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Vol. 1, No. 15March, 2016

Printed ISSN: 2223-2257Online ISSN: 2225-0751

INTERNATIONAL JOURNAL OF

SCIENCE,

INNOVATION

NEW Technology

www.ijsint.org

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INTERNATIONAL JOURNAL OFSCIENCE, INNOVATION AND NEW TECHNOLOGY

Vol. 1, No. 15, March, 2016Printed ISSN: 2223-2257 - Online ISSN: 2225-0751

Editors Co-editorsProf. Dr. Kozeta SEVRANI Dr.Edlira MARTIRIProf. Dr. Fatmir MEMAJ Dr.Edlira KALEMI

Editorial Advisory and Academic Board Agni DIKA, University of South Eastern Europe, Macedonia Anita MIRIJAMDOTTER, Linnaeus University, Sweden Arjan GJONÇA, LSE, UK Artur BAXHAKU, University of Tirana, Albania Bashkim RUSETI, University of Tirana, Albania Betim ÇIÇO, Polytecnic University of Tirana, Albania Dhimitri TOLE, University of Tirana, Albania Edmond HAJRIZI, University for Business and Technology, Kosovo Fatmir MEMAJ, President of ASET, Albania Francesco PROTA, University of Bari, Italy Carles Gispert-Pellicer, Universitat Autònoma de Barcelona, Spain Giuseppe TARDIVO, University of Torino, Italy Gudar BEQIRAJ, Academy of Sciences, Albania Heinz-Dieter WENZEL, University of Bamberg, Germany Ilia NINKA, University of Tirana, Albania Iraj HASHI, Staffordshire University, UK Kozeta SEVRANI, University of Tirana, Albania Kurt MATYA, Vienna University of Technology, Austria Larry STAPLETON, Waterford Institute of Technology, Austria Lule AHMEDI, University of Prishtina, Kosova Marcus HUDEC, University of Vienna, Austria Marianna GRETA, University of Lodz, Poland Mehtap HISARCIKLILAR, Staffordshire University, UK Neki FRASHËRI, Research and Development Center, Albania Norbert JESSE, Dortmund University, Germany Peter KOPACEK, Vienna University of Technology, Austria Rodica PRIPOAIE, Danubius University Galaţi, Romania Silvana TRIMI, University of Nebraska, USA Sang LEE, University of Nebraska, USA Shanggeun RHEE, Kean University USA Vittorio NICOLARDI, University of Bari, Italy Zamir DIKA, University of South Eastern Europe, Macedonia

The International Journal of Science, Innovations and New Technology is published under the auspice of ASET (Albanian Socio-Economic Think Tank) in collaboration with the Department of Statistics and Applied Informatics, University of Tirana, and the Department of Computer Science, University of Durres. IJSINT is indexed in EBSCO Host databases.The views presented in the Journal present opinions of the respective authors. The views presented do not necessarily reflect the opinion of the editors, editorial and academic board or staff. All rights reserved by ASET. No part of this journal may be reproduced or used in any form or by any means without written permission from the publisher, except for noncommercial, educational use including teaching purposes.

Publisher: ASETRr."Ismail Qemali", Pall.27/1 Shk.2, Apt.13, P.O. Box 1506, Tirana, ALBANIATel/Fax: ++355 (4) 258 171, www.aset-al.com, E-mail: [email protected]

Correspondence and questions: E. Martiri: [email protected] E. Kalemi: [email protected]

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INTERNATIONAL JOURNAL OFSCIENCE, INNOVATION AND NEW TECHNOLOGY

Vol. 1, No. 15, March, 2016Printed ISSN: 2223-2257 - Online ISSN: 2225-0751

Table of Contents

1. Financing options and implications for a health insurance fund for Kosovo Edmond MUHAXHERI, Besnik BISLIMI 1

2. Possible disaster recovery site solutions for organizations in AlbaniaGjergji MULLA 9

3. Technological trends and the use of ICT by public entities, with a focus on e-Government and Interoperability Framework in KosovoKujtim GASHI, Kozeta SEVRANI 13

4. School Leader Self-DevelopmentIbrahim CAN KORKUT, Shyqyri LLACI 19

5. Risk management & analyses in the oil industryBlerta MJEDA 27

6. Evaluation of the models used to create vector space representation of wordsAlkid ADEMI 31

7. Analysis and comparison of Gauss and Gauss – Jordan methods and their algorithms, for the selection of linear equation systemFlorim IDRIZI, Burhan RAHMANI, Agon MEMETI 35

8. Cultural differences between public and private organizationsAlma SELMANI 39

9. Statistical literacy for decision makingArjana MISHA 45

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FINANCING OPTIONS AND IMPLICATIONS FOR A HEALTH INSURANCE FUND FOR KOSOVO

Edmond MUHAXHERI1, Besnik BISLIMI1 1American University in Kosovo,

Germia Campus, 21 Nazim Gafurri, Prishtina, 10000, Kosovo

AbstractIn this paper we present the logic behind the derivation of the contribution rate as set out in Kosovo’s law on health insurance. This work is a follow up from Bislimi and Muhaxheri (2012), and employs more recent data, thus providing a ten year funding strategy that complements the current tax-based system in Kosovo. Our analysis uses the most current economic data, and makes use of preliminary reports from Kosovo’s 2011 census. A number of scenarios are included, based under different economic outlooks. The structure of contributions is geared towards an equal (percentage) split between employers and employees, which allows for a great degree of solidarity, and is in line with the law.Keywords health insurance, universal healthcare, insurance fund.Jel classification codes I13, I15, I18

IntroductionAs the youngest country in Europe, coming at back end of former Yugoslav wars, Kosovo happened (and in many aspects still is!) at crossroads as to what is the best way forward for its people. Since declaring indepence in 2008, successive governments have promissed many reforms, including the transformations of its beleaguered health sector. Lack of political will, however has dictated in a negative impact on country’s medical indicators. In April of 2014, the Government of Kosovo passed a Law on Health Insurance. This law is the first meaningful step in achieveing a universal coverage that would provide the agreed package for all, and would increase solidarity since the poor would not face financial hardship from catastrophic health expenditures. Moreover, it is (indirectly) in line with United Nations Millendium Development Goals (MDG), whose eight goals include: eradicting extreme poverty; reduce child mortality; improve maternal health; and combat HIV/AIDS, malaria and othe diseases. This paper id based on Bislimi and Muhaxheri (2012)1, and it serves two purposes. First, it sets out the methodology developed, and then makes use of current data in order to test is robustness. The rest of the paper follows through a brief introduction of different financing methods that are typical for health sectors; next it details the data used and methodology employed in obtaing the different simulations; followed by scenarios under different economic conditions.

Health Sector FinancingIn general there are four basic methods of financing the health sector. The sector could be financed directly from the government budget (tax financed system – TFS); from health contributions (HIF); patient payments (co-payments and/or out-of-pocket payments); and donations. In most of the cases financing involves a combination of more than one of these methods, with the name depending upon the dominating factor of income. If the greatest part of income is generated from the budget (or local government), then the system is classified as a TFS. Several factors will determine the method of financing for health care adopted by a particular country. Amongst the most important factors are: level of national income, level of informal sector, tax evasion, country’s previous experience with different alternatives, administrative

1 Financial Sustainability of a Health Insurance Fund for Kosovo

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Journal of Science, Innovation and New Technology Vol. 1, No. 15 – March, 2016

capacities, cultural values, etc. However, the selection of one method does not provide a universal fit. There are countless cases when countries deciding to move from one financing system to another expected thet the new system would avoid shortcomings of the previous system. The success depends heavily on the sustainability of reforming policies. These policies can be successful only if they rely on the economic, political, social and cultural context of the country. In the majority of countries worldwide, the largest proportion of health spending comes from taxation. Respective domestic revenue agencies collect all taxes and then allocates them to different ministries at agreed proportions. If health spending (or most of it) is covered from taxation, then it is clear that resources dedicated for the health sector could increase if the volume of distributed cake is increased. Both TFS and HIF have a range of advantages, including the protection from catstrophic expenditure through risk pooling; thus allowing vulnerable groups to access health services. Direct or out-of-pocket payments are an important method of financing for health services in Kosova. According to some surveys, Kosovar families spend more than 80 million € (around 2% of GDP) annually for health care0. These payments can take mainly two forms:1. direct, officially sanctioned payments for health services2. informal payments to health staffOut-of-pocket payments do not involve any risk pooling thus increasing the exposure of households to catastrophic expenditures. Like other methods of financing for health care, direct financing has some advantageous and disadvantageous. One positive aspect of direct payments is hat they reduce overall costs of health care.

Contributions for health insuranceIn HIF, specific groups of population (employees, employers, self-employed) contribute to the fund, and the government covers the rest of costs0. Specific health packages are then bought with collected funds (from private or public providers), which is offered to all insured persons. People are then offered the option to buy private insurance or to cover individually the rest of health services demanded. Problems of fiscal nature are usually the main reason for governments to consider the introduction of insurance systems. Low level of GDP and the government’s inability to collect sufficient revenues, increase the attractiveness of HIFs0. Governments do hope that the implementation of HIFs will increase financial stability of the health sector and at the same time alleviate financial burden when used by vulnerable groups. Another important factor is the growth of acceptance by people for the new way of financing. Chances for acceptance growth depend on government’s ability to persuade its citizens of what will be included in the package, to convince the contributors that they will not have to offer informal payments when demanding health services. At initial stages there is a need for serious dedication and leadership by the government.

Data and MethodologyThe nature of this study requires the extensive use of statistics. Unfortunately, economic and social development with a healthy base of data represents an Achilles heel in Kosovo and other countries with a comparable level of national income. In Kosovo there is a lack of data in general, therefore most of information used are more estimates than actuals. Furthermore, there are contradictions about the level of GDP, income per capita, household’s spending for health, labor market statistics, and many more. As a consequence, while presenting scenario analysis for the possible implementation of HIF, we were forced to use indirect methods and obtain data that 0 Including co-payments.0 This is set out in the Law on Health Insurance0 Despite this empirical findings show that developing countries are not successful in setting up of mandatory health insurance schemes, see Guy Carrin, “Social health insurance in developing countries: A continuing challenge”.

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Financing options and implications for a health insurance fund for Kosovo

come to reality as close as possible. Lack of healthy databases for a wide range of statistics and data in Kosovo, and contradicting reports for economic indicators such as GDP, household spending, labour market statistics, health indicators, etc, ensured that indirect methods were used to obtain data in order to construct sensitivity analysis, for possible implementation of a HIF. Therefore the following set of basic assumptions were used:1. Average wages and cost of health care packages grow in line with GDP2. Public and private sectors have equal productivity growth3. Population grows 1.18% per year4. Public sector employees numbers grow 1% per year5. GDP for 2015 is 5.9bn Euros (Medium Term Expenditure Framework)6. Public sector employees are 81,952 (Republic of Kosovo Budget [2015])7. Total number of employed persons is estimated at 298,300 (Kosovo Pensions and SavingsTrust)8. Average annual wages is 3500 Euros (Ministry of Finance) 9. Population of Kosovo is 1.8 million (Census [2011]), so estimated population in 2015 is just over

1,9 million.10. Cost of family health package is estimated at 245 Euros per year. Individual and individual plus

spouse were also considered, but were deemed to be inappropriate and that they would gather sufficient support without other family members being covered, and would have also increased the burden on the government.

11. Cost for individuals covered by the government estimated at 150 Euros per year.12. Covered persons multiplier (CPM): 20% of all employed persons have another family member

employed; therefore, 80% of employed persons’ families are covered. Household size is 5.88 (Census [2011]), therefore, CPM = 4.7

Using these assumptions, we have prepared a number of scenarios depending on GDP growth and employment trends, for a ten year period (2015-2025). However, we do refer again to our recommendation of introducing HIF only after a careful period of capacity building.

Conclusions from analyzed scenariosIn our simulations, we have ignored alternatives for individual insurance and those covering only employees and their spouses as they are financially not feasible. The only option discussed is a family cover (included are contributor, spouse and children under 18). According to our analysis it has been concluded that in order to get the full volume of expenses, there is a need to multiply the cost of the benefit package by a factor of 4.7, called multiplier (the cost of the family benefits package after adjustment for all family members, family structures, and families with two persons employed). This option has valued the initial benefits package at €245, which was then multiplied by the number of contributors. The development of health expenditures has then been calculated for the number of active contributors reported by KPST. Considering that health insurance contributions would be collected jointly with pension contributions, then the KPST figures should be more relevant. As for economic growth, we have decided to consider three possible scenarios (3%; 4% and 5%). With a low scenario for economic growth of 3% per annum, gathered funds could reach €191.4 million in year 2025 (with €102.2 million or 53% realized from the private sector – see scenario table 1, columns 8-12). With an optimistic scenario of 5% annual growth (table 2, columns 8-12), gathered funds in 2025 could reach €232 million (with €123.9 million coming from the private sector). To raise these funds according to these scenarios, contribution rate should be set at 7% of gross earning, which is in line with Kosovo’s Law on Health Insurance; with equal contributions of 3.5% from employers and employees. The following two table’s present scenarios tables 1 and 2, which are most interesting for our analysis. Assuming that will continue to grow, the tables represent the two extremes, low (3%) and optimistic economic growth (5%).

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Low Economic Growth (3%), Family Insurance, Public Employment with 1% Annual Increase

1 2 3 4 5 6 7 8 9 10 11 12

Year Population GDPAverage

WageCost of health

package EmploymentCovered Persons Total Cost

% from private sector

% of average wage

Non -Contributors

Cost for Non-Contributors

2015 1,939,225 5.9000 3,500 245.00 298,300 1,403,203 73,083,500 73% 7.0% 536,022 80,403,303

2016 1,962,108 6.0770 3,605 252.35 307,249 1,445,299 77,534,285 73% 7.0% 516,809 79,846,957

2017 1,985,261 6.2593 3,713 259.92 316,466 1,488,658 82,256,123 74% 7.0% 496,603 79,026,867

2018 2,008,687 6.4471 3,825 267.72 325,960 1,533,318 87,265,521 74% 7.0% 475,369 77,917,282

2019 2,032,390 6.6405 3,939 275.75 335,739 1,579,318 92,579,991 75% 7.0% 453,072 76,490,475

2020 2,056,372 6.8397 4,057 284.02 345,811 1,626,697 98,218,113 75% 7.0% 429,675 74,716,601

2021 2,080,637 7.0449 4,179 292.54 356,186 1,675,498 104,199,596 76% 7.0% 405,139 72,563,558

2022 2,105,188 7.2563 4,305 301.32 366,871 1,725,763 110,545,351 76% 7.0% 379,425 69,996,825

2023 2,130,030 7.4739 4,434 310.36 377,878 1,777,536 117,277,563 77% 7.0% 352,494 66,979,295

2024 2,155,164 7.6982 4,567 319.67 389,214 1,830,862 124,419,767 77% 7.0% 324,302 63,471,103

2025 2,180,595 7.9291 4,704 329.26 400,890 1,885,788 131,996,930 77% 7.0% 294,807 59,429,430

Legend:

GROWTH RATE INITIAL INPUTS

GDP 3.00% Public Sector Employees 81,952

Population 1.18% GDP (EURO bn) 5.90

Employed Persons 3.00% Population 1,939,225

Average Wage (Annual) 3.00% Employed Persons 298,300

Costs of Health Care Package 3.00% Average Wage (Annual) 3,500.00

Public Sector Employees 1.00% Costs of Health Care Package 245.00

Costs of Health Care Package for Non-Contributors 150.00

Covered Persons Multiplier 4.7

Total Employment: 298300

Table 1. Low Economic Growth (1%), Family Insurance, Public Employment with 1% Annual Increase

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Optimistic Economic Growth (5%), Family Insurance, Public Employment with 1% Annual Increase

1 2 3 4 5 6 7 8 9 10 11 12

Year Population GDPAverage

WageCost of health

package EmploymentCovered Persons Total Cost

% from private sector

% of average wage

Non -Contributors

Cost for Non-Contributors

2015 1,939,225 5.9000 3,500 245.00 298,300 1,403,203 73,083,500 73% 7.0% 536,022 80,403,303

2016 1,962,108 6.1950 3,675 257.25 307,249 1,445,299 79,039,805 73% 7.0% 516,809 81,397,383

2017 1,985,261 6.5048 3,859 270.11 316,466 1,488,658 85,481,549 74% 7.0% 496,603 82,125,668

2018 2,008,687 6.8300 4,052 283.62 325,960 1,533,318 92,448,296 74% 7.0% 475,369 82,544,857

2019 2,032,390 7.1715 4,254 297.80 335,739 1,579,318 99,982,832 75% 7.0% 453,072 82,606,772

2020 2,056,372 7.5301 4,467 312.69 345,811 1,626,697 108,131,433 75% 7.0% 429,675 82,257,874

2021 2,080,637 7.9066 4,690 328.32 356,186 1,675,498 116,944,144 76% 7.0% 405,139 81,438,734

2022 2,105,188 8.3019 4,925 344.74 366,871 1,725,763 126,475,092 76% 7.0% 379,425 80,083,466

2023 2,130,030 8.7170 5,171 361.98 377,878 1,777,536 136,782,812 77% 7.0% 352,494 78,119,089

2024 2,155,164 9.1528 5,430 380.08 389,214 1,830,862 147,930,611 77% 7.0% 324,302 75,464,850

2025 2,180,595 9.6105 5,701 399.08 400,890 1,885,788 159,986,956 77% 7.0% 294,807 72,031,475

Legend:

GROWTH RATE INITIAL INPUTS

GDP 5.00% Public Sector Employees 81,952

Population 1.18% GDP (EURO bn) 5.90

Employed Persons 3.00% Population 1,939,225

Average Wage (Annual) 5.00% Employed Persons 298,300

Costs of Health Care Package 5.00% Average Wage (Annual) 3,500.00

Public Sector Employees 1.00% Costs of Health Care Package 245.00

Costs of Health Care Package for Non-Contributors 150.00

Covered Persons Multiplier 4.7

Total Employment: 298300

Table 2. Optimistic Economic Growth (5%), Family Insurance, Public Employment with 1% Annual Increase

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Additional information on inputs and results: Data on columns 2, 3, 4, and 6 are statistical information obtained from various sources. Data on column 4 describe average annual wage, Data on column 5 include estimetd annual costs for a family health package for insured

individuals and covered dependants. Data on column 7 are calculated by multiplying employment with the multiplier. Data on column 8 include all direct costs of health package for covered persons. It is simply

employment multiplied with costs of the health package. Data on column 9 shows the percentage of total costs of health package (column 8) that will come

from the private sector. It is calculated as total costs minus part that comes from employment in the public sector. It can be also interpreted as the net revenue generated after introducing HIF. An illustration, table 1: Figure “73%” in column 9 for 2015 means that 73% of total costs from column 8 (€73,083,500), or €53 million are net revenues generated from the private sector.

Data on column 10 show the percentage of annual wage (column 4) that is necessary to cover pure costs of health package (administrative costs and eventual commission to the tax administration is not included).

Data on column 11 show persons that will be covered from the government transfers to HIF. Figures are calculated as population minus covered persons.

Data on column 12 show the amounts of transfers from the state budget that are necessary to cover pure costs of the health package for those not covered (column 11). Data are found by multiplying the number of not covered persons with the estimated costs of the health package for non-contributors (which is set at €150 for the year 2015.).

● Calculation of the multiplier: we start from the assumption that around 20% of employed persons have another employed person in their family. Hence the number of employed persons, -let’s say 298,300- is equivalent with 238,640 families that would be covered from HIF via direct contributions. Then based on 2011 census data we know that average household size is 5.88 persons, thus 5.88 members from each family will be covered from the system. So: 238,640 families x 5.88 = 1,403,203 covered persons. This means that 298,300 employed persons need to pay for costs of health package for 1,403,203 covered persons. By dividing 1,403,203 with number of employed persons, we get the multiplier of “4.7”.

ConclusionKosovo health sector is currently in deep crisis. Continuous budget cuts, old and ill maintained infrastructure, high level of private payments, co-payments or informal payments, worsening of many families’ financial status due to catastrophic expenditures, and other, are key elements that could be used to diagnose the state of health sector.In order to improve this situation, in April of 2014 the Parliament of Kosovo voted in a the Law on Health Insurance, which foresees the creation of a mandatory health inruance fund (HIF). The law setsout a number of provisions, including the categories of those who are exempt from contributions, and more importantly the rate of contribution for those that are not exempt. As an advancement on Bilsimi and Muhaxheri (2012), this paper is also a contribution in the same direction and focuses on the financial sustainability of a HIF. We test its robustness by using more current data, and observing that benefits for Kosovo society still hold. Two scenarios are presented as more attractive and we provide a tabular form to present the financial sustainability. Both scenarios would lead to an increase in health spending so our calculations suggest that implementation of HIF would help improving the actual health situation.According to our scenarios, government is right in passing a law that proposes an initial contribution rate of 3.5% + 3.5% (employer and employee). This is a very low percentage compared to other countries. As such, it is not expected to have implications in the labor market. But this contribution rate would be only enough to finance spending for a rather restrictive health package. If government wants to improve the situation in health care and to reduce the incidence of catastrophic expenditures, a higher pooling of risks would be necessary. Of course, some preliminary surveys would be necessary to guestimate the willingness of citizens to pay that contribution rate.

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Literature“Achieving universal health coverage: Developing the health financing system” Technical Brief for Policy makers, Number 1 2005Balabanova, Dina; McKee, Martin, “Reforming health care financing in Bulgaria, the population perspective”, Social Science & Medicine 58, 2004Blair, D. Roger; Jackson, R. Jarry; Vogel, J. Ronald; “Economies of Scale in the Administration of Health Insurance”, The Review of Economics and Statistics, Vol. 57, No. 2. May 1975 Campbell, Rita Ricardo; Campbell, W. Glen, “Compulsory Health Insurance: The Economic Issues”, The quarterly Journal of Economics, Vol. 66, No. 1, February 1952Carrin, Guy; “Social Health Insurance in Developing Countries; A Continual Challenge”, 2000.Carrin, Guy and James, Chris, “Reaching universal coverage via social health insurance: key design features in the transition period” Health financing policy: Issue paper, 2004 Cho, Sungnam. “The Emergence of a Health Insurance System in Developing Countries: The Case of South Korea”, Journal of Health and Social Behavior, Vol. 30, No. 4, 1989.Dean A. Shuey, Fatime Arenliu Qosaj, Erik J. Schouten, Anthony B. Zwi, “Planning for health sector reform in post-conflict situations: Kosovo 1999-/2000”, Health Policy 63, 2003. “Designing health financing systems to reduce catastrophic health expenditures”, Technical Brief for Policy makers, Number 2 2005Falk, I. S., “The Economic Issues of Compulsory Health Insurance: Comment”, The Quarterly Journal of Economics, Vol. 66, No. 4. November 1952.Fifty-seventh World Health Assembly: “Sustainable health financing, universal coverage and social health insurance”, may 2005.Government of Republic of Kosovo, “Budget 2015”.Government of Republic of Kosovo, “Law on Health Insurance 2014”.Government of Republic of Kosovo, “Medium Term Expenditure Framework 2016 - 2018”.Jenkins, Sue Dr; Sadiku, Nexhbedin Dr; “Health Needs and Health Priorities, Kosovo”, in “Kosovo Education and Health Project; Health Component”, Institute for Health Sector Development, London, 2003Ke Xua, David B. Evansa, Patrick Kadamaa, Juliet Nabyongab, Peter Ogwang Ogwalc, Pamela Nabukhonzod, Ana Mylena Aguilara; “Understanding the impact of eliminating user fees: Utilization and catastrophic health expenditures in Uganda”, Social Science & Medicine 62, 2006.Kosovo Pension and Savings Trust, Quarterly Reports, 2015. “Kosovo Poverty Assesment: Promoting Opportunity, Security, and Participation for All”, World Bank Report No. 32378-XK, June 2005Kosovo Statstics Agency, Census 2011. “Labor Market Study: Policy Challenges of Formal and Informal Employment - Kosovo” World Bank, Report No. 25990, June, 2003 “Macroeconomics and Health: Investing in Health for Economic Development” Report of the Commission on Macroeconomics and Health Chaired by Jeffrey D. Sachs, December 2001.Rakia Moalla-Fetini, Heikki Hatanpää and Shehadah Hussein (EUR) and Natalia Koliadina (PDR), “Kosovo—Gearing Policies Toward Growth and Development”IMF, November, 2004Ron, Avia; Carrin, Guy; and Tien, Van Tran; “Viet Nam, The development of national health insurance”Savedoff, William, “Tax-Based Financing for Health Systems: Options and Experiences” Discussion Paper Nr. 4, 2004Savedoff, William, “How Much Should Countries Spend on Health” Discussion Paper Nr. 2, 2003Schultz, T. Paul, “Health economics and applications in developing countries”, Journal of Health Economics 23, June 2004 “Strategy on health care financing for countries of the Western Pacific and South-East Asia regions (2006-2010), WPR/RC56/9 Rev.1, 2005

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POSSIBLE DISASTER RECOVERY SITE SOLUTIONS FOR ORGANIZATIONS IN ALBANIA

Gjergji MULLA1, 1University of Tirana

[email protected]

AbstractThe purpose of the article is to point out some of the possible disaster recovery site solution in Albania. To ensure continuity of critical business activities in the event that all or part of the Primary Site building is rendered unavailable, Every institution needs to have a Disaster recovery site as part of their business continuity plan. Selection of the disaster recovery site is a crucial part of the business continuity plan. Disaster might occur anytime, so each business must be prepared in advance in order to face such events. Depending on the size and nature of the business, we design a plan to minimize the disruption of disaster and keep our business remain competitive. Considering the Albanian local constrains for choosing disaster recovery site solutions have been assessed two possible scenarios in order to select the one that better satisfy the requirements that a financial or banking institute needs.Keywords: Recovery, Continuity, Disaster, Incident, Rental, Hosted, Solutions

IntroductionDisaster Recovery is the process of rebuilding your operation or infrastructure after the disaster has passed. According to Business Continuity Institute’s Glossary “Business continuity plan is a collection of procedures and information which is developed, compiled and maintained in readiness for use in the event of an emergency or disaster.”To ensure continuity of critical business activities in the event that all or part of the Primary Site building is rendered unavailable, Every institution needs to have a business continuity plan solution.An important part of the business continuity plan is the selection of the disaster recovery site.Disaster recovery site bare considerable cost in order to be implemented properly.Beside the primary function of the disaster recovery site, in order for the investment to be more cost effectively, the disaster recovery site could be used also for other purposes like training purposes etc. The scenarios that should be fulfilled from the disaster recovery site are: accommodate the staff members that should work in case the business continuity plan is activated

and provide them will all the tools needed for their work (meaning that the user will be perform their tasks on the disaster recovery site),

have an IT Server room similar to the primary site that could host all the sensitive applications and services in which the critical processes depends. This means that the business continuity plan requires a mirror server room to be available in the disaster recovery site

to host an archive area where will be stored all required physical documents.

To achieve the above goals, considering the Albanian local constrains for such a service have been assessed two possible solutions in order to select the one that better satisfy the requirement that a financial or banking institute needs.The first solution (referring below like the Rental Solution) is the rental of a location that meets the following conditions:The rental site and the primary site should not be affected from the same risk at the same time. Meaning that at a specific time the incident can effect only one site. Like this at least the other site is available. Exposition to risks and risks evaluation depend strongly on characteristics of the area where organization primary site is settled and where the organization is considering building the disaster recovery site. In both cases, risks analysis is necessary for primary site and the disaster recovery one in order to insure the above rule.

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Till nowadays, regulator (in Europe and even in Albania) hasn’t defined any recommendation concerning the distance between the primary site and the disaster recovery site and the perception is that recommendations of this type will never be proposed. But in case of wrong choice, regulator wouldn’t be in support of this choice and “moral responsibility” would be engaged.It is probable that regulations will check that the disaster recovery sites are not exposed to the same type of risks as primary site (for instance, both of them settled in liable to flooding area).In this case, regulator can ask to justify enterprise’s choice and can compare this choice to solutions chosen by other institutions, having similar activities.The second solution (referred below as Hosted Solution) is to host the disaster recovery site to a company that offers Data Centre and Recovery services. Such sites are designed to comply with the strictest of international standards in construction and building safety (Tier IV). The evaluation of both sites is based on the following criteria:

relevance of technical choices: architecture required for operation, justification of the components of the recommended solution.

flexibility, functionality and technical adaptability of the solution, implementation lead times the costs associated with:

- the required technical architecture,- the assistance and support services supplied by the provider- operation and maintenance- the provider's ability to meet the requirements of the organization

In the below tables are given the evaluations for both sites related to Technical and Service characteristics.

I. Technical characteristicsTechnical characteristics Rental Solution Hosted SolutionGeographic location related risks -Flooding -Mudslide -Earthquake -Lightening -Wind -Thunderstorm -Cyclone

Acceptable(regarding Earthquake the building should have a certification that is able to resist 8 righters scale)

Acceptable(Tier 4 Data Center)

Physical security standards -Surveillance of the building -Fire protection -False ceilings

Not offered The cost will be included in the construction costs

Offered from the company as service Included in rental price

Dimensions of the space Acceptable AcceptableIT and telephony equipment (workstations, networks, servers, telephone/fax, printers)

Not offered from the company Not offered from the company

Specific equipment Not offered from the company Not offered from the companyFlexibility, functionality and technical adaptability of the solution Acceptable Acceptable

Implementation lead times

Construction adaption work could start immediately after signing the Rental Contract. Installation could start after signing the construction has finished.

Installation could start on immediately after signing the Rental Contract.

Based on the technical and Service Characteristics the difference between Hosted Solution and Rental Solution is that services like Surveillance of the building, Fire protection, false ceilings, AVAC, Backup generators, Inverters etc, are offered and maintained by Company which offer the Hosted Solution. The mention features are standard features for a Data Centre Building

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Possible disaster recovery site solutions for organizations in Albania

Hosted Solution is built with the main purpose to host DRP solutions while Rental Solution needs additional construction costs in order to be adapted to function as DRP site. This is the main advantage that Hosted Solution has against Rental Solution

II. Service characteristicsServices Rental Solution Hosted SolutionLead time for bringing the alternate site into service

Depending on organization solution

Depending on organizations solution

Maximum length of stay No restriction but the contract should be no more than 10 year based on the local legislation

No restriction

Assistance Will be handled by organization resourcesfor both IT and Administration activities

Done by organization resources only for IT services. Administration maintenance will be responsibility of company that offers the hosted solution

Maintenance resources Will be handled by organization resourcesfor both IT and Administration activities

Will be handled by organization resourcesonly for IT services. Administration maintenance will be responsibility of company that offers the hosted solution

Resource sharing No resource sharing Administrative resource sharingModification of the configuration Will be handled by organization

resourcesfor both IT and Administration

Will be handled by organization resourcesonly for IT services. Administration maintenance will be responsibility of company that offers the hosted solution

Testing Will be handled by organization resourcesfor both IT and Administration

Will be handled by organization resourcesonly for IT services. Administration service will be tested by the company that offers the hosted solution

III. PROJECT COSTS AND CHARGESIII.1. Difference in the costs The difference in the costs for each solution is mainly related to - Construction costs, - Rental fees- Administration maintenance services (Alarm Systems, AVAC, Inverters, Power Generators etc)

CAPEX Rental Solution Hosted SolutionConstructions General Constructions Costs

Server Room Construction CostsNo construction costs

If we suppose the Rental Solution will require a rental contract at least 10 years, capex will amortized on 10 years with a specific amount per year. This amount is composed by depreciation costs for the general construction and depreciation costs for the construction and installation of the Server Room

OPEX (Yearly) Rental Solution Hosted SolutionRental Lower rental costs Expensive Rental CostsMaintenance Fee Considerable maintenance fee Included in rental feeDepreciation (10 years rental contract) Considerable depreciation fee as

detailed aboveIncluded in rental fee

III.2. Common Investment in both location.A significant part of the investments for the BCP is related to IT infrastructure that consist on Servers, workstations, telephony, printer, fax and other devices.The above investments are the same for both solution and are detailed in the table below:

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CAPEX Rental Solution Hosted Solution

SoftwareHardware (workstations)ServersTelephony System

Telephone Device

Printers

Fax

Photocopy

Scanners

OPEX Rental Solution Hosted Solution

Network connections(Yearly)

Total OPEX expenses including IT and construction cost should be presented in the table below:OPEX (Yearly) Rental Solution Hosted SolutionRentalMaintenance FeeDepreciation of Construction costs (10 years rental contract)Network connections(Yearly)Depreciation(IT equipment and software’s will be depreciated in 4 year period)Depreciation (Equipment’s of Server Room Will be depreciated in 4 year period)

ConclusionReferring Albania, there is a lack of disaster recovery site solution. The options are very limited. Based on the analysis performed over the above selected cases we have the following results:

1. Hosted Solution Advantage DisadvantageIs designed to Host DRP.Is better from Service and Technical point of view

Higher yearly OPEX . It is more expensive solution that the other one

2. Rental SolutionAdvantage DisadvantageLower yearly OPEX and in total is less expensive that the other solution

Will be adapted to function as DRP Site

References1. https://www.isc2.org/cissp/default.aspx.2. Sybex CISSP, Certified Information Systems Security Professional Official Study Guide 7th (2015)3. http://project-management.truesolutions.com/2010/09/22/practical-business-continuity-planning/ 20104. Michael Whitman, Herbert Mattord and Andrew Green: Principles of Incident Response and Disaster Recovery, 2nd Edition. Thomson, 2014.5.http://www.simcoe.ca/EmergencyManagement/Documents/Introduction%20to%20Business%20Continuity%20Planning.pdf6. Official (ISC)2 Guide to the CISSP CBK7. Gj.Mulla, A.Ademi, “Business Continuity Plan”, 2014.

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http://project-management.truesolutions.com/2010/09/22/practical-business-continuity-planning/

TECHNOLOGICAL TRENDS AND THE USE OF ICT BY PUBLIC ENTITIES, WITH A FOCUS ON E-GOVERNMENT AND

INTEROPERABILITY FRAMEWORK IN KOSOVO

Kujtim GASHI1 Kozeta SEVRANI2

1PhD student, Faculty of Economy, University of Tirana1Prof. Dr., Head of Statistics & Applied Informatics Department

Faculty of Economy, University of [email protected]; [email protected]

AbstractRecent developments and trends are becoming an important factor in every segment of the modernization of public administration worldwide; a progress that could be called revolutionary is achieved by the development of Information and Communication Technology (ICT).Contemporary ICT development influenced the governance methods and created the opportunity to develop strategies and frameworks regarding e-Government as a national objective of public policy in the majority of states in the whole world, in which is aimed democracy and transparency.An important element in consolidation of the e-Government is interoperability (interaction) of electronic systems developed for electronic services in relations Government-to-Government (G2G), Government-to-Citizens (G2C), Government-to-Businesses (G2B).European Union Programme - Interoperability Solutions for European Public Administration (ISA) - is based on four types of initiatives (actions): Strategies, Frameworks, Guidelines and Services & Tools [1]. Therefore, development of interoperability framework of Kosovo’s has been based on the European Interoperability Framework model.Interoperability (interaction) is based on validity of multiple use and sustainable, transparency, open sources and innovation, continuous improvement, common interest and mutual confidence.Keywords: e-Government, Interoperability, modernization, Public Administration, services, development, ICT, Kosovo

Introduction:One of the main achievements of this century is considered to be the use of Information and Communication Technology (ICT) in order to pursuit a better life and easier access to resources, with a special emphasis in the embrace of ICT by the public authorities in order to ensure modern and efficient services. In the prevailing circumstances, the level of achievements in various areas as well as communication and interaction opportunities regardless of distance is facilitated by the opportunities that provide revolutionary advancement of Information and Communication Technology (ICT). Therefore, the aim of this paper is to note the importance and value of management by applying digital services and e-governance as a very important resource of the time. Another important element in the content of this paper is the fact that the implementation of e-governance is becoming a reality and an objective of the contemporary world as well as the primary instrument for building confidence of the people in government. As it was stated on a study by [23], except the confidence of people in government, the use of e-government is creating a centralized solution from which the governmental entities will be able to create so called one-stop-shop for their citizens. On the other side, e-governance is utilized as a requirement and as an opportunity to connect electronic services and systems that are being developed to enhance the quality of services for citizens and to enhance democracy by creating architecture where electronic systems function on the basis of interoperability and coherence.Regarding the level of application of e-government, international institutions such as the European Commission, the United Nations have established a set of implementation criteria and are measuring the performance of this modern management method by institutions along with creating better

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mailto:[email protected]



opportunities for businesses and citizens in all countries of region, specifically the Western Balkans region at both central and local level.As many countries in the world, the institutions of the Republic of Kosovo too are committed to meet the contemporary criteria and to this end, they have carried out a series of activities and investment in terms of creating the preconditions for modern governance. Important steps in this direction range from the development of e-government strategy and other legal and sub-legal acts to development and approval of Interoperability Framework, which precede the specific actions in terms of use of ICT in public administration institutions of the Republic of Kosovo.E-government as a state objectiveInstitutions of different countries and governments, as driving agents behind the efforts of administration modernization are increasingly pursuing the highest levels of electronic services.Electronic Governance (e-Governance) essentially pertains to the approach of the government and institutions with regards to the use of information and communication technology (ICT), other technologies and web-bases to improve and increase the efficiency and effectiveness of service delivery in the public sector. E-governance is increasingly becoming a permanent requirement to be applied in practice, particularly in circumstances where society in general is keeping the pace with the trends of the time. Therefore, the move towards information society in the modern world and the need for continued reform in every segment of life and administrative services in every institution, are becoming a criterion towards the integration of society, irrespective of borders and distance between countries. The whole progress is being achieved through a great revolution in the field of information and communication technology (ICT). This is a goal, which is now pursued by our country as well, which is in accordance with contemporary trends and with enhancement of the quality and capacity of the public administration in the country.The term e-Government in recent years has become almost the most frequent expression of public policy glossary of governments and institutions, which aim towards modern and transparent management methods.An important element is the fact that the level of implementation of e-Government for various countries has become an instrument for measuring the performance level of modernization of the administration of the country and an important indicator for promoting economic and social development in general by offering online services for citizens and businesses [2].It should also be noted that the large-scale use of information and communication technology (ICT) has become an indispensable criterion. The nature of this technology allows generation and dissemination of essential social and economic information through distant spaces, thus allowing circumvention of widely used methods to date.In recent years information technology revolution has driven radical economic and social transformation. The potential of information technology lies in the ability to process, store, retrieve, duplicate and transmit information without the influence of time, distance and volume. With integrated information systems, products and services are disseminated worldwide, becoming accessible to smaller companies or to the most remote regions [3]. It is now evident across the globe that the use of ICT has the potential to revolutionize the way people interact with the government and each other.

Opportunities provided by e-government Broader access of citizens to information related to government activities; Use of information and technology which allow participation of citizens in administrative services

and public policy-making. Raise the level of government’s accountability by restricting bureaucratic procedures and curbing

opportunities for corruption; and Create equal opportunities for all, irrespective of the distance and zone, 24 hours a day and 7 days

a week (24/7).E-governance is delivery of government institutions services through information and communication technology (like internet, mobile network, etc.) to citizens, businesses and other categories. With the introduction of e-government, citizens, businesses and government itself will have more effective services and quick access to services from any point of distance; boost economic and social

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Technological trends and the use of ICT by public entities, with a focus on e-Government and Interoperability Framework in Kosovo

development, capacity building opportunities, permanent services at any time, presentation of national achievements.

E-government and Interoperability Framework of the Republic of Kosovo Based on the objectives of the Government of the Republic of Kosovo to upgrade and modernize the public administration in the Republic of Kosovo institutions, special attention is given to electronic governance. Activities aimed at inclusion and interoperability of all electronic systems within institutions have been carried out, along with involvement of citizens in the process to ensure quality service, by increased use of Information and Communication Technology (ICT) resources and capacities.There is a range of actions and activities that were conducted by local institutions, based on European Union criteria and standards, with the aim of enhancing the level of electronic services, such as: Develop legislation and other sub-legal acts, such as:

o Electronic Governance Strategy 2009-2015, Law on electronic communication, Law on Information Society Services, Law on Government Bodies for Information Society, Kosovo Digital Agenda 2013-2020, Interoperability Framework of the Republic of Kosovo, etc... and other sublegal acts.

Building infrastructure capacities, such as:o Expand and upgrade the state telecommunication network, which covers the entire

territory of the Republic of Kosovo, which is basic pre-requisite for application and operation of e-governance services and systems interaction.

o An important component was the establishment of the National Data Centre, which would allow collecting (centralization) of electronic data of all the institutions of the Republic of Kosovo.

Develop electronic applications and their application in practice.o An important element is to continue developing electronic systems, which are used to

deliver services in three primary areas of electronic governance, such as: Services for citizens - G2C Services for businesses - G2B Services for government - G2G

Push e-services and enhancing the quality of services through ICT systems, raise the level of application and use of these systems and enhancing interoperability of existing systems and those in development represent a challenge in all modern countries around the world.However, still there is a lack between the interoperability of systems used by the Kosovo institutions in one side, and also the budget cuts created a gap between the systems that were created before and the ones that are in progress, since the technology is changing rapidly and the investments in the past year were quite low [24].Interoperability as a conceptIn the research community in the field of e-Government, and in many e-governance development processes in many countries, the interoperability process (interaction) was inevitably seen as a key factor in effective and attractive developing for raising the level of e-Services for Citizens and Businesses. It is worth noting that researchers agree that interoperability represents more than mere superficial technical standards and interface; rather it involves organizational, legal and cultural aspects.It is important to note that interoperability specifically can be a challenge to traditional modes of governance in institutions and public sectors at large, in the processes of introducing new ways of intergovernmental cooperation. However, until now, very little is known about the level of configuration of the information and communication technology (ICT), where the governance evolved in practice towards achieving interoperability in the e-governance process, including the public sector governance, where it is worth noting the impact of systems interoperability within the broad related framework or through operation in government networks[4].In most developed countries, interoperability frameworks have been introduced at national and international level. Recommendations are made for adapting the architecture of the public sector in

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terms of creating opportunities for implementation of interoperability in practice by planning and developing interactive systems and policies in order to create opportunities to increase the quality of services and increase the overall safety.

Fig.1.The Comparative Interoperability Project views an interoperability strategy as an understanding of and sensitivity to configurations of technology, organization, and community [5]

Introduction into the European Interoperability Framework European Interoperability Framework relates to interoperability in a very specific context of delivering European Public Services. Although in almost all cases, the delivery of European Public Services includes data exchange between the ITC systems, interoperability is a broader concept and includes possibility for organizations to work together towards joint mutually approved useful goals.Therefore, the following definition is applied in the European Interoperability Framework [6]: "Interaction, in the context of delivering European Public Services, is an opportunity for various and diverse organizations to interact towards joint, mutually agreed goals, including exchange of information and know-how between organizations, through work processes they support, through exchanging data on their respective ICT systems."[7]It is worth noting that interoperability, by nature, is versatile and is best perceived as a common value of a community.

Purpose and legal frameworkThe purpose of the European Interoperability Framework (EIF) is: To promote and support delivery of European Public Services by support cross-border and cross-

sector interoperability; To manage the public administration’s efforts in delivery of European Public Services for

businesses and citizens; To complement and connect various National Interoperability Frameworks (NIF) to the European

dimension.As a document of non-technical nature the EIF targets all those involved in defining, designing and implementation of European Public Services.EIF should be taken into account when deciding on the implementation of European Public Services and in particular in the development of services and systems to support the implementation of the policy initiatives of the EU. Further, EIF should be taken into account during the implementation of public services, which in the future could become part of the European Public Services.EIF contributes to the better functioning of the Internal Market through increased interoperability between European public administrations.

Kosovo’s Interoperability Framework and its importanceInteroperability Framework (interaction) of the Republic of Kosovo (KIRK) has emerged as a requirement stemming from current state of e-government and information technology, and derives

16

Technological trends and the use of ICT by public entities, with a focus on e-Government and Interoperability Framework in Kosovo

further as one of the obligations under the e-Government Strategy and Action Plan for the implementation of this strategy. This framework is also necessary in the case of European integration, European interoperability respectively.Services to citizens, businesses, institutions and organizations, orientation towards reducing administrative burdens in the internal social processes and the European integration processes, require serious efforts to increase the efficiency and transparency of public administration.To attain a full success, cooperation between participants in the process must be established a along with automation of information exchange and integration processes between the segments of the public administration, with the aim of building services for all. All this can be achieved only if achievements and opportunities provided by the systems and information technology infrastructure are properly utilized. Interoperability is necessary in order to increase the performance of each institution, and to increase the level of integrated and automated processes.The framework provides all the necessary elements and actions that should be taken by all institutions in the country in order to achieve interoperability in pursuit of important objectives of development and increase of living standards.

Fig 2. The basic elements of the conceptual model are depicted in the diagram below: [8]

Levels of interoperability under the FrameworkThe levels of interoperability are described based on the European Interoperability Framework (EIF) and the Interoperability Framework of the Republic of Kosovo.There are four levels of interoperability:[9] Legal interoperability, Organizational interoperability, Semantic interoperability, and Technical interoperability.Political levels have decisive impact in establishing electronic services in public administration. In introducing electronic services, laws, priorities, administrative capacity, etc. are of decisive importance..The structure of the Interoperability Framework of the Republic of Kosovo The Interoperability Framework describes in detail: Basic principles of the Framework Conceptual Model of Public Services Levels of Interoperability, and Services infrastructure Recommendations of the Interoperability Framework of Kosovo Kosovo should consistently its Framework to EIF, by incorporating best practices and prohibiting

any norms outside of these standards.

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The Framework document as well as interoperability of systems and services in practice must be in full compliance with national and international legislation;

Public administration institutions should develop the services model based on multiple components, by introducing public services that make extensive use, where possible, of the existing service components;

Public administration institutions that are yet to integrate in the common infrastructure scheme must proceed quickly to also connect their components under this scheme during the introduction of their public services.

Public administration institutions should make their authentic data available to others, coordinated by the agency that manages these joint systems and electronic security services.

Public administration institutions should review and harmonize legislation with the aim of reducing and optimizing processes. [10]

In view of the above, including the basic e-Government and interoperability concepts, we may note that upgrading public administration and enhancing the quality of services for citizens is likely to be realized in practice only if ICT resources are used, along with pursuit of interoperability of as many electronic systems and services as possible, based on the practices of the best models of European interoperability of systems.

References[1] http://ec.europa.eu/isa/[2] Measurement of e-services: factors than can improve the e-Government, e-participation- Prof. Dr. Kozeta Svrani, Irena Malolli, MBA – International Conference “ISTI and their importance in the economic development”, Proceedings Book/ 2011 Tirana, Albania[3] The Role of the Information and Communications Technology Sector in Expanding Economic Opportunity- William J. Kramer, Beth Jenkins and Robert S. Katz [4] Herbert Kubicek l Ralf Cimander, Hans Jochen Scholl- Organizational Interoperability in E-Government[5] Comparative Interoperability Project - http://interoperability.ucsd.edu/[6] Vendimi i Parlamentit Evropian dhe të Këshillit mbi solucionet e interoperabilitetit për administratat publike (KEI)[7] European Interoperability Framework for European Public Services (EIF) Version 2.0[8] European Interoperability Framework for European Public Services (EIF) Version 2.0[9] The Framework of Interoperability in the Republic of Kosovo[10] The Framework of Interoperability in the Republic of Kosovo [11] The Government of Kosovo - "Electronic Government Strategy 2009-20015[12] United Nations E-Government Survey 2008, From e-Government to Connected Governance[13] Local Governments Network - Vienna 2005.[14] Klaus Lenka - The University of Oldenbergut, Oldenberg - Local Governments Netvork[15] Christine Leiter (ed.) e-Government in Europe: state of affairs. Maastricht: European Institute of Public Administration, 2003[16] OECD Public Sector Modernisation, OECD Policy brief, Paris October (LOGON 2005)[17] E-Governance Academy-2009 Tallinn Estonia - study visit[18] United Nations E-Government Survey 2010,[19] www.rks-gov.net[20] www.ceec-logon.net- Lokal Government Network[21] www.cdt.org/egov/handbook- e-Government Handbook - Estoni[22] www.ega.ee- e-Governance Academy –Tallinn, Estoni[23] Maurizio Marchese, Gianni Jacucci, Mike Martin, Bridgette Wessels, Yvonne Dittrich, Sara Eriksé - A participatory design approach for the development of support environments in e-Goverment services to citizens[24] Kosovo Budget: https://mf.rks-gov.net/sq-al/Buxheti/Buxheti-i-Republikes-se-Kosoves/Buxheti-qendrore

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http://www.ceec-logon.net-

http://www.ega.ee-

http://www.cdt.org/egov/handbook-

http://www.rks-gov.net/

http://interoperability.ucsd.edu/

http://ec.europa.eu/isa/

SCHOOL LEADER SELF-DEVELOPMENT

Ibrahim CAN KORKUT1, Prof, Dr. Shyqyri LLACI2 1 PhD Candidate, Faculty of Business Administration, Epoka University, Tirana, Albania

2 Prof, Dr, Faculty of Economy, Tirana University, Tirana, AlbaniaEmail: [email protected], [email protected]

AbstractSchool leadership concept has been changing due to societal, methodical and technological developments, changes in pedagogy and teaching environment, the global and national markets, etc. In today's world, the challenges that educational/school leaders face are incomparable with the challenges that they faced one hundred even fifty years ago. Furthermore, in comparison with the history of learning, teaching and school leadership that have been under discussion for long, the history of school leadership standards, assessment tools and certification progresses are quite new. There is not a consensus even on the definitions of some basic concepts as it is in human’s nature to have different ideas and search for better which makes it almost impossible to come to an agreement for long. However, in our globalized world, standards have gained importance and there has been a significant progress in school leadership development. Efforts for setting standards for education and school leadership have gained speed since the 1980s. In addition, private schooling has become a great business and self-development is a part of the competition. Yet, lack of quality educational leaders and certification methods are under discussion. Albania, as a country with valuable educational tradition, has taken part in new debates about the school concept and development since the 1990s. This progress has been followed by the adaptation to the Bologna system, the participation in Centre for Effective Learning Environments (CELE), partnership with Educational Testing Service (ETS) and co-operation with The World Bank under the authority and guidance of Ministry of Education and Science. Key Words: School leadership, school leader shortage, development programs, certification, Albania

1. Definition of School LeadershipAlthough most standards starts with "A school leader is an educational leader that is responsible for all students' success by ...", Leithwood et al (1999) argues that there is no consensus on the definition of leadership since there are innumerable number of definitions. Yet, successful leadership is a key element for a successful/outstanding school and it is essential to find a definition that suits well in most cases as Beare et al (1992) argues that extraordinary leadership has appeared to be an important characteristic of great schools and there can no longer be doubt that those seeking quality in education must ensure its presence. He claims that development of potential leaders must be given high priority.One of the difficulties in providing a permanent definition of a school/educational leader is that what can be a valid definition for the past cannot be comprehensive for the present and future. The shift in leadership conception also causes changes in educational leadership definitions and consequently in standards. The shift in term started as early as the early 1990s and the phrase "school leadership" started being used instead of "educational administration". In school-level, there was movement from concerns with knowledge related to school and system-based roles to change-oriented knowledge and evidence-based performance outcomes (Murphy & Shipman, 1999). It led to the conception of transformative leadership and was applied to educational leadership standards. Although there is no agreement on a clear definition of leadership, three types of school leadership which are transformational, distributed and teacher leaderships have been settled in the center of interest. In broader perspective, leadership also moved from property of individuals to dynamics of relations. However, this movement has not influenced educational leadership standards as educational standards are closely linked to tasks of individuals as educational leaders. Reasons for change can be counted as societal and global changes, improvements in teaching and learning, environmental changes, different expectations and changes in the market. Aarons (2010) claims that three important items that have dramatically changed the concept of educational / school leadership are transformation in management, information technology and accountability.

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If it is necessary to apply to the standards, the Interstate School Leaders Licensure Consortium (ISLLC) by the Council of Chief State School Officer provides standards which are used by more than 40 states in the USA for school leaders. ISLLC 2008 uses six standards to define school leadership. According to the six standards of ISLLC 2008 (Council of Chief State School Officer, 2008), a school administrator is an educational leader who promotes the success of all students by:facilitating the development, articulation, implementation, and stewardship of a vision of learning

that is shared and supported by the school community. advocating, nurturing, and sustaining a school culture and instructional program conducive to student

learning and staff professional growth. by ensuring management of the organization, operations, and resources for a safe, efficient, and

effective learning environment. collaborating with families and community members, responding to diverse community interests and

needs, and mobilizing community resources. by acting with integrity, fairness, and in an ethical manner. by understanding, responding to, and influencing the larger political, social, economic, legal, and

cultural context.

2. Reasons and Core Components Behind School Leader Self-DevelopmentBefore dealing with the self-development of school leaders, it is important to mention school leaders’ shortage to emphasize the importance of development and self-development. As school leaders are becoming more and more responsible for students' outcome (Archer, 2003), the problem has got three aspects to be understood as;a) there is a real shortage b) candidate quality is lessening and c) fewer people are seeking to become a school leader. “Some countries, particularly United States and Flanders, find it difficult to attract suitable candidates for what is seen as an increasingly onerous job” (OECD, 2001). According to a research by Grady et. al (1994), 92% percent school leaders in Australia are planning to retire before they have to become a school leader and there are fewer applicants for the post and 88% of school leaders in Australian state of Victoria in 2000 did not have any intentions to become a school leader (Lacey, 2002). This rate in Ontario, Canada is 40% and in South Africa only 30% (Merrill, 2001). As school leadership is a less desirable career than it was before (MacBeath, 2006) and fewer teachers who are more likely and more naturally supposed to be future administrators are willing to take an administrative position (Andrianaivo, et al, 2005), self-development and job-satisfaction of present school leaders are becoming a very important issue. One of the crucial points here is to see school leadership as a career. Because if it is considered as a career, it will be easier to apply development trainings and school leaders will take some steps to improve themselves. As a matter of nature, today's working school leaders are aging and younger ones should be replaced soon. In the USA by the year 2000, 60% of school leaders were over 50 years old and in urban schools the percentage was five percent higher (Owings, et al, 2010). In fact, the problem is not the number of school leaders but their preferences, not their certification but quality. Wealthy regions and schools do not have difficulties in finding quality staff and school leaders as they can solve their staff and leadership shortage by hiring more qualified and experienced leaders instead of spending years for younger ones to become qualified and experienced ones (Gajda & Militello, 2008). Main reasons for school leaders shortage can be counted as:stress caused by the poor circumstances at school, isolation from their colleagues owing to the lack of time to spend with them, environmental and societal problems surrounding the school such as pollution or high percentage of

unemployment, continuous change in educational systems and policies some of what are not necessary, more accountability issues and responsibilities without considerable income raise, the profession becoming increasingly demanding and competitive as schooling is turning into a big

business in which school leaders are turning out to be a tool less time and freedom on personal, family life, less respect from students and the society and consequently less job security,

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School Leader Self-Development

no compensations for the duties after lessons and at weekends, high expectations against limited human and material resources (Andrianaivo,, et al, 2005).

However, raising quality should not only mean individual improvements but also developing the quality of circumstances in which school leaders’ work.

3. Dimensions of School Leadership Self-DevelopmentThree dimensions of professional development of school leaders are proposed for attention. These dimensions are content/design, delivery mode and measures of success (Mulfordb, 2003). For the content, school leadership concept and expectations play the key role. Therefore, when a school leader takes steps into self-development, the first thing to take into consideration should be that he should know basic concepts of school leadership and know what is expected from them. Hipp and Huffman (2003) suggest "Educational administration programs need to prepare potential school leaders to move beyond issues of management, and provide practical experiences that focus on relationships and learning outcomes. Leadership preparation programs must guide potential leaders in the following: establishing collaborative decision-making, developing a shared vision, aligning the energies of diverse groups of people, supporting the interdependency of individuals in the organisation, and providing opportunities for shared learning among staff.”A school leader should concentrate on core elements of leadership, read some material on them and visit some successful school leaders at work. Furthermore, they should attend a program that provides sources and experience on the issues. The basic questions to choose such a program are who, where, how and when. These questions also apply to course providers for the delivery mode. For Mulford (1984) such trainings should take learners' characteristics, context and place into consideration for the fruitful outcomes. Day & Harris’ (2002) comparison of five sets of school leadership development standards from New Zealand, Australia, UK and USA summarizes that "all five sets had in common an emphasis on financial management including hiring appropriate staff, being a role model, establishing professional development as an ongoing school-wide activity, monitoring and evaluating teacher and pupil progress, using test score to guide curriculum and instruction, wide consultation, parent and community involvement and effective communication to all stakeholders, and valuing diversity”. Missing from the lists were teacher leadership, balancing the full range of duties expected of the school leader, teacher stress and morale, endorsing new programs in order to aid implementation, consequences of high-stakes testing, marketing, working effectively with school councils, outreach or entrepreneurial functions, and acknowledgement of the political, social and organizational features of the contexts in which leaders work (Mulforda, 2003). In some cases there are no choices to choose between but if there are, a school leader or a future school leader should carefully examine the components of the available courses to be well-trained on the basic concepts and expectations.

4. CertificationDespite the arguments that certification does not guarantee quality and certification processes face the same problems as the gap between the theory and real life practices, standards have gained importance in today’s world. Furthermore, people who spare extra efforts for self-development should be rewarded if they meet the requirements. It is clear that applying the right certification progress is the first step to start with. Different certificates evaluate different aspects of school leadership and finding the appropriate certification method for the current leadership environment is crucial. Credibility and the appropriate measurement methods for the candidates are vital. For the credibility, the certificate should be based on (inter)nationally accepted standards and the measurement methods should focus on evident-based daily practices taking the work environment and conditions into consideration. The link among (inter)national standards, development, certification and creditability must be identified well and the basic concept behind certification should be quality but not number of certified people. Therefore, the requirements to apply and obtain certificates must be defined and applied so well that certificates should not lose their values and they should not cause conflicts in hierarchy while not discouraging young talents. Certification processes should also depend on a mixture of theory and daily work practices such as development in school environment and students’ outcome. However, outside

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course/certification providers may neglect some practical issues (Kronley, 2000). One of them is an educational leader’s commitment and it can play a significant role in successful school management (Elmore, 2000).

5. School Leadership Development in AlbaniaIn Albania school leaders are supposed to be a teacher and a manager at the same time. Many have difficulty in concentrating on leadership tasks due to teaching loads. On the contrary, the number of the lessons is limited in private schools from six to sixteen in accordance with the level of leadership. Leadership tasks in state and private schools may vary. State school leaders are limited within the school borders since the tasks are identified by the state laws in particular with the requirements of Regional Educational Directory (DAR) and Educational Office (ZA). They do not have much freedom to act outside of these borders and it can be considered as too much responsibility in comparison with their salaries while success of private school leaders heavily depends on their outside activities and influence although they also have to follow some institutional orders or task by the superior powers of the institutions. One of the main reasons for it is that state school leaders are not much worried for the selection of new students as they mostly take students from their regions whereas one of the most important tasks of private schools and their leaders is to attract the best possible students available, not only form their regions but all over the country. To do so, they need to act as business leaders to some extent. They are also planner and implementer, critique and school advocate at the same time.The highest state authority of pre-university education is The Council of Ministers and Ministry of Education and Science (MASH) that provides and assesses all the educational programs and plans in Albania in accordance with world trends. The National Council of Pre-university Education System is a subdivision of MASH for advisory purposes. Regional Educational Directory (DAR) and Educational Office (ZA), which is responsible to train educational staff, are the formal authorities on the level. These three institutions are responsible for monitoring leadership implementations. DAR organizes periodic meetings with school leaders to inform them on instructions such as methods of teaching, events to be organized, policies and initiatives etc. But school leaders do not have any freedom or control in such meetings on implementations as a whole. Zone inspectors who are part of DAR staff and whose jurisdiction cover four or five schools can play the role of mediators between DAR and school leaders. Having founded in 1970, The Institute of Curricula and Standards is the unit whose responsibility is to provide in-service training and research for the development of school leaders. Two of the core guideline principles of the decentralization process in Albania are performance-based management and a new role of school leaders. A Policy Paper of the Ministry of Education and Science defines the perspective and orientation of the educational leadership capacity building and professional development system reform in Albania. The role of the school leader as a promoter of participation in school management is not emphasized (World Bank Data on Education, VII Ed., 2011). Society does not play any significant role on school inspection or development. If a parent is not satisfied with the quality or implementations of the school, the usual remedy is to change it.As career options for a teacher in Albanian educational system is very limited and a teacher can start his/her career as a teacher and may end up as a school leader. However, teachers may not see a prospect as a school leader and when it is the case, it makes the profession a daily routine. Normally, career stages start from being a teacher, followed by being a director and finally regional educational director. The legislation on pre-university education provokes collective and distributed leadership styles. However, in Albania the influence of school leaders is very limited and mostly within the borders of school and there is not a working network among school leaders. In addition, teachers' freedom to leave schools as soon as lessons finish makes it difficult to distribute the responsibilities and experience.By law (No: 7952) Regional Educational Directory (DAR) leaders and Educational Office (ZA) leaders are chosen by the Ministry of Education and Science. For school leaders, although there are some basic requirements such as holding a university degree and teaching experience in the field of education, private schools may ask some additional qualities such as human resource management skills and ability to communicate in English since there is a competition among private schools.

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The governmental organizations concerning the development of school leaders can be counted as the Ministry of Education and Science, its subsidiary elements such as DAR, Department of Pre-university Education, the Institute for Educational Development (IZHA) and a unit for human resources within MASH. A formal list of school leaders' competences and powers, procedures to be followed to become a school leader and selection criteria are set up in MASH Directive. While choosing the criteria, experience of successful school leaders and international standards have been taken into account and there are some examples of such training programs.Pre-university education system school leaders training program includes modules such as leader's role in development and management, theories and legislation of management of institutions, communication, cooperation with other organizations and communities, technology and ethics (MASH, 2011). Directive no: 5310/2, dated 12/10/2011 expresses the purpose of the program for the school leaders at present and for those who aspire to meet the standards of school leadership and management at the expert level. Also according to the directive No: 7170, dated 22/11/2010 for 'qualification of pre-school and school leaders' of the Ministry of Education and Science, it is compulsory to obtain the 'Leader Certificate' to be considered as a school leader candidate.According to the Pre-university education system law no: 7952 (1995) some of the DAR requirements for school leaders are as follows;a university degree long-period experience in teaching management mastership

In addition, the following chart gives school leadership application criteria for the applicant of social studies.

Table 4.1: School leadership application criteria for the applicant of social studiesSocial Studies - Vol. 6 - No. 2 - 13 -The national program for qualifying contemporary leaders Application Criteria: - holds a teaching degree based on the respective level of education system; - is at least a qualified teacher of the third category and it is rewarded at least "good" at this qualification test - he is not subject to administrative or criminal proceedings; - presents two positive recommendations from direct superiors with whom has worked during the last five years

Individuals who have hold the position of an education institution leader at least for ten consecutive years until the passing date of the Directive of Minister on Leaders’ qualification

Individuals who have hold a university degree of second or third level in education management and administration of accredited universities.

Individuals who have hold the position of the school leader at least for five consecutive academic years until the passing date of the Directive of Minister on Leaders’ qualification

(Source: Nathanaili, V., School Leadership in Pre-university Education System in Albania, Politics, Issues and Trends, Social Studies 2012)

The National Program for Leaders’ Qualification aims to teach to the participants the knowledge, practices and necessary competences to perform the duty of the leader in compliance with contemporary successful headmasters and practices. The leader certificate whose assessment is based on bonuses is valid for eight years and it includes 'the Final Qualification Test'. 10% of the bonuses come from holding a degree, professional master, PhD and a foreign language qualification and 90% come from the test results (MASH, 2011). As a part of school and school leadership development, there are some co-operational efforts and two of those international co-operations are as follows; Ministry of Education and Science in Albania is an associate participant of Centre for Effective Learning Environments (CELE) that focuses on environmental effects on education. CELE explains its approach as to promote collaboration between designers, school leaders and communities to create learning environments that stimulate and inspire students. CELE also promotes effective approaches to planning, design, construction, management and evaluation of policy and practice (OECD, 2012).

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Albania is also a partner of Educational Testing Service (ETS) that develops and implements worldwide educational and development testing tools (ETS Global Institute, 2012). Moreover, Albania has been implementing international treaties of educational matters as constitutional obligation since the 1990s and it is explicitly stated in constitution Article 4 and 116 that ratified international treaties are prior to Albanian laws. The draft law of pre-university education system grants teachers and school leaders as similarly treated as other professions.In 'Education Excellence and Equity Project (PO76933) Report (no: ISR6367) dated 23/Nov/2011 by The World Bank, 'strengthening leadership, management and governance of the system' was one of the four core components of the project objectives. Also as one of the project objectives which was set on 03/May/2006, percentage aim of training school leaders in management and education leadership was 70% till 31/December/2011 and by 17/May/2012 100% of all school leaders had been trained in school management (World Bank, 2011). Under the task number MOES/CQ/CS/016/07 in the completed tasks section of 'Education Excellence and Equity Project Revised Procurement Plan 2010' in Albania by World Bank, the completed task is mentioned as 'Local TA: Create database for school teachers and principals and respective consultancy', under the task number MOES/CQ/CS/060/07 as 'Train RED/EO personnel and school principals and heads of subject committees in charge of qualification on implementing teacher professional development system reform and under the task number MOES/IC/CS/017/07 as 'Local TA: Design effective school indicators and a model for school self-evaluation and develop a professional development program and the program for training for school principals; define professional criteria for school principals and design manual for school principals (World Bank, 2012).

However, there are some serious weaknesses of the system. One of the most important ones is that there is lack of quality for the accountability which leads to unsatisfactory achievement reports and measurements. Except for it, some political reasons, non-transparent school leader selection criteria and appointment of unqualified candidates make school leadership unsteady post or profession. Although there is a significant improvement in networking systems at schools, there are not any professional networking and supporting systems specifically designed for school leaders and different types of school leadership as it has previously mentioned that school leaders have not got any freedom and control over the implementations and school management initiatives. Nor do school leaders have freedom or time and flexibility to follow any development programs on their own. Although the findings of Western Balkan Regional Competitiveness Initiative (RCI) in 2011 claim that there is a very serious lack of management skills (Western Balkan Regional Competitiveness Initiative, 2011), Training Needs Analysis was conducted in Albania in the same year. One of the reasons behind that can be the decline in the educational quality during the chaos in 1997 (Country Assistance Evaluation report, No: 21390).

6. ConclusionEducational concepts, including assessment and certification standards, and leaders’ development, should be taken as a gradually altering and interactive progress in our globalized and fast changing world. Primary and secondary education have become compulsory in most countries and the world population keeps growing. About 1398 million students were enrolled in pre-primary, primary and secondary education around the world in 2010 (The World Bank, 2016). With more and more people taking part in educational progresses and increasing number of private educational institutions, education has also become a significant field of business forcing educational establishments and specifically leaders to develop and compete. However, quality and development still stay as crucial concepts to be dealt with due to lack of quality human resources. Therefore, it is of great importance to take part in international co-operations to provoke self-development, build educational policies, standards, assessment tools and creditable certificates without neglecting environmental and practical concerns. One solution for the lack of quality in educational leadership can be that individuals can be more motivated if they see school leadership as a professional carrier. It might be also worth trying – at least as a pilot experiment – to employ successful professional managers in education although they may face difficulties in schooling. However, it should be kept in mind that economy plays a very

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important role in today’s societies and development is a very slow progress unless individuals see their careers in education financially and environmentally attractive.

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RISK MANAGEMENT & ANALYSES IN THE OIL INDUSTRY

Blerta MJEDA1, PhD Candidate1Ass/Lecture at Epoka University, Department Business-Administration

[email protected]

AbstractRisk is part of life. Avoiding all risk would result in no achievement, no progress and no reward defining risk as "The combination of the probability of an event and its consequence. Consequences can range from positive to negative." All organizations have objectives at strategic, tactical and operational levels - anything that makes achieving these objectives uncertain is a risk. As our world becomes increasingly volatile and unpredictable, we must cope with greater uncertainty. Risk management is the systematic process of understanding; evaluating and addressing these risks to maximize the chances of objectives being achieved and ensuring organizations, individuals and communities are sustainable. Risk management also exploits the opportunities uncertainty brings, allowing organizations to be aware of new possibilities. Essentially, effective risk management requires an informed understanding of relevant risks, an assessment of their relative priority and a rigorous approach to monitoring and controlling them. The use of oil as raw material and energy marks one of the highlights of the industrial revolution that swept the world. In these conditions, the demand for this source of energy, therefore for oil began to increase being accompanied by an ever-increasing interest in terms of investment for exploration, discovery and elaboration of her. The ever-an increase of oil and its by-products as a source of energy for the economy have described it as "black gold".To be effective, risk management must be proportionate to the size and nature of an organization. It can range from a risk assessment for a community event up to a sizeable, integrated process for a multi-national.Enterprise risk management (ERM) refers to an integrated or holistic approach to managing risk across an organization, using clearly articulated frameworks and processes led from board level. Risk management should be embedded in the general management of an organization and fully integrated with other business functions such as finance, strategy, internal control, procurement, continuity planning, HR and compliance. The degree of integration will differ between organizations depending on their culture, implementation process, and ways of operating and external environment.Keywords: risk management, uncertainty, Enterprise risk management, risk assessment.

Re-Evaluating the Relation of Risk to Risk Management In decision making there is ideally a set tolerance for risk. This tolerance applies regardless of how much of the risk is due to uncertainty from incomplete data, uncertain model parameters, or uncertain outcomes of future random processes. If the risk is too high, the decision maker is committed to select some action to address it. If the risk is low enough, the decision maker may proceed without specifically taking action to address the risk. When the calculated risk is too high, then the subsequent decision of what to do about it will require knowing the magnitudes of the contributions of various sorts of uncertainty to the total risk. The decision may seek to reduce the risk through management actions to control the feared outcomes, through collecting additional data and then recalculating the risk on the basis of new data and determining whether the risk is still too high, or through some combination of more data collection and control of outcomes. The tradeoff between the management action to control outcomes and choosing instead to collect more data (“to reduce uncertainty”) is essentially a matter of cost-benefit analysis, not a policy call: sometimes the cost of data is so high relative to its information content that it is more rational to accept the uncertainty and by default select the management action even though there is some probability that it is not actually necessary; in other circumstances the cost of new data is so low, and the value of new data is so high, that the choice definitely is to collect more data. Either way, what is being controlled is risk, and uncertainty appears in the equation only as a contributing factor in the risk.

How Do Data Quality and Quantity Affect Risk? A risk calculation is not just a model prediction, and so a real risk assessment will behave rather differently from a simple predictive model as its information input is reduced. Indeed, a simple predictive model may well predict a more favorable outcome when it is given less detailed or less

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precise input information. That is because the model prediction is a single scenario: the best estimate of the outcome. Furthermore, these kinds of models have “out of sight, out of mind” behavior. With less detailed input information, the model will default to assumptions of intermediate input values and will therefore deliver an intermediate output prediction. The way risk assessment takes account of our uncertainty about input values is to represent every uncertain input as a probability distribution. The greater our uncertainty about the input, the broader that distribution. Now, the uncertainty about inputs gets propagated through the prediction component of the risk assessment. The output of the risk assessment is also a probability distribution, showing the distribution of outcomes. The greater the uncertainty about inputs, the greater the uncertainty about outcomes. The uncertainty about outcomes is reflected in the spread of the probability distribution that describes the result. If there is little uncertainty, the probability distribution is concentrated over a narrow range of outcomes, and then the acceptability of the risk depends merely on whether the outcomes in that narrow range are themselves in a range that we consider acceptable. But if there is great uncertainty, the probability distribution is spread over a broad range of outcomes. If the spread is great enough, a considerable portion of the distribution can “spill over” into a range of unacceptable outcomes, even if the peak is centered over outcomes that are acceptable. This would describe a situation where the “best estimate prediction” is an acceptable outcome, but the risk is still unacceptable because the uncertainty leaves too high a probability of outcomes that are far from the best estimate. In very conventional applications of statistics to risk calculations, the uncertainty about inputs is factored into the calculation through the use of confidence limits. We might consider a map of our oil development site and ask what portions of the site should be off limits to road construction because the risk is “too high.” If our input information were of low resolution, a good deal of the off-limits area would be so classified only because our uncertainty about those locations was so high that the equivalent of the “upper 95% confidence level” would spill over into the range of unacceptable values [3] Basically, the map would contain a lot of gray areas that would be classified as high risk. With better input information (more spatial resolution, more precision) many of the gray areas on the map would be resolved into black and white. In the black and white areas the predictions are more certain, and some of the gray area that was previously classified as high risk because of the uncertainty will be reclassified as low risk, and more of the remaining areas that are still classified as high risk will be so classified because of a reasonably secure prediction that the outcome there really will be unacceptable. The high-risk gray area of the map shrinks as the input information improves [7].

Building Risk ModelsThe practical challenges related to building representative risk models are most often related to analysts. They must have the project-specific knowledge and yet the creativity to identify all relevant risks surrounding the project. They also need the appropriate mathematical modeling and statistical skills to build representative probabilistic models. The data required as input for risk models come from many different sources and are provided by professionals with diverse backgrounds that usually use different jargons. As such, risk analysts are expected to measure different items in common terms in order to “translate” the data into what their model needs [4].But more importantly, analysts must know how talk to each other. While no one analyst must have all of the skill sets mentioned, each skill must be contributed to a collective whole, giving a company the requisite skills for a holistic understanding of a model at hand. Seems obvious, but you’d be amazed how many talented professionals are simply isolated or unknown in their own companies [1]The process of acquiring data for risk models is sometimes noisy, too. Risk analysts often walk a fine line between, “just asking for more a little more information,” and creating an extra burden for their peers or–even worse–being perceived as an additional layer of corporate bureaucracy [6]. This is why it is ultra-critical to explicitly show peers the benefits of risk analysis and how it will be used to provide decision makers with better information. Doing so will help get everyone onboard.The software and hardware tools available today for risk analysis are good and many. Processing power is not a restraint anymore in most applications, so the tools are not an issue. Still, risk models are harder to understand and validate than their deterministic pairs.

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Risk management & analyses in the oil industry

Decision-Maker’s ProfileSome practical challenges are married to the methods by which decision makers perceive risk and to their appetite for taking risk. Although interpreting the results of deterministic models is a straight forward process, agreeing on the relevant criteria to make decisions based on risk is not always an easy task [5]. Furthermore, the interpretation of risk model results adds additional complexity to the process.For example, imagine a project in the oil company whose deterministic net present value (NPV) is positive—representing a successful project that adds to the company’s portfolio. Obviously, a company should move forward with it. (Of course, other metrics are also taken into account when making wise decisions, but I’m limiting the discussion to the NPV only for the sake of the comparison.) While the risk analysis of the project might indicate that the expected NPV is indeed positive, it may also have a 30 percent probability of generating negative NPVs [2].For a company with a diversified portfolio, one new project with a positive expected NPV but 30 percent odds of bad returns might be acceptable. But for another company owning a much smaller portfolio, the potential project is considered relatively large and the 30 percent probability of negative NPVs is simply unbearable. So, depending on the company size, portfolio makeup, and risk sensitivity, the results of a single risk analysis will be interpreted along a wide continuum of preferences. Understanding these characteristics of the decision makers, project managers can adjust their project model.

ConclusionRisk analysis is a science, but it’s not an exact science. Having a well-informed and honest view of an oil company will greatly enhance the ability to construct risk models helping to make decisions most applicable and beneficial to a company and its stakeholders. While the practical challenges facing the implementation of risk analysis in oil companies are many, the benefits greatly outweigh them.

Biblography[1] C.M. Bishop (2005). Neural networks for pattern recognition.[2] Carlin, Bradley P.; Louis, Thomas A. (2000). Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.).[3] Bent Flyvbjerg and Alexander Budzier, 2011, "Why Your IT Project May Be Riskier Than You Think", [1] Harvard Business Review, vol. 89, no. 9, [4] Dorfman, Mark S. (2007). “Introduction to Risk Management and Insurance” Englewood Cliffs, N.J: Prentice Hall(9 ed.).[5] Hassenzahl, D. M., Finkel, A. M. (2008). “Risk assessment, environmental/occupational.International Encyclopedia of Public Health”, 5 [6] Hubbard, Douglas (2009). “The Failure of Risk Management: Why It's Broken and How to Fix It”. John Wiley & Sons[7] Giovanni Parmigiani, Lurdes Inoue 2009 “Decision Theory: Principles and Approaches”.

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EVALUATION OF THE MODELS USED TO CREATE VECTOR SPACE REPRESENTATION OF WORDS

Alkid ADEMI1, 1 Head of Analysis and Programming Sector, IT Department, Bank of Albania, Tirana, Albania

[email protected]

AbstractOne of the main areas of interest today in the field of data mining and artificial intelligence is to design algorithms to allow the computers to “understand” the natural language. The natural language processing is composed of many tasks, like creating and getting the corpus of words, creating the model, training the model against the corpus and finally analyzing the result. The common denominator of all the recent models is that they try to create a learning vector space representations of words which succeeds in capturing semantic and syntactic regularities between the words using vector arithmetic.This article will focus on explaining the models currently in use and evaluate the performance of the models in word analogy, word similarity and named entity recognition. For our evaluation to be as much accurate as possible we will try to find a very large corpus of words for training the models.Keywords: Data Mining, Natural Language Processing, Word Space Model, Training Data, Corpus of Words.

IntroductionIn the vector space models of language each word is a presented with a real valued vector. The resulting vector space can be used in many application such as retrieval of information, question answering, classification of documents, parsing and entity recognition. A variety of models and methods have been developed to create the vector spaces. Some of the models rely on the closeness similarity or distance to evaluate the quality of such sets of word representations. Other models evaluate the variance in the difference of vectors. For example the analogy “Square is to hexagon like four is to six” will be encoded in the vector space by the equation Square-Hexagon=Four-Six. In our work we will analyze some of the models: Hellinger PCA (HPCA), Continuous Bag of Words (CBOW), Skip Gram (SG) and Global Vectors (glove). Finally we will make some experiments on these models to try to determine the performance of each model on real datasets of data taken from different corpuses.

Hellinger PCA Model (HPCA).Hellinger PCA Model is a derivative of a class of SDV models, which try to find word vectors, by first looping over a massive dataset and accumulating word co-occurrence counts in the form of a matrix X, and then perform Singular Value Decomposition (SVD) on X to get a USVT decomposition. The rows of U are used as the word vectors for all the words in our corpus. The assumption is that words that are somewhat related will appear frequently in the same sentence. For instance the words “banking”, “finance”, “money” and “bond” will most probably appear together within the same sentence while it will be probably impossible or unlikely to find the words “banking”, “football”, “fish” and “crop” in the same sentence. Let’s take into consideration a corpus of 3 simple sentences. I enjoy riding. I like football. I like movies. The co-occurrence matrix will be:

I Enjoy Riding Like Football Movies .I 0 1 0 2 0 0 0Enjoy 1 0 1 0 0 0Riding 0 1 0 0 0 0 1Like 2 0 0 0 1 1 0Football 0 0 0 1 0 0 1Movies 0 0 0 1 0 0 1. 0 0 1 0 1 1 0

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We now perform SVD on X, observe the singular values (the diagonal entries in the resulting S matrix), and cut them off at some index k based on the desired percentage variance captured:

∑i=1

k

σ i

∑i=1

¿V∨¿ σi

¿¿

We then take the submatrix of U1:|V|,1:k to be our word vector matrix. This would thus give us a k-dimensional representation of every word in the vocabulary. This model will give us word vectors that are more than sufficient to encode semantic and syntactic (part of speech) information. A main problem with this model is that the most frequent words contribute a disproportionate amount to the similarity measure: the number of times two words co-occur with “the” or “to”, for example, will have a large effect on their similarity despite that as matter of fact they have no semantic similarity between them. To avoid this shortcoming many techniques have been proposed. One of this techniques is transform the co-occurrence matrix with a square root transformation in the form of Hellinger PCA ( HPCA).

Iteration based models: Continuous Bag of Words Model (CBOW) and Skip Gram (SG).Instead of computing and storing information about a huge dataset with just one operation, the Iteration based models try to learn one iteration at a time. Finally this models will be able to predict the probability of a word given its context. At every iteration the iteration based models evaluate the errors and update the rules in order to avoid the parameters which caused the errors. Let’s take into consideration a simple sentence “the horse jumped over the fence”. A good model with give this sentence a high probability because it’s correct both syntactically and semantically. By assuming that the word occurrences are completely independent we can present the probability of the sentence with the following formula:

P(w(1), w(2), ... , w(n))=∏i=1

n

P (w ( i ))

The assumption that the word occurrence is independent from the other words is simplistic because we know that the presence of the word in a sentence is highly dependent from the previous word. Let’s elaborate further this idea by stating that the probability of the sentence is the probability of a pairwise of word and the word next to it.

P(w(1), w(2), ... , w(n))=∏i=2

n

P (W (i)∨W (i−1))

Let turn back to our sentence. We treat {“The”, “”horse”, “over”, “the”, “fence”} as a context and try to predict the center word “jumped”. This approach is used in Bag of Words Model (CBOW). The input to the model would be w(i−2),w(i−1),w(i+1),w(i+2) the preceding and following words of the current word we are. The output word w(i) is represented by a Huffman binary tree in the classification section. The objective function is a hierarchical softmax. Stochastic Gradient Descent. (SGD) is used to train CBOW while the gradient is calculated with the back-propagation algorithm.Another approach is to create a model such that given the center word "jumped", the model will be able to predict or generate the surrounding words "The", "horse", "over", "the", "fence". In this case we call the word "jumped" the context. We call this type of model a Skip Gram model (SG). Skip-gram is less sensitive to word proximity. In Skip-gram, the neural network input is one single word inside a context window around the output word. When enumerating the word pairs (input and output) in a training corpus, Skip-gram actually captures the total co-occurrences of two words.

Global Vectors (Glove) Model.GloVe is essentially a log-bilinear model with a weighted least-squares objective. The main algorithm used in the model derives from the observation that ratios of word-word co-occurrence probabilities have the potential for encoding some form of meaning. For example, let’s take into consideration the co-occurrence probabilities for target words ice and steam with some example words taken from the

32

Evaluation of the models used to create vector space representation of words

vocabulary. The table below gives these probabilities on a 6 billion word corpus:Probability and ratio

k=solid k=gas k=water k=fashion

P(k|ice) 1.9 X 10-4 6.6X10-5 3.0X10-3 1.7X10-5

P(k|steam) 2.2X10-5 7.8X10-4 2.2X10-3 1.8X10-5

P(k|ice)/P(k|steam) 8.9 8.5X10-2 1.36 0.96

As it is expected the probability rates in the table shows that ice co-occurs more frequently with solid and steam co-occurs more frequently with gas that is does with solid. We can see that both ice and steam co-occurs with water frequently, which again is expected because there is a strong semantic relationship between them. Both of them co-occurs with the fashion very infrequently. Now let’s focus in the last row of the table, the ratio between the probabilities of ice and probability of steam. The values much greater than 1 correlate well with properties of ice and values much less than 1 show specific properties of steam. The ratio of probabilities in the table encodes some form of meaning which is associated with the abstract concept of thermodynamic relation in the real word.The training objective of GloVe is to learn word vectors such that their dot product equals the logarithm of the words' probability of co-occurrence. Owing to the fact that the logarithm of a ratio equals the difference of logarithms, this objective associates (the logarithm of) ratios of co-occurrence probabilities with vector differences in the word vector space. Because these ratios can encode some form of meaning, this information gets encoded as vector differences as well.

Evaluation of the HPCA, CBOW, SG and GLOVE models.We conducted our experiment on word analogy tasks and on some specific word similarity task.Word analogy. The word analogy task consists of questions like, “a is to b as c is to _?” The dataset contains 19,544 such questions, divided into a semantic subset and a syntactic subset. The semantic questions are typically analogies about people or places, like “Paris is to France as Berlin is to _?” The syntactic questions are typically analogies about verb tenses or forms of adjectives, for example “fly is to flying as jump is to _?” To correctly answer the question, the model should uniquely identify the missing term, with only an exact correspondence counted as a correct match. We answer the question “a is to b as c is to _?” by finding the word d whose representation wd is closest to wb−wa

+wc according to the cosine similarity.Word similarity. While the analogy task is our primary focus since it tests for interesting vector space substructures, we also evaluate our model on a variety of word similarity tasks. These include WordSim-353 (Finkelstein et al., 2001) and RW (Luong et al., 2013).We trained our model on five corpora of varying sizes: a 2010 Wikipedia dump with 1 billion tokens; a 2014 Wikipedia dump with 1.6 billion tokens;Gigaword5 which has 4.3 billion tokens; the combination Gigaword5 + Wikipedia2014, which has 6 billion tokens; and on 42 billion tokens of web data, from Common Crawl5. For the Glove model we tokenize and lowercase each corpus with the Stanford tokenizer, build a vocabulary of the 400,000 most frequent words, and then construct a matrix of co-occurrence counts X. In constructing X, we must choose how large the context window should be and whether to distinguish left context from right context. In all cases we use a decreasing weighting function, so that word pairs that are d words apart contribute 1/d to the total count. We use a context of ten words to the left and ten words to the right.With word2vec, we train the skip-gram (SG) and continuous bag of words (CBOW) models on the 6 billion token corpus (Wikipedia 2014 + Gigaword5) with a vocabulary of the top 400,000 most frequent words and a context window size of 10. We used 10 negative samples, which are a good choice for this corpus.Model Dimension Corpus Size Semantic Syntactic TotalHPCA 100 1.6B 4.2 16.4 10.6Glove 100 1.6B 66.2 55.6 60.8SG 300 1B 61 61 61CBOW 300 1.6B 16.2 53.8 36.7Glove 300 1.6B 80.6 61.8 70.2HPCA 300 6B 56.8 63.2 61.1CBOW 300 6B 63.6 67.4 65.7

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SG 300 6B 73.0 66.0 69.1Glove 300 6B 77.4 67.0 71.7CBOW 1000 6B 57.5 68.7 63.7SG 1000 6B 66.2 65.3 65.6HPCA 300 42B 81.7 69.1 74Glove 300 42B 81.8 69.2 74.9

For the HPCA model we generate a truncated matrix X trunc which retains the information of how frequently each word occurs with only the top 10,000 most frequent words. This step is typical of many matrix-factorization-based methods as the extra columns can contribute a disproportionate number of zero entries and the methods are otherwise computationally expensive.The results on the word analogy task are presented on the following table. Underlined scores are the highest within models similar in size, bold scores are the highest overall.Spearman rank on word similarity tasks are presented on the following table. Underlined scores are the highest within models similar in size, bold scores are the highest overall.Model Dimension Corpus Size WordSim-353 RWHPCA 300 6B 65.8 37.0CBOW 300 6B 57.2 32.5SG 300 6B 62.8 37.2Glove 300 6B 65.7 38.1HPCA 300 42B 74.0 39.0Glove 300 42B 75.9 47.8CBOW 300 100B 68.4 45.5

ConclusionIn the word analogy task: The Glove model performs significantly better than the other, often with smaller vector

dimensions and smaller corpora. In the semantic subtask there is a substantial increase in performance for all the models. This is

expected because larger corpora typically produce better statistics. The same is not true for the syntactic subtask where in most of the cases there is no performance

increase when learning on a larger corpus. In some cases Wikipedia corpus gave better result that GigaWord, despite the fact of having a smaller size. This is due to the fact that Wikipedia has a lot of articles that have country –city or other geographical information which are very helpful for answering word analogy syntactic subtasks.

In the word similarity task: HPCA model performs slightly better than Glove and the other modes on a corpus of 6 billion

word tokens. By increasing the corpus size the Glove model gives better accuracy compared to all the models, in both Wordsim353 and RW similarity tasks.

For the CBOW model we take some data from the word2vec website which have used a corpus with about 100 billion tokens of news data. Glove outperforms this model even by using a corpus with half a size.

When comparing the models, there are a number of parameters which should be taken into account: Vector length, context window size, and corpus and vocabulary size. We talked in detail about these parameters and the strategy for comparing them in the previous section. The last, but not the least important parameter is the number of iteration for Glove Model and the number of negative sampling for the CBOW and SG model (this parameter does not exist in the HPCA model). We tried to increase the number of negative sampling in the CBOW and SG model and the performance in accuracy begins to drop after 10 and 15 negative sampling respectively. We Glove model continually increases the accuracy by increasing the number of iteration until 25 iteration. After that the increase in performance stops to be substantial so we train this model with 25 iteration during our analysis.

References [1] Models for Word Vectors explained.Peter D. Turney, 2010, “From Frequency to Meaning:Vector Space Models of Semantics”.

34

Evaluation of the models used to create vector space representation of words

Mikolov et al. , 2013, “Distributed representations of words and phrases and their compositionality”, Advances in neural information processing systems[2] Linear Algebra Review and ReferenceZico Kolter (updated by Chuong Do), 2015, “Linear Algebra Review and Reference”3] Probability TheoryArian Maleki and Tom Do, 2015, “Review of Probability Theory”

35

ANALYSIS AND COMPARISON OF GAUSS AND GAUSS – JORDAN METHODS AND THEIR ALGORITHMS, FOR THE SELECTION OF

LINEAR EQUATION SYSTEM

Florim IDRIZI1, Burhan RAHMANI2, Agon MEMETI1,2,3 Departament of Informatics - State University of Tetova, Macedonia

[email protected], [email protected]

Abstract In this paper we primarily, theoretically and in brief tried to explain the methods of Gauss and the Gauss-Jordan, later the code written in c ++ was conducted for solving a system of linear equations by the two methods. Through programmed programs in c++ comparisons are achieved from attained results, and measurement time. In relevant codes we used files where we stored the obtained results in certain files and then we set them in the table where we can see more clearly the difference between the obtained results and the time of execution of these two methods. Keywords: method of Gauss, Gauss-Jordan method, the solution of system of linear equations, c ++.

1. INTRODUCTIONThe linear systems equations are the most dense patterns that occur in practical scientific experimentation, hence for the solutions there were many methods developed. Numerical analysis deals with the studying, constructing and analyzing the algorithms for the numerical of a large number of mathematical problems. In this area there are two types of methods studied for solving systems of linear equations, direct methods (or correct) and those iterative.0

The two main methods for solving systems of linear equations are the methods of Gauss and Gauss-Jordan method, which take part in the direct methods. Gaussian method is the most efficient and it is widely used in practice, as well as the Gauss-Jordan method is a variant of the Gaussian elimination. The difference stands in that that when the unknown is eliminated in the method of Gauss-Jordan, it is then eliminated from all equations in the system.0

In addition, all the rows are normalized by dividing by their elements in the shaft. These methods are also named after the discoverers of the selecting linear system: Carl Friedrich Gauss and Wilhelm Jordan. The system with n linear equations and unknown n.

(1)

Equations 1 Gaussian elimination for the solution of linear systems of equations, G´erard Meurant, France, 1992

Can be written in matrix form: Ax = b (1.2), where A is the matrix with m x n coefficients and b ε Rn free vector limitations.Although there are the formulas of Kramer for solving these problems, their meta lies in the large number of actions which need to be carried out. So for example for a system with n = 10, the size needs to be conducted with 400 million arithmetic operations. This makes this method unusable and has led to the finding of new methods such as the method of Gauss and Gauss-Jordan.

2. CASE STUDY

0 Methods of numerical analyses. Fatmir Hoxha, Tirana 2012.0 C++ solutions for mathematical problems, Arun Ghosh, New Delhi 2005

37

Figure 2 The result for six equations linear system using Gauss Elimination MethodFigure 1 The result for six

equations linear system using

Since both algorithms are direct methods for solving systems of linear equations, the difference between these two algorithms is very small. To see the difference we will choose systems of different sizes, where we choose systems with 3 equations up to 188 equations. As to obtain a linear system of equations 188 is impossible, we have implemented a code in c ++ which generates itself a system of how many equations we ourselves want, whereas the internal variables are initialized under a certain value. In our case we use the value 50, and from this value the internal variables of the system are initialized.The obtained values from the systems with different number of equations:

Gauss Gauss-Jordan Gauss Gauss-Jordan Gauss Gauss-Jordan3 equations 6 equations 9 equations0.51607198 0.51605976 1.3283973 1.3284222 0.24236241 0.242362891.4632939 1.4632941 -0.4362427 -0.43623549 1.451591 1.4515263

-0.15687889 -0.15687902 -0.31092268 -0.31092951 -1.6402919 -1.6402512 0.62217069 0.62217289 1.6584058 1.658378

Time:1.661 Time:1.811 -0.85849142 -0.85850626 2.614852 2.61479450.20137934 0.20137885 -3.4035788 -3.4035177

-0.58583409 -0.58581096Time:1.655 Time:2.029 1.6757369 1.6757174

0.21137545 0.21139024

Time:1.58 Time:2.146*Time is displayed in seconds

Gauss Gauss-Jordan Gauss Gauss-Jordan Gauss Gauss-Jordan18 equations 36 equations 72 equations -0.67658776 -0.67656785 -11.944526 -11.936118 -0.41985142 -0.41959-0.4115546 -0.41152006 -10.428529 -10.444694 -0.46110001 -0.46106-1.3336753 -1.3336536 -10.942468 -10.94721 1.1543683 1.154318

-0.36234656 -0.36238316 6.154151 6.1798272 0.30003577 0.299840.73397225 0.73386359 30.545483 30.570272 -1.2473426 -1.24724

2.944546 2.9445605 -13.773993 -13.793234 -0.37648118 -0.37635-0.37873152 -0.37876308 -10.968795 -10.978383 -0.098503515 -0.09852

2.0008762 2.0008924 21.339567 21.347904 -0.69671267 -0.6968-0.47441751 -0.47435522 -7.8983221 -7.9111028 0.43472642 0.43468

38

Analysis and comparison of Gauss and Gauss – Jordan methods and their algorithms, for the selection of linear equation system

0.50770843 0.5078001 -2.7387993 -2.746423 1.1032553 1.1031251.3449454 1.3449082 -10.752131 -10.768005 -0.086750858 -0.08674

-1.4082731 -1.4083148 12.292426 12.308265 -0.20554976 -0.20551.4436799 1.4437097 -8.6864309 -8.6926994 1.1356065 1.135561-2.131799 -2.1318228 -13.672196 -13.694575 -0.39516664 -0.39513

-2.2582226 -2.258215 -0.82785302 -0.81720752 -0.16174485 -0.161731.5476201 1.5475745 1.9561856 1.9758722 -0.95867109 -0.95867

0.91867048 0.91865128 9.2687864 9.2856388 0.53512722 0.535077-0.66540194 -0.66537732 -7.5539093 -7.567462 -0.32488641 -0.32474

-13.519744 -13.548623 -1.1810715 -1.18091Time:2.917 Time:2.654 22.056425 22.066263 0.81644338 0.81646

20.995138 21.022581 1.2311064 1.23097-7.4121981 -7.4037271 -0.18959506 -0.1895313.225112 13.256004 0.54537427 0.545403

-2.6820548 -2.7014248 1.0354438 1.03529612.951962 12.946257 -0.93963766 -0.9395

-2.0421021 -2.0439954 -0.20861195 -0.2085528.916759 28.924288 -0.57670802 -0.576726.3081393 6.331336 1.8481532 1.848032

-4.7896156 -4.7892375 -0.1793666 -0.17929-29.678482 -29.708702 -2.0277512 -2.027524.5567393 4.5627966 -0.89632237 -0.8962523.817978 23.827656 -0.4094044 -0.40936-31.26532 -31.290989 0.038098987 0.03814617.753698 17.776989 0.60764575 0.607553

0.11109202 0.096038148 -0.21349698 -0.21361-8.570281 -8.5731792 0.85840249 0.858378

-0.67287302 -0.67278Time:2.999 Time:5.191 0.71558857 0.715477

-0.18822111 -0.188211.2066945 1.206553

0.15429835 0.154158-0.64873642 -0.64872-0.89056021 -0.89034-0.34951118 -0.349470.74509448 0.745035

-0.11085235 -0.110831.2703414 1.2702481.1275012 1.127433

-0.66554171 -0.66535-0.24943432 -0.24943

1.0943261 1.09412-0.42173481 -0.42175-0.71239436 -0.71226-0.84863883 -0.84839-1.2201345 -1.220061.2325025 1.232324

0.70502919 0.7049340.29010117 0.290042-0.7089994 -0.70902-0.1158774 -0.11599

-0.86883801 -0.86865

39


0.35537869 0.3553490.76839566 0.7681840.26314241 0.2631120.63715518 0.637204

-0.32524174 -0.325171.3137032 1.3135

-0.87787348 -0.87784-0.083442919 -0.08332-0.28055725 -0.28050.50161636 0.501615

-0.52942419 -0.52956Time: 5.798 Time: 6.229

3. ConclusionFrom the table we can clearly see that whichever method we will use, the obtained results will be very similar, in both methods we have used a precision output of 8, whereas the error between the obtained results from both methods is 10-5, which means that we get a very small difference. While in the program also we have measured the time of execution of the operation, which depends on the speed of the computer and the program itself and also it should be noted that the timing changes in each re-execution of the codes, but we can say that the values in the obtained time are very similar to each re-execution.

Reference[1] Fatmir Hoxha - Methods of numerical analyses, Tirana 2012.[2] Arun Ghosh - C++ solutions for mathematical problems, New Delhi 2005[3] G´erard Meurant - Gaussian elimination for the solution of linear systems of equations, France, 1992

40

CULTURAL DIFFERENCES BETWEEN PUBLIC AND PRIVATE ORGANIZATIONS

Alma SELMANI1, 1University of Tirana

[email protected]

AbstractThis paper aims at researching existing literature to identify differences in the culture of public and private organizations using the synthesis of empirical studies and research in this field. For this research interest using the synthesis of secondary data it turned out that some contradictions and limits on actual studies as long as past study efforts are focused on different aspects of the construct of the organizational culture which results in different directions especially in the organizational aspects and less in terms of individual employee conduct. Although, the actual literature revealed that there are some similarities and differences in preferred system of employee’s values in public and private organizations. Also it was found as Important the existence of some general organizational values of traditional public sector such as "accountability", "legality", "efficiencies" and "neutrality" (Eikenberry and Kluver 2004). Further, a set of organizational values highly associated with quality management in the private sector practices have been found as follows: Focus on the customer; Focus on human resources; The emphasis on quality processes; Continuous Improvement; and Cooperation (Cartwright, 1999). On the other hand considering the Individual level, this paper refers to Van Der Wal et.al (2008) study, which have found some of the public sector most important values such as: "accountability", "lawfulness", "incorruptibility "; "expertise"; "reliability", "effectiveness" and "impartiality" following those of the private sector, "profitability"; "accountability"; "reliability"; "effectiveness" and "innovation".Key words: Organizational culture, Organizational values

IntroductionBoth types of public (government agencies) and private (businesses) organizations vary widely in terms of their financing, ownership and governance (Perry & Rainey 1988). A government agency is funded by the government also is in public ownership and public interest-oriented. In contrast, a business organization is privately funded and driven by market forces. A public sector organization is supposed to operate in a different way comparing with a private sector organization in some organizational and individual practices. So far, public administration researchers evidences assume that all employees in the public sector organizations exhibit different behaviors compared to private sector employees. An important construct that speaks to the behavior and practices of organizations in fulfilling their existence is their organizational culture, which has a direct impact on the performance of the organization. As an example, one of the most relevant to theoretical and practical contribution in this area are the studies by Cameron and Quinn (2005), which discovered the importance of culture in the development of competitive advantage, arguing that the success of organizations derives not only from some circumstances or conditions that are outside of their environment, such as market entry barriers, industry rivalry and power of suppliers and buyers but more related to the company's internal values. When the values of the employees are not congruent with the values of the organization, then we have a clash of values and an organizational culture that prevents high performance results (Cameron & quin, 2005, p.4). Connecting the culture of the organization with its performance is considered by many scholars (Ogbonna & Harris, 2000; Kotter & Heskett, 1992; Kandula, 2006; etc.) and referring to Kandula (2006) the key to good performance is a strong culture. He further states that due to changes in organizational culture, the same strategy will not yield the same results for both organizations in the same industry and in the same place. A positive and strong culture can make an average employee to achieve excellent performance levels while a negative and poor culture may discourage an employee in his performance. Therefore,

41

organizational culture has an active and direct role in performance management. Moreover, the culture of the organization is being increasingly identified as a key factor for this purpose. Against the background of a rapidly changing external environment, most organizations are concerned how best to achieve their goals and objectives. Part of this interest is reflecting on the type of culture that is necessary to advance these goals and objectives. Consequently, the organization's culture seems to play an important role in maximizing asset motivation and intellectual and human capital in particular (Baker, 2002). In addition, it seems of interest to identify if there are cultural differences in public organizations and private and for this purpose we have looked up into several studies by various authors which converge in the existence of some differences due to structural, managerial and individual (values and behaviors) aspects.

1. Organizational culture constructSchein (2004) is perhaps the leading analyst of the concept of culture which, according to him, may appears on several different levels into the organization, where the term "level" refers to the rate at which it is visible to an observer the cultural phenomenon. In this way, the author adopts the functionalist opinion describing culture as a model of underlying assumptions, invented, discovered or developed by a group as the way that this group learns to cope with his problems of adaptation to external environment and integration with the domestic environment, which worked quite well to consider these assumptions as valuable models and therefore should learn to the new members as the correct way to perceive, think and feel regarding these problems. Although in a rich literature related to organizational culture, many authors (Johnson & Schules, 1990; Schein, 1992; Hatch, 1993; etc.) recognize that culture’s concept basically consists of visible and invisible aspects. Where the visible part represents the organization's practices otherwise the invisible represent the basic values that lie at the center of these practices. The organization's values are not always directly visible to its employees, but often we have to assume that the organization values are shown or embedded in the daily practices of them. Also, the set of values itself is more difficult to measure and study because it involves psychometric assessments and thus organizations can show more differences in their practices rather than values system. Further, it is noted that the organizational culture serves as a control tool where it is sufficiently affirmed (strong cultures) as long as replacing the formalization or bureaucracy. While operating regulations serve to regulate the conduct of the employee’s behavior they could be rooted in a set of values that regulate the behavior of employees anticipating those as desired behaviors and unwanted by members of the organization. Consequently, the bureaucracy / high formalism is not seen necessary in cases where the culture is consistent with bureaucratic rules (as shown in Table 1.1).Table 1.1 Culture as a form of controlFormalism as a form of control Culture as a form of controlOperating regulations serve to regulate employee’s behavior

Common values serve to regulate employee’s behavior

High Formalism, need for written papers/documents, offering predictability, rules and consistency

Strong culture no need for written papers/documents, offering predictability, rules and consistency

Employees are asked to take specific job works. Work is motivated by financial rewards and fear from penalties.

Strong culture can be powerful than formal structure of control because culture controls mind, spirit and body.

Source: Kunda, 1992

Thus, organizations supported by high formalism (bureaucratic) may reduce the need for high bureaucracy by creating strong cultures. For this reason, it can be said that the stronger

42

Cultural differences between public and private organizations

culture is less concerned should be the management body about developing formal regulations to govern the employee’s behavior. Further, referring Daft (1992), there are 10 variables identified as 10 cultural features associated with the structural dimensions of the organization (Table 1.2.). Karakteristikat kryesore të Kulturës Lidhja Strukturore1. Personal initiative: Level of responsibility, and autonomy Centralization/ formalism2. Risk Tolerance: the extent to which individuals are encouraged to be " aggressive ", innovators and risk takers.

Formalism

3. Direction : The degree to which the organization creates clear objectives and performance expectations.

Formalism

4. Integration: The degree to which units within the organization are encouraged to act in a coordinated manner.

Standardization/ formalism

5. Management support : degree to which managers provide clear communication , assistance and support to their subordinates.

Extent of Control

6. Control . The number of rules and the amount of direct supervision used to control the behavior of employees.

Formalism/ Extent of Control

7. Identity: The degree to which members identify the organization as a whole instead as their group work or their area of expertise.

Complexity/Differentiation/ specialization

8. Reward systems : the extent to which the rewards distribution ( salary increase , promotion )is based on the performance of employees in contrast to favoritism, etc.

Hierarchy of authority

9. Conflict Tolerance: the extent to which employees are encouraged to handle conflict and criticism openly.

Centralization

10. Communication systems : the extent to which communication within the organization is limited to formal hierarchical authority.

Hierarchy of authority

Source: Daft , 1992Next considering the link between organizational performance and its culture, in a structural system the role of cultural values within organizations which operates under different structural systems as mechanistic or organic systems is taken into consideration by Daft (1992 ). According to this author organizations operating under mechanistic systems tend to have a bureaucratic culture where the focus is on compliance and enforcement of regulations rather than to employees matters. Otherwise organizations operating under organic systems tend to develop a culture mainly focused on employees matters (Table 1.3).

Table 1.3 Mechanistic systems / Organic - Internal / External variables Organizational Output Mechanistic Systems Organic SystemsContextual Variables

Environment Technology Size Goals and Strategy Culture

SafeRoutineBigEfficiency Employees are taken for granted, bureaucracy, systematic

Unsafe Non-routineSmallEffectiveness Based on working teams, flexible, innovation, efficiency

Internal Output Structure Mechanism of control Communication Innovation Cross-department relationship Decision making

Functional, centralizationBureaucracyFormal system of information Non- frequentCooperationRational analysis

Teams, decentralizationClanFace to faceFrequentConflictProve- Mistake

Source: Daft (1992)

Pollitt and Bouckaert (2004) distinguish between two models of administrative culture (public organizations): The Rechtsstaat model with a strong legal basis, as in France and Germany, where the actions of civil servants are based on a strong understanding of the law and the model of public interest, very common in Anglo- Saxon places which grants the state a broader or dominant role in

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society. Of course the law is an essential component of governance, but its prospects and specific procedures are not as dominant as in the Rechtsstaat model. The law is not in the background and civil servants are regarded as simply citizens who work for government organizations.

2. Organizational differences on cultural valuesThere is a growing empirical research on some key organizational values of public and private organizations where organizational values refer to the principles underlying the models and norms of individual’s behavior. Kernaghan (2003) defines values as “stable beliefs witch influence the choices we make." Values are desirable goals or behaviors which individuals place a high value and allow them to choose between available options. The commitment of public servants to the values they offer through public service leads to a significant positive relationship between public employees' motivation and employee’s attitudes of and the level of its performance (Sangmook, 2005).Given the historical diversity, social and cultural rights in different parts of the world it arises difficult to unify a set of universal values for public organizations. However, within the EU, there are many shared values between public service organizations. In most European countries, public administration exists to provide public service politically neutral and be faithful to public interest. Attempting to ensure neutrality and loyalty, there are some formalized rules relating to issues such as fairness and respect for the law (Demmke 2000). These rules include maintaining confidentiality, acting in the public interest, through the avoidance of conflicts of interest, ensuring accountability for a range of stakeholders and treatment of all parties in a non-preferential, also. Even though this set of values varies according to the type of sector or organizational unit, there is an interesting study (Sherman, 1998) who has summarized a set of values overall public organizations. Including some important public values as: Honesty and integrity; impartiality; Respect for the Law; Respect people; Efficiency and Effectiveness; Responsiveness and accountability. Later it was found that traditional public sector shows some main organizational values such as "impartiality", "legality", "efficiencies" and "neutrality" (Eikenberry and Kluver 2004). Other important efforts on this topic are taken by various researchers and referring Toonen (2003) there are three sets of public administration values:

Savings and economy: family values related to this optimal and efficient use of resources, and basically standing on public management from a financial standpoint.

Justice, equality and fairness: These values have to do with the integrity and public confidence in the development of government.

Power, flexibility and sustainability: These values ensure public administration and government to be stable while facing different pressures, but at the same time stay flexible enough to learn and adapt to changes while preserving the public trust.

Many researchers claim that organizational values of agencies should differ from those of business organizations. Given the main purpose of their existence we know so far that those public were created to fulfill the public interest and private organizations are created to fulfill some private interests. Thus, is easily understood that may exist some differences into the desirable value system for each of them but similarities should not be excluded also. As the attention of the private sector remains to management practices focused on clients then the quality of what will be offered makes a source for the articulation of a set of values itself oriented on quality practices. As Cultural values associated with quality management can be mentioned: focus on the customer; focus on human resources; the emphasis on quality implied in any activity or process within the organization; continuous improvement; and cooperation (Cartwright, 1999).

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Cultural differences between public and private organizations

Beyond the differences of these groups organizational values may have similarities on these two sectors at the level of employee behavior. According to a study of Van Der Wal et.al (2008) a set of key values was identified which relate to the employee in public and private organizations and the results showed that the most important values for the public sector, in a descending order were: Accountability, which was related to public decision-making followed by "lawfulness ", "incorruptibility"; "Expertise"; "reliability", "effectiveness" and "impartiality". As well some less important values were “self-fulfillment" and "profitability". Further, in the private sector the most important values were listed as "profitability"; "accountability"; "reliability"; "effectiveness" and "innovation", while less important were found values as "self-fulfillment" and "social justice". These results demonstrate the emerge of clear differences but also some similarities in desirable values among the employees of the two sectors such as: accountability, expertise, reliability and effectiveness. Yet another conclusion, was that the preferences of employees were related specifically to the employment sector rather than to demographic variables such as age, gender or work experience.

Conclusions• Arises difficult to unify a set of universal values for public organizations given the historical diversity, social and cultural rights in various countries of the world. Also, there are difficulties measuring the values of the organization, which are not always directly visible to its employees, but often have to assume that the organization values are shown or embedded in the daily practices.• Organizations which operate under mechanistic systems tend to have a bureaucratic culture where the focus is on compliance and enforcement of regulations rather than employees. In contrast, organizations under organic systems tend to developed a culture focused on employees (Daft, 1992).• Commitment of public servants to the values of public service leads to a significant positive relationship between public employees' motivation and employee’s attitudes and the level of performance (Sangmook, 2005).• As the attention of the private sector remains to client management practices then the quality of what is offered leads to the articulation of a set of values oriented on quality. Cultural values associated with quality management can be: focus on the customer; focus on human resources; the emphasis on quality implied in any activity or process in the organization; continuous improvement; and cooperation (Cartwright, 1999).• Some of the most important values in the public sector, following a descending order were: accountability, which was related to public decision-making followed by “lawfulness", "incorruptibility"; "expertise"; "reliability", "effectiveness" and "impartiality". Less important values to the public sector were "self-fulfillment" and “profitability ". For the private sector the most important values were listed as “profitability "; "accountability"; "reliability"; "effectiveness" and "innovation". While less important were found "self-fulfillment" and "social justice". The similar desirable values among the employees of the two sectors were “accountability”, "expertise", "reliability" and "effectiveness" (Van Der Wal et.al, 2008).

References Bakker, A.B. And Demerouti, E. (2008). Towards a Model of Work Engagement. Career Development

International, 13:3, pp. 209-223 Budhwar, P.S. And Boyne, G.A. (2004). Human Resource Management in the Indian Public and Private

Sectors: An Empirical Comparison. International Journal of Human Resource Management, 15:2, pp. 346–370.

Cameron, K. and R. Quinn (2005). Diagnosing and changing organizational culture: Based on the competing values framework. Jossey-Bass Inc Pub.

Cartwright J (1999). Cultural Transformation, Financial Times, Prentice Hall.45


Daft, R.L. 1992. Organizational Theory and Design. New York: West Publishing Company. Demmke, C. European Civil Services between Tradition and Reform: European Institute of Public

Administration, 2000 Eikenberry, A.M. and J.D. Kluver. 2004. ‘The Marketization of the Nonprofi t Sector: Civil Society at

Risk? ’ , Public Administration Review , 64 , 2 , 132 – 40 Hatch, M.J. (1993). The dynamics of organizational culture. Academy of Management Review, 18(4), 657-

693 Johnson, G. and Scholes, K. (1997) Exploring Corporate Strategy. Prentice-Hall Kandula, S. R. (2006).Performance Management. New Delhi: Prentice Hall of India private limited Kernaghan, K. (2003). Integrating values into public service: The values statement as centerpiece. Public

Administration Review, 63, 711-719 Kernaghan, K., (2003). ‘Integrating Values into Public Service: The Values Statement as Centrepiece’ in

Public Administration Review Vol. 63 (6), 2003, pp.711-19 Kotter, J. P., J. L. Heskett. (1992). Corporate Culture and Performance. New York:The Free Press Kunda, G. (1992). Engineering culture. Philadelphia, PA: Temple University Press Ogbonna, E and Harris, L. (2000). Leadership style, organizational culture and performance: Empirical

evidence from UK companies. International Journal of Human Resources Management, 11(4), 766-788 Pollitt , C. and G. Bouckaert, (2004) . Public Management Reform: A Comparative Analysis . Oxford:

Oxford University Press Poór, J., Karoliny, Zs., Musztyné Bártfai, B.V., Pótó, Zs. And Farkas, F. (2009). Similarities and

Differences of Human Resource Management in Public and Private Sector Organizations in the Light of New Public Management in International Comparison. Acta Oeconomica, 59:2

Rainey, H.G. and B. Bozeman . 2000 . ‘ Comparing Public and Private Organizations: Empirical Research and the Power of the A Priori ’ , Journal of Public Administration Research and Theory , 10 , 447 – 69 .

Rocheleau, B. And Wu, L. (2002). Public Versus Private Information Systems: Do They Differ in Important Ways? A Review and Empirical Test. American Review of Public Administration, 32:4, pp. 379-397.

Sangmook, K. (2005). Individual-level factors and organizational performance in government organizations. Journal of Public Administration Research and Theory, 15, 245-261

Schein, E.H.1992, Organizational Culture and Leadership, Jossey-Bass, San Francisco Sherman, T. ‘Public Sector Ethics: Prospect and Challenges’ in Sampford, C., Preston, N. and Bois, C-A.

Public Sector Ethics: Finding and Implementing Values (London: Federation Press/Routledge, 1998), pp.13-25

Toonen, T.A.J. ‘Administrative Reform: Analytics’ in Peters, B. G and Pierre, J. Handbook of Public Administration (London: Sage, 2003), pp.467-77

Van Der Wal.Z., et.al, (2008). Public Administration Vol. 86, No. 2, 465–482.

46

STATISTICAL LITERACY FOR DECISION MAKING

PhD candidate Arjana MISHAPhD candidate, Faculty of Economy, University of Tirana, E-mail [email protected]

AbstractThe statistical literacy for decision making represent one of the multiple functions for which this discipline exists and progresses. When decision makers think of a specified amount to spend to fight poverty, they first have to think about whether the program for which the money will be spent will reduce poverty in reality. Otherwise, it would be a waste of time and resources while poverty would remain the same. How do we find out if we achieved this goal?When policies aimed at certain support group / strata of population to strengthen their economic growth, increasing their prosperity, how can we judge whether they have succeeded?Economic aid is a social policy that the government applies to a certain stratum of the population below a certain level of poverty. How can we assess if this policy will be effective in helping the poor? By using theories already discovered and widely accepted, as well as official statistical data, this paper will attempt to help with the orientation of the use of statistics in decision making.Key words: statistical knowledge, variables, indicators, decision-making, public policies.

1. Introduction1.1. The importance of statistical knowledge and cultureStatistics means drawing conclusions from data. The basis of statistics is the theory of probability, which is based on mathematics. Statistics can be useful at all levels of life.Statistical literacy is needed at the level of officials. Decisions can be made based upon sound, high quality data and reliable statistical analysis. The essence of statistics is precisely to translate the complexities we face into meaningful and relevant information for policy makers, but also for citizens, in order to ensure transparency of decisions and of the decisions making process ("Statistics for policymaking: Europe 2020").Statistics are also crucial when setting our targets and using indicators for monitoring and evaluation purposes, particularly the economic indicators on the basis of which macro economic situations are evaluated and analyzed. Statistics are commonly used in arguments regarding social policy. Issues such as poverty, welfare, distribution of wealth, crime and drugs are all presented using statistics. These statistics are crucial in preparing forecasts and projections for the future on the basis of which policymakers update and design new policies.

Statistics also play a key role in communicating the policies. Diagrams and tables expressing analysis and data are helpful to communicate the essence of various phenomena and situations. Statistical literacy is an important tool for the public. Statistical literacy is useful when judging whether or not an advertisement is reasonable. For the regular citizen, statistical literacy is important in two aspects: it is needed to understand everyday life, a must to undertake personal decisions and a tool to exercise democratic control over the public administration.

1.2 The Statistics in the policy monitoring and evaluationInformation and statistics are powerful tools in creating a culture of accountability in the implementation of human development. Statistical indicators for development, measured periodically, enable monitoring of development strategies and policies of development. One of the indicators of the Millennium Development Goals (MDGs) is the poverty indicator. The target was set so that the poverty indicator would be halved by 2015. This commitment was taken by the Albanian Government in the framework of the commitments to the MDGs. The achievement of this objective will be measured toward its comparative values in a base year, the year in which the commitment was undertaken.The LSMS 2002 survey data were used as the data source. This instrument is established to be used in order to measure the poverty and inequality and to monitor the indicators. According to data, following recommended methodologies for this purpose, the poverty indicator as calculated by LSMS

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2002 was at the rate of 25.4%. This indicator has a corresponding metadata that makes it reliable to be a monitorable indicator.Table 1: The Millennium indicators and the commitment for their reduction

2002 2006 2015

1.1 – Percentage of population under the poverty line 25.40% 22.0% 13.0%1.2 - The poverty gap (incidence x depth of poverty) 5.7% 5.0% 3.0%

On this basis, the National Strategy for Economic and Social Development established targets to reduce the poverty indicator to 20% by 2006 and 13% in 2015 (Table 1).The measures during years show how the data have been changed toward the commitments. (Tab 2).

Table. 2 The poverty indicators 2002 2005 2008 2012 Percentage 25.4 17.9 12.5 14.3 Gap 5.7 3.9 2.4 3.0 Severity 1.9 1.3 0.7 1.0Source: Living Conditions Measurement Survey on, LSMS 2002, 2005*, 2008*, 2012**After the publication of the final figures of the population in may 2014 regarding the years 2001-2014, the time series for the LSMS 2005, 2008, 2012 have been revised.

1.3 The basis and principles of statisticsStatistics are a valuable tool for the public. Statistics are an irreplaceable instrument of information on every aspect of the life of a country. Through them, we are informed on the state’s social and economic developments, be they static or dynamic. Through indicators, tables, and charts, analysis, and comments, we can understand the current situation and make it possible through projections and forecasts to estimate the expected future. They hive us a basis on which to raise hypotheses, studies, or policies for development.For those who have responsibilities, public statistics are a valuable instrument to recognize the context and define policies for intervention; for citizens, it is a valuable instrument to assess the actions of the policy makers. Their purpose is to serve decision-makers, scientists and the general public, offering a comprehensive picture of the life of a country. They are based on principles such as: (in accordance with what established the UN in 1994 and the Regulation of the Council of the European Union n.322 / 97) principles of impartiality, reliability, relevance, effectiveness, confidentiality and transparency. Adherence to these principles ensures the quality of information produced and distributed.

2. Introduction to statistics, basic conceptsThe term "data" realizes significant value of various facts collected about people, things, objects, through surveys, measurements and registrations. This data make up the raw information. They themselves are not knowledge. To gain an understanding of data, we must go from data to information, from information to the facts and in the end from the facts to knowledge (Figure 1).Figure 1

Level of accuracy of thestatistical model

Knowledge

Facts

InformationThe level of improvements over Decision-making

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Statistical literacy for decision making

Data

Data becomes information when a decision needs to be taken. Information becomes fact, when supported by data. Facts become knowledge when used in the successful fulfillment of a decision-making process. Figure 1 shows the statistical thinking process based on the statistical data to build statistical models in order to serve decision-making. That’s why we need statistical data analysis. Statistics are derived from the need to establish regular evidence-based knowledge. This requires knowledge of the probability laws, measurement of the characteristics of data and the links that exist. Statistics offers a variety of methods used to collect, analyze, present and interpret the data.

When working with data, we encounter the notion of a variable. A variable is a characteristic of people, objects or events that differ in value from each other.Variables differ from the facts based on how much measurable information they provide. Variables also differ from the types of the measurement scale. Thus, a variable measurement scale is classified into the following: nominal, ordinal, interval, or ratio.Nominal variables are variables in the form of a text, without any order (qualitative). For purposes of analysis, we codify them in a conventional manner. In this way, we convert this variable into a number, or a quantitative variable, which we are then able to count and describe its characteristics (Table 3).Table 3 Nominal data and their codificationROW DATA TableNr. Name Gender Code Gender Total1 Fatmir Male 1 Meshkuj 22 Besa Female 2 Femra 33 Arjan Male 1 Total 54 Diana Female 25 Elena Female 2

Ordinal variables has ordered values. Numerical variable attributes are sorted in ascending or descending order. For example, observations about welfare are sorted into 5 categories: very good, good, without changes, bad and very bad. Socio-economic status of families is an example of ordinal variables (Table 4). A variable interval contains attributes which are divided equally. They are ordered and allow us to make comparisons. Proportional variables (ratios) are supposed to have attributes, which have equal intervals and start from zero.Table 4: Financial situation of households compared to the previous year

Urban

Much improved 0.6Somewhat improved 30.9No change 48.4Somewhat worse 7.3Much worse 3Don’t know 9.9

Source:: LSMS2002

Variables are divided into dependent and independent. Dependent and independent variables are widely used in analysis and research studies. Studies require discovering the laws, the rules, and the relationships between variables. Two variables are considered correlated when in a range of observation each of their values correspond to one another. An example that shows the connection of a dependent and independent variable is given in Table 5. Variable "employer" is independent and is the cause for which the variable "average monthly salary" changes in the function of the employer.Variables contain features that vary depending on several other characteristics. This variability reflects the simple processing of data by calculating frequencies.

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Table 5The average wage of the employee according to the employer 2002 The independent variable

The employer

Theaverage monthly wage in leks

Dependent variable

Non-governmental organization 25685Private associations 21309Private individual 19368Government 17242State woned enterprise 13903Country average 18680 Source:: LSMS2002

Table 6 “The frequencies of the variable “The highest diploma”.The highest Diploma achieved Frequency Percentage Cumulative percentage

No diploma 275689 10.7 10.7Elementary school 540876 20.9 31.6Primary school 1053654 40.7 72.3General middle school 303307 11.7 84Professional 2 - years 40197 1.6 85.6Professional 4 - years 240023 9.3 94.8University diploma 127765 4.9 99.8Postuniversity 5632 0.2 100Total 2587144 100 Source::LSMS2002

A graphical introduction is the pie graph, which presents the distribution of a continuous or a categorized variable. In cases when a variable is comprised of too many categories, it is not recommended to use a pie graph. In the graph below, we present the variable “income” divided into 5 categories.

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The frequency tables become more informative when we provide the cumulative sum for each data category to better understand the distribution. From the calculation of the cumulative frequency we get a statistic called a percentile.Percentiles are a special case of quantiles. Quantiles divide a range of values into groups with known proportions in each group. The quartiles, quintiles, and deciles are examples of quantiles.Quartiles are groups with 25% each, so they are values that divide the range of values in 4 equal parts, where each part includes 25% of the observations.Deciles are groups with 10% each, so they are values that divide the range of values in 10 equal parts, where each part includes 10% of the observations.

Table 7. The distribution of consumption per capita by quintiles

Quintiles of the consumption per capita Consumption per capita in %

First quintile 12.4Second kuintile 16.0Third quintile 19.6Fourth quintile 21.8Fifth quintile 30.2Total 100 Source:: LSMS2002

Table 7 shows the distribution of a population (households) according to quintiles of the continuous variable “consumption per capita”. According to the results in the table, the consumption by the individuals of the first quintile represents 12.4% of the total consumption of the population. Meanwhile, the consumption of the individuals in the fifth quintile represents 30.3% or one third of the population’s total consumption. Therefore, the purpose of the calculation of the cumulative frequencies, the division in five equal classes of the values ordered from the lowest to the highest, allow us to compare results and draw conclusions from the data collected and analyzed.2.1Descriptive statisticsStatistics have two functions depending on whether the data is obtained from a random sample. If a statistic is not from a random sample, then one can only work with descriptive statistics and statistical modeling, even if the data is the entire population. If a statistic is from a random sample, then one can also work with inferential statistics: sampling distributions, confidence intervals and hypothesis tests (M.Schield, 2001).A statistic is a number calculated from a range of data. For example, “the average wage of 18,680 leks” is a statistic collected from the data of each individual interviewed in a household survey about household monthly wages. Statistical analysis methods help to measure and interpret the statistics. Statistics that help to describe a population are called descriptive statistics. Examples of descriptive statistics are percentages, mean, mode, mediane, variance and correlation.Statistics that come as a result of generalizing from a probability sample are called inferential statistics. They are used to describe a population by studying the characteristics of a selected sample of the population. Therefore these statistics may also be descriptive.

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What are the measures of the central tendency? A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. The mean (often called the average) is the measure of central tendency that you are probably most familiar with, but there are others, such as the median and the mode (Laerd Statistics).In the case of the normal distribution, mean, mode and median fall close to each other. In random cases of the asimetric distribution, these three measures of central tendency differ from each other. In a random distribution of data, the median value is about 20% lower than the mean.The mean and the median differ. When we calculate the mean, we incude all values and extreme values influence the mean, which in our case are : the minimum is 12,000 leks and the maximum is 90,000 leks (Table 8). The median, being the value that divides a range of ordered values into two equal halves, does not include the highest extreme values. Therefore, median is used in social statistics, rather than mean, to analyse the income/consumption data and others.Table 8Mean 26,797.5 Median 21,250.0 Minimum 12,000.0 Maximum 90,000.0 Mode 26,000.0

Variation: There are many ways to measure the variations of the discrete and continuous variables. They include: diversity index, index of qualitative variation, the range, the variance and standard deviation.The curve in graph 4 is the representative, meaningful presentation of the distribution. Statistics are also considered the science of distribution. A normal distribution curve, or better known as the Gauss curve, expresses a probability distribution for continued variables.The term normal distribution does not actually reflect reality. It is very rare that resulting data have a normal distribution. The form of any normal distribution is defined by two values: the population mean and variance.Graph 5 shows the distribution of the families according to the consumption per capita. In this case we notice a shift in the Gauss curve. A shift in the left side which means that the most frequent cases are the minimal values of consumption. Theoretically, this distribution must have the wings equally distant from the mean (graph 4), but this may happen very rarely in real life. When curve is narrow, the variance is small and the contrary. The two extremes of the curve vary from - ∞” to + ∞” as they approach the horizontal axis but never actually touch it.2.2. Correlation:Correlation measures the relationship between two or more variables. There are several correlation coefficients, often denoted with ρ or r, measuring the degree of correlation. The most common of these is the Pearson correlation coefficient, which is sensitive only to a linear relationship between two variables (which may exist even if one is a nonlinear function of the other). The Pearson correlation is +1 in the case of a perfect direct (increasing) linear relationship (correlation) and −1 in the case of a perfect decreasing (inverse) linear relationship (anticorrelation), and some value between −1 and 1 in all other cases, indicating the degree of linear dependence between the variables. As it approaches zero, there is less of a relationship (closer to uncorrelated). The closer the coefficient is to either −1 or 1, the stronger the correlation between the variables.

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Graph 4 Graph 5

2. 3 Qualitative data – quantitative analysesThe data are qualitative or quantitative. They can be nominal, numerical, interval and ratio. When they are in nominal form, they can be codified by codes ordered or not, so they return to numerical data and can be manpulated like numeric variables. The difference between them is that these will be categorical variables with two or more categories and can use measures like frequencies, percentages, simple and cross tables, graphs, etc. The same logic works for qualitative questionnaires or surveys. These are instruments that combine data and semi-structured or structured questions, which are converted to numbers using codification, which enable further processing.Qualitative research is intended to achieve specific findings regarding the hypothesis raised previously. Researchers involved in qualitative research (Lincoln & Guba, 2000) set 4 criteria for judging qualitative research. We mentioned features of public statistics at the beginning of this document. Researchers specify 4 criteria that distinguish quantitative and qualitative analysis (see Table 9).What are the indicators, selection of appropriate indicators for monitoring and evaluation of projects, programs, policies and measure development?

Table 9

Traditional criteria to evaluate a quantitative study

Inside Validity Outside validity Reliability Objectivity

Traditional criteria to evaluate a qualitative study

Credibility Transferability Dependabiity Confirmability

3. What are the indicators3.1. Indicator is a simple variable or a variable constructed using a combination of two or more variables.There are economic and social indicators. Gross Domestic Product (GDP ) is an economic indicator that shows a country's economic development. The definition of economic development given by Professor Michael Todaro is an increase in living conditions, improvement of the citizens’ confidence, and an increase of justice in that society. He suggests that the most accurate method to measure economic development is the Human Development index, which takes into account the literacy rates & life expectancy, which in turn has a direct impact on productivity and could impact Economic

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growth. Indicators can be simple or combined. Simple indicators are those represented by a basic variable. For example, population at a certain point in time, is a simple indicator. The population of Albania in 2011 was 2,821,977 inhabitants (© INSTAT 2012). This is a simple indicator that is derived from the population census.The combined indicators are constructed combining two or more variables. For example, the number of inhabitants for 1 km2 is an indicator comprised of two variables: the total number of inhabitants and the size of the country space in km2. The child mortality indicator is an indicator calculated based on the number of deaths of children from 0 to 1 years old and the total number of children born in the same period.Indexes are a combination of indicators. Construction of indexes in the social field is the result of the purpose to get synthetic indicator measuring the standard of living and development of a country. One of these indexes is the Human Development Index, which is a fusion of three indexes: real GDP per capita, life expectancy and educational attainment. Indicators are divided into input and output types. For example, the cost of building a school, is an indication of the input; the number of schools built is an indicator of output.The number of students enrolled and the number of those students enrolled who are satisfied with the school are indicators of the outcome or result or performance indicators. Meanwhile, the indicator of the level of education, measuring the tests conducted in schools, is an indicator of impact, that the program has had in a given community (target), in our case to school children. The ratio between the output and the result is called effectiveness.The report of input/ output over the indicators of outcome and impact is an indicator of efficiency in the implementation of a program, in our case of the programs related the education.3.2 How the indicators must be selectedIndicators should be selected to monitor the policies and programs and projects should be eligible for such a role. They should be specific, measurable, appropriate, realistic and time bound.

4. The instruments of the policy monitoringA benchmark is defined as ‘a standard or point of reference against which things may be compared or assessed’ (Pearsall, 1999: 125). Benchmarks must be chosen by reference to the objectives set. This is because benchmarking is a tool by which a government assesses how well it is meeting its objectives and how they could be met more effectively. 4.1 The measure of indicators for policy designPublic expenditure statistics are a very useful tool to monitor policies. Construction of appropriate indicators makes it possible for government decision makers to know the state of play and to forecast where to go. Policy orientation indicators are indicators of poverty, as they have a social and economic context. Analysis of the sources of income of the poor shows us how the government's social programs for the poor perform. Economic aid programs are aimed at populations under a certain minimum subsistence. But how can we measure how effective these programs are in reaching these target families? Table 9

Poor households

Non poor households

Poor households

Non poor households

Receive economic aid 100% 0%Receive economic aid 25% 8%

No economic aid 0% 100%No economic aid 75% 92%

The measurement of poverty and inequality indicators for small areas, would help the policymakers to distribute public funding to proper strata of the population in need. There are special techniques for this calculation. Authors Elbers, Lanjouw and Lanjouw (2002, b) have developed methodologies that are based on data from censuses and surveys LSMS or similar observations with their families.Detailed maps of smaller areas enable policymakers to have in hand indicator for these areas on which they can build policies. These help in making decisions to make redistribution of funds and budgets. Indicators of inequality and poverty differentiated by administrative units at various levels are a very

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valuable information for judging later in the design of such social policies to reduce poverty and improve the living standards of families.

4.2 Public spendingIt is important to build indicators that inform us about the distribution of budget items, to understand how and where the money has gone. These indicators are constructed for sectors of the economy reflecting government priorities.

Graph 7In the graph above, it is shown that the government’s budget dedicated to agriculture toward the total budget has had an increase during the period between 2007-2011, a decrease in 2012 and 2013 and a significant increase in 2014. The trend has not been linear during this period with regards to the support on agriculture.

Conclusions and recommendations If the data sources are different, it is important to clearly define the reasons for building

information and facts and why the goal will be used.

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Statistics are a very useful tool to monitor policies. Construction of appropriate indicators makes it possible for government decision makers to know where they stand and where they want to go.

Basic statistical literacy is necessary at different levels of decision making because they help in understanding the situations and facilitate appropriate decision making.

Indicators selected to monitor policies, programs, and projects should be used for such a role. They should be specific, measurable, appropriate, realistic and time bound. They help results-based management to measure achieved results.

Reeferences: Bohrnstedt and Knoke, Statistics for Social Data Analysis, second Edition. Schield M., Augsburg College, Statistical Literacy and Evidential Statistics. Schield M., Augsburg College, Describing rates and Përcentages in Tables. Holmes P., Nottingham Trent University Statistical Literacy, numeracy and the future. Schield M., Augsburg College Statistical Literacy: reading tables of rates and përcentages . GAO, Quantitative Data Analysis, an Introduction. The statistics homepage. Schield M., Augsburg College, Correlation, Determination and causality in introductory statistics. UNDP, Raporti i Zhvillimit Njerëzor për Shqipërinë, 2005. Strategjia Kombetare e Zhvillimit Ekonomik dhe Social, Raporti i Progresit 2003. World Bank, Albania Poverty Assessment 2003. INSTAT, Hartat e varfërisë dhe pabarazisë, 2004. INSTAT, Anketa e Matjes së Nivelit të Jetesës në Shqipëri, 2012. UNDP, Social Indicators handbook, studies and methods. IMF, Achieving the MDGs and related outcomes:a framework for monitoring policies and actions. Ministry of Agriculture, Rural Development and Water Administration Audrey R. Chapman, Ph.D. Indicators and Standards for Monitoring Economic, Social and

Cultural Rights. HDPC 2002, The Albanian response to the Millenium Development Goals. Ministria e Financave, www.minfin.gov.al Albanian Agency for Rural Development

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http://www.minfin.gov.al/

CALL FOR PAPERSInternational Journal of Science, Innovation and New Technology

Department of Statistics and Applied Informatics, University of Tirana,Department of Computer Science, University of Durres

A publication of ASET

The Editors and co-editors of the International Journal of Science, Innovation and New Technology (IJSINT) would like to invite you to consider submitting a manuscript for inclusion in this scholarly journal. Authors should e-mail their manuscripts to [email protected], [email protected] or [email protected]. There is no charge for paper reviews. The normal review time for submissions is 30 days.

Authors submitting a manuscript for publication consideration must guarantee that the document contains the original work of the authors, has not been published elsewhere and is not under publication consideration elsewhere. In addition, submission of a manuscript implies that the author is prepared to pay the publication fee should the manuscript be accepted.

Topics of interest include but are not limited to:

Information Systems

Web 2.0 and Web 3.0; Enterprise 2.0; social media in the enterprise; IS Control and Innovation Systems: Groupware; Financial Systems; Business Intelligence; Global IS and Development; IT Trends and Future Studies; Information Technology and Culture

Data Management

Intelligent Systems; Data Mining; e-Publishing and Digital Libraries; Information Search and Retrieval; Knowledge Management

Platforms

High performance computing systems; Distributed intelligent systems; Mobile systems; Hardware issues; Networks and Data Transmission

e-Science

Natural sciences in digital society; Financial Mathematics and Computation; Statistical Modeling; Mathematical Modeling in Sciences; Applied Mathematics and Computational Science Education; Bioinformatics; Intellectual Property Rights; Decision Support Systems; Simulations;

Communication and Secure Technologies

Internet security; Web services and performance; Cryptography; e-Risk; Information assurance; Mobile social networks; Peer-to-peer social networks

Education and Learning

Virtual Learning Environments and Issues; e-Learning Tools and e-Education

e-Services

E- government; Democracy and the Citizen; e-Administration; Policy Issues; Virtual Communities

Ideas for special theme issues may be submitted to [email protected]: www.ijsint.org

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http://www.ijsint.org/
