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Pennsylvania Economic Association 2018 Conference Proceedings

Pennsylvania Economic Association

Annual Conference May 31- June 2, 2018

Penn State Altoona Altoona, PA


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PENNSYLVANIA

ECONOMIC

ASSOCIATION

ANNUAL CONFERENCE

May 31- June 2, 2018

Penn State Altoona

Altoona, PA

Visit the Pennsylvania Economic Association Home Page at http://econpea.org/


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Pennsylvania Economic Association

2017 – 2018

Executive Board President: Jui-Chi (Rocky) Huang, Penn State-Berks President-Designate: Sunita Mondal, Slippery Rock University Vice President, Program: Tufan Tiglioglu, Alvernia University Vice President, Publicity: Xuebing Yang, Penn State - Altoona Vice President, Membership: Kosin Isariyawongse, Edinboro University of Pennsylvania Secretary: Stephanie Brewer, Indiana University of Pennsylvania Treasurer: Steven Andelin, Penn State - Schuylkill Editor, Pennsylvania Economic Review: Orhan Kara, West Chester University of Pennsylvania Webmaster: Orhan Kara, West Chester University of Pennsylvania Immediate Past President: Kosin Isariyawongse, Edinboro University of Pennsylvania

Board of Directors Riza Emekter, Robert Morris University Sandra McPherson, Millersville University Mike Trebing, Federal Reserve Bank of Philadelphia Divya Balasubramaniam, Saint Joseph University Adora Holstein, Robert Morris University Guhan Venkatu, Federal Reserve Bank of Cleveland Aram Balagyozyan, University of Scranton Soumendra (Ben) Banerjee, Misericordia University John Ruddy, University of Scranton


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Ex-Officio Directors

Ralph Ancil (Retired), Geneva College Thomas O. Armstrong, Wilson College Ron Baker, Millersville University of Pennsylvania Gerald Baumgardner, Pennsylvania College of Technology William Bellinger, Dickinson College Charles Bennett, Gannon University David Culp (Retired), Slippery Rock University Donald Dale, Muhlenberg College Robert D'Intino, Rowan University James Dunn (Retired), Edinboro University of Pennsylvania Andrew Economopoulos, Ursinus College Joseph Eisenhauer, University of Detroit Mercy Mark Eschenfelder, Robert Morris University Soma Ghosh, Albright College Deborah Gougeon, University of Scranton Michael Hannan, Edinboro University of Pennsylvania Jolien Helsel, Youngstown State University Andrew Hill, Federal Reserve Bank of Philadelphia Elizabeth Hill (Retired), Penn State-Mont Alto Mehdi Hojjat, Neuman University Tahereh Hojjat, DeSales University James Jozefowicz, Indiana University of Pennsylvania Ioannis N. Kallianiotis, University of Scranton Timothy Kearney, Voya Investment Management Donna Kish-Goodling, Muhlenberg College Daniel Y. Lee, Shippensburg University of Pennsylvania Robert Liebler, King's College Johnnie Linn, Concord University Patrick Litzinger, Robert Morris University Stephen Mansour, University of Scranton John McCollough, Lamar University Jacquelynne McLellan, Frostburg State University Tracy Miller, Grove City College Lawrence Moore, Potomac State College of West Virginia University Heather O'Neill, Ursinus College J. Brian O'Roark, Robert Morris University Abdul Pathan, Pennsylvania College of Technology Natalie D. Reaves, Rowan University Margarita M. Rose, King's College William Sanders (Retired), Clarion University of Pennsylvania Mark Schweitzer, Federal Reserve Bank of Cleveland Yaya Sissoko, Indiana University of Pennsylvania Brian Sloboda, University of Phoenix Kenneth Smith, Millersville University of Pennsylvania Lynn Smith (Retired), Clarion University of Pennsylvania Osman Suliman, Millersville University of Pennsylvania Thomas Tolin, West Chester University Sandra Trejos, Clarion University of Pennsylvania John Walker, Kutztown University of Pennsylvania Roger White, Whittier College Paul Woodburne, Clarion University of Pennsylvania Bijou Yang-Lester, Drexel University

David Yerger, Indiana University of Pennsylvania


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Publish Your Paper in the:

Pennsylvania Economic Review

The Pennsylvania Economic Review (PER), a publication of the Pennsylvania Economic Association (PEA), publishes scholarly articles and book reviews submitted from anywhere in the world and in all economic areas. The PER also welcomes submissions containing an analysis of the Pennsylvania economy. It encourages members of the Pennsylvania Economic Association who present papers at the Association’s Annual Conferences to submit their papers for possible publication. Referees evaluate articles that authors submit for possible publication. Manuscript submission instructions can be found at the Pennsylvania Economic Association home page: http://www.econpea.org.

Publish Your Presentation or Discussion in the:

PEA 2018 Conference Proceedings

Submit an electronic copy of your paper or comments to: Dr. Tufan Tiglioglu Alvernia University 400 Saint Bernardine Street Reading, PA, 19607 [email protected]

For submission information, see the style sheet posted at the association website – http://www.econpea.org/pub/proceedings.html.

The deadline for proceeding submissions is July 31, 2018.

mailto:[email protected]


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Acknowledgement

Dr. Lori Bechtel-Wherry, Chancellor and Dean, Penn State Altoona Dr. Darin Zimmerman, Associate Dean for Research, Penn State Altoona Dr. Karin Jordan, Associate Dean, Academic Affairs, Penn State Altoona Dr. Jungwoo Ryoo, Head of Division of Business, Engineering, and Information Sciences and Technology (BEIST), Penn State Altoona Jack Sinclair, Director of Continuing Education and Training and Career Services, Penn State Altoona Debra Flaig, Continuing Education and Training, Penn State Altoona Lucinda Royal, Continuing Education and Training, Penn State Altoona Stephanie Eakin, Housing and Foodservice Manager, Penn State Altoona Toni Feret, Assistant Director of Development, Development and Alumni Relations, Penn State Altoona Deborah Hommer, Business Program Coordinator, Division of Business, Engineering, and Information Sciences and Technology (BEIST), Penn State Altoona Dr. Mark Agee, Division of Business, Engineering, and Information Sciences and Technology (BEIST), Penn State Altoona Dr. Tulay Girard, Division of Business, Engineering, and Information Sciences and Technology (BEIST), Penn State Altoona Dr. Erica Groshen, Keynote Speaker, Visiting Senior Scholar at the ILR School of Cornell University Mekael Teshome, Vice President and Senior Regional Officer, Federal Reserve Bank of Cleveland Dr. Stephan D. Whitaker, Research Economist, Federal Reserve Bank of Cleveland Cengage


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CONFERENCE AGENDA

THURSDAY, May 31 04:00 pm – 09:00 pm Registration (Harry E. Slep Student Center, Lobby) 05:00 pm – 06:00 pm Board of Directors’ Dinner (Port Sky Cafe, Mountain View Room) 06:00 pm – 08:00 pm Board of Directors’ Meeting (Port Sky Cafe, Mountain View Room) 06:00 pm – 09:00 pm Reception (Harry E. Slep Student Center, Pond View)

Sponsored by Academic Affairs, Penn State Altoona

FRIDAY, June 1 08:00 am – 12:00 pm & 2:00 pm – 4:00 pm Registration (Hawthorn Building Lobby) 07:30 am – 10:30 am Cengage Breakfast (Hawthorn Building Room 134)

Continental Breakfast Sponsored by Cengage 09:00 am – 10:15 am Concurrent Sessions

(Hawthorn Building Rooms 140, 141, 142, 143, 144, 146) 10:15 am – 10:30 am Cengage Coffee Break (Hawthorn Building Room 134) Coffee/Refreshment 10:30 am – 11:45 am Concurrent Sessions

(Hawthorn Building Rooms 140, 141, 142, 143, 144, 146) 12:00 pm – 12:45 pm Luncheon (Harry E. Slep Student Center, Pond View) 12:45 pm – 01:45 pm Keynote Speaker (Harry E. Slep Student Center, Pond View)

Dr. Erica Groshen, Visiting Senior Scholar at the ILR School of Cornell University “Update on the Labor Market, the Importance of the Bureau of Labor Statistics and Critical Issues it Faces”

02:15 pm – 03:30 pm Concurrent Sessions (Hawthorn Building Rooms 140, 141, 142, 143, 144, 146, 150)

03:30 pm – 03:45 pm Coffee Break (Hawthorn Building Room 134) – Coffee/Refreshment 03:45 pm – 04:45 pm Fed Lecture - (Misciagna Family Center for Performing Arts Theatre)

Mr. Mekael Teshome, Federal Reserve Bank of Cleveland Dr. Stephan D. Whitaker, Federal Reserve Bank of Cleveland “Economic update and discussion on intergenerational educational mobility”

05:00 pm – 08:00 pm Fed Reception (Misciagna Family Center for Performing Arts Rooms 101, 102, 103) Sponsored by Federal Reserve Bank of Cleveland

SATURDAY, June 2 07:30 am – 10:30 am Registration (Hawthorn Building Lobby) 07:30 am – 09:00 am Cengage Breakfast (Hawthorn Building Room 134)

Sponsored by Cengage 09:00 am – 10:15 am Concurrent Sessions

(Hawthorn Building Rooms 140, 141, 142, 143, 144, 146) 10:30 am – 11:45 am General Membership Meeting

(Misciagna Family Center for Performing Arts Theatre) 11:45 am Closing


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FRIDAY, June 1, 2018

Conference Registration – Hawthorn Building Lobby 08:00 AM – 12:00 PM 02:00 PM – 04:00 PM

Cengage Continental Breakfast – Hawthorn Building Room 134

07:30 AM – 10:30 AM

Session F1: Friday, June 1, 2018 09:00 AM – 10:15 AM

F (Friday); F1 (Friday Concurrent 1); F1A (Friday Concurrent 1 Session A)

Session F1A: General Economics and Teaching

Hawthorn Building Room 140 (09:00 – 10:15 am)

Chair: Yaya Sissoko, Indiana University of Pennsylvania

[1] Hey Grad Students! Looking for a Career in Teaching Large Courses? How to (Correctly) Apply Dave Brown, Penn State University

[2] Superheroes Disguised as the Underdogs: A Study of Rural Pennsylvania

Mary Milford, Morey's Beverage Carlos Liard-Muriente, Central Connecticut State University

[3] The Leadership of Judith Rodin and the West Philadelphia Initiative Helen Midouhas, Alvernia University

Discussants: [1] Alexi Thompson, Indiana University of Pennsylvania [2] Travis Yates, Penn State - Behrend (Erie) [3] Muhammad Hashim, Government College of Management Sciences Peshawar


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Session F1B: Financial Economics Hawthorn Building Room 141 (09:00 – 10:15 am)

Chair: Jui-Chi (Rocky) Huang, Penn State-Berks

[1] Conditional Tail Risks in REITs Return

Babatunde Odusami, Widener University [2] Financial Anxiety in the Orthodox Jewish Community

Daria Newfeld, Albright College

[3] Systemic Importance of Financial Institutions; The Increasing Role of NBFIs. Amir Rafique, COMSATS, Islamabad Campus

[4] Portfolio Diversification: A Case of Growth Mutual Funds Sunita Mall, Mudra Institute of Communication Ahmedabad

Discussants: [1] Mehdi Hojjat, Neumann University [2] Michael Malcolm, West Chester University [3] Sunita Mall, Mudra Institute of Communication Ahmedabad [4] Samouel Beji, University of Sousse


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Session F1C: Business Administration and Business Economics, Marketing, Accounting and Personnel Economics


Chair: Brian Sloboda, University of Phoenix [1] Here I Come To Save the Day? The Efficacy of Small Business Development Centers in

Pennsylvania M. Garrett Roth, Gannon University

Ryan Morris, Gannon University

[2] Assessing the Impact of First Person Constructs in Introductory Accounting Courses Brian Trout, Millersville University Eric Blazer, Millersville University

[3] AFLAC - Crossing the Line? Melanie Anderson, Slippery Rock University [4] Testing claims that Transkeian District Councils led to increased black enrollment and attendance.

Farai Donald Nyika, Stellenbosch University Discussants: [1] John Ruddy, University of Scranton [2] David Nugent, Robert Morris University [3] Ahmed Abou-Zaid, Eastern Illinois University [4] Brian Sloboda, University of Phoenix

Session F1D: Macroeconomics and Monetary Economics Hawthorn Building Room 143 (09:00 – 10:15 am)

Chair: Jolien Helsel, Youngstown State University

[1] Three Strategies for Protecting the National Economic Integrity

Jeffrey Forrest, Slippery Rock University David Jordan, Slippery Rock University Kostas Karamanos, Technological & Educational Institute of Athens

[2] Inflation, exchange rate and growth dynamics in Ghana (An ARDL model to cointegration and VECM approach)

Isaac-Ohene Agyei, University of New Brunswick

[3] Monetary Union or Monetary Cooperation in North Africa? Lessons from an Optimization Exercise

Aram Belhadj, University of Carthage


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[4] Does Islamic Banking favors Price Stability? Tamsir Cham, Islamic Development Bank

Discussants: [1] Niraj Koirala, Texas Tech University [2] Hari Krishna Bagavatam, Osmania University [3] Gao Chen, George Washington University [4] Malik Shahzad Shabbir, University of Brunei Darussalam

Session F1E: Mathematical and Quantitative Methods Hawthorn Building Room 144 (09:00 – 10:15 am)

Chair: Jeff Salavitabar, Delaware County Community College

[1] A Simple Algorithm for Selecting a Set of Samples or Observations with Approximately Equal Mean Values

William Galose, McNeese State University Daryl Burckel, McNeese State University Lonnie Turpin, Jr., McNeese State University

[2] GSM Network Uncertainty, Social Media and Consumption Theory: Challenges & Prospects of harnessing ICT platform for inclusive growth in Nigeria

Chukwuemeka Eke, University of Abuja

[3] Stock Returns and Inflation: A tale of two periods in India Niyati Bhanja, Mudra Institute of Communication

Discussants: [1]Behzod Ahundjanov, Texas Tech University [2] Hanadi Taher, Beirut Arab University [3] Andrew Alola, Eastern Mediterranean University


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Session F1F: Panel on Pedagogy in the Economics Classroom Hawthorn Building Room 146 (09:00 – 10:15 am)

Chair: Sandra Trejos, Clarion University

Panelists: [1] Soma Ghosh, Albright College [2] Kayhan Koleyni, Clarion University [3] Sunita Mondal, Slippery Rock University [4] Sandra Trejos, Clarion University [5] Abdul Pathan, Pennsylvania College of Technology


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Session F2: Friday, June 1, 2018 10:30 AM – 11:45 AM


Session F2A: Urban, Rural, Regional, Real Estate, and Transportation Economics

Hawthorn Building Room 140 (10:30-11:45 am)

Orhan Kara, West Chester University [1] Impact of Recent Volatility in Oil and Gas Production on North Dakota: A Dynamic Analysis of

Employment Change David Doorn, West Chester University

[2] The Changes in Workforce Patterns on a County Level in the Commonwealth of Pennsylvania 2002-2013 Brian Sloboda, University of Phoenix Yaya Sissoko, Indiana University of Pennsylvania

[3] Sense of Community and Economic Outcomes in Racially Diverse, Post-industrial Pennsylvania Lisa Wilder, Albright College

Discussants: [1] Daria Newfeld, Albright College [2] Lisa Wilder, Albright College [3] Orhan Kara, West Chester University


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Session F2B: Labor and Demographic Economics Hawthorn Building Room 141 (10:30 – 11:45 am)

Chair: Tufan Tiglioglu, Alvernia University

[1] A Spatial Analysis of Youth Disconnectedness in the U.S.

Jolien Helsel, Youngstown State University David Helsel, John Carroll University

[2] Reasons Behind the Disappearing Routine Jobs Anurud Rankoth Gedara, Texas Tech University

[3] Customer Attitude and Judgment: A Case of Islamic, Conventional and Islamic standalone branches in Pakistan

Malik Shahzad Shabbir, University of Brunei Darussalam [4] A Study on How People with Disabilities are Adjusting to Emerging Technology in Modern Society

Shin Hyoung Hwang, Choice Research Group Lois Kim, Choice Research Group Richard Kyung, Choice Research Group

Discussants: [1] Usamah Alfarhan, Sultan Qaboos University [2] Theresa Phipps, Slippery Rock University [3] Shin Hyoung Hwang, Choice Research Group [4] Soumendra Banerjee, Misericordia University

Session F2C: Economic Development, Innovation, Technological Change, and Growth


Chair: Jeffrey Forrest, Slippery Rock University [1] Achieving Sustainable Development Goal 11: An Analysis of India’s Smart Cities

Soma Ghosh, Albright College [2] Structural Change, Productivity Growth and Labor Market Turbulence in Africa

Solomon Owusu, United Nations University Emmanuel Boadi Mensah, United Nations University

[3] M-tourism in the context of mobile culture in Northern African destinations Moez Kacem, Research Unity ENVIE


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[4] Aggregate impact of tourism-refugeeism on house prices in Cyprus and Malta: A dynamic ARDL approach

Andrew Alola, Eastern Mediterranean University Uju Violet Alola, Eastern Mediterranean University

Discussants: [1] Abdul Pathan, Pennsylvania College of Technology [2] Atif Awad, University of Sharjah [3] Jeff Salavitabar, Delaware County Community College [4] Samson Getachew, Guest House

Session F2D: Public Economics Hawthorn Building Room 143 (10:30 – 11:45 am)

Chair: Michael Malcolm, West Chester University

[1] Public Utility Realty Tax (PURTA) in Pennsylvania

Thomas Armstrong, Wilson College

[2] A Seventh Look at Swedish Municipal Public Housing – Operating under Business-Like Principles: Some Unanticipated Consequences

Timothy Wilson, Umeå School of Business and Economics Lars Lindbergh, Umeå School of Business and Economics

[3] Institutional Economics Approach for Addressing Underproduction

Ashutosh Sarker, Monash University Malaysia

[4] Fiscal Austerity and Income Inequality: Who Bears the Costs? Vivian Norambuena, Universidad de Chile

Discussants: [1] Tahereh Alavi Hojjat, DeSales University [2] Amir Rafique, COMSATS, Islamabad Campus [3] Nguyen Ngo, SUNY at Buffalo [4] Ashutosh Sarker, Monash University Malaysia


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Session F2E: Microeconomics Hawthorn Building Rooms 144 (10:30 – 11:45 am)

Chair: Mehdi Hojjat, Neumann University

[1] Labor Market Survey of Non-Industrial Ceramics Production in the U.S.

Sunita Mondal, Slippery Rock University David Culp, Slippery Rock University

[2] Estimating Production Functions When Output Prices and Quality are Unobservable

Xuebing Yang, Penn State Altoona [3] The Evolution of Price and Performance for the Porsche 911

Steven Andelin, Penn State Schuylkill Discussants: [1] Xuebing Yang, Penn State Altoona [2] William Galose, McNeese State University [3] Niraj Koirala, Texas Tech University

Session F2F: International Economics Hawthorn Building Room 146 (10:30 – 11:45 am)

Chair: John Ruddy, University of Scranton

[1] Rethinking the Balassa-Samuelson Effect: Central Bank’s Capital Control Policy and Real Exchange Rate Misalignments

Gao Chen, George Washington University [2] Relationship between FDI distribution and regional economic aggregates: An evidence from India

Sunita Mall, Mudra Institute of Communication

[3] Aid versus Remittances Which Works Better? Imad El Hamma, Paris East University

Discussants: [1] Tuan Le, West Virginia Wesleyan College [2] Jui-Chi (Rocky) Huang, Penn State-Berks [3] Farai Donald Nyika, Stellenbosch University


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Keynote Speaker Luncheon

Friday, June 1, 2018 12:00 PM – 01:45 PM

Harry E. Slep Student Center, Pond View


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Keynote Speaker

Friday, June 1, 2018 12:00 PM – 01:45 PM

Harry E. Slep Student Center, Pond View

“Update on the Labor Market, the Importance of the Bureau of Labor Statistics and Critical Issues it Faces”

Dr. Erica Groshen

Visiting Senior Scholar at the ILR School of Cornell University


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Dr. Erica L. Groshen is a Visiting Senior Scholar at the ILR School of Cornell University, and served as the 14th Commissioner of Labor Statistics from January 2013 to January 2017. Prior to joining the Bureau of Labor Statistics (BLS), Dr. Groshen was a Vice President and economist in the Research and Statistics Group at the Federal Reserve Bank of New York.

Dr. Groshen’s research focuses on jobless recoveries, regional labor markets, wage rigidity and dispersion, the male-female wage differential, service-sector employment, and the role of employers in labor market outcomes. She co-authored the book How New is the “New Employment Contract”? from the W.E. Upjohn Institute Press and co-edited Structural Changes in U.S. Labor Markets: Causes and Consequences, from M.E. Sharpe, Inc. She has published numerous papers in academic and Federal Reserve journals and co-led the sixteen-country International Wage Flexibility Project.

She has served as a member of the BLS Data Users’ Advisory Committee and of the Census Bureau’s 2010 Census Advisory Committee and Advisory Committee of Professional Associations. She was a Research Fellow of the Institute for the Study of Labor (IZA) and served on the Board of Reviewers for Industrial Relations: A Journal of Economics and Society.

In 1999-2000, Dr. Groshen visited the Bank for International Settlements in Basel Switzerland. Before joining the New York Fed, Dr. Groshen was a visiting assistant professor of economics and economic advisor at Barnard College at Columbia University and an economist at the Federal Reserve Bank of Cleveland.

Dr. Groshen holds a Ph.D. and M.A. in economics from Harvard University and a B.S. in economics and mathematics from the University of Wisconsin-Madison.

Source: https://www.ilr.cornell.edu/people/erica-groshen

https://www.ilr.cornell.edu/people/erica-groshen


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Session F3: Friday, June 1, 2018 02:15 PM – 03:30 PM


Session F3A: Economic Development, Innovation,

Technological Change, and Growth Hawthorn Building Room 140 (2:15 – 3:30 pm)

Chair: Travis Yates, Penn State - Behrend (Erie)

[1] Comparative Advantage and Economic Policy Tools based on a Local Economy’s Imports and Exports

Travis Yates, Penn State - Behrend (Erie) Hannah Carlino, Economic Research Institute of Erie (E.R.I.E)

[2] Institutions, Financial Freedom, and Remittances: Evidence from MENA Countries Ahmed Abou-Zaid, Eastern Illinois University

[3] The Future of Economic Growth under Agricultural Development and Structural Transformation in Nepal

Jeeban Amgain, World Connections Inc, Japan

Discussants: [1] David Doorn, West Chester University [2] Soma Ghosh, Albright College [3] Gbolahan Olowu, Cyprus International University

Session F3B: Public Economics Hawthorn Building Room 141 (2:15– 3:30 pm)

Chair: Daria Newfeld, Albright College

[1] Retirement and Interstate-Banking Deregulation: Do the Self-Employed Work Longer?

Nguyen Ngo, SUNY at Buffalo

[2] Public Expenditure and its impact on Economic Growth: a case of Pakistan Malik Shahzad Shabbir, University of Brunei Darussalam


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[3] The Myth & Reality of Government Expenditure on Primary Health Care in Nigeria: Way Forward to Inclusive Growth

Oserei Kingsley, University of Lagos Godwin Uddin, Pan-Atlantic University

Discussants: [1] Thomas Armstrong, Wilson College [2] M. Garrett Roth, Gannon University [3] Timothy Wilson, Umeå School of Business and Economics

Session F3C: Business Administration and Business Economics, Marketing, Accounting and Personnel Economics

Hawthorn Building Rooms 142 (2:15 – 3:30 pm)

Chair: Steven Andelin, Penn State Schuylkill

[1] Motivation Factors of Female Entrepreneurs Orhan Kara, West Chester University

[2] The Pros and Cons of Family Business Succession David Nugent, Robert Morris University

[3] Abortion and Property Crime: What becomes of the Mothers?

Alexi Thompson, Indiana University of Pennsylvania Christopher Jeffords, Indiana University of Pennsylvania

[4] An Analysis of Bank Financial Strength Ratings and Other Credit Rating Data

John Ruddy, University of Scranton

Discussants: [1] Brian Trout, Millersville University [2] Vivian Norambuena, Universidad de Chile [3] Moez Kacem, Research Unity ENVIE [4] Steven Andelin, Penn State Schuylkill


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Session F3D: Business Administration and Business Economics, Marketing, Accounting and Personnel Economics

Hawthorn Building Room 143 (2:15– 3:30 pm)

Chair: Xuebing Yang, Penn State Altoona

[1] A Case Study of the Voluntary Implementation of SOX at the University of Pittsburgh Theresa Phipps, Slippery Rock University

[2] Career Development Strategies for Business Students Mehdi Hojjat, Neumann University

[3] Are corruption and insurgency nemesis of tourism development? Evidence from Nigeria Violet Alola, Eastern Mediterranean University Andrew Alola, Eastern Mediterranean University

Discussants: [1] Babatunde Odusami, Widener University [2] Melanie Anderson, Slippery Rock University [3] Yaya Sissoko, Indiana University of Pennsylvania

Session F3E: General Economics and Teaching Hawthorn Building Room 144 (2:15– 3:30 pm)

Chair: Dave Brown, Penn State University

[1] Is it Ethical (or Expected) for Instructors to Game the System of Teaching Evaluations?

Dave Brown, Penn State University [2] Servant Leadership and Job Satisfaction among Academicians

Muhammad Hashim, Government College of Management Sciences Peshawar

[3] The Creation of the Online Economics Major at a Large University: Perspectives from an Administrator and Instructor

Dave Brown, Penn State University Jamie Brown, Penn State University

Discussants: [1] Helen Midouhas, Alvernia University [2] Dave Brown, Penn State University [3] Muhammad Hashim, Government College of Management Sciences Peshawar


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Session F3F: Health, Education and Welfare Hawthorn Building Room 146 (2:15– 3:30 pm)

Chair: Elsy Thomas K, Bowling Green State University

[1] An Overview of Health Systems across Select Countries

Elsy Thomas K, Bowling Green State University [2] Prescription Opioid Misuse and Property Crime

Michael Malcolm, West Chester University McCaslin Giles, West Chester University

[3] Eight-hour Work Day, Impractical to Modern Economic Conditions Samson Getachew, Guest House

[4] Rehabilitation and Wealth Distribution Methodology for Minorities with Disabilities for Their Health and Social Self-Sufficiency

Lois Kim, Choice Research Group David Sang E Kyhm, Choice Research Group Yeon Soo Kang, Choice Research Group

Discussants: [1] Mary Milford, Morey's Beverage [2] Elsy Thomas K, Bowling Green State University [3] David Sang E Kyhm, Choice Research Group [4] Chukwuemeka Eke, University of Abuja


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Session F3G: Student Paper Session 1 Hawthorn Building Room 150 (2:15– 3:30 pm)

Chair: Kayhan Koleyni, Clarion University

[1] A Multi-Discipline Economic Analysis of Bitcoin Zachary Shifflett, Mount Saint Mary's University

[2] Exports and Firm Productivity in Ethiopian Manufacturing Firms Ali Yibrie Esmaile, University of Milan

[3] Institutional Quality, Schoolenrolment and Mobile Subscribers in ECOWAS-5: FDI Perspective Using Panel Data

Isaac Oladele AlabiIsaa, University of Uyo [4] Food Security in a Changing Climate in West Africa; Evidence from Non-Stationary Heterogeneous Panel

Richmond Silvanus Baye, University Degli Studi di Milano Discussants: [1] Sandra Trejos, Clarion University [2] Kayhan Koleyni, Clarion University [3] Jeff Salavitabar, Delaware County Community College [4] Anurud Rankoth Gedara, Texas Tech University


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Federal Reserve Lecture

Friday, June 1, 2018 03:45 PM – 04:45 PM

Misciagna Family Center for Performing Arts Theatre

“Economic update and discussion on intergenerational educational mobility”

“Economic Update”

Mekael Teshome

Vice President and Senior Regional Officer Federal Reserve Bank of Cleveland


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Mekael Teshome

Mekael Teshome is vice president and senior regional officer of the Pittsburgh Branch of the Federal Reserve Bank of Cleveland. Mr. Teshome is responsible for managing relationships with regional stakeholders in western Pennsylvania, southeastern Ohio, and the northern panhandle of West Virginia. He is also responsible for monitoring the region’s economic environment and conducting economic research and analysis. Before joining the Cleveland Reserve Bank, Mr. Teshome was an assistant vice president and economist at the PNC Financial Services Group in Pittsburgh where he contributed industry-focused, regional, national and international economic analyses and forecasts. Prior to PNC, he served as an assistant director and economist at Moody’s Analytics. He is a founding board member of the Bethel Environmental and Agricultural University and Training Center, a member of the board of economic advisors of the North Carolina Chamber Foundation, and a board member of the Economic Club of Pittsburgh. He is also a member of the National Association for Business Economics (NABE) and the Ethiopian Economics Association. Mr. Teshome holds a BA in political science from Taylor University and an MA in economics from Vanderbilt University.


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“Intergenerational Educational Mobility”

Dr. Stephan D. Whitaker

Research Economist Federal Reserve Bank of Cleveland

Dr. Stephan D. Whitaker

Stephan Whitaker is a research economist in the Research Department at the Federal Reserve Bank of Cleveland. His current work includes research on housing markets and studies of state and local public finance. Dr. Whitaker holds a BA in economics from Columbia University and an MS in statistics from Colorado State University. Before earning his PhD in public policy from the Harris School at the University of Chicago, he served as a lieutenant in the US Air Force.


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Federal Reserve Reception

Friday, June 1, 2018

05:00 PM – 08:00 PM

Misciagna Family Center for Performing Arts Rooms 101, 102, 103


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SATURDAY, June 2, 2018

Conference Registration – Hawthorn Building Lobby 07:30 AM – 10:30 AM

Cengage Continental Breakfast – Hawthorn Building Room 134

07:30 AM – 9:00 AM

Session S1: Saturday, June 2, 2018 09:00 AM – 10:15 AM

S (Saturday); S1 (Saturday Concurrent 1); S1A (Saturday Concurrent 1 Session A)

Session S1A: Macroeconomics and Monetary Economics


Chair: Mike Trebing, Federal Reserve Bank of Philadelphia

[1] Several Issues Facing the Designer of Economic Policies Jeffrey Forrest, Slippery Rock University Erkan Kose, Nuh Naci Yazgan University, Turkey Roger Solano, Slippery Rock University

[2] Testing Lucas Critique with Phillips Curve and Term Structure Models

Mehmet Orhan, University of West Indies [3] Labor Share of Income and Labor Market Regulation

Niraj Koirala, Texas Tech University

[4] Demonetization - Impact on Money Laundering and Terrorist Financing in the Indian Banking System

Hari Krishna Bagavatam, Osmania University

Discussants: [1] Yaya Sissoko, Indiana University of Pennsylvania [2] Isaac-Ohene Agyei, University of New Brunswick [3] Niyati Bhanja, Mudra Institute of Communication [4] Mehmet Orhan, University of West Indies


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Session S1B: Agricultural and Natural Resource Economics, Environmental and Ecological Economics


Chair: Tahereh Alavi Hojjat, DeSales University

[1] Exporting and Pollution Abatement Expenditure: Evidence from Firm-Level Data Soumendra Banerjee, Misericordia University Jayjit Roy, Appalachian State University

Mahmut Yasar, University of Texas, Arlington [2] Nonparametric Analysis of the Growth Process of CO2 Emissions: Testing the Law of Proportionate Effect

Behzod Ahundjanov, Texas Tech University

[3] Case Study of Sustainability Education in Economics Courses Tahereh Alavi Hojjat, DeSales University

[4] Climate Change and Economic Growth in Lebanon

Hanadi Taher, Beirut Arab University

Discussants: [1] Xuebing Yang, Penn State Altoona [2] Brian Sloboda, University of Phoenix [3] Steven Andelin, Penn State Schuylkill [4] Oserei Kingsley, University of Lagos

Session S1C: International Economics Hawthorn Building Room 142 (09:00 – 10:15 am)

Chair: Tuan Le, West Virginia Wesleyan College

[1] Exchange Rate Pass-Through: What Have We Learned?

Jui-Chi (Rocky) Huang, Penn State-Berks [2] Does FDI from Corrupt Countries Increase Corruption in Host Countries?

Tuan Le, West Virginia Wesleyan College [3] Microfoundations of the New Keynesian Phillips Curve in an Open Emerging Economy

Zouhair Ait Benhamou, Paris Nanterre University

Discussants: [1] Sunita Mall, Mudra Institute of Communication [2] Zouhair Ait Benhamou, Paris Nanterre University [3] Nguyen Ngo, SUNY at Buffalo


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Session S1D: Economic Development, Innovation, Technological Change, and Growth & Labor and Demographic Economics

Hawthorn Building Room 143 (09:00-10:15 am)

Chair: Dave Brown, Penn State University [1] Business Environment and Economic Performance in Emerging Economies: A Cross-Country Analysis of the BRICS

Jeremiah Durotola, University of Bradford Temitope Laniran, University of Bradford

[2] Knowledge of Cybercrime on Information Communication Technology Growth among Higher Institutions of Learning in Selangor, Malaysia

Olayiwola Tokunb Taofeek, Management and Science University Asif Iqbal, Management and Science University

[3] The Local-Migrant Earnings Dilemma: Failing Nationalization Policies Usamah Alfarhan, Sultan Qaboos University

[4] The Impact of Foreign Labour on Average Real Wages of Residents: Evidence from Malaysia’s Construction Sector

Atif Awad, University of Sharjah

Discussants: [1] Jolien Helsel, Youngstown State University [2] Anurud Rankoth Gedara, Texas Tech University [3] Solomon Owusu, United Nations University [4] Jeremiah Durotola, University of Bradford


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Session S1E: Agricultural and Natural Resource Economics, Environmental and Ecological Economics


Chair: Babita Srivastava, William Paterson University

[1] Mitigation of the Gap in Energy Supply and Demand in the KAVAL Cities of India Babita Srivastava, William Paterson University

[2] The nexus between relative GDP Share of Agriculture, poverty and employment in selected West African countries

Gbolahan Olowu, Cyprus International University

[3] The Role of Incentives for Sustainable Implementation of Bio Sphere Reserves: Evidence from Developing Countries

Gashaw Kassahun, Bologna University

[4] Economic and Health Impacts of Climate change; a case of Air pollution in Ethiopia Aemade Mistru Terefe, University of Bologna

Discussants: [1] Aemade Mistru Terefe, University of Bologna [2] Babita Srivastava, William Paterson University [3] Gbolahan Olowu, Cyprus International University [4] Gashaw Kassahun, Bologna University


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Session S1F: Industrial Organization Hawthorn Building Room 146 (09:00 – 10:15 am)

Chair: M. Garrett Roth, Gannon University

[1] The Link between Kindness, Effective Teamwork and Employee’s Well-being

Susanh Perez, iStart Services Group Toni Didona, Albizu University Reina Daugherty, Albizu University Jose Gonzalez, Albizu University

[2] The Determinants of Industrialization: Empirical Evidence from Africa

Samouel Beji, University of Sousse Aram Belhadj, University of Carthage

[3] Positive Emotions in the Workplace and Employee Affective Well-Being Susanh Perez, Albizu University Toni Didona, Albizu University Reina Daugherty, Albizu University Jose Gonzalez, Albizu University

[4] Review of India's Crude Oil Production History and Its Peak Oil Period Estimation Using Hubbert's Theory and a Novel Technique Based on Statistical Analysis

Meet Shah, Pandit Deendayal Petroleum University Jatin Agarwal, Pandit Deendayal Petroleum University

Discussants: [1] Jeeban Amgain, World Connections Inc, Japan [2] Susanh Perez, iStart Services Group [3] Samouel Beji, University of Sousse [4] Aram Belhadj, University of Carthage


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General Membership Meeting

Saturday, June 2, 2018 10:30 AM – 11:45 AM

Misciagna Family Center for Performing Arts Theatre

Our Annual Business Meeting of the General Membership of the Pennsylvania Economic Association is open to the entire membership of the PEA, including all registrants of the conference.

“Door Prizes” will be awarded!

Closing 11:45 AM


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2018 Program Author and Participant Index Last Name First Name E-mail Session Abou-Zaid Ahmed [email protected] F1C, F3A Agyei Isaac-Ohene [email protected] F1D, S1A Ahundjanov Behzod [email protected] F1E, S1B Ait Benhamou Zouhair [email protected] S1C Alabilsaa Isaac Oladele [email protected] F3G Alfarhan Usamah [email protected] F2B, S1D Alola Andrew [email protected] F1E, F2C, F3D Alola Violet [email protected] F2C, F3D Amgain Jeeban [email protected] F3A, S1F Andelin Steven [email protected] F2E, F3C, S1B Anderson Melanie [email protected] F1C, F3D Armstrong Thomas [email protected] F2D, F3B Awad Atif [email protected] F2C, S1D Bagavatam Hari Krishna [email protected] F1D, S1A Banerjee Soumendra [email protected] F2B, S1B Baye Richmond Silvanus [email protected] F3G Beji Samouel [email protected] F1B, S1F Belhadj Aram [email protected] F1D, S1F Bhanja Niyati [email protected] F1E, S1A Brown Dave [email protected] F1A, F3E, S1D Cham Tamsir [email protected] F1D Chen Gao [email protected] F1D, F2F Doorn David [email protected] F2A, F3A Durotola Jeremiah [email protected] S1D Eke Chukwuemeka [email protected] F1E, F3F El Hamma Imad [email protected] F2F Elsy Thomas [email protected] F3F Esmaile Ali Yibrie [email protected] F3G Forrest Jeffrey [email protected] F1D, F2C, S1A Galose William [email protected] F1E, F2E Getachew Samson [email protected] F2C, F3F Ghosh Soma [email protected] F1F, F2C, F3A Hashim Muhammad [email protected] F1A, F3E Helsel Jolien [email protected] F1E, F2B, S1D Hojjat Mehdi [email protected] F1B, F2E, F3D Hojjat Tahereh Alavi [email protected] F2D, S1B Huang Jui-Chi [email protected] F1B, F2F, S1C Hwang Shin Hyoung [email protected] F2B Kacem Moez [email protected] F2C, F3C


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Last Name First Name E-mail Session Kara Orhan [email protected] F2A, F3C Kassahun Gashaw [email protected] S1E Kim Lois [email protected] F2B, F3F Kingsley Oserei [email protected] F3B, S1B Koirala Niraj [email protected] F1D, F2E, S1A Koleyni Kayhan [email protected] F1F, F3G Kyhm David Sang E [email protected] F3F Kyung Richard [email protected] F2B Le Tuan [email protected] F2F, S1C Malcolm Michael [email protected] F1B, F2D, F3F Mall Sunita [email protected] F1B, F2F, S1C Midouhas Helen [email protected] F3E Milford Mary [email protected] F1A, F3F Mondal Sunita [email protected] F1F, F2E, F3B Newfeld Daria [email protected] F1B, F2A, F3B Ngo Nguyen [email protected] F2D, F3B, S1C Nguyen Liem [email protected] F3G Norambuena Vivian [email protected] F2D, F3C Nugent David [email protected] F1C, F3C Nyika Farai Donald [email protected] F1C, F2F Odusami Babatunde [email protected] F1B, F3D Olowu Gbolahan [email protected] F3A, S1E Orhan Mehmet [email protected] S1A Owusu Solomon [email protected] F2C, S1D Pathan Abdul [email protected] F2C Perez Susanh [email protected] S1F Phipps Theresa [email protected] F2B, F3D Rafique Amir [email protected] F1B, F2D Rankoth Gedara Anurud [email protected] F2B, F3G, S1D Roth M. Garrett [email protected] F1C, F3B, S1F Ruddy John [email protected] F1C, F2F, F3C Salavitabar Jeff [email protected] F1E, F2C, F3G Sarker Ashutosh [email protected] F2D Shabbir Malik Shahzad [email protected] F1D, F2B, F3B Shah Meet [email protected] S1F Shifflett Zachary [email protected] F3G Sissoko Yaya [email protected] F1A, F2A, F3D, S1A Sloboda Brian [email protected] F1C, F2A, S1B Srivastava Babita [email protected] S1E Taher Hanadi [email protected] F1E, S1B


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Last Name First Name E-mail Session Terefe Aemade Mistru [email protected] S1E Thompson Alexi [email protected] F1A, F3C Tiglioglu Tufan [email protected] F2B Tokunb Taofeek Olayiwola [email protected] S1D Trejos Sandra [email protected] F1F, F3G Trout Brian [email protected] F1C, F3C Wilder Lisa [email protected] F2A Wilson Timothy [email protected] F2D, F3B Yang Xuebing [email protected] F2E, F3D, S1B Yates Travis [email protected] F1A, F3A

NOTE

Tufan Tiglioglu PEA Vice President, Program

Associate Professor of Business Alvernia University

[email protected]

mailto:[email protected]


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PUBLIC UTILITY REALTY TAX (PURTA) IN PENNSYLVANIA

Thomas O. Armstrong Wilson College

1015 Philadelphia Ave Chambersburg, PA 17201

ABSTRACT

The Public Utility Realty Tax, PURTA, in Pennsylvania is imposed on certain entities furnishing utility services regulated by the Pennsylvania Public Utility Commission or a similar regulatory body. PURTA is in place of local real estate taxes and distributes the local realty tax equivalent to local taxing authorities. In addition, 7.6 mills, is added to PURTA and transferred to the Public Transportation Assistance Fund. Total average PURTA revenues is $44,182 million which represents .152 percent of property tax revenue collected for 2015. After examining five taxation criteria, PURTA may not meet the criteria of ease of administration and accountability.

I. INTRODUCTION The Public Utility Realty Tax Act, PURTA, is imposed on public utilities furnishing services under the jurisdiction of the Pennsylvania Public Utility Commission, PUC. PURTA was enacted in 1968 to satisfy the requirements of Article VIII, Section 4, of the PA Constitution, which required that in lieu of the assessment and collection of local Pennsylvania property taxes upon public utility real property, the Commonwealth must collect through a gross receipts tax or “other special tax”, PURTA, an amount of tax which is equivalent to the sum that all local taxing authorities would collection if they imposed a local real estate tax on such realty.1 PURTA is levied on Pennsylvania taxable value of property owned by utilities furnishing utility service and regulated by the Pennsylvania PUC. The taxable value is the current market value of utility realty.2 The tax rate is set each year by the Pennsylvania Department of Revenue to raise the required total revenue equal to the realty tax equivalent in lieu of local real estate tax revenue for distribution to the local taxation authorities: counties, municipalities and school districts. Beginning in fiscal year 2003-04 and afterwards, 7.6 mills, seven and six-tenths upon each dollar, surcharge of the state taxable value of utility realty is added to PURTA for the Public Transportation Assistance Fund, PTAF (Tax Reform Code of 1971, Section 1112-A) and remains within the General Fund upon appropriations to PTAF.

In 2015, the total revenue collected from PURTA was $38,157 million and adding in the 7.6 mills addition, PURTA revenue was $42,758 million. When compared to a total of county, municipality and school district property tax revenue of $26,912 billion, PURTA represents .142% without the 7.6 mills revenue or .152% with the additional millage relative to total property tax revenue for 2015.3 It has been argued that Pennsylvania needs state tax reform to improve economic performance (Armstrong, 2002). The purpose of this paper is to provide a PURTA revenue background and a PURTA tax analysis benchmarked against a set of fundamental tax principles to judge whether the tax needs reform. Afterwards, a conclusion will be provided.

II. PURTA REVENUE BACKGROUND Utility realty for PURTA purposes are “all lands, together with all buildings, towers, smokestacks, dams, canals, cooling towers, storage tanks, reactor structures, pump houses, supporting foundations, enclosing structures, supporting structures, containment structures, reactor containment outer shells, reactor containment vessels, turbine buildings, recovery tanks, solid waste area enclosures, primary auxiliary buildings, containment auxiliary safeguard structures, fuel buildings, decontamination buildings, and, all other structures and enclosures whatsoever which are physically affixed to the land, no matter how such structures and enclosures are designated and without regard to the classification thereof for local real estate taxation purposes, but not including machinery and equipment, whether or not housed within such building, structure or enclosure, or, after December 31, 1999, land and improvements to land that are indispensable to the generation of electricity, located within the Commonwealth that at the end of the taxable year or owned by a public utility or its affiliate either directly or by or through a subsidiary…and which are not subject to local real estate taxation under the law in effect on April 23, 1968…” (Tax Reform Code of 1971, Article XI-A).4 Table 1 provides a summary PURTA nominal revenues from 2010 to 2017, available PURTA revenues for 2018, budgeted revenues for 2019, and forecasted PURTA revenues from 2020 to 2023 from the Pennsylvania Department of Revenue. The additional PURTA revenues are the 7.6 mills surcharge on top of the PURTA base.


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Average PURTA revenues from 2010 to 2023 is $39,428 million distributed to local governments with an average annual percentage change over the same period of 1.71%. The additional 7.6 mills surcharge provides an average to the General Fund of $4,754 million. The total average PURTA revenues is $44,182 million. Annually, public utilities and Local Taxing Authorities, LTA’s are required to file a Public Utility Realty Tax Report (RCT-900 by April 1st) listing all Public Utility Reality Tax Act (PURTA) property and local assessed value(s). The utilities must remit tax based on the equalized statewide value of the property and an effective tax rate established annually each August 1st by the Department of Revenue (DOR). The taxable value of an individual PURTA property is calculated by multiplying the local assessed value by the equalization rate [Common Level Ratio (CLR) Factor] provided by the State Tax Equalization Board. The PURTA distribution process includes input from each LTA and the appropriate county assessment office. Annually, each LTA (county, city, borough, township, school district or institutional district) that chooses to participate in the distribution provides the Secretary of Revenue with the following information:

• Real estate tax rate [mills] per $1000 assessed value • Total assessed value of all public utility real estate in its jurisdiction • Total tax receipts from all sources for the preceding year

Each count0y assessment office declares annually and provides the local assessed value of every parcel classified for that tax year as public utility property for all LTAs within the county. All participating LTAs (those filing the tax report information RCT-900) are eligible to share in the distribution regardless of whether public utility real estate is located within their jurisdictions. Each year on Oct. 1, tax distributions checks are mailed to each reporting LTA for its share of the total realty tax equivalent. As an example, for public data available for 2015, a total of 1,713 local County governments and municipalities participated last year receiving a total of $12,295,558.48, compared to their own Realty Tax Equivalent (RTE) of $10,417,224.12. The average receipt of PURTA dollars was $7,178 per LTA. 458 municipalities received no PURTA distribution funding given they failed to file their PURTA tax report required annually by April 1st.

III. PURTA TAX ANALYSIS PURTA is examined relative to the five broad tax principles.5 It is recognized that no one tax will meet high standards to a

full set of tax principles; nevertheless, a tax that meets high standards to as many of the fundamental tax principles as possible would constitute good state tax policy. While the list of fundamental principles is open to debate, tax policy experts tend to agree on five broad principles (Brunori, 2001 and State Policy Reports, January 2002). 1. Adequacy. State revenue systems must raise revenue to pay for current and future public expenditures. Pennsylvania is required by the state’s constitution for the final state budget to be in balance. To understand the adequacy tax principle, one needs to understand the demand for tax revenue, which is the demand by government for private sector resources. Wagner’s Law (1958) suggests that government expenditure, as a share of the economy will continue to rise over time. Now, some of the increase in resources demanded by government is for efficient enhancing activities while others are for inefficient activities. The risk to Pennsylvania is lower economic growth and living standards, when more resources over time are directed towards inefficient activities as a greater share of government spending (Armstrong, 1994). Pennsylvania is required to impose the PURTA tax on public utility realty on exempt local real estate tax base and distribute the PURTA revenue to local taxing authorities based on the realty tax equivalent. Generally, Pennsylvania is a pass-through entity that collects property tax on public utility property, PURTA, in lieu of local real estate tax collection and remits PURTA, by a redistribution mechanism, to the locals. Given that 458 municipalities in 2015 have not filed their PURTA tax report and have not received PURTA redistribution funds, full funding of municipality public expenditures are under greater fiscal stress than would need be if filing have occurred. An additional 7.6 mills are added to the PURTA tax base and transferred to the Public Transportation Assistance Fund, PTAF. PTAF (Pennsylvania Tax Compendium, March 2017) receives taxes and fees including the 7.6 mills of PURTA funds upon appropriations from the General Fund for Pennsylvania mass transportation funding. The predominant funding is provided to mass transportation for large urban areas while the rest is shared among the other urban and rural transportation agencies in the Commonwealth. The additional PURTA millage is either not redistributed to the local jurisdictions or a very small percentage is redistributed to the local transportation agencies enhances the fiscal stress upon funding local government budgetary expenditures. 2. Neutrality. The imposition of taxes should minimally influence market decisions. Businesses and individuals should


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not take actions based significantly on tax consequences but on market conditions. With PURTA tax revenues representing such a small percentage of property tax revenues, .142% or .152% of property tax revenue for 2015 collected, the overall incentive for firms to alter pricing, output and investment decisions is most likely minimal. Nevertheless, the tax base at the local and PURTA levels differ in terms of what is considered exempt from tax as machinery and equipment. After the decision in Commonwealth v. Philadelphia Electric Company, 372 Aa.2d 815, 472 Pa. 530, 1977, the state amended the definition of “utility realty” to specifically include items that otherwise could have been considered machinery and equipment. This resulted in a much broader exclusion on the local level. Therefore, with a narrow PURTA tax base relative to a local real estate property tax base, there is a tax incentive to challenge the PURTA tax base to be applied at the local level. Business decisions may be influenced by this differential tax base consequence where resources involved in these tax disputes result in a government administrative and an uneconomic use of resources. 3. Equity. The equity concept generally revolved around two considerations: horizontal and vertical equity. Horizontal equity suggests that two entities that receive the same taxable income should be taxed equally. Vertical equity suggests that differently situated taxpayers should be taxed differently. Tax expenditures, by way of exemptions, deductions or credits, may harm horizontal equity but may or may not advance vertical equity. Utility property subject to PURTA and non-utility property subject to local real estate taxation are treated differently with respect to machinery and equipment exemptions with PURTA property as a narrower tax base where horizontal equity being violated with respect to property taxation. 4. Ease of Administration. Sound tax policy requires minimizing the costs of compliance for taxpayers and collection costs by government. As a tax system becomes more complex for businesses and individuals, more time and resources will be spent determining the requirements and attempting to minimize the tax burdens of the law. Economic inefficiency results where resources could be better spent in other areas. Filing by utilities and local taxation authorities are submitted electronically to the Department of Revenue. Required electronic filing has reduced the administrative costs relative to paper filing of previous years for regulated utility taxpayers and LTAs. The 458 municipalities that failed to file their PURTA tax report suggest an administrative cost greater than

an expected PURTA return that should be analyzed more closely. In addition, there is state resource use to meet the requirement for the Pennsylvania Department of Revenue to reconcile both properties and values reported by the Local Taxing Authorities and the values reported by the regulated utilities. If excessive, opportunity costs may impact administrative time in other state revenue tax settlement activities. 5. Accountability. Good tax policy should provide taxpayers with the true costs of providing public services (Oakland and Testa, 1994). Transparent tax policy encourages the economic link between government spending and taxing. The more visible the link, the more likely the government sector will conform to taxpayer preferences. Transparent tax policy also requires continuous review of existing laws and regulations to help reveal the tax costs to taxpayers of paying for state government services. Without accountability, business and individual taxpayers would consistently underestimate these tax costs payments for government services. The risk is businesses and individuals would support too large a range of state services than economically justified. There is a direct relationship between the revenue that local public utilizes would have paid in local real estate taxes relative to the revenue paid through PURTA but is not complete due to the narrower tax base of PURTA relative to local real estate taxes. Nevertheless, there is no accountable relationship between the additional tax on the PURTA base and the funds transferred to the Public Transportation Assistance Fund and used for transportation funding. Those firms who pay the PURTA 7.6 mills tax generally do not receive the benefits from mass transportation primarily used in the Philadelphia and Pittsburg areas.

III. CONCLUSION Improvements in current PURTA state tax policy should be considered in the areas of administration and accountability through tax simplification initiatives. Tax simplification is a search for tax systems, with reasonable compliance costs and administrative burdens, which promote a high degree of voluntary compliance among taxpayers. The result of tax simplification is the use of less administrative and taxpayer resources per collection of certain revenue (Armstrong, 2013). The administration of PURTA should be evaluated for barriers to filing by local tax authorities. Any simplification will allow more municipalities to file and receive their due PURTA revenue.


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Transparent tax policy is to conduct routine evaluations of their tax revenue systems (Armstrong, 2013). A PURTA tax evaluation should be conducted to include an incidence analysis to determine who is paying what share of public

transportation funding relative to additional PURTA taxation for an evaluation of the link that who pays the taxes should receive the benefits from the taxes.

Table 1: Public Utility Realty Tax Revenues (Dollar Amounts in Thousands)

ENDNOTES * The author would like to thank Thomas D. Kimmett, Greg Skotnicki, and a discussant for comments. All possible errors are the author’s. 1. For an historical background on PURTA, see the various back issues of the Pennsylvania Tax Compendium. 2. Electric generation assets stopped being exempt and is subject to local real estate tax beginning January 1, 2000, due to deregulation of electric generation that is no longer regulated by the Public Utility Commission (Hassell, July 21, 1997). 3. The Pennsylvania real estate tax is the only tax authorized by law to be levied by all classes of local government. Every property owner generally pays real estate taxes to three independent classes: the county, the municipality and the school district. Real estate taxes in 2015-16 accounted for about 88% of $15.59 billion total local tax revenues of school districts, about 33% of $7.9 billion 2015 total tax revenues of municipalities, and 85% of $3.39 billion 2015 total tax revenues of counties (Pennsylvania Departments of Education and Community and Economic Development). 4. There are specified items not subject to the PURTA base: (i) easements or similar interests, (ii) railroad rights-of-way and superstructures thereon, (iii) pole, transmission tower, pipe, rail or other lines, whether or not attached to such land or to any structure or enclosure which is physically affixed to the land.

5. See Armstrong (December 16, 2003) and Armstrong and Benzing (1996) for a general discussion concerning the controversy of property taxes.

REFERENCES Armstrong, Thomas. 1994. “An Inquiry into the Pennsylvania Constitution: Do Constraints Effectively Lessen Inefficient Government Activities?” Pennsylvania Economic Association Proceedings. 130-144. Ibid and Cindy Benzing. 1996. “The Property Tax and Pennsylvania,” Pennsylvania Economic Association Proceedings, 348-353. Ibid. December 16, 2003. “Real Property Taxes in Pennsylvania,” The Shenango Institute for Public Policy, 1-10. Ibid. 2002. "State Taxation Reform Proposals for Pennsylvania," Pennsylvania Economic Association Proceedings, 1-10 Ibid. 2013. "Pennsylvania Tax Simplification: Nuisance Tax Credit, Obsolete Taxation and Administration Provision Repeals, Including Proper Placement within the Tax Reform Code," Pennsylvania Economic Association Proceedings, 28-33. Brunori, David. 2000. State Tax Policy: A Political Perspective. Washington, DC: The Urban Institute Press. Governor's Executive Budget 2018-19. February 6, 2018. Commonwealth of Pennsylvania.

Dates 2010 2011 2012 2013 2014 2015 2016PURTA 39,549 34,434 28,721 43,884 37,048 38,157 39,211Additional PURTA1 4,769 4,152 3,463 5,291 4,467 4,601 4,728Total 44,318 38,586 32,184 49,175 41,515 42,758 43,939Dates 2017 2018* 2019** 2020*** 2021*** 2022*** 2023***PURTA 40,185 40,800 41,200 41,600 42,000 42,400 42,800Additional PURTA1 4,845 4,920 4,968 5,016 5,064 5,112 5,161Total 45,030 45,720 46,168 46,616 47,064 47,512 47,961* Available; **Budget; ***Estimated

Sources: 2018-19 Governor's Executive Budget, February 6, 2018, Commonwealth of Pennsylvania; Pennsylvania Tax Compendium, March 2017, and Statistical Supplement for the Pennsylvania Tax Compendium, November 2017, Pennsylvania Department of Revenue, Commonwealth of Pennsylvania

1. Calculations based on PURTA Revenues


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Hassell, C. Daniel. July 21, 1997. “Switching On Competition: The Tax Implications of Consumer Choice in Pennsylvania, State Tax Notes, 179-184. Oakland, H., William and William A. Testa. 1994. “State-Local Business Taxation and the Benefits Principle,” Economic Perspectives, 2-19. Pennsylvania Tax Compendium. March 2017. Pennsylvania Department of Revenue, Commonwealth of Pennsylvania. State Tax Policy Reports. January 2002. Washington, DC: Federal Funds Information for the States.

Statistical Supplement for the Pennsylvania Tax Compendium. November 2017. Pennsylvania Department of Revenue, Commonwealth of Pennsylvania. Tax Reform Code of 1971, Article XI-A, Sections 1101-A through 1112-A (72 P.S. 8101-A through 8110-A). Act of March 4, 1971 (P.L. 6, No.2). Commonwealth of Pennsylvania. Wagner, A. 1958. Three Extracts of Public Finance. In Classics in the Theory of Public Finance. London: Macmillan.


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SEVERAL ISSUES FACING THE DESIGNER OF ECONOMIC POLICIES

Jeffrey Yi-Lin Forrest1), Erkan Kose2), Roger Solano1) 1)School of Business, Slippery Rock University, Slippery Rock, PA 16057

2)Department of Industrial Engineering, Nuh Naci Yazgan University, Kayseri, Turkey

ABSTRACT

By employing the feedback mechanisms this paper investigates the following two problems related large-scale, sudden movements of capital that flow either into or away from a particular economy: 1) How can the consequent economic shocks be minimized while the desirable economic growth can still be on track; and 2) when economic performance indicators approach pre-determined targets as appropriate policies are introduced, how can the economy also evolve without experiencing much severe up-and-down fluctuations. In particular, to address the first problem, this paper provides a method of designing economically stabilizing strategies when the economic system suffers from external shocks. And as for the second problem, it is addressed by looking at placing the poles of the general control-theory model of the economic system in particular areas on the complex plane. In terms of policy making, what is presented in this paper implies that a disturbed economic system by outside forces could be stabilized by designing and adopting appropriate economic policies. The established results are expected to provide practically useful guidelines for policy makers.

INTRODUCTION

When large amounts of money enter or withdraw from an economy within a short period of time, various state variables or performance indicators of the economy will experience drastic changes, causing major shocks to the originally orderly operation of the economy (Forrest, et al., 2013a; 2013b; Forrest, 2014). These shocks most likely create uncertainties in the previously established relationship and equilibria between the state vector and the output vector of the economy, and pose major challenges for how the participants of the economy could potentially handle the difficult or disastrous aftermath.

If the function 𝜑𝜑(𝑡𝑡) represents the environmental interference or the external shock with t being the time variable, then a key question policy makers need to address is: Under the effect of 𝜑𝜑(𝑡𝑡), what decision input 𝑢𝑢(𝑡𝑡), a set of policies that change with time t, should be adopted so that the changes in the output or performance 𝑦𝑦(𝑡𝑡) of the economy would satisfy a certain pre-determined objective? Specific to this paper, our issues of concern are: if large amounts of foreign capital enter or leave an economy within a short period of time, that is, the shock 𝜑𝜑(𝑡𝑡) is known, then what kind of fiscal policy and/or

monetary policy (that is, the decision input 𝑢𝑢(𝑡𝑡)) should be adopted by the government to accomplish the following objectives, seen as the output 𝑦𝑦(𝑡𝑡) of the economic system: 1) maintain a stable price level; and 2) the gross domestic production still grows as originally expected.

To materialize these objectives, the state and the output of the economy have to be continuously measured in order to determine whether or not the actual values of relevant variables deviate from the pre-determined targets. If a deviation does appear, the policy maker will need to adjust the control vector (or policies) to make the deviation approach zero with time. To illustrate the situation, one can imagine a ship that is sailing in the ocean. No matter how the helmsman constantly adjusts the compass, it is most likely that the ship is deviating from the desired route (the control objective). Even for the best helmsman, he still cannot be assured that he would stay perfectly on the pre-determined course at any given moment of time. That is because in its sailing, the ship, as a metaphor of an economy, experiences various shocks from the environment; and random disturbances of the environment constantly interrupt the realization of the control applied on the ship (or regulation applied on the economy) to approach the objective of the control (respectively, regulation).

As for environmental shocks, since the 1990s, with the economic globalization, the frequency and contagion of financial crises have increased tremendously. That has profoundly impacted the world economy and captured the attention of many governments and scholars from around the world. In particular, the U.S. subprime mortgage crisis in 2007 led to a global financial crisis, confirming the fact that a non-systemic risk of a local region could become a global financial systemic risk within the framework of financial globalization and economic integration (Chen and Ying, 2012; 2014). This reality brings forward new challenges to the existing theories of financial crises and regulations and makes it necessary to consider building a supervision system for the outbreak of crises and consequent contagion. To face this challenge, this paper attempts to look for sufficient conditions of controllability for the dynamic path of economic systems, while this effort surely has great theoretical value for follow-up studies on the prevention of financial crises and the reduction of the disastrous consequences.

An effective method for addressing our issues of concern is to make policy adjustments based on feedback (Granger, 1963; Infante and Stein, 1973; Norman, 1974; Moe, 1985; Hui, 1991;


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Chong and Calderón, 2000; Kiekintveld, et al., 2004; Kirk,2012; Zhang, et al., 2014). That is, by using the mechanism of feedback to make necessary regulatory changes, the policy maker is able to feed the deviation between the result of control, such as the “actual state” of the output of the system (for example, the actual route of the ship), and the pre-determined objective of control, such as the “ideal state” (for example, the pre-determined route), back into the input of the controlled system as the basis for applying the next level of adjustment. Such a system with feedback is referred to as a closed control system. This kind of thinking is known as feedback control. To this end, (Chong and Calderon, 2000) is a good example on how similar idea has been successfully employed in the study of economic systems.

In the process of regulating an economy, sometimes it is not enough for the policy maker to just require the output ( )y t to approach the target value. Beyond this requirement, it also often requires the process of approaching the target to experience fewer up-and-down fluctuations with less magnitude. That is, the control strategy that is designed for adoption must have relatively good characteristics. As a matter of fact, no matter whether it is about maintaining the stability (Shen and Zheng, 2015) of the economy that performs at a certain desirable level, or the tracking control (Nguyen and Krylov, 2015) that chases after a mobile and dynamic target of the actual system, the essence is about how to resist the interference of external factors of the environment, and how to reduce or eliminate the occasional deviation between the actual output of the regulated economy and desired pre-determined targets so that the performance of the economy reaches the expected objective of regulation. In other words, this work mainly addresses the feedback control and adjustment of economic systems in order to develop practically operational methods to effectively counter the shocks on the economy as caused by external interferences.

The rest of this presentation is organized as follows. The next section studies two kinds of feedback mechanisms: the state feedback and the output feedback, and then looks at how to specifically design feedback controls for economic systems. After that, the following section considers the problem of how to reduce the fluctuation severity under the effects of economic policies. Then, the presentation of this paper is concluded in the last section.

ECONOMIC POLICIES BASED ON PERFORMANCE INDICATORS

This section presents a method about how to design economically stabilizing strategies when the economy suffers from external shocks such as sudden inwards or outwards flows of capital. The results show that such a severely disturbed economic system by outside factors can still be stabilized if appropriate economic policies are designed and adopted.

For an economy to be healthy, measures of its performance need to stay within some range or to be equal to some fixed ideal values. At the same time, when the environment experiences major shocks, the policy maker would like to maintain the performance of the domestic economy within the ideal range through adjusting her policies. In such a reactive control process of decision making, the state and/or the output of the economic system are the key information used in the process of policy making. At the same time, when the economy is manipulated by changing policies, it also experiences constantly various random disturbances from the outside world (Lin, 2008). The effect of the latter relentlessly interrupts the effects of the former, making it difficult for the performance of the economy to approach the target objectives. To face this challenge, we will apply the method of feedback adjustments to effectively handle various interfering factors of the environment. When doing so, the deviation between the result of manipulation and the expected objective of regulation is fed back into the controlled system (economy) as input and as the basis to design and to modify the control strategy of the next step. Such feedback mechanism represents a closed-loop regulation (or control) of economic systems.

Technically, different pieces of information can be used as the feedback input, such as the information of the state vector of the economy or the information of the system’s output vector. Hopefully, such important characteristics of the economy as observability and controllability are kept by the regulated (or controlled) economy. In particular, assume that the economy of our concern can be modelled by the following constant-coefficient linear system (for details, see (Forrest, et al., 2018))

�𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

= 𝐴𝐴𝐴𝐴 + 𝐵𝐵𝑢𝑢𝑦𝑦 = 𝐶𝐶𝐴𝐴

(1)

Although the state of an economic system at time t can be, in theory, completely describable by all relevant economic factor variables (Zhang and Zhang, 1981), known as the state 𝐴𝐴 of the economy, the literature indicates that this state is finite dimensional (Forrest, et al., 2013a; 2013b; Myoken, 1979). And the state of the economy is affected by monetary and fiscal policies, summarized within the input vector 𝑢𝑢, which shape the state of the economy of the next time moment (Westcott, 1977). That is why these policies can be treated as input variables that regulate the economic system.

In general, the evolution of the economic system can be described as in equation (1) except that the right-hand sides of the equations should be general functions 𝑓𝑓(𝐴𝐴,𝑢𝑢, 𝑡𝑡) and 𝑔𝑔(𝐴𝐴,𝑢𝑢, 𝑡𝑡), which might not be linear. Considering the fact that fiscal and monetary policies tend to work for the near term instead of long term into the future, these functions could be generally linearized. That is why we can use equation (1) to model the economy of our concern.

If the input is


45

𝑢𝑢(𝑡𝑡) = 𝑟𝑟(𝑡𝑡) − 𝐾𝐾𝐴𝐴(𝑡𝑡), (2)

or

𝑢𝑢(𝑡𝑡) = 𝑟𝑟(𝑡𝑡) − 𝐾𝐾𝑦𝑦(𝑡𝑡), (3)

we have a state feedback control or an output feedback control, where 𝑟𝑟(𝑡𝑡) is a reference input of the system and 𝐾𝐾 the gain matrix of the feedback. When some aspects of the state of the economy are not observable to the policy maker, she might look at using an output feedback mechanism.

When the state feedback in equation (2) is employed, the state equation of the closed loop system is

𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑

= (𝐴𝐴 − 𝐵𝐵𝐾𝐾)𝐴𝐴 + 𝐵𝐵𝑟𝑟. (4)

And applying the output feedback in equation (3) provides the following state equation of the resultant closed loop system:


= (𝐴𝐴 − 𝐵𝐵𝐾𝐾𝐶𝐶)𝐴𝐴 + 𝐵𝐵𝑟𝑟. (5)

If the state of the economy at the initial time 𝑡𝑡0 is 𝐴𝐴(𝑡𝑡0) and an appropriate monetary or fiscal policy 𝑢𝑢 within a bounded time interval �𝑡𝑡0, 𝑡𝑡𝑓𝑓� can be selected to make the economy to reach the expected state 𝐴𝐴(𝑡𝑡𝑓𝑓) at time 𝑡𝑡𝑓𝑓, then the economy is said to be controllable at time moment 𝑡𝑡0. For the economy described in equation (1), if any of its state is controllable, then the economy is said to be completely controllable or (𝐴𝐴,𝐵𝐵) controllable for short. Then we have the following conclusion from control theory (Perko, 2013): Applying state feedback does not affect the controllability of the system.

Assume that the input 𝑢𝑢 of the economy in equation (1) is zero. If for any given initial state 𝐴𝐴0 = 𝐴𝐴(𝑡𝑡0), there is a finite time moment 𝑡𝑡1 > 𝑡𝑡0 such that the output 𝑦𝑦(𝑡𝑡) on the time interval [𝑡𝑡0, 𝑡𝑡1] can be used to determine the initial state 𝐴𝐴0, then this economy is said to be completely observable or (𝐶𝐶,𝐴𝐴) observable for short. Then similar to the previous result, the following (Perko, 2013) holds: Applying output feedback does not affect the economy’s observability.

What these two results say is that as long as monetary or fiscal policies are internally introduced to the economy to counter various interfering factors, the over-arching properties of the economy, such as controllability and observability, are not altered.

The mechanism of the feedback, when applied to regulate the economy, could suffer from problem(s), such as the phenomena of imbalance or severe fluctuations in the performance of the economy. When an imbalance is the case, it generally means that the applied feedback is insufficient so that no matter how the economy is adjusted, the performance of the economy still stays in an erroneous state that is far away from the expected target of regulation. For example, during the

1997 financial crisis in Thailand, the failure of preventing the worst from happening was closely related to not only how the policies were implemented, but also the relevant problems existing in the economic development of Thailand (Lauridsen, 1998; Phongpaichit and Baker, 2000; Laird, 2000). On the other hand, severe economic fluctuations indicate the fact that the feedback regulation either comes a little too late or is applied with too much force. Therefore, the problem we try to address is really about the stability of feedback control of the economy. In other words, the economy was in a state of equilibrium before being forced to deviate from that state by an environmental interference, then the problem is: Can the regulator eliminate the interference and help the system return to a state of equilibrium through introducing economic policies?

The concept of stability of control systems has been well studied in the area of differential equations (Coddington and Levinson, 1955; Perko, 2013). In particular, for constant coefficient linear system


= 𝐴𝐴𝐴𝐴,𝐴𝐴(0) = 𝐴𝐴0 (6)

let 𝜙𝜙(𝑡𝑡; 𝐴𝐴0, 0) represent its state trajectory whose initial state is 𝐴𝐴(0) = 𝐴𝐴0at the initial time moment 0. If lim𝑑𝑑→∞

‖𝜙𝜙(𝑡𝑡; 𝐴𝐴0, 0)‖ = ∞, then the initial state 𝐴𝐴0 is said to be instable. When the system in equation (6) does not have any instable initial state, then the system is said to be stable. So, when lim

𝑑𝑑→∞‖𝜙𝜙(𝑡𝑡; 𝐴𝐴0, 0)‖ = ∞holds true for any 𝐴𝐴0 ∈ 𝑅𝑅𝑛𝑛, then

the system in equation (6) is said to be asymptotically stable. Because for the real-life economy of our concern what is important is the concept of asymptotical stability, let us refer the economy’s asymptotical stability as economic stability, or that A in equation (1) is a stable matrix. In the following, let us see how to design a feedback control 𝑢𝑢 = 𝑟𝑟 + 𝐾𝐾𝐴𝐴 so that the resultant closed loop system is asymptotically stable for the economic system in equation (1).

If we let the reference input be 𝑟𝑟 = 0, we have the following feedback control 𝑢𝑢 = 𝐾𝐾𝐴𝐴 and the closed loop system is

𝑑𝑑𝐴𝐴𝑑𝑑𝑡𝑡

= (𝐴𝐴 + 𝐵𝐵𝐾𝐾)𝐴𝐴.

Based on results of control theory (Perko, 2013), the matrix 𝐾𝐾 that we obtain by solving the following equation is the desired feedback gain matrix.

(𝐴𝐴 + 𝐵𝐵𝐾𝐾)𝑇𝑇 + (𝐴𝐴 + 𝐵𝐵𝐾𝐾) = −𝐼𝐼. (7)

Example 1. Assume that the following system models a simply economic system,

0 1 1 12 2 2 1

dx x udt

= + −

.


46

Let us see how we can find a feedback 𝑢𝑢 = 𝐾𝐾𝐴𝐴 such that the resultant closed loop system is stable.

First, equation (7) is specified into

11 21 12 22 11 21 12 22

11 21 12 22 11 21 12 22

1 1 1 02 2 2 2 2 2 2 2 0 1

Tk k k k k k k kk k k k k k k k+ + + + + +

+ = − − + − + − + − +

from which we can obtain the following feedback gain matrix

𝐾𝐾 = �𝑘𝑘11 𝑘𝑘12𝑘𝑘21 𝑘𝑘22

� = � 1 1− 3 2⁄ − 1 2⁄ �.

It can be checked that when this matrix K is used as the feedback gain, the poles of the resultant closed loop system, (that is, the eigenvalue of the matrix 𝐴𝐴 + 𝐵𝐵𝐾𝐾. In the rest of this paper, we say the poles are determined by the matrix of the state vector), have −0.5 as the real part, and the closed loop system is asymptotically stable.

As a practical application of this established theory, let us now take a closer look at the Thai crisis in the 1990s.

When the financial crisis was on the brink of breaking out, the available foreign exchange reserves were not enough to meet the demand for exchange rate intervention. So, instead it should be more practical for the Thai authority to address how to minimize disastrous consequences of the coming crisis within the credit environment in order for the economy to perform as normally as possible.

Other than illustrating the applicability of various control methods, such as regulating the interest rate, intervening with the foreign exchange rate, tightening the fiscal policy, restructuring the financial sector, etc., as employed by the Thai government during the crisis, our theory developed here suggests also the following additional actions:

• The exchange rate and interest rate need to be stabilized simultaneously

• The confidence of the stock market needs to be supported;

• The speculation cost of currencies needs to be lifted higher; and

• Short selling needs to be limited.

Due to the lack of relevant data, we will have to stop the discussion of this case right here.

ECONOMIC POLICIES THAT LEAD TO SMOOTH

RECOVERIES

This section studies how the economy could develop as expected under the effect of adopted policies without experiencing much severe up-and-down fluctuations. It

associates economic policy making with the pole placement of the control-theory model of the economy in equation (1). Speaking practically, the economic system is required, to a certain degree, not only to be controllable (or it can be regulated) so that the controlled variable, such as the GDP, the inflation, etc., approaches the target value, but also to develop reasonably well in its process of approaching the target value. To achieve this end, it involves addressing the problem of pole placement of the systemic representation of the economy (Cobb, 1981; Kaczorek, 1985; Ogata, 1995; Ram, et al., 2011; Kirk, 2012; Zubov, et. al., 2013). Results from control theory (Perko, 2013) and the discussions in the previous section indicate that when the poles of the economic system can be arbitrarily placed, then the policy maker can alter some of the important characteristics of the economy through using the feedback mechanism. That is, an ability to relocate the poles is closely related to that of regulation or controllability of the economy. Although what is discussed in the previous section can help to materialize the goal of regulating the economy, the development for the economic performance to approach the desired target might still experience major fluctuations and might take a long time to actually reach the target. This end has a lot to do with the eigenvalues of 𝐴𝐴 in equation (1) which are also known as the poles of the economic system. In particular, if the poles of the economic system, or the eigenvalues of matrix 𝐴𝐴, are located on the left half plane but near the imaginary axis, then in its process of approaching the targeted, the performance of the economy will suffer from severe up-and-down fluctuations. Such severe fluctuations had been experienced during the process of dealing with the 1998 Russian financial crisis.

In October 1997, a financial crisis broke out in Southeast Asia. And in the following ten months, Russia made unremitting efforts to survive. During the time period, Russia issued large amounts of national debts and sacrificed a lot of foreign exchange in the market, drastically reducing Russian foreign exchange and gold reserves. Hence, the government faced the following dilemma: either continue to maintain the floating exchange rate policy of the “currency corridor” or support the bond market. Although the government decided to go with the former choice, the financial situation did not turn for the better; and ruble started to drastically depreciate.

Facing the continuing turmoil in the financial market, the Russians introduced an economic program to stabilize the financial situation. However, the program did not generate sufficient investor confidence. On August 13, 1998, George Soros, a renowned international speculator, publically suggested for Russian government to depreciate ruble in the scale of 15% - 25%, causing the price index of 100 industrial company stocks, as calculated by Interfax, to lose over 70% of its value. At the same time, the tax collected during the month of July was about 12 billion ruble, while the operational budget for each month was no less than 20 billion ruble, representing a huge gap between the government’s income and expense.


47

Under the pressure, on August 17, the government launched three tough emergency measures:

• The first was to expand the floating range for the exchange rate of ruble while lowering the upper limit of the ruble’s exchange rate against the dollar to 9.5∶1. That in fact declared the depreciation of ruble against the dollar from 6.295 to 9.5, more than 50% depreciation. So, the market predicted based on this emergency measure that the exchange rate of ruble would continue to drop drastically. And indeed, in the next 10 days, ruble fell to 20 – 21:1, which busted the stable exchange rate of the past three plus years.

• The second was to delay the payments, which were due, of foreign debts, which were estimated to be around US$15 billion, for 90 days.

• The third was to lengthen the repayment periods of domestic debts, making all national debts, totaling about US$20 billion, that would mature by December 31, 2099, become mid-term debts that would mature in the next 3, 4, or 5 years. And before finishing the change of maturities, the national debt market was closed for trading.

These measures immediately caused public outcry, the stock market crashed and closed for trading, and the exchange rate of ruble plunged. Afterward, the central bank altogether declared that it would allow ruble to float freely, causing the public to either run for ruble in order to exchange for the U.S. dollar or buy anxiously consumer goods and the stock market plummeted much further. At end of August 28, the price index of the 100 industrial company stocks, as calculated by Interfax, fell to US$15.92 billion, a fall of 85% compared to the level of US$103.356 billion reached at the start of the year. And then the market closed down, making the price index worthless.

Although all the adopted responses helped to reduce the economic loss of the nation, they surely created major obstacles for the recovery of the domestic economy. In particular, firstly, half of the deposits of the domestic residents were lost. The prices of imported goods rose 2 to 3 times. In September, the consumer prices went up 40%, the highest since the start of the economic reform. People’s actual wages went down 13.8%, making nearly 1/3 of all the residents live under the poverty line. The overall economy dropped 5%, industry 5.2%, agriculture 10%, and foreign trade 16.1%.

Secondly, many commercial banks, especial those big ones, suffered heavy losses. The SBS agriculture bank, one of the seven financial giants, at the time held short-term national debts in the equivalent amount of US$1 billion, which became worthless instantly. It was estimated that about one half of the commercial banks were on the verge of bankruptcy.

Thirdly, this Russian financial crisis spread across its national

border and affected Europe, the United States, Latin America, becoming a global effect. Foreign investors lost about US$33 billion in this huge financial storm, where American long term capital management firms (or hedge funds) lost about US$2.5 billion, American Bankers Trust lost somewhere near US$0.49 billion. Germany was the largest creditor of Russia, which owed Germany 75 billion marks (about US$44.4 billion), most of which were government-guaranteed bank loans. So, any trouble that appeared on the Russian financial market affected the safety of German creditors, creating shocks on the German market. Then the shock waves were spread over to the entire European financial market. For example, Frankfurt DAX index once fell over 3%, the CAC40 index of Paris stock market dropped 1.76%, Amsterdam stock market lowered 2%, Zurich stock market lost 1.6%, etc. For related details, see (Dabrowski, 2012; Kenourgios and Padhi, 2012; Kenourgios, et al., 2011; Bisignano, et al., 2012; Gluschenko, 2015; Razin and Rosefielde, 2011).

This real-life example shows that in dealing with environmental disturbances and shocks, it is necessary to make sure the adopted feedback control strategies possess desirable qualities so that consequent systemic fluctuations could be reduced as much as possible.

Additionally, when not all of the eigenvalues of matrix 𝐴𝐴 in equation (1) are located on the left half of the complex plane, the economic system is not asymptotically stable, and the feedback strategies as designed in the previous section cannot make the performance of the economy approach the pre-determined targets. Hence, it is necessary for the policy maker to have new methods not only to guarantee that the process of economic development that approaches the pre-determined target exhibits desirable qualities, but also to make the performance of the economy actually approach the target by designing relatively good feedback control strategies, even if the economy itself is not asymptotically stable. To this end, the design of such feedback control strategies is closely related to the poles of the economic system, known as pole placement.

Assume that an economy is modelled by the constant coefficient linear system in equation (1), which takes the following structural form and is (𝐴𝐴1,𝐵𝐵1) controllable.

1 12 1

20 0

c

c

NcNc

dxxA A Bdt uxAdx

dt

= +

(8)

[ ]1 2c

Nc

xy C C

x

=

(9)

whose set of poles consists of those determined by 𝐴𝐴1 and those by 𝐴𝐴2. Using the feedback


48

𝑢𝑢 = 𝑟𝑟 − 𝐾𝐾𝐴𝐴 = 𝑟𝑟 + [𝐾𝐾𝑐𝑐 𝐾𝐾𝑁𝑁𝑐𝑐][𝐴𝐴𝑐𝑐 𝐴𝐴𝑁𝑁𝑐𝑐]𝑇𝑇,

where −𝐾𝐾 = [𝐾𝐾𝑐𝑐 𝐾𝐾𝑁𝑁𝑐𝑐], rewrites this model of the economy as the following closed loop system:

1 1 12 1 1

20 0

c

cc Nc

NcNc

dxxA B K A B K Bdt rxAdx

dt

+ +

= +

whose set of poles consists of those of 𝐴𝐴1 + 𝐵𝐵1𝐾𝐾𝑐𝑐 and those of 𝐴𝐴2, respectively. From the previous section, it follows that the poles determined by 𝐴𝐴2 stays the same with the applied feedback. That is, if the economic system in equation (8) is not completely controllable, then the system’s poles cannot be arbitrarily relocated through using feedback. On the other hand, if a constant coefficient linear system is completely controllable, results from control theory then indicate that the system’s poles can be arbitrarily placed, and that when 𝐴𝐴 is not a stable matrix, there is 𝐾𝐾 such that the matrix 𝐴𝐴 + 𝐵𝐵𝐾𝐾 of the resultant closed loop system as obtained from using the feedback 𝑢𝑢 = 𝑟𝑟 + 𝐾𝐾𝐴𝐴 is a stable matrix, if and only if all poles determined by 𝐴𝐴2 are of negative real parts. The problem of finding K to make 𝐴𝐴 + 𝐵𝐵𝐾𝐾 stable is referred to as the problem of stabilizing the economic system.

Example 2. For the following economic system

1 1 0 11 1 0 00 1 3 0

dx x udt

= − +

= 𝐴𝐴𝐴𝐴 + 𝑏𝑏𝑢𝑢,

find a state feedback 𝑢𝑢 = 𝑟𝑟 + 𝐾𝐾𝐴𝐴 such that the resultant closed loop system is stable with −1, −2+3i, and −2−3i as its poles.

Solution. Step 1: Define 𝑈𝑈 = [𝑏𝑏,𝐴𝐴𝑏𝑏,𝐴𝐴2𝑏𝑏]−1, 𝑞𝑞 =[0 0 1]𝑈𝑈−1, and 𝑇𝑇 = ([𝑞𝑞, 𝑞𝑞𝐴𝐴, 𝑞𝑞𝐴𝐴2]𝑇𝑇)−1. Then we have

𝑈𝑈 = �1 1 20 1 00 0 1

�−1

= �1 −1 −20 1 00 0 1

�, 𝑞𝑞 = [0 0 1],

and

𝑇𝑇 = �0 0 10 1 31 2 9

�−1

= �−3 −2 1−3 1 01 0 0

�.

By taking the transformation 𝐴𝐴 = 𝑇𝑇𝐴𝐴′, the given economic system becomes:

𝑑𝑑𝐴𝐴′

𝑑𝑑𝑡𝑡= 𝑇𝑇−1𝐴𝐴𝑇𝑇𝐴𝐴′ + 𝑇𝑇−1𝑏𝑏𝑢𝑢 = 𝐴𝐴′𝐴𝐴′ + 𝑏𝑏′𝑢𝑢

= �0 1 00 0 1−6 2 3

� 𝐴𝐴′ + �001� 𝑢𝑢.

If we apply the state feedback

𝑢𝑢 = 𝑘𝑘𝐴𝐴′ + 𝑟𝑟 = [𝑘𝑘1 𝑘𝑘2 𝑘𝑘3]𝐴𝐴′ + 𝑟𝑟

on this transformed economic system, then the characteristic polynomial of the resultant close loop system

𝑑𝑑𝐴𝐴′𝑑𝑑𝑡𝑡

= (𝐴𝐴′ + 𝑏𝑏′𝑘𝑘)𝐴𝐴′ + 𝑏𝑏′𝑟𝑟

is

|𝑠𝑠𝐼𝐼 − (𝐴𝐴′ + 𝑏𝑏′𝑘𝑘)| = 𝑠𝑠3 + (−3 − 𝑘𝑘3)𝑠𝑠2

+(−2 − 𝑘𝑘2)𝑠𝑠 + (6 − 𝑘𝑘1),

which is supposed to be equal to

(𝑠𝑠 + 1)(𝑠𝑠 + 2 − 3𝑖𝑖)(𝑠𝑠+2 + 3𝑖𝑖)

= 𝑠𝑠3 + 5𝑠𝑠2 + 17𝑠𝑠 + 13.

That is, we have:

6 − 𝑘𝑘1 = 13, −2 − 𝑘𝑘2 = 17, and −3 − 𝑘𝑘3 = 5.

This implies that 𝑘𝑘 = [𝑘𝑘1 𝑘𝑘2 𝑘𝑘3] = [−7 −19 −8].

Now, by transforming back to the original state variable 𝐴𝐴, the state feedback is

𝑢𝑢 = 𝑘𝑘𝑇𝑇−1𝐴𝐴 + 𝑟𝑟 = 𝐾𝐾𝐴𝐴 + 𝑟𝑟,

where 𝐾𝐾 = 𝑘𝑘𝑇𝑇−1 = [−8 −35 −136].

When an economy is represented as a control-theory model, the model generally is high dimensional involving multiple economic policy factors. In this case, the problem of placing the poles of the economy is very technical and all details are omitted. For the interested reader, please consult with (Forrest, et al., 2018).

When some of the states of the economy cannot be used as feedback, such as the performance of the underground economy, one can consider using output feedback to place the poles. From comparing equations (4) and (5), it follows that this problem is equivalent to replacing 𝐾𝐾0 by 𝐾𝐾𝐶𝐶 within the closed loop system. Let 𝐾𝐾𝑠𝑠 stand for the gain matrix of state feedback, and 𝐾𝐾0 the gain matrix of output feedback. When 𝐾𝐾𝑠𝑠 = 𝐾𝐾0𝐶𝐶, we solve for 𝐾𝐾0 from 𝐾𝐾𝑠𝑠 = 𝐾𝐾0𝐶𝐶 so that we can use the output feedback to arbitrarily place the poles of the controllable economic system. However, in general, 𝐾𝐾𝑠𝑠 might not be equal to 𝐾𝐾0𝐶𝐶. That means that generally not all poles can be arbitrarily placed when an output feedback is employed.


49

All the detailed discussions along this line are omitted.

FINAL WORDS

When the economy is represented as a symbolic control system, by using methods of control theory many issues of policy design and introduction can be rigorously investigated, producing practically useful conclusions. In particular, when the economy experiences shocks of the environment, the parameters of the control-theory model of the economy change quickly. That often means that the performance of the economy deviates from its expected trajectory of development, while such deviations most often bring about severe damages to the otherwise well performing economy and lead to massive losses of wealth.

That explains why it is important for us to investigate whether or not one could effectively introduce regulatory strategies to bring the economy back to a stable state of operation when it suffers from external interferences. To this end, this paper establishes ways to design stabilizing strategies when the economy is impacted by external forces by employing the concept of state and output feedback. Although what is developed in this paper could stabilize a disturbed economy in theory, in practice, the process of approaching the desired performance target might experience relatively major fluctuations and might take a long time to materialize the goals.

When the economy is treated as a control system as what is done in this paper, the dynamic characteristics of the economy can best be described by where the poles of the closed loop system are located. If the poles can be placed at some pre-determined locations on the left side of the complex plane, then the policy maker will be able to make the economy possess desired dynamic characteristics and an important degree of stability so that the economy can withstand shocks of the environment. Due to this reason, this paper considers when and how to arbitrarily place the poles of a constant coefficient linear model of the economy so that the resultant economic system possesses a good quality of stability and fast response speed.

Beyond what has been established above, the policy maker should also think about the potential economic losses that might be associated with the adoption of relevant policies. That is, a placement of poles is good only if it does not cause a lot of economic losses while it makes the economy more able to withstand adverse effects of the outside world. So for future research, it is necessary to consider combining the methods of optimal control and pole placement to make the matrix of feedback gain able to not only place the poles of the closed loop system at expected locations but also lead to the design of policies that could potentially result in optimal regulation consequences.

REFERENCES

Bisignano, J. R., W. C. Hunter, and G. G. Kaufman. (Eds.). 2012. Global Financial Crises: Lessons from Recent Events. New York: Springer. Chen, K., and Y. R. Ying. 2012. China’s Capital Account Liberation in the Post-Financial Crisis Era. Shanghai: Press of Shanghai University. Chen, K., and Y. R. Ying. 2014. Impacts and Monitoring System of Financial Crisis Based on Differential Dynamics. Chengdu: Press of University of Electronic Science and Technology of China. Chong, A., and C. Calderon. 2000. Causality and feedback between institutional measures and economic growth. Economics & Politics, 12(1): 69 – 81. Cobb, D. 1981. Feedback and pole placement in descriptor variable systems. International Journal of Control, 33(6): 1135 – 1146. Coddington, E. A., and N. Levinson. 1955. Theory of Ordinary Differential Equations. New York: McGraw-Hill Education. Dabrowski, M. 2012. Currency Crises in Emerging Markets. New York: Springer. Forrest, J. 2014. A Systems Perspective on Financial Systems. Balkema, the Netherlands: CRC Press, an imprint of Taylor and Francis. Forrest, J., Z. Hopkins, and S. F. Liu 2013a. Currency wars and a possible self-defense (I): How currency wars take place. Advances in Systems Science and Application, 13: 198 – 217. Forrest, J., Z. Hopkins, and S. F. Liu. 2013b. Currency wars and a possible self-defense (II): a plan of self-protection. Advances in Systems Science and Application, 13(4): 298 – 315. Forrest, J., Y. R. Ying, and Z. W. Gong. 2018. Currency Wars: Offense and Defense through Systemic Thinking. New York: CRC Press, an imprint of Taylor and Francis. Gluschenko, K. 2015. Impact of the global crisis on spatial disparities in Russia. Papers in Regional Science, 94(1): 3 – 23. Granger, C. W. J. 1963. Economic processes involving feedback. Information and control, 6(1): 28 – 48. Hui, Y. V. 1991. Economic design of a complete inspection plan with feedback control. International Journal of Production Research, 29(10): 2151 – 2158.


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Infante, E. F., and J. L. Stein. 1973. Optimal growth with robust feedback control. Review of Economic Studies, 40(1): 47 – 60. Kaczorek, T. 1985. Pole placement for linear discrete-time systems by periodic output feedbacks. Systems & Control Letters, 6(4): 267 – 269. Kenourgios, D., and P. Padhi 2012. Emerging markets and financial crises: Regional, global or isolated shocks? Journal of Multinational Financial Management, 22(1): 24 – 38. Kenourgios, D., A. Samitas, and N. Paltalidis. 2011. Financial crises and stock market contagion in a multivariate time-varying asymmetric framework. Journal of International Financial Markets, Institutions and Money, 21(1): 92 – 106. Kiekintveld, C., M. P. Wellman, S. P. Singh, J. Estelle, Y. Vorobeychik, V. Soni, and M. R. Rudary. 2004. Distributed Feedback Control for Decision Making on Supply Chains. In: ICAPS (pp. 384 – 392). Kirk, D. E. 2012. Optimal Control Theory: An Introduction. Courier Corporation. Laird, J. 2000. Money Politics, Globalization, and Crisis: The Case of Thailand. Graham Brash. Lauridsen, L. S. 1998. The financial crisis in Thailand: causes, conduct and consequences? World Development, 26(8): 1575 – 1591. Lin, Y. 2008. Systemic Yoyo Model: Some Impacts of the Second Dimension. New York: Auerbach Publications, an imprint of Taylor and Francis. Moe, T. M. 1985. Control and feedback in economic regulation: The case of the NLRB. American Political Science Review, 79(04): 1094 – 1116. Myoken, H. 1979. A comparative study on the use of state-space representations for multivariable economic systems and the structural properties. Kybernetes, (8): 193 – 201. Nguyen, Q. C., and S. Krylov. 2015. Nonlinear tracking control of vibration amplitude for a parametrically excited microcantilever beam. Journal of Sound and Vibration, 338:

91 – 104. Norman, A. L. 1974. On the relationship between linear feedback control and first period certainty equivalence. International Economic Review, 15(1): 209 – 215. Ogata, K. 1995. Discrete-Time Control Systems. Englewood Cliffs, NJ: Prentice Hall. Perko, L. 2013. Differential Equations and Dynamical Systems (Vol. 7). New York: Springer. Phongpaichit, P., and C. J. Baker. 2000. Thailand's crisis. Institute of Southeast Asian Studies. Ram, Y. M., J. E. Mottershead, and M. G. Tehrani. 2011. Partial pole placement with time delay in structures using the receptance and the system matrices. Linear Algebra and Its Applications, 434(7): 1689 – 1696. Razin, A., and S. Rosefielde. 2011. Currency and financial crises of the 1990s and 2000s. CESifo Economic Studies, 57(3): 499 – 530. Shen, J., and X. W. Zheng. 2015. Positivity and stability of coupled differential–difference equations with time-varying delays. Automatica: A journal of IFAC the International Federation of AutomaticControl, 57: 123 – 127. Westcott, J. H. 1977. An experiment on controlling a national economy. In: Bensoussan, A., and Lions, (J. J. (eds.): New Trends in Systems Analysis (pp. 591 – 611). Berlin: Springer. Zhang, J., S. Liu, and J. Liu. 2014. Economic model predictive control with triggered evaluations: State and output feedback. Journal of Process Control, 24(8): 1197 – 1206. Zhang, Z. J., and Q. R. Zhang. 1981. State space realization of linear econometric system. Information and Control, (4): 1 – 14. Zubov, N. E., E. A. Mikrin, M. S. Misrikhanov, and V. N. Ryabchenko. 2013. Modification of the exact pole placement method and its application for the control of spacecraft motion. International Journal of Computer and Systems Sciences, 52(2): 279 – 292.


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THREE STRATEGIES FOR PROTECTING THE NATIONAL ECONOMIC INTEGRITY

Jeffrey Yi-Lin Forrest1), David Jordan1), Kostas Karamanos2) 1)School of Business, Slippery Rock University, Slippery Rock, PA 16057

2)Department of Energy Technology, Technological & Educational Institute of Athens, Aghious Spyridona 17, GR-12210 Egaleo, Athens, Greece

ABSTRACT

Motivated by how currency attacks could be potentially raged against a nation, this paper presents three strategies on how a nation could protect itself from disastrous consequences of such attacks. The first strategy is constructed by using the theory of feedback systems, where the national economy is fictitiously divided into three sectors so that the purchasing powers of money are different for the sectors. The second strategy is developed by utilizing control theory so that the performance indicator would approach the pre-determined objective and could withstand disturbances of environmental factors. The third strategy focuses on how a nation could possibly counter large scale sudden flight of foreign investments in order to avoid the unnecessary disastrous aftermath. To show how these strategies could be practically employed, each of them is accompanied with illustrations and/or examples.

INTRODUCTION

From the study on currency wars (Forrest, et al., 2013; 2018) and recent cases of speculative attacks in the area of international finance (Forrest, 2014), the following predicament appears. When a nation attempts to join the economic globalization, it welcomes foreign investments with open arms. However, due to the very nature of capital that it chases after profit opportunities, large amounts of such foreign investments would strategically rush into the nation to ride along the forthcoming economic boom. As shown by Forrest et al (2013), if a big proportion of the foreign investments later leave suddenly, then the hosting nation would most likely suffer from an economic disaster. To face the dilemma, by employing the concept of feedback systems this paper looks at designing several strategies of self-protection in order to avoid the potential economic disasters or to reduce the severity of the disasters if they are unavoidable.

Although our results established herein are brand new, such an association between control theory and economics has been investigated by various scholars, see, for example, (Chow, 1975) and (Shefrin and Thaler, 1981), and theoretical results and practical procedures for real-life applications developed (Kendrick, 1981; Seierstad and Sydsaeter, 1986), including research on economic policies (McKinnon, 1993). For instance, Pindyck (1977) works on a control model developed for the American economy by using such control variables as

excess tax, government spending, and money supply. Moe (1985) presents an empirical analysis of the National Labor Relations Board by using feedback control. Kydland and Prescott (1980) develop recursive methods for designing optimal taxation plan. Since economic systems are generally nonlinear, various authors have used the economic model predictive control (MPC) system to design an economic estimator (Rawlings, et al., 2012; Diehl, et al., 2011; Heidarinejad, et al., 2012; Ellis, et al., 2014).

Considering the theoretical continuity and practical discreteness of time in the operation and regulation of economies, Wu and Liu (2004) employ systems theory and control theory to simulate the operation of macroeconomic systems by developing their replaceable cyclic control model and discrete successive input-control-decision model for macroeconomic systems. For dynamic input-output economic systems, Chow (1976) investigates a general production strategy based on monitors of consumption. Yang et al (2004) derive an optimal economic adjustment scheme for the optimization of linear quadratic forms. They use this scheme to materialize the optimal tracking of the actual output compared to the ideal output.

Because economic systems are nonlinear and sometimes behave chaotically, Yao and Sheng (2002) develop a prediction feedback to control discrete chaotic systems. Although each theoretical model of the macroscopic economy is incomplete and suffers from errors of parameters’ estimation and from signal interference from the environment, Xiao and Lu (2002) successfully analyze and optimally control a general macroscopic economic system. By making a quadratic performance index equivalent to the observed information of control, Wang and Wang (2006) transform from the angle of information fusion the original problem on the regulation of the macroscopic economy into one of solving for the optimal estimation of the controlled variable.

Comparing what has been accomplished this paper enriches the literature by addressing the problem of self-protection against adverse movements of money through establishing three theoretical strategies, while showing their practical usefulness. Here, we fully utilize the concept of feedback systems, and show how fiscal and monetary policies could both directly and indirectly work on altering the performance of the economy.


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The rest of this paper is organized as follows. The next section discusses the underlying motivation of this research and its significance. The following three sections are the main contents of this paper. They develop respectively a defense strategy by focusing on exchange rates, see how the concept of feedback can be employed in designing a different method of defense, and design a third strategy through partitioning the national economy into divisions. The last section concludes this presentation while pointing to some open problems for future research.

MOTIVATION

Over time since World War II, the form of war has changed. Modern warfare has quietly shifted away from that of direct military clashes to that of economic tactics. And fundamentally, currency plays an important role in all forms of modern warfare. Other than the absence of physical battlegrounds, the scales of economic conflicts and benefits these conflicts generate out of the competition for financial highlands are no less than those of any wars in history.

If a currency (or currencies) is employed as the weapon of mass destruction, then the relevant economic maneuvers will be seen as a currency war. In this regard, each financial crisis can be seen as a signal of a currency war. Currently, the world on the average experiences about 10 massive financial crises each year with the consequence that the relevant countries lose their leadership, if there was any, and have to stay in the subsequent economic shadows for years to come, or might be worse, they can no longer recover and return to their previous glory. For example, although the British sterling crisis, Japan's decade of recession after the Plaza Accord, southeastern Asia's financial crisis, and so on, did not involve any military conflict, the relevant countries suffered magnificent losses. The related currency attacks had made these countries pay a much greater economic cost than expected. The reality is that most nations today have no need, and are unable to resolve conflicts by employing conventional wars, because by using means of currency maneuvers one can achieve his desired objectives.

In terms of what could lead to currency attacks, Forrest, Ying and Gong (2018) establish the fact that economic instability generally opens up a particular country as the target of currency attacks by the international hot money. As for instability, Li and Zhang (2008) analyze the impact of capital account liberalization on economies and show that opening up the domestic market for direct investments in developing countries or countries with economies in transition will result in a stronger impact than in developed countries. Zhang (2003) analyzes the conduction of a financial crisis as the breakthrough point and tries to investigate financial crises from one side. Li (2007) establishes the currency substitution VEC model and dynamically analyzes the extent of China's currency substitution and the relationship between its

influence factors. His conclusion is that the main factor that has effects on Chinese currency substitution is RMB’s nominal effective exchange rate in both long- and short-terms. Frequent fluctuations in the nominal effective exchange rate will lead to currency substitution and even cause instability in the demand for money.

In other words, when a nation attempts to accelerate its economic development, it will generally try to introduce changes, such as liberalizing its capital account, loosening its monetary policies, and others. That tends to create economic imbalances, which in turn encourage large inflows of foreign capital while a lot of the inflows would and could be positioned strategically to take advantages of newly appearing opportunities for quick profits. So, it is critically important for the nation to look at how to protect itself against all potential adverse effects of the inward movement of capital.

A STRATEGY BASED ON EXCHANGE RATE

Riding the present wave of economic globalization, nations from around the world try to develop their economies through loosening their economic and monetary policies and cordially welcoming foreign investments. However, Forrest et al (2018; 2013) show that if the foreign investments leaves suddenly and massively, then the hosting nation would most likely suffer from a burst of the economic bubble. In the rest of this section, we look at how to possibly design a measure to counter such sudden departure of foreign investments in order to avoid the undesirable disastrous consequences.

Consider the following model of a national economy:


= 𝑘𝑘(𝐷𝐷 − 𝑆𝑆) (1)

where D stands for the demand for money, S the money supply, P the purchasing power of money, k > 0 is a constant, and t represents time.

According to Allen and Goldsmith (1972), a stable society generally meets the following four requirements: 1) minimum disruption in ecological processes; 2) maximum conservation of materials and energy; 3) a population in which recruitment equals loss; and 4) a social system in which individuals can enjoy rather than feel restricted by the first three conditions. So, let us divide the national economy of concern into three sectors 𝐸𝐸𝑖𝑖, 𝑖𝑖 = 1, 2, 3: 𝐸𝐸1 stands for the goods, services, and relevant production needed for maintaining the basic living standard, 𝐸𝐸2 those used to acquire desired living conditions, and 𝐸𝐸3 those that are utilized for the enjoyment of luxurious living. Accordingly, the aggregate demand D of money and the purchasing power P of money are divided into three corresponding categories 𝐷𝐷𝑖𝑖 and 𝑃𝑃𝑖𝑖 so that 𝐷𝐷𝑖𝑖 (𝑃𝑃𝑖𝑖) is the demand (purchasing power) of money for sector 𝐸𝐸𝑖𝑖, 𝑖𝑖 = 1,2,3. Hence, to stabilize the economy, 𝑃𝑃1 should stay relatively constant, while 𝑃𝑃2 decreases slightly, and 𝑃𝑃3 drops drastically


53

in order to attract and trap the additional money supply away from 𝐸𝐸1.

Letting 𝑆𝑆𝑖𝑖 be the money supply that goes into economic sector 𝐸𝐸𝑖𝑖, 𝑖𝑖 = 1,2,3, rewrite equation (1) into the following feedback system:

�̇�𝑃 = 𝐾𝐾𝐾𝐾 + 𝑄𝑄 (2)

where P = [P1 P2 P3]T so that �̇�𝑃 = �𝑑𝑑𝑑𝑑1𝑑𝑑𝑑𝑑

𝑑𝑑𝑑𝑑2𝑑𝑑𝑑𝑑

𝑑𝑑𝑑𝑑3𝑑𝑑𝑑𝑑�𝑇𝑇, 𝐾𝐾 =

[𝐾𝐾1, 𝐾𝐾2, 𝐾𝐾3]𝑇𝑇 = [𝐷𝐷1 − 𝑆𝑆1,𝐷𝐷2 − 𝑆𝑆2,𝐷𝐷3 − 𝑆𝑆3]𝑇𝑇, and 𝐴𝐴 =[𝐴𝐴1, 𝐴𝐴2, 𝐴𝐴3]𝑇𝑇 (monetary policies dealing respectively with economic sector 𝐸𝐸1, 𝐸𝐸2, and 𝐸𝐸3), K = �𝑘𝑘𝑖𝑖𝑖𝑖�3×3

and Q = �𝑞𝑞𝑖𝑖𝑖𝑖�3×𝑛𝑛

constant coefficient matrices.

Although the relationship between the purchasing power vector [P1 P2 P3]T of money and the difference vector 𝐾𝐾 =[𝐾𝐾1, 𝐾𝐾2, 𝐾𝐾3]𝑇𝑇 of demand and supply of money is mostly nonlinear in real life, monetary policies in practice aim at alleviating the performance of the economy for the near future. So, such nonlinearity that exists over long periods of time can be linearized for near terms without loss of generality as follows:

�𝑃𝑃1𝑃𝑃2𝑃𝑃3� = 𝑅𝑅3×3 �

𝐷𝐷1(𝑡𝑡) − 𝑆𝑆1(𝑡𝑡)𝐷𝐷2(𝑡𝑡) − 𝑆𝑆2(𝑡𝑡)𝐷𝐷3(𝑡𝑡) − 𝑆𝑆3(𝑡𝑡)

� + �𝜀𝜀1𝜀𝜀2𝜀𝜀3� (3)

where 𝑅𝑅3×3 is a constant square matrix with real entries, and [ε1 ε2 ε3]T a random vector with a none zero mean. By taking the mathematical expectations of the variables in equation (3), and substituting the result into equation (2), we have

𝑅𝑅3×3�̇�𝐾 = 𝐾𝐾𝐾𝐾 + 𝑄𝑄𝐴𝐴 (4)

If in general the categorized purchasing power of money is completely determined by the categorized differences of demand and supply of money, then 𝑅𝑅3×3 is invertible so that equation (4) can be rewritten as follows:

�̇�𝐾 = 𝐴𝐴𝐾𝐾 + 𝐵𝐵𝐴𝐴 (5)

where A = 𝑅𝑅−1𝐾𝐾 and B = 𝑅𝑅−1𝑄𝑄.

If similar to the concept of consumer price index (CPI) we introduce an economic index vector y = [𝑦𝑦1 𝑦𝑦2 𝑦𝑦3]𝑇𝑇 such that yi measures the state of the economic sector i, i = 1, 2, 3. Then from equation (5), it follows that the national economy of our concern can be modelled as follows:

𝑆𝑆: ��̇�𝐾 = 𝐴𝐴𝐾𝐾 + 𝐵𝐵𝐴𝐴𝑦𝑦 = 𝐶𝐶𝐾𝐾 + 𝐷𝐷𝐴𝐴𝐾𝐾(0) = 0

(6)

where z = [D1 – S1 D2 – S2 D3 – S3]T, referred to as the state of the economic system, A, B, C, and D are constant 3 × 3

matrices, such that D is non-singular (meaning that each introduction of monetary policies does have direct effect on the performance of the economy), 𝐴𝐴 the policy inputs, and 𝑦𝑦 the economic performance of the three economic sectors.

According to (Lin, 1994), the 3-dimensional system in equation (6) can be decoupled into three independent systems of the same kind with one-dimensional input and output:

𝑆𝑆𝑖𝑖 ∶ ��̇�𝐾 = 𝐴𝐴𝐾𝐾 + 𝐵𝐵𝑖𝑖𝐴𝐴𝑖𝑖 𝑦𝑦𝑖𝑖 = 𝐶𝐶𝑖𝑖𝐾𝐾 + 𝐷𝐷𝑖𝑖𝐴𝐴𝑖𝑖𝐾𝐾(0) = 0

, 𝑖𝑖 = 1, 2, 3, (7)

where 𝐵𝐵𝑖𝑖 is the ith column of B, 𝐶𝐶𝑖𝑖 the ith row of C, 𝐷𝐷𝑖𝑖 a non-zero constant.

The previous decoupling of the 3-dimensional system S into component systems Si, i = 1, 2, 3, implies that when monetary policies are established individually and respectively for each economic sector Ei, i = 1, 2, 3, although the sector specific policies most definitely have joint effects on the economy, there is at least one way to design a feedback mechanism so that the overall performance of the economy can be controlled through adjusting individually each of the economic sectors.

In terms of designing a strategy to protect the national economic integrity in the case that a significant amount of foreign investments turn out to be an aggressive act by suddenly withdrawing from the hosting nation, the theory above suggests the following countermeasure: To protect the nation from potential economic turmoil, caused by sudden departure of foreign investments, the government could maintain a stable exchange rate, increase the money supply, and divide the economy into three sectors E1, E2, and E3, as described earlier. So, the sector specific CPI for E1 evolves as stably as possible; the specific CPI for E2 outpaces that of E1 by a large amount; and the government manages to trap most of the additional money supply in E3. Our established theory above indicates that by managing the market reactions appropriately, these three economic sectors can be well separated from each other. And when sector E1 evolves stably based on the history pattern, the nation would not need to worry about maintaining the desired societal stability and peace.

A STRATEGY USING THE CONCEPT OF FEEDBACK

This section looks at how to design economic policies based on system feedback so that the chosen performance indicator would approach the pre-determined objective. By showing that the feedback controller could automatically regulate the economy’s supply and demand, we design feedback controls that could withstand disturbances of the environment. To model this problem, let 1x be the state variable of the

economy that will be regulated, 2x the variable that reflects


54

the environment interference on the economy, and u and y respectively the control vector (either fiscal or monetary policies) and the output vector. Then the economy can be written as follows, where the reasoning as given in the previous section also applies here for why linear differential equations are employed. For more in-depth discussion on such modelling, see (Forrest, et al., 2018).

⎩⎪⎪⎨

⎪⎪⎧ 1

1 1 3 2 1dx A x A x B udt

= + + ,

22 2

dx A xdt

= ,

1 1 2 2y C x C x= +

(8)

When the economy is affected by the environment, the economic regulator adopts policies to reduce the deviation of the state variables from the targeted values. The strength of the adopted policies is roughly proportional to the deviation of the observable state variables. That proportionality is referred to as the pure gain of the adopted policies. The observed values of the state variables of the original economy and changes in these variables when the economy is influenced by external factors are then employed as inputs in controlling the action strength of the adopted policies. The product of the gain of the control policies and the input value is referred to as the feedback gain. And when the changes in the state variables are used as input, the product is known as pure feedback gain. So, let us use both 1x and 2x as feedback to design a feedback controller of pure gains as follows:

1 1 2 2u K x K x= + (9)

satisfying the requirement of regulation of the output: lim ( ) 0t

y t→∞

= , where matrices 𝐾𝐾1 and 𝐾𝐾2 need to be

determined. Substituting equation (9) into equation (8) produces the following closed loop system

11 1 1 1 3 1 2 2( ) ( )dx A B K x A B K x

dt= + + + (10)

Results of control theory indicate that if the elements of A, B, and C of the economy or the interference input 2x change, as long as the real parts of the eigenvalues of the state matrix

1 1 1A B K+ in equation (10) stay negative, then the control strategy in equation (9) guarantees that the controlled variable will approach its target. In particular, to design a feedback controller in the form in equation (9), we first solve for

1K so

that the eigenvalues of 1 1 1A B K+ are located at the n pre-determined locations in the left-half open plane; second, solve the matrix equation system 1 2 1 3A X XA BU A− + = and

1 2C X C= for X and U; and then compute 2 1K K X U= − . The

resultant 1 1 2 2u K x K x= + is the desired pure gain feedback controller, where the first term is the state feedback, while the second term the interference feedback. The former makes the closed loop system stable, while the latter eliminates the effect of the environmental disturbance and adjusts the output.

In the following, let us look at an example. Assume that the state equation of a small economy is

11 2

1 0 1 1 00 1 2 0 1

dx x x udt

= + +

such that the environmental interference equation and the output equation are respectively given as follows:

2 0dxdt

= and 1

1 00 1

y x =

.

Design a pure gain feedback controller as shown in equation (9).

Solution. By referencing to equation (8), we have

𝐴𝐴1 = �1 00 1� ,𝐴𝐴2 = 0,𝐴𝐴3 = �12� ,𝐵𝐵1 = 0,

𝐶𝐶1 = �1 00 1� ,𝐶𝐶2 = 0.

First, let us calculate K1 = �𝑘𝑘𝑖𝑖𝑖𝑖�2×2 so that 1 1 1A B K+ has poles

−2 and −2, which can be any negative numbers. In particular, we have

𝐴𝐴1 + 𝐵𝐵1𝐾𝐾1 = �1 + 𝑘𝑘11 𝑘𝑘12𝑘𝑘21 1 + 𝑘𝑘22

� = �−2 00 −2�,

which leads to

1

3 00 3

K−

= − .

Second, we solve the matrix equation system

1 2 1 3A X XA BU A− + = and 1 2C X C= for X and U. In

particular, we solve the following for X and U

1 0 1 0 10 1 0 1 2

X U + =

1 00

0 1X =


55

That gives: X = 0, U = [1 2]𝑇𝑇, and 𝐾𝐾2 = 𝐾𝐾1𝑋𝑋 − 𝑈𝑈 = −[1 2]𝑇𝑇. So, the desired pure gain feedback controller is

1 2

3 0 10 3 2

u x x−

= − − .

To summarize, what is accomplished in this section is that it shows the fact that when the control-theory model of an economy is established and the appropriate parameters of the model are determined, one can design a feedback economic strategy to make the state of the economy approach the pre-determined objective without being adversely affected by external disturbances.

A STRATEGY THROUGH PARTITIONING THE ECONOMY INTO DIVISIONS

This strategy attempts to address how a nation could possibly design a countermeasure against sudden large scale flight of foreign investments in order to avoid disastrous consequences. Most recent economic dramas from around the world indicate clearly the fact that how to avoid the disastrous aftermath and how to reduce its severity when a large scale flight of foreign capital appears suddenly is still not well comprehended.

Let 𝑤𝑤 be the vector [𝑊𝑊1,𝑊𝑊2,𝑊𝑊3]𝑇𝑇 of categorized fiscal and/or monetary policies, grouped accordingly into three categories as described in previous sections:

𝑊𝑊1= policies that provide the population with the basics of meeting the standard of basic living;

𝑊𝑊2 = policies that provide the population with ways to acquire desired living conditions; and

𝑊𝑊3 = policies that provide the population with means to enjoy luxurious living conditions.

Like the concept of overall balance in international payments, let 𝐾𝐾 = [𝐾𝐾1, 𝐾𝐾2, 𝐾𝐾3]𝑇𝑇 be an economic index vector such that 𝐾𝐾𝑖𝑖 measures the state of the economic sector 𝐸𝐸𝑖𝑖, i = 1, 2, 3. When the purchasing power rises, people generally purchase more foreign assets and foreign products, the overall balance of international payments will drop because foreign exchange expenditure increases. When the purchasing power declines, people generally sell more domestic assets and domestic products; so the overall balance of international payments increases because foreign exchange revenue increases.

In the following systemic model (Chen, et al., 2017) with polynomial lag variables

��̇�𝐴 = 𝐴𝐴𝐴𝐴(𝑡𝑡) + ∑ 𝐴𝐴𝑖𝑖𝐴𝐴(𝑡𝑡 − ℎ𝑖𝑖)𝑛𝑛

𝑖𝑖=1 + 𝐵𝐵𝑤𝑤(𝑡𝑡) + 𝐵𝐵1𝑢𝑢(𝑡𝑡)𝐾𝐾 = 𝐶𝐶𝐴𝐴(𝑡𝑡) + ∑ 𝐶𝐶𝑖𝑖𝐴𝐴(𝑡𝑡 − ℎ𝑖𝑖)𝑛𝑛

𝑖𝑖=1 + 𝐷𝐷𝑤𝑤(𝑡𝑡) + 𝐷𝐷1𝑢𝑢(𝑡𝑡)𝐴𝐴(𝑡𝑡) = 𝜑𝜑(𝑡𝑡), 𝑡𝑡 ∈ �−ℎ�, 0�

(11)

let the symbol 𝐾𝐾 represent the state of the national economy, w1, w2, and w3 the positive and/or negative effects of the fiscal and monetary policies on the performance of the economy directly, or on the currency demand and supply to have an impact on the economy indirectly. Here u(t) is a random vector with a none zero mean. Because economic development can be seen in theory as a continuous process, the current change in the money stock is determined by the current monetary policies, money stock, and the previous money stock. And the current performance of the economy is also determined by the current fiscal and monetary policies, money stock, and the previous money stock.

Let x be the 3 × 1 matrix [D1 – S1 D2 – S2 D3 – S3]T of the categorized differences of demands and supplies of money of the three economic sectors 𝐸𝐸𝑖𝑖, i = 1, 2, 3. Then the study (Chen, et al., 2017) of the systemic model of the national economy in equation (11) indicates that this separation of the economy into these three sectors can help properly manage the market reaction to fiscal and monetary policies. When the policies have positive effects on the economy, people will consume more in every economic sector with the rising purchasing power of their income. Therefore, foreign exchange expenditure increases. When the policies have negative effects on the economy, people tend to sell more in every economic sector with the declining purchasing power of their income. Hence, foreign exchange revenue increases.

To demonstrate how this model works, let us use a one-dimensional case to illustrate. That is, the three economic sectors described above now become one sector. Substituting the demand and supply of money, let x be an exchange rate, and the same symbol w represent the vector [w1 w2 w3]T of categorized fiscal and monetary policies. The first equation in (11) implies that the current exchange rate is not only determined by the current fiscal and monetary policies, but also by the previous policies. By fitting actual data into this model, Chen et al (2017) show that when the recent financial crisis occurred during 2008 – 2010, Chinese government maintained the exchange rate of its RMB against the US dollar at around 6.8 by implementing a series of policies. Based on the systemic model structure for the second-order lag, the degree of the model fitting that contains parameters for policy implications increases 16.8% from that of the model without any parameters for policy implications. This fact validates the effectiveness of the introduced policy parameters.

When 𝐾𝐾 is identified as the overall balance of international payments, the second equation in (11) indicates that the overall balance is determined by the current exchange rate and the previous exchange rates. Once again, Chen et al (2017) show that the degree of model fitting that contains parameters for policy implications is better than that of the model without any parameter for policy implications. That implies that policy parameters are useful and necessary in the process of model fitting. Additionally, the model considered in this section


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implies that 𝐾𝐾 is also determined by the current fiscal and monetary policies directly and previous policies indirectly. So the nature of changing 𝐾𝐾 is determined by quantitative continuous-deferred policies.

One of many potential applications of this strategy is in the internationalization of a currency. In such a process, policies of the national government become an extremely important factor. For example, in history Britain was the first to develop modern financial institutions. British National Order in 1694 passed a bill to establish the world’s first central bank. During the years from 1816 to 1819, the British government introduced various policies about Mint and exchange, and implemented the first gold standard. Consequently, from the middle Ages to the 19th century Britain became the "sun" Empire with a financial system that dominated the world. For more details see (Yu and Xie, 2011). Similar roles played by various governments can be vividly seen with the internationalization of the US dollar, Japanese yen, German mark, the Euro, and currently the Chinese Renminbi.

FINAL WORDS

This paper presents three different ideas on how a nation could protect its economic integrity against currency/economic attacks and disturbances of external factors. Corresponding to the great number of different ways of investing money, there should be a similar number of ways one could protect himself. Speaking differently, more scientific efforts should be allocated to the study of how to protect the financial wellbeing of an economic system, be it an individual family, or a region, or a nation.

Due to the complexity of any national economic system, economic changes occur constantly, making it difficult for the policy maker to introduce effective control strategies so that the stability of the economy could be maintained. To face this challenge, this paper presents strategies of self-defense against adverse effects of external influences by employing the concept and theory of feedback systems. Other than developing the theory, examples are used to illustrate how the theory could play out in practice. That is, this paper demonstrates both theoretically and practically that the established strategies should work well, making the regulated economic indices approach the ideal targets even under the influence of environmental disturbance.

What is accomplished in this paper leads to the following important suggestions on the future research of defense solutions against adverse effects of factors that are external to the economic system of concern:

• It is theoretically limiting if the study of economic interactions only focuses on the dynamics between two countries. Such study should cover a much

bigger dynamic system involving many mutually reciprocating feedback countries; and

• The following is still an open problem: How can one improve the accuracy of assessing and quantifying the impact of different policies on the economy?

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A STUDY ON THE GROWING IMPLEMENTATION OF ASSISTIVE TECHNOLOGY FOR THE PEOPLE WITH DISABILITIES AND RECEPTIVE LANGUAGE IMPAIRMENTS

Andrew Kyung, Lois Kim, Yeon Soo Kang, David Sang E Khym, Eva Kim

Choice Research Group Cresskill, NJ 07627

ABSTRACT Developmental language disorders occur when children struggle to learn language despite showing normal development in other areas. People with receptive language impairments have difficulty learning, understanding, and using language. This results in poor verbal communication and decreased exposure to social environments. The goal of this research is to understand and propose solutions for issues that people with receptive language problems commonly experience. Although technology and media business can assist people with receptive language problems, many individuals lack access to such technology. Creating community programs that support individuals’ implementation of assistive technology will allow people with receptive language problems to live more fulfilling lives.

INTRODUCTION

Communication allows people to exchange information and meaning. It is undeniably a ubiquitously essential skill for people to interact with each other. “Complex communication needs” is a term that describes people who have little or no speech ability. These communication impairments may be caused by physical, sensory, cognitive, or environmental factors. Speech and cognitive impairments prevent people with complex communication needs from communicating in conventional ways. In addition to speech impairments, individuals may have multiple disabilities, including physical, vision, hearing, sensory, or cognitive impairments [1]. “Developmental causes of complex communication needs” refers to the conditions people are born with, such as autism or cerebral palsy; “acquired complex communication needs” refers to progressive neurological conditions such as dementia or the effects of traumatic brain injury. People with complex communication needs often use different augmentative and alternative communication (AAC) strategies and systems to assist their communication. According to the American Speech-Language-Hearing Association, AAC stands for “all forms of communication -- other than oral speech -- that are used to express thoughts, needs, wants, and ideas. We all use AAC when we make facial expressions or gestures, use symbols or pictures, or write.” AAC serves as an alternative to spoken language, which can be used to further assist comprehension of what is verbally stated. While AAC

includes actions as simple as the look in one’s eyes, facial expressions, and tones, it also encompasses visual props and technological devices that may be used to help communication. AAC tools do not have to be high tech. Picture exchange communication systems (PECS) and homemade binders with picture symbols are examples of low tech AAC systems. High tech AAC includes apps and programs on tablets, as well as “durable medical equipment” (DME). [2]

PREVALENCE OF CCN & AAC USE

A significant proportion of the population experience complex communication needs. A longitudinal study in 2002 reported that out of the 1875 responses, 34.4% of elementary school students had trouble communicating or did not communicate at all; 34.2% reported having a little trouble with communication. Communication difficulties are exhibited by a large proportion of individuals who have other disabilities: a different national longitudinal transition study revealed that 55% of individuals with intellectual disabilities, 61% of people with autism, and 72% of people with multiple disabilities have communication difficulties. Because the percentage of people who have communication difficulties is high, the proportion of people requiring AAC use is consequently high. A survey conducted by Binger and Light on speech-language pathologists (SLP) revealed that 12.5% of preschoolers in Pennsylvania receiving special education services required AAC. The surveys were distributed to early intervention SLPs in Pennsylvania between January and June of 2003. Advisors at 11 different SLP agencies were contacted to distribute the surveys to the Speech-Language Pathologists. 144 surveys were returned, and at least one survey was collected from 10 out of 11 SLP agencies. A total of 1,009 preschools between the ages of 3;0 and 5;11 from the 10 agencies in Pennsylvania required AAC. This is out of the 8,742 who were receiving services, which means that 12.5% of the preschoolers enrolled in special education services required AAC in Pennsylvania. The total number of preschoolers receiving SLP services, according to the SLPs who completed the surveys, was 4192, which means that a mean of 24% of the preschoolers receiving speech or language services required AAC. [4] Figure 1 illustrates the different disabilities that preschooler AAC users have. As mentioned previously, communication complex needs are evident along with many other disabilities.


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More than one third of the preschoolers who require AAC had a primary diagnosis of developmental delay; another third was diagnosed with autism or pervasive developmental disorder (PDD). The findings indicate that a substantial number of preschoolers with a wide variety of disabilities on the caseloads of SLPs require AAC technology. For these children who have complex communication needs, early intervention is critical. [4]

TRADITIONAL AAC TECHNOLOGY AND DEVICE

The traditional high-tech AAC device has many shortcomings and limitations in their use. Although the use and acceptance of AAC has been increasing, many people in the United States still do not have access to AAC assistance that may significantly help their communication. According to Assistive Technology Law Center, only 2-3% of people who actually need speech generating devices in the US actually have the devices available to them. Annually, the average of 11,000 of these devices are sold in the United States; it is a prevalent problem if only 2-3% of the population with a need has assistance. This data suggests that over 300,000 individuals would benefit from a speech generating device. [5] One of the major barriers is the beliefs and misconceptions on AAC use. The potential users have a lack of knowledge on these technological devices, and those who are aware of AAC may not be aware of the recent advances in the field. The mindset of the families of AAC users is important as well; the family members may be devastated when they learn that their loved one has a language disorder and be concerned about the loved on fitting in with others with the use of AAC. The public is also unaware of the different possibilities of AAC. The lack of exposure to AAC leads to the community not knowing how to react to people who use this technology. In general, there is a social stigma associated with the use of these devices, and therefore a lack of community acceptance and understanding. Professional training and services is another major category of barriers of AAC use, which includes the accessibility to services, limitations of the provided services, and training. The overall need for AAC services is much greater than the availability of help. Additionally, many people may have to travel a long time to repair the device or seek help on device use. Many professionals may not even adequately be trained to provide the best services for its client, which causes much frustration among the users, families, and the professionals. Furthermore, there are limitations of services provided, which includes choosing a technology that fits the user’s culture and family preferences as well as how the technology will be updated for the user as the user’s vocabulary grows. Limitation in user and communication partner training is specific to the SLPs who may not sufficiently train the users in addition to family members or friends. Miscommunication may result from the lack of adequate vocabulary on the AAC device and lack of practice between the user and the partner. [5] The device itself has many shortcomings which drive the potential users away. General limitations include the inability

of some AAC devices to be compatible to mobile technologies. The need for backup equipment when a device breaks and the amount of time the device is spent in repair is one of the main general issues. Specific device access limitations focus on the characteristics such as the physical size and weight of the device. High tech devices may be inappropriate for places with water hazards. Users may have difficulty mounting and carrying the device as well. Voice output limitations include complaints on low quality of speech output and only having English as an option. The final barrier to AAC access is opportunities for its use. The situations with the ability to use AAC to communicate are limited: for instance, a student may have an AAC device he can use in a resource room but not in a mainstream classroom. In another case, a student may only have access to a device inside a classroom, but not during playtime or even at home. [5]

THE EFFECT OF EMERGING TECHNOLOGY ON AAC USERS

The recent boom in technology has introduced smartphones and tablets as tools for efficient communication and attempts to solve the issues that have arisen from the traditional AAC systems. Mobile technology, including cell phones, smartphones, and tablets, is found everywhere in this day and age. Cell phone use has reached the majority of the population all over the world, including Africa, which has recently become the second largest mobile phone market after Asia. [6] The use of these devices has become ubiquitous, and now there are a wide variety of mobile technology devices and operating systems. The development mobile technology has not only impacted the lives of the general public but also has influenced the lives of those who have communication and language disabilities. Mobile technology can play a crucial role assisting people with complex communication needs who require augmentative and alternative communication (AAC) approaches. Smartphones can be interconnected with an individual’s AAC device, which allows the user to effectively operate the device more independently [6]. Even better, AAC applications can be downloaded on to smartphones and tablets, which are cheaper and more convenient than the traditional AAC devices; therefore, there has been an increase in specialized applications that may be used as AAC that one can access from phones and tablets. [7] There are several benefits that come from the introduction of smartphones and tablets for AAC use. One of the benefits is that AAC has been brought into the mainstream by the use of iPads. Instead of using specialized speech-generating devices (SGD), people can use mainstream technology to assist them [7]. Because mobile devices are mainstream and more stylish, AAC users may be less hesitant to utilize them than when they had to carry a bulky device which were “markers” of disability [6]. Furthermore, everyone has access to AAC applications on


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the app store. With increased accessibility of AAC applications, there has been an increased awareness of AAC and more social acceptance for its use. Because the use of mobile technologies is socially valued, the users are free of the stigma that may be associated with the use of typical assistive technology. Additionally, current technology has increased consumer empowerment in AAC access and increased adoption of this technology. Traditionally, the typical SGDs are very expensive and provided through licensed professionals. They were hard to access and very costly. However, the wide availability of smartphones and tablets, the relatively low price of these devices, and easy access to the app store has allowed greater access to AAC systems for those who need them. The democratization of access to AAC technology - highlighted by the relatively low price of these devices and applications - means that families and schools who formerly were not able to afford AAC technology now can [7]. With the prevalence of mobile technology, the public is recognizing its possibly beneficial effect on the population of people with disabilities by increasing independence and participation in the community [6]. A survey conducted in 2015 by AssistiveWare revealed the prevalence of mobile technology use for AAC. The survey was shared through blogs, newsletters and social media in US, Canada, Australia, and UK and focused on AAC users. No further information ethnic or socioeconomic status of the demographics of the survey was provided. A survey conducted in 2015 by AssistiveWare revealed the prevalence of mobile technology use for AAC. The survey was shared through blogs, newsletters and social media in US, Canada, Australia, and UK and focused on AAC users. No further information ethnic or socioeconomic status of the demographics of the survey was provided. There were 216 participants, with 147 child AAC users and 69 adult AAC users. In all three groups, the use of iOS (iPads) for AAC is dominant, primarily due to the fact that AAC apps became widely available on consumer devices. There is a significantly higher proportion of AAC users using iPads or smartphones compared to a traditional, dedicated AAC device. Many AAC users use more than one AAC system and have a secondary device or a paper-based system as a backup. [8]

BARRIERS AND COMPLICATIONS OF EMERGING

TECHNOLOGY FOR AAC USERS

Although there are many benefits from the use of modern technology to meet the communication needs of people who require AAC, there are still several challenges that must be addressed. One of the major challenges is still the lack of access. Smartphones and tablets, while cheaper than the previous options for AAC technology, are still pricey.

Therefore, there are still people with language disabilities who do not have access to these mobile technologies. Furthermore, sometimes modern technology may be too advanced, requiring complex motor skills in addition to high sensory perceptual and cognitive skills. The complexity of the software as well as the smartphone may be too difficult for one to use comfortably: the design of the application may not be suitable for its users. [7] A survey conducted on adult AAC users in the United States and South Africa to describe the use of consumer mobile technology demonstrates how, despite all the benefits of mobile technology use for AAC, there is little information on the actual use and adaptation of these technologies. The participants were 38 adults from the United States and 30 from South Africa, who rely on AAC. The Survey of User Needs (SUN) was used to survey the participants. There are four parts to SUN: demographic variables, participant abilities and disabilities, use of mobile device, and functions and frequency of mobile device. [6]

The results indicate that participants from both countries experience multiple disabilities. While more than 75% of participants from both samples have complex communication needs, the majority also have physical difficulties using their arms, fingers, and legs. Table 3 shows the types of assistive technology the participants use to address their disabilities. The majority of both samples use high-tech Speech-generating AAC devices (SGDs) and Text-to-speech software. Data from table 4 illustrates mobile technology device ownership. Not surprisingly, most of the participants own and use smartphones. People from both samples stated that they use mobile technology for professional and personal use. However, several participants stated that mobile technology can be difficult to use. It is also necessary to note that several changes and modifications were made to these mobile devices for use – more in the United States than in South Africa. The vast majority of the participants stated that they use mobile devices in order to text or call. This data emphasizes how communication and social interaction is important to these people as well. Texting bypasses the need for speech and allows the use of abbreviations, which makes it easier for AAC users to communicate with others. Although mobile technology enhances the lives of people with complex communication needs, data from the study revealed that the use of mobile technology was difficult for one-third of the participants. For people who use this technology, a variety of modifications and device changes were necessary. The finding emphasizes the need to promote the design, development, and production of devices with easier access for people with disabilities. [6]


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POSSIBLE SOLUTIONS AND RECOMMENDATIONS

FOR IMPROVEMENT

Research on solving this problem is urgent to ensure the availability of AAC technologies to meet the needs and skills of the wide spectrum of users. Instead of developing products for the regular, “average,” consumer, developers should brainstorm different ways to improve access for those with more specialized and complex needs. However, this is difficult to do, since there are very few financial resources available, and since there cannot be a specialized app for each individual user based on his or her specific needs. The software developers who work on the AAC apps have limited knowledge on the field of AAC and the challenges its users face. To take a step closer to addressing this problem, more communication is necessary among the researchers, software developers, educational teams, and consumers who require and use AAC. From these partnerships formed, developers may be able to redesign the AAC apps and mobile technologies that are easier and more efficient for the consumers to use. [7] To improve mobile technology use for people with complex communication needs, several things could be done. For instance, technology designers and manufacturers can expand the built in accessibility features to address the needs of people with complex communication needs who may also have other physical disabilities. Physical and motor accessibility features, such as a switch control and assistive touch, could make a big difference. Additionally, manufacturers of specialized AAC devices should expand their software and application designs so that they could be easily downloaded onto mainstream mobile devices. The use of mainstream devices for AAC will decrease abandonment rates and will be more powerful and image enhancing. [6] The use of mobile devices is not always easy for everyone, especially those with disabilities. Oftentimes, the burden of adapting and modifying the devices for easier use lies on the person, rather than the special features already coming built-in with the device. To encourage social and economic inclusion, this current situation must change to provide people with complex communication needs the technology they need to access to the growing technological world. [6] Figure 3 shows that the language disorders affect one or more fundamental aspects of language: form, content, and function. Deficits may involve morphology (understanding and use of the building blocks of words), syntax (grammar), and semantics (vocabulary). Phonology, the ability to distinguish and use speech sounds appropriately, is affected in Speech Sound Disorder. Disorders of pragmatics, the use of language, are encompassed within Social Communication Disorder.[18]

OTHER EQUIPMENTS FOR INDIVIDUALS WITH DISABILITIES

New developments in technology provide the elderly and those with disabilities the opportunity to feel safe in their homes. For example, alarm systems and video-monitoring allow individuals to quickly alert the authorities or a loved one in the case of an emergency. Wireless alarm systems can be connected to a family member’s smartphone, which allows them to be notified when an event triggers the system. Furthermore, pressure mats, a device that detects a person mobilizing from a bed or chair, are a form of communication between the user and a caregiver or relative. They are useful to detect night-wandering, to prevent falls, and to notify the carer of movement. These mats can also be connected to an illumination system that turns on lights when the user arises. These technologies can be applied to one’s life in numerous ways. First, through the use of alarm and communication systems, emergencies can be prevented and relatives can feel relieved knowing their loved one is safe at home. Sensors could also be used to collect information on one’s lifestyle. For instance, weight; agility; and movement patterns can be detected, building a profile on that person’s activity. So if a deviation from normal activity is detected, the caregiver can be informed and help. Finally, sensors can be equipped in a home and can turn off any devices (e.g. light switches, stoves, televisions). This ensures the safety of the home’s occupants and assists them of their daily functions.

DISCUSSIONS AND CONCLUSIONS

The goal of this research is to understand and create solutions for issues that people with receptive language problems experience. Assistive technology can help people with receptive language problems overcome the language barrier. This includes software that converts long, complex sentences to simple, easy-to-understand sentences and websites that offer information in the form of pictures, videos, and diagrams rather than text. Although there are many benefits from the use of modern technology to meet the communication needs of people who require AAC, there are still several challenges that must be addressed. One of the major challenges is still the lack of access. Smartphones and tablets, while cheaper than the previous options for AAC technology, are still pricey. Therefore, there are still people with language disabilities who do not have access to these mobile technologies. Although mobile technology enhances the lives of people with complex communication needs, data from the previous studies have revealed that the use of mobile technology was difficult for one-third of the participants. For people who use this technology, a variety of modifications and device changes were necessary. The finding emphasizes the need to promote


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the design, development, and production of devices with easier access for people with disabilities.

FURTHER STUDIES AND CONSIDERATIONS

Current research will delve further into the arena of emerging technologies for the people with disabilities and receptive language impairments. For the health of those people, we will discuss what new types of wearable electronics are being developed to help monitor health needs, store data and deliver feedback to the wearer and to health care professionals, and can then deliver medication remotely. And we can consider the phenomena of “Sixth Sense Technology” and check how we use this technology to bridge the gap between our digital devices and our physical world, allowing us to interact with devices and information via gestures. The use of robotics for encouraging social interaction for young children on the Autism spectrum, and telepresence robotics for remote elder care and as an alternate way for homebound students to interact with their classmates and teachers will be addressed. During this research, we have noticed trends with Smart Home technology, environmental control, and complete home automation however, not many articles have addressed the ethics surrounding in home monitoring of the individual; we will touch on this topic as well. Finally, we will showcase devices that were specifically developed to address specific functional needs of individuals with disabilities, such as smart memory aids for individuals with cognitive disabilities, speech controlled devices that allow individuals with physical disabilities to perform multiple functions hands free, 3D printing as a teaching tool for individuals with visual impairments, smart apps that allow deaf or hard of hearing individuals to have face to face communication with hearing individuals in real time, and the importance and implications of crowdsource funding in reaching the financial goals of developing these types of technologies.


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Figure 1. Disabilities of Preschoolers Who Require AAC

Table 1. Primary Communication Device of AAC Users

AAC users iOS Dedicated Device Paper-based Other

Child AAC users (n=147) 71% 18% 10% 1%

Adult AAC Users, reported by family (n=35) 63% 14% 17% 6%

Adult AAC User, self-reported (n=34) 68% 26% 3% 3%

Developmental Delay, 378, 38%

Autism/PDD, 323, 32%

Speech/language, 165, 17%

Multiple disabilities, 103,

10%

Deaf-blind, 6, 1% Traumatic brain

injury, 6, 1%other, 13, 1%

Disabilities of Preschoolers Who Require AAC

Developmental Delay Autism/PDDSpeech/language Multiple disabilitiesDeaf-blind Traumatic brain injury


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Figure 2. Primary Communication Device of AAC Users

Figure 3. Language disorders [18]


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Table 2. Type of Difficulty Experienced by Participants from the United States (US) and South Africa (SA)

Type of difficulty % of US participants (N=38)

% of SA participants (N=30)

Frequent worry, nervousness, or anxiety 24% 17% Difficulty concentrating, remembering, or making decisions

21% 27%

Difficulty seeing 21% 13% Difficulty hearing 34% 7% Difficulty using arms 61% 60% Difficulty using hands and fingers 66% 70% Difficulty walking and climbing stairs 66% 73% Difficulty speaking so people can understand 82% 100%

Table 3. Percentages of Specialized Assistive Technologies used by Participants from the United States (US)

and South Africa (SA)

Type of specialized assistive technology % of US participants

(N=38)

% of SA participants

(N=30)

Screen reader 16% 7%

Screen magnifier 5% 3%

Hearing aid 26% 3%

Speech-generating AAC device 100% 57%

Text-to-speech software 45% 53%

Fabricated AAC communication board N/A 50%

Wheelchair 61% 70%

Crutches, cane, or walker 21% 7%

Table 4. Percentages of Participants’ Ownership of Mobile Device the United States (US) and South Africa (SA)

Mobile devices % of US participants (N=33) % of SA participants (N=30)

Owns a mobile device 85% 100% - Basic cell phone 6% 23%

- Smartphone 49% 67% - Tablet 21% 10% - Other (ex: laptop) 9% 0%


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Table 5. Percentages of Changes made to Mobile Device the United States (US) and South Africa (SA)

Changes/additions made to mobile devices % of US

participants (N=28)

% SA participants

(N=30) No changes or additions 14% 47% Physical accessories (protective skin or case, headset, Bluetooth, etc) 61% 33% Assistive devices (head switch, EMG switch, AAC device, TTY) 32% 7% Software added (text-to-speech, screen reader, screen magnifier, app store downloads)

39% 27%

Improvised solutions (hand strap, Velcro, wheelchair mount) 32% 7% Other (larger font, protective screen, different screen glass) 18% 10%

REFERENCE [1]http://www.adhc.nsw.gov.au/__data/assets/file/0006/338829/Complex_Communication_Needs_Practice_Guide.pdf [2]https://vkc.mc.vanderbilt.edu/ebip/augmentative-and-alternative-communication/ [3] Natalie R. Andzik, John M. Schaefer, Robert T. Nichols & Yun-Ching Chung (2018) National survey describing and quantifying students with communication needs, Developmental Neurorehabilitation, 21:1, 40-47, DOI: 10.1080/17518423.2017.1339133 [4]Binger, C., & Light, J. (2006). Demographics of preschoolers who require AAC. Language, Speech & Hearing Services in Schools, 37(3), 200-8. [5]https://scholarworks.uni.edu/cgi/viewcontent.cgi?article=1113&context=hpt [6]https://www.atia.org/wp-content/uploads/2017/11/ATOB_ATOBN1V11_ART-6.pdf [7] David McNaughton & Janice Light (2013). The iPad and Mobile Technology Revolution: Benefits and Challenges for Individuals who require Augmentative and Alternative Communication, Augmentative and Alternative Communication, DOI: 10.3109/07434618.2013.784930 8] http://www.assistiveware.com/state-aac-english-speaking-countries-first-results-survey [9]http://pubdocs.worldbank.org/en/123481461249337484/WDR16-BP-Bridging-the-Disability-Divide-through-Digital-Technology-RAJA.pdf [10]https://www.ncbi.nlm.nih.gov/books/NBK97336/ [11]https://gerontology.usc.edu/resources/articles/exploring-technologys-impact/

[12]https://gerontology.usc.edu/resources/articles/how-technology-will-impact-aging-now-and-the-near-future/ [13] Lancioni, G. E., & Singh, N. N. (Eds.). (2014). Assistive technologies for people with diverse abilities. New York: Springer. [14] Lancioni, G. E., O’Reilly, M. F., Sigafoos, J., Campodonico, F., Perilli, V., Alberti, G., Ricci, C., & Miglino, O. (2018a). A modified smartphone-based program to support leisure and communication activities in people with multiple disabilities. Advances in Neurodevelopmental Disabilities [15] Puanhvuan, D., Khemmachotikun, S., Wechakarn, P., Wijarn, B., & Wongsawat, Y. (2017). Navigation-synchronized multimodal control wheelchair from brain to alternative assistive technologies for persons with severe disabilities. Cognitive Neurodynamics, 11, 117–134. [16] Williamson, B., Aplin, T., de Jonge, D., & Goyne, M. (2017). Tracking down a solution: exploring the acceptability and value of wearable GPS devices for older persons, individuals with a disability, and their support persons. Disability and Rehabilitation Assistive Technology [17] Desideri, L., Negrini, M., Malavasi, M., Tanzini, D., Rouame, A., Cutrone, M. C., Bonifacci, P., & Hoogerwerf, E.-J. (2018). Using a humanoid robot as a complement to interventions for children with autism spectrum disorder: a pilot study. Advances in Neurodevelopmental Disorders, 2. [18]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5801738/ [19] American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5th. Arlington, VA: American Psychiatric Association; 2013.

https://vkc.mc.vanderbilt.edu/ebip/augmentative-and-alternative-communication/

https://vkc.mc.vanderbilt.edu/ebip/augmentative-and-alternative-communication/

https://scholarworks.uni.edu/cgi/viewcontent.cgi?article=1113&context=hpt

https://scholarworks.uni.edu/cgi/viewcontent.cgi?article=1113&context=hpt

https://www.atia.org/wp-content/uploads/2017/11/ATOB_ATOBN1V11_ART-6.pdf

https://www.atia.org/wp-content/uploads/2017/11/ATOB_ATOBN1V11_ART-6.pdf

http://www.assistiveware.com/state-aac-english-speaking-countries-first-results-survey

http://www.assistiveware.com/state-aac-english-speaking-countries-first-results-survey

http://pubdocs.worldbank.org/en/123481461249337484/WDR16-BP-Bridging-the-Disability-Divide-through-Digital-Technology-RAJA.pdf



https://www.ncbi.nlm.nih.gov/books/NBK97336/

https://gerontology.usc.edu/resources/articles/exploring-technologys-impact/

https://gerontology.usc.edu/resources/articles/exploring-technologys-impact/

https://gerontology.usc.edu/resources/articles/how-technology-will-impact-aging-now-and-the-near-future/

https://gerontology.usc.edu/resources/articles/how-technology-will-impact-aging-now-and-the-near-future/


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A SEVENTH LOOK AT SWEDISH MUNICIPAL PUBLIC HOUSING – OPERATING UNDER BUSINESS-LIKE PRINCIPLES: SOME UNANTICIPATED CONSEQUENCES

Lars Lindbergh and Timothy L. Wilson

Umeå School of Business, Economics and Statistics SE901 87 Umeå, Sweden

ABSTRACT

The purpose of this paper is to reflect on some specific observations that Municipal Housing Companies (MHCs) in Sweden have made as they complied with the Public Municipal Housing Companies Act of 2011. This act, PMHCA 2011, required MHCs to perform in a business-like manner. It was not explicitly noted, but undoubtedly assumed, by authors of this legislation that the municipal sector of housing would proceed much as before. In other words, the effect of passage would be a tweaking of present operations. As is frequently the case in examples of New Public Management (NPM) applications, however, there can be unexpected consequences to legislative activities. Results are reported here for a situation in which a particular MHC made a significant sale of properties to a private company, and an assessment is made of the apparent strategies involved for the two companies. Although specific in nature, it is emblematic of the manner in which all MHCs must be assessing their operations.

INTRODUCTION

Previously, we have reported on a number of aspects of Swedish public housing at PEA conferences (Lindbergh et al, 2017, 2016, 2015, 2004, 2003, 2002). It is a system of interest insofar as it has avoided some of the problems associated with social housing across Europe and the U.S. That is, allowances in the Swedish system were not made by income, but rather need, and public housing was universally accessible. In July 2002, the European Property Federation lodged a complaint with the European Commission, objecting to the Swedish practice of allocating state aid to house “well-off people” (cf. Czischke, 2014, pp. 338-339). After a state inquiry and much debate, the Swedish parliament abolished public service compensation for municipal housing companies in order to maintain the principle of universal access. The Municipal Housing Act, which entered into force on 1 January 2011, liberalized the sector and set out the objectives and ground rules for public housing companies. The State’s aim was to promote public benefits and the supply of housing for all kinds of people (still) and/but Municipal Public Housing Companies needed to operate under “business-like principles”. Under the new legal framework, public companies should charge market rents, including a certain profit margin. Furthermore, municipalities should require a market rate of return on

investment, reflecting industry practice and level of risk. Last year, our paper related to the analysis of financial results to ascertain whether companies’ walking was consistent with the previous talking (Lindbergh and Wilson, 2017, 2016). That is, an attempt was made to ascertain if financial observations paralleled the verbiage in owner directives. As colorfully suggested (Lindbergh and Wilson, 2016), the outputs of the owner directives represented companies’ ability to “talk the talk”. In other words, it remained to be seen if they really have learned to operate more like businesses, i.e., had MHCs learned to “walk the walk” as affected by their financial performance? Results were mixed. Data suggested that the industry had adjusted overall to the business-like mandate insofar as revenue generation illustrated an upturn starting in 2011. Results for return on assets, operating profit margin and solvency, however, did not show statistically significant results. Performance of two individual firms illustrated how extreme reactions might occur. Put another way, easy to say, hard to do. This year the study is continued. In the promulgation of PMHCA 2011 it was not explicitly noted, but undoubtedly assumed, by legislators that the municipal sector of housing would proceed much as before. That is, municipalities would continue to promote public benefits and the supply of housing for all kinds of people. The only difference would be the “business-like” addition to activities. To be sure there would be adjustments but Swedish residents would continue to be content with their housing system, and the European Property Federation and the European Commission would be off the State’s back. As is frequently the case in examples of New Public Management (NPM) applications, however, there can be unexpected results. In the Swedish example, one change had to do with the relationship between public and private housing provision. Until PMHC 2011, rents were negotiated between the local Union of Tenants and MHCs, which were also binding upon the private sector and thus further affected the importance of MHCs. Nevertheless, because of PMHCA 2011 private landlords have been equal parties in negotiations and sign their own agreements with unions – business-like in this context could include price competition, something rather new to this sector. Further, market rents, profit margins, rates of return on investment and levels of risk had companies analyzing and reassessing their operations. The rental sector is important in Sweden. The sector


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comprises nearly 30 percent (28.6) of all units and is primarily served by three forms of organizations - Public Housing Companies (MHCs), Limited Liability Companies, and Individual Ownership of Multi-Units (See Appendix 1). Among rentals, MHCs comprise over 50 percent (52) of the sector. Although the holdings of limited liability companies are growing at a rate greater than 10 percent (12.3), the rental sector overall is growing at less than three percent (2.7). In fact, the growth rate of MHCs is presently negative. One reason for this negative rate is that parts of MHCs have been sold to Limited Liability Companies. The purpose of this paper is to reflect on some specific observations that Municipal Housing Companies (MHCs) in Sweden have made as they complied with the Public Municipal Housing Companies Act of 2011. In particular, an assessment is made of the apparent strategies in which a particular MHC made a significant sale of properties to a private company. Although specific in nature, the transaction reflects the general situation in Sweden as companies reflect on their asset portfolios as they deal with PMHCA 2011. The study is important because PMHCA 2011intentionally set out to change practice in a system that had served the country for over half a century. Results therefore have implications for both state and municipal policy makers as well as residents of Swedish public housing who must live with changes. Further, because the Swedish system tends to be contrasted to social housing, there may be international implications as well. The paper is organized as follows: now that the problem and approach has been described, the next section will give some background on Swedish housing and some of the business concepts useful in analyzing the case. Subsequent to that comes a short description of the case, then its analysis and a short, general discussion.

BACKGROUND The Current Status of Swedish Housing Lind (2014) has written a comprehensive description of housing in Sweden. It was concluded “The debate on housing policy is making it increasingly clear that a new model is needed. There is agreement that MHCs should no longer bear any special social responsibility, even though in practice many still do to a lesser degree. There is no new construction of affordable housing, which makes it more and more difficult for outsiders to rent apartments in metropolitan areas. If housing allowances do not go hand-in-hand with new construction, then helping one household leads to problems for another household just above the support line. As house prices and apartment prices have not fallen, access to owner occupation remains difficult. There are indications of increased overcrowding and illegal subletting. It is becoming more and more obvious that a new program for large-scale production of affordable housing is needed, but how this can be carried out is very much and open question” (Lind, 2014).

The situation continues to evolve. That is, they are moving from a prior position that might be characterized as part of a planned economy to one that would be better characterized as a free market economy. Even in the prior situation, problems were recognized. As noted previously, Turner (1999) who first noted that a low solidity, in combination with increasing vacancies, had created a negative yield for some companies. Somewhat along the same lines, Nesslein (2002 and 1981) was critical of the Swedish planned system. In brief, the very high cost Swedish housing had been made affordable for the general Swedish population through large housing production subsidies and massive income redistribution (Nesslein, 2016). Specifically, it was indicated that Swedish building and operating costs were significantly higher that private rental housing in other countries, and it appeared that the supply of housing was not well-matched to the kind of housing that consumers desire. Presently, as the industry goes further, more difficulties may be encountered. To name a few, new construction has not kept pace with population growth particularly in the larger cities (Ho, 2015); in all three major metropolitan areas, the increase in the number of finished residential properties has only been one-fifth of the population increase (Housing Crisis Committee, 2014). According to the latest survey carried out by the National Board of Housing, Building and Planning, 240 out of 290 local municipalities have a shortage of housing (Swedish Radio-1, 2017). The average wait time for a Stockholm County apartment has grown to 9.1 years. For a contract on a city-center apartment, the wait is 13.5 years (Swedish Radio-2, 2017). Immigration of course has accentuated the capacity problem. A regulation introduced in 2016 meant that municipalities were required to house more than 20,000 immigrants who have been distributed throughout the country (Swedish Radio-1, 2017). Whatever the case, the situation is not the same everywhere; individual companies have entered this period facing different local environments and with different resources. All firms in the industry will not adopt the same strategy, nor should they. Nevertheless, those that possess superior resources and make appropriate decisions in their adaption might be expected to do well. Those that have not and/or do not – not so well (cf. Hunt, 2000, pp. 127-132). Going forward, Swedish Municipal Public Housing Companies must now operate under “business-like principles” – by law. Czischke (2014, pp. 338-339) has described how this situation arouse, citing the circumstances leading to the Public Municipal Housing Companies Act, which entered into force in 2011.

“In July 2002, the European Property Federation (EPC) lodged a complaint with the European Commission, objecting to the Swedish practice of allocating state aid to house well-off people. After a state inquiry and much debate, the Swedish parliament abolished public service compensation for


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municipal housing companies in order to maintain the principle of universal access (although the phasing of state subsidies for housing construction had begun in the early 1990s. ... The Municipal Housing Act, which entered into force on 1 January 2011, liberalized the sector and set out the objectives and ground rules for public housing companies. Their aim is to promote public benefit and the supply of housing for all kinds of people, and they must operate under ‘business-like principles’ (emphasis added). … Under the new legal framework, public companies should no longer apply the cost-rent principle but instead should charge market rents, including a certain profit margin. Furthermore, municipalities should require a market rate of return on investment, reflecting industry practice and level of risk”.

In summary, it might be said that Swedish housing policy is at a turning point (cf. Lind, 2014). On the one hand, the historical feeling among political parties has been that the country would not have social housing. That need was covered adequately and affordably by the Municipal Public Housing system. Allowances were not made by income, but rather need, and public housing was universally accessible. The common opinion within the country was that the system worked well. Nevertheless, municipal companies, which for 50-60 years provided housing now have found themselves operating under new guidelines. It remains to be seen what system these guidelines will produce.

FIVE CONCEPTS AS USED IN BUSINESS There are five concepts that are used in the analysis of the case that receive some exposure in this section. Individual sections are short and quite basic. Typically these sections rely on a single, fundamental citation and thus may be considered more along the lines of working definitions as opposed to developed concepts. They are 1.) the concept of a business: if MHCs are to become more business-like, this concept is certainly relevant. 2.) the concept of an investment portfolio, particularly that of the property holdings and their returns. 3.) the idea of strategy –intended and emergent, 4.) unexpected consequences and 5.) coopetition, the simultaneous pursuit of cooperation and competition among firms. Unexpected Consequences Unexpected consequences (sometimes called unanticipated, unintended, or unforeseen consequences) are outcomes that are not ones anticipated, intended, or foreseen by a purposeful action, decision, or legislation (the situation considered here). Manuscripts on the topic generally cite Merton’s (1936, 899-900) seminal paper, which suggested that chance consequences are occasioned by the interplay of forces and circumstances so complex and numerous that prediction is

quite beyond our reach. In essence this may be a formalization of something that Niels Bohr, a contemporary, had noted, “Prediction is very difficult, especially when it relates to the future”. Or perhaps the concept is a corollary of Murphy’s Law, “Anything that can go wrong will go wrong”. The Concept of a Business Over the past 30 to 40 years, much of the philosophy of business has come from Peter Drucker and his Practice of Management (1982/1954). Traditionally, “businesslike principles” tend to be associated with operations, i.e., “it is a first duty of a business to survive” (Drucker, 1982/1954, p. 46). Consequently, operations tend to be concerned with managing revenue intakes in a manner that exceeds cost over a sufficiently long time horizon to assure profitability. In general, “What is our business and what will our business be” are covered in chapter 6. His eight objectives, which seem especially relevant as an organization becomes more business-like, are covered in chapter 7 (Drucker, 1982/1954, pp. 62-63). They are, in his order,

1. Market standing 2. Innovation 3. Productivity 4. Physical and financial resources 5. Profitability 6. Manager performance and development 7. Worker performance and attitude 8. Public responsibility

The Concept of an Investment Portfolio, Particularly that of the Property Holdings and Their Returns “Portfolio” is a word that takes on a range of meanings depending upon an application and/or usage. It can mean anything from a valise, an office, to a collection of holdings or representative works. In this paper, the meaning is limited to investment portfolios, i.e., the collection of property investments specifically held by municipal public housing companies. In particular, an attempt is made to analyze the nature of these holdings. In this regard, it is presumed that analyses can be associated with the product portfolio concepts involved in developing marketing strategies, i.e., the use of scarce cash and managerial resources for optimum long-run gains (cf. Day, 1977). Product classes in this analysis are characterized in the now relatively familiar terms as cash cows, stars, dogs and problem children. The Idea of Strategy – Intended and Emergent From all the possibilities and proponents, in this paper, we follow Mintzberg (1978) who defined strategy in terms of patterns as in patterns in a stream of decisions. In this regard, he identified two aspects of strategies: intended and realized. These two, at least in theory, were combined in three ways: (1) Intended strategies that get realized, which may be called


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deliberate strategies. (2) Intended strategies that do not get realized, perhaps because of unrealistic expectations, misjudgments about the environment, or changes in either during implementation; these may be called unrealized strategies, (3) Realized strategies that were never intended, perhaps because no strategy was intended at the outset or perhaps because, as in (2), those that were got displaced along the way; these may be called emergent strategies. Coopetition, the Simultaneous Pursuit of Cooperation and Competition Among Firms Coopetition is the practical recognition that firms in a given sector who are nominally competitors find in some occasions the need to cooperate for mutual benefit (cf. Bengtsston and Kock, 2000). The degree to which this behavior occurs varies from country to country and industry to industry. Price fixing, for instance is restricted in most developed countries, but industry standards and organizations tend to be tolerated and even promoted. In the particular case covered in this paper, the two companies, Bostaden and Heimstaden, eventually will become competitors. During the transaction and subsequent transition, however, they have cooperated and show indications of continuing that cooperation in the future.

METHODOLOGY The financial transaction covered in this study has elements of a participative case study. One of the authors lives permanently in the city in which the transaction is occurring and has an ongoing relationship with one of the principals involved. Additionally, one of our colleagues in our group lives in one of the flats that will change hands. Thus, to the extent that outsiders have knowledge of developments, we are well informed. Nevertheless, observations reported here tend to have hard, secondary support for their inclusion in the study. In Sweden, there tends to be a high degree of transparency in such matters.

OBSERVATIONS The Nature of the Case The two primary players in the case are AB Bostaden, the established Municipal Public Housing Company and Heimstaden AB, the established national private real estate rental company. Subsequently, an introduction will be made of the Apartments, an established, private rental company that has focused its rental efforts on seniors. The primary facts in the case are relatively straightforward. That is, on or about 19 June 2017, Bostaden, the local MHC in Umeå, Sweden, formally agreed to sell 1,601 of its apartments in two residential locales (Mariehem and Carlshem) to Heimstaden, an international, private rental firm, in a deal estimated to be worth just over SEK 1.1 billion (AB Bostaden, 2017). AB Bostaden, the case company in this study, builds and

manages housing in the Umeå municipality, and its history dates back to 1953 when it started as a foundation. In 1995, the company became a municipal public utility tasked with contributing to the municipality’s growth by providing its housing. With 15,400 apartments before the sale, it was the biggest participant in the Umeå housing rental market, with a market share of approximately 45 percent of the rental sector, and 27 percent of the Umeå housing market overall (AB Bostaden, 2016). It also had a large stock of student housing. According to the latest financial report (2016), the Company had a rental revenue (turnover) of ~ 1,048 million SEK (~ $125 million), 177 employees and an asset value in excess of 5250 MM SEK (~ $630 million). Company literature suggests that through its growth, Bostaden has helped turn Umeå into a city that in many ways is the capital of Norrland. The company’s business concept is to provide “value-for-money” rentals. Its aspiration has been to be the natural choice for people who wish to rent their own homes in Umeå. Heimstaden AB is a subsidiary of Fredensborg AS who acquired it in 2005. It has about 19,800 apartments and a property value of approximately 26.3 billion SEK ($3.1 billion) in value (Heimstaden AB, 2018). It is one of the leading residential real estate companies in the Nordics, and in Sweden, the business is divided into four geographical areas; North, Middle, South and Skåne. Heimstaden owns, refines, develops and manages apartments from Luleå in the North to Trelleborg in the South. They see themselves as creating value for shareholders and partners by developing and optimizing an attractive property portfolio, and their expressed strategy involves striving for • Excellence in customer experience, • Sustainable and profitable growth, • Sustainable development of real estate, and • A value-driven organization

Corporate headquarters are located in Malmö, Sweden, and sees its mission as to acquiring, developing and managing real estate in Sweden and Denmark. In pursuing this mission, its apartments are mostly located in city central areas. With the acquisition of the Umeå properties, the company now has holdings in 18 Swedish communities - Falköping, Huddinge, Katrineholm, Klippan, Landskrona, Luleå, Lund, Linköping, Ljungby, Malmö, Norrköping (2), Trelleborg, Uppsala, Umeå, Vetlanda, Vällingby, Växjö and Örkelljunga.

The transaction, because of its strategic nature was secreted until completed, but was under consideration for some time. Bostaden needed a capital infusion and its three alternatives were – an increased shareholder contribution, increased borrowing, or sale of assets (AB Bostaden, 2017). The Municipality holds all shares in the Company and unlikely to increase its capital holdings; borrowing weakens the balance sheet, so it was a sale of assets by a process of elimination.


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The FAQ Sheet put out by Bostaden (AB Bostaden, 2017) reads “we asked a total of ten housing companies to submit their prices for the portfolio. Three of these were selected to submit a final bid”. “Heimstaden (as the selected buyer) is a strong, long-term player that owns and manages tenant properties. They also take great social responsibility on the markets in which they own properties and they share our management strategy. Heimstaden also submitted the highest bid for the property portfolio”.

The final price of 1.1 billion SEK indicates Heimstaden treated its offer as one for an ongoing enterprise and not a purchase of assets at book value, which would have been about half of that (~ 52.5 million SEK). Appendix 2 suggests that Heimstaden must have been looking at 20 to 25 year time horizon at an internal capital cost of about eight percent. The transaction benefitted both parties. Bostaden has already indicated very specifically how its receipts will be invested in new production for the elderly (AB Bostaden, 2017): • Mariehem centre: around 80 retirement apartments • Lodet block (current bus station): around 180 retirement

apartments • Västteg: total of 300 apartments. Elderly centre with

housing for the elderly (130 apartments) and retirement housing (100 apartments). We are also building 70 standard apartments.

Likewise, Heimstaden has benefitted insofar as they now have an established position in Umeå. Its intended strategy has been and is “Part of our mission is to build new homes. We operate in cities where we already are established as well as cities that have a growth potential. We build rental apartments, condominiums and property rights, depending on what is in demand and what is considered sustainable”. Heimstaden apparently has seen potential in Umeå and has tapped in via an ongoing, profitable business.

BUSINESS REFLECTIONS

In this section we look at the case with regard to some elements of proactive business management. Unexpected Consequences PMCHA 2011 was promulgated in an apparent attempt to shake up the Municipal Housing sector. In the extreme, it may have affected a shakeout in the industry. Appendix 1 indicates that the Public Housing rental sector is declining as Limited Liability Company holdings are increasing. That transition in itself was probably unanticipated. Nevertheless, the case suggests how the transition is occurring. Basically, there is a shortage of housing in Sweden, particularly in the larger cities (Ho, 2015). Normally, some, if not most, of that would be

satisfied by MHC expansion. The municipal sector, however, has a limitation in acquiring funds. Limited Companies, on the other hand, appear to have access to funds. Some of these funds seem to be finding their way into the purchase of existing municipal properties instead of new construction. Ergo, the holdings of one sector declines while the other increases, with no new net expansion. The Concept of a Business Drucker counseled (1982/1954, p. 46) “it is a first duty of a business to survive”. Bostaden management did what they thought they had to do. Ten percent of its present business was sold so that expansion into more profitable businesses might be pursued. Presently, it is getting about 5,700 SEK/mo/apt (See Appendix 1). At the same time, there is a private company in town servicing seniors that is getting approximately 8,000 SEK/mo/apt. New construction costs do not increase markedly whether 8,000 SEK or 5,700 SEK apartments are built. Thus, the MHC is betting some portion of its survival upon being successful in higher margin holdings. Heimstaden, on the other hand, is a private company that competes in housing at the national level. Any purchase it makes of established housing represents an expansion in its holdings. Given that there tends to be economies of scale in the sector, Hemstaden provides for its survival by making the purchase. With regard to Drucker’s eight objectives, only worker performance and development do not seem affected by the transaction. Depending on future developments, public responsibility may or may not be improved. Presently, existing tenants seem to be concerned with the change in ownership. Otherwise, market standing, innovation, productivity, physical and financial resources, profitability, manager performance and development all seem to have been improved with the transaction. The Concept of an Investment Portfolio The concept of an investment portfolio folds into the concept of a business. Municipal housing can be either a dog or a cash cow in an investment portfolio, depending upon market share. That is, holdings tend to be low growth, but they can be profitable. A private company in an expansive mode covets cash cows for the cash spin-offs they provide to finance expansion. The Idea of Strategy – Intended and Emergent The two elements of strategy may be at work here. For Bostaden it seems that it pursued an intention; for Heimstaden an opportunity emerged, although historically it has been a buyer of existing holdings (see Heimstaden AB, 2017). Thus,


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when the opportunity to purchase Bostaden’s properties presented itself, the company competed and won the competition. Coopetition, the Simultaneous Pursuit of Cooperation and Competition among Firms By cooperating with Heimstaden in this transaction, Bostaden has created a competitor for itself and it is a formidable competitor. It is 25 times larger and whereas Bostaden sold assets to inject capital, Heimstaden had the muscle to buy the same. Bostaden reflections on the transition indicate that Heimstaden will play by the same rules in operations, i.e., Heimstaden is keen to do things in a proper manner and to create good relationships with the tenants (AB Bostaden, 2017). The presence of Heimstaden is also seen as a plus insofar as it may have the impact of bringing a capital infusion into the community.

ASSESSING THE FUTURE We start off this section by repeating the common quote, “Prediction is very difficult, especially when it relates to the future”. Given that caveat, the plumb in this case may not be the Mariehem and Carlshem apartments, but another firm, which we call the Apartments. It is this firm that built the 8,000 SEK/mo/apt for seniors. At this date, this firm is not for sale, although anything is possible in the future. Bostaden, however, has indicated a desire to build housing like that and has indicated that some portion of the income will go for that construction (AB Bostaden, 2017). Heimstaden also suggests that it also pursues a strategy of building in those communities in which it has a position (Heimstaden, AB, 2017). Given a choice between building 8,000 SEK apartments and 5,700 SEK apartments, the decision would seem to go with the more expensive ones – that is where the money and growth appears to be. This situation is shown in Figure 1. Of course, when firms in a sector look at expansion opportunities and each one proceeds as if they will be the one that will benefit from the opportunity, then one gets saturation or excess of a product – sometimes called a glut. Thus, in the medium term, the municipality may end up with upscale apartments for seniors without enough seniors to fill them. The other aspect of looking into the future is that Bostaden has indicated that it will need another 1billion SEKs of capital to get through the next five years. Its situation remains the same – shareholders adverse to a new stock issue, while borrowing is also limited. It would thus appear that another offering is in the making, and its nature is now known, i.e., 1,600 apartments for 1 billion SEK. Such a sale is speculation at this time. Neither the reality nor the timing are known at this time, but it would appear to be probable within a two year time frame. Thus, there would be 3,200 of 15,400 apartments going from

the public sector into the private sector. They are still available, of course, and are still being rented. For the present, they have the same tenants that they had before, just the administration has changed. That is occurring around Sweden. One has to wonder, however, if that is what the legislators had in mind in a State with welfare leanings when PMHCA 2011 was promulgated.


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Figure 1 – Status in the Specific Case of Umeå and of Sweden in General

ENDNOTES 1. During the same period, population increased 3.6 percent.

Statistics Sweden (2018) http://www.scb.se/en/finding-statistics/statistics-by-subject-area/ Downloaded 30 March, 2018.

2. It is recognized that this chapter was written before the even more recent influx of immigrants into the State. That pressure on housing will just put more pressure on MHCs, which historically have been the first living quarters for incoming persons.

3. cf. http://www.merriam-webster.com/dictionary/portfolio, downloaded 5 October 2015.

4. This puts them in the top six percent of MHCs in Sweden (cf. Turner, 2007, p. 149).

5. Actually, a one room apartment is approximately 8,700 SEK/month and a three room apartment is approximately 11,100 SEK/month. Cleaning and other additional types of services are available for an extra charge (Private communication, 04/05/2018).

Heimstaden

Bostaden

Apartments

Transaction

Aspiration B

Aspiration H

http://www.scb.se/en/finding-statistics/statistics-by-subject-area/

http://www.scb.se/en/finding-statistics/statistics-by-subject-area/

http://www.merriam-webster.com/dictionary/portfolio


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APPENDICES Appendix 1 – The Housing Sector in Sweden: Units, by Organizational Form and Holdings (Statistics Sweden, 2017)

http://www.statistikdatabasen.scb.se/pxweb/en/ssd/ Downloaded, 30 March, 2018.

2013 2014 2015 2016 % Ch Comment Single Unit Homes (All Forms) 1,999,962 2,007,664 2,018,064 2,053,665 2.7 Pri Individual Cooperative Tenant Associations 980,835 946,926 967,953 985,410 0.5 Pri Individual Public Housing Companies 682,453 684,183 685,282 681,038 -0.2 Pri Rental Limited Liability Companies 433,155 445,361 461,797 486,300 12.3 Pri Rental Individual Owned Multi-Units 159,705 155,476 151,085 142,107 -11.0 Pri Rental All Others1 126,105 124,885 122,454 129,258 2.5 Miscellaneous

TOTALS 4,382,215 4,364,495 4,406,635 4,577,778 4.5 Appendix 2 – Estimate of “Fair” Market Value of Apartments Average rental revenue of Bostaden apartment = Total Rental Revenues / No. Apartments = 1,078 Billion / 15,400 = 68.05 Thousand / Apartment / Year Expected annual revenue for 1601 apartments = 68.05 * 1601 = 109 Million / Year Net Present Value of 109 Million / Year for Varying Time Horizons and Costs of Capital (Figures in Billions SEK)

1 All Others includes, other legal entities, condominium associations, government holdings of all types and missing data (< 1%).

Capital Cost Years 6% 8%

15 1.06 0.93 20 1.25 1.07 25 1.39 1.17 30 1.49 1.26

http://www.statistikdatabasen.scb.se/pxweb/en/ssd/


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REFERENCES AB Bostaden. 2017: 11-17. FAQ about the selling of properties. www.bostaden.umea.se/ faq-about-the-sale-of-properties (Downloaded 25 March, 2018). AB Bostaden. 2016. Bostaden. AB Bostaden, Umeå, Sweden. www.bostaden.umea.se (Downloaded 25 March, 2018). Bengtsson, M. and Kock, S. 2000. Coopetition in business networks – to cooperate and compete simultaneously. Industrial Marketing Management. 29, 5: 411-426. Czischke, C. 2014. Social housing and European Community competition law. in Scanlon, K., Whitehead, C. and Fernandez, A. (eds.) Social Housing in Europe. London: John Wiley & Sons, Ltd. 333-346. Day, G. S. 1977. Diagnosing the product portfolio. Journal of Marketing 41, 2: 29-38. Drucker, P. F. (1982/1954). The Practice of Management. New York: Harper Perennial. Heimstaden AB 2017. https://heimstaden.com/om-oss/heimstaden/marknader?lang=en (Downloaded 25 March, 2018). Ho, G. 2015. Housing supply constraints in Sweden. Chapter in IMF Country Report No. 15/330, Sweden: Selected Issues: 24-33 (pdf – downloaded 11/21/2016). Hunt, S. D. 2000. A General Theory of Competition. London: Sage Publications, Inc. Lind, H. 2014. Social housing in Sweden. in Scanlon, K., Whitehead, C. and Fernandez, A. (eds.) Social Housing in Europe. London: John Wiley & Sons, Ltd. 91-102. Lindbergh, L. and Wilson, T. L. 2017. A fifth look at Swedish housing: Operating under business-like principles – walking the walk. PEA 2017 Conference Proceedings. Mondal, Sunita (ed.) 121-137. Lindbergh, L., Jacobsson, M. and Wilson, T. L. 2016. A fourth look at Swedish housing: The business model. Pennsylvania Economic Association Annual Conference Proceedings. Huang, Jui-Chi (ed.) 136-147. Lindbergh, L. and Wilson, T. L. 2015. A third look at public housing in Sweden: The influence of owner directives.

Proceedings of the Pennsylvania Economic Association Conference. Isariyawongse, K. (ed.) 127-135. Lindbergh, L., Larsson, C.-G. and Wilson, T. L. 2004. Public housing in Sweden: metropolitan observations. Proceedings of the Pennsylvania Economic Association 2004 Conference. Tolin, T. W. (ed.) 213-218. Lindbergh, L., Larsson, C.-G. and Wilson, T. L. 2003. A second look at public housing in Sweden. Proceedings of the Pennsylvania Economic Association 2003 Conference. Hannan, M. (ed.) 361-367. Lindbergh, L., Larsson, C.-G. and Wilson, T. L. 2002. A look at public housing in Sweden. Proceedings of the Pennsylvania Economic Association 2002 Conference. Yerger, D. B. (ed.) 213-219. Merton, R. K. 1936. The unanticipated consequences of purposive social action. American Sociological Review 1, 6: 894-904. Mintzberg, H. 1978. Patterns in strategy formation. Management Science. 24, 9: 934-948. Nesslein, T. 2016. Private communication in manuscript review for Journal of Modelling in Management. Nesslein, T. 2003. Markets versus planning: An assessment of the Swedish housing model in the post-war period. Urban Studies. 40, 7: 1259-1282. Nesslein, T. 1982. The Swedish housing model an assessment. Urban Studies. 19, 3: 235-246. Swedish Radio 2017-1. Archives: Migrants face cramped housing in Sweden. 3 January 2017 sverigesradio.se/sida/artikel.aspx?programid=2054&artikel=6606671 (Downloaded 18 January 2017). Swedish Radio (2017-2). Archives: Over half a million in Stockholm apartment waiting line. 12 January 2017. sverigesradio.se/sida/artikel.aspx?programid=2054&artikel=6606671, (Downloaded 18 January 2017). Turner, B. 2007. Social housing in Sweden in Social Housing in Europe, Whitehead, C. and Scanlon, K. (eds.) London: London School of Economics and Political Science. 148-164. Turner, B. 1999. Social housing finance in Sweden. Urban Studies. 36, 4: 683-697.

https://www.bostaden.umea.se/news/faq-about-the-sale-of-properties

https://www.bostaden.umea.se/news/faq-about-the-sale-of-properties

http://www.bostaden.umea.se/

https://heimstaden.com/om-oss/heimstaden/marknader?lang=en

https://heimstaden.com/om-oss/heimstaden/marknader?lang=en

http://sverigesradio.se/sida/artikel.aspx?programid=2054&artikel=6606671





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THE PROS AND CONS OF FAMILY BUSINESS SUCCESSION

David A. Nugent Robert Morris University

Moon Township, PA 15108

ABSTRACT This is a theoretical paper that addresses factors that determine whether or not a family business is transferred to a younger generation. Issues addressed include whether continued ownership by a founder’s family maximizes value. If highly motivated, well qualified relatives want to join the family business, transfer to the next generation may be appropriate. If potential successors are not motivated and/or not qualified to manage the business, a sale of the business to outside investors may be more appropriate. If a retiring owner feels that family pride, maintaining traditions, and providing employment to family members is more important than profits, the owner may transfer ownership knowing that a trade-off is being made. If well qualified potential successors have opportunities for careers outside the family business and are not interested in joining the family business, sale to outside investors may be the best choice for the younger generation as well as the retiring owner.

INTRODUCTION Studies of family business succession (Bailey, 2016; Scouler, 2014; Stalk and Foley, 2012) report that approximately 30 percent of family businesses are transferred to the second generation, 12 percent are transferred to the third generation and 3 percent are transferred to the fourth generation. These figures seem to suggest that low succession rates may lead to economic losses. If a business owner were to fail to find a suitable successor, and if the business were to close, the result might be the loss of employment of relatives who work for the business, the loss of employment of non-relatives, the loss of capital assets that the owner spent a lifetime building, the loss to customers of their supply of the products and services provided by the business, the loss to the economy of the products and services that will no longer be produced, and other adverse consequences. However, consequences may not be as dire if employees are able find to new jobs, customers find new suppliers and if assets can be sold to other business that produce goods and services to make up for the lost production of the closed family business. A seemingly low multi-generational family business survival rate may be a normal outcome for businesses in a dynamic, changing economy. Aronoff (2018) points out that of the corporations that comprised the original Dow Jones Industrial Average (DJIA) in 1896, only one corporation, General Electric, remained in the DJIA (as of the date of publication).

It might be inferred that mergers and acquisitions and other changes are to be expected over periods of decades and generations. This paper addresses conditions and circumstances that the retiring owner of a family business might consider when deciding whether or not to transfer the business to a younger generation.

FACTORS THAT INFLUENCE FAMILY BUSINESS SUCCESSION

Suppose that the owner of a family business wants to maximize family well-being. The goal could include not just the maximization of the market value of the business, but non-financial concerns as well. Transfer of ownership to a younger generation may provide continued employment for family members, satisfaction associated with knowing that the family name and the family business will carry forward and that the traditions of the family will be maintained. Similarly, family members who join the family business may be drawn by pride and loyalty to the family and the traditions of the family business. The retiring owner, as well as the younger generation, may also feel satisfaction knowing that loyal customers will continue to be served by the family business. If the family business is profitable and growing, continuation of ownership through multiple generations may provide for a growing legacy of value to provide for the economic well-being of future generations. Success of a family business may be affected by a number of attitudes and attributes of family members. Parker (2016) suggests that if family members who own and/or manage a business are altruistic and have a desire to keep the business in the family, those family members may expend more effort than mangers of non-family businesses. The result may be more profitability and productivity than comparable non-family businesses. Gedajlovic, Carney, Chrisman and Kellermanns (2012) suggest that effort and abilities affect performance. If managers of a family business have superior abilities and expend high levels of effort, the business should do well. In contrast, if managers of a family business have inferior abilities and expend low levels of effort, the result could be deterioration of the business.


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Inadequate abilities and low levels of effort may arise if a business employs unqualified family members out of a sense of loyalty. Stalk and Foley (2012, page 26) suggest that if a business owner tells family members that “There’s always a place for you here”, the result could be unqualified managers who make decisions that lead to business failure. Stalk and Foley (2012) suggest that family members should be required to compete with non-family members, perhaps be required to earn university degrees, to gain experience working for other businesses, and otherwise achieve qualifications. If family members were to achieve qualifications that would enable them to find alternate employment, a business owner may have difficulty persuading qualified family member to be successors. Opportunity cost may be a factor for family member choosing whether or not to join the family business. Accounting textbooks (Garrison, Noreen and Brewer, 2018; Lanen, Anderson and Maher, 2008) define opportunity cost as the potential benefit that is given up when one alternative is chosen instead of another. A family member who has the opportunity to either join the family business or work for another company would consider the benefits of each alternative. Sharma and Irving (2005) suggest that if a business is successful, the opportunity cost of not joining the family business would include not just potential income, but also the market value of the business. The opportunity cost of not joining the family business may be too great to forego. If a business is only moderately successful, the opportunity cost of not joining the family business may be less than the perceived benefits of a career outside of the family business. In that case, a business owner may not be able to find a willing successor within the family. A retiring business owner may want to consider other options, such as selling the business to outside investors. A FAMILY BUSINESS AS A STAGE IN A FAMILY’S

FINANCIAL PROGRESS If a business owner encourages the next generation to pursue higher education, a family business may serve as a source of funds that may allow the younger generation to progress beyond the family business. Consider how a family’s financial well-being might progress over a series of generations. Imagine an era when few people had substantial education and a person with limited education could find a job that provided a modest standard of living. Such a person might live in an apartment and have children who graduate from high school. The generation who are high school graduates may have somewhat better jobs than their parents. The high school graduates may be able to purchase modest houses and send their children to college. The college graduates may be able to purchase bigger houses and send their children to graduate school. Also imagine that at some point a family business was established.

As the generations achieve higher levels of education, career opportunities may expand. The expanding opportunities for younger generations may decrease the likelihood that a family member will want to join the family business. For example, suppose that members of a younger generation graduate from medical school, or dental school or law school. It seems unlikely that they would join the family business. If the founder of a family business were to consider the business to be a source of income and wealth for the benefit of the family, the founder may not be overly upset by the prospect of ending a family tradition. If a founder’s objective were to provide a legacy of value to be passed down to future generations, the founder may consider transfer of ownership to family members to be just one possible action. An alternative action could be the sale of the business to non-relatives. If sale of the business is an option, the founder may want to consider which alternative would maximize value.

MAXIMIZING BUSINESS VALUE To maximize business value, a family business owner may want to consider business valuation models. For example, Finance textbooks (Block and Hirt, 2005; Ross, Westerfield and Jordon, 2006) present corporate stock valuation as the present value of future dividends. If a company’s dividends are expected to grow at a constant rate, the stock value would be calculated as: Po = D1 / Ke – g Symbols are defined as: Po is the market price of the stock today. D1 is the dividend expected at the end of the coming year. Ke is the required total rate of return. g is the constant growth rate for dividends. Ke – g is the required current yield Although a family business may not pay dividends, the above model can be used to draw general conclusions regarding valuation maximization strategy. If income were a proxy for dividends, greater income would mean greater value. If growth of sales and income gives rise to a lower required current yield, a faster growth rate would lead to greater value. If a business owner approaching retirement wants to leave a legacy in the form of the proceeds of the sale of the business, preparation for retirement may include taking steps to enhance income and growth. That might mean hiring non-relative managers who have the education, experience and abilities that enable them to keep up with technology, meet customers’ changing needs and otherwise successfully compete. If the retiring owner has relatives who want to join the family business, maximization of the family legacy may entail running the family business in a manner similar to a non-


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family business. Family members could compete with non-family members. Those family members could pursue university degrees compatible with the family business, such as MBA degrees, and otherwise prepare for successful management of the family business.

CONCLUSIONS This paper has addressed factors that the owner of a family business might consider in deciding whether or not to transfer ownership of the family business to a younger generation. Low multi-generational survival rates of family businesses suggest that there may be good reasons to not transfer ownership to a younger generation If a family business is highly profitable and growing, young family members may perceive a career in the family business to be preferable to other careers. Those family members may make efforts to become qualified to manage the business by attaining the appropriate education and experience. They may enthusiastically embrace their roles as managers of the family business and make substantial effort leading to success of the business. Under these circumstances, transfer to a younger generation may be warranted. If highly qualified family members do not enthusiastically embrace the opportunity to pursue a career with the family business, but instead reluctantly join because they feel a sense of obligation and loyalty to the family, their lack of enthusiasm and motivation may lead to the deterioration of the business. Similarly, if family members with low levels of ability are hired because they have difficulty finding good paying jobs outside of the family business, the result might be poor performance. If a retiring owner judges that transferring the business to relatives who lack motivation and/or ability may lead to a drop in the value of the business, the owner may want to sell the business to outside investors while the business can still command a high price. If the owner of a family business seeks to enhance family well-being in ways that do not necessarily maximize market value, the owner may welcome the participation of all family members, even those who have imperfections. The owner may feel that family pride, maintaining traditions, seeing the family name perpetuated by the family business, and providing employment to family members is more important than profits. The owner may transfer ownership to the next generation knowing that a trade-off is being made. If potential successors have attained education and experience outside of the family business, those potential successors may have a broad range of career opportunities. If those other opportunities are more appealing than working for the family business, no potential successors may express interest in joining the family business. Rather than attempt to persuade

reluctant family members to fulfill their duty to take over the family business, the retiring owner may choose to sell the business to outside investors. If appropriate steps have been taken to put in place a management team that achieves high profitability and growth, the result could be proceeds of sale that would maximize the retiring owner’s legacy.

REFERENCES Aronoff, C. E. 2018. Family Business Survival: Understanding the Statistics. https://www.thefbcg.com/family-business-survival-understanding-the-statistics/ Bailey, D. 2016. More Than 8 Out Of 10 Family Businesses Have No Succession Plans. http://sponsored.bostonglobe.com/rocklandtrust/more-than-8-out-of-10-family-businesses-have-no-succession-plans/ Block, S. B., and G. A. Hirt, G. A. 2005, Foundations of Financial Management. New York: McGraw-Hill Irwin. Garrison, R. H., E. W. Noreen, and P. C. Brewer, 2018. Managerial Accounting. Sixteenth Edition. New York: McGraw-Hill. Gedajlovic, E., M. Carney, J. J. Chrisman, and F. W. Kellermanns. 2012. The Adolescence of Family Firm Research: Taking Stock and Planning for the future. Journal of Management. Vol. 38, No 4, July 2012. pp. 1010-1037. Lanen, W. N., S. W. Anderson, and M. W. Maher. 2008. Fundamentals of Cost Accounting. New York: McGraw-Hill Irwin. Parker, S. C. 2016. Family Firms and the “Willing Successor” Problem. Entrepreneurship: Theory and Practice. November 2016, pp. 1241-1259. Ross, S. A., R. W. Westerfield, and B. D. Jordan. 2006. Fundamentals of Corporate Finance New York: McGraw-Hill Irwin. Scouler, D. 2014. The Frequently Fatal Family Business Flaw: Denial. https://www.entrepreneur.com/article/231757 Sharma, P., and P. G. Irving. 2005. Four Bases of Family Business Successor Commitment: Antecedents and Consequences. Entrepreneurship: Theory and Practice. January 2005, pp. 13-33. Stalk, G., and H. Foley, 2012. Avoid the Traps That Can Destroy Family Businesses. Harvard Business Review. January-February 2012, pp. 25-27.

http://sponsored.bostonglobe.com/rocklandtrust/more-than-8-out-of-10-family-businesses-have-no-succession-plans/

http://sponsored.bostonglobe.com/rocklandtrust/more-than-8-out-of-10-family-businesses-have-no-succession-plans/

https://www.entrepreneur.com/article/231757


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HERE I COME TO SAVE THE DAY? THE EFFICACY OF SMALL BUSINESS DEVELOPMENT CENTERS IN PENNSYLVANIA

M. Garrett Roth and Ryan Morris

Department of Economics and Finance Gannon University

Erie, PA 16501

ABSTRACT

This paper assesses the efficacy of the eighteen Small Business Development Centers (SBDC) located throughout the state of Pennsylvania. More specifically, we compare the sales growth of SBDC clients, as reported in post-consultation surveys, to comparable growth measures for the corresponding business population. The results show that respondent clients clearly outperform the broader population, both in aggregate and when decomposed by region and industry. The results are, however, tempered by low overall survey response rates.

I. INTRODUCTION

Of late, “big business” has become a pejorative term invoking cartoonish ideas of corporate executives wallowing in lavish profits born of exploitation and avarice (Denning, 2016). Conversely, small business and entrepreneurship have become buzz-words for laudable economic activity, even among those who bristle at the term “capitalism” (Baysal, 2018). The most widespread means for state and local governments to visibly foster such entrepreneurship is via Small Business Development Centers (SBDC), which provide free consultation services to existing and would-be businessmen. While these organizations, typically headquartered at colleges and universities throughout a state, may be politically beneficial to legislators, no research currently exists on whether or not such expert “intervention” by SBDC consultants actually improves firm performance. We undertake such a study using comprehensive survey data from SBDC clients throughout Pennsylvania from 2013 to 2016. Our results are stark; clients clearly outperform the broader business population (as measured by percent change in sales) in the year following their SBDC consultation.

Despite the aforementioned push by civic leaders to cultivate entrepreneurship in their communities, little empirical examination exists on the efficacy of such services, governmentally funded or otherwise, in developed economies. Bruhn et. al. (2018), for example, consider randomized interventions on small and medium-sized businesses in Mexico. While the authors show positive effects from consulting services, their results may or may not apply to firms operating within an advanced economy such as the United States. Uniquely, Sobel et. al. (2018) consider the efficacy of television reality shows that center on interventions to failing

businesses (e.g. Gordon Ramsey’s Kitchen Nightmares). They find that reality TV participants fare no better than those in the broader industry (which may well be a mark of success, given the obvious incentive of television producers to select ostentatiously terrible businesses).1 Our analysis differs from Sobel et. al. in two important respects (i) SBDC clients are more representative of struggling firms than those few businesses selected to appear on television and (ii) we have better metrics of firm performance than customer reviews and binary, “open / closed” categories.

The remainder of the paper proceeds as follows: Section II summarizes the funding and activities of the Pennsylvania SBDC in further detail. Section III provides the empirical analysis, via mean comparison, of SBDC clients and the relevant business population as a whole. Section IV offers qualifications to the empirical findings. Section V briefly concludes.

II. PENNSYLVANIA SMALL BUSINESS DEVELOPMENT CENTERS – AN OVERVIEW

The SBDC is a publicly-funded, state-wide network of eighteen branches hosted by various Pennsylvania colleges and universities. The Commonwealth of Pennsylvania and the U.S. Small Business Administration each provide $4.1 million toward the annual SBDC budget. The host university is responsible for paying overhead expenses (electricity, internet, telecommunication services, etc.) and providing office space. To maximize accessibility to prospective clients, the branches are geographically widespread.2 Each SBDC is overseen by a branch manager, who controls the branch’s budget and reports to the main SBDC office.3 The branch manager employs a small support staff as well as a diverse cadre of business consultants.4 While the branches operate independently, they are connected through customer relationship management software (NeoSerra) that facilitates the exchange of information and business expertise. The SBDC provides no-cost, confidential consulting and low-cost training to both start-up and existing for-profit business ventures. In the case of pre-revenue start-ups, the SBDC works with the lead entrepreneur to assess the viability of the business idea and formulate a working business plan that addresses both internal organization and funding possibilities vis-a-vis local loan institutions. The SBDC typically continues


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consultation until start-up ventures either achieve operational stability or the entrepreneur voluntary ends the relationship. Existing businesses commonly request consultation services from the SBDC when they are either facing hardships or undergoing rapid expansion. Once established as a client, the business is assigned a consultant based on their particular needs; the client is then free to use SBDC resources as much or as little as they please. Services provided to existing businesses might include, for example, developing financial statements, providing market and industry research, analyzing company expenses, and, if warranted, re-evaluating the business’s core operations. In addition to consultation services, the SBDC also hosts educational lectures, seminars, and conferences to better inform and connect the local business community.

III. EMPIRICAL ANALYSIS

At year end, SBDC branches send a detailed questionnaire to their clients of the previous year, which are then typically returned between February and April. Of the 30,671 SBDC clients receiving consultation between 2013 and 2016, 4,846 (15.8%) returned the survey.5 The survey form asks a variety of questions, primarily focused on the business owner’s satisfaction with SBDC consulting services. The survey also includes quantitative questions about workforce changes and, to the purposes of this paper, the firm’s sales in the year prior to as well as after their SBDC consultation(s).6 The percent change in client sales can thereby be compared to relevant population values. We consider three measures of population growth rate: (i) statewide private production by year (i) production by metropolitan statistical area (MSA) by year and (iii) statewide production by industry by year, all of which are taken from standards figures available from the Bureau of Economic Analysis (BEA, 2018).

The MSA decomposition is difficult, as many SBDC branches are located outside of MSA geographic areas. Consequently, these branches were assigned to the nearest contiguous MSA. Kutztown SBDC was, for example, paired with Reading, Bucknell SBDC with Bloomsburg-Berwick, etc.7 A list of locations and their associated MSA are provided in the Appendix. Most self-reported categories for client industry pair neatly with the decomposition of the BEA. However, the SBDC category of “technology” is, of necessity, constructed from BEA categories of “computer and electronic product manufacturing”, “data processing, hosting, and other information services”, and “computer systems design and related services”. To maximize sample size, the SBDC industry category “other” i.e. “none of the above” is assigned to the BEA values for private industry as a whole.

Because our firm performance data is more precise than that of Sobel et. al. (2018) we need not resort to any elaborately constructed metrics in comparing sample to population. However, SBDC clients may (i) decide not to proceed with a

prospective business, (ii) develop a previously non-existent entrepreneurial idea, or (iii) close an existing business. The first two categories represent a non-trivial 40.5% of survey respondents; the third is a much less consequential 1.1%. Because our focus is on whether or not SBDC services improve the performance of existing businesses, those with zero initial sales are eliminated from the sample. However, “existing business” must be further qualified, since “percent change in sales” as measure of economic success can be distorted by very small initial sales levels. Rather than impose an ad hoc lower bar on which clients are permissible, we eliminate the problem of (severe) outliers by restricting our attention to respondents with growth within one standard deviation of the aggregate mean. However, this restriction only eliminates (a trivial) five respondents that began, comparatively speaking, with near-zero sales levels.8

The aforementioned median values are quite high, with an overall median of 0.143 over 2,014 respondents. Categorical medians also outpace typical economic growth rates as detailed in Table 1. Thus, a safe presumption is that more thorough econometric testing will reveal SBDC clients out-performing the relevant business population. It is important to note that these figures are not driven by the comparative smallness of SBDC clients already in business. Only 5.6% of respondents have pre-consultation sales below $1,000 and only 17.2% have sales below $10,000. Also, no significant difference exists between ordinary SBDC consultations and “outreach” services to more rural clients; I therefore make no such distinction in the analysis.

Full econometric testing is done via one-sample t-test by year (where sample sizes are consistently large), and two-sample t-test across MSA’s and industries. Although, in all cases, we know true population values as measured by the BEA, a paired, two-sample t-test is preferable in the latter two variations because it allows for aggregation across industries and MSA’s; where some samples (e.g. agriculture) are relatively small, we need not specify a single comparison mean value. Thus, it is econometrically equivalent to a one-sample test aggregated across several samples with different population means. The results of these tests are provided in Table 2. In all comparisons, SBDC respondents clearly outperformed the broader population.9 Qualifications to this result are discussed in the subsequent section.

IV. QUALIFICATIONS TO THE EMPIRICAL RESULTS

The endogeneity problem, typical in econometric testing, is present here: our sample is not random and (existing) business owners seeking consultation with the SBDC are likely to be those whose firms are under-performing. However, endogeneity is not a concern as it works against the main empirical result: that SBDC clients out-perform the broader population immediately following intervention. A


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countervailing factor, less obvious but worth noting, is that already savvy businessmen may be more likely to avail themselves of free advice, providing that advice is of sufficiently high quality.

The much larger but unavoidable caveats to the empirical analysis are two-fold: (i) sales data are self-reported and (ii) survey response rates are low. Whether self-reporting of sales data actually biases the analysis (toward our result) is not straightforward; a businessman who generally inflates his figures will over-report both pre-consultation and post-consultation sales levels. Thus, percent change in sales is largely unaffected.10 It is also possible (but highly speculative) that respondents, eager to overstate the usefulness of SBDC services (due to the psychic benefit of appeasing their benefactor), are both under and over-estimating sales figures, respectively.

Low response rates to the client survey are a significant qualification to the empirical results. While thriving businesses may be less inclined to spend their time filling out such a survey (where no tangible benefit for completion exists), it is more probable that generally disorganized owners and/or firms that have folded completely will be less inclined to provide feedback, particularly regarding accurate sales data.

The striking discrepancies between the business population and the SBDC sample nonetheless suggest that those who took the time to respond are doing very well indeed; thus, our fundamental conclusion is not seriously compromised by the previous qualifications.

V. CONCLUSION

The main result of the paper is simple and clear: given available data, existing firms who avail themselves of SBDC consultation services outperform the broader business population by a large margin. This result holds both is aggregate and when the sample is decomposed by industry and geography. Thus, it appears as though Pennsylvania’s Small Business Development Centers are generating a benefit for their intended customer base. The major qualification to this result is the relatively small response rate for the client survey from which the data is drawn. Thus, we must emphasize the caveat “given available data”.

Whether the existence of the SBDC generates a net benefit is beyond the scope of this paper. More broadly, it appears as though expert intervention can prove effective in assisting troubled businesses but only insofar as those firms are proactive in soliciting such assistance where available.

TABLE 1 – MEDIAN SALES GROWTH OF SBDC RESPONDENTS

Year Median Client Growth n 2013 0.143 625 2014 0.158 387 2015 0.140 541 2016 0.143 461

Total 2,014 Industry Agriculture 0.325 20 Construction 0.158 130 Manufacturing 0.0929 336 Retailing 0.145 389 Services 0.227 567 Technology 0.100 93 Wholesale 0.133 97

Total 1,632 MSA Allentown-Bethlehem-Easton 0.094 134 Altoona 0.187 21 Bloomsburg-Berwick 0.174 185 East Stroudsburg 0.0983 10 Erie 0.124 72


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Harrisburg-Carlisle 0.286 66 Johnstown 0.0548 18 New York-Newark-Jersey City 0.121 10 Philadelphia-Camden-Wilmington 0.138 409 Pittsburgh 0.133 571 Reading 0.165 204 Scranton-Wilkes-Barre 0.125 124 State College 0.258 115 Williamsport 0.0351 40 Youngstown-Warren-Boardman 0.0696 29

Total 2,008

TABLE 2 – SBDC CLIENT SALES GROWTH TO BUSINESS POPULATION

n t-statistic 95% lower bound

95% upper bound

2013 624 4.06* 1.08 3.05 2014 386 3.71* 0.735 2.30 2015 540 4.26* 0.758 1.99 2016 459 6.42* 0.627 1.16 Aggregate industry (full) 2,009 7.56* 1.12 1.89 Aggregate industry (restricted) 1,628 6.81* 1.15 2.05 Aggregate MSA 2,005 7.53* 1.13 1.89

* indicates significance at the 1% level

ACKNOWLEDGEMENTS

The authors wish to thank Maggie Horne for assistance in SBDC data collection and John A. Ruddy for helpful comments at the PEA 2018 conference.

ENDNOTES

1. Curiously, Sobel et. al. (2018) expect expert intervention to bolster firm performance to above average, which seems somewhat unreasonable given that such firms began as the “worst of the worst”. 2. See the Appendix for a complete list of Pennsylvania SBDC branches. 3. Due to funding reorganization, the main SBDC office has recently been shut down. The new headquarters will be located at one of the 18 host universities with the relocation decision to be announced on June 22nd, 2018. 4. Support staff usually include an administrative assistant, a marketing officer, and student research analysts recruited from the host university.

5. Client totals were obtained via the same information management software, NeoSerra, that compiles client survey responses. All SBDC consultations are digitally logged and archived. 6. Prior to 2013, the client survey did not ask for pre-intervention sales level. While changes in client workforce may be interesting metrics in and of themselves, they are a less precise gauge of firm success than sales figures, as their economic value is contingent on hours per week, wage rate, etc. 7. Shippensburg, being nearly equidistant to Harrisburg-Carlisle and Chambersburg-Waynesboro, was paired to Harrisburg-Carlisle as the larger economic center. Where SBDC outreach was not contiguous to any MSA (e.g. Bradford), it was omitted from the analysis. 8. The elimination of these outliers (with growth rates exceeding 28,300%!) does meaningfully affect p-values. However, eliminating the highest-performing clients (outliers do not exist on the lower tail of the distribution) also works against the core result (that SBDC clients outperform the broader business population). Thus, the data restriction actually strengthens the conclusions of the analysis.


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9. In the industry-specific analysis, the “full” sample includes clients categorized as “other”. The “restricted” sample excludes these firms.

10. There are virtually no publicly traded companies in the client sample such that sales figures could be externally verified.

APPENDIX

TABLE A1 - MSA CLASSIFICATION OF SBDC BRANCHES AND OUTREACH LOCTIONS

Branch / Outreach Location MSA Bucknell University*B Bloomsburg-Berwick Clarion University*B Pittsburgh Beaver Pittsburgh Butler County Pittsburgh Cranberry Pittsburgh Duquesne UniversityB Pittsburgh New Castle* Pittsburgh Gannon UniversityB Erie Meadville* Erie Mercer Youngstown-Warren-Boardman Warren* Erie Indiana University of PennsylvaniaB Pittsburgh Kutztown University*B Reading Lehigh UniversityB Allentown-Bethlehem-Easton Lock Haven UniversityB Williamsport Penn State UniversityB State College Greene County* Pittsburgh Pittsburgh UniversityB Pittsburgh Washington County Pittsburgh Bradford* --- Lackawanna Scranton-Wilkes-Barre Monroe East Stroudsburg Pike County New York-Newark-Jersey City University of ScrantonB Scranton-Wilkes-Barre Susquehanna* Scranton-Wilkes-Barre Tioga* Williamsport Wayne* Scranton-Wilkes-Barre Wyoming Scranton-Wilkes-Barre Bedford* Altoona Blair Altoona Cambria Johnstown Huntington* Altoona St. Francis UniversityB Johnstown Shippensburg University*B Harrisburg Fayette Pittsburgh St. Vincent CollegeB Pittsburgh Temple UniversityB Philadelphia-Camden-Wilmington Widener UniversityB Philadelphia-Camden-Wilmington Wharton School of BusinessB Philadelphia-Camden-Wilmington Bloomsburg Bloomsburg Hazelton Scranton-Wilkes-Barre Jim Thorpe Allentown-Bethlehem-Easton Pottsville* Reading Wilkes UniversityB Scranton-Wilkes-Barre * indicates area / branch outside of an MSA proper. B indicates an SBDC branch location


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REFERENCES Baysal, Nur. 2018. Millennials dig entrepreneurship but dislike ‘capitalism’. Foundation for Economic Education. 9 Mar. Online article. https://fee.org/articles/millennials-dig-entrepreneurship-but-dislike-capitalism/ Bureau of Economic Analysis. 2018. Regional economic accounts. Online database. https://www.bea.gov/regional/index.htm Accessed 10 May 2018. Bruhn, Miriam, D. Karlan, and A. Schoar. 2018. The impact of consulting services on small and medium enterprises: Evidence from a randomized trial in Mexico. Journal of

political Economy. 126, 2: 635-87. Denning, Steve. 2016. What’s wrong with big business? Forbes. 11 Apr. Online article. https://www.forbes.com/sites/stevedenning/2016/04/11/is-big-business-destroying-the-fabric-of-america/#372577fb5d22 Sobel, Russell S., R. N. Sobel, D. M. Walker and P. T. Calcagno. 2018. How effective are expert TV hosts at saving failing businesses? Contemporary Economic Policy. Published online in advance of print.


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THE CHANGES IN WORKFORCE PATTERNS ON A COUNTY LEVEL IN THE COMMONWEALTH OF PENNSYLVANIA 2005-2015

Yaya Sissoko

Department of Economics Indiana University of Pennsylvania

Indiana, PA 15705

Brian W. Sloboda School of Advanced Studies

Center for Management and Entrepreneurship University of Phoenix

ABSTRACT

Prior research identified the connections between regions based on economic conditions (Amin, 1999; Meyer 1964; Porter (2003). This paper will explore mobility patterns on a county level within Pennsylvania between 2005-2015, inclusive and the plausible reasons for these patterns. The examination of these patterns will use the LODES data in the LEHD Origin-Destination Employment Statistics (LODES) from the Census Bureau. Because of the heterogeneity of the sixty-seven counties in Pennsylvania, these differences offer different opportunities for specialization. Consequently, when these opportunities are exploited, they may add to the aggregate income to the county by attracting workers and/or firms from other counties. The analysis will primarily focus on the population and labor force/workforce data. This analysis will tell us how these counties interrelate by showing the distance and direction in general and provides a destination analysis.

INTRODUCTION

Regional economic growth is not static as espoused by North (1955) or many regional economies evolved from the very beginning as export economies and built their development around the export sector. More specifically, location theory and the theory of regional economic growth have described a typical sequence of stages through which regions develop. Hoover and Fisher (1949) remarked that, “there is now a fairly well accepted body of theory regarding the normal sequence of development stages in a region.” That is, regional economic growth is not static, and its economic growth can be disaggregated into five stages as given in Table 1.

From Table 1, the economic advantage in the form of employment in the extractive industries, or in government or manufacturing, has provided the impetus for migration patterns between regions such as counties. Thus, the ability to successfully interpret changes in migration patterns has provided a useful tool for community leaders and for economic developers who are tasked with attracting new jobs and

promoting economic development of a region. In fact, numerous studies have confirmed the importance of both economic and non-economic (or amenity) factors on net domestic migration flows across county boundaries in the United States over the past several decades. More specifically, we take a closer look at gross out-migration, net migration, outmigration, and in-migration for Pennsylvania for the period 2005-2015. Specifically, we examine the Census Bureau’s LODES data to better understand the worker migration patterns on a county level in Pennsylvania. The balance of this paper is as follows: Section 2 presents the existing literature. Section 3 presents the methodology and the data sources. Section 4 presents the empirical results and Section 5 concludes the paper.

EXISTING LITERATURE

Empirical Evidence on the Income and Employment Determinants of Migration

The expansion of economic opportunity was often credited with providing a pull-factor that counties would count on to attract new residents to their county (Muth 1971; Olvey 1972; Greenwood 1975, 1985; Partridge and Rickman 2006). On the other hand, other studies have shown that the benefits derived from improvements in the quality of life plays a major role in migration (Cushing 1987; Cebula 2005; Cebula and Payne 2005) as well as the role of location specific amenities in particular locations in the decision to migrate (Green 2001; Gunderson and Ng 2006.). Earlier studies have concluded that increased levels of income and wealth are related to location-specific amenities and leisure activities, which could influence people to migrate into a region (Graves, 1973, 1979, 1980). Johnson and Stewart (2005) used urban proximity to demonstrate the relationship between second-home ownership and eventual migration to areas influenced by recreation and amenities in southeastern Wisconsin. Porell (1982) addressed tradeoffs between economic and amenity factors to explain migration occurring in metropolitan regions between 1965 and 1970. Brennan et al. (2008) explore that the perceptions, economic bases, and mean for development in rural areas for


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future policy efforts. They find out that local culture plays a central role in shaping community development, local character, and options for responding to community needs. Culture and attachment can be used as a motivating factor in opposing anti-agency activities such as extra-local development and exploitation. Culture can also be used to motivate community members and can serve as a tool for policymakers and others interested in encouraging local level development.

Recently, economists have started to conceptualize migration as a part of a search and matching problem (Dahl, 2002; Shimer, 2007). The latter idea would be a logical extension of labor market search theory that incorporates frictions (like that pioneered by Peter Diamond, Dale Mortenson, and Christopher Pissarides in the work for which they shared the 2010 Nobel Prize). The latter is important because the geographic search that migration often entails is an important component of general labor market search.

Shields (2004) investigates the rural economic growth trends in Pennsylvania by focusing on the causes of industry growth and identifying new opportunities for rural communities. He finds that there is an increasing earning gap between urban and rural Pennsylvania in average per-worker earnings, even though employment growth rates have been similar over time. He determines that higher education plays a prominent role in rural economic development. Latzko (1999) uses the method of Sala-i-Marttin (1997) to identify which variables are strongly correlated with county income growth. The method by Sala-i-Martin method suggests computing the entire distribution of the estimators to conclude if two variables are correlated. He concludes that a county in Pennsylvania has probably experienced rapid per capita income growth since 1970 if it had a college educated labor force, agricultural income was a large share of county total earnings, and its population was largely urban.

Latzko (2003) investigates the dynamic correlations among Pennsylvania Counties for employment and income to examine the co-movement of county economies by using time series data. He uses correlations to construct 5 multi-county and 3 single county regional economies centered on major urban areas consisting of counties displaying a high degree of economic cohesion. His results indicate that counties tend to co-move more strongly with neighboring counties than with the state. Another important finding is that suburban Philadelphia counties comprise a cohesive regional economy separate from the city of Philadelphia.

Empirical Evidence on the Location Specific Determinants of Migration

A second aspect considered in migration studies is the importance of location specific amenity factors such as weather and public services. The theoretical argument is that

amenities provide non-earnings-based utility to households, drawing new residents to the region (Graves, 1979; Graves and Linneman, 1979). In this framework, equilibrium is achieved as people move into an amenity rich region, increasing the local labor supply and subsequently reducing wages to the point that regional wage differentials exactly compensate for local amenities.

Graves (1979) provides one of the earliest examinations into the importance of weather in household location decisions. Examining net population migration in the 1960s, Graves demonstrates that when income levels and unemployment rates are considered, certain climatological amenity variables are important. These variables include the influence of heating and cooling-degree days, annual temperature variance, relative humidity and wind speed. Other researchers have investigated the importance of local public services on the migration decision. In a survey of migration and the local level of local public services Charney (1993) concludes that higher levels of public expenditures on several goods serve as an incentive for migration. Of course, higher expenditures could also mean higher taxes, a factor that can discourage the household location decision (Yinger, 1982). In sum, the theoretical and empirical evidence suggests that expected earnings, regional employment opportunities and locational amenities influence migration. From this, a basic migration modeled looks like: migration=m(empgrow,relwage,relunemp,A) where empgrow is local employment growth, relwage is the relative average local wage, relunemp is relative local unemployment, and A is a vector of location specific amenities.

DATA AND THE METHODOLOGY

The data are derived from the LEHD Origin-Destination Employment Statistics (LODES) version 7.3 which are the job data that are delivered in the OnTheMap application.1 These data are disseminated by the US Census Bureau. Since the focus of this paper is on jobs, the definition of jobs is quite specific in the LODES. Under LODES, a job is counted if a worker is employed with positive earnings during the reference quarter as well as in the quarter prior to the reference quarter. This is called a “beginning of quarter” job because the assumption is that the worker was employed on the first day of the reference quarter. The time period for this analysis will be from 2005 through 2015, and 2015 is the last year of data for LODES.2 The analysis will include the 2008 Financial Crisis and to avoid starting the analysis from 2009 as originally intended, we started from 2005. Since the LODES data is based on geography, LODES use 2010 census blocks, defined for the 2010 Decennial Census, as their base geography for years 2010 and later. For data released in previous versions of LODES and OnTheMap used 2000 census blocks as the geographical base. In fact, LODES Version 7.3 is based on 2016 TIGER/Line shapefiles. The data are enumerated with 2010 census blocks.3


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Table 2 provides a list of all the variables by which the LODES data are categorized. Keep in mind that not all variables are released on the origin-destination matrix or for the inclusion in the OnTheMap application. The following variables will be extracted from the LODES 7.3 data for this analysis. The Methodology To complement the migration patterns from the LODES data, we estimated the Hoover Index. Hoover (1941) formulated the following:

𝐻𝐻𝑑𝑑 = 0.5∑ 100|𝑝𝑝𝑖𝑖𝑑𝑑𝑟𝑟𝑖𝑖=1 − 𝑎𝑎𝑖𝑖| (1)

where pi is the share of the entire study regions population P found in a subarea i at a time t, and ai is the share of the entire study regions land area A in subarea i. The appeal for this index is the ease in its interpretation. The index is theoretically bound by 0 (meaning the population is equally distributed across all areas relative to their size) and 100 (meaning the population is concentrated entirely in a very small area of the larger study region). In practicality, these two theoretical bounds are extremely unlikely. Essentially, the index tells us the percentage of the total population that would have to move to achieve equal densities in each area. Knowing that current and incoming population is not evenly distributed among all the counties in Pennsylvania, we want to see how it varies by each county. After the estimation of the Hoover Index, other measures of migration streams of the workers will also be estimated. For these migration streams, the population of workers at risk is the population around origin or the county in Pennsylvania. Measures of migration of jobs begin with rates for in-migration (arrivals to the county), out-migration (departures from the county), net-migration and gross-migration, where O represents the size of the workers moving out of an area, I the size of the workers moving into an area, and P is the size of the worker population in the area during the years. The measures are multiplied by a constant, usually 10004, to create a rate. These various measures of migration are summarized below.

Outmigration rate = 𝑂𝑂𝑑𝑑∗ 1000

In-migration rate = 𝐼𝐼

𝑑𝑑∗ 1000

Net migration rate =𝐼𝐼−𝑂𝑂

𝑑𝑑∗ 1000

Gross migration rate = 𝐼𝐼+𝑂𝑂

𝑑𝑑∗ 1000

In-migration is the process of workers moving into a county from another county while the out-migration is the process of workers moving out of a county to another county. These estimates are based on 1,000 people because these measures

are multiplied by 1,000. Net migration represents the difference between in-migration and outmigration during a given period. A positive net, or net in=migration, indicates that more workers entered the county than left it during that period. A negative net, or net out-migration, means that more workers left the county than entered the county. Finally, gross immigration is the sum of in-migration and outmigration for a county for a given period. In other words, this measure shows the total amount of movement in and out of an area.

THE EMPIRICAL ESTIMATES Table 3 presents the results from the estimation of the Hoover Index. Part A shows the index for the entire state of Pennsylvania. Part B shows the Hoover Index for each county. Panel A of Table 3 shows the Hoover index for all continuously counties in Pennsylvania from 2005 to 2015 as well as the changes in the index over time. The examination of the Hoover Index over time indicates that the population of Pennsylvania is becoming slightly more concentrated. As of 2015 almost 49 percent of the population residing in Pennsylvania would have to move to have equal population densities. It was surprising that during the 2008 Great Recession that the Hoover Index managed to increase somewhat when the housing market nearly collapsed throughout much of the country. Beginning with 2009, the population in Pennsylvania was becoming more concentrated. Table 3 Panel B shows the Hoover Index for each county for 2005 through 2015. Based on the estimates in Panel B, the increases in the concentration in Pennsylvania may be occurring in the larger counties, e.g., Allegheny, Berks, Chester, Delaware, Philadelphia, Montgomery, Lancaster, Bucks, and York counties. The very low Hoover values across all the remaining counties in Pennsylvania indicate a dispersed or evenly distributed population. This means that the population densities within each of the counties are more uniform than they are when all the counties are analyzed together as the state of Pennsylvania. The aggregated state estimate exhibits much higher Hoover values (more concentration) over time than the counties except for Philadelphia County. This stems from the increasing concentration occurring in the Philadelphia County in comparison with the other counties. Table 4 represents the averages for Pennsylvania for the in-migration rates, outmigration rates, the net migration rates, and the gross migration rates for 2005 to 2015. Let’s look at the net migration rate first. The net migration rate for Pennsylvania is -89.20 per 1,000 people. This means that for every 1,000 people in Pennsylvania at the beginning of the 2005, 89.20 would have left by the end of the year. The interpretation of the net-migration measures would the same for the remaining estimates. Quite interestingly, the net


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migration rates were not too high until the onset of the 2008 Financial Crisis. The net migration peaked in 2011 the decreased to 2015. Looking at the in-migration and the out-migration rates, the outmigration rates were much higher than the in-migration rates. That is, it shows that workers were leaving their jobs and moving elsewhere. As for the gross migration estimates, these measures essentially remained stable from 2005 to 2015 or that this measure shows a stable amount of movement in and out of Pennsylvania. Upon inspection of these estimates on a county level which are provided in the Appendix of this paper. Let’s look at the in-migration estimates on the county level. Many of the rural counties, e.g., Butler, Cameron, Carbon, Crawford, Franklin, Fulton, Mifflin, Monroe, Perry, Sullivan, Tioga, Venango, Warren, Wayne, Washington, etc., have negative migration rates consistently from 2005 to 2015. Some of these net migration estimates are quite high indicating that more workers were leaving their jobs in the county and going elsewhere. On the other hand, some of the rural counties started off with small positive net migration estimates than over time their net migration estimates increased, e.g., Indiana, Snyder. Quite interestingly, York County though quite economically developed has experienced negative net migration rates consistently from 2005-20015. A plausible reason for these consistent negative net migration rates is the lack of employment opportunities in their respective county, so the workers would be required to find suitable employment outside of the county. Also, Lackawanna County started off with negative net migration rates then in 2007 before the start of the 2008 Financial Crisis experienced positive net migration rates and maintained a positive rate throughout the 2008 Financial Crisis and afterwards. The more urbanized counties, e.g., Allegheny, Erie, Montgomery, and Philadelphia experienced positive net migration rates. The rational for their consistent positive net migration rates is a larger number of employment opportunities, so they can retain and attract workers. That is, workers with these urbanized counties can simply find a new job in the area and transfer while other employment opportunities simply attract workers from outside of the county to partake employment in their respective county. By looking at the out-migration and in-migration estimates, we can confirm that those counties with negative net migration rates have high out-migration rates and lower in-migration rates. Recall that the gross immigration rate is the sum of in-migration and outmigration for a county for a given period or it show the total amount of movement in and out of an area. As expected, those counties with negative net migration rates have smaller gross migration rates, and those counties with positive or highly positive net migration rates have larger gross migration rates. Typically, the gross migration rates often follow the business cycle. For some of the rural counties, the gross migration rate experienced small declines from 2007 to 2008 while some of the urbanized counties, namely Allegheny

and Philadelphia, the gross migration rates went down quite substantially.

CONCLUSIONS AND CLOSING THOUGHTS

Our results suggest a few interesting things. The results from this paper would be consistent with some of the micro-level labor search literature, which suggests people make migration decisions regarding their employment opportunities in a two-step process, first deciding to where to work then deciding to pursue these employment opportunities. Our findings would support the notion that people want to leave for someplace better, but that there are numerous better places from which to choose. The main intent of this analysis is to take a closer look at the extent and nature of rural-metro differences in employment patterns. Given that the net migration rates were generally higher for the rural counties, those workers are more likely overall to go outside of the county for employment opportunities because that is where the employment opportunities are. In fact, they could tend to migrate to urbanized counties in Pennsylvania, but the LODES data does not trace the workers and where they migrate to for their employment. In a nutshell, the workers will gravitate to where the jobs are located either in the same county (most likely in the urbanized counties) or in a different county if the employment opportunity is available. That is, in rural counties, the employment opportunities may be limited, so the workers may be forced to pursue employment opportunities elsewhere.


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TABLES

Table 1: Stages of Regional Economic Growth Stage Description One This is self-sufficient subsistence economy in which there is little investment or trade. The basic

agricultural stratum of population is simply located according to the distribution of natural resources. Two With improvements in transport, the region develops some trade and local specialization. “A second

stratum of population comes into being, carrying on simple village industries for the farmers. Since the materials, the market, and the labor are all furnished originally by the agricultural populations, the new ‘industrial superstructure’ is located with reference to that ‘basic stratum.’”

Three With the increase of interregional trade, a region tends to move through a succession of agricultural crops from extensive grazing to cereal production to fruit-growing, dairy farming, and truck gardening.

Four With increased population and diminishing returns in agriculture and other extractive industries (mining, logging, ranching and farming), a region is forced to industrialize. “Industrialization means the introduction of so-called secondary industries (mining and manufacturing) on a considerable scale.” Typically, the early stages of industrialization are based on the products of agriculture and forestry and include such activities as the processing of food, the manufacture of wood products, and the preparation of textile fibers. If industrialization is to continue, mineral and energy resources become critical.

Five A region specializes in tertiary industries producing for export. Such a region exports to less advanced regions capital, skilled personnel, and special services.

Table 2: Variables in LODES

Variable (Category Count) Categories Age (3) 29 or younger

30-54 55 or older

Earnings (3) $1250/month or less $1251/month to $3333/month Greater than $3333/month Industry Group (3) Goods Producing industry sectors Trade, Transportation, and Utilities industry sectors All Other Services industry sectors Industry Sectors (20) 20 categories, available at www.census.gov/eos/www/naics. Race (6) White alone

Black or African American Alone American Indian or Alaska Native Alone Asian Alone Native Hawaiian or Other Pacific Islander Alone Two or More Race Groups

Ethnicity (2) Not Hispanic or Latino Hispanic or Latino

Educational Attainment (4) Not available (29 or younger) Less than High School High school or equivalent, no college Some college or Associate degree Bachelor’s degree or advanced degree

Sex (2) Male Female

Job Dominance (2) Primary Job Nonprimary Job

http://www.census.gov/eos/www/naics


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Table 2 (continued) Ownership (2) Private

Public Firm Age (5) 0-1 Years

2-3 Years 4-5 Years 6-10 Years 11+ Years

Firm Size (5) 0-19 Employees 20-49 Employees 50-249 Employees 250-499 Employees 500+ Employees

Source: https://lehd.ces.census.gov/doc/help/onthemap/OnTheMapDataOverview.pdf. Table 3: Part A of Hoover Index

Hoover Index 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Index for Pennsylvania 40.381 40.518 40.699 40.875 41.051 41.244 41.421 41.598 41.731 41.829 41.927 Changes in Hoover Index -- 0.137 0.181 0.176 0.176 0.193 0.177 0.176 0.133 0.098 0.098

Part B: County Index Index

Adams County 1.978 Greene County 4.663 Allegheny County 47.789 Huntingdon County 4.358 Armstrong County 3.351 Indiana County 2.490 Beaver County 1.198 Jefferson County 4.393 Bedford County 4.191 Juniata County 5.316 Berks County 11.640 Lackawanna County 2.969 Blair County 0.798 Lancaster County 16.373 Bradford County 3.642 Lawrence County 2.390 Bucks County 21.193 Lebanon County 0.545 Butler County 1.754 Lehigh County 8.952 Cambria County 0.116 Luzerne County 7.596 Cameron County 6.150 Lycoming County 1.216 Carbon County 3.553 McKean County 4.465 Centre County 0.325 Mercer County 1.240 Chester County 15.628 Mifflin County 4.328 Clarion County 4.628 Monroe County 0.961 Clearfield County 2.759 Montgomery County 28.748 Clinton County 4.678 Montour County 5.573 Columbia County 3.455 Northampton County 6.739 Crawford County 2.477 Northumberland County 2.272 Cumberland County 3.998 Perry County 4.368 Dauphin County 5.284 Philadelphia County 61.694 Delaware County 18.296 Pike County 3.841 Elk County 4.963 Potter County 5.613

https://lehd.ces.census.gov/doc/help/onthemap/OnTheMapDataOverview.pdf


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Part B (continued) Erie County 5.979 Schuylkill County 0.110 Fayette County 0.239 Snyder County 4.642 Forest County 6.056 Somerset County 2.964 Franklin County 0.241 Sullivan County 6.097 Fulton County 5.724 Susquehanna County 4.526 Tioga County 4.536 Union County 4.421 Venango County 3.972 Warren County 4.562 Washington County 2.778 Wayne County 4.095 Westmoreland County 9.636 Wyoming County 5.135 York County 12.618

Table 4: The Average In-Migration Rates, Outmigration Rates, the Net Migration Rates, and the Gross Migration Rates, 2005 to 2015

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Outmigration Rates 469.339 470.522 504.437 512.589 519.535 531.930 547.761 552.890 556.902 547.392 537.718 InMigration Rates 380.131 389.999 403.957 406.151 400.320 395.867 406.836 412.190 418.057 416.566 407.621 Net Migration Rates -89.208 -80.522 -100.480 -106.438 -119.215 -136.06 -140.92 -140.700 -138.846 -130.826 -130.097 Gross Migration Rates 29.307 29.822 32.251 32.608 32.577 33.283 34.583 34.966 35.469 35.686 35.436

ENDNOTES

1. To avoid confusion, the LODES data was originally referred to as “OnTheMap data.” “OnTheMap” now refers only to the internet application produced by Census Bureau and these data are now exclusively known as LODES.

2. LODES Version 7.3 includes data for 2002-2015, for

which Quarter 2 (April – June) is the reference period in each year. Not all states have data available for each year and not every variable is available in each year. See the documentation for LODES for the details on the availability of states and variables by year.

3. For data previously released in 2000 census blocks, the

LODES data has been “crosswalked” into the base of 2010 census blocks. Further information on how OnTheMap and LODES implement the 2010 census blocks can be found in OnTheMap: Updating the Base Geography.

4. Typically, it is 1000 but a researcher could also multiply by 100 if their analysis requires a rate based on 100 workers.


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APPENDIX

Outmigration Rates 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 Adams County 653.218 646.792 655.509 672.543 653.995 645.563 638.564 616.926 605.207 593.189 579.437 Allegheny County 187.673 191.081 185.012 180.011 180.353 175.943 177.491 172.793 163.637 153.976 153.085 Armstrong County 699.859 703.775 714.940 692.873 687.997 695.288 670.147 668.650 651.979 611.746 597.785 Beaver County 612.627 607.233 599.952 583.476 586.557 596.029 586.920 586.284 581.128 568.757 563.976 Bedford County 577.804 599.980 561.593 570.323 556.860 566.953 541.381 526.482 549.258 508.027 527.211 Berks County 420.659 427.585 431.663 425.425 433.341 419.917 422.678 406.365 405.313 380.044 378.927 Blair County 378.836 355.466 355.909 356.101 357.042 367.809 360.719 343.498 342.970 318.344 312.017 Bradford County 449.841 458.782 517.922 500.000 496.912 459.802 446.421 455.367 425.279 385.761 367.076 Bucks County 592.538 593.417 588.998 590.341 583.862 583.648 578.605 569.021 561.644 532.854 543.388 Butler County 553.778 558.328 553.590 542.438 550.741 538.799 535.193 533.745 529.609 507.631 507.620 Cambria County 447.687 458.272 455.750 450.778 436.885 429.291 427.276 412.097 417.042 384.183 384.609 Cameron County 421.403 467.258 615.691 610.597 612.194 473.232 434.601 402.024 416.515 439.243 455.959 Carbon County 708.155 711.131 734.737 722.249 708.856 698.373 690.278 689.354 687.790 644.870 659.740 Centre County 323.119 342.238 342.569 340.184 334.519 331.767 337.277 368.089 304.803 254.221 244.898 Chester County 540.022 545.882 544.048 541.050 539.000 528.103 526.871 514.804 515.528 492.247 490.353 Clarion County 543.067 555.448 598.241 610.695 597.480 528.621 506.037 496.627 493.112 447.014 438.240 Clearfield County 493.051 507.427 539.158 533.817 520.961 482.594 470.514 471.825 445.337 394.845 392.888 Clinton County 594.379 598.624 611.180 611.829 589.092 604.901 590.600 563.710 560.846 524.405 522.126 Columbia County 559.061 578.730 533.981 501.750 493.874 552.970 536.292 521.181 518.714 490.469 477.745 Crawford County 793.494 808.550 798.385 806.420 795.332 794.821 792.677 788.691 765.321 766.751 764.326 Cumberland County 523.105 535.744 505.374 507.599 501.486 535.524 524.287 514.662 515.024 488.391 479.133 Dauphin County 442.910 450.661 447.191 426.207 436.447 427.795 423.557 415.487 417.793 382.357 378.004 Delaware County 621.636 611.072 610.857 607.704 603.812 603.524 605.750 587.939 586.593 583.492 585.907 Elk County 319.931 326.902 427.766 410.319 397.959 346.651 343.879 312.524 320.101 264.957 233.577 Erie County 177.288 182.346 180.621 174.981 172.799 170.334 175.839 162.149 154.810 142.925 136.478 Fayette County 551.608 565.685 554.275 549.663 536.043 522.346 524.943 502.369 497.850 480.705 478.512 Forest County 772.232 779.154 885.269 880.481 879.603 738.189 629.289 734.286 691.674 668.199 649.669 Franklin County 502.634 511.814 519.213 506.823 516.055 504.215 487.856 464.213 448.863 442.739 443.533 Fulton County 717.409 679.474 649.894 651.310 634.921 656.049 666.313 608.702 636.338 567.820 560.460 Greene County 625.649 633.822 633.100 659.872 637.813 611.187 623.629 615.743 598.926 581.354 577.701 Huntingdon County 606.594 644.063 726.285 737.149 735.641 630.447 582.765 556.915 579.262 531.467 531.089 Indiana County 501.998 520.128 552.621 545.121 547.366 482.533 476.205 464.797 453.406 408.997 409.965 Jefferson County 550.120 569.914 540.911 553.850 548.451 561.953 554.210 530.889 537.740 507.096 511.072 Juniata County 653.906 684.068 727.560 757.088 749.461 701.310 622.165 631.982 619.171 570.661 575.488


93

Lackawanna County 371.575 376.425 382.000 378.927 365.830 352.491 342.352 331.401 336.427 320.544 321.122 Lancaster County 334.060 338.069 336.897 339.081 326.508 324.537 315.438 301.647 296.510 272.662 265.207 Lawrence County 559.011 564.195 568.406 559.501 562.053 535.444 527.846 521.153 516.668 459.025 479.633 Lebanon County 588.107 588.438 582.558 587.562 589.401 583.537 591.571 584.236 560.180 541.577 533.928 Lehigh County 463.916 469.873 465.004 455.303 452.504 458.703 454.573 441.847 445.385 421.527 408.438 Luzerne County 351.324 363.230 385.443 370.424 377.041 355.713 355.137 347.128 341.262 325.017 316.370 Lycoming County 389.414 404.686 400.648 382.798 384.536 400.925 388.111 364.922 362.142 320.271 331.212 McKean County 408.191 416.077 342.635 361.787 346.442 403.304 404.498 385.732 370.551 305.496 320.626 Mercer County 430.307 438.910 436.670 430.779 422.377 425.587 413.785 403.612 365.037 373.375 364.386 Mifflin County 525.519 541.514 587.506 591.874 583.716 516.036 506.959 496.641 501.280 435.256 441.438 Monroe County 581.793 594.895 604.783 594.153 571.047 560.709 542.231 468.770 465.243 417.526 424.475 Montgomery County 513.771 521.728 523.449 505.316 511.760 500.084 497.633 480.923 482.231 462.712 469.781 Montour County 601.263 606.620 661.015 664.922 669.231 571.574 581.797 592.302 571.840 574.383 542.898 Northampton County 627.181 637.746 634.013 637.543 636.491 634.370 636.658 627.701 624.849 605.717 600.746 Northumberland County 668.935 677.215 647.309 641.734 636.361 649.715 638.029 626.013 634.966 597.477 581.230 Perry County 810.543 819.799 865.177 858.759 857.670 797.956 797.255 798.910 804.210 761.972 766.727 Philadelphia County 389.208 389.106 384.653 400.780 365.216 373.858 375.678 395.143 380.355 370.799 370.131 Pike County 762.092 773.673 823.742 803.055 793.458 735.955 707.167 669.735 677.533 611.762 626.224 Potter County 509.542 525.750 544.015 512.320 500.282 530.758 506.999 500.281 490.621 405.852 387.355 Schuylkill County 534.373 537.914 543.606 544.804 538.482 524.388 529.317 523.882 522.490 483.416 479.544 Snyder County 626.302 630.467 661.218 648.669 652.459 602.389 632.444 625.845 616.457 544.195 553.583 Somerset County 551.853 550.929 585.597 556.744 562.709 526.072 514.658 513.503 502.990 459.067 464.237 Sullivan County 708.751 742.780 743.289 736.259 757.865 709.492 582.196 712.021 661.006 617.177 591.362 Susquehanna County 688.676 713.721 743.288 746.061 745.740 730.428 699.224 703.481 682.937 609.208 625.706 Tioga County 530.057 537.481 455.397 442.767 429.686 503.612 469.308 474.691 455.061 381.059 401.171 Union County 615.738 622.711 588.588 593.925 579.788 603.391 597.057 616.101 588.950 517.650 530.470 Venango County 484.008 497.562 458.398 458.516 437.813 459.799 445.630 426.291 429.444 386.756 394.663 Warren County 423.336 449.218 574.293 580.441 585.689 424.161 413.529 407.962 385.446 335.855 356.449 Washington County 553.217 565.294 579.480 567.207 579.176 567.864 571.515 546.222 555.340 523.671 518.694 Wayne County 642.861 670.571 635.433 632.226 630.985 659.603 630.977 611.484 580.117 538.179 549.311 Westmoreland County 537.101 543.006 547.399 537.221 528.537 527.894 524.376 520.403 519.784 490.232 485.163 Wyoming County 659.712 683.927 656.055 648.651 644.999 661.742 645.212 663.328 639.011 627.127 619.752 York County 468.050 470.897 464.729 462.455 460.401 460.766 458.449 449.950 432.377 408.668 411.666


94

Net Migration Rates2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005

Adams County -359.562 -354.059 -372.982 -405.411 -389.266 -372.226 -375.060 -349.405 -364.023 -346.985 -335.096Allegheny County 200.190 208.676 213.372 212.140 212.529 225.843 217.851 208.648 230.512 197.146 196.483Armstrong Count -439.410 -443.880 -446.829 -409.222 -413.267 -451.969 -385.971 -371.901 -349.234 -287.037 -284.554Beaver County -307.909 -285.329 -264.711 -258.573 -270.743 -294.553 -291.890 -312.625 -323.706 -331.760 -330.064Bedford County -276.494 -303.675 -247.017 -256.595 -230.802 -227.185 -160.651 -177.348 -220.840 -227.247 -240.777Berks County -102.631 -102.039 -111.806 -117.263 -124.761 -114.247 -119.504 -113.392 -122.355 -119.013 -114.634Blair County 82.984 132.107 127.330 116.327 120.332 87.584 89.366 98.770 94.813 72.282 79.428Bradford County -95.071 -104.263 -180.153 -147.761 -138.116 -196.044 -172.820 -157.108 -149.274 -105.783 -88.041Bucks County -193.161 -196.679 -202.378 -202.779 -184.825 -194.188 -189.095 -167.976 -169.949 -97.671 -176.007Butler County -30.105 -37.224 4.391 -0.613 -13.783 -21.818 -29.254 -49.518 -48.515 -76.323 -88.348Cambria County -71.302 -69.288 -54.503 -44.586 -28.033 -50.294 -56.169 -56.756 -48.606 -72.462 -76.083Cameron County -91.142 -129.000 -340.202 -330.370 -328.607 -98.990 -39.083 12.879 47.901 -89.427 -69.660Carbon County -443.166 -451.583 -470.669 -471.315 -466.327 -470.981 -460.898 -447.705 -460.313 -439.679 -455.247Centre County 204.551 221.681 246.229 221.540 218.949 195.219 198.510 116.182 187.670 240.855 241.525Chester County 16.496 -2.770 -10.815 -6.927 -0.885 14.303 11.354 34.178 4.601 9.230 5.011Clarion County -142.818 -121.455 -163.396 -161.172 -155.686 -95.565 -71.595 -50.215 -69.672 -53.405 -31.426Clearfield County -46.682 -43.420 -51.661 -36.594 -33.597 -27.700 -16.942 -38.673 -3.918 14.081 6.724Clinton County -248.726 -232.113 -240.970 -226.578 -208.604 -258.543 -226.728 -115.188 -125.862 -171.720 -166.905Columbia County -94.749 -95.001 -5.525 37.771 53.730 -123.104 -72.911 -91.899 -97.020 -99.499 -83.049Craw ford County -531.789 -588.987 -561.948 -564.689 -565.273 -584.217 -591.149 -544.465 -401.947 -570.277 -536.655Cumberland Coun 296.883 277.709 348.446 328.847 340.373 265.543 229.567 228.431 124.929 164.734 730.211Dauphin County 448.132 458.556 467.111 498.417 460.064 465.890 460.694 412.578 418.545 393.124 333.103Delaw are County -170.248 -134.915 -147.407 -151.797 -156.618 -172.852 -189.073 -186.441 -184.388 -225.440 -202.094Elk County -80.612 -80.385 -202.143 -179.651 -175.815 -113.977 -93.710 -86.063 -96.943 -58.350 -21.805Erie County 17.163 28.636 23.598 28.378 27.358 28.418 23.572 11.464 8.723 -1.248 6.690Fayette County -284.582 -305.973 -295.833 -291.764 -278.608 -266.005 -226.616 -185.314 -194.425 -242.577 -240.654Forest County -188.755 -180.599 -468.284 -493.342 -479.816 -129.265 215.063 413.333 307.411 327.206 298.395Franklin County -180.837 -188.337 -179.070 -180.071 -189.636 -201.812 -188.113 -168.951 -91.000 -211.358 -237.356Fulton County -449.675 -199.204 -124.064 -117.964 -123.205 -232.802 -281.752 -162.629 -231.497 -112.099 -134.406Greene County -2.064 -8.456 11.703 -36.081 -61.098 -109.577 -173.116 -121.360 -165.698 -180.949 -175.710Huntingdon Coun -267.011 -331.186 -451.950 -472.432 -487.117 -306.451 -196.479 -185.031 -209.329 -159.511 -167.999Indiana County -12.866 -22.461 -56.485 -62.648 -79.624 -11.025 -0.155 19.067 -1.493 80.080 89.986Jefferson County -169.564 -194.795 -114.563 -120.794 -122.502 -221.155 -200.295 -176.982 -215.954 -133.975 -207.951Juniata County -374.229 -408.972 -471.297 -500.274 -492.275 -468.347 -359.059 -333.573 -303.270 -285.987 -284.060Lackaw anna Cou 25.149 28.000 30.645 21.031 31.447 51.631 42.490 22.955 14.665 -18.950 -29.802Lancaster County -51.449 -49.848 -59.363 -65.018 -56.915 -59.257 -51.338 -45.312 -53.084 -56.533 -40.155Law rence County -248.745 -240.933 -271.821 -262.023 -265.206 -225.602 -209.991 -215.746 -229.044 -86.342 -168.868Lebanon County -239.055 -228.002 -209.558 -233.527 -228.221 -255.273 -269.160 -257.228 -204.848 -251.885 -251.164Lehigh County 144.356 144.978 152.758 162.780 148.710 139.214 138.069 128.751 121.078 92.527 107.127Luzerne County 1.993 2.190 -37.056 -22.625 -44.116 -32.456 -38.897 -41.089 -16.501 -46.054 -36.113Lycoming County 13.900 -5.368 -1.527 6.012 -27.855 -66.783 -63.556 -62.337 -72.456 -76.404 -88.936McKean County -109.134 -94.664 20.823 23.035 -6.323 -114.844 -155.084 -134.630 -124.533 -34.603 -84.010Mercer County 4.065 9.720 18.836 18.434 5.725 -24.299 -8.574 -22.753 148.559 -59.627 -46.423Miff lin County -224.575 -235.420 -315.202 -317.378 -283.611 -200.955 -228.196 -203.375 -229.716 -181.908 -194.643Monroe County -235.505 -257.133 -274.448 -254.072 -197.978 -199.277 -170.310 -4.198 -9.363 12.448 -16.148Montgomery Coun 217.315 196.934 200.281 213.510 195.819 218.368 221.813 234.629 234.537 214.811 225.817Montour County 922.475 960.871 750.683 663.371 589.817 983.473 918.396 837.195 802.706 1373.827 580.990Northampton Cou -230.401 -240.241 -235.384 -246.888 -262.955 -272.590 -286.227 -297.345 -297.158 -301.022 -292.096Northumberland C -316.654 -312.363 -257.018 -243.397 -247.003 -298.859 -289.710 -281.474 -295.768 -291.597 -263.894Perry County -622.681 -631.438 -710.821 -699.547 -693.866 -595.862 -600.414 -598.811 -609.255 -546.648 -582.971Philadelphia Coun 151.888 168.538 186.148 197.907 204.278 180.543 168.294 77.302 113.291 79.655 111.497Pike County -538.234 -549.344 -624.494 -590.650 -531.839 -458.250 -416.405 -287.876 -314.866 -164.291 -161.769Potter County -152.488 -166.985 -146.295 -151.122 -129.765 -187.053 -175.839 -111.049 -112.164 67.248 101.963Schuylkill County -224.886 -215.681 -227.519 -239.591 -236.161 -210.142 -230.607 -240.785 -246.569 -230.953 -231.909Snyder County -72.948 -84.754 -123.075 -112.753 -122.557 -16.598 -32.778 12.021 27.502 114.351 116.358Somerset County -275.708 -265.505 -311.099 -280.941 -286.633 -243.675 -249.161 -261.424 -252.083 -238.943 -237.833Sullivan County -309.936 -335.289 -288.433 -338.209 -375.596 -470.640 -224.172 -264.861 -186.792 -162.450 -162.126Susquehanna Co -502.820 -531.627 -562.464 -596.241 -594.320 -593.142 -534.873 -519.504 -491.269 -387.698 -405.434Tioga County -239.149 -228.963 -109.702 -98.655 -104.177 -263.188 -242.470 -238.247 -221.999 -168.815 -185.800Union County 22.931 -0.251 131.675 111.271 125.163 12.148 0.000 -64.527 0.878 153.357 114.344Venango County -131.744 -133.646 -32.061 -23.858 -21.598 -85.265 -56.752 -71.505 -77.083 -53.632 -59.842Warren County -160.657 -185.760 -366.599 -385.746 -407.325 -186.652 -184.257 -186.137 -159.297 -133.759 -156.242Washington Coun -80.693 -85.064 -109.582 -85.538 -137.164 -156.821 -170.134 -147.890 -174.840 -156.895 -154.434Wayne County -318.089 -349.840 -249.284 -298.727 -297.992 -379.629 -337.924 -236.764 -191.786 -156.807 -204.236Westmoreland Co -197.921 -201.202 -211.421 -211.177 -196.293 -189.272 -177.348 -171.025 -194.153 -157.773 -152.374Wyoming County -167.170 -186.357 -98.926 -109.043 -46.269 -196.159 -165.930 -199.358 -54.051 -185.043 -179.389York County -181.151 -172.192 -166.861 -173.695 -175.638 -187.016 -184.219 -183.983 -152.576 -153.543 -187.399


95

Gross Migration Rates2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005

Adams County 13.466 13.753 13.388 12.382 12.411 12.677 12.031 12.563 11.868 11.561 11.185Allegheny County 214.428 219.953 221.963 215.831 213.031 213.050 206.909 201.722 207.563 187.813 182.709Armstrong Count 7.560 7.670 7.547 8.072 7.630 7.111 8.002 7.780 7.985 7.819 7.636Beaver County 22.062 23.527 23.900 23.895 22.732 21.820 21.239 20.480 19.761 18.378 17.456Bedford County 6.023 6.032 5.854 5.959 6.111 6.725 7.343 7.094 6.705 5.545 5.459Berks County 58.576 59.501 57.920 55.915 54.241 53.248 52.725 53.643 52.015 46.349 45.885Blair County 22.589 23.953 24.088 23.551 23.779 22.077 21.420 21.350 21.549 19.736 19.802Bradford County 8.788 9.014 9.368 9.851 9.352 6.614 6.092 6.766 6.599 6.241 6.475Bucks County 119.286 117.867 114.649 114.407 117.999 113.178 111.998 114.845 114.465 116.168 105.719Butler County 41.975 41.915 44.226 43.334 41.764 39.762 38.170 37.532 37.039 33.659 31.503Cambria County 19.980 20.974 21.622 21.728 22.504 21.145 20.455 20.598 20.110 18.029 17.648Cameron County 0.645 0.687 0.763 0.789 0.801 0.741 0.759 0.902 1.018 0.665 0.671Carbon County 7.046 6.994 7.289 6.785 6.656 5.968 6.008 5.955 5.985 5.345 5.089Centre County 26.622 27.654 28.731 27.716 27.695 25.572 24.591 19.674 24.373 23.225 22.929Chester County 129.377 125.468 123.460 121.132 121.028 119.416 117.796 121.113 114.744 106.050 101.830Clarion County 5.777 6.290 6.428 6.600 6.559 6.318 6.154 6.552 6.424 6.110 6.149Clearfield County 13.683 14.214 14.486 15.219 14.986 13.302 13.306 12.914 13.631 12.691 12.362Clinton County 5.154 5.646 5.392 5.544 5.617 5.160 5.196 5.795 5.738 4.770 4.713Columbia County 12.159 12.532 11.478 11.871 12.067 10.770 11.320 11.310 11.054 10.071 10.047Craw ford County 1.063 0.946 0.938 0.995 0.898 0.855 0.766 0.921 1.009 0.781 0.883Cumberland Coun 85.725 83.271 82.941 80.299 79.890 76.873 73.936 77.267 66.558 63.491 76.006Dauphin County 106.124 105.490 103.595 106.304 102.568 102.259 101.672 101.356 98.969 94.494 89.672Delaw are County 111.753 117.135 112.846 110.598 108.900 101.747 98.083 99.012 98.308 86.819 90.020Elk County 3.613 3.769 3.642 3.997 3.788 3.401 3.337 3.526 3.446 3.077 3.069Erie County 21.685 23.164 22.674 22.444 21.919 21.450 20.777 19.521 18.898 16.461 16.456Fayette County 13.508 13.211 13.479 13.077 13.218 12.786 14.449 15.791 15.032 11.453 11.230Forest County 1.018 1.161 1.204 1.193 1.130 0.929 1.010 1.206 1.093 1.084 1.005Franklin County 20.223 20.153 20.130 19.181 19.011 18.261 17.808 18.745 20.473 14.468 13.192Fulton County 1.605 2.776 2.739 2.872 2.709 2.499 2.178 2.892 2.911 2.924 2.781Greene County 8.763 9.172 8.927 8.386 7.996 7.014 6.369 6.470 5.889 4.940 5.128Huntingdon Coun 5.326 5.247 5.756 5.532 5.306 5.079 5.443 5.715 5.513 5.208 5.134Indiana County 15.056 15.998 17.076 16.682 16.132 15.225 15.345 15.099 14.529 15.134 15.162Jefferson County 6.808 6.689 6.629 6.913 7.056 5.997 5.991 6.314 6.076 6.206 5.285Juniata County 2.675 2.736 2.916 2.809 2.864 2.117 2.159 2.496 2.677 2.386 2.493Lackaw anna Cou 34.515 34.694 35.360 34.212 34.324 33.273 32.289 31.846 30.381 26.961 25.963Lancaster County 65.438 65.631 62.503 59.843 60.091 57.956 57.714 58.845 55.931 48.377 50.407Law rence Count 11.313 12.016 11.187 11.395 11.250 11.256 11.199 11.371 11.019 11.883 10.681Lebanon County 20.786 21.461 21.403 20.757 21.415 19.254 18.322 18.924 19.217 16.483 15.786Lehigh County 93.860 94.044 93.717 93.210 89.044 87.342 85.079 86.974 85.626 77.852 75.886Luzerne County 46.805 47.554 45.701 45.610 43.567 40.785 39.131 39.326 40.386 36.059 36.661Lycoming County 19.643 19.565 19.080 19.402 17.645 16.406 15.713 15.358 14.537 12.397 12.068McKean County 4.790 5.222 5.184 5.563 4.895 4.697 3.937 4.273 4.279 4.431 4.022Mercer County 19.019 19.663 19.661 19.592 18.768 17.324 17.061 17.358 19.661 15.133 15.274Miff lin County 5.644 5.848 5.803 5.730 6.278 5.807 4.968 5.370 5.199 4.635 4.524Monroe County 19.675 19.782 19.796 20.044 21.223 20.106 19.856 22.574 21.327 19.274 18.712Montgomery Cou 279.335 272.312 270.257 269.220 262.394 260.968 259.543 269.552 262.204 249.491 242.098Montour County 12.069 12.740 14.989 13.705 13.255 12.233 11.638 11.477 10.974 11.211 9.354Northampton Cou 51.980 51.352 51.840 50.056 47.870 45.675 43.676 43.824 42.995 39.902 38.727Northumberland C 13.152 13.618 13.557 14.087 13.611 12.882 12.553 13.140 12.841 11.351 11.758Perry County 3.768 3.856 3.794 3.899 4.044 4.074 3.902 4.039 3.974 3.823 3.467Philadelphia Coun 295.200 294.151 292.655 287.526 294.518 276.364 269.834 247.779 253.178 236.992 241.002Pike County 4.319 4.495 4.187 4.617 5.264 4.860 4.634 5.352 4.963 5.274 5.169Potter County 2.003 2.070 1.885 1.980 1.974 1.923 1.728 2.076 1.957 2.603 2.817Schuylkill County 18.720 19.634 19.355 18.503 18.133 18.852 17.949 17.705 17.392 15.657 14.896Snyder County 8.770 8.835 9.087 9.103 8.977 8.683 7.886 8.491 8.688 8.651 8.528Somerset County 8.221 8.584 8.318 8.536 8.500 8.417 7.835 7.851 7.679 6.913 6.834Sullivan County 0.876 0.904 0.933 0.899 0.803 0.542 0.552 0.678 0.755 0.684 0.647Susquehanna Co 3.131 3.182 3.456 2.863 2.800 2.335 2.414 2.628 2.789 3.008 2.887Tioga County 4.458 4.771 4.689 4.810 4.590 3.729 3.178 3.410 3.371 2.947 2.906Union County 10.000 9.928 9.847 9.799 9.711 9.223 8.358 8.113 8.734 8.555 7.958Venango County 7.313 7.687 8.178 8.508 8.383 7.933 7.510 7.646 7.611 6.907 6.826Warren County 4.480 4.581 4.590 4.331 3.780 3.993 3.769 3.661 3.667 3.259 3.092Washington Coun 41.911 42.472 42.329 42.662 38.700 35.273 33.943 34.089 32.743 30.742 30.310Wayne County 6.513 6.601 7.015 6.053 5.871 5.603 5.278 6.031 5.958 5.500 4.987Westmoreland Co 53.020 53.855 53.695 52.336 53.108 52.317 52.583 53.060 51.625 49.964 49.243Wyoming County 5.549 5.632 5.706 5.682 5.850 5.358 5.206 4.772 5.726 4.600 4.616York County 57.807 59.669 58.634 56.364 56.055 53.349 52.588 52.240 53.427 47.405 42.697


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InMigration Rates2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005

Adams Coun 293.656 292.733 282.527 267.131 264.730 273.337 263.504 267.521 241.184 246.204 244.342Allegheny Co 387.863 399.757 398.384 392.151 392.882 401.786 395.343 381.441 394.149 351.122 349.568Armstrong C 260.449 259.896 268.112 283.651 274.731 243.318 284.177 296.750 302.745 324.708 313.231Beaver Coun 304.718 321.904 335.241 324.903 315.814 301.476 295.030 273.659 257.422 236.997 233.912Bedford Cou 301.311 296.305 314.576 313.728 326.058 339.768 380.730 349.134 328.418 280.780 286.434Berks Count 318.028 325.546 319.857 308.162 308.580 305.670 303.174 292.973 282.958 261.031 264.293Blair County 461.820 487.573 483.239 472.428 477.375 455.393 450.085 442.267 437.783 390.626 391.444Bradford Co 354.770 354.519 337.769 352.239 358.795 263.758 273.601 298.259 276.005 279.978 279.035Bucks Count 399.377 396.738 386.620 387.562 399.037 389.460 389.510 401.045 391.694 435.183 367.381Butler Count 523.673 521.105 557.981 541.825 536.958 516.981 505.938 484.227 481.094 431.308 419.272Cambria Cou 376.385 388.984 401.247 406.192 408.851 378.997 371.106 355.340 368.436 311.721 308.526Cameron Co 330.261 338.257 275.488 280.228 283.587 374.242 395.518 414.903 464.416 349.816 386.298Carbon Coun 264.989 259.548 264.068 250.934 242.529 227.392 229.380 241.649 227.477 205.191 204.493Centre Coun 527.670 563.919 588.798 561.724 553.468 526.986 535.787 484.271 492.473 495.076 486.423Chester Cou 556.518 543.112 533.233 534.123 538.115 542.406 538.226 548.982 520.129 501.478 495.364Clarion Coun 400.249 433.994 434.844 449.523 441.795 433.057 434.442 446.413 423.439 393.609 406.814Clearfield Co 446.369 464.007 487.497 497.223 487.365 454.894 453.572 433.152 441.418 408.925 399.612Clinton Coun 345.653 366.511 370.210 385.252 380.488 346.358 363.872 448.521 434.984 352.685 355.221Columbia Co 464.312 483.729 528.456 539.521 547.604 429.866 463.381 429.282 421.693 390.970 394.697Craw ford Co 261.705 219.563 236.437 241.732 230.059 210.604 201.528 244.226 363.374 196.474 227.672Cumberland 819.988 813.454 853.819 836.446 841.858 801.067 753.854 743.092 639.953 653.126 1209.343Dauphin Cou 891.042 909.216 914.302 924.624 896.511 893.685 884.250 828.065 836.339 775.482 711.107Delaw are Co 451.388 476.156 463.450 455.907 447.194 430.673 416.677 401.498 402.205 358.052 383.813Elk County 239.319 246.517 225.623 230.667 222.144 232.674 250.169 226.461 223.158 206.607 211.772Erie County 194.451 210.983 204.219 203.360 200.157 198.753 199.411 173.613 163.533 141.677 143.168Fayette Coun 267.026 259.712 258.442 257.899 257.436 256.341 298.327 317.056 303.425 238.128 237.858Forest Coun 583.477 598.555 416.984 387.139 399.788 608.924 844.351 1147.619 999.085 995.404 948.064Franklin Cou 321.796 323.477 340.143 326.752 326.419 302.403 299.742 295.262 357.863 231.381 206.177Fulton Count 267.735 480.270 525.831 533.346 511.716 423.246 384.561 446.073 404.841 455.722 426.054Greene Coun 623.586 625.367 644.802 623.791 576.715 501.609 450.513 494.383 433.228 400.405 401.992Huntingdon C 339.583 312.876 274.335 264.717 248.524 323.997 386.286 371.884 369.933 371.955 363.090Indiana Coun 489.132 497.667 496.136 482.472 467.741 471.508 476.050 483.865 451.913 489.077 499.951Jefferson Co 380.556 375.119 426.348 433.056 425.949 340.798 353.914 353.907 321.786 373.121 303.121Juniata Coun 279.678 275.096 256.264 256.814 257.186 232.963 263.107 298.409 315.901 284.674 291.428Lackaw anna 396.724 404.425 412.645 399.958 397.278 404.122 384.842 354.356 351.091 301.594 291.320Lancaster C 282.611 288.221 277.535 274.063 269.593 265.280 264.100 256.335 243.426 216.129 225.051Law rence C 310.266 323.262 296.585 297.478 296.847 309.842 317.854 305.407 287.624 372.683 310.765Lebanon Cou 349.052 360.436 373.000 354.035 361.180 328.264 322.411 327.008 355.332 289.692 282.764Lehigh Coun 608.272 614.852 617.762 618.083 601.214 597.918 592.641 570.598 566.463 514.054 515.565Luzerne Cou 353.317 365.421 348.387 347.799 332.926 323.257 316.241 306.039 324.761 278.963 280.257Lycoming Co 403.314 399.318 399.121 388.810 356.681 334.141 324.555 302.585 289.686 243.867 242.276McKean Cou 299.057 321.413 363.458 384.823 340.120 288.460 249.414 251.102 246.019 270.893 236.616Mercer Coun 434.373 448.630 455.506 449.214 428.102 401.288 405.211 380.858 513.597 313.748 317.963Miff lin Count 300.944 306.094 272.305 274.496 300.105 315.081 278.763 293.266 271.564 253.348 246.795Monroe Coun 346.288 337.761 330.335 340.081 373.068 361.432 371.921 464.572 455.880 429.974 408.327Montgomery 731.086 718.662 723.730 718.826 707.579 718.452 719.446 715.552 716.769 677.523 695.598Montour Cou1523.737 1567.491 1411.698 1328.293 1259.048 1555.047 1500.193 1429.497 1374.546 1948.210 1123.888Northampton 396.780 397.506 398.630 390.655 373.536 361.780 350.432 330.356 327.691 304.695 308.650Northumberla 352.281 364.852 390.292 398.337 389.358 350.857 348.318 344.539 339.198 305.880 317.337Perry County 187.862 188.361 154.356 159.213 163.804 202.094 196.841 200.099 194.956 215.324 183.756Philadelphia 541.096 557.644 570.801 598.687 569.494 554.401 543.972 472.445 493.646 450.454 481.627Pike County 223.858 224.330 199.248 212.406 261.620 277.705 290.762 381.860 362.666 447.471 464.456Potter Count 357.054 358.765 397.720 361.197 370.516 343.705 331.160 389.233 378.457 473.101 489.317Schuylkill Co 309.487 322.233 316.087 305.213 302.321 314.246 298.710 283.096 275.921 252.463 247.635Snyder Coun 553.354 545.713 538.143 535.916 529.901 585.790 599.665 637.866 643.958 658.546 669.940Somerset Co 276.145 285.424 274.498 275.803 276.076 282.397 265.496 252.079 250.907 220.124 226.404Sullivan Cou 398.815 407.491 454.856 398.050 382.269 238.852 358.025 447.160 474.214 454.727 429.236Susquehann 185.856 182.094 180.824 149.819 151.420 137.286 164.351 183.976 191.668 221.510 220.272Tioga County 290.908 308.518 345.694 344.112 325.509 240.423 226.838 236.444 233.061 212.243 215.371Union County 638.669 622.460 720.263 705.196 704.951 615.539 597.057 551.574 589.828 671.007 644.814Venango Co 352.264 363.916 426.337 434.658 416.216 374.534 388.877 354.786 352.361 333.124 334.821Warren Coun 262.680 263.458 207.694 194.694 178.364 237.509 229.272 221.825 226.149 202.096 200.207Washington 472.524 480.229 469.899 481.670 442.012 411.043 401.381 398.331 380.500 366.776 364.260Wayne Coun 324.772 320.731 386.148 333.499 332.993 279.974 293.053 374.720 388.331 381.372 345.076Westmorelan 339.181 341.804 335.978 326.044 332.245 338.621 347.028 349.378 325.632 332.459 332.788Wyoming Co 492.543 497.570 557.129 539.609 598.731 465.583 479.282 463.970 584.960 442.084 440.363York County 286.899 298.705 297.867 288.760 284.763 273.750 274.230 265.967 279.800 255.125 224.267


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REFERENCES

Cebula, R. J. 2005. Internal migration determinants: Recent evidence. International Advances in Economic Research. 11, 267-74. Cebula, R. J. and J. E. Payne. 2005. Net migration, economic opportunity and the quality of life, 1999-2000. International Review of Economics and Business. 52, 245-54. Cushing, B. J. 1987. Location-specific amenities topography, and population migration. Annals of Regional Science. 21, 74-85. Dahl, G. B. 2002. Mobility and the return to education: Testing a Roy Model with multiple markets. Econometrica. 70(6), 2367–420. Graves, P. E. 1973. A reexamination of migration, economic opportunity and quality of life. Journal of Regional Science 13. 205-11. Graves, P. E. 1979. Income and migration reconsidered. Journal of Human Resources. 14, 112-21. Graves, P. E. 1980. Migration and climate. Journal of Regional Science. 20, 227-38. Green, G. P. 2001. Amenities and community development. Journal of Regional Analysis and Policy. 31, 2:61-76. Greenwood, M. J. 1975. A simultaneous-equations model of urban growth and migration. Journal of the American Statistical Association. 70, 797-810. Greenwood, M. J. 1985. Human migration: Theory models, and empirical studies. Journal of Regional Science. 25, 521-44. Gunderson, R. J. and P. Ng. 2005. Analyzing the effects of amenities, quality of life attributes and tourism on regional economic performance using regression quantiles. Journal of Regional Analysis and Policy. 35(1), 1-22. Hoover, E. M. 1941. Interstate redistribution of population, 1850-1940. The Journal of Economic History. 1(2), 199-205. Hoover, E. M. and J. Fisher. 1949. Research in regional economic growth. In Universities-National Bureau Committee for Economic Research, Problems in the Study of Economic Growth National Bureau of Economic Research, Chapter V.

Johnson, K. M. and S. I. Stewart. 2005. Recreation, amenity migration and urban proximity. In Gary Green, Steven Deller and David Marcouiller, (eds.) Amenities and Rural Development. Northampton, MA: Edward Elgar Publishing, Inc. Latzko, D. 1999. Why have some Pennsylvania counties grow fast and others slowly? Presented at the Pennsylvania Economic Association Conference, Carlisle, PA, June 4, 1999. Latzko, D. 2003. Urban centers and regional economic cohesion in Pennsylvania. Presented at the Pennsylvania Economic Association Conference, West Chester, PA, May 30, 2003. Muth, R. F. 1971. Migration: Chicken or egg? Southern Economic Journal. 57, 295-306. North, D. C. 1955. Location theory and regional economic growth. Journal of Political Economy. 63(3), 243-58. Olvey, L. E. 1972. Regional growth and interregional migration‒Their pattern of interaction. Review of Regional Studies. 2, 139-63. Partridge, M. D. and D. S. Rickman. 2006. An SVAR model of fluctuations in U.S. migration flows and state labor market dynamics. Southern Economic Journal. 72, 958-80. Porell, F. W. 1982. Intermetropolitan migration and quality of life. Journal of Regional Science. 22, 137-58. Sala-i-Martin, X. 1997. I just ran two million regressions, American Economic Review. 87, 178-83. Shields, M. and C. Vivanco. 2004. Rural Pennsylvania in the new economy: Identifying the causes of growth and developing new opportunities. Center for Rural Pennsylvania. available at: https://files.eric.ed.gov/fulltext/ED485790.pdf. Shimer, R. 2007. Mismatch. American Economic Review. 97(4), 1074–1101. U.S. Census Bureau. 2018. LEHD Origin-Destination Employment Statistics 2002-2015 (computer file). Washington, DC: U.S. Census Bureau, Longitudinal-Employer Household Dynamics Program (distributor), accessed on May 1, 2018 at https://onthemap.ces.census.gov. LODES 7.3.

https://files.eric.ed.gov/fulltext/ED485790.pdf

https://onthemap.ces.census.gov/


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MITIGATION OF THE GAP IN ENERGY SUPPLY AND DEMAND IN THE KAVAL CITIES OF INDIA

Babita Srivastava William Paterson University

300 Pompton Rd Wayne, NJ 07470

ABSTRACT The case studies analyze the requirement of electricity with respect to the future population for the major forms of energy in the KAVAL cities of Uttar Pradesh in India. A model consisting of significant key energy indicators has been used for the estimation. The accuracy of the model has been checked using standard statistical techniques and validated against the past data by testing for export forecast accuracy. The study identifies the urgent need for special attention in evolving effective energy policies to alleviate an energy famine in the near future.

INTRODUCTION As power is one of the fundamental infrastructure inputs for development, its prospects and availability sets significant constraints on the socio-economic growth of the state. A careful long-term power plan is imperative for the development of the power sector. This need assumes more importance in the state of Uttar Pradesh where the demand for electrical energy is growing rapidly. Since electricity is one of the necessities in ordinary business of life and a major driving force for economic growth and development, the non-storable nature of electricity means that the supply of electricity must always be available to satisfy the growing demand. Since the commission of power plants and the transmission may take between 5-7 years and power purchase from another source is limited, it is imperative that the power development plan must be well conceived. Inevitably, a reliable medium and long run load forecasts are prerequisites for a well- conceived power development plan. An under forecasted load leads to an under expanded power system, which leads to the black out of the power system. On the other hand, an over forecasted load leads to an over expanded power system. In this case, the unnecessary costs are passed on to the power consumers through a higher power tariff. Load forecasting has always been important for planning and operational decision conducted by utility companies. However, with the deregulation of the energy industries, load forecasting is even more important. With supply and demand fluctuating and the changes of weather conditions and energy prices increasing by a factor of 10 or more during peak situations, load forecasting is vitally important for utilities.

There are several factors that affect electricity demand. The key factors are electricity price, number of electricity appliances, income, temperature and consumer load pattern that differ by regions and consumer groups (Bansal and Pandey, 2005; Yee et al., 2009). A reliable load forecast methodology must correctly gauge the effects of the key factors on electricity demand. The electricity demand which includes public lighting, will be forecasted by customer groups (NIDA Consulting Center, 2006). On the other hand, utilization hours for electric power generation equipment has continued to drop since 2000 and will continue to decline in 2008. According to predictions of the Power Grid Corporation, de-rated output operation or even shutting down units of power generation during low demand periods not only increase energy consumption but also affect equipment life span and is overall uneconomical. Uttar Pradesh is a state located in the northern part of India with a population of over 19 million people and an area of 93,933 mile2 (243,286 km2). It is India's most populous state, as well as the world's most populous sub-national entity. Kaval Cities in Uttar Pradesh (UP) are: Kanpur, Agra, Varanasi, Allahabad, and Lucknow. Kanpur is known as the Commercial Capital of UP. Agra is known as the Tourism Capital, Location of the Taj Mahal. Varanasi is considered to be the Religious Capital of India. Allahabad is considered the Judicial Capital of UP and Lucknow is the Capital of UP. These are the oldest cities in UP and they are rich in history; they are also the most populated cities in UP. Due to its population size, the Kaval cities of UP are in need of an energy solution as their demand for energy is growing rapidly. Since the commission of power plants and the transmission of energy may take between 5-7 years and power purchase from other sources is limited, it is imperative that the power development plan be well conceived. With supply and demand fluctuating, the changes of weather conditions, and energy prices increasing by a factor of 10 or more during peak situations, load forecasting is vitally important for utilities. The demand of power is increasing day by day in daily life. Therefore, the forecasting of power is very important for the future. The state had a deficit in power supply of -12.6% of total demand in June 2007 while in June 2008, it had the deficit of -13.7%. In another way, transmission and distribution losses in 2002-03 was 36.64% and in 2005-06 it was 37.17%


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of the total power supply in the state. As per the 2001 Indian Census (Government of India, 2001), population and percentage with respect to the total population with temperature range are shown in Table 1. This study has studied the power forecast in 2023 corresponding to the city temperatures with respect to the population in the cities (Bhardwaj and Bansal, 2010). Few researches has been conducted with respect to the population (Sharma et al., 2002), as well as with the temperature of the particular areas (Barakat and Al-Rashed, 1992) individually while few are written with consideration of both factors (Bhardwaj et al., 2009; Bhardwaj and Bansal, 2010).

CALCULATION OF POPULATION AND DEMAND FORECAST

Population Calculation Growth rate is an essential value to find the population in a specified year based on a particular time. The time frame can be defined between the standard base year and final year with respect to the time span of 9 months. The following formula can be suggested for growth rate calculation (Bhardwaj et al., 2009). (N = B[{(100+G)/100}I – 1]) Where: N = Net increase in population between 2001 and 1991 B = Base year value of 1991 G = Growth rate (1.28%) T = Total period between 2001 and 1991 Since the correct data of 2008 is not available, in this particular calculation we will take the 2001 value, from Table 1, as the base year value and time span between 2001 and 2008 i.e., 9.34. Population in the cities using the relation = B{(100+G)/100}T are shown in Table 2. In all the cases of estimate of population, we have taken the base year value of 2001 and time periods between 2001 and the required year. Power Demand Forecast Power demand is projected to increase for KAVAL cities as seen in Table 3. For many years, most of the UP power capacity is fossil sourced, such as coal, natural gas and oil. Since using renewable energy sources and schemes, there has been some progress in developing cities such as powering the area, as well as establishing new industry. The power supply demand gap remained above 10% for over the last 10 years.

MINIMIZING THE SUPPLY-DEMAND GAP The biggest challenge is minimizing the supply and demand

gap prevailing in the state, with insufficient generating capacity and dilapidated transmission and distribution network that require huge investments. As a result, the manifestations reflect in the form of rostering, restrictions, breakdowns, voltage fluctuations, which are the order of the day and the pockets still not served from the grid resulting in perpetual migration to urban areas. We should open access of power in which it is drawn from outside the state. Outlining the critical flaws in the power distribution system of Uttar Pradesh was addressed in a government meeting with officials from various distribution companies. The need for immense efforts in removing the constraints being faced and for steps to be taken to overcome the critical flaws for bringing down the losses and measuring inefficiencies with IT-backed activities, is crucial in achieving to make power affordable as well as accessible round the clock (Pandey, 2017). The government has stated the commission feels a lot of effort is required to push the reforms so that issues related to quality, reliability, availability and affordability are addressed in an efficient manner. The challenges can be gauged from the size and the population of the state as compared to its number of consumers, consumption, and the connected load. On the generation and transmission front allocation of ultra mega power project to the state based on the Gadgil formulae has been a matter of concern. Steps must be taken to post a team of liaison officers at New Delhi under a senior officer to take up all pending issues related to fuel, clearances, etc. with various ministries on a regular basis. The refurbishment schemes at the power plants, and above all planning and making huge investments from private and public sector in adding new capacities in generation to make the power available for 24 hours in the state so as to make it a power surplus by 2017. The government has stated that power generation by thermal plants in the state need to be upgraded (Pandey, 2017). While many of the Centre's power plants have been functioning at 95% plant load factor, many of those in the state were working much below it. The state should get into a time-bound action plan and that will show results.

SOLAR ENERGY OPPORTUNITIES IN KAVAL CITIES

The majority of power generated in Uttar Pradesh is reliant on coal, while the limited availability and high prices of coal have aggravated the precarious power situation in UP. Hence, there is an obvious need to develop alternate sources of energy. Uttar Pradesh is blessed with a good solar irradiation to the tune of 1,800 KW/h per m² on an annual average basis, which is considered necessary for operating a solar photovoltaic power plant. Thus, there are immense possibilities in this sector (Bisht, 2013). Growth of renewable energy would definitely help the state in meeting its energy requirements (Bandhu, 2012).


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In January 2014, the Uttar Pradesh Cabinet approved the first-ever solar energy policy of the state. Under this policy, a target of producing 1000 MW of electricity through solar energy has been set by March 2017. For the purpose of promoting production of electricity by solar energy, a U.P. renewable energy fund has been created (Bisht, 2013). The UP government has zealously taken up the agenda of augmenting solar power generation and consumption, both on-grid and off-grid. The government had announced a solar energy policy, which promises incentives to entrepreneurs setting up solar power plants. Besides, the state has been promoting rooftop solar plants for small companies and individuals. This would not only provide a green and renewable energy source, but also reduce the burden on the grid. The 30% central subsidy provided on solar power installations has only added to the attractiveness of the proposition. After subsidy, a typical solar power system costs about Rs 1.40 lakh per kw capacity, although the final price largely depends upon the battery strength. State government finalized agreements with seven private power players and a public sector undertaking to produce 230 MW of solar power (Natural Group, 2013). At a function on Thursday, Chief Minister Akhilesh Yadav handed out Letter of Intents to Jakson Power Solutions, Moser Bayer Clean Energy Ltd, Sree Developers, DK Infracon, Refax Energy, Azure Surya Ltd and Essel Infra. These private sector firms would together be setting up solar power plants of a total 130 MW capacity. These companies would be purchasing land directly from the farmers and are expected to be commissioned within six months after the land is in possession. The Joint MD, Jakson Power Solutions, Sundeep Gupta said that they would be investing Rs 80 crore within one year to establish a 10 MW

solar plant. He said that they have identified some land parcels in the Bundelkhand region of the state and expect their project to be commissioned in 6 months’ time. Jakson power Solutions has already commissioned a 2×10 MW solar project in Rajasthan in February this year and is ambitious on growing their solar power portfolio. “We are scouting for several opportunities and would also be exploring the option of setting up one more solar plant in Uttar Pradesh” Gupta said. The state government has also signed an MoU with the public undertaking the National Hydro Power Corporation Ltd (NHPC) to set up a 100 MW solar plant in Jalaun district of Bundelkhand region.

CONCLUSION KAVAL is an oldest and fasted growing cities in the state of Uttar Pradesh of India. Growth of the KAVAL cities may get hurt unless the energy crisis is taken care of. Renewable energy, especially solar energy could be an answer for increasing energy demand. Government and private sectors has taken several strong measures to promote renewable energy in the majority of the country but UP has been developing in a much slower pace. The region of UP typically gets plenty of natural sunlight and has been able to utilize wind power. With the use of these forms of renewable energy, the KAVAL cities will develop quickly and improve in all areas. These cities often have blackouts and this region has a very isolated and large rural area. Renewable energy will not only improve the power supply of the region but will also improve the livelihood of the people who live there. If the key factors stated are considered and the plans and incentives created by the government are followed, the KAVAL cities should improve quickly and efficiently.


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Table 1: Population and maximum temperature

Table 2: Estimated Population

Table 3: Power Demand Forecast


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REFERENCES Pandey, A.C. 2017. IAS Chief Secretary, Government of U.P. Retrieved from http://upinvestorssummit.com/htm/01/img/pdf/Uttar_Pradesh_Solar_Energy_Policy-2017_English_.pdf

Bandhu, U. 2012. Renewable Energy in Uttar Pradesh. Retrieved from http://udyogbandhu.wordpress.com/2012/04/17/renewable-energy-in-uttar-pradesh/

Bisht, A.S. 2013. Next big move in UP, energy from the sun. The Times of India. Retrieved from http://articles.timesofindia.indiatimes.com/2013-02-13/lucknow/37078773_1_solar-energy-renewable-purchase-obligation-solar-power

Central Electricity Authority (CEA) 2013. Monthly Executive Report, March 2013. Retrieved from http://www.cea.nic.in/reports/monthly/executive_rep/mar13/mar13.pdf

Gurtoo, A. and Pandey, R. 2001. Power Sector in Uttar Pradesh: Past Problems and Initial Phase of Reforms, Economic and Political Weekly 36 (31), pp. 2943-2953.

Natural Group. 2013. Uttar Pradesh government finalises 230 MW solar power projects. Solar for India. Retrieved from http://natgrp.org/2013/08/16/uttar-pradesh-government-finalises-230-mw-solar-power-projects/

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PHD Chamber of Commerce and Industry. 2011. Comparative Study on Power Situation in the Northern and Central States of India. Retrieved from http://www.phdcci.in/admin/admin_logged/banner_images/1334554564.pdf.

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Review on Power Sector Reforms in Uttar Pradesh. Retrieved from http://bit.ly/1i0Xfr2

The Economic Times. 2011. Greenpeace called upon Uttar Pradesh government to demand a bigger share of decentralised renewable energy. Retrieved from http://articles.economictimes.indiatimes.com/2011-10-21/news/30306656_1_renewable-energy-rggvy-arpana-udupa

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The Times of India. Feb 2013. Next big move in UP, energy from the sun. Retrieved from http://articles.timesofindia.indiatimes.com/2013-02-13/lucknow/37078773_1_solar-energy-renewable-purchase-obligation-solar-power

UPPC. 2010-11. Statistics at a Glance 2010-11. Retrieved from http://www.uppcl.org/uppcllink/documents/14022013030910Statistics_UPPCL_2010_11.pdf.


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ASSESSING THE IMPACT OF FIRST PERSON CONSTRUCTS IN INTRODUCTORY ACCOUNTING COURSES

Brian Trout Department of Business Administration

Millersville University 43 E. Frederick Street Millersville, PA 17551

ABSTRACT

The first accounting courses are typically compulsory for business majors regardless of their concentration. The variety of students enrolled in these courses make instruction a challenging endeavor. Studies indicate that students lack interest in these courses and often consider them irrelevant to their future careers. This study is the first to examine how point of view impacts students’ perceptions and performance in the first accounting courses. Students were given two quizzes which contained the same questions and content except one version’s questions were phrased from the first person perspective and the other version’s questions were phrased from the third person perspective, akin to the point of view taken by popular textbooks. Analyses of matched pairs showed scores on the first person quizzes were significantly better than scores on the third person quizzes. Overall perceptions of first person quizzes were more favorable than perceptions of the third person quizzes, although not statistically significantly different. Students’ overall perceptions of accounting immediately following the first person quizzes exceeded their perceptions of accounting following the third person quizzes, but not by a significant margin. Results will be of interest to educators striving to enhance relevance, affect authentic interest, and improve learning outcomes in these important classes.

INTRODUCTION

Some view accounting as being as difficult as learning a new language (Borja, 2003). Wells (2015) found that one of the major factors which contributed to students’ unfavorable perceptions of accounting was an absence of context. When material is delivered from an unrelatable perspective, it can be difficult for students who do not have accounting experience to make the personal connections often required to affect authentic interest.

When students fail to see the relevance of accounting in their personal lives, they often resort to memorizing just enough material to pass the class (Phillips & Graeff, 2014). This passive memorization is a major characteristic of surface learning (Ballantine, Duff, & P., 2008). Conversely, deep learning takes place when students search for meaning by integrating new material with their personal experiences and interests (Duff & McKinstry, 2007). It is this “interest” in a

subject that precipitates a search for understanding and studies indicate a connection between the level of interest and deep learning (Fransson, 1977; Nolen, 1988).

Wygal & Stout (2015) surveyed 105 accounting educators that were formally recognized for their teaching excellence. They were asked to list, in their own words and in ranked order of importance, the factors or qualities of teaching that they believed have helped distinguish them as an effective teacher. Among the top ranked responses were the ability to convey complex ideas in as simple a fashion as possible and finding the right example that makes the material relevant.

Like accounting (Larkin, 1991; Picard, Durocher, & Gerdron, 2014; Stivers & Onifade, 2014), many mathematics students show a lack of interest in the subject and find classes boring and irrelevant (Boaler, 2000; Mtetwa, Mudehwe, & Munyira, 2010). Darby (2008) notes that education committees in this field contend that one major element of student disengagement is the lack of connection between students’ lives and the subject. Researchers recommend curriculum approaches that focus on relevance to students’ lives, claiming that students develop a deeper understanding when they are encouraged to relate material to their own world (Harvey & Averill, 2012; Mtetwa et al., 2010).

If relevance to students’ lives does in fact stimulate interest and foster deep learning, then accounting educators must develop ways to make the seemingly impersonal personal. Much has been written about active learning methods and how these can positively affect student engagement and performance (Accounting Education Change Commission, 1990; Adler, Milne, & Stringer, 2000; Biggs, 1993; Gordon Boyce, Greer, Blair, & Davids, 2012; Cullen, Richardson, & O’Brien, 2004; Johnstone & Biggs, 1998; Kirschner, Sweller, & Clark, 2006; Milne & McConnell, 2001; Shawver, 2015; The Pathways Commissioners, 2012). This study is unique in that it specifically examines how a student’s perspective affects performance and perceptions of accounting.

To the best of my knowledge, this study is the first of its kind but there are studies which indirectly relate to the first person perspective and personal relevance. The literature review describes the importance of the courses under study, summarizes articles related to the first person perspective, and discusses a view on problem presentation from the field of


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mathematics. This is followed by a description of the methodology employed, results and a discussion.

LITERATURE REVIEW

This study focuses on the first accounting courses: Introduction to Financial Accounting and Introduction to Managerial Accounting. These courses are usually required for both accounting majors and other business majors regardless of concentration. The variety of students enrolled in these classes make teaching these courses a challenging and important undertaking. The AECC believes these courses must serve the interests of students who are not going to enter the profession as well as those who will become accountants (Accounting Education Change Commission, 1990).

The influence of a student’s experiences in these courses is consequential in terms of their perceptions of accounting and subsequent career pursuits (Geiger & Ogilby, 2000; Jackling & Calero, 2006; Nelson, Vendrzyk, Quirin, & Kovar, 2008; The Pathways Commissioners, 2012). Research indicates that many students have unfavorable experiences (Chen, Jones, & McIntyre, 2004; Geiger & Ogilby, 2000; Marriott & Marriott, 2003; Tickell, Lim, & Balachandran, 2012).

As noted, accounting educators have employed a variety of active learning methods to improve students’ experiences in the first accounting courses. The following summarizes strategies which specifically concentrate on educators’ efforts to make accounting personal to students and approach the material from a first person perspective.

Educators’ Efforts To Make Accounting Personal

Role play. Many accounting instructors has employed role play in an effort to make accounting personal. Phillips & Graeff (2014) designed a scenario where students assumed different roles, bought and sold merchandise, and accounted for transactions by preparing journal entries and maintaining inventory records for a Mom and Pop candy shop. Students reported that they felt more confident after the activity in terms of their understanding of how a business maintains accounts and prepares financial statements. This is consistent with Buckhaults and Fisher's (2011) assertion that role playing can reduce student anxiety toward accounting. Students’ attitudes regarding understanding of accounting concepts increased significantly. Scott's (1972) experiment also involved students preparing journal entries and financial statements from a first-hand perspective in a partnership context. Student performance improved and the students were “deeply engrossed in what they were doing” (p. 612).

Bearden's (2004) role play was also centered on students accounting for transactions from first person perspective except they were not provided a business or pre-planned transactions. Instead, students worked together in groups to choose a hypothetical business and plan how they would earn

revenue, what assets they would require, what expenses they would incur, and how they would finance the company. Next students created transactions for their hypothetical businesses. The author notes that the process of students thinking deeply about what kinds of transactions their company would encounter, made the exercise a rich learning opportunity as students applied their self-generated transactions to journalizing.

The first person perspective of Haskins and Crum's (1985) exercise allowed students to gain a deeper understanding of cost allocations and how various methods personally affect different parties. The authors posit that cost allocations are difficult for accounting students to grasp not because of the technical components but because students do not comprehend the behavioral issues typically involved business situations. The exercise centered on the allocation of a franchise tax over four segments of a company. First students made a normative choice among allocation alternatives. Later they made subsequent choices when assuming roles as CFO and Segment Manager. Their subsequent choices differed from their initial choices which was made absent of a personal perspective. By stepping into roles, students can discover the influence of self-interest and grasp the drivers and consequences of allocation decisions.

Games. Many games are used as an active learning method to enhance student engagement and stimulate interest in accounting. Some of these approaches include the employment of bingo games or the replication of game shows (Cook & Hazelwood, 2002; Haywood, McMullen, & Wygal, 2004; Oldfield & Slessor, 2010; Rhodes & Smith, 2004). Other accounting educators have utilized games which implicitly place participants in the first person perspective.

The first business simulation game known to be used in a university class dates back to 1957 (Faria & Nulsen, 1996). Bruns' article from 1965 discusses a game developed at Yale where groups of students assume the role of managers for fictitious companies and compete against other groups in class. Participants order parts, plan production, set pricing, develop cash budgets and prepare financial statements. Students are forced to consider the importance of questions related to product costing methods, inventory valuation choices, and depreciation alternatives and how these choices affect net income for their business. Bruns notes that all of the topics included in the game, as well as their importance, are discussed at length in classes prior to the game but student reactions and comments reveal that it is the game which drives home the salience of these concepts.

Hoffjan (2005) created a game, called Calvados, for a cost accounting course where students were split into groups of four with each group responsible for one company. The groups all face the same situation and compete against each other to maximize profits. The exercise concentrates on


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product mix decisions, transfer prices, and the complex issues involved in decision making. Students learn, tacitly from the first person perspective, how managerial accounting decisions affect reported profits and ultimately rewards for managers. Such a perspective allows students to understand the critical nature of orienting performance evaluation toward the organization as a whole.

Finally, a classic and effective game for accounting instruction has been Monopoly (Knechel, 1989; Mastilak, 2012; Warren & Young, 2012). The game affords students opportunities to account for personal transactions such rental property investments, depreciation, other expenses and revenue. It also allows for the construction financial statements and financial analyses.

Case studies. Case studies are said to engage and motivate accounting students (G. Boyce & Greer, 2012; Healy & McCutcheon, 2010) by fostering insight into business situations that students would otherwise not experience (Dellaportas, 2015). This can have a significant effect on student perceptions and attitudes (Burton & Sack, 1991). The rationale for the case based learning approach is that students are more receptive to teaching when they are immersed or “living” in the learning experience (Grant, 2015). Biggs (1989) asserts that instructors need to create a climate that stimulates students “proprietorial” interest.

Some case-based learning studies have explicitly situated students as owners or employees of the company under study. For example, Krumwiede and Walden (2013) instruct students to “put yourself in Kay Johnson’s (owner) shoes” when considering analyzing a special order. Everaert and Swenson (2014) employ a an exercise where students design toy trucks and consider the role of accounting during new product development. The authors use the possessive pronoun “your” when phrasing questions such as “Calculate your truck’s product cost”. Swain, Charles, Hobson, Stocks, and Pratt (2010) instruct students to assume they are senior staff accountants at a CPA firm when examining direct and indirect costs, breakdown of variable and fixed costs and a target net income. Results from these studies indicate student perceptions related to the cases’ overall effectiveness were significantly higher than a neutral point.

Insight From Mathematics

Interesting work on the topic of personal relevance and question phrasing comes from mathematics. Koedinger & Nathan (2004) found that differences in problem representation can affect student performance and learning when one representation is easier to comprehend than another. The study focused on high school students enrolled in a first year algebra course. Students completed quizzes with problems that ranged in various degrees of representation from exclusive story problems to equation-only problems without

stories and blended or mixed problems that the authors called “word equations”. Although contrary to commonly held assumptions, Koedinger and Nathan’s results aligned with Baranes, Perry, and Stigler's (1989) contention that the situational context of story problems can make them easier than equivalent symbolic problems. Koedinger and Nathan found students were more successful in solving algebra story problems than mathematically equivalent equation problems and offered an explanation which relates to the cognitive processes of students. Baranes, Perry, and Stigler posit this is because story problems can activate real world knowledge which students can leverage to arrive at a solution.

Koedinger and Nathan, as well as other authors (Cummins, Kintsch, Reusser, & Weimer, 1988; Hall, Kibler, Wenger, & Truxaw, 1989) assert that the process of story problem solving can be divided into two phases: the comprehension phase and solution phase. The comprehension phase involves processing the text of a story and developing internal representations of the quantitative and situation-based relationships (Nathan, Kintsch, & Young, 1992). Problem solvers use the representations from the comprehension phase to arrive at a solution (Koedinger & Nathan, 2004). Researchers believe that the difficulties students experience in solving story problems is primarily due to errors in the comprehension phase. Cummins et al., (1988) notes that certain word problems are difficult for students because they use linguistic forms that do not intuitively map to existing conceptual knowledge structures. Conversely, if problems are presented in a language familiar to students, they can leverage intuitive strategies to solve them. “Grounding new representations to familiar representations has the potential to promote reliable performance by facilitating meaning making” (Koedinger & Nathan, 2004, p. 158).

METHODOLOGY

Research Questions

• Research Question 1: To what extent does the use of first person question phrasing affect student performance as measured by pop quizzes?

• Research Question 2: To what extent does the use of first person question phrasing affect student perceptions of quizzes?

• Research Question 3: To what extent does the use of first person question phrasing affect student perceptions of accounting?

Population

The population was comprised of students enrolled in four sections of Introduction to Financial Accounting and four sections of Introduction to Managerial Accounting during the Fall 2017 and Spring 2018 semesters at a public university in the Northeastern United States. These courses are required for business majors at most colleges and universities.


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Instruments and Collection of Data Related To Student Performance

Two pop quizzes in each course were used to assess the effect of question phrasing on student performance. Students were not given advanced notice regarding what dates the quizzes would be administered. When each quiz was presented, they were delivered as closed book, in-class quizzes. A twenty-five minute time limit was enforced for each quiz.

Questions and content included in each quiz were identical except one version’s questions were phrased from a first person perceptive and the other version’s questions were phrased from a third person perspective utilizing generic “textbook” descriptors.

The first pop quiz was given to all participants on the same day of the ninth week of each semester. The second round of quizzes were delivered one week later during the tenth week of each semester. The order of the quiz versions was alternated to eliminate any effect that the sequencing of quizzes could have on student performance. For example, during the first round of quizzes, one section of Financial Accounting students received the third person/textbook quiz and the other section of Financial Accounting students received the first person quiz. During the section round, each section received the alternate version of their respective quiz. Neither general grades nor specific answers for the first pop quiz were discussed or publicized before the second pop quiz. Only students who took both quizzes were included in the results in an effort to compare performance at an individual level and control for moderator variables.

The same instructor taught each of the four sections of Financial Accounting which were included in this study. The quizzes administered to Financial Accounting students were comprised of ten questions. Nine questions contained transactions for which students were required to prepare journal entries. The tenth question required students to calculate the total revenue earned from the nine transactions according to accrual based accounting. The third person/textbook quiz contained transactions that related to a fictitious company called “Red River, Inc.” The first person quiz contained transactions related to a tutoring business where the reader was situated as the owner. Each version’s question structure and order was the same except for the difference in perspective. The correct answers, in terms of account names, were the same for both quizzes.

The author taught each of four sections of Managerial Accounting included in this study. The quizzes administered to Managerial Accounting students were comprised of seven questions. The first question provided students with five cost descriptions and asked them to identify each expense as variable or fixed. The second and third questions related to the computation of product costs using activity based costing. The

remaining questions related to cost-volume-profit relationships. The third person/textbook quiz utilized fictitious and real corporations in the questions. The first person quiz situated students as owners of a business which produced study guides for various college courses. Each version’s question structure and order was the same except for the difference in perspective.

Instruments and Collection of Data Related to Student Perceptions

After students submitted each quiz, they were provided a hard copy questionnaire to complete. Confidentiality was maintained by using a unique identifier rather than using identifying information such as students’ names. Only paired samples were included in the results. The questionnaire consisted of two sections and also gathered demographic data including gender, age, GPA, class level, and major.

One section was comprised of five statements designed to assess students’ perceptions of the quizzes as assessment tools. This portion of the instrument was based on student feedback gathered in a pilot study focus group and input from another experienced accounting professor. Students rated their agreement with the following statements using a five-point Likert type scale with 5 representing strongly agree to 1 representing strongly disagree:

• The questions made sense • I relate to the situations presented in the questions • The questions sparked my interest • I understand the material in the quiz • The questions helped me demonstrate my knowledge

The other section of the questionnaire included six statements intended to measure students’ perceptions of accounting immediately following each quiz. These statements were driven by studies which find students perceive accounting as boring and irrelevant (Lehman, 2001; Picard et al., 2014; Stivers & Onifade, 2014). These perceptions are commonly formed based on students’ experience in the first accounting course and heavily influence students’ decisions related to their major and career pursuits (Accounting Education Change Commission, 1992; Geiger & Ogilby, 2000; The Pathways Commissioners, 2012). Accordingly, six statements were created to assess the association between different quiz versions and students’ perceptions of accounting. Students rated their agreement with the following statements using a five-point Likert type scale with 5 representing strongly agree to 1 representing strongly disagree:

• Accounting is interesting • Accounting is relevant to my career aspirations • Accounting is just a lot of rule memorizing which does

not require in depth critical thinking (reverse scored for construct)

• I like accounting


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• Accounting is boring (reverse scored for construct) • Accounting skills can add value to departments outside of

accounting (such as sales, marketing, production, supply chain, HR) and help in making strategic decisions

Limitations

The population was limited to undergraduate students enrolled in one university. The advantage to using this approach was consistency in instruction and timing. Results are restricted to the first financial accounting and managerial accounting courses. This limits the generalizability of results to other accounting courses and disciplines. In addition, the experiment only tested select topics from typical financial and managerial accounting courses. The quizzes are were not meant to be representative of all learning objectives. Generalizability of the results to other topics or learning objectives within the courses under study may not be appropriate.

RESULTS Demographics The sample consists of 187 undergraduate students who took both versions of the quizzes. 71 matched pairs were enrolled in Financial Accounting and 116 were enrolled in Managerial Accounting. 184 of these matched pairs completed post-quiz questionnaires which collected demographic data and measured their perceptions of the quizzes and accounting. Forty-six percent were female and 54% were male. The mean age was 20.24 years. The sample included 22 seniors, 47 juniors, 82 sophomores and 33 freshmen with a mean GPA of 3.06.

Quiz Scores

Total quiz scores. There was a significant difference in scores for the first person pop quiz (M=.7607, SD=.2053) and third person/textbook pop quiz (M=.6944, SD=.2218); t(186)=-5.188, p = .000. One hundred out of 187 students performed better on the first person quiz, 41 did worse and 46 received the same score on both versions. First person quiz scores were higher than third person/textbook quiz scores regardless of major.

Financial Accounting quiz scores. There was a significant difference in scores for the first person pop quiz (M=.7013, SD=.2176) and third person/textbook pop quiz (M=.6110, SD=.2409); t(70)=-6.129, p = .000. Forty-eight financial accounting students performed better on the first person quiz, 11 did worse and 12 received the same score on both versions. First person quiz scores were higher than third person/textbook quiz scores for all majors except International Business which only included one student.

Managerial Accounting quiz scores. Managerial Accounting students’ first person pop quiz scores (M=.7972, SD=.1892) were significantly higher than their third person/textbook pop quiz scores (M=.7455, SD=.1932); t(115)=-2.8, p = .006. Fifty-two Managerial Accounting students performed better on the first person quiz, 30 did worse and 34 received the same score on both versions. First person quiz scores were higher than third person/textbook quiz scores for all majors except Marketing majors whose average third person/textbook score exceeded the first person version by 0.013%.

Perceptions

Overall perceptions of quizzes. Overall perceptions of the first person quizzes (M=3.53) were more favorable than perceptions of the third person / textbook quizzes (M=3.48), although the difference was not statistically significantly different. Mean ratings related to the first person quizzes exceeded mean ratings of the third person / textbook quizzes on four out of five questions. “The questions sparked my interest” resulted in the largest number of students who rated a question differently on each version of the quiz. After taking the first person quizzes, 101 students agreed with this statement to higher degree when compared to their ratings of the same question after taking the third person / textbook quizzes. The largest variance in terms of mean rating was “I relate to the situations presented in the questions”: 3.17 relative to the first person quizzes and 3.04 relative to the third person / textbook quizzes. Students reported slightly lower agreement with “The questions helped me demonstrate my knowledge” relative to the first person quizzes.

Overall perceptions of accounting. Overall perceptions of accounting after taking the first person quizzes (M=3.38) exceeded students’ perceptions of accounting immediately following the third person / textbook quizzes (M=3.32). Mean ratings after taking the first person quizzes were higher for four out of the five questions when compared to mean ratings post third person / textbook quizzes. “Accounting is boring” resulted in the same ratings for both versions of quizzes. The differences in student perceptions of accounting were not statistically significantly different. “I like accounting” and “Accounting is interesting” demonstrated the largest difference in mean scores with post first person quiz ratings exceeding post third person / textbook quiz ratings by 0.09. “Accounting is relevant to my career aspirations” was the largest difference in terms of the frequency of students that rated a question differently on both versions of the quizzes. Ninety-two students agreed with this statement to higher degree when compared to their ratings of the same question after taking the third person / textbook quiz.

Perceptions by Course

Financial Accounting students’ perceptions. Overall, students enrolled in Financial Accounting rated the first person quiz


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(M=3.34) higher than the third person / textbook quiz (M=3.25), although the difference was not significantly different. These students rated four out of five questions higher for the first person quizzes. Mean ratings for “The questions helped me demonstrate my knowledge” were the same for both versions of the quiz. “The questions made sense” demonstrated the largest difference in mean ratings a first person quiz mean rating of 4.10 and third person quiz mean rating of 3.95. “The questions sparked my interest” was the largest difference in terms of the frequency of students that rated a question differently on both versions of the quizzes. Forty-three students agreed with this statement to higher degree after the first person quiz when compared to their ratings of the same question after taking the third person / textbook quiz.

Students enrolled in Financial Accounting had more favorable perceptions of accounting immediately following the first person quiz with a mean overall score of 3.13 and 3.04 following the third person / textbook quiz. All five individual questions related to perceptions of accounting were rated higher immediately following the first person quiz compared to those following the third person / textbook quiz. The mean ratings for “Accounting is relevant to my career aspirations” showed the largest difference for an individual question with a 3.44 average rating after the first person quiz compared to a 3.30 mean rating after the third person / textbook quiz. “Accounting is relevant to my career aspirations” was the largest difference in terms of the frequency of Financial Accounting students that rated a question differently on both versions of the quizzes. Thirty-eight students agreed with this statement to a higher degree after taking the first person quiz.

Managerial Accounting students’ perceptions. Managerial Accounting students’ perceptions of the first person quiz were similar overall (M=3.63) to perceptions of the third person / textbook quiz. “I relate to the situation presented in the questions” showed the largest difference in mean rating for any individual question with an average rating of 3.35 relative to the first person quiz and 3.25 relative to the third person / textbook quiz. Consistent with Financial Accounting students, “The questions sparked my interest” was rated higher than any other question after Managerial Accounting students took first person quiz (58) when compared to their ratings of the same question after taking the third person / textbook quiz. “The questions helped me demonstrate my knowledge” was the only question which averaged a higher mean rating for the third person / textbook quiz.

Overall, Managerial Accounting students reported more favorable perceptions of accounting following the first person quiz (M=3.52) as opposed to overall perceptions following the third person / textbook quiz (M=3.48). The difference was not statistically significantly different. The question which demonstrated the largest variance between first person and third person / textbook quizzes was “I like accounting”. The

average rating for this question was 3.27 for the first person quiz and 3.15 for the third person / textbook quiz. Consistent with students enrolled in Financial Accounting, “Accounting is relevant to my career aspirations” demonstrated largest difference in terms of the frequency of Managerial Accounting students that rated a question differently on both versions of the quizzes. Fifty-four students agreed with this statement to a higher degree after taking the first person quiz when compared to their ratings of the same question after taking the third person / textbook version.

DISCUSSION

This study adds a unique approach to the evaluation of factors which may stimulate interest and enhance learning outcomes. It is the first study to specifically examine point of view as a variable affecting student performance and perceptions. The study focuses on two of the most critical courses in accounting: the first Financial Accounting and Managerial Accounting courses. These courses heavily influence students’ perceptions of the profession which ultimately impact their decisions to pursue accounting as a major and career. The experiment was designed to control for differences between participants by employing a within-subjects design. The population is drawn from four sections of each course over two semesters.

Perceptions of Quizzes

While not statistically significantly different, students rated the first person quiz higher on four out of five categories. The results suggest that the allusive “interest” described by Nolen (1988) was stimulated to a higher degree through questions phrased from the first person point of view. The statement demonstrating the largest difference in mean ratings was “I related to the situations presented in the questions” with mean ratings of 3.17 and 3.04 for the first person and third person / textbook quizzes respectively. Personal relevance and interest seem to go hand in hand. Educators contend that relating material to students’ lives stimulates an interest which promotes deep learning (Darby, 2008; Harvey & Averill, 2012; Mtetwa et al., 2010). Consistent with students reporting a higher level of relatability to the first person quiz, 101 students rated “The questions sparked my interest” to a higher degree after taking the first person test when compared to their responses to the same question after taking the third person.

When viewed by course, the first person ratings relative to “I related to the situations presented in the questions” and “The questions sparked my interest” also exceeded students’ ratings for the third person / textbook quizzes by a larger margin than other statements. This implies that increased interest and perceived relatability relative to the first person quizzes are not restricted to specific course material. The Financial Accounting quiz content was centered on journal entries while the Managerial Accounting quiz content focused on product


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costs and cost-volume-profit analyses. While these findings are not generalizable to upper level accounting courses, the results are applicable to both accounting principles courses.

Perceptions of Accounting

Students’ post-quiz perceptions of accounting were more favorable after the first person quizzes, although the difference was not statistically significant. The two questions with the largest mean differences were “Accounting interesting” and “I like accounting”. Such results, in courses where students often have unfavorable experiences (Chen et al., 2004; Tickell et al., 2012), are encouraging. Research consistently shows that the first courses in accounting are instrumental in the formation of students’ perceptions of accounting (Nelson et al., 2008; The Pathways Commissioners, 2012). These perceptions ultimately can impact students’ choice of major and career goals. Ninety-two students rated “Accounting is relevant to my career aspirations” to a higher degree after taking the first person quiz when compared to their ratings post third person / textbook quiz. In my experience as an accounting educator, a common question asked by students is “Why do all business majors have to take accounting courses”. The results from this study indicate that students perceive accounting to be more relevant to their careers after viewing business situations from a first person point of view.

In addition to academic performance, the potential positive impact of perceived relevance could have favorable effects on the profession. Favorable perceptions following the first person quiz were found for both Financial Accounting and Managerial Accounting students. This comes at a critical period when qualified entry level accountants are needed to fill a soon-to-be void left by retiring baby boomers. In addition, the favorable perceptions of accounting were consistent across major. It is important for all business majors, regardless of specific concentration, to understand the importance of accounting outside of public accounting. Methods which can help all business students appreciate the relevance of accounting allows them to accurately view the current role of accounting as “business partner” rather than “bean counter” (“Institute of Management Accountants,” n.d.).

Student Performance

Overall, students achieved significantly higher scores on the first person versions of the quizzes. Every major included in the study achieved higher quiz scores on the first person versions. The two courses in this study are challenging to teach considering the variety of majors which typically comprise them. Instructors are called upon to provide a solid foundation for accounting majors while simultaneously providing non-accounting majors the tools required to be successful in their chosen field. If academic performance indicates professional preparedness, the results may have implications for employers who are struggling to find talent

that meets their expectations.

First person quiz scores were significantly higher in both courses, with Financial Accounting demonstrating the larger difference. Every major enrolled in Financial Accounting earned a higher average quiz score on the first person version except for International Business which was represented by only one student. Every major enrolled in Managerial Accounting achieved a higher mean score on the first person quiz except Marketing majors whose score on the third person / textbook quiz exceeded the first person quiz by 1%.

A focus group was conducted after the experiment to provide a deeper understanding of Financial Accounting students’ experiences with the two quizzes. Four students participated in the focus group. Two performed better on the first person quiz, one performed better on the third person/textbook quiz, and one had the same score on both versions of the quiz. When asked to describe their experiences taking both versions of the quiz, three students described going through the first person quiz with more confidence, less anxiety, or less intellectual strain. This sensation seemed to have different impacts on student performance. One student noted that he overlooked a mistake on the first person quiz because he did not review his answers before submitting the quiz. In contrast, he said he did review the third person/textbook quiz before submitting it because he did not feel as confident in his answers. Another student described struggling with homework problems from the textbook as “not knowing what side I am on”. This student found the first person phrasing beneficial in helping her understand which party to account for. For example, she said the first person perspective made it “easy” to think about which party was increasing liabilities in a question that related to borrowing money from a bank. Overall, students described the experience with the first person quiz as more natural or intuitive. Under the first person scenario, it seemed they tried to account for transactions. Under the third person/textbook scenario, the researcher inferred that they tried to give the right answers.

Feedback from students also provided a possible explanation related to students’ ratings of “The questions helped me demonstrate my knowledge”. Students consistently rated this question slightly lower for the first person quiz when compared to the third person quiz. This finding is counterintuitive considering their actual performance on first person quizzes exceeded their mean performance on third person quizzes by a significant degree. One student found the first person questions to be less familiar than the homework he had practiced in the publisher’s online learning management system. He noted that the different perspective, although more relatable, negatively impacted his performance. This student readily admitted that he had taken a surface learning approach to the class where he had memorized patterns of questions and trigger words from the textbook. This differs from the “meaning making” which Koedinger and Nathan (2004)


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contend can be the product of applying new material to familiar representations.

Feedback from students indicate that they may have employed more intuitive or critical thinking strategies, even unknowingly, to solve the problems phrased from the first person point of view. If this is the case, an argument could be made that a similar method of teaching could not only facilitate deeper learning, but also this method of testing may allow instructors to assess learning more accurately. Most accountants, including those who begin their careers in public accounting, ultimately wind up in managerial accounting roles (Siegal, Sorensen, Klammer, & Richtermeyer, 2010). These types of accounting roles view situations and problems from an insider’s perspective (the first person point of view). This first person perspective mirrors real world scenarios that most business students will encounter after graduation, including accounting majors.

REFERENCES

Accounting Education Change Commission. (1990). Position statement number one: Objectives of education for accountants.

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SENSE OF COMMUNITY IN RACIALLY DIVERSE, POST-INDUSTRIAL READING PENNSYLVANIA

Lisa Wilder Department of Business, Accounting and Economics

Albright College Reading, PA 19604

ABSTRACT How bonded we are to our community has a significant influence on quality of life. In 2017, a sample of Reading Pennsylvania households completed the SCI (sense of community index) survey to measure their sense of connection to their neighbors and feelings of neighborhood empowerment and ownership. Participants also indicated their race, age, tenure in the neighborhood and family characteristics. It is proposed that sense of community will be lower in neighborhoods with greater diversity and those with greater renters. It is commonly found in the literature that demographic and neighborhood characteristics often dominate economic characteristics. “Community connectedness is not just about warm fuzzy tales of civic triumph. In measurable and well-documented ways, social capital makes an enormous difference in our lives...Social capital makes us smarter, healthier, safer, richer, and better able to govern a just and stable democracy.” – Robert Putnam The City of Reading Pennsylvania has ridden the wave of industrialization in the United States Mid-Atlantic. In the days of the Revolutionary War, the production of iron in the local area exceeded that of all of England. The Reading Railroad further fueled early industrialization connecting the coal mines of the region with Philadelphia, New York and other East coast cities. Local manufacturing was also a key to Reading’s past including the large hosiery mills and other small manufacturers of the turn of the century. Neighborhoods of Victorian row homes harken back to the age of mills and the wealthy industrialists. Ask those growing up in the 1960’s to 1980’s though what they remember about Reading and they will tell you about the trips to the Reading outlets. As the large hosiery and clothing mills began to lose business to intense competition, the owners dove into retail outlets. Once just a way to clear excess goods by offering them at a discount to employees, the outlets became a pilgrimage for many middle class families from hours away. But, as many good ideas go, innovation bred competition and the outlets of Reading have gone the way of the iron foundries and railroad and the hosiery mills. Reading today is a microcosm of the post-industrial US city. In 2011, The New York Times named Reading the poorest city of its size in the nation (Tavernise, 2011). With the closing of

two major skilled manufacturers, Lucent Technologies and Dana car parts manufacturing, and increasing poverty in the city, Reading moved from the 32nd poorest in 2000 to edge out Flint Michigan for the poorest city in 2011. However, unlike many cities of its type, Reading is growing (PA State Data Center, 2010). Population growth in Berks County (which includes both the City of Reading and surrounding suburban and rural lands) was 3.6 times the rate of Pennsylvania and the Pennsylvania State Data Center projects Berks County to be one of the fastest growing areas of the state through 2030, with a projected growth of 31.7% between 2010 and 2030 (second in Southeastern PA to Chester County and only following behind the astronomical growth of the Northeast Shale oil region). Pennsylvania as a whole is expected to grow by a total of 7.4%. In Reading PA and Berks County, we see the changing face of America. A combination of agricultural and manufacturing jobs, along with reasonable housing costs compared to major East coast cities has brought waves of immigrants to the area. Writing in Five Thirty Eight in 2016, Clare Malone notes that “By 2044, the U.S. will be a majority minority nation. By 2060, 29% of the population will be Latino”. She continues “nestled in the center of Berks County, Reading had a Latino population of 60 percent in 2014, up 37 percent from 2000. It is a city living, at least demographically, in the future” (Malone, 2016). Understanding the City of Reading, with its common challenges and demographics of the future is a route to better understanding the changes taking place in much of the country. With a deeper understanding of community dynamics within the city as well, we are better posed to step up and make sensible changes in guiding Reading toward a brighter future. In the spring of 2015, the City of Reading, under the direction of the Mayor’s office, and a non-profit organization titled ReDesign Reading Community Development launched the STAR Community Index initiative in the City of Reading. This research is an outgrowth of the STAR Communities project and examines community dynamics and satisfaction using an externally developed and validated instrument, the Sense of Community Survey. It is intended that this current study will be a pilot for the City of Reading to undertake a larger scale Sense of Community study and continue in its efforts to improve upon its Star rating. In Section 1, I discuss the Star communities program and its


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potential for a city like Reading. In Section 2, I examine the theoretical foundations of Sense of Community and, in Section 3, its measurement. Results presented in Section 4 consider the role of economic factors with particular emphasis on community based influences. Cautions, direction for further study and general conclusions follow.

SECTION 1. STAR COMMUNITIES

“There can never be a goal without a goalpost”. Ikechukwu IzuaKor

In 2007, ICLEI- Local Governments for Sustainability, the US Green Building Council and the Center for American Progress launched a project called the Green City Index. At the same time, they created the concept for a STAR community index. By 2010, they released 81 goals that would enable communities to monitor their progress along with guiding principles for a ratings system. In 2012, the first STAR community rating index was released with 7 goal areas and 44 objectives that guide communities toward sustainable development. As stated in the STAR Community’s tag line, “Set Goals, Measure Progress, Improve Your Community”. The STAR rating system was created by local governments for use by local governments in measuring performance on numerous metrics and tracking their performance over time. Standards have been developed the meet STAR rating board objectives which are measures that are Relevant, Feasible, Timely, Useful, Systematic, Reliable and Valid (Star Communities, 2018). The STAR project provides ample opportunity for institutions of higher education in an area to collaborate with local government in assessing the community standards and developing plans for improvement. Faculty and students can offer the analytical support necessary to measure some of the 44 metrics while providing a natural laboratory for research and student learning. The areas of focus measured in the STAR rating system, as shown in Figure 1, are:

• Built Environment • Climate and Energy • Economy and Jobs • Education, Arts & Community • Equity & Empowerment • Health & Safety • Natural Systems • Innovation & Process

Under each of these areas, the STAR rating system defines up to 7 measures of performance. For the purpose of this paper, the metric of interest is in the area of Education, Arts & Community and specifically focused on EAC-2, Community

Cohesion. At the time of Reading’s application for STAR community status, no data was available for Sense of Community thus no submission was made for standard EAC-2. The current paper is motivated to address one of the data needs of the STAR Communities project, that of a measurement of sense of community in Reading PA based on the direct experience of community residents.

SECTION 2. THEORITICAL FOUNDAITONS OF

COMMUNITY COHESION Our place in our community and our satisfaction with that place have a strong influence on our happiness overall. Sarason (1974) defines sense of community as “the perception of similarity to others, an acknowledged interdependence with others, a willingness to maintain this interdependence by giving to or doing for others what one expects from them, and the feeling that one is part of the larger dependencies and stable structures.” The question is, why should we, as economists, care about the sense of community? Where we live is often a constrained choice limited by employment, social structures in increasingly complex families. At the same time, our housing is one of our largest lifetime investments and those we spend our time with are equally a significance investment in our social wellbeing. As such, both the components of a sense of community and the determinants of a strong sense of community have been greatly under examination and there is even a new field of economics termed happiness economics which considers more than the traditional structures of growth. Instead, it examines multiple, potentially conflicting, measures of performance. As economists, we also can ask if there are any benefits to a sense of community. Does it fulfill a role in society other than making us feel better? Research on altruism has pointed out that our satisfaction is not limited to our own personal wellbeing and even Adam Smith in “The Theory or Moral Sentiments” expressed that

“Though our brother is upon the rack, as long as we ourselves are at ease, our senses will never inform us of what he suffers. They never did and never can carry us beyond our own persons, and it is by the imagination only that we form any conception of what are his sensations...His agonies, when they are thus brought home to ourselves, when we have this adopted and made them our own, begin at last to affect us, and we then tremble and shudder at the thought of what he feels.” The Theory of Moral Sentiments (1759)


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Robert Putnam, noted author of Bowling Alone: The Collapse and Revival of American Community (2000) notes that societies with generalized reciprocity operate more efficiently as he puts it “in the same way that money is more efficient than barter”. If one must be resistant and cautious in the face of rent-seeking behavior of others, we may see a waste of time and resources. Social cooperation also breeds positive externalities in the traditional forms of shared benefits, access to needed resources and information sharing. In addition, sense of community can be a useful tool in community economic development, both formal and informal. The World Bank has devoted research time to exploring the impact of community building on the effectiveness of its programs. They point out that “a growing body of empirical evidence suggests that density of social networks and institutions, and the nature of interpersonal interactions that underlie them, significantly affect the efficiency and sustainability of development programs” (Mansuri and Rao, 2003). The economic impact of sense of community can be linked to many aspects of society and improved economic performance. These include better local government (Putnam, 2001; Rice, 2001), more volunteerism (Wilson and Musick, 1997), lower crime (Messner, Baumer and Rosenfeld, 2004), and overall better economic outcomes (Flora, 1997). However, in some of these cases, reverse causality is a worry (Iyer, Kitson and Toh, 2005). For example, lower crime in a community may increase active taking place on sidewalks and in parks, leading to stronger social networks among residents. What does it mean to be a part of a community? An interesting view can be traced back to Helene Sjursen writing in the Journal of Common Market Studies (2002) who defines 3 reasons that unions come together. We can think of these three motivations as well in terms of the ties that bind communities together. First, unions form when the solve problems. The participants in such cases may have little in common other than the desire to affect a change and the motivation to do so. In the second case, unions form when participants have common views on important values. For example, religious or social action groups are often formed by people who share common mindsets and beliefs about rights, not just to solve a problem, but because of a unified belief. Finally, unions also form among participants with a similar culture or fundamental value system. Shared traditions or experiences may bring people together who have little in common in other aspects of their lives. For example, ethnic festivals bring together strangers with a common desire to express their cultural traditions and, in the case of family reunions, one may drive hours to sit in a room with people who are strangers and relatives at the same time. Knowing why community forms can help us to identify in theory the demographic and neighborhood characteristics

most likely to lead to strong social ties and a sense of community. Research in economics, sociology and psychology has identified several factors. Gender matters. Females tend to show deeper levels of trust and engagement socially (Shapiro and Mahajan, 1986). Racial heterogeneity within a neighborhood has been often examined as a source of declining social cohesion (Alesina and Ferrara, 2000; Costa and Kahn, 2003) while others note that including neighborhood characteristics diminishes the role of race (Letki, 2008) and measurement error of the sense of community when cultural differences are present may also explain some influence of race (Coffman and BeLue, 2009). In addition, household characteristics may trigger a greater or lesser sense of community. These include planned or actual housing tenure and ownership and the presence of children in the household (Leviten-Reid & Matthew, 2017). Specifically, those who intend to live in a neighborhood for less time or who are renters are less likely to form social networks and the presence of children often promote interactions between neighbors, whether at the bus stop or through school functions. Neighborhood characteristics also have an impact on sense of community. More densely populated areas have been noted to create a sense of anonymity as shown in both surveys and in experiments (Levine, Martinez, Brase and Sorenson, 1996). Other factors in neighborhood relationships could also include crime, income and heterogeneity – that is, it is not just your income or your race that matters, but instead how your income or race are related to the race and income of those around you).

SECTION 3. MEASUREMENT The Sense of Community has been studied in various venues including traditional studies within various geographies – countries and cities in particular – as well as among online communities, schools and universities, workplaces, sports teams and clubs. The survey instrument itself by McMilian and Chavis (1986), lends itself well to these various environments as it allows users to comment on their participation as a part of the group and the ability of the group to work together. As with any survey instrument, it is important that the tool assess what is intended. This is one of the purposes of using this tool in the current study as it has been validated externally. The Sense of Community Index (SCI) is composed 4 subcategories which contribute (in an equally weighted index) to the overall sense of community experienced by a respondent. Spinkio (2013) points out why each of these factors enhances our sense of community. The constructs measured in the SCI are:

1. Membership – A feeling of belonging and personal


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relatedness 2. Influence – A sense of mattering, when they have an

influence on the community and they are concerned with and aware of the influence of others in the community on them

3. Integration and the Fulfillment of Needs – The ability of the community to meet the needs that the respondent hopes for

4. Shared Emotional Connection – A personal connection to each other, based on interactions

The specific questions of the SCI survey are provided in Figure 2. Questions 6 and 8 are reverse scored and responses to the questions in the basic form of the SOC are yes and no. A more detailed SOC survey has been used in some contexts as well but this is the standard format. Membership is measured by the sum of the responses to questions 4, 5 and 6 (recognition of others, feeling at home and being known by my neighbors). Influence is measured by the sum of questions 7, 8 and 9 (I care what my neighbors think, I have influence over life here, if there is a problem we can solve it). To measure how well a neighborhood meets the needs of the resident, questions 1, 2 and 3 are summed (It’s a good place for me to live, People here share the same values and we want the same things). Finally, the sense of emotional connection is based on questions 10-12 which ask how long the person expects to live in the neighborhood, how important the neighborhood is to them and if people generally get along in the neighborhood. In addition to the SCI 1 instrument, additional information was gathered including the responses to open ended questions about the best and worst aspects of their neighborhood and the City of Reading as a whole. Demographic information was also gathered. Additional questions asked in the survey are listed in Figure 3. In Spring of 2017, an anonymous email survey was conducted of residents in what is defined as Greater Reading Pennsylvania. Typical of Pennsylvania, Berks County is composed of numerous municipalities, many of which share the same school district but different local governments and, in many cases, public service departments. There are 72 municipalities in Berks County with the largest being the City of Reading with a population of 88,000 people. Since Reading City is the core of economic activity of Berks County, the survey was not limited to just the City of Reading. Email addresses for a sample of 1800 households were purchased through a data syndicator who validated that the email addresses were for residents who were over the age of 18. While not ideal, the purpose of this study is to form a pilot and encourage a more comprehensive sense of community study in the future for Greater Reading.

SECTION 4. EMPIRICAL RESULTS A total of 105 households responded with complete survey responses. While only a 7 percent response rate, this is not unusual for surveys of this type. It is hoped that this study will stimulate interest in a more comprehensive census of the sense of community in Reading by the City government. Responses were as expected in terms of age, presence of children and renting. One difference compared to the City as a whole is in the race variable. The overall percentage Hispanic or Latinx was 16% overall in Greater Reading which is statistically indifferent from the 20% Hispanic in Berks County. However, the percentage Hispanic in the City of Reading in our survey was 47% compared to the 63% according to the Census Bureau. Greater outreach in the Hispanic population will be necessary to ensure that future Sense of Community studies are representative. Weighting of the responses by race is possible but has not been applied to this small sample. Overall, the Sense of Community reported was 7.96 on a scale of 1-12. Figure 4 shows the mean and standard deviation for Conditions Overall (the higher the average, the better the conditions), each construct of Sense of Community, Sense of Community overall and Importance of Sense of Community. In terms of the Overall Sense of Community, the maximum score of any subcategory is 3. The highest was meeting needs and the lowest was influence. The low score on ability to influence conditions is not surprising as Reading has undergone very difficult years in terms of local government effectiveness with recent political controversies. Most people considered a strong sense of community was important with a mean of 2.32 out of 3. The mean for the question “Are conditions better than 5 years ago (=3), the same as 5 years ago (=2) or worse than 5 years ago (=1) was disheartening. The p-value for a One Way Anova to test for differences between groups is reported also in Figure 4. In terms of age groups, younger respondents reported worse conditions in the City overall but this difference was not significant. Younger people reported weaker connection to their neighborhood and this difference was statistically significant with a p-value of .001. Younger people also reported a lower overall sense of community with the maximum score of 8.86 among the older adults (ages 45-65) followed by those over 65. Since older residents may have longer tenure in their neighborhood, this result can be understood. Renters, as expected, demonstrate lower connections in their community and also a lower total sense of community compared to those who own or are paying a mortgage on the property. Renters may anticipate not living in a home for long. In other measures of sense of community including needs, membership and influence, renters also show a lower sense of


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community, though the difference is not statistically significant. Those with children living in the household demonstrate a slightly lower overall sense of community than those without children in the home though the result is not significant at 90% confidence level. This may be a spurious relationship as a result of differences in the age of the householder. The result is counter to the intuition which states that those with children are more likely to be participating in school or community events which would connect the households. Instead, we see a significantly lower score for connections and the difference in means for influence is statistically significant at a 90% confidence level. Controlling for age will make a difference in this result. Finally, Figure 4 shows the differences in the dependent variables on the basis of the race of the respondent. Overall conditions compared to 5 years ago were rated the worst by whites, followed by Hispanics though the differences are not significant. Likewise, there is no overall difference in Sense of Community by race with Blacks showing an average of 9.09 compared to 8.03 for whites and 7.41 for Hispanics. The only statistically significant difference again is in the Connections construct where the connections where highest for whites (2.23 out of 3) compared to Hispanics (1.59 out of 3), Blacks (2.18 out of 3) and other races (1.43 out of 3). All demographic groups rated the Sense of Community as important to them. Significant variation occurs primarily in the Connections subcategory of Sense of Community which asks how long they expect to live in their neighborhood, if their neighborhood is a good place for them to live and if it is important for them to live in that neighborhood. Since age varies by demographic group, a more complete analysis is necessary to assure that racial differences exist. Anova analysis of these 3 questions (important to live here, Expect to live here a long time and people get along) shows only a weak (90% confidence level) statistically significant difference among races in the Important to live on this block question (Anova p-value = .082). In analyzing this question by age and renter/owner, both the important to live on this block and expect to live here a long time showed statistically significant differences among groups at the 99% confidence level which indicates a more transient lifestyle for younger and renting households. For those with children in the household, these two questions again showed differences at the 99% confidence level for important to live here and 99% confidence level for expect to live here a long time with parents indicating that they would not live there as long. One important result is that no demographic group showed a statistically significant difference on the connections question about getting along with their neighbors.

To consider how age, renting and presence of children interact, a regression analysis is conducted. Due to the small sample size and high variability, this is really a model of how regression could be particularly useful in a larger study. In addition, data for the census tract of the household is included in the regression to account for the level of income inequality in the neighborhood with the hypothesis that neighborhoods with greater inequality will have lower levels of social cohesion and therefore lower sense of community scores. In addition, since the Sense of Community survey did not ask about income, the inclusion of Census tract data allows to us control for, though imperfectly, the influence of income and level of crime in a community. Sense of Community is first estimated using the variables shown in Figure 3. That is,

SOCi = β0 + �βj

3

j=1

Ageji + β4Renteri + β5Tenurei

+ �βjiRacei

7

j=6

+ �βjiChildreni + εi

10

j=8

(1)

And the results are shown in Figure 5. As shown by the p-values, Hispanics have a lower overall sense of community when other factors are considered with a 95% confidence level. In addition, those who are between the ages of 45-65 have a higher sense of community. The presence of children at any age and renting have the expected signs but are not statistically significant. Respondents represented 74 different census tracts and 6 different zip codes which allows us to gather information about average income, income inequality as measured by the Gini Coefficient and average crime rate (of many different forms). To be meaningful, the survey collection should be repeated with a census of all households in Reading so that a large enough sample can be gathered from each tract or zip code. This analysis is planned in the future if cooperation is obtained from the City.

CONCLUSIONS AND FURTHER RESEARCH Overall, this study demonstrates the use of the SCI survey in a small sample of households in one of America’s rapidly changing post-industrial cities. Reading is experiencing rapid demographic changes and suffers from the decline in manufacturing that is being experienced in many cities across the country. Its proximity to major immigration centers (New York City in particular), ease of transportation and low housing costs have created an immigration boom in the city unseen in many other Pennsylvania cities.


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Through a household survey administered in 2017, we see the largest determinants of sense of community are age (with younger households demonstrating a lower attachment to community), renters and race. Within sense of community, there were no differences in sense of influence over the community, which unfortunately was low for all groups or membership, where all groups indicated a uniform sense of inclusion. The questions demonstrating the greatest differences were importance of living in a particular block and, in a few cases, expectation of living long on the block. Once controls were instituted using multiple linear regression, those in the age group 45-65 showed the strongest sense of community and sense of community was significantly lower

in the Hispanic community. This result is similar to the literature however the literature points out that controlling for neighborhood income and other characteristics diminishes the role of race in sense of community studies. If the SCI was measured across the City of Reading through a more broad sample, then census tract characteristics including income, crime and income inequality would be incorporated into this study. It is the intention to offer this study as both useful information and as a pilot for a more detailed survey of community members so that census tract level data can be used to control for broader neighborhood characteristics.

FIGURE 1. STAR COMMUNITY CRITERIA


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FIGURE 2. MEASURING SENSE OF COMMUNITY (SCI) 1. I think my neighborhood is a good place to live 2. People in this neighborhood believe the same things are important 3. My neighbors and I want the same thing 4. I can recognize many people who live in my neighborhood 5. I feel at home in my neighborhood 6. Very few of my neighbors know me (reverse) 7. I care about what my neighbors think of my actions 8. I have almost no influence over what this neighborhood is like (reverse) 9. If there is a problem in this neighborhood, the people here can solve it 10. It is very important to me to live in this neighborhood 11. I expect to live in this neighborhood for a long time 12. People around here are willing to help their neighbors

FIGURE 3. ADDITIONAL QUESTIONS

1. Overall, are the conditions in the City (3) Better than they were 5 years ago, (2) the same as they were 5 years ago or (1) worse than five years ago?

2. My favorite thing about my neighborhood is (open ended) 3. If I could change one thing about my neighborhood, it would be (open ended) 4. The biggest challenging facing the City of Reading is (open ended) 5. My favorite thing about the City of Reading is (open ended) Demographic and Household Characteristics 1. Age of Householder (1=18-30; 2= 31-45; 3=46-65; 4=Over 65 2. Renter (1=Renter; 0 = owner occupied) 3. Tenure (number of years in this property) 4. Number of children in the home under age 5 5. Number of children in the home between the ages of 6 and 13 6. Number of children in the home between the ages of 14 and 18 7. Race (Caucasian, non-hispanic; Black, non-hispanic; Hispanic/Latinx; Other)


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FIGURE 4. OVERALL RESULTS

Conditions SCI - Needs

SCI- Member

SCI- Influence

SCI-Connect

SCI-Total Importance N

Overall 1.52 2.25 1.98 1.66 2.07 7.96 2.32 104 (0.91) (0.97) (0.99) (1.06) (0.96) (3.20) (0.79) 18-30 1.33 1.89 1.89 1.17 1.39 6.33 2.11 18 (0.91) (1.02) (0.96) (1.04) (0.98) (3.18) (0.90) 31-45 1.50 2.26 1.91 1.62 1.94 7.74 2.35 34 (0.86) (1.02) (0.90) (0.99) (0.95) (3.00) (0.92) 46-65 1.57 2.46 2.09 1.89 2.43 8.86 2.49 35 (0.92) (0.85) (1.07) (1.11) (0.85) (3.15) (0.56) 66+ 1.65 2.18 2.00 1.82 2.29 8.29 2.12 17 (1.06) (1.01) (1.06) (1.01) (0.77) (3.24) (0.78) Anova p-value 0.754 0.345 0.870 0.112 0.001 0.049 0.267 Owner 1.54 2.31 2.01 1.72 2.26 8.29 2.32 78 (0.94) (0.96) (1.01) (1.10) (0.87) (3.22) (0.73) Renter 1.46 2.08 1.88 1.50 1.50 6.96 2.31 26 (0.86) (1.02) (0.91) (0.91) (0.99) (2.96) (0.97) Anova p-value 0.712 0.297 0.568 0.368 0.000 0.065 0.943 No Children in House 1.49 2.32 1.98 1.87 2.34 8.51 2.30 47 (1.00) (0.96) (1.07) (1.03) (0.81) (3.24) (0.66) Children in House 1.54 2.19 1.98 1.49 1.84 7.51 2.33 57 (0.85) (0.99) (0.92) (1.05) (1.01) (3.12) (0.89) Anova P-Value 0.764 0.51 0.99 0.07 0.01 0.11 0.82 White 1.45 2.25 1.87 1.68 2.23 8.03 2.33 69 (0.95) (1.03) (1.07) (1.10) (0.86) (3.36) (0.61) Black 1.73 2.45 2.55 1.91 2.18 9.09 2.36 11 (0.90) (0.82) (0.52) (1.04) (0.98) (2.98) (1.12) Hispanic 1.53 2.29 2.12 1.41 1.59 7.41 2.12 17 (0.80) (0.77) (0.86) (1.00) (1.12) (2.74) (1.22) Other 1.86 1.86 1.86 1.71 1.43 6.86 2.57 7 (0.90) (1.07) (0.69) (0.76) (0.98) (2.79) (0.53) Anova P-value 0.592 0.651 0.177 0.667 0.020 0.439 0.606

Standard Deviations in parenthesis


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FIGURE 5. Dependent Variable: Total SCI.

Coefficient Std. Error P-value (Constant) 2.180 .234 .000 *** Age 18-30 -.046 .262 .862 Age 45-65 .461 .230 .048 ** Age 66+ .157 .301 .605 Renter -.013 .204 .948 Tenure -.010 .008 .190 Hispanic -.410 .221 .067 ** Black -.022 .247 .930 # under5 .144 .135 .289 # Children 5-13 .114 .097 .246 # Children 14-

.117 .131 .373

Rsq = .318


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