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Journal of Applied Business and Economics

North American Business Press

Atlanta - Seattle – South Florida - Toronto

Journal of Applied Business and Economics

Editors Dr. Adam Davidson Dr. William Johnson

Editor-In-Chief

Dr. David Smith

NABP EDITORIAL ADVISORY BOARD

Dr. Andy Bertsch - MINOT STATE UNIVERSITY Dr. Jacob Bikker - UTRECHT UNIVERSITY, NETHERLANDS Dr. Bill Bommer - CALIFORNIA STATE UNIVERSITY, FRESNO Dr. Michael Bond - UNIVERSITY OF ARIZONA Dr. Charles Butler - COLORADO STATE UNIVERSITY Dr. Jon Carrick - STETSON UNIVERSITY Dr. Mondher Cherif - REIMS, FRANCE Dr. Daniel Condon - DOMINICAN UNIVERSITY, CHICAGO Dr. Bahram Dadgostar - LAKEHEAD UNIVERSITY, CANADA Dr. Deborah Erdos-Knapp - KENT STATE UNIVERSITY Dr. Bruce Forster - UNIVERSITY OF NEBRASKA, KEARNEY Dr. Nancy Furlow - MARYMOUNT UNIVERSITY Dr. Mark Gershon - TEMPLE UNIVERSITY Dr. Philippe Gregoire - UNIVERSITY OF LAVAL, CANADA Dr. Donald Grunewald - IONA COLLEGE Dr. Samanthala Hettihewa - UNIVERSITY OF BALLARAT, AUSTRALIA Dr. Russell Kashian - UNIVERSITY OF WISCONSIN, WHITEWATER Dr. Jeffrey Kennedy - PALM BEACH ATLANTIC UNIVERSITY Dr. Dean Koutramanis - UNIVERSITY OF TAMPA Dr. Malek Lashgari - UNIVERSITY OF HARTFORD Dr. Priscilla Liang - CALIFORNIA STATE UNIVERSITY, CHANNEL ISLANDS Dr. Tony Matias - MATIAS AND ASSOCIATES Dr. Patti Meglich - UNIVERSITY OF NEBRASKA, OMAHA Dr. Robert Metts - UNIVERSITY OF NEVADA, RENO Dr. Adil Mouhammed - UNIVERSITY OF ILLINOIS, SPRINGFIELD Dr. Roy Pearson - COLLEGE OF WILLIAM AND MARY Dr. Veena Prabhu - CALIFORNIA STATE UNIVERSITY, LOS ANGELES Dr. Sergiy Rakhmayil - RYERSON UNIVERSITY, CANADA Dr. Robert Scherer - CLEVELAND STATE UNIVERSITY Dr. Ira Sohn - MONTCLAIR STATE UNIVERSITY Dr. Reginal Sheppard - UNIVERSITY OF NEW BRUNSWICK, CANADA Dr. Carlos Spaht - LOUISIANA STATE UNIVERSITY, SHREVEPORT Dr. Ken Thorpe - EMORY UNIVERSITY Dr. Robert Tian – SHANTOU UNIVERSITY Dr. Calin Valsan - BISHOP'S UNIVERSITY, CANADA Dr. Anne Walsh - LA SALLE UNIVERSITY Dr. Thomas Verney - SHIPPENSBURG STATE UNIVERSITY Dr. Christopher Wright - UNIVERSITY OF ADELAIDE, AUSTRALIA

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This Issue

Inflation and the Purchasing Power Parity in South Africa ................................................................. 11 Glenville Rawlins This paper seeks to ascertain whether current inflation differentials can explain the differences between contemporaneous and previous exchange rate levels for South Africa and each of five of her trading partners in the developed world: the US, the UK, France, Germany and Japan. With the help of the absolute version of the Power Parity Theory (PPP) theory, the nominal exchange rate and national price levels are treated as integrated processes that will allow for trade between a pair of countries to make the exchange rate a stationary variable. After performing Ordinary Least Squares Regression tests, unit root tests are conducted on the various price and exchange rate variables. Next, using the Johansen Cointegration method, the paper investigates whether there is an underlying long-term relationship between the price differentials and the exchange rate for South Africa and each of these five developed countries (DCs). These various tests yield results that are generally mixed to somewhat unfavorable to the PPP Theory. Finding and Developing New Product Ideas: An Ideation Process for Entrepreneurs ...................... 19 Michael J. Swenson, Gary K. Rhoads, David B. Whitlark Ideation is the creative process of generating, developing, and communicating new ideas (Johnson, 2005). In this paper, we develop an ideation process for entrepreneurs, with a focus on problems customers are trying to solve. This research evolves from the relevant marketing literature, from field interviews, and from observations. We present a four-step ideation process. Grading Technology Allows Teachers to Infuse Technology in the Economics Classroom............... 26 Ian J. Shepherd, Brent Reeves, Darryl Jinkerson Education courses now involve homework assignments that require technology skill as well as domain knowledge. Yet there is little pedagogical and technological support for teaching “What” (statistical mean) while simultaneously teaching “How” (use the =average (Range) function in Excel). We describe a conceptual approach and a methodology that helps teachers leverage their domain knowledge and helps students learn both a new topic and a new information technology skill. While teachers will allocate more time towards preparing homework, far less time is spent overall in administering and grading assignments. This approach scales to any class size, thus removing grading burdens imposed by large class sizes. The huge burden of grading lessons is removed, leaving that time free to improve the teaching. The Impact of the Institutional Environment on SME Internationalization: An Assessment of the Environmental Assumptions of Emerging Integrated Models of Internationalization .............................................................................................. 43 Pat H. Dickson, K. Mark Weaver, George S. Vozikis This study provides an empirical test of the assumptions that many existing models of internationalization make regarding the institutional factors influencing internationalization by small to medium-sized firms (SMEs). Utilizing a sample of over 2100 SMEs in nine countries, the roles of such institutional attributes as the nature of the economic and legal systems and the levels of economic and political risk are examined in light of the impact that these factors have on the levels of international activity of SMEs. The results indicated that each of these factors may play an important role in either motivating or enabling internationalization.

Junior Mining Sector Capital-raisings: The Effect of Information Asymmetry and Uncertainty Issues......................................................................................................... 56 Casey Iddon, Samanthala Hettihewa, Christopher S. Wright While prospecting by junior mining companies (JMCs) is a vital contributor to modern wealth creation, attributes of the junior mining sector (JMS) limit JMC-fund raisings to external equity (shares). In considering responses by JMC principals to deep discounting and other JMC-investor strategies, potential responses were found to: increase returns to principals, increase JMS moral-hazard issues, and further deepen price discounts on JMC share offerings, especially IPOs. It is suggested that the attractiveness and moral-hazard consequences of these potential responses can be greatly diminished if mining-tenement fees are raised and JMC prospecting costs are allowed as an offset against those fees. From Washington to Wall Street: The Relationship Between National Politics and Stock Market Performance ................................................................................................. 68 James M. Day, Thomas W. Harvey The question explored in this paper is the relationship between national politics and the stock market. There is an interesting relationship between Washington and Wall Street that has existed in the United States for decades that many citizens either do not know or do not understand. Government has had an increasingly influential role in the economy of the United States, particularly since the creation of the Federal Reserve. We seek to understand and explain the impact the balance of power in Washington and political activity can have on the performance of the stock market. Capacity and Employment in the Great Recession ................................................................................ 91 Piero Ferri, Anna Maria Variato Although it is difficult to compare the present events to those of the “Great Depression”, they re-propose two old questions: Does a fall in aggregate demand have a direct impact on the labour market? Does a rebound in production imply a recovery in employment? The paper stresses the role of aggregate demand and slack capacity. While spare capacity may stimulate quantity adjustment strengthening the relationship between aggregate demand and labour performance in recession, the rebound can imply other forces (technical and structural change). However, the peculiarity of the present recovery is better understood within the aftermath of a financial bubble. The Day-of-the-Week Effect: The CIVETS Stock Markets Case....................................................... 102 Julio César Alonso Cifuentes, Beatriz Eugenia Gallo Córdoba Finding patterns in the behavior or performance of financial markets has been a subject of interest for both analysts and academics. We use GARCH and IGARCH models with covariates to estimate the day-of-the-week (DOW) effect on both volatility and daily returns of the stock exchange markets for the CIVETS. We found a DOW effect on the daily returns for all of the CIVETS’ stock markets. DOW effect was also found for the daily returns’ volatility of some of the stock markets. Finally, there is evidence of lags in the DOW effect for the stock markets we analyze.

Dynamic Behavior of Inflation in Nigeria: A P-Star Approach ......................................................... 117 Ikechukwu Kelikume The P-star model performance in predicting inflation renders it a valuable tool in analyzing dynamic behavior of prices. This study tests the performance of the P-star model in Nigeria with a view to ascertaining its usefulness in predicting price movement. Using quarterly data obtained from the Central Bank of Nigeria statistical Bulletin over the period 1970 to 2011, we obtained estimates of the price-gap, velocity-gap and output-gap model. The result obtained shows the usefulness of the price-gap model in explaining and predicting inflation in Nigeria. Exchange Rate Pass-Through to External and Internal Prices: A Developing Country Perspective ........................................................................................................ 128 M. Nusrate Aziz, Muhammad Sabbir Rahman, Md. Alauddin Majumder, Somnath Sen We estimate the exchange rate pass-through to external and internal prices of a developing country, specifically, Bangladesh. The study also examines whether the tradition view that exchange rate pass-through should be ‘full’ for developing countries. We construct some variables which are not readily available in existing databases. A full sample estimation indicates that exchange rate pass-through to external prices is ‘full’, however, pass-through to internal prices is ‘partial’. Rolling regressions indicate that the response of external prices to exchange rate movement has been constantly around unity until 2003, however, it has fallen rapidly in subsequent years. Response to internal prices has been found unstable and relatively small.

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Inflation and the Purchasing Power Parity in South Africa

Glenville Rawlins Montclair State University

This paper seeks to ascertain whether current inflation differentials can explain the differences between contemporaneous and previous exchange rate levels for South Africa and each of five of her trading partners in the developed world: the US, the UK, France, Germany and Japan. With the help of the absolute version of the Power Parity Theory (PPP) theory, the nominal exchange rate and national price levels are treated as integrated processes that will allow for trade between a pair of countries to make the exchange rate a stationary variable. After performing Ordinary Least Squares Regression tests, unit root tests are conducted on the various price and exchange rate variables. Next, using the Johansen Cointegration method, the paper investigates whether there is an underlying long-term relationship between the price differentials and the exchange rate for South Africa and each of these five developed countries (DCs). These various tests yield results that are generally mixed to somewhat unfavorable to the PPP Theory.

INTRODUCTION

This paper applies two of the more recent techniques in econometrics, unit root and cointegration, to one of the oldest and certainly unresolved issues in economic theory, namely the Purchasing Power Parity theory. The PPP derives its policy importance from the fact that if there were to be a real depreciation of a nation's currency away from its underlying equilibrium level this would precipitate countervailing trade flows that would ultimately lead the real exchange rate back to the previous equilibrium level, (Peter C Lui 1998). Further, deviations of the real exchange rate away from equilibrium could quantify the degree of currency misalignment (Edwards 1999, Machlup1973), and signal the necessary policy intervention as well as the level of aggressiveness by the monetary authorities in executing such a policy.

It must be noted that extensive research on the PPP over the last 30 years on this subject appears to have approached a number of consensus points: that the PPP works best in the long run; that there is no assurance of precisely a value of negative and a positive 1 on the lagged coefficients of domestic and foreign price levels respectively in a regression equation where the dependent variable is the nominal exchange rate; and that the PPP has greater explanatory power in the context of shocks of a monetary nature, usually in high inflation situations (Taylor 1988).

As South Africa is one of a handful of less developed countries that have for reasonably extended period of time kept its currency (the RAND) as a convertible unit (see Gunnar Jonsson 2001) a full examination of the effects of price differentials on the exchange rate should be revealing.

First a few observations are in order regarding South Africa’s recent economic performance. Table 1 compares the inflation experience of all six countries involved in this paper. Over the relevant study (1971 to 2012), South Africa had an inflation rate that was more than double that of all of the DCs, except

Journal of Applied Business and Economics vol. 15(3) 2013 11

for the UK where the margin was 9.61% to 6.04% over the UK. Also over every five-year period, South Africa's inflation rate exceeded that of every other developed country with the exception of the UK, 1971 to 1975 and 1976 to 1980, and Japan 1971 to 1975.

As did the rest of the world, South Africa suffered a severe decline in real GDP during the 2007 to 2009 financial crisis, but experienced a rapid return to growth, up to 4% by the end of the first quarter of 2011. Since then economic growth has relented, with unemployment remaining very high, in a range of 23 to 26% over the last four years, (see Moody’s Analytics 2013). While South Africa traditionally sported a Merchandise Trade surplus over most of the 1971 to 2005 period, a growing Current Account deficit opened up over the last dozen years, with the Current Account deficit going from just under 1.3% of GDP in 2000 to about 2.5% in 2005, and reaching an estimated 4.3% in 2012,(see Bloomberg News). Finally, we note that the data used in this study are quarterly from 1972-2010, and are obtained from the IMF Monthly Financial Statistics. The nominal exchange rate used is a bilateral exchange rate between South Africa and each of the DCs, and is measured as the number of South African Rands that commands a single unit of the foreign currency.

TABLE 1 THE AVERAGE ANNUAL INFLATION RATE

Year US UK France Germany Japan South Africa

1971-1975 6.79% 13.16% 8.84% 6.13% 11.57% 9.40% 1976-1980 8.94% 14.47% 10.51% 4.04% 6.66% 12.04% 1981-1985 5.52% 7.24% 9.67% 3.87% 2.77% 14.01% 1986-1990 3.97% 4.76% 3.05% 1.37% 1.34% 15.34% 1991-1995 3.13% 3.80% 2.23% 3.60% 1.37% 11.33% 1996-2000 2.48% 1.60% 1.21% 1.26% 0.32% 6.68% 2001-2005 2.55% 1.45% 1.91% 1.53% -0.45% 4.48% 2006-2012 2.35% 3.00% 1.67% 1.75% -0.10% 5.94% 1971-2012 4.40% 6.04% 4.75% 2.90% 2.89% 9.61%

Note: Author’s Calculations based on data published at http://www.inflation.eu. The paper is organized as follows. The next section lays out the traditional empirical formulation of the PPP Hypothesis, to be followed by a brief description of the two techniques (the cointegration and unit root tests) that will be featured as the primary empirical framework for this study. The next three sections present the empirical results from the Ordinary Least Squares (OLS) regression analysis of the PPP, the Non-Stationary and the Cointegration tests. This section is followed by a brief conclusion. FORMULATING THE PURCHASING POWER PARITY THEORY

The exchange-rate between two currencies that equates the purchasing power in two countries in the face of likely divergent national price levels is the purchasing power parity rate. If we measure the nominal exchange rate as the number of domestic currency units that commands a single unit of the foreign currency, this can be expressed as:

EQUATION 1 Sit = βi(Pit − Pit∗) + µt

12 Journal of Applied Business and Economics vol. 15(3) 2013

Where: Sit is the nominal exchange rate Ρit is the domestic price level Pit∗ is the foreign price level.

To make Equation 1 more amenable to empirical testing and to the traditional interpretation of the coefficients, it is converted to the following log linear expression:

EQUATION 2 Sit = β0 + β1Pt + β1Pt∗ + µt

Note that this is the absolute version of the PPP accompanied by either the symmetry or the

proportionality condition.1 A milder version (the Relative PPP) merely requires that a nominal exchange rate adjustment provide a percentage match for any relative price adjustment. This formulation implies that deviations from parity (such as excessive inflation in one country) create potentially profitable arbitrage opportunities which when exploited would result in an automatic adjustment of the nominal exchange rate change that would restore the purchasing power to its previous level. It also implies that for a country with a flexible exchange rate system, the exchange rate becomes an endogenous variable. THE UNIT ROOT AND COINTEGRATION TESTS

The unit root technique begins from a first order regression equation where Y the dependent variable is expressed as:

EQUATION 3 YT = ρYt−1 + δXt + Et

Where Xt represents regressors such as a constant or a time trend, ρ and δ represent the parameters

and Et the white noise. Equation 3 is said to have a unit root and is thus stationary if the value of the coefficient ρ is

estimated to be 1 in a simple Dickey-Fuller test. Also higher order series lag correlation can be incorporated into this formulation by adding lagged differential terms of the dependent variable Y. This results in the standard Augmented Dickey Fuller (ADF) test construct that is used in this paper, and is represented below as follows:

EQUATION 4

∆Yt = αYt−1 + β1∆Yt−2 +⋯+ βt∆Yt−p + δXt + Vt

Engle and Granger postulated that if each of two variables X and Y possesses a unit root then the residual from a regression of Y and X can be written out as:

EQUATION 5 ut = Yt − β0 − β1Xt

Once a unit root test confirms that ut is itself stationary i.e. I(0), without stochastic trends, it can be

concluded that Y and X are cointegrated with the implication of the existence of a long-term relationship between them.


TABLE 2 ORDINARY LEAST SQUARE (OLS) RESULTS

Country CPISA CPISA(-1) CPIDC CPIDC(-1) R- square DW

USA -1.59 (-32.09) -3.19 (-3.07) 1.51 (1.50)

1.63 (14.15) -1.61 (-1.08) 3.43 (2.27)

0.99 0.99

0.29 0.40

UK -1.19 (-26.62) -3.86 (-2.82) 2.60 (1.95)

0.36 (4.88) 1.23 (1.07) -0.76 (-0.66)

0.98 0.98

0.22 0.28

France -1.09 (-25.31) -2.86 (-2.02) 1.85 (1.34)

0.36 (4.02) 11.93 (5.69) -11.62 (-5.50)

0.97 0.98

0.17 0.33

GERMANY -3.07 (-8.20) -7.11 (-3.65) 3.96 (2.12)

7.03 (5.24) 11.86 (2.74) -4.63 (-1.06)

0.84 0.84

0.28 0.42

JAPAN -0.80 (-33.24) -1.21 (-0.87) 0.39 (0.29)

0.94 (10.72) -1.51 (-1.06) 2.46 (1.76)

0.94 0.94

0.20 0.22

Note: The t-statistics are in brackets after the respective coefficient EMPIRICAL RESULTS OLS Regression

Table 2 presents the results of Equation 2, where the bilateral real exchange rate for South Africa with each of the five DCs is regressed against its domestic price level and that of the corresponding DC. For all five countries, the domestic price level carries the expected negative sign while the sign on the DC countries’ price level is positive. Further, each of the 10 coefficients is shown to be statistically significant at the 1% level. While the proportionality condition of the absolute version of the PPP is not borne out, as none of these coefficients has a value of exactly 1(absolute value), only in the case of Germany do the coefficients noticeably diverge from absolute 1. Finally, it can be pointed out that in general the coefficient of determination (the adjusted R2 ) is generally quite high, exceeding .97 in the US, UK and France. When the regressions were run with both price levels and their lags, the results were mixed with the contemporaneous price levels retaining the expected sign, while the lagged variables alternated in sign without much of a pattern. Finally the quite low values of the reported Durbin Watson statistics imply the likely presence of Autocorrelation, while a comparison of the size of that latter statistic with the relatively high coefficient for determination (R2) suggests the possibility that the estimated OLS regressions could be spurious. This raises the question of whether or not these variables do possess a unit root. Unit Root Tests

The level and first difference stationary tests results are presented in Table 3 for all variables used in this paper, with the null hypothesis being the existence of a unit root. The Augmented Dickey Fuller construct as outlined above in Equation 4 is used together with an automatic lag length following the Schwartz Information Criterion. For this test we accept the null hypothesis of a unit root if the ADF statistic is either more positive or less negative than the reported critical value, and for the first difference test to reject the existence of multiple roots if the ADF statistic is less negative than the reported critical value.


TABLE 3 LEVEL AND FIRST DIFFERENCE STATIONARY TEST

Level Test First Difference Test

Variable ADF Statistics ADF Statistic BXUS -0.3176 -6.4389 BXUK -0.6025 -5.1099 BXF -0.6379 -11.4704 BXG -0.1085 -5.8183 BXJ -0.2914 -11.5387 DCPI -2.585 -3.3436 CPIUS -2.8936 -3.4132 CPIUK -2.424 -3.1509 CPIF -0.9705 -3.5589 CPIG -0.4736 -6.6056 CPIJ -3.7918 -4.2568

Note: For the BX variables, BXUS represents the Bilateral exchange rate between the US and South Africa; DCI is South Africa’s domestic price level, while CPI is the US price level, etc.

Although there are minor variations in the individual country sample size, the ADF test critical values for both the level and first difference tests at the 1 percent and 5 percent levels are – 3.47 and – 2.88 respectively.

In general, the level test allows us to accept the null hypothesis of a unit root at the 1 percent level. However there are a few variables that would marginally cause us to reject this hypothesis namely Japan's price level (in the level test) and the UK's and South Africa's price level (in the first difference test). However we can reasonably conclude that the variables used in this study are non-singular, possessing one root. The weight of this evidence points to the possibility of a long-run underlying relationship between each bilateral exchange rate and the price levels. The logical next step is the execution of a cointegration test to further investigate the existence and nature of any such relationship. Cointegration Results Table 4 reports the results of the cointegration tests where the Johansen method was employed. The test is run using each of the five bilateral exchange rates, the country’s domestic price level and the foreign price level as shown in Equation 2. The first column of this table provides the number of cointergrating relations as per the null hypothesis, with the number of reported cointergrating equations shown in the column after each statistic4. For the Trace Test, the 5 percent critical values for a null hypotheses of at most 0, 1 and 2 cointegrating vectors are 29.78, 15.49, and 3.84 respectively. The corresponding values for the Maximum Eigen Value test are 21.13, 14.26 and 3.84. All tests are run with an assumed linear deterministic trend.


TABLE 4 COINTEGRATION TEST

TRACE TEST

CE US r UK r FRANCE r GERMANY r JAPAN r

0 35.44 38.83 35.9 18.94 48.39 1 20.39 23.11 18.57 3.97 27.07 2 6.85 3 9.27 3 6.76 3 0.14 0 11.41 3

MAX EIGENVALUE TEST

CE US r UK r FRANCE r GERMANY r JAPAN r

0 15.05 15.73 17.33 14.97 21.32 1 13.54 13.83 11.81 3.83 15.66 2 6.85 0 9.27 0 6.76 0 0.14 0 11.41 3

Note: r denotes the number of cointegrating equations. Note: the number at the bottom of the columns after the coefficients represent the actual number of cointegrating vectors found.

Up to this point the empirical evidence has clearly supported the relevance of the PPP hypothesis in determining South Africa's nominal exchange rate. The results however of the cointegration tests are decidedly mixed. For three of these DC's, the US, the UK, and France there is an open disagreement between the Trace and the Maximum Eigen Value versions of the test, with the former confirming the existence of three cointegration relations while the latter denies the existence of any such relation. As it is generally thought that the Maximum Eigen Value method carries the higher threshold of proof in the Johansen cointegration test, we will defer to it and conclude that for these three countries there is no fundamental underlying relationship between South Africa's exchange-rate with each of these countries and the corresponding national price levels.

For Germany both tests agree that no cointegration exists, but confirm that there are three cointegrating relations for Japan. These results are difficult to explain if one adheres to the PPP hypothesis. First, the greater explanatory power that might have been expected for the bilateral exchange rate with Britain, given South Africa’s longer period of trade relations with Britain, is not borne out by the results. Second, the clear-cut evidence of the PPP hypothesis in the case of South Africa's relations with Japan would seem to contradict earlier findings of the proof of the PPP in cases of countries with historically high inflation rates. After all Japan has enjoyed a lower average annual rate of inflation over the last four decades that each of the other four DCs, while South Africa has seen a higher average than all five DCs (please see http://www.inflation.eu). Thus the overall picture that emerges from these cointegration tests is at best, tepid support for the PPP hypothesis. CONCLUSION

The goal of this paper was to examine the long run PPP using regression, unit root and cointegration tests on data depicting the relationship between South Africa and five developed countries with which it trades. While the proportionality condition of the absolute PPP is not supported, the OLS results do suggest that the bilateral exchange rate between South Africa and each of these DCs is impacted by both South Africa's and each DC’s price level with the appropriate sign found for each coefficient and with all coefficients being statistically significant. While the coefficient of determination is high in each case, the suggestion of autocorrelation by the low reading on the Durbin Watson statistic warrants an examination of the stationary nature of these variables. For the most part the ADF test confirms that these variables are


non-stationary, leading us to the Johansen cointegration test. This test produced decidedly weak evidence in support of the PPP hypothesis, since for all countries except Japan, the bilateral exchange rate and the relative prices did not appear to be cointegrated.

Given that South Africa maintained a convertible currency for much of this period, and had inflation rates significantly above these five trading partners, these results have to be considered somewhat unfavorable to the PPP hypothesis. However, it must be noted that the significant apparatus of trade restrictions assembled against Apartheid was in place for more than half of the study period. Further studies can investigate this anomaly by breaking the data into two distinct periods to learn whether these trade sanctions impacted the relationship between prices and the nominal exchange rate. These results do add to the collection of findings of a mixed relationship between these variables and point to the need for more research into the causes and consequences of a possible long term drift of relative prices away from the nominal exchange rate. ENDNOTES

1. The symmetry and proportionality conditions are supposed measures of the accuracy of the empirical results from an OLS regression as provided by the coefficients on the domestic and foreign price variables β1 and β2. The symmetry condition requires that these be both equal and of opposite sign, while the proportionality condition (the more binding of the two) requires that β1=1 and β2 = -1.

2. It must be noted that for most LDCs there is a limited extent to which a central bank can influence the trading range of its currency if it operates a reasonably flexible exchange rate regime.

3. It must also be noted that while the quarterly data sample size averages about 155 for the other four DCs, for Germany there are only 72 observations.

4. In about two of the cases for the CPI, the lag length was fixed at 4 by the author. 5. Please see http://www.inflation.eu.

REFERENCES Dickey, D.A. & Fuller, D.A. (1979). Distribution of the Estimations for Autoregressive Time Series with a Unit Root. Journal of American Statistical Association, 74, 427-431. Edwards, S. (1998). Capital Flows, Real Exchange Rates, and Capital Controls: Some Latin American Experiences. National Bureau of Economic Research. Working paper 6800. Edwards, S. (1988). Exchange Rate Misalignment in Developing Countries, Maryland: Johns Hopkins University Press. Engle, R. F. & Granger, C.W.J (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55, (2), 251-276. Frenkel, J.A. (1978). Purchasing Power Parity: Doctrinal Perspective and Evidence from the 1920s. Journal of International Economics, 8, (2), 169-191. Frenkel, J.A. (1981). The Collapse of Purchasing Power Parity during the 1970s. European Economic Review, 16, 145-165. Johansen, S. & Juselius, K. (1990). Maximum Likelihood Estimation and Inferences on Cointegration – with applications to the demand for money. Oxford Bulletin for Economics and Statistics, 52, (2), 169-210. Johansen, S. (1991). Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models. Econometrica, 59, (6), 1551-1580.


Jonsson, G. (2001). Inflation, Money Demand and the Purchasing Power Parity in South Africa. IMF Staff Papers, 48, (2), 243-265. Lui, P.C. (1992). Purchasing Power Parity in Latin America: A Cointegration Analysis. Review of World Economics (Weltwirtschaftliches Archiv), 128, (4), 662-680. Machlup, F. (1972). The Misalignment of Foreign Exchange Rate. Praeger Publishers, 1972. Moody’s Analytics Inc. (2013). South Africa: Economics Analysis. Retrieved from http://economy.com/dismal/outlook/country Nagayasu, J. (2002). Does the Long Run PPP Hypothesis Hold for Africa? Evidence from a Panel Cointegration Study. Bulletin of Economic Research, 54, 2. . Sarno, L. & Taylar, M. (2002). Purchasing Power Parity and the Real Exchange Rate. IMF staff Papers, 49, (1), 65-105. Taylor, M. P. (1988). An Empirical Examination of the Long Run Purchasing Power Parity Using Cointegration Techniques. Applied Economics, 20, (10), 1369-1381. Triami Media BV (2013). Inflation- current and historic inflation by country. Retrieved from http://www.inflation.eu Bloomberg News, South Africa. http://www.bloomberg.com/news


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Finding and Developing New Product Ideas: An Ideation Process for Entrepreneurs

Michael J. Swenson

Brigham Young University

Gary K. Rhoads Brigham Young University

David B. Whitlark

Brigham Young University

Ideation is the creative process of generating, developing, and communicating new ideas (Johnson, 2005). In this paper, we develop an ideation process for entrepreneurs, with a focus on problems customers are trying to solve. This research evolves from the relevant marketing literature, from field interviews, and from observations. We present a four-step ideation process. INTRODUCTION

Long ago, Peter Drucker (1954), the father of modern management, noted, “Business has only two basic functions—marketing and innovation.” Everything else is detail. Certainly, innovation is critical for any enterprise. Many practitioners, entrepreneurs in particular, note that innovation is the lifeblood of their organizations. Accordingly, they continually search for better ways to identify and develop new product ideas.

Finding and developing successful new product ideas depends, in part, on asking the right people the right questions. Henry Ford exclaimed, “If I’d asked my customers what they wanted, they’d have said a faster horse.” Fortunately, Henry Ford did not think like a product consumer, he thought like an entrepreneur. He asked, “What problems are people buying this horse to solve?” By asking the right people the right questions, he discovered that they needed a quicker, more convenient, more comfortable way to get from Point A to Point B. Similarly, Christensen, Allworth, and Dillon (2012) note, “many products fail because companies develop them from the wrong perspective. Companies focus too much on what they want to sell their customers, rather than…what problems customers are trying to solve.” A deep understanding of the problems customers are trying to solve is the starting point for entrepreneurial innovation.

Ideation is the creative process of generating, developing, and communicating new ideas (Johnson, 2005). In this paper, we develop an ideation process for entrepreneurs, with a focus on problems customers are trying to solve. This research evolves from the relevant marketing literature, from field interviews, and from observations. We present a four-step ideation process: (1) select a problem area; (2)


explore the specific problem; (3) propose and test alternative product ideas; (4) perfect the top product idea. SELECT A PROBLEM AREA

Begin by considering the problems customers are trying to solve. Gather data on where there is pain

in the market. Find the pain by observing customer frustration first-hand. We note two generic approaches for selecting problem areas—(1) broad and opportunistic or (2) narrow and focused.

With the broad approach, entrepreneurs consider customer pain points with no particular product category in mind. These opportunistic entrepreneurs look for big market opportunities, then uncover the associated pain points and sell a solution. For example, if they see a big market for cell phones that scratch or damage with use, then they sell protective cases for cell phones. Opportunistic entrepreneurs use engaged observation with potential customers to identify problem areas by encouraging discussion with the following statements and questions:

1. Name some great products that have some annoying little problems. 2. Describe some bad or annoying service experiences you’ve had recently. 3. Over the past year, which product purchases have you regretted the most?

Problem areas emerge as customer pain points are identified.

Conversely, with the focused approach, entrepreneurs leverage their expertise or passion in a specific product category or industry. They search familiar landscapes for pain-points in their specialty area. For example, if they have a passion for scrapbooks, they invent problem-solving scrapbooking tools, paper, cards, and accessories. These entrepreneurs engage potential customers with specific questions/statements such as:

1. What keeps you from really enjoying [product]? 2. If you could change one thing about [product], what would it be? 3. I know it is not possible with [product], but wouldn’t it be wonderful if [product] could …

For entrepreneurs, the bigger the problem area, the bigger the opportunity. We classify these

opportunities into three categories: (1) solve everyday pain; (2) ride waves of interest; and (3) stretch or entertain to the extreme.

Both opportunistic and focused entrepreneurs can address hard-to-ignore pain points that most people experience every day. The pain point must have enough intensity so that potential customers have (1) strong reason to believe the product claim and (2) strong willingness to overlook the risk associated with purchasing a new product from a relatively new company.

Riding the coattails of successful products provide big opportunities for entrepreneurs. For example, the popularity of Crocs shoes makes possible the success of Jibbitz decorative shoe buttons. The popularity of iPhone makes possible the success of apps like Angry Birds and accessories like Zagg’s InvisibleShield. Tapping into engaged buyers that are already on the lookout for ways to make their favorite products even better enables entrepreneurs to ride the waves of interest.

Stretching an idea to the extreme or entertaining to the extreme provides additional opportunities for entrepreneurs. For example, energy drinks succeed because they take the energy benefit of cola drinks and stretch it to the extreme. 5-hour Energy Drink succeeds because it stretches the energy benefit even more by eliminating the extra-calorie distraction. Good ideas also entertain to the extreme. The JetLev water-propelled jetpack shoots out twin streams of water that will keep 500 pounds airborne, move a person across the water’s surface at 22 mph and send one over 30 feet in the air. This extreme fun supports an extreme price tag. While vacationers spend upwards of $250 for 90 minutes of instruction and flying time, celebrities have spent $5,000 for an afternoon of jetpack entertainment.


EXPLORE THE SPECIFIC PROBLEM

After identifying the problem area, probing, open-ended questions are used to deep dive into the pain and identify product attributes that address the core problem. The objective is to detect customers’ desired functional and emotional outcomes. Furthermore, outcome barriers are revealed in this exploration mode. With this information, product requirements begin to emerge.

The big question for entrepreneurs is “How do customers measure value?” Laddering research method provides a means to answer this question by identifying the linkages potential customers make between product attributes and personal benefits (Reynolds, Dethloff, and Westberg, 2001). Specifically, the method enables researchers to identify customer’s personal outcomes, both functional and emotional, along with the barriers that prevent the desired outcomes and attributes that lead to the desired outcomes.

1. What outcomes (functional and emotional) are customers seeking? 2. What barriers stop those outcomes? 3. What attributes lead to those outcomes?

The Case of Prescription Drug Containers Consider the typical brown or white containers for prescription drugs. They all look the same, yet

they are different: label size, label position, text font, information positioning, amount of information, and format of instructions. These differences from bottle to bottle and from pharmacy to pharmacy can cause confusion, and confusion can cause medication errors. Of course, medication errors and even the anxiety over the possibility of making such errors can cause pain—the problem area. Figure 1 delineates the process for identifying how customers measure value. With this information, graphics designer Deborah Alder and industrial designer Klaus Rosburg turned the traditional prescription drug bottle on its head so that the label could be wrapped around the top (Rhoads, Swenson, and Whitlark, 2010). Then they flattened the container so patients could read the label without rotating the container. The label is divided into primary and secondary information. Colored rubber rings attached to the neck of the bottle enable family members to identify their own medications to minimize confusion among household members. Today, this problem-solving prescription bottle minimizes confusion and reduces pain for many Target pharmacy customers across the United States.

FIGURE 1 EXPLORE THE SPECIFIC PROBLEM--PRESCRIPTION DRUG CONTAINERS

1. What outcomes are customers seeking?


2. What barriers stop those outcomes?

3. What attributes lead to those outcomes?

PROPOSE AND TEST ALTERNATIVE PRODUCT IDEAS

Propose and compare alternative product ideas with potential customers using Wow Groups (Rhoads, Swenson, and Whitlark, 2010). To illustrate this concept, we note the experience of Ken Hakuta, founder of the Wacky Wallwalker. When he was asked how he knew that the Wacky Wallwalker was going to be a hit he answered, “It’s really pretty easy. I show people the new product and look to see if they tilt their head slightly and say WOW!” He continued, “If they do, I’ve got another hit.” Although we believe this is a useful starting point for testing product ideas, a deeper understanding of the meaning behind the WOW provides richer test information for alternative ideas.

To understand meaning behind WOW responses, we use a framework proposed by Edward de Bono, the Six Thinking Hats (1985). The framework provides a means for potential customers to be creative in seeing the total picture when discussing and evaluating product ideas. Each thinking that addresses a different activity carried out in what we call a WOW! Group to distinguish it from traditional focus groups often used in marketing research. Briefly, the Six Thinking Hats are:


White: Facts about the product, service, or people who might use them Red: Emotional reaction to the product or service Yellow: Perceived benefits or positive support for the product or service Black: Perceived shortcomings or objections to the product or service Green: Creative thoughts around improving the product or service Blue: Summary thoughts regarding the WOW! Group process and results

When testing alternative product ideas, we invite 10-12 people from the target market to participate in

the WOW! Group. We begin with the White Hat by describing the facts of the product. Next, using the Red Hat, we ask participants, “How would you rate this product from 1 to 10, where 1 means the product is just okay and 10 means WOW! Here is my credit card!” We look for average ratings at 7 or above. Then we ask group members to describe the perceived benefits (Yellow Hat) followed by perceived shortcomings (Black Hat). Next, creative thoughts about improving the product are solicited (Green Hat). We conclude by asking for summary comments and observations (Blue Hat).

Using the Six Thinking Hats creates an opportunity for entrepreneurs to bring together 10-12 people from the target audience, introduce several product concepts, compare concepts fairly without leading participants, identify key positive and negative aspects of each alternative, and get a quick emotional reaction from 1-10. WOW! Group results indicate (1) how to best move forward with the current idea or (2) how to best move forward to find a better idea. The approach enables entrepreneurs to develop new products by validating customer pain points.

Along with measuring the WOW! Factor, entrepreneurs and researchers can test whether they have found a core group of customers that truly loves the product idea by asking additional questions such as, “Who would be interested in this type of product and how would they use it?” PERFECT THE TOP IDEA

A product’s competitive angle defines the pain point and highlights the solution it provides. Trout and

Rivkin (2000) define a competitive angle as an element of differentness, such as smaller, bigger, lighter, heavier, cheaper, or more expensive, and note that profitable products must have one. We add more specificity to the definition. We define a competitive angle as an element of differentness that solves a problem and sparks a personal connection with the customer.

We expand the definition because there are many examples of failed products with elements of differentness that did not solve a problem or spark a personal connection. For example, consider Crystal Pepsi. Launched in the 1990s, Crystal Pepsi is a well-known example of a product with an element of differentness--the only clear colored cola--that flopped.

To perfect the idea, we suggest a litmus test of the five dimensions of a product’s competitive angle (Rhoads, Swenson, and Whitlark, 2010). Products cannot afford to fail on even one of the dimensions. Each dimension demands reflection and customer feedback.

1. Unique Product Claim. How is the product distinctly better at solving problems and more memorable than its closest competition?

2. Need to Believe. Which significant pain point does the product address that is personally relevant to the target market?

3. Dominate Situations. Which usage situations or buying situations does the product dominate in a way that delivers superior value?

4. Reason to Believe. How effective is the demonstration of product problem-solving benefits in capturing the imagination of the target market?

5. Quantifiable Support. What are the relevant facts and figures that can be used to support or enhance the product claims?


The Case of Del Sol We illustrate the five dimension of a competitive angle with a case study--Del Sol. The company

successfully leverages NASA-invented technology to add an element of differentness to hats, T-shirts, shorts, sunglasses, nail polish, glitter, and other fashion accessories. Every street vendor offers similar items except that Del Sol products change color when exposed to the UV rays of the sun. Del Sol carved out a profitable niche from a very competitive market be creating a new product category of color-changing casual clothing. Unique Product Claim

Del Sol color-change technology gives their products a bright and vibrant element of differentness that stands out from all competitors. Furthermore, the differentness also solves a problem by creating distinctive and memorable souvenirs and sparks a personal connection by offering a cool-factor that thrills the target market. Need to Believe

Del Sol started out selling products in shopping malls via carts and kiosks. They shifted the marketing channel to put their products in position to better address relevant pain points. Indeed, Del Sol managers soon learned that vacationers look to bring home a little of the magic from their vacation as well as share some of the magic with family and friends. The frustration is that all of the souvenirs look the same. Del Sol addresses an important pain point for vacationers by providing distinctive, memorable, and fun souvenirs and gifts. Dominate Situations

Del Sol picked a usage situation--vacationers visiting distinctive and memorable vacation sites--and provided a distinctive and memorable souvenir and/or gift at an affordable price. Value for dollar is high and customer expectations are exceeded because vacationers can bring home some vacationing magic. Reason to Believe

Del Sol products are problem solvers that demonstrate themselves. Take them into the sun and their graphics delightfully transform into a palette of bright and vibrant colors. Quantifiable Support

Del Sol uses relevant facts and figures by highlighting NASA color-change technology to support and enhance product claims.

Few, if any, competitive angles are great right out of the gate. The five dimensions of a competitive angle are not go-no-go hurdles. They are not meant to choose between winners and losers, or feel-good benchmarks designed to lull marketers into a sense of false security. The five dimensions are meant to challenge the product and improve it. The competitive angle evolves from good to great when good ideas become great products. DISCUSSION

At this stage we are not claiming that our ideation process is complete. Our purpose is to initiate a process for entrepreneurs to generate, develop, and communicate new ideas, with a focus on problems customers are trying to solve. We believe the proposed ideation process for entrepreneurs provides a framework for finding and developing new product ideas that is amenable to systematic evaluation by researchers and deliberate application by practitioners.


REFERENCES Christensen, C., Allworth, J. & Dillon, K. (2012). How Will You Measure Your Life? New York: Harper-Collins. Drucker, P. (1954). The Practice of Management, New York: Harper-Collins. Johnson, B. (2005). Design Ideation: The Conceptual Sketch in the Digital Age. Design Studies, 26 (6), 613-624. Reynolds, T., Dethloff, C. & Westberg, S. (2001). Advances in Laddering. In T. J. Reynolds and J. C. Olson (Eds.), Understanding Consumer Decision Making: The Means-End Approach to Marketing and Advertising Strategy (pp. 91-118). Mahwah, NJ: Lawrence Erlbaum Associates. Rhoads, G., Swenson, M. & Whitlark, D. (2010). Boom Start: Principles of Entrepreneurial Marketing, Dubuque, IA: Kendall Hunt. Trout, J. & Rivkin, S. (2000). Differentiate or Die: Survival in Our Era of Killer Competition, New York: John Wiley & Sons, Inc.


Grading Technology Allows Teachers to Infuse Technology in the Economics Classroom

Ian J. Shepherd

Abilene Christian University

Brent Reeves Abilene Christian University

Darryl Jinkerson

Abilene Christian University

Education courses now involve homework assignments that require technology skill as well as domain knowledge. Yet there is little pedagogical and technological support for teaching “What” (statistical mean) while simultaneously teaching “How” (use the =average (Range) function in Excel). We describe a conceptual approach and a methodology that helps teachers leverage their domain knowledge and helps students learn both a new topic and a new information technology skill. While teachers will allocate more time towards preparing homework, far less time is spent overall in administering and grading assignments. This approach scales to any class size, thus removing grading burdens imposed by large class sizes. The huge burden of grading lessons is removed, leaving that time free to improve the teaching. INTRODUCTION

Higher education courses now involve homework assignments that require skill at a technology as well as an understanding of a domain concept. Yet there is little pedagogical and technological support for teaching “What” (Average) while simultaneously teaching “How” (use the Average function in Excel). This paper describes a conceptual approach and a system implementation that helps teachers leverage their domain knowledge and helps students both learn a new topic and new information technology skill. While teachers might allocate more time towards preparing homework, far less time is spent overall in administering and grading assignments. This approach scales to any class size, thus removing grading burdens imposed by large class sizes.

Large class sizes have made it more difficult for teachers to provide individual feedback and attention to each student [Chamilliard 2002, Meiselwitz 2002]. It is not unusual at the university level to have sections of classes with hundreds of students [Kay 1998]. These large classes, while financially lucrative for the schools, cause concern for teachers because they can no longer provide one-on-one feedback to students.

Large classes have led to a change in teaching philosophy from the Socratic Method where the professor operates in a mentoring type environment, to straight lecture, where the topic is presented at a


pace and with a style that makes little room for individual learning styles of students. One-on-one interaction is limited and individual feedback is rare. Piaget maintained that individuals learn through interaction with the real world and that social interaction develops knowledge [Piaget 1969].

The Socratic process provided an individualistic teaching methodology. Students were prompted with questions to explore and develop their own understanding of the topics at hand. Individual questioning by the teacher guided the learners to new levels of understanding. The Socratic method of teaching was seen as a powerful tool in developing critical thinking through self-discovery. The teacher used guided questions to develop the thinking skills of the student. Each question was specifically designed for that individual to assist in the development of their learning skills.

Instructional technologies have been limited in their ability to gauge an individual’s progress and offer the teacher the ability to iteratively guide the student towards new propositions using the manipulation of information. Traditional methods of instruction would require significant teacher time commitments and exceptional time management techniques to provide unique one-on-one feedback and guidance through a series of questions. The authors’ prototype developments, however, will provide tools that are capable of providing one-on-one instructional feedback to unlimited number of students.

Marketplace pressures have also resulted in recent curriculum changes at all school levels. More emphasis is being placed on the integration of information technology in all courses [Horgan 1998]. It is typical for visiting committees and school boards to recommend spreadsheet and database software are integrated throughout all curriculums. So in addition to the usual domain knowledge (accounting, finance, management, BCIS, statistics), the student must also learn database, spreadsheet, presentation, data-mining software etc.

Common approaches to integrating technology in the curriculum include requiring introductory courses in, for example, Microsoft ™ Excel. Although one must begin somewhere and the learning curve of most current information technology tools is steep, research shows that the ideal learning environment is in the context of real problems [Suchman 1987]. If the best learning takes place in context of a real problem, an ideal Macroeconomic assignment would introduce new spreadsheet concepts in the context of an economics issue, for example elasticity. The student would improve their skill set (spreadsheet knowledge) as well as their interpretation skill (elasticity). THE PRIMARY GOAL - DOMAIN KNOWLEDGE

The primary goal of an assignment is to teach domain knowledge. The student is challenged to demonstrate their new knowledge in the context of some problem. Two things interfere with this. First, the increased pressure to infuse technology interferes with domain learning. The learning curve of desktop software applications is steep so much time must be allocated to learning the technology itself. This time and energy can detract from learning about the domain. Second, learners make two kinds of errors: syntactic and semantic [Histova 2003]. Syntactic Errors

A syntactic error is frequently referred to as a “typo” or typographical error. An example of a basic syntactic error is when a spreadsheet user forgets to type “=” before entering a formula. The spreadsheet software doesn’t recognize the text “A1+B1” as something to be calculated, but as text to be displayed. A more insidious syntactic error is when the formula is “correct” only in the sense of being accepted by the spreadsheet software. In our context, an example is when a student intends to write =A1 + B2, but instead, enters =A1 – B2. The plus and minus keys are side-by-side on many keyboards and is easy to mistype these keys.

The challenge that syntactic errors introduce in technology intensive courses is that they confuse the semantic issues. It is difficult to understand elasticity when the formulas are not correct. A small typographical error can cause much confusion.


Semantic Errors Semantic errors are true misunderstandings. An example is when a student does not understand how

to calculate a slope. The student enters a formula which produces erroneous results. Spreadsheet software cannot know that the formula was intended to calculate “slope,” so there is no way to catch this error other than to recognize an incongruity between expected and actual values. Misunderstandings at the semantic level can cause a student to spend much time adjusting formulas that are technically correct, but not appropriate. Information Technology Skills

The marketplace has increased demand for business graduates with skills in desktop software applications. Most often this is the Microsoft ™ Office suite, but there is also more interest in SAS, SPSS and SAP. The intent of this article is to focus on spreadsheet assignments using Microsoft TM Excel.

The System

In design and development for twelve semesters by the authors, the original desktop system managed the distribution, grading, and feedback of spreadsheet homework assignments. The prototype system was designed around some simple steps:

FIGURE 1

STEP 1 – THE PERFECT ANSWER

The professor prepares a template containing the perfect answer and decides what is important in this assignment. By spending a little more time on the assignment, we can test different levels of learning, both semantic and interpretive kinds of understanding. This preparation takes more time than before, but our experience shows that, for example, with a class of 200 students, the grading time is reduced by over 90% [Shepherd 2005].


FIGURE 2 STEP 2 – CREATE GRADING RULE CRITERIA

The teacher creates grading rules for the perfect answer. The desktop tool has checkboxes the teacher uses to indicate which aspects of the assignment should count. The teacher can also assign grade weights to each criteria being checked. Our context being spreadsheets, the desktop set-up options include formula, value, and cell attributes such as font, style, or colors. The software can focus on syntactic issues: is the formula correct? Is the answer correct? Is the data shown correctly? The system can also focus on semantic issues: what data meets a certain criteria? What does this chart mean? How might this be interpreted?

Additional time during rule development ensures clear grading criteria are maintained. It is during this process that the teacher can decide on the level of feedback to each student. Assignment intent and teaching philosophy are handled by allowing the teacher to provide simple feedback: “This is wrong – fix it” to “You did not calculate the average correctly. To do this you need to….”

FIGURE 3 STEP 3 – CREATE A STUDENT TEMPLATE FOR DISTRIBUTION

Having finalized assignment creation, the teacher creates a blank template by removing from the perfect answer those items to be completed by the student. Instructions are clearly given as to what the student must complete to receive full assignment credit.


FIGURE 4 STEP 4 – DISTRIBUTE THE ASSIGNMENT TEMPLATE FOR COMPLETION

In the prototype system the blank template assignment was distributed to the student via common directory, email attachment, or drop/return box systems. The new web based version of the product, removes this step by allowing assignments to be downloaded from the web. The new web based distribution system removes all local architecture problems for teachers. Common barriers to mass distribution of the prototype were: lack of an email system to send these files out or, lack of file distribution system, difficulty processing files by email attachment. Now, all that is required is access to the web- not infrastructure is necessary for the school.

FIGURE 5 STEP 5 – THE STUDENT COMPLETES THE ASSIGNMENT


After downloading the assignment, the student uses the usual spreadsheet software, in our case, Microsoft Excel, and then submits the file to the school dropbox system.

FIGURE 6 STEP 6 – GRADE THE ASSIGNMENT

In the prototype desktop system the teacher could (on a scheduled basis) grade all files in the submissions folder. The prototype system checked answers, based on the rules created by the teacher i.e. formulas, formats, ranges, and correct answers. On a typical desktop system, the system graded 200 workbooks in under 2 minutes. The new web based system grades each file instantaneously as the student uploads the file.

Part of the prototype functionality of the system included email notification. After grading, the students were informed via email of their assignment grade and exactly what was wrong. The student email contained feedback directions pertaining to each of the student’s deficient areas in the assignment.

As part of the grading process, the prototype created feedback for the teacher that allowed them to focus on those areas within the assignment where the majority of students fail to understand a concept or fail to grasp a technology skill. This enabled early diagnosis of problem areas and helped the teacher clear up confusion and give extra instruction in specific areas. The teacher could address these deficient areas either in class or in a special session with the students.

The web based system grades each student assignment immediately as the assignment is dropped on the web site. Immediate feedback from the web removes time delays in the old desktop system where the student was relying on the teacher to manually run a grading process.

FIGURE 7 STEP 7 – REVIEW, REPAIR, AND RETURN THE ASSIGNMENT


In the prototype desktop system, the student reviewed the feedback, amended the file and returned the assignment to the teacher for re-grading and possible re-submission back to the student with further directed instructions on areas where the student has failed to comply with the assignment instructions. Iterative assignment grading is an option for the teacher. Iterate Early and Often

One of the most important factors in learning is iteration. Humans learn best in small, iterative steps. Because the prototype tool graded so quickly, the part of an assignment that used to take the most time now took the least amount of time. This enabled the teacher to give feedback “early and often.” Rather than accepting homework only once right before a deadline, the prototype system allowed the teacher to accept assignments early and grade them often.

Iterative assignments contributed to learning in an important way. The nature of technology integration was that small errors (syntax) could lead to large penalties (one formula is wrong and all dependent cells thus also wrong) [Hristova 2003]. Although we live in a world in which small errors can certainly lead to large consequences, the creators of the process do not believe this is the best way to teach. On the contrary, the authors believe that allowing iteration on assignments helps the student find syntax errors which have resulted in serious semantic errors. Clearly the syntax must be correct before the semantics can be considered correct. Students cannot speak intelligently about elasticity if the formulas that create data used to understand that concept are incorrect.

Once the syntax is correct, how can we also evaluate semantics? The authors have discovered that by attention to learning outcomes and careful phrasing of questions, teachers can use syntactic markers to communicate semantics. For example, referring to a table with data, one can challenge the student to “use bright green for all cells that show inelastic demand.” To get this question right, the correct technology skills must be in place (right formulas - syntax) and the domain concepts must be understood (elasticity - semantics).

By allowing iteration, the student receives feedback on both the “how” and the “what” of the assignment. This feedback is directly related to the skill level and competency of each student. The ability of this system to manage large numbers of students not only allows schools to maintain the economic benefits of larger class sizes, but begins to focus more closely on individual performance and instruction. A counter argument to allowing iteration is that students must learn how to get it right the first time. Our experience with under graduates causes us to be more interested in the lower 99% than the top 1%, who are capable of getting it right the first time. There are certainly times to teach that precision is needed right now, but that lesson is not the most important lesson and we believe most students benefit more from a gradual and iterative approach.

THE HYPOTHESIS

The authors propose that assignment iteration decreases technology errors (those errors that fog the interpretation of economic data - syntax errors), improves technology competence (I can repeatedly generate correct economic formulas), and improves domain knowledge (I understand how to interpret this economic data - semantic errors). Research was conducted with a regular and online Macroeconomics and Microeconomics courses and data was collected from two self-selecting groups. The first student group chose to use an iterative learning approach. The second student group chose to use single submissions of the required excel assignments. The iterative assignment option was offered to all students. Students who iterated at least once during the semester were counted in the iterative group. No measurement of student motivation was made during the courses. Self-selection and use of iteration might imply a more motivated student. The students took one of two routes: Iteration or No Iteration. A summary of the differences in each rout appears below.


FIGURE 8 GROUPS – ITERATION AND NO ITERATION

Iterative Feedback and No Feedback

Feedback was conveyed in the prototype system to those who chose to iterate via email. Email feedback error messages were classified as either: syntax (SY) or semantics (SE) based on the requirements of the assignment.

1. Syntax feedback typically dealt with Excel skill issues; such as the student’s inability to create a formula. Syntax feedback instructions were designed to specifically guide the student in correcting the syntax error prior to making any interpretation of the data for the assignment.

2. Semantic feedback typically dealt with interpretation issues surrounding the data; such as elasticity ranges. If the data were correct, and the student misinterpreted that data, then instructions were given on where to focus to correct this misinterpretation i.e. inelastic data is less than 1.

In addition to the syntax and semantic feedback, the emails weighted the student errors showing the

student where the greatest percent of their grade was missed. This allowed the student to focus on the errors of greatest magnitude, and thus offered the student the greatest opportunity to improve their grades. Careful consideration was given to feedback to ensure that prior dependencies were noted so that cascading errors could be tracked.

Students choosing not to iterate received only the first graded email and chose to take the first and final grade for their assignments. CURRENT RESEARCH

Goffe and Sosin (1995) note that while the use of computers and the web within the classroom has increased, there is hesitancy for instructors to use computer modeling tools to dynamically test students understanding of economic concepts. Resistance, they maintain, comes in two forms: both instructor and student hesitance in using the new technologies. The difficulty in implementing these new techniques is compounded by two factors: the instructor must redefine modeling assignments to convey the economic concepts, and the student must overcome poor technology skills to be able to use the modeling technique.

Experimentation within the classroom with assignments and models that allow the student to build data and understand relationships helps students improve both their attitudes and understanding of economic concepts (Grimes, Ray 1993). The problem then becomes, how might the instructor “crest the technology wave, increase modeling within the economics course load, and reduce student resistance to learning new technology skills.” Goffe and Sosin (Goffe, Sosin 2005) discuss the need to also measure improved performance on the part of the student i.e. is the technological effort worthwhile?

Seven years of data collection and twelve years of program development at Abilene Christian University produced the prototype system (and now the new web based system) that addresses the


concerns of instructors wishing to design, implement, and measure the use of technology in the classroom modeling environment. Shepherd and Reeves (Shepherd, Reeves 2006) describe the prototype system. The instructor distributes model templates to students who complete the economics assignment. The students return the assignments to the instructor. The assignments are graded and feedback sent via email. With the burden of grading removed, assignments that are submitted early can be graded, feedback generated at the individual level, and error information returned to the student for review via email, allowing correction and resubmission by the student. Feedback design is important and requires the instructor to spend time defining the requirements for the assignment. Here it is up to the instructor to define the types of errors i.e. incorrect formula, failure to provide formulas, failure to use the right function, and failure to interpret the data correctly. Additional presentation skills can be developed at the instructor’s request to enhance the student’s ability to present visibly pleasing data in formats that convey the correct interpretation of the data i.e. graphs, titles, and data formats. Once problems with feedback are reduced, and the ability to address individual errors is addressed, email (or a web page presentation about the errors) becomes a powerful tool in correcting modeling errors. Iteration now becomes manageable and in fact desirable. Along with the submission of electronic assignments came the need to step up the students’ skills in managing data movement over the web. Experience in using the prototype system showed that strict rules with regard to assignment submission actually enhanced the student’s ability to diagnose delivery problems i.e. in the drop box by 11:55 pm on due date. Delivery methods could vary; ftp, Blackboard file move, Explorer copy, Explorer move, Save to from Excel, Save as from Excel, and now upload to a web page. All students became aware that on-time delivery of a correct product had its benefits - a good grade. Novak etal (Novak etal 1999) first suggested that students would benefit from interactive activities in the classroom accompanied by web based resources that helped the students develop basic economics skills. They defined this technique as JiTT or Just-in Time Teaching. The basis of JiTT is that class activities and homework should encourage outside development by the students, provide quick feedback, and allow the instructor to modify future classes and assignments to address learning deficiencies. With grading and feedback instantaneous to students, the instructor is able to identify problem areas quickly, refocus either class instruction, and/or redesign future assignments to follow a track that helps the students clarify learning problems. Simkins and Maier (Simkins, Maier 2004) developed an innovative teaching technique in their introduction to economics classes that designed future classes based on question feedback from students. The prototype system can be used both in (where students have access to computers) and outside the classroom to determine exact areas of deficiency. Instructors are presented with weighted errors and can focus attention on correcting errors in semantics or syntax based on full class responses to assignments i.e. 42% of the class cannot identify the inelastic range of this data and 15% cannot correctly create the formula for elasticity. Research in the computer science area has shown that Web-CAT automatic grading systems help students focus their efforts through graphic representation of the student’s relative position within the class allowing the students to iteratively improve their assignments (Edwards etal 2006). They maintain that students need to not only see their problem areas, but that they need to be able to place themselves in positions of comparison to other students on the same assignments. Edwards (2003) maintains that this feedback is also invaluable to the instructor as it helps focus the instructor on areas of deficiency thus allowing a modified JIT teaching approach to resolve areas of deficiency. Malmi (2004) maintains that “it is often much better to get instantly even simple feedback than to get advanced human feedback many days afterwards, or even worse to get no feedback at all.” The purpose of the prototype system was to provide this feedback on a timely basis. Malmi’s research also directed further research be done to focus on the types of errors involved by the students that limit their understanding of the course content. The authors support Malmi’s request for error tracking through data collection at the error and feedback level. The teacher is now empowered with the ability to assess and analyze error data to adjust teaching methodology.


The ability of the system to categorize errors based on instructor requirements is a major step forward in removing barriers to learning while enhancing student interaction and feedback so as to remove these errors. Improved Student Scores Improved Domain Knowledge

A total of 45 students enrolled and completed the courses. Of the 45 students, 39 chose to iterate assignments at least 1 time and 6 chose not to iterate assignments. Comparisons of students who did not iterate and did iterate found that on average students who iterated improved their grades by 23% compared to those who do not iterate.

An independent Samples T-Test was performed on the average final grades for both groups (iterate vs. not-iterate). As shown in Table 1 and 2 below:

TABLE 1

GROUP STATISTICS – AVERAGE GRADES COMPARISON BETWEEN GROUPS

TABLE 2 INDEPENDENT SAMPLES TEST – AVERAGE FINAL GRADES

COMPARISON - BETWEEN GROUPS

Table 1 displays the mean points for both groups. The group of students that iterated had a mean of 665 points compared to 458 for the non-iterate group. Table 2 displays the results of a Levene’s test for equality of variances. This analysis was conducted due to the large standard deviations associated with each group. Further analysis is being done to identify the source of this large deviation (possible problems include: a student starting the course and not finishing the course – dropping out and not submitting all the work required).

As shown, the Levene’s test was significant (p < .05), and therefore the Equal variances not assumed t-test must be used. Unfortunately, those results are not statistically significant despite the large difference in the mean points for the two groups.

Average assignment scores for students who did not iterate were 7.5 out of a possible 10 while students who did iterate averaged 9.2 out of a possible 10 for their assignments. The average score and standard deviation per assignment for those students that chose to iterate was calculated. The maximum number of iterations was four. Table 3 below displays the average score per iteration (out of 10).


TABLE 3 AVERAGE SCORE AND STANDARD DEVIATION FOR THOSE WHO ITERATED

An ANOVA was conducted on the average score per iteration and is displayed in Table 4.

TABLE 4 ANALYSIS OF VARIANCE FOR COMPARISON OF AVERAGE SCORES

FOR THOSE WHO ITERATED

As shown, the ANOVA is statistically significant indicating that average grades improved

significantly as students elected to iterate.

Improved Technology Competence The study grouped the learning of new Excel skills into the first five assignments. No new technical

skills were required after assignment five. Visual data groupings imply that by assignment five, the number of times students iterated dropped from three iterations to one iteration.


TABLE 5 ASSIGNMENT COUNT BY ITERATION TYPE GREEN AREA

Examining the last 10 assignments, we wanted to see if there was a difference in the number of total

errors between those who iterated and those who did not. Expectations were that there would be a difference as students who iterated were more likely to resolve errors earlier in the learning process than students who did not iterate. Table 6 shows the results for the comparison of the last 10 assignments. Students who iterated had .23 mean errors compared to .36 mean errors for non-iteration students.

TABLE 6 GROUP STATISTICS – TOTAL

Given that the equal variances assumed results were significant, we must use equal variances not assumed results. These indicated that there was no significant difference between the mean results (however, differences were indicated).


Further breakdown of the error analysis allowed us to compare just syntax errors over the last 10 assignments. Expectations were that we would see a significant difference in syntax errors between the two groups. Those who iterated would be expected to have a lower occurrence of syntax errors.

TABLE 7 COMPARISON OF SYNTAX ERRORS ON LAST 10 ASSIGNMENTS

Group Statistics

571 .45 .498 .02180 .59 .495 .055

571 .24 .428 .018

80 .33 .471 .053

571 .16 .363 .01580 .19 .393 .044

571 .17 .376 .01680 .18 .382 .043

571 .12 .330 .01480 .21 .412 .046

571 .05 .212 .00980 .09 .284 .032

571 .04 .205 .00980 .11 .318 .036

571 .05 .223 .00980 .06 .244 .027

571 .03 .180 .00880 .04 .191 .021

571 .02 .138 .00680 .04 .191 .021

571 .01 .102 .00480 .01 .112 .013

571 .01 .118 .00580 .01 .112 .013

571 .00 .042 .00280 .00 .000 .000

571 .00 .042 .00280 .00 .000 .000

571 .00 .042 .00280 .00 .000 .000

student_iterateYNYN

YNYNYNYNYNYNYNYNYNYNYNYNYN

number of syntax1 occurences






number of syntax7occurences









N Mean Std. DeviationStd. Error

Mean


Table 8 shows that there is a significant difference between the two groups with regard to syntax errors. Iteration here appears to have improved the student’s ability to avoid Excel errors. Table 8 compares each individual assignment for syntax errors.

TABLE 8 INDEPENDENT COMPARISON OF SYNTAX ERRORS BY ASSIGNMENT

Independent Samples Test

2.427 .120 -2.313 649 .021 -.137 .059 -.254 -.021

-2.322 102.663 .022 -.137 .059 -.255 -.020

7.893 .005 -1.608 649 .108 -.083 .052 -.185 .018

-1.497 98.171 .138 -.083 .056 -.194 .027

1.948 .163 -.722 649 .470 -.032 .044 -.118 .054

-.681 98.848 .498 -.032 .046 -.124 .061

.051 .821 -.114 649 .909 -.005 .045 -.093 .083

-.112 101.579 .911 -.005 .046 -.095 .085

15.753 .000 -2.164 649 .031 -.088 .041 -.168 -.008

-1.834 93.785 .070 -.088 .048 -.184 .007

8.670 .003 -1.514 649 .130 -.040 .027 -.092 .012

-1.218 91.761 .226 -.040 .033 -.106 .025

24.790 .000 -2.597 649 .010 -.069 .026 -.121 -.017

-1.879 88.408 .064 -.069 .037 -.141 .004

.537 .464 -.369 649 .712 -.010 .027 -.063 .043

-.346 98.510 .730 -.010 .029 -.067 .047

.152 .697 -.196 649 .845 -.004 .022 -.047 .038

-.186 99.511 .852 -.004 .023 -.049 .041

4.331 .038 -1.052 649 .293 -.018 .017 -.052 .016

-.824 90.812 .412 -.018 .022 -.062 .026

.104 .747 -.162 649 .872 -.002 .012 -.026 .022

-.151 98.336 .880 -.002 .013 -.028 .024

.047 .829 .108 649 .914 .002 .014 -.026 .029

.112 105.057 .911 .002 .013 -.025 .028

.562 .454 .374 649 .708 .002 .005 -.007 .011

1.000 570.000 .318 .002 .002 -.002 .005

.562 .454 .374 649 .708 .002 .005 -.007 .011

1.000 570.000 .318 .002 .002 -.002 .005

.562 .454 .374 649 .708 .002 .005 -.007 .011

1.000 570.000 .318 .002 .002 -.002 .005

Equal variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumedEqual variancesassumedEqual variancesnot assumed







number of syntax7occurences









F Sig.

Levene's Test forEquality of Variances

t df Sig. (2-tailed)Mean

DifferenceStd. ErrorDifference Lower Upper

95% ConfidenceInterval of the

Difference

t-test for Equality of Means


SUMMARY

• Student Grades improved if they used iteration through the prototype system. • Iteration improved the student’s ability to avoid Excel errors and thus remove technology barriers

to learning economic concepts. CONCLUSION

Tools are now available that provide individual feedback related to the skill level and competency of each student. The focused use of the system provides feedback that enhances learning through iteration. The ability of this tool to manage large classes allows us to maintain the economic benefits of larger class sizes, but begin to focus more closely on individual performance and instruction. The successful application of this tool enhances the technology skills required for the business world, and the subject knowledge skills required to successfully fulfill course content requirements.

Finally, this tool facilitates a change of focus in instructional methods that leads to an improved quality of teaching experience. As professors become comfortable with this tool they are able to focus on what they need to teach students, rather than the drudgery of grading. REFERENCES Chamilliard, A.T., and Merkle, Laurence D. (2002), Management Challenges in a Large Introductory Computer Science Course, Proceedings of the 32nd SIGCSE technical symposium on Computer science education, SIGCSE’02, pp. 252-255, Feb 27, 2002 Stephen H., Perez-Quinines, Manuel A., Phillips, Mathew, and RajKumar, Johnny, (2006) Graphing Performance on Programming Assignments to Improve Student Understanding, 9th International Conference on Engineering Education, Session R1H. San Juan, PR. Edwards, Stephen H. (2003), Teaching Software Testing: Automatic Grading Meets Test-First Coding. OOPSLA 03, October 26-30, 2003, Anaheim, California, USA. Goffe, William L., Sosin, Kim (2005). Teaching with Technology: May You Live in Interesting Times. The Journal of Economic Education, Vol. 36, No. 3, pp. 278-291. Grimes, P. W., and M. A. Ray (1993). Economics: Microcomputers in the College Classroom - A review of the academic literature. Social Science Computer Review 11 (Winter): 452-63 Histova, Maria, Misra, Ananya, Rutter Megan, and Mercury, Rebecca (2003), Identifying and Correcting Java Programming Errors for Introductory Computer Science Students, Proceedings of the 33rd SIGCSE technical symposium on Computer science education, SIGCSE ’03, Reno, Nevada, pp. 153-156, Feb 19, 2003 Horgan, Barbara (1998), "Transforming Higher Education Using Information Technology: First Steps" The Technology Source, January 1998. Available online at http://ts.mivu.org/default.asp?show=article&id=1034. Kay, David G (1998). Large Introductory Computer Science Classes: Strategies for Effective Course Management, Technical Symposium on Computer Science Education, Proceedings of the twenty-ninth SIGCSE technical symposium on Computer science education, Atlanta, Georgia, pp. 131-134, 1998


http://ts.mivu.org/default.asp?show=article&id=1034�

Malmi, Lauri, and Korhonen, Ari (2004). Automatic Feedback and Resubmissions as Learning Aid. Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALT’04) Meiselwitz, Gabriele (2002), Using the Web to maintain the benefits of small class instruction in large classes, Journal of Computing Sciences in Colleges, Volume 17, Issue 3, pp. 141-148, 2002 Novak, G., Patterson. E., Gavrin. A., & Christian, W (1999). Just-in-time teaching: Blending active learning with web technology. Upper Saddle River, NJ; Prentice Hall. Piaget, J (1969). The Mechanisms of Perception, New York; Routledge Kegan Paul, 1969 Shepherd, I. & Reeves, B (2006). How to Structure and Evaluate Information Technology Assignments. Association of Business Information Systems, Federation of Business Disciplines, 1 (1), 53 - 56. Shepherd, Ian J. (2005), Faculty Assignment Load Analysis, College of Business Administration, Abilene Christian University, Technical Report COBA2005031, 2005. Simkins, S. & Maier, M (2004). Using Just-in-Time Teaching Techniques in the Principles of Economics Course. Social Science Computer Review 2004; 22; 444 Suchman, L (1987). Plans and Situated Action, Cambridge University Press


APPENDIX

ASSIGNMENT DATA


The Impact of the Institutional Environment on SME Internationalization: An Assessment of the Environmental Assumptions of Emerging

Integrated Models of Internationalization

Pat H. Dickson Wake Forest University

K. Mark Weaver

University of South Alabama

George S. Vozikis California State University, Fresno

This study provides an empirical test of the assumptions that many existing models of internationalization make regarding the institutional factors influencing internationalization by small to medium-sized firms (SMEs). Utilizing a sample of over 2100 SMEs in nine countries, the roles of such institutional attributes as the nature of the economic and legal systems and the levels of economic and political risk are examined in light of the impact that these factors have on the levels of international activity of SMEs. The results indicated that each of these factors may play an important role in either motivating or enabling internationalization. INTRODUCTION Evidence of the growing internationalization of small to medium-sized enterprises continues to emerge in both popular literature and academic publications. A vigorous debate has ensued focused on whether traditional theories of internationalization, developed almost exclusively in respect to larger firms, are applicable to smaller more entrepreneurial firms. In response to this debate a number of integrated models of the internationalization of SMEs has been offered (Bell, McNaughton, Young and Crick, 2003; Oviatt, Shrader and McDougall, 2004; Zahra and George, 2002). While each of these models provides a unique perspective on SME internationalization the one common foundation of all is an assumption of the critical role of environmental factors in motivating or constraining internationalization. Each proposes a theoretically derived grocery list of environmental attributes that should impact both the choice and opportunity to internationalize. To date, little empirical research has been offered to support or discount these assumptions. In order to provide a foundation for the assumptions of these models this research provides a limited test of the environmental assumptions of these integrated models of SME internationalization. We will first review the arguments for considering SME internationalization as a unique phenomenon and the integrative models suggested for understanding such behavior. Secondly we will present research hypotheses


specifically designed to test assumptions regarding environmental or institutional factors influencing internationalization by SMEs. Finally, we will describe a limited test conducted to assess the impact of a range of institutional factors on SME internationalization and the implications of the results obtained. STUDY MOTIVATION AND THEORETICAL FOUNDATIONS SME Internationalization International business operations, long considered to be the primary province of large multinational firms, is now widely observed in firms of all sizes (Fillis, 2001). A recent large scale study of UK firms noted that almost one-half of all SMEs surveyed maintained international operations (Kalantaridis, 2004) and Knight (2000), writing in 2000, noted that SMEs were believed to account for 35% of all exports from Asia and over 25% of all exports from developed countries elsewhere in the world. This growing internationalization of SMEs is underscored by Oviatt and McDougall’s (2000) estimate that by 2005 one-third of all SME manufacturing firms would derived at least 10% of their revenues from foreign sources. The increasing internationalization of SMEs has been reflected in a growing academic interest in understanding the nature of such activity and the intersection between internationalization and the entrepreneurial process. There have been special academic journal issues devoted to the topic by Entrepreneurship Theory and Practice, Small Business Economics, the Journal of Business Venturing, and the Academy of Management Journal as well as others. A special focus journal, the Journal of International Entrepreneurship, was founded in 2003 specifically to address research internationalization by entrepreneurial firms. The growing body of research aimed at understanding the nature of the internationalization process for SMEs suggests that the unique characteristics of such firms, particularly their resource constraints and timing of entry into the international marketplace, make the application of traditional theories of internationalization, dealing primarily with large multinational organizations, tenuous at best (Bell, McNaughton, Young and Crick, 2003: Ibrahim, 2004: Madsen and Servais, 1997; and Oviatt and McDougall, 1994). Fillis (2001) argues that many SMEs exhibit behaviors that don’t fit the traditional stages of internationalization and that globalization effects along with the impact of technology and industry specific changes make the application of existing models of internationalization impractical. Emerging Integrated Models of SME Internationalization Some of the more popular theories drawn from business research and utilized in an attempt to understand the internationalization of SMEs include “Innovation-Related Internationalization” models (Bilkey and Tesar, 1977), “Foreign Direct Investment Theory” (Buckley and Casson, 1993), and “Network Theory” (Johanson and Vahlne, 1998; Sharma 1992). Additionally, such traditional economic theories as Transaction Cost Theory (Zacharakis, 1997) and Resource Theory (Woodcock, Beamish and Makino, 1994; Yeoh and Jeong, 1995) have been applied. The most widely explored theories of internationalization in business research that have also been applied in SME-based research, are those labeled as the “stage” theories of internationalization or the “Uppsala Internationalization Models” (Aaby and Slater, 1989; Petersen and Pedersen, 1997). The most influential of the stage models is the one developed by Johanson and Vahlne (1977). These theorists suggest that firms internationalize through various processes slowly and incrementally over time. The underlying assumption of the model is that as firms learn more about distant markets the risk-reward valuations improve allowing the firm to incrementally increase commitments of resources to foreign markets. Johanson and Vahlne (1977) suggest that firms will move first into international markets that are most similar to their home markets and then with time and knowledge acquisition will take increasingly greater risks in entering markets that are more distant or more dissimilar to their own home markets. Although the stage models of internationalization seem appropriate in characterizing the behavior of larger firms, many scholars, as noted previously, have begun to question their applicability to SMEs. In response to the criticisms aimed at applying traditional theories of internationalization to SMEs, a number


of integrated models of SME internationalization have been proposed. Three models that have gained increasing notice are ones developed by Zahara and George (2002), Bell, McNaughton, Young and Crick (2003) and Oviatt, Shrader and McDougall (2004). Each of these integrative models provides a unique perspective for understanding the SME internationalization process. Oviatt and McDougall began developing the basic elements for a process theory of SME internationalization built around their foundation research (Oviatt and McDougall, 1994) on “born global” firms. In 2004, they along with Shrader (Oviatt, Shrader and McDougall, 2004) proposed a “risk management” model of new venture internationalization. The model, focusing on firms that rapidly internationalize from inception, describes a complex set of interactions between the founders and the general environment of the venture which are mediated by the industry environment and the characteristics of the entrepreneur. The traditional stage models of internationalization suggest that firms gradually move into increasingly more risky international transactions while the “born global” perspective outlined by Oviatt and McDougall (1994) focuses on firms that internationalize from inception. A second perspective has been enunciated by Bell and Young (2001) that incorporates a process understanding of SMEs that are well established in their domestic markets but which suddenly, based on some critical triggering event, rapidly internationalize. Madsen and Servais (1997) earlier described this process as a “leapfrog” process. Bell, McNaughton, Young and Crick (2003) propose a model of internationalization that seeks to accommodate all three processes of internationalization—incremental internationalization, born global, and leapfrog. They argue that the pathway that an SME takes is dependent upon the knowledge resources of the firm, the strategic posture of the firm and the unique attributes of the firm’s internal and external environments. A third integrative model of the internationalization process is proposed by Zahra and George (2002). Arguing that existing models do not adequately consider the international entrepreneurial behavior of established firms, these theorists propose a model intended to accommodate such behavior. Building on a definition of international entrepreneurship as “the process of creatively discovering and exploiting opportunities that lie outside a firm’s domestic markets in the pursuit of competitive advantage (Zahara and George, 2002, p. 261), they focus their model on the forces that influence the degree, speed and geographic scope of international activities. The model outlines a complex interaction between firm attributes and strategic and environmental factors that lead to internationalization. Although each of these models has a different temporal focus as it relates to internationalization one common attribute of each is the acknowledgement of both firm-specific and environmental or institutional antecedents to internationalization. While firm-based antecedents of internationalization have a long history of exploration far less explored are the institutional factors that motivate or constrain internationalization. Oviatt et al (2004) suggest such institutional variables as political systems and government policies, economic and social conditions and certain attributes of the natural environment as playing a role in internationalization. Although they do not provide specific attributes, Bell et al (2003) suggest an important role for environmental factors in internationalization. Zahra and George (2002) argue that in addition to competitive forces and growth opportunities in the environment such factors as national culture and attributes of the institutional environment play a role in the internationalization process. Building a Framework for Assessing the Role of Institutional Variables

The primary goal of this research is not to provide a new model of SME internationalization but rather to begin testing the assumptions the models have in common regarding the institutional factors impacting internationalization. It is hoped that such research will provide a starting point for integrating all three temporal perspectives of SME internationalization into an empirically testable framework. Both the resource based view (RBV) of the firm as well as institutional theory is used in this analysis to understand the role and importance of the institutional environment of the SME to internationalization. The institutional environment has been defined as the set of political, economic, social and legal conventions that establish the foundational basis of production and exchange (Oxley, 1999). Kalantaridis


(2004) argues that changes in the institutional framework of international trade have created a new age of global economic integration. Institutional variables are of particular importance in understanding the capacity of SMEs to internationalization. Given the limited internal resources base of the SME there is a far greater reliance on the external environment for the resources necessary for internationalization. A review of existing institutional theory research as well as internationalization research yields a wide range of factors that may impact internationalization. For purposes of this study we have chosen to look at four components of the institutional framework assumed to influence internationalization and the range of individual variables reflecting each component. Environmental Munificence

Keats and Hitt (1988) suggest that a munificent environment, an environment characterized by a broad range of resources supporting growth (Dess and Beard, 1984), will support expansion and the generation of slack resources supporting growth. First explored by Staw and Szwajkowski (1975) as an important variable in organizational growth and decision capacity, environmental munificence has been linked both to the survival and growth of firms as well as the ability of new firms to enter a specific market (Castrogiovanni (1991). It has traditionally been assumed that the more abundant the resources available to firms the more likely the firms are to pursue strategies beyond those necessary for survival. As resources become scarce, competition intensifies and firms have limited ability to pursue goals beyond survival (Castrogiovanni, 1991). A wide range of resources have traditionally been employed as reflecting the level of environmental munificence. These include, particularly as it related to technology-based industries, such things research and development spending by governments and businesses. Variables assumed to impact all firms operating in a specific environment include such things as population growth, workforce educational levels, personal income, commercial and industrial loan volume and venture capital investments. Because most often SMEs must seek resources external to the firm for both growth and the pursuit of strategic options such as internationalization, it is assume that environmental munificence has a particularly significant impact. Given these assumptions and the preponderance of historical research linking environmental munificence to strategic options we propose the following relationship between environmental munificence and internationalization.

Hypothesis 1: The greater the levels of environmental munificence of the home market of an SME the greater will be the level of internationalization.

Economic Risk

Environmental risks, particularly economic and political, have been link to the nature and extent of transactional interactions of firms (Ghosal, 1987). The unattractive nature of economically risky environments has been shown to limit the ability of SMEs to form alliances (Dickson and Weaver, 2011) as well as deterring foreign investment (Oxley, 1999), particularly in those settings in which there is a history of government expropriation of foreign-held assets. Economic risks are driven by the institutional forces that impact stability. For firms in countries with high levels of economic risk the lack of external investment and the limited attractiveness of potential alliance partners would seem to suggest a greater need to increasingly seek growth outside the home market of the firm. This suggests the following hypothesis:

Hypothesis 2: The greater the level of economic risk of the home market of an SME the greater the greater will be the level of internationalization

Economic and Political Freedom

According to Hitt, Ahlstrom, Dacin, Levitas and Svobodina (2004) institutional factors have great power in shaping economic activity and the strategic choices of firms. One such institutional factor, economic freedom is a widely utilized construct that reflects the freedom and protection that individuals have in acquiring property and engaging in voluntary transactions (Gwartney, Lawson, and Block, 1996).


Traditionally it has been assumed that the freest economies are those that have a minimal level of government interference and least free are those where significant restrictions are placed on what can be produced, how much is produced and for whom production is intended (Karabegovic and McMahon, 2005). Economic and political freedom has most often been assumed to include such reflective attributes as low taxes, low regulations on business transactions and secure property rights (Kreft and Sobel, 2005). Past research has empirically linked the level of economic freedom in a region to economic growth, the development of democratic institutions, civil and political freedoms and other economic outcomes (Karabegovic and McMahon, 2005). More recently the level of economic freedom has been linked to the level of entrepreneurial activity (Kreft and Sobel, 2005). To our knowledge there is limited research linking the level of economic freedom uniquely to the likelihood of SMEs to internationalize.

The level of economic and political freedom in the home market of the SME is assumed to impact the decision and timing of internationalization in three key ways. First, due to the limited resource capacity of the SME, the lower the economic burden placed by local, regional and national taxes the greater the resource capacity available to internationalize and thus the greater the SMEs tolerance for economic risk associated with internationalization. Second, in order to choose to internationalize the owner or manager of the SME must be able to assume a reasonable capacity for the expatriation of earnings from international operations with no unreasonable regulations or taxation. Finally, again due to the limited resource base of the SME and a consequential limited capacity to manage restrictive legal barriers to international trade, internationalization by the SME will be significantly impacted by the level of burdensome regulations to international trade placed by local, regional and national governments. Given these assumptions we propose the following relationship between economic freedom and SME internationalization.

Hypothesis 3: The greater the economic and political freedom of the home market of an SME the greater will be the level of internationalization.

Nature of Legal Systems

The legal systems governing the transaction environment of firms has often been viewed as creating regulatory pressures that impact strategic choices (Oxley 1997; 1999). Legal systems impact organizational actions through the establishment the basis for production, exchange and distribution in an effort to establish order (Davis and North, 1971; Yiu and Makino, 2002). Because of the resource constraints faced by most SMEs there is a need for a strong system of equitable and enforceable laws that support the successful operation of the firms. La Porta, Lopez-de-Silanes, and Shleifer (1997; 1998; 1999) argue that the primary issue determining the strength of a legal system to protect transactions is the legal origin of laws. Their research, which has been widely utilized in financial research, utilizes four legal systems based on origin. These include English common law and French, German and Scandinavian civil law. While their work suggests that in general wealthy countries enforce laws better the economically poor countries, in general they find that French civil law countries have the lowest quality of legal enforcement in terms of contracts and property rights.

Utilizing similar logic as that relating to economic risks, it would seem that firms located in countries with lower levels of legal protection for transactions would be more likely to seek transactions outside their home market. This logic would suggest the following hypothesis:

Hypothesis 4: The legal system governing an SME will be significantly related to the SME’s likelihood of internationalization. In comparison to SMEs in French Civil Law Countries, SMEs in countries with legal origins in English Common Law, German Civil Law or Scandinavian Civil Law will have lower levels of internationalization.


RESEARCH DESIGN Sample

SME manufacturing firms in Australia, Costa Rica, Finland, Greece, Indonesia, Mexico, Netherlands, Norway and Sweden were randomly selected for inclusion in the study. The study sample was based on a methodologically rigorous survey sampling program conducted by the authors over a five year period. For purposes of the study, an SME was classified as any firm with less than five hundred employees, but no less than six employees as consistent with both European and U.S. classifications (European Network for SME Research 1995, U.S. Government Printing Office 1995). A key informant design was chosen for the survey given the individual and firm level attributes of interest. This design was deemed appropriate based on the strong theoretical argument that firms of this size are in extensions of the key individuals in charge (Lumpkin and Dess 1996). The total sample included 8578 SMEs in the nine countries included in the research. This sampling resulted in a total of 2141 useable responses. Lists of potential firms were developed, where possible, from data base listings and where such listings were not available, lists were developed from organizational affiliation lists of commercial firms in each country. Mailing lists for some European countries were developed through the use of KOMPASS On-Line systems, which is an electronic database that provides addresses for manufacturing firms. Manufacturers were selected at random from eleven different industry groups representing major industrial classifications in the Gross Domestic Product (GDP) of each country. Only SMEs with 6 or more employees and less than 500 employees were included in the study. A complete report on the sampling demographics can be found in Table 1.

TABLE 1 SAMPLE DEMOGRAPHICS

Country Total Sampled Useable Surveys Percent Useable Australia 1373 313 22.8 Costa Rica 1500 87 5.8 Finland 400 121 30.3 Greece 400 228 57.0 Indonesia 890 285 32.0 Mexico 650 363 55.8 Netherlands 300 131 43.7 Norway 2465 433 17.6 Sweden 600 180 30.0 Totals 8578 2141 25.0

A key informant design was used. Consistent with definition of a key manager used in this study the surveys were addressed and completed by either the owner or general manager of each firm selected. The strategic decisions regarding alliance relationships for firms of the size surveyed in this study are generally assumed to be determined by the key decision leader within the firm. This is one of the primary reasons that firms with 500 or less employees were utilized in the study. There is strong theoretical support that firms of this size are extensions of the individuals that are in charge (Lumpkin and Dess, 1996).

Survey items, developed originally in English, were translated with care and a back-translation process was utilized. Teams of experts reviewed the final survey translations for meaning and consensus was reached prior to the development of a final survey. In some of the countries the surveys were mailed (Norway, Sweden, Australia, Finland and the Netherlands) since past experience has shown a reasonable response rate for mailed surveys. Past experience has also shown that in some national settings mailed


surveys have a very low response rate and a much higher response level is obtained when surveys are hand-delivered.

The representative nature of the final samples was assessed in two ways. First, a series of analysis of variance procedures were used to test for significant differences across all study variables when wave was considered as a main effect. No significant differences were found. A second assessment was completed through a random telephone survey of a select group, 50 in each country, of non-respondent SMEs. Because the surveys were anonymous, calls were placed randomly to firms selected until 50 firms had been identified that had not responded. The results of this telephone survey indicated that there were no significant differences between the responding and non-responding SMEs in terms of demographics alliance participation or industry classification. Study Measures

The study variables were measured utilizing items drawn from existing research, macro-economic country data and a series of objective-type measures developed for the study. In order to control for the potential effects of time of collection of our sample, we measured the institutional factors associated with internationalization over a range of years. The natural log of the five year average (ending with the year of data collection) of real GDP (in 1990 U.S. dollars) was drawn from UNESCO Statistical Yearbook (2002) and used as a measure of environment munificence. Although this is a very broad measure of a country’s economy it is assumed to reflect a broad range of resources available to SMEs. The Euromoney (1998) Country Risk Index, which is a measure of a country’s debt in international finance markets, was uses as a measure of economic risk. The Freedom in the World Index (Karantnycky & Piano, 2002) was utilized as a measure of economic and political freedom in each country. The survey rates political rights and civil liberties utilizing a broad range of measures including civil liberties, organizational and human rights and economic freedoms. The origin of laws measure utilized in this study was drawn from La Porta, et al. (1997; 1998; 1999). A dummy coding scheme was utilized in which SMEs were coded as being from “French Civil Law,” “Scandinavian Civil Law,” or “English Common Law.” No “German Common Law” countries were included in the current study. The study outcome variable, SME internationalization, was assessed by asking respondents to indicate the total percentage of their annual business that was derived from markets external to their primary country of operations. Control Measures

In order to provide a rigorous test of the hypothesized relationships a wide range of control variables was utilized in the study. SME size was based on the number of total employees. Differences in the individual risk profile of the firm were controlled utilizing a widely used measure of entrepreneurial orientation developed by Covin and Slevin (1988; 1989). Twelve different industry groups were sampled in the study. In order to aid in the stability of the regression analysis these twelve groups were collapsed into four industry groupings based on the level of technological sophistication. The categorization scheme was based on one provided by the Organization for Economic Co-Operation and Development (OECD): Science, Technology and Industry Scoreboard (2003). A dummy coding scheme was utilized for the four industry groupings.

Controls for the perceptions of the respondent regarding environmental conditions that might impact their strategic choices were also included in order to clearly picture the impact of the actual institutional environment of the SME. Perceptions regarding general market uncertainty, technological uncertainty and uncertainty specific to the firm’s industry were measured utilizing scales drawn from the work of Covin and Slevin (1989) and Schultz, Slevin and Covin (1995). Reliability tests showed that reliability was above generally excepted levels for all measures in the study. Analysis

The study utilized hierarchical linear regression to assess the factors associated with the level of internationalization of SMEs. In order to provide the most conservative test possible, all control variables were entered into the regression model first. Second, the variables measuring the firm owner’s


perceptions of the environment were entered. The institutional variables were entered into the model last. All continuous variables were converted to standardized scores in order to minimize as much as possible potential multicolinearity of variables. RESULTS

Tables 2 and 3 provide an overview of the study demographics and the study measures.

TABLE 2 STUDY DEMOGRAPHICS

English

Common Law

Scandinavian Civil Law

French Civil Law

High Tech

Medium High Tech

Medium Low Tech

Low Tech

Total 313 734 1094 352 406 742 641

TABLE 3 STUDY VARIABLES

Variable Mean Standard Deviation International Intensity1 18.21 26.8529 SME Size2 64.10 107.43 Risk propensity 3.00 .7737 General market uncertainty 3.02 .6858 Technological uncertainty 2.87 .9307 Industry uncertainty 2.84 .8371 GDP (log) 12.09 .7800 Economic risk index3 21.15 19.189 Freedom index4 2.26 1.6158 1Total volume of business external to home country. 915 firms had no international involvement. 11.8% had over 50% of total business external to home country. 2Firms with less than 6 employees are more than 500 employees are not included in the study. 3Variable coded such that lower index numbers indicate lower risk 4Variable coded such that higher index numbers indicate lower levels of freedom Correlation matrix available upon request from the authors. There was no significant evidence of multicolinearity among predictor variables.

An overall response rate of 25 percent was obtained for the study resulting in 2141 complete and useable surveys. Of the survey total, 915 SMEs had no international involvement while just over 11 percent had over 50 percent of their total business derived from outside their primary country of operation. Tests of Hypotheses

Table 4 provides the results of the hierarchical linear regression. Hypothesis 1 posits a positive relationship between the munificence of the environment of the SME’s primary country of operation and internationalization. It is assumed that there must be at least a minimum level of resources and infrastructure available to the SME in order to aid internationalization. The results of the regression analysis support this assumption.


TABLE 4 HIERARCHICAL LINEAR REGRESSION FOR INTERNATIONALIZATION

Variables Model 1 Model 2 Model 3 Constant .005 .001 .508 Controls Firm Size (Loq) Risk Propensity High Tech Industry Medium High Tech Industry Medium Low Tech Industry1

.293*** .054** -.110 .046 -.106*

.289*** .025 -.102 .053 -.103*

.225*** .044 .004 .116* .011

Environmental Perceptions General market Uncertainty Technological Uncertainty Industry Uncertainty

.021 -.033 .111***

.041 -.042 .081***

Institutional Environment GDP (log) Economic Risk Index2

Freedom in the World Index3

English Common Law Scandinavian Civil Law4

.120*** .308*** -.181*** -.765*** -.150*

R2 Adjusted R2 ∆ R2 F Change

.101 .099 .101*** 47.831

.111 .108 .011*** 8.449

.158 .153 .047*** 23.533

1Comparison group is “low tech” industry 2Variable coded such that lower index numbers indicate lower risk 3Variable coded such that higher index numbers indicate lower levels of freedom 4Comparison group is “French Civil Law” N = 2141 *p< .05, **p< .01, ***p< .001

Hypothesis 2 predicted a positive relationship between the level of economic risk and the level of internationalization while Hypothesis 3 predicted a positive relationship between economic and political freedom and levels of internationalization. It was reasoned that concerns about risks in home markets would drive SMEs to internationalize while the freedom from unreasonable regulations and the ability to expatriate earnings without excess taxation would support them in doing so. Both assumptions proved to be correct. It should be noted that in the regression analysis the coefficient for the Freedom Index is negative. For this index higher index numbers indicate lower levels of freedom. Thus the negative coefficient indicates that has freedom increases (lower scores) internationalization increases.

Hypothesis 4 predicted a significant relationship between the origin of the legal system of the home country of the SME and the SME’s levels of internationalization. The results suggest that in comparison to “French Civil Law” countries, both “English Common Law” and “Scandinavian Civil Law” countries have lower levels of internationalization. This supports the assumption that lower levels of protection of trade in the SMEs home country might encourage higher levels of internationalization. Discussion

The findings of this study help to provide a framework for understanding the role of the institutional environment in motivating or enabling the internationalization of SMEs. Taken in total, the results of the


study suggest that the greater the risks in a SMEs home country the higher will be the levels of internationalization by the SME. At the same time, internationalization is enabled when there are high levels of economic and political freedom and when the institutional economy provides a foundation and infrastructure supporting of business operations. The origin of laws within the home country of the SME was also found to have a significant impact on internationalization.

While the study illuminates the role of institutional variables in the internationalization activities of SMEs, it has a number of limitations. It provides a broad sampling of SMEs from multiple countries and industries but generalizations made to countries and industries other than those included in the study should be made with care. The study utilizes survey methodology, that while providing unique information has potential limitations. Also, a limitation potentially present in all cross-national research is the potential of misinterpretation of questions by respondents and interpretations of results by researchers. Study Implications

The current debate regarding the unique nature of SME internationalization is theoretically robust with a range of proposed “process-based” models driving the discussion. This study finds its significance in presenting research hypotheses aimed at assessing one attribute each of these process models have in common, the assumptions regarding the role of the institutional environment in the internationalization of SMEs. The goal of the study is to provide a starting point from which all three temporal perspectives of SME internationalization outlined by the process models reviewed, might be integrated and empirically tested. Extant research is rich regarding the role of firm resources in enabling internationalization. It is hoped that the present research will expand our knowledge by providing a unique view of the enabling or constraining attributes of the institutional environment of the SME and in so doing stimulate additional research in this area.

This research also promises to have important policy implications as it relates to SME internationalization by illuminating the role of various factors of the institutional environment, many reflective of local and regional trade and industrial support policies, in supporting or constraining SME internationalization. For example, it is hoped that answers to such key questions as to what aspects of economic freedom, i.e. tax structures, trade regulations, etc, are most critical in supporting or constraining SME internationalization. Likewise based on the industry of the SME, what attributes of environmental munificence, i.e. availability of financial capital, R&D expenditures, educational preparation, etc, are most relevant in understanding the ability of SMEs to internationalize. Finally, does the international diversity of a region make a difference in the ability of SMEs to internationalize and if so what are the linkages between a diverse population and SME firms that are most relevant in support of internationalization? CONCLUSIONS AND FUTURE RESEARCH

Recent research into SME internationalization has called into question the adequacy of traditional models of internationalization in explaining the strategic choice of SMEs to enter the international marketplace and the timing of such entry. Several “process-based” models of SME internationalization have been proposed by modern theorists. One commonality across all such models is the assumptions regarding the role of both firm-specific resources and institutional factors in impacting SME internationalization. Although there has been an abundance of research focusing on firm-specific resources and internationalization the research focusing on the relationship between the attributes of the institutional environment and SME internationalization has been limited. This research provides four specific research hypotheses designed to empirically test the institutional framework proposed by the process models of internationalization. These hypotheses focus specifically on the relationship of economic munificence, economic risks, economic and political freedom, environmental munificence and the nature of the home country legal systems in motivating or constraining SME internationalization decisions. We believe this research to be of significant importance both in developing a common ground


for integrating the various process models of internationalization and in illuminating the important policy implications of the relationship between the institutional environment and SME internationalization.

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Junior Mining Sector Capital-raisings: The Effect of Information Asymmetry and Uncertainty Issues

Casey Iddon

University of Ballarat, Australia

Samanthala Hettihewa University of Ballarat, Australia

Christopher S. Wright

Burgundy School of Business, France

While prospecting by junior mining companies (JMCs) is a vital contributor to modern wealth creation, attributes of the junior mining sector (JMS) limit JMC-fund raisings to external equity (shares). In considering responses by JMC principals to deep discounting and other JMC-investor strategies, potential responses were found to: increase returns to principals, increase JMS moral-hazard issues, and further deepen price discounts on JMC share offerings, especially IPOs. It is suggested that the attractiveness and moral-hazard consequences of these potential responses can be greatly diminished if mining-tenement fees are raised and JMC prospecting costs are allowed as an offset against those fees. INTRODUCTION

Prospecting by the Junior Mining Sector (JMS) is a key input to renewal and growth in the mining sector which (via mineral commodities) is a major driver of the wealth that is created by manufacturing and flows on through its derivative markets. About 60 percent of Australian gold, nickel, and base metal discoveries, since the 1960s, have been attributed to JMC prospecting efforts (Geo-science Australia, per Hogan et al.,, 2002). The JMS is dominated by small firms who are vitally dependent on their capacity to raise external capital. The JMS are solely in an exploring and/or pre-mine developmental phase. Firms (involved in those activities) with one or more operating mines, are part of the senior mining sector.

However, the JMS is beset with information asymmetry and at times moral-hazard issues that, consistent with Akerlof’s (1970) conundrum, drive massive discounts (i.e. 50 percent or more of intrinsic value) in IPOs and secondary equity offerings (SEOs). The fundamental issue is that, while Junior Mining Companies (JMCs) can potentially generate spectacularly massive future earnings, they are more likely to exhaust all their capital (before generating earnings) and the uncertainty is so great as to leave little means to differentiate between future winners and losers among JMCs (where JMCs are engaged in exploration and/or have undeveloped finds). If a JMC succeeds in developing a working mine, it graduates from the JMS and becomes a large mining company (LMC) in the senior mining sector. Thus, for the most part, JMCs have wonderful but highly uncertain earnings potential with little or no past or current earnings or positive cash flows to indicate their potential or to otherwise commend a given JMC or group of JMCs to


investors. The research question being considered in this paper is: How do JMC principals organize their firms, in response to strategies used by investors to value JMC share offerings?

In answering the aforementioned research question, this paper seeks to: i) Show how JMCs transcend their generally dismal risk/return profiles and their lack of access to

internally generated funds, to raise the capital they need to perform their vital link/service in the creation of wealth,

ii) Discuss the nature of information asymmetries and the associated fears issues that may rise in the JMS and how and why they drive massive discounts in IPOs and other share offerings, and

iii) Consider policies and other remedies that may mitigate the harmful effects of the information asymmetries and associated investor fears in the JMS

It is important to note that, without ongoing prospecting services from a vibrant JMS, those services

would need to be done by large mining companies (LMCs) or as government public good. The rest of the paper is structured as follows: 2) The JMC business model; 3) Trade-offs and other equity market issues for JMCS; 4) Moral hazard and other opportunistic JMC-manager behaviour; 5) Conclusions.

THE JMS BUSINESS MODEL

The JMS has mostly been ignored by the literature examining the capital raising process. Specifically, that literature emphasizes firms with earnings, and/or expected earnings that can be estimated within a tolerable confidence bandwidth and the JMS business model precludes that type of analysis. Specifically, JMCs generate revenue by acquiring mineral tenements from governments (usually Federal) and either explore them for mineable deposits or hold them until prospecting by another firm (JMC or LMC) generates evidence of mineable deposits in neighboring tenements. If a tenement increases in value due to a discovery, or proximity to a discovery, its JMC owner will likely sell it on to another JMC or a LMC. Only rarely, after discovering a mineable deposit, does a JMC develop it into a major mine and grow from a JMC into a LMC. Thus, JMCs can be like financial firms who acquire and hold onto options—in some cases JMCs can earn very good returns without ever prospecting. This potential separation of rewards from prospecting effort, cost and risk creates an enormous moral hazard issue for the JMS. Specifically, without active prospecting, there is little or no means for any tenement to rise in value. However, those who incur the costs and risks of prospecting can only capture the benefits from that prospecting that accrue to their tenement(s). Gains to neighboring tenements, from that prospecting, will accrue as external benefits to the holders of those tenements. Thus, the best business model for a JMC is to acquire and sit on tenements in the hope that some other JMC will incur all the cost and risk of prospecting. Given that there are few generous fools and bankruptcy tends to thin their numbers quickly, the sit-and-wait JMC plan works best with a degree of low cunning. Specifically, firms using a strategy of wait-for-others-to-take-the-risk may need to spinoff and sell a subsidiary JMC to incur the costs and risks of prospecting but (that subsidiary) will have limited rights to tenements that could benefit from that prospecting (e.g. a scattering of tenements owned by the subsidiary and all or most of the surrounding tenements owned by the parent JMC). Such a structure concentrates the prospecting costs and risks in the subsidiary and concentrates potential gains from prospecting in the parent JMC.

The fears of investors, that JMC principals may exploit the potential information asymmetry, lead to deep discounting of JMC share issues and are an excellent example of the phenomena described by Akerlof (1970). Thus, as noted by Akerlof’s (1970) assertion, unchecked information asymmetry can lead to behaviours with costs that are borne by all participants in the market.

JMCs must obtain funding to acquire tenements, to administer their tenements, and to prospect on their tenements for mineable deposits. If and when a JMC finds a mineable deposit on one of their tenements, they then need to acquire more funding to go through the very risky and uncertain process of early-stage development of the deposit and even more funding if they choose to develop the deposit into a viable mine. Many JMCs forego this last stage and sell the rights to their discoveries to LMCs. The potential returns to winning JMCs are massive, with their shareholders receiving multiples of their


investments of 10-fold or even several100-fold—what needs to be considered in future research is to what degree are those returns reflective of risk and effort and to what degree might they be attributable to manipulation by a few less scrupulous JMC principals.

During the vast majority of the business life of a JMC, it bleeds money and only a lucky few will ever have positive cash-flows that are of sufficient magnitude to justify either loans or an investment. Also, as Low (2011) notes, risk within the JMS is further aggravated by the cyclical nature of the mining industry. As a result, loans and other credit are rarely possible for JMCs and they must, for the most part, rely on equity funding. As an added complication, potential investors need to be convinced of the wisdom of either investing in JMCs on a portfolio basis or on a lottery basis (if investing in a single firm or tenement).

TRADE-OFFS AND OTHER EQUITY MARKET ISSUES FOR JMCS

Similar to venture capital, capital-raising for JMCs is not auxiliary to its business operations, but is a necessary precondition for any business operations to occur. Capital-raising involves treacherous tradeoffs, such as balancing the raising of funds with the risk of diluting existing equity-holders, which may off-side key market participants, whose support may be needed in future capital raisings.

Pecking order theory (Myers, 1984; Myers and Majluf, 1984) suggests that firms, unable to access internal funding, first elect to use debt, then preferred shares, then debt-convertible-to-common shares, and finally (as a last resort) equity. This is supported by Lee et al. (1996) who found that IPOs are the most expensive capital raising process, followed by SEOs, convertible bonds and, finally, least expensive, straight bonds. The JMS, given its high-risk and initial high cash-out-flow business model, is forced to seek the most costly form of financing—external equity.

Equity financing is also the quickest method for raising funds (Low, 2011). Thus the JMS is largely reliant on equity markets, and the capacity to periodically tap equity markets is vital to support ongoing business development and/or survival of these firms. Consequently, the prudential management of the capital raising process is of vital importance to a JMC; a well managed capital-raising insures the viability of the firm as a going-concern (at least until the next capital raising), and demonstrates an endorsement by the market of the company’s strategy. In contrast, a poorly managed or inopportune capital-raising can inflict serious harm on a company’s prospects, market standing and share price – even if there is no underlying deterioration of the firm’s assets or the viability of its business plan. The history of the Australian Securities Exchange (ASX) reveals that JMS has a number of a poorly managed capital-raising and not many of those firms survived into a second capital-raising process.

These concerns, when combined with the information asymmetries and associated fears discussed in the previous section, may encourage a few unscrupulous JMC principals to organise the opportunities, costs and risks of their firms such that their interests and those of a select few are served at the expense of investors that they can attract to deviously designed subsidiaries. It is important to note that, while investors in such subsidiaries may make a fair- to-excellent return on their investment, that return may be a mere fraction of the gain that their investment created. Specifically, the principal of the JMCs may have organised their firms to maximise the external benefits of the prospecting efforts of the subsidiary and to concentrate as much of those benefits as possible into the parent and/or related firms. IPOs, SEOs, and Share Pricing

The IPO is only the start of a JMC’s close association with equity markets—as the firm seeks to raise ever more funds to sustain itself and/or grow its business and market value. Kreuzer et al. (2007) studied junior exploration floats on the ASX from 2001-06 and found that the typical JMC raised A$4 million during the IPO process, but, given an average A$2.6 million annual cash-burn rate, it usually required a fresh capital infusion within two years of listing. Thus, while industrial firms with earnings can concentrate on protecting, optimising and growing its operations earnings (confident that those efforts will be reflected in its share value), a JMC often must improve market value to acquire tenements and/or prospect for potential mineral deposits to generate earnings flows—all this must occur after it has sought


its capital infusion and before it proves itself. Once a JMC has a developed mine, it moves toward becoming a LMC and can justify and build its market value in the manner of an industrial company.

Wrapped up in the capital raising process is the phenomenon of self-fulfilling prophecy, where rising market valuations often allow funds to be raised more easily and with relatively less dilution (which, in turn, increases market value and facilitates future capital raisings and associated business development)t in a fortuitous self-sustaining cycle. Similarly, falling share values makes it profoundly more challenging to execute capital raisings, and failed capital raisings can have a devastating impact on market values and sentiment, which again vastly increases the difficulties involved, and the required dilution of existing holders, of the still required capital raise. Thus, raising capital is a crucial first step for most JMCs and is an ongoing need, until a firm proves itself by finding and developing a significant ore body.

The literature in Australian markets, on SEOs, includes Denhart (1992 and 1993), who investigated the market’s response to SEO announcements, in addition to Allen and Soucik (1999a; 1999b) and Brown et al. (2006), who looked at long run performance in the wake of a SEOs. More recently, Brown et al. (2008) analyzed share purchase plans, a particular species of SEO, which involve an offer to shareholders of up to $5,000 worth of new shares over a one year period at a discounted price and without brokerage. The Brown et al. (2008) study sampled 591 share purchase plans from 1991 to 2005 and found that firms who elected to pursue this style of SEO typically displayed lower levels of liquidity (i.e. current ratio) and net cash holdings in addition to having a more disparate share register (percent of share registrar held by non-top 20 shareholders). In addition, the study found that the immediate and long-term impact of share purchase plans was under-performance of share prices relative to the market. Factors found to influence immediate underperformance include the size and the level of discount of the share purchase plan, prior share price performance, whether non-shareholders have time to participate in the offer and the industry within which the firm is located. Long-run underperformance was mitigated in the case of mining firms, where the share purchase plan was underwritten and when the firm was audited by a “big-N” firm.

A select minority of JMCs operates mines and the earnings derived from such productive assets provide internally generated funding and access to a wider store of external capital raising options, at a reasonable cost.

IPO and the Winner’s Curse

The winner’s curse, in Rock’s (1986) model, refers to the tendency for attractively priced IPOs to be secured by well-informed investors, leaving little chance for less-informed investors to participate in the potential gains. Lee et al. (1996) suggested a measure for this effect, namely the speediness in which new IPOs are funded—where more attractively priced issues are more quickly funded. This effect creates an empirical means for testing—where the speed of the IPO is positively related to the perceived degree of under-pricing in the industrial setting by How et al. (1995) and in the mining sector by How (2000). However, How (2000), finds that mining IPOs which are slower to be funded, typically enjoy greater performance in the longer-term (e.g. three-years after listing). Such results suggest that a perception of an attractively priced IPO may not be a good reflection of reality—unless the well-informed investors “flip” their acquisitions, shortly after the listing firm debuts on the market. The JMC Investor

As noted in previous sections, information asymmetry and moral hazard issues are concerns that will affect the decisions of every prudent investor in the JMS. Other concerns and issues are discussed by Baker (2009). Baker (2009) contends that, until recently, corporate finance literature has focussed on various factors influencing the demand for corporate capital, to the neglect of the factors influencing the supply of corporate capital. Further, according to Baker’s (2009) view: corporate finance demand effects involve specific characteristics of the firm and how these characteristics influence the capital raising process, while, supply effects are grouped into the following categories: (1) investor tastes (where the sentiment and demands of investors changes over time in a way which is not due to changing fundamentals and is potentially irrational); (2) limited intermediation (which recognizes that financial intermediaries are not always effective in ensuring the market prices are efficient; e.g., due to poor


capitalization, competition or incentives); and (3) corporate opportunism (where the corporate managers seek to raise funds when prices are high and to make repurchases when prices are low.

The boom in the JMS, as witnessed by the upsurge in the IPO market for JMCs over the past decade, is undoubtedly partly in response to the demand by investors for new investment vehicles in this suddenly prosperous and prominent investment space. Research demonstrates that market prices are influenced by changing demand by investors which is not underpinned by changing fundamentals (see Shleifer, 1986; Wurgler and Zhuravskaya, 2002; Mitchell et al., 2004; Greenwood, 2005).

Investor taste has a powerful impact, within the JMS, via the fad formation and speculative bubbles—such phenomena, likely facilitate the creation of market inefficiencies. Although inefficiencies might be assumed to be arbitraged-away by effective financial intermediaries, the requirements of such an benign outcome may be difficult to satisfy within the JMS (i.e., due to the sector’s difficulties in generating effective and efficient valuation techniques, spotty coverage by reputable analysts, specialized knowledge, illiquidity, hyperbole, rumor, and high risk and uncertainty). The failure of financial intermediaries to ensure efficient market pricing is highlighted by studies such as Shleifer and Vishny (1997) and Brunnermeier and Pedersen (2005). Theoretical foundations arguing for deviations between investor demand and fundamentals includes Barberis et al. (1998) and Daniel et al. (1998) and (empirically) Odean (1998, 1999).

Where financial institutions have difficulty correcting market inefficiencies (because the investor tastes potentially cause prices to separate from fundamental values), such financial institutions can actually exacerbate the divergence and may be encouraged to do so by the, managers of JMCs—who will then attempt to exploit such inefficiencies by raising capital at favorable (high) prices and reacquiring shares at low prices for later re-sale at higher prices. Similarly, if a particular commodity-type is coveted and ripe with speculative interest, JMCs are incentivized to tap this demand and refocus their stated prospecting efforts towards the new in-vogue commodity-type. As part of this process, JMCs may seek to change their names to signal their new focus to investors. Indeed the Cooper et al. (2001) study shows how companies change their name in an effort to appeal to changing investor sentiments.

DOES JMC-MANAGER BEHAVIOUR REFLECT A MORAL HAZARD?

Baker (2009) argues that corporate managers are ‘opportunistic’ in issuing securities when prices are relatively high and repurchasing securities when prices are depressed. Taggart (1977), Marsh (1982), Asquith and Mullins (1986), Korajczyk et al. (1991), Jung et al. (1996) Hovakimian et al. (2001), and Virolainen (2009) are among those suggesting SEOs are issued at high prices. Ikenberry et al. (1995) provides supporting evidence of repurchases during times of depressed prices. The propensity to issue new funds at times of high prices is also evidenced by research finding subsequent poor returns post new equity issues, as seen in Stigler (1964), Ritter (1991), Loughran and Ritter (1995). Applied to the JMS, corporate managers will seek to issue securities when prices are high, but, without the luxury of time, these managers may be forced to issue securities when prices are less favorable, so as to fund the continued development of the JMC to sustain it as a going concern.

Erel, et al (2012) employed a large sample of debt and equity issuances in the United States from 1971 to 2007 to investigate the impact of macroeconomic conditions on capital raisings. Their findings support the important influence of macroeconomic conditions on the capacity of firms to raise capital. Among other findings, they found that, for non-investment grade borrowers (as in the JMS) capital-raisings typically flourish during economic upturns but are greatly diminished during economic downturns.

There is also evidence to suggest that the participation by management may be related to performance (Datar et al., 1991; Balatbat et al., 2004). Leland and Pyle’s (1977) signaling model implies that high-levels of retained ownership by the issuer post-listing is supportive of a firm’s value and prospects and so, should bring about lower levels of under-pricing. However, this contention has been questioned by the empirical findings of Lee et al. (2000), who found no significant relationship.


Future research should empirically test for the presence of the moral hazard issue discussed earlier in this study, where the JMC principal(s) seek to organize the structure of their firms to concentrate prospecting costs and risks in a subsidiary, to maximize the external benefits from the prospecting, and to concentrate those external benefits in the parent firm and/or in related firms. It should be noted that such actions, while difficult to prove, are clearly a social wrong and may possibly even be a variant of fraudulent preference. Further, the harm arising from such actions is not limited to the artificially-reduced returns to JMC investors but must also include the reduced JMS investment activity and increased deep-discounts imposed on the IPO and SEO share offerings of other JMCs.

Reputation and IPO Share Pricing and Performance

Reputation is often seen as an effective counter to information-asymmetry and moral-hazard issues. Chemmanur and Fulghieri (1994) contend that the reputation of the underwriter involved in a share issue (e.g. their historical of underwriting quality firms), is drawn upon by investors as an important signal to assess the quality of the issuing firm. This notion is supported by Michaely and Shaw (1994) in addition to Carter et al. (1998).

The reputation of a mining industry share issue may also be enhanced by the participation of other firms or persons of note—e.g., the 2007 Poseidon Nickel announcement that Andrew Forrest (founder of Fortescue) would participate in the firm’s capital-raising and future direction and by the 2010 entry of Lynas Corp as a major shareholder in Northern Uranium.

The literature also suggests that reputation may play a pivotal role in the success or failure of IPOs (Block and Stanley, 1980; Beatty, 1986; Beatty and Ritter, 1986; Balvers et al., 1988; Beatty, 1989; Schiller, 1990; How et al., 1995). The general conclusion of the studies is that auditors and underwriters with strong reputations gravitate towards IPOs with less under-pricing and more certainty concerning the post-listing price. However, the How 2000 study found no empirical support within the ASX mining-sector for reputation positively affecting IPO share prices. Nevertheless, firms going to market with an IPO or SEO often develop narrates and attestation to attract sufficient capital, the veracity of which is mostly a function of reputation. Further, some researchers (e.g., How, 2000) allude to a possibility that sector specific factors of the mining industry may strengthen the role the reputation of firm auditors play and may mean that they have at least as strong a role for the JMS as they do for industrial firms.

The effect of prevailing market conditions on the initial performance of IPOs is well known (Ibbotson and Jaffe, 1975; Ritter, 1984; Ibbotson et al., 1988; How et al., 1995). How (2000) investigated the performance of 130 mining IPOs, on the ASX from 1979 to 1990 and found an average under-pricing of more than 100 percent, significantly more than the under-pricing of the IPOs of industrial companies. The How (2000) study was based (in part) on earlier work, in the USA, by Ritter (1984)—who found that mining company IPOs tended to be relatively more underpriced than those of other firms. Further, in reviewing the post IPO performance of mining firms, How (2000) found that they did not underperform the broader market (as described in the broader body of literature) for ASX listed industrial companies (see, also, Lee et al., 1996). How (2000) found that the primary variables related to the initial under-pricing were the prevailing market conditions at the time of the IPO and the time elapsed between the registration of the prospectus and listing. How (2000) found support for size of the listing firm, underwriter reputation, and period of time from the firm’s incorporation to listing, as related variables.

Rock’s (1986) model implies that the extent of under-pricing in the IPO market is related to the level of uncertainty around the post-listing price. Risk increases with uncertainty--thus, IPOs which are difficult to value and for which it is difficult to predict a post-IPO market price, are typically more underpriced, likely to compensate investors for the added risk. This relationship is empirically supported by Ritter (1984), Beatty and Ritter (1986), Beatty (1989), Wolfe and Cooperman (1990), How et al. (1995) and Lee et al. (1996a). This study suggests that there is a sharp dichotomy in the JMS as to the reliability of valuation: Class 1 firms consist of mining and late-stage development firms capable of relatively robust NPV-based valuations; and Class 2 firms, consist of exploration and early-phase feasibility firms (the majority of firms in the JMS) who have few if any reliable methods of valuation. It is suggested that future research seek to transfer insight from Rock’s (1986) model into the IPO market,


into the SEO market for the JMS, by determining whether Class 1 SEOs are less discounted than Class 2 SEOs. Information Asymmetry and Other Risks Afflicting JMC IPO Investors

While underlying causes for the under-pricing of IPOs remains contentious, leading theories focus on the conflating issues of moral hazard and information asymmetry. Specifically, the principal(s) of the issuing firm possesses greater knowledge regarding the firm’s true value than potential investors, who fear that their lack of knowledge will result in them being duped into paying for external benefits that are organized to accrue to the principal(s) of the issuing firm while providing little or no benefit to the other shareholders of the issuing firm. As a result, the issuing firms must under-price their IPO offer, relative to intrinsic value. Thus, honest principals suffer because they cannot be differentiated from less honest principals, as do investor who want a fair investment for a fair price.

The long-run underperformance of IPOs is generally supported throughout the literature (Ritter, 1991; Aggarwal and Rivoli, 1990; Lewis, 1993; Keloharju, 1993; Aggarwal et al., 1993) and, within the Australian context, Finn and Higham (1988) and Lee et al. (1996). A notable exception to this finding is Lee et al. (1996b) who studied the Singaporean IPO market for mining sector. Intriguingly, the How (2000) finding that mining IPOs are substantially more underpriced than industrials was entirely a product of gold firms within the sample—reinforcing the special place of gold-focused firms within the larger JMS. Gold firms are often heralded in the literature as a special case, because they are often a hedge for most other investment opportunities (Low, 2011).

Bowen et al. (2008) investigated the capacity of investment analysts to reduce information asymmetry and, thereby, reduce the costs of capital-raisings. They looked at the level of under-pricing for 4,766 SEOs in the U.S. issued between 1984 and 2000. The principal finding was that increased analyst coverage does decrease under-pricing. Additionally, the quality of the analysis and whether they were employed by the lead underwriter, also, decreased the under-pricing. This is in contrast to other studies which questioned the value of analysts, given the conflict of interest risks—e.g., Zhang (2005) argues that analysts disproportionately benefit already relatively informed investors and, thus, exacerbate information asymmetries.

Cranstoun (2010) tested the value-relevance of capital-raisings from Aug/08-Mar/10 on gold firms with market valuations from $100-to-$800 million, listed on major US and Canadian Exchanges. That research yielded a sample of 42 public and 59 private capital transactions. Tested potential-value-relevant factors were: (1) dilution percentage (i.e. Total Transaction Volume/Market Cap on day of Transaction), (2) prior stock performance (i.e. LTM returns/ benchmark, ARCA:GDX), warrant issuance (Y/N), presence or absence of producing facilities (Y/N) and underwriters’ domicile (Canada/Other). Cranstoun (2010) suggests that his finding of no significant value-relevant factors may be due to a small sample size and because capital raisings are linked with issues that both depress and enhance prices (e.g. respectively, dilution and imminent mine development/production) resulting in a negligible little net impact. The outcomes of Cranstoun’s study may also have been hindered by the study’s target period (i.e. share price performance on the day prior to the announced capital-raising was compared to the share price on the day of the announced capital raising). Such a selection presupposes a near-perfect market and runs counter to more recent empirical research (which suggested that insider trading is relatively rampant within the mining sector). Bird et al. (2010) found that 40 percent of the value-content of exploration announcements, 50 percent of the value-content of resource announcements and fully 100 percent of the value content of reserve announcements was already priced by the market, prior to the price-sensitive announcement. As a result, the value-relevance of the capital raisings assessed by Cranstoun may already have been priced-in by the market, the day before the announcement.

The literature clearly identifies why JMCs must seek equity funding and eschew credit funding. Specifically, credit-market imperfections (e.g. asymmetric information between lenders and borrowers) cause credit to be inordinately costly. Further, it is suggested that the magnitude of external finance premiums is an inverse function of the borrowing firm’s net worth (defined as the sum of liquid assets and the collateral value of illiquid assets; Bernanke et al., 1996)—given that JMCs are most highly


uncertain potential with very high moral hazard risks, they have little to commend them to prudent creditors.

CONCLUSIONS

This study highlights the importance of prospecting by the JMS to wealth creation in modern economies, the difficulties JMC have in funding their prospecting efforts, and that JMCs need to seek external equity funding. Information asymmetries in the JMS were discussed with possible occurrence of moral hazard and their baleful influence on the pricing of JMC IPO and SEC share issues.

The role of reputation as a possible means of moderating the information asymmetries effects in JMC IPO and SEC share issues was considered but the empirical evidence was mixed and the continuing presence of deep-discounting of JMC share issues suggests that it is, at best, only marginally effective.

The analysis suggests that, given the great difficulties in resolving information asymmetries and possible moral hazard in the JMS, a few unscrupulous JMC principals may have been encouraged to organize the opportunities, costs and risks of their firms benefit their interests and those of a select few at the cost of investors in subsidiaries that were carefully crafted to concentrate the costs and risks of prospecting in the subsidiaries while shifting the gains to externalities concentrated in the parent JMC and/or related firms. It is important to note that, investors in such subsidiaries may make a fair to excellent return on their investment in the JMC subsidiary, but that return is a mere fraction of the gain created by their investment. If such manipulations are occurring, they are at least a social wrong and may even be a variant of fraudulent preference.

A quick policy offset to the moral hazard issue in the JMS would be a significant increase in the tenement fees and to allow JMCs to deduct their bona fide prospecting costs from those fees or a significant portion of those fees.

Future Research

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From Washington to Wall Street: The Relationship Between National Politics and Stock Market Performance

James M. Day

Ashland University

Thomas W. Harvey Ashland University

The Foundation for Ethics in Financial Education

The question explored in this paper is the relationship between national politics and the stock market. There is an interesting relationship between Washington and Wall Street that has existed in the United States for decades that many citizens either do not know or do not understand. Government has had an increasingly influential role in the economy of the United States, particularly since the creation of the Federal Reserve. We seek to understand and explain the impact the balance of power in Washington and political activity can have on the performance of the stock market. INTRODUCTION

Ever since its founding, the New York Stock Exchange has served as one of the most prominent leading indicators of economic conditions in American society (Hirt and Block, 2012). Throughout its history, this exchange and the broader stock market have reflected economic growth in the United States and around the world. It has provided opportunities for individual and institutional financial gain through risk taking in investments. Whether serving as an avenue of tremendous wealth accumulation or plainly as a supplemental retirement income source, the stock market has impacted the lives of the American people over time. Coinciding with the market’s impact on society has been the role of the federal government and political establishments on the American people. Since the founding of the United States, free enterprise capitalism and democracy have shared a common bond of freedom (Friedman, 2002). These forces have arguably been the greatest two contributors of American affluence and exceptionalism, especially since the turn of the 20th century. It is clear that, over time, democratic principles in government pave the way for prosperity in the financial markets.

Political and economic interests, particularly financial interests, have consistently been intertwined in such a free and prosperous nation (Friedman, 2002). The question, then, becomes what is the best political power structure in American government for the stock market to thrive? Does the makeup of Democrats and Republicans in Washington have an impact on the performance of market indices such as the Dow Jones Industrial Average and the Standard & Poor’s 500? Is there a particular balance of power between the White House and Congress that leads to higher investment returns? By examining historical data, we discover if there is a correlation between political party power and stock market performance.


This paper asserts that the balance of political power influences, or is directly related to, actions and events in Washington. These actions and events, carried out by elected officials in Congress and the White House, have a heavy impact on economic activity and development for the country. It is widely accepted that the stock market indices are a major leading indicator of the state of the economy in the country (Hirt and Block, 2012). Stock market performance on Wall Street fluctuates daily based on the latest economic and geopolitical news from anywhere and everywhere in the world. Politics and investments have something in common: they both directly impact the lives of millions of Americans. Are these two critical components of activity in the United States separate and impartial to each other, or is there a subtle or potentially deep relationship between the variables? How great of an influence does Washington have on the stock market? In other words, does political action have a cause-and-effect relationship with the stock market?

While many hold the belief that government and business (free enterprise) ought to be kept separate, it is evident that the federal government has played a continually increasing role in the nation’s economy. One can look to the financial crisis of 2008 to see the role government played in the economy, and, therefore, the stock market. Following the collapse of the investment banks, Bear Stearns and Lehman Brothers, the financial markets went into turmoil (Paulson, 2010). Other large financial institutions such as AIG and Citigroup were on the verge of collapse as well and were in desperate need of assistance. These institutions were so large and heavily leveraged that other institutions were unable to come to their rescue financially. Only the federal government had the capital necessary to save such failing institutions deemed too big to fail (Sorkin, 2009). President Bush, advised by Federal Reserve Chair Ben Bernanke and then Secretary of Treasury Henry M. Paulson, Jr., was convinced that those institutions needed to be saved in order to prevent the rest of the economy from crashing as well (Paulson, 2010). The fear was that if the nation’s largest financial institutions failed, they would bring down the rest of the economy possibly into levels not seen since the Great Depression or even worse. Paulson testified before Congress to convince the legislators that it was necessary to issue what became known as the TARP funds (Troubled Asset Relief Program) in the initial amount of $700 billion to save firms like Citigroup, AIG, among other major financial institutions in the country.

In this circumstance, the government acted decisively in intervening in the economy. Many conservative politicians compromised their principles, which were based on the teachings of Adam Smith, of free market capitalism without government interference, for the good of the markets, the economy, and the country (Heilbroner, 1986). The balance of power in Congress had a significant influence on the Congressional willingness to proceed with the bailouts and the corresponding passage of the Dodd-Frank Wall Street Reform and Consumer Protection Act. As a result of this action, the stock market eventually stabilized after suffering significant losses. Proponents of the government bailouts and resulting legislation argued that the stock market would have collapsed even lower had the federal government not intervened.

This paper contends that political events impact the financial markets, positively and negatively, as part of an intertwined web of geopolitical and economic activity. It is reasonable to assume that the balance of political power in Washington has a large bearing on fiscal and even monetary policy, which, in turn, impact markets. Therefore, it is also reasonable to assert that politicians have a significant impact on the stock market. This leads to the question of whether the market has a preference for a certain political party or ideology. Does the stock market prefer Republicans in power, Democrats in power, or does it not matter?

BACKGROUND Campaign Contributions from Wall Street

The relationship between Washington and Wall Street runs deep. When the New York Stock Exchange was founded in March of 1817, James Monroe was the fifth President of the United States. Almost 200 years later, Barack Obama is the nation’s 44th President. One can argue that the power held by those in charge in Washington and Wall Street is the strongest, most concentrated amount of authority


and influence anywhere in the world. When these two forces work together, or are forced to work together in instances of crisis, the entire population of the United States may be affected for better or worse (Paulson, 2010).

Historically, Wall Street has been one of the most influential forces in political campaign funding. Despite various measures at campaign reform to protect the public from incredibly rich forces such as those on Wall Street, such forces remain as a powerful player in campaign funding. Wealthy donors and interest groups have become accustomed to using money to influence politics and are usually eager to find new ways to do so. The floodgates of unlimited funds were blown open in 2010 when “the Supreme Court issued a famous free-speech decision called Citizens United that allowed corporations and unions to spend unlimited amounts out of their own treasuries to voice their opinions in an election. Later, a separate court allowed groups to run campaign advertisements any time they wanted” (Mullins, 2012). While institutions cannot donate unlimited funds directly to candidates, they can donate unlimited amounts to political action committees (PACs) or to run their own campaigns for whatever candidate or political interest they desire to support. This influx of funding for campaigns led to what are known as super PACs. “Republican strategist Karl Rove, who was an adviser to President Bush, helped create two of the biggest super PACs, including a group called American Crossroads, which, along with a sister group, could raise and spend as much as $300 million for Mitt Romney. President Obama’s backers responded with their own super PAC, called Priorities USA, which could raise as much as $100 million for his re-election campaign”(Mullins, 2012). This trend is likely to continue and potentially increase in significance for one major reason: the winners of political campaigns write the rules. That is, elected representatives to Congress write the laws regarding campaign financing. Because this newfound source of funding is likely to continue to serve their benefit, they are very unlikely to change the status quo for it is the wealth from special interests and super PACs that help get them elected.

Wall Street executives give money to political candidates for one of two reasons: 1) they believe in that candidate’s policies and ideology that would benefit business or 2) they believe that candidate is likely to win and thus want to make sure they are on the winning side in the hope of getting preferential treatment from government and/or political favors. Wall Street embraced George W. Bush during both of his Presidential election bids. In 2000, he won the White House after collecting nearly $4 million from the financial industry versus Democrat Al Gore's $1.4 million (Hook and Morain, 2008). In 2004, Bush received $8.8 million, twice what Democratic Sen. John Kerry collected (Hook and Morain, 2008).

Due to their conservative, free market philosophy, it is reasonable to assume that Wall Street firms are more likely to donate money to candidates that represent their interests, i.e. Republican candidates. However, Wall Street has actually been a relatively positive supporter of President Obama, who received a heavy percentage of his top funding from Wall Street for his successful 2008 White House run. “In the entire 2008 cycle, bundlers in the finance sector accounted for about $16 million of $76.5 million brought in by top Obama fundraisers, the Center for Responsive Politics said. Obama raised a total of $745 million in his first White House run” (Eggen, 2011). Wall Street viewed Obama as a fresh alternative to Bush’s policies, which fair or unfair, were in place during the financial crisis of 2008. Obama capitalized on his populist economic platform and was able to raise significantly more than Republican John McCain among Wall Street donors. Table 1 shows a breakdown of the top twenty institutional donations for the Presidential campaign of 2008. In total, Obama's top twenty contributors gave $13,382,825, while McCain's gave $4,034,622, meaning Obama had a $9,348,203 advantage (Hicks, 2008).

The President also performed well with Wall Street in his 2012 reelection bid. “About a third of the money his top fundraisers have brought in this year has come from the financial sector, suggesting that strained relations with Wall Street have not hurt the President’s ability to attract donations there for his reelection campaign, according to data released Friday by the Center for Responsive Politics” (Eggen, 2011). During the campaign of 2012, Republicans sought to create a rift between Obama and Wall Street, highlighting a perceived desire of the President to increase taxes on high income earners, as well as on capital gains and dividends on investments. “Republicans have sought to take advantage of the rift by openly courting Wall Street donors. The top sources of corporate money for Presidential candidate Mitt Romney, who reported raising $18.3 million, include contributions from employees of Morgan Stanley,


Bank of America, Goldman Sachs and other financial firms, according to Federal Election Commission data” (Eggen, 2011). Romney was successful in pulling many of Obama’s supporters on Wall Street away to his side, but clearly it was not enough to win the election. “Mitt Romney's six largest campaign donors in 2011 were from Wall Street. Romney got $1.8 million from Wall Street execs, according to the Center for Responsive Politics” (Drawbaugh, 2012). Thus, it can be seen that the nation’s largest financial firms play a significant role in national elections, particularly Presidential ones. “Even after the 2008 financial crisis and the 2010 passage of the Dodd-Frank laws that put new restrictions on the banks and markets, the power of Wall Street in Washington is unmitigated, said Richard Parker, a public policy lecturer at Harvard University's Kennedy School of Government” (Drawbaugh, 2012). The significant wealth possessed by those on Wall Street gives them immense power and influence over politicians and political policy in Washington.

We can see, therefore, that Wall Street plays a critical role in political campaign funding, as it continually serves as one of the wealthiest sources upon which candidates rely for donations. In return for their patronage, the influential forces that exist from the nation’s largest banks and financial services firms expect positive (if not preferential) treatment from the political candidate whom they helped elect.

TABLE 1

Source: Hicks (2008)


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Government’s Role in Ending the Financial Crisis of 2008 Government provides the legal and regulatory framework as well as stimulus, to promote market

growth. It also plays an even more important role in providing a floor for equity markets. While the United States theoretically has a free market, the government has power to affect financial markets (Friedman, 2002). The economy is a set of interrelated parts, and government is, indeed, a crucial part of the equation. Further, the federal government is able to infuse cash into banking institutions, thus providing a floor against stock market crashes. The Federal Reserve, which receives its authority from Congress, can take various actions to either lower interest rates, increase the money supply in the economy, or lower bank reserve requirements, all of which are advantageous for economic growth (Rose and Hudgins, 2010).

One only has to look to the financial crisis of 2008 as an example of how the government, through its practically unlimited resources, can protect institutions and asset classes during times of great turmoil. In 2007, things began to look bleak in the American economy and stock market due to what became known as the subprime mortgage fallout in which there was a rampant amount of default on high risk loans made to borrowers with high credit risk (Sorkin, 2009). Many of these loans were made on terms of low initial interest rates and no down payment and were made to many people who could not afford them (McDonald and Robinson, 2009; Lowenstein, 2010; Morgenson and Rosner, 2011). Such subprime loans were then pooled together and sold as securities through financial engineering to various investment firms, securities companies, and hedge funds (Firms were able to once again combine commercial lending and investment practices when Congress replaced the previous stipulations of the Glass-Steagall Act). The magnitude of the defaults was amplified because the subprime loans had been sold as security products in the derivatives market, putting the entire financial industry at stake in a significantly intertwined and overly complicated housing bubble that led to the financial crisis of 2008 (Paulson, 2010).

The government took action to prevent a complete stock market crash that was pending as a result of the turmoil from the bursting of the housing bubble. “According to the free market theory, any institution with enough clout to sway the movement of the market -- like the government -- should stay out of the way and let nature take its course. While the U.S. government doesn't directly intervene in the stock market (say, by inflating the prices of stocks when they fall too low), it does have power to peripherally affect financial markets” (Clark, 2012). Government can serve as the most prominent source of liquidity to financial markets because it, theoretically, has unlimited capital potential since it has the ability to print money. In 2008, the U.S. government went to great monetary measures in an effort to keep the economy from plunging into a depression (Paulson, 2010; Sorkin, 2010; Morgenson and Rosner, 2011). First, it announced it would infuse money into the economy in the form of tax rebate checks in the amount of hundreds of dollars per taxpayer. The hope was that the money would spur Americans to spend on goods and services in America to help revive the economy.

Government can also guard the economy and protect against stock market crashes by providing liquidity to financial institutions. The federal government’s main instrument for doing so is the Federal Reserve Bank. This is a network of government-related banks that serve as a standardization and regulatory force for commercial banks. The Fed also has the power to aid banks and took full advantage of this power in aiding distressed and failing banks during the financial crisis. In 2008, the Fed announced the creation of the new lending arm: the Term Securities Lending Facility (Paulson, 2010; Clark, 2012). The facility would offer $200 billion in loans to non-deposit banks, thus aiding many of the investment banks that were in dire trouble (Clark, 2012). The Fed was even more hands-on when it guaranteed $30 billion of debt when JP Morgan Chase bought Bear Stearns (Kelly, 2010; Clark, 2012). Since investment banks are such a driving force for injecting capital into the markets, the Federal Reserve, acting with support from Congress, took necessary actions to make sure that these large firms did not stop investing because that would have brought the entire financial system to a halt.

Congress also took direct action during the financial crisis of 2008 in creating the Troubled Asset Relief Program, commonly known as TARP (Members of the Federal Crisis Inquiry Commission, 2011: FRB TARP Program Information, 2011). This government program created the establishment and


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management of a Treasury fund in an attempt to stop the financial crisis. Global credit markets had come to a near standstill as major financial institutions had gone under or were on the brink of going bankrupt. TARP gave the U.S. Treasury purchasing power of up to $700 billion to buy mortgage-backed securities from institutions across the country in an attempt to reestablish liquidity in financial markets. Information from the Federal Reserve’s website reads as follows:

“On October 14, 2008, the U.S. government announced a series of initiatives to strengthen market stability, improve the strength of financial institutions, and enhance market liquidity. Treasury announced a voluntary Capital Purchase Program to encourage U.S. financial institutions to build capital to increase the flow of financing to U.S. businesses and consumers and to support the U.S. economy. Under the program, Treasury will purchase up to $250 billion of senior preferred shares on standardized terms. Treasury's Capital Purchase Program and the FDIC's Temporary Liquidity Guarantee Program complement one another. Through these programs, fresh capital and liquidity are available to foster new lending in our nation's communities.” (FRB TARP Information Program, 2011)

Critics of the Federal government’s intervention into the American economy believe that markets

would have corrected and recovered on their own without government action. They view the government’s intervention more as interference than as an action of support. Critics say that government action can only prolong the problem in the free market and that the best course of action would have been to do nothing. While this idea can be debated one way or another, the fact is that the federal government did intervene in the marketplace in order to protect the nation’s citizens and did what it felt was best for the well-being of the country.

Banking institutions have benefited from the government’s actions. They have accumulated an increasingly large amount of liquidity since TARP was implemented. The Fed, with support and approval from Congress, has pumped billions of dollars into the financial system in an effort to strengthen financial institutions and increase liquidity in the market. In March of 2009, The Washington Post said that the Fed would “flood the financial system with an additional $1.2 trillion” (Irwin, 2009). Such a move by the Fed was intended to stimulate the economy by lowering borrowing costs for home mortgages and other types of loans (Irwin, 2009). With this influx of capital, banks have been putting more money to work. “They do what they believe is prudent and in addition to buying treasuries they buy other assets that diversify their portfolios. The banks aren’t necessarily acting illegally or corruptly. The banks are using their balance sheets to invest in assets that will increase their earnings. Based on their analysis, the assets they have been buying are “good” investments” (Business Insider, 2010). Results have been great for such institutions as shown in Figure 1.

As Figure 1 indicates, U.S. financial profits rebounded quicker and increased at a much faster rate in 2009 than non-financial profits as the Treasury and Federal Reserve directly injected capital into the financial institutions amid the financial crisis in 2008.

In an environment of extremely low interest rates, markets are, theoretically, likely to flourish (Friedman, 2002). In such an environment, investors are likely to move money from less risky accounts with banks such as checking accounts, savings accounts, and certificates of deposit, to more aggressive accounts such as equity portfolios. Investors wish to get a sufficient return on their money, and when rates are extremely low (almost zero), they cannot get a sufficient return by just keeping their money in the bank. Theoretically, capital should flow from “risk off” accounts to “risk on” accounts i.e. the stock market. Essentially, through monetary policy, the Federal Reserve is encouraging investors to move capital out of cash and into securities (Rose and Hudgins, 2010).

The policy of the Fed has the greatest impact on the actions of institutions, since it has the largest amounts of capital. Large financial institutions, especially the investment banks such as Morgan Stanley and Goldman Sachs, have been increasingly putting more money to work in equity markets as a result of such a low interest rate environment. The Federal Reserve has a heavy influence on the activity of


investment firms. “Toxic assets get exchanged for cash and cash gets exchanged for whatever the banks feel like buying on a particular day. In this case, it’s approximately $1.5T worth of firepower. The results have been “shock and awe” on steroids. $1.5T certainly does wonders for an equity market, bond market or municipal bond market (all of which have rallied substantially in the last year)” (Business Insider, 2010). The Fed is considered independent. However, some critics argue that the Fed should be considered the fourth branch of the U.S. federal government due to its increasingly powerful role in injecting money into the country’s economy through its dealings with financial institutions. The Federal Reserve has been serving as the continuous support floor to prop up the struggling economy since the financial crisis in 2008. Its continued quantitative easing program has served as a buttress for financial markets. It is widely accepted in finance and accounting that a company’s cash flow is the most important element to successful growth in its business. By putting more cash into the financial system, the Fed has artificially inflated cash flows for financial institutions and the resulting businesses to which they lend. Research has shown that increased cash flows lead to higher earnings for companies, and these higher earnings are then reflected in the increases of company stock prices (Johnson and Zhao, 2012; Cheng, Warfield, and Ye, 2011; Bali, Demirtas, and Tehranian, 2008).

FIGURE 1

Source: Deutche Bank, Bloomberg

Political Parties and the Stock Market: The Presidency

Since 1929, Republicans and Democrats have each controlled the Presidency for nearly 40 years. Because each party has controlled the White House for approximately the same period of time, it is possible to examine stock market performance under each President and do a comparison of market performance under a Republican administration versus a Democratic administration.

In 2008, The New York Times published an interesting illustration showing the growth of a $10,000 investment in the S&P 500 index under Republican and Democratic administrations (McCall, 2008). One would think that the market would perform better under Republican administrations due to their tendency toward policies more favorable to business (lower taxes, less regulation) compared to their Democratic counterparts. However, surprisingly, the S&P 500 has performed better under Democratic Presidents over the long-term. The illustration suggests “a $10,000 investment in the S & P stock market index would


have grown to $11,733 if invested under Republican Presidents only, although that would be $51,211 if we exclude Herbert Hoover’s presidency during the Great Depression. Invested under Democratic Presidents only, $10,000 would have grown to $300,671 at a compound rate of 8.9 percent over nearly 40 years” (McCall, 2008). Figure 2 details that information.

Note: Information for President George W. Bush does not reflect full term because the article was published in October of 2008.

FIGURE 2

Source: The New York Times


The S&P 500 has had a positive return under every Democratic President since Franklin D. Roosevelt. There are, of course, potential outliers to consider in The New York Times’ illustration. First is Herbert Hoover’s presidency which the paper does cite. Hoover was President during the worst financial crisis in United States history: the Stock Market Crash of 1929 and the resulting Great Depression. A second, less obvious potential outlier to consider in this illustration is the Clinton presidency in which the market took off as a result of the technology bubble that resulted from tremendous developments in the industry and usage of the Internet during the 1990s. The housing market boomed during the Clinton years as well, resulting in a very rare occurrence where both the housing market and the stock market saw monumental gains. If the Clinton presidency is taken out as an outlier, the results of the illustration are much closer between the two parties. Also, if the Clinton presidency is discounted, the Democrats do not have a President that served during a double-digit percentage gain in the S&P 500 (10 percent or higher). The Republicans interestingly have four Presidents that saw double digit percentage gains in Eisenhower, Ford, Reagan, and George H.W. Bush. In solely examining this illustration, one can see that the market has been more consistent under Democratic Presidents and more volatile under Republican Presidents. However, this illustration does not suggest any potential causes or reasons for such results.

Another interesting study was conducted in 2003 regarding market return comparisons under Presidential administrations for each party. Pedro Santa-Clara and Rossen Valkanov published a study titled “The Presidential Puzzle: Political Cycles and the Stock Market,” which was featured in The Journal of Finance in October of 2003. They analyzed market returns using the Center for Research in Security Prices indexes, including the value-weighted and equal-weighted portfolios. Such portfolios track major market indexes and are created in a systematic, unbiased manner for academic research purposes. Santa-Clara and Valkanov focused their study not on total return, but on excess return over the three-month Treasury bill. As shown in Figure 3, when a Republican held the office of the White House, both the value-weighted and equal-weighted portfolios yielded a much lower return over a Treasury bill than did the same portfolios under a Democratic President (Investopedia, 2010).

Further investigation reveals the results were generated by higher real returns and lower interest rates under Democratic administrations. Business cycle fluctuations did not show any correlation to the results, demonstrating statistically significant outperformance for the Democrats regardless of underlying economic conditions.

FIGURE 3 EXCESS RETURNS OF CRSP INDEXES OVER 3 MONTH TREASURY BILL

1927 – 1998

Portfolio

Returns Under Republican

Administrations

Returns Under Democratic

Administrations Value Weighted 1.69% 10.69% Equal Weighted -0.01% 16.52% Source: Investopedia

Value-weighted portfolios posted a steady 10% premium in favor of the Democrats, while equal-

weighted portfolios came in at around 20% in the study. “Examination of additional business cycle variables revealed that expected returns (those anticipated by the markets) were 1.8% higher under the Republican administrations analyzed in the study, while unexpected returns were 10.8% higher when Democrats were in power, suggesting that stock market results may be driven by Democratic policies that surprise investors. Interestingly, the results do not show up in close proximity to election dates, but rather grow over time during the President's term” (Investopedia, 2010).


The authors of this study admit that the results may be inconclusive due to the small sample size, but they do believe the extensive testing of data suggests a legitimate connection between the data and the returns. In their own words, the authors acknowledge that "it might just be the case that we have stumbled upon a variable that tests significantly even when there is actually no underlying relation between the presidency and the stock market" (Investopedia, 2010). The study does not consider the impact of Congress on stock market returns.

Other articles, on the other hand, suggest the market can perform just as well under either Republican or Democratic Presidents. “Those who believe the markets perform best during Democratic presidencies can point to the 52 years from 1928 until 1980. That’s when the Standard & Poor’s 500 index had average annual gains of 12 percent compared with average gains of only 2.6 percent when Republicans were in the White House” (Deener, 2012). However, the tables were turned from 1952 through 1992 (does not include Clinton Presidency) when Republican Presidents presided over annual gains of 11.6 percent to the Democrats’ 11.5 percent. (Deener, 2012).

James Stack, President of InvesTech Research, is a market historian who holds the opinion that the argument that Wall Street prefers one party’s President to another can be made either way depending on the time period being analyzed, and he believes that the stock market can do well regardless of which party holds the White House and that the market conditions do not run coinciding with political cycles (Deener, 2012). When political parties take credit or blame another party for stock market performance, they fail to keep in mind that business cycles and political cycles are two completely separate things.

“Stock market and economic cycles don’t fall neatly into Presidential terms. These are broad cycles that span several years, if not decades. For example, the seeds of the Internet bubble were sown well before Republican President George W. Bush took office in 2001 — and yet he is often blamed for the vicious bear market that ensued” (Deener, 2012). Sometimes, Presidents can be lucky or unlucky in terms of the economy they inherit upon entering the White House. For example, Herbert Hoover was President for less than eight months when the stock market crashed and the Great Depression began that had been years in the making. Stack says, “Market gains or losses are less dependent on which political party wins the White House and more reflective of economic conditions, trends and risks that are already in place prior to Election Day” (Deener, 2012).

It is understood that statistics can be deceptive and formulated to support either party’s political cause. Politicians generally attempt to use such economic statistics to claim supremacy over the other party for self-serving interests. The data is inconclusive, suggesting that there is no real relationship between which party holds the White House and stock market return. Based on the data that has been cited, it is our conclusion that the political party holding the Presidency has little to no effect on the stock market. Political Parties and the Stock Market: Congress

Many Americans credit or blame the President for the conditions of the markets and economy, but all legislation and regulatory authority that impacts economic activity resides in Congress. The federal legislature is responsible for creating the laws that impact tax policy and regulatory policy. Congress is responsible for the nation’s fiscal policy which has a direct impact on the economy. Since the stock market is a reflection of the day-to-day fluctuations of the economy, Congressional activity and lawmaking can have a tremendous effect on stock market movements. Because it makes the laws that create fiscal policy, Congress is more important to economic stability and stock market success than is the President.

Is there any evidence suggesting better stock market performance under a certain type of Congress? Robert Schumacher of Van Kampen Investments examines the issue of political power on stock returns and found some interesting, perhaps telling results. He notes:

In every Presidential election year, voters and investors alike focus on the race for the White House, and rightfully so. You see, as shown in the accompanying chart from Ned Davis Research, the historical data depicts market returns that vary greatly under Republican or Democratic leadership. The same data also suggest that while Presidential races may dominate the statistical landscape, a more interesting


interaction between politics, the public and stock prices is likely to take shape. And it has very little to do with who wins or who loses (Schumacher, 2006).

Figure 4 is a study of market performance under various combinations of federal government, including different party combinations of Presidents and Congress working together. As Figure 4 demonstrates, the Dow Jones Industrial Average has historically performed best under a Democratic President and Republican Congress combination with the Dow returning 9.6%. While there is no guarantee of causation from these results, it is plausible for one to conclude that a Democratic President and Republican Congress would benefit the markets in that the two sides would serve as checks on each other and provide certainty to markets in that nothing too drastic is likely to be accomplished in the federal government under such split power. These results support the theory that political gridlock is good for equity markets.

Statistically, the best combination for the stock market is a Democrat in the White House and Republican majorities in the both chambers of Congress. Republicans can constrain a Democratic President when they control both chambers of Congress, and both sides are forced to compromise if they wish to pass any new pieces of legislation. Evidence suggests that political gridlock is usually beneficial to the stock market for, when nothing in Washington changes, there is more certainty. Interestingly enough, the stock market performed very well during the Clinton Administration with a Republican House of Representatives and is also performing well under the Obama Administration and a Republican House of Representatives. Figure 5 shows price movements on the Dow Jones Industrial Average.

FIGURE 4 GAINS (%) FOR STOCKS BY PARTY OF THE PRESIDENT AND MAJORITY

PARTY IN CONGRESS 03/04/1901–10/23/2006

Political Variable Stocks (DJIA)

Democratic President 7.19% Republican President 3.85% Democratic Congress 6.46% Republican Congress 3.51% Dem Pres, Dem Cong 6.53% Dem Pres, Rep Cong 9.60% Rep Pres, Rep Cong 1.54% Rep Pres, Dem Cong 6.37%

All Periods Buy & Hold 5.34% Sources: Van Kampen, Ned Davis Research


FIGURE 5

Source: Daily Kos

Again, it is important to keep in mind that Republicans controlled the House of Representatives

(where legislation begins) in Congress for the majority of Clinton’s presidency and have done so during the Obama presidency as well. This continues to illustrate two important considerations for stock performance: 1) the importance of Congress and 2) the impact of political gridlock on the market. Figure 6 shows S&P 500 returns from 1940 to 2008 based on political party control for the federal government.

FIGURE 6

Note: R=Republican, D=Democrat Source: Gallup Inc., Factset, J.P. Morgan Asset Management


Perhaps the most fascinating aspect of this study performed by Ned Davis Research and endorsed by Van Kampen Investments has to do not with political power but instead simply whether or not Congress is in session. "Using historical pricing on the Dow Jones Industrial Average (DJIA), the Standard and Poor’s 500 Stock Index (S&P 500), the Center for Research in Security Prices (CRSP) Equal-Weighted Returns Index and Value-Weighted Returns Index, Ferguson and Witte find that, depending on the index, daily returns when Congress is in session range from 1 to 4 basis points per day. When Congress is out of session returns range from 5 to 15 basis points a day” (The Big Picture, 2006). The market is more likely to flourish under conditions of certainty, so once again, perhaps the market prefers the certainty that accompanies an out-of-session Congress. Figure 7 details the idea that a Congress out of session is more advantageous for the stock market.

Since 1965, the S&P 500 has historically performed much better on days when Congress is out of session. An interesting observation can be made from the above chart: starting in 1965, as the years go on, the two lines generally grow farther apart. The federal government has become larger in size and scope over time within the United States economy; it therefore has an increasingly powerful impact on stock market activity and performance. Daily activity (or inactivity) in Congress moves the stock market now more than ever before. Because markets prefer certainty, they prefer an out-of-session Congress.

FIGURE 7

Source: Congressional Effect Management

Investors have recognized this pattern of market outperformance while Congress is out of session. There has even been a mutual fund created on this premise. Launched in 2008, the fund invests on the premise that this relationship between the congressional calendar and stock market returns will continue in the future (Conrad, 2012). Based on data on the fund company’s website, the annualized daily price gain of the Standard & Poor’s 500 (S&P 500) from Jan. 1, 1965 through 2011 was 0.72 percent when Congress was in session and 16.60 percent when Congress was out of session (Conrad, 2012). Similar results have been experienced over the most recent ten years as well.


Does the Stock Market Determine Elections? Data suggests that conditions and returns in the stock market can have a profound effect on elections,

particularly Presidential elections. InvestTech Research, an investment firm based in Montana, has put together a model showing that the overwhelming majority of Presidential elections are decided by the Dow Jones Industrial Average. The firm claims that the stock market has been the most reliable indicator of who will win the presidency for more than a hundred years. Eric Vermulm, senior portfolio manager at the firm, says, “The election is a reaction to the stock market. If you see strength in the market, consumer sentiment and confidence among the voters is higher. If you see volatility, you are going to see investors take that out on the incumbent” (Fox, 2012). According to Vermulm, the math is simple: the incumbent party in the White House wins when the stock market increases the two months leading up to the Presidential election. When the stock market declines during the two months before the election, the incumbent party loses the White House (Fox, 2012). Since 1900, this has held true for approximately 90 percent of Presidential elections, the only exceptions being in 1958, 1968, and 2004 (Fox, 2012). Cathy Hetrick, a senior portfolio adviser at InvestTech and author of the study writes, “Wall Street typically worries about how politics might affect the stock market, perhaps, Presidential candidates should worry about how the stock market might affect their political outcome” (Fox, 2012).

Perhaps the stock market has just as a significant impact on national politics as national politics has on it. It is reasonable to believe this conclusion because the stock market is a reflection of economic conditions and consumer confidence. Politics and the stock market share in common the idea of perception being more important than reality. It is also reasonable to assume that when the stock market is doing well, Americans have more confidence in the economy and are more likely to re-elect their public officials. On the contrary, if the stock market is struggling, Americans may have less confidence and are more likely to elect new or different public officials. HYPOTHESIS DEVELOPMENT

Based on previous research and literature, we know that there is long-lasting relationship between business people from Wall Street firms and politicians in Washington. We know that fiscal policy in the United States is established by Congress. We also know that monetary policy is established and carried out by the Federal Reserve and that its Chairman is nominated by the President, confirmed by the U.S. Senate, and can be called to testify before Congress. Together, these regulatory bodies and their corresponding policies help establish economic conditions for business activity in the country.

Both fiscal and monetary policy have a significant influence on the American economy. Fiscal policy entails the handling of conditions concerning factors such as taxes (individual, small business, and corporate), as well regulatory authority over various sectors and industries of the economy. Monetary policy most directly affects liquidity in the market and interest rates. Unemployment is a lagging indicator of economic growth since it is affected by decisions made regarding monetary policy and taxes. Changes in monetary policy can have drastic impacts on the level of liquidity in the economy and GDP, and these impacts are reflected in the stock market. Therefore, we hypothesize that:

1a) A change in monetary policy that lowers interest rates and increases the money supply causes gross domestic product to increase.

1b) A change in monetary policy that raises interest rates and decreases the money supply causes gross domestic product to decrease.

2a) As gross domestic product increases, the stock market will increase. 2b) As gross domestic product decreases, the stock market will decrease.

DATA AND METHODOLOGY

For this study, we are assuming that while there may be a correlation between political party power and stock market performance, there is no specific proof of causation. It is legislative laws that form fiscal


policy that enable such market returns over the long-run, not the political party itself. There is also an argument to be made that legislative policies do not have an immediate impact on the economy and the stock market but that they take a while, perhaps years, to go into full effect. Therefore, it is plausible that laws and regulations under a Republican Congress and President may go into full effect under a Democratic Congress and President, or vice versa. Overall, general fiscal policy from Washington has more of a long-term effect on the stock market in that it provides the framework for certainty and stability for the United States economy.

On the contrary, the Federal Reserve has the ability to take action more quickly. It can inject liquidity into the market through a process known as quantitative easing. We know that increased liquidity in the economy and low interest rates are advantageous to market growth. It is plausible to believe that more money in circulation in the economy means more money for institutions and individuals to invest in the stock market. Low interest rates are advantageous for individuals and companies to borrow and invest in their businesses. A larger money supply and lower interest rates, theoretically, should lead to more consumer spending in the economy. An increased money supply means more disposable income for people and more sales for companies which increase earnings and eventually stock prices. The Federal Reserve sets the guidelines for monetary policy which includes both interest rates and the amount of money in circulation. Such actions by the Fed influence the economy and coincidentally, the stock market. Therefore, secondly, we assume that the Federal Reserve and its Chairman have more of an immediate impact on the stock market. The Federal Reserve Chairman may be more important to positive stock market performance than is Congress or the President, at least in the short-term.

We also know that the stock market is reflective of projected GDP growth and how the economy is perceived by investors. It is important to keep in mind that the stock market is a reflection of future expectations than it is of past results. Therefore, growth projections and consumer confidence are two key metrics to the price of equities. The primary link between the stock market and the economy, in the aggregate, is that an increase in money and credit increase both GDP and the stock market simultaneously. A growing economy naturally produces more wealth. We also know that government can increase the money supply either through monetary policies carried out by the Federal Reserve or through the printing of money by the Treasury Department. If GDP is rising, either the money supply must be increasing, or the amount of products and services produced in the economy must be increasing, or both are occurring simultaneously. Variables

For this study, there are a variety of variables to consider. We seek to determine whether market return in excess of GDP, known as Market Alpha, is a way to measure the political effect on the stock market. We include monetary changes by the Federal Reserve as part of this political effect. To do this, we will study the Standard & Poor’s 500 index to gauge changes in the stock market using historic prices over a ten year period since 2003. The following are the variables that will be considered in the study:

1. Historical index averages as they relate to changes in monetary policy (interest rates and money supply).

2. Changes in GDP as related to changes in monetary policy. Stock market index changes as they relate to changes in GDP.

3. Stock market index changes as they relate to changes in GDP.

Figure 9 demonstrates the Standard and Poor’s 500 index from 2003 to 2013. In comparing Figure 8 and Figure 9, we can see a trend of the stock market generally increasing when interest rates are lower and decreasing when interest rates are higher. It is interesting to note that, according to the charts showing the last ten years, both the Federal Funds rate and the S&P 500 reached their peaks in 2007, followed by severe declines. Both the Federal Funds rate and the S&P 500 then bottomed in 2009. Since 2009, the S&P 500 has seen its value double in price while the Federal Funds rate has remained below 1%. It is


plausible that both the bull runs in the S&P 500 from 2003-2008 and from 2009-2013 were either started by or aided from a low interest rate environment.

FIGURE 8

Source: http://www.Economagic.com

FIGURE 9

Source: Federal Reserve Bank of St. Louis

Monetary policy consists of two main parts: interest rates and money supply. We have illustrated how interest rates can affect the stock market, particularly how a low interest rate environment is conducive to


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stock market gains. The other part of monetary policy, the money supply, has a significant impact on the economy as well, as more money in circulation means more liquidity in the financial system and more money that consumers can potentially spend in building the GDP of the nation. The question then becomes are there any noticeable trends between changes in the money supply and changes in the stock market’s performance? Again, we examine the charts.

FIGURE 10


The Federal Reserve has characterized the money supply using the terms M1, M2, and M3. M1 refers

to notes and coins in circulation, travelers’ checks of non-bank issuers, demand deposits, and other checkable deposits. M2 consists of M1 plus saving deposits and time deposits less than $100,000 and money-market deposit accounts for individuals. M3 consists of M1 and M2 plus large and long-term time deposits, including institutional money market funds. Since 2006, M3 is no longer tracked by the Federal Reserve. However, there are still estimates of M3 produced by various private institutions. Figure 10 presents annual U.S. money supply growth year over year percentage change by month from 2003 to 2013. The chart is followed by another chart of the S&P 500 over the same time period for comparative purposes.


FIGURE 11


According to the figures, there appears to be about a one-year lag between percentage change in M3 and the performance of the S&P 500 as the market index bottomed in early 2009 whereas the chart for M3 shows its low point in early 2010. However, the general trends between the two variables appear to be similar. It is reasonable to believe that an increase in the money supply would drive the stock market higher as more money in circulation means more money that can potentially be spent or invested. From early 2010-2013, there has been a combination of low interest rates and a graduating percentage change in money supply. The total return for the S&P 500 was 15.1% in 2010, 2.1% in 2011, and 16.0% in 2012.

Ultimately, economic growth trends and expectations for economic growth are the primary reflections in the stock market. Figure 12 indicates the relationship between the quarterly change in GDP growth percentage in the United States and percentage change in the S&P500 index. Based on the chart, the two variables share a common trend as they appear to fluctuate in a similar pattern.

As the GDP growth rate fluctuates, the S&P 500 fluctuates even more. While the trend for both variables is approximately the same, the deviation of the S&P 500 variable is much greater than GDP growth. This implies what we call the “Market Alpha”, a term which represents excess return over GDP based on external economic and political conditions. While GDP plays a role in stock market returns, it is clearly not the only factor. The Market Alpha details market movement in excess of GDP in that it considers inflation, as well as fiscal policy actions put forth by Congress and monetary policy actions put forth by the Federal Reserve.


FIGURE 12

Source: Authors DISCUSSION

Throughout this paper, we have attempted to demonstrate correlating relationships between external political decisions and events with the performance of the stock market. Of course, this does not mean that correlation implies causation. However, some interesting points and parallels can be taken away from the information presented. The stock market is so complex and is dependent on so many variables that it is impossible to guarantee its results. Analysts can forecast market conditions and make target price predictions, but they cannot guarantee outcomes of the future of the unknown.

First, we posited that political party power has significant influence on the stock market. It was reasonable to assume that due to its conservative, low tax, low regulatory tendencies, the Republican Party would be more favorable to the stock market. In examining historical data, data is inconclusive as to whether either the Republican Party or Democratic Party is more advantageous to the stock market. Based on the numbers, the stock market has performed better overall under Democratic Presidents. However, the market has also performed better overall under a Republican Congress. The best combination, according to historical information, is a Democratic President and a Republican Congress. The take-away from this is that the party in political power does not have a significant impact on the performance of the stock market. If anything, the market prefers political gridlock, such as a Democratic President and Republican Congress, because this increases the chances for stability in an uncertain economic environment. Political gridlock is beneficial in that it prevents substantial change from taking place in fiscal policy, something that the stock market embraces. According to the data presented, the stock market has actually performed better on days when Congress was out-of-session when compared to days in session, again suggesting the market prefers the certainty associated with a do-nothing Congress.

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Change in U.S. GDP Growth Rate S&P 500 Return


On the contrary, the political force that is the Federal Reserve can have a significant impact on the stock market. Since the financial crisis of 2008, the Fed has taken an aggressively active role in the economics of the United States. It’s most aggressive programs, known as quantitative easing, have injected billions of dollars into the financial system through the consistent buying of mortgage-backed securities and other financial bond instruments. Led by Ben Bernanke, the Federal Reserve has actively pursued and maintained an environment of extremely low interest rates, the theory being that such a low interest rate environment is conducive to borrowing and spending in the economy (Paulson, 2010). This paper concluded that the combination of low interest rates and an increasing money supply is beneficial for the stock market’s performance. Data and charts in the thesis suggest correlating relationships between changes in interest rates and changes in the money supply with changes in the stock market. In studying such an environment, it appears such conditions of continued low rates and increasing money supply are ideal for stock market gains for the following reasons:

1) Lower interest rates make it easier for borrowers to take loans which they then spend in the economy or on their own business, increasing gross domestic spending.

2) Increasing the money supply weakens the value of the dollar, thus increasing exporting as such action makes exporting cheaper for domestic companies.

3) Aggressive investors borrow at low interest rates to then invest in assets (real estate, precious metals, and financial instruments).

4) Record low treasury yields and bank account yields have forced many investors to reconsider where they put their money. Actions taken by the Federal Reserve encourage investors to move money out of cash and bond markets and into the stock market.

The stock market has clearly thrived in the low interest rate environment that has existed from 2009

through April, 2013. During this time the S&P 500 has gone from approximately 890 points to approximately 1,580 points, a return of approximately 78%. Both individuals and companies are more likely to borrow at such low levels of interest. This borrowed money can then be either spent, or invested, both of which produce a beneficial impact for the economy and GDP. Investors are also more likely to take on more risk in a low interest rate environment as they search for higher returns.

Lastly, we examined GDP and its impact on the stock market. Gross Domestic Product and Gross Domestic Product growth are often reviewed and referred to as the ultimate economic indicator by economists. It is important to keep in mind that GDP is factual data from the past, whereas the stock market is more of a reflection of projected earnings data for the future. When viewed from a long-term perspective, the relationship between GDP and the stock market has been positive. Over long periods of time (decades) in the United States, both GDP and the stock market have increased significantly. However, it is much more difficult to examine short-term fluctuations and how they correlate between the two variables. When we compared quarterly growth rates between GDP and the S&P 500, the S&P 500 fluctuated much more. This implied that fluctuations in the stock market are dependent on more than solely GDP growth rates. CONCLUSION

In tying everything together, we confirm our hypothesis development. As demonstrated in the figures presented, a stable yet gridlocked political environment, combined with an aggressively easing monetary policy, makes for a very conducive environment for positive stock market performance. We consider political gridlock as a beneficial force for financial markets as gridlock decreases the chances of changes to fiscal policy. This increases the element of certainty, something the market prefers.

The Federal Reserve is a powerful political force in that its decisions and actions have a tremendous impact on U.S. financial institutions and markets. As stipulated by the Banking Act of 1935, the President appoints the members of the Board of Governors of the Federal Reserve System (Rose and Hudgins, 2010). The appointment of the Chairman of the Federal Reserve is subject to confirmation by the U.S.


Senate. If the Federal Reserve’s policies are considered political, as we have contended, this current political environment can be considered the perfect storm for the stock market’s continued advance. It is yet to be determined how effective the Federal Reserve’s policies are on the actual economy. However, from an investment standpoint, quantitative easing has been stimulative to positive stock returns. The Federal Reserve has put out multiple statements that it intends to keep interest rates at record low rates for some time to come, possibly until 2015, when it projects the economy will be fully recovered with the unemployment rate substantially improved. However, until this time, one can expect continued low rates and measures of liquidity. If the stock market is a prediction of future economic conditions, we can project continued positive performance in domestic market indices at least until external political conditions change. REFERENCES Bali, G., K. Dermitas, and H. Tehranian. (2008). Aggregate earnings, firm-level earnings, and expected stock returns. Journal of Financial and Quantitative Analysis, 43 (3): 657 – 684. Business Insider. (2010). Does government really manipulate the stock market? Business Insider. Retrieved December 23, 2012 from http://www.businessinsider.com/does-the-government-actually- manipulate-the-stock-market-2010-2. Cheng, Q, T. Warfield, and M. Ye. (2011). Equity incentives and earnings management: evidence from the banking industry. Journal of Accounting, Auditing, and Finance, 26 (2): 317 – 349. Clark, J. (2012.). Can the government control a stock market crash?. How Stuff Works. Retrieved December 22, 2012, from money.howstuffworks.com/government-control-stock-market-crash2.htm. Conrad, L. (2012). Personal finance insights: politics and the stock market. Lexington Minuteman. Retrieved December 27, 2012, from http://wickedlocal.com/lexington/newsnow/x735231215/ personal-finance-insights-politics-and-the-stock-market Deener, W. (2012). Numbers show neither political party can claim stock market supremacy. Dallas Morning News. Retrieved December 27, 2012 from http://wwwdallasnews.com/business/columnists/will-deener/20121028-numbers-show-neither-political-party-can-claim-stock-market-supemacy. Drawbaugh, K. (2012). Mitt Romney draws more Wall Street donations than Obama – CSMonitor.com. The Christian Science Monitor - CSMonitor.com. Retrieved December 22, 2012, from http://www.csmonitor.com/USA/Elections/From-the-Wires/2012/0202/Mitt-Romney-draws-more-Wall- Street-donations-than-Obama. Eggen, D. (2011). Obama campaign attracts Wall Street money, despite tensions. Washington Post. Retrieved December 22, 2012 from http://articles.washingtonpost.com/2011-07-22/politics 35266863_1_bundlers-orin-kramer-obama-campaign FRB TARP Program Information. (2011). Board of Governors of the Federal Reserve System. Retrieved December 23, 2012, from http://www.federalreserve.gov/bankinforeg/tarpinfo.htm. Fox, L. (2012). Stock market picks 90 percent of Presidential elections. U.S. News. Retrieved December 28, 2012, from www.usnews.com/news/blogs/washington-whispers/2012/02/24/stock-market-picks-90- percent-of-Presidential-elections.


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Capacity and Employment in the Great Recession

Piero Ferri University of Bergamo

Anna Maria Variato

University of Bergamo

Although it is difficult to compare the present events to those of the “Great Depression”, they re-propose two old questions: Does a fall in aggregate demand have a direct impact on the labour market? Does a rebound in production imply a recovery in employment? The paper stresses the role of aggregate demand and slack capacity. While spare capacity may stimulate quantity adjustment strengthening the relationship between aggregate demand and labour performance in recession, the rebound can imply other forces (technical and structural change). However, the peculiarity of the present recovery is better understood within the aftermath of a financial bubble.

INTRODUCTION

Although it is difficult to compare the present events to those characterizing the “Great Depression”, (see Alumnia et alia, 2009, for a discussion), they seem to put forward again two questions that were both on the agenda during those times: does a fall in aggregate demand have a direct impact on the labour market? Is the rebound in production capable of restoring the previous level of employment?

A debate is taking place within the so called “new synthesis paradigm” (or DSGE models) (see for instance Hall, 2011) which, according to some authors (see Woodford, 2009), is unifying macroeconomics. This synthesis seems to accommodate both productivity shocks as in the Real Business Cycles theories (RBC) and demand shocks. In particular, while demand shocks are used in order to explain the downturn, the supply aspects are invoked in order to explain the jobless recovery. Wage rigidity (see Shimer 2012), productivity-led recoveries (Gordon, 2003), sectorial adjustment processes (see Chen et alia, 2011) and credit contraction (see Calvo, Coricelli and Ottonello, 2012) are the most quoted explanations.

A thesis of the present paper is that the role of aggregate demand is strategic in both contraction and expansion. It serves three purposes. First of all, aggregate demand characterizes the present events with respect to other business cycle experiences. This gives us a benchmark in order to measure the performance of the labour market. In the second place, aggregate demand, through the vehicle of capital utilization can explain the fall in employment during the contraction. Finally, along with a process of interdependence with supply aspects, it helps explaining the aftermath of the “Great Recession” which can be better understood as the aftermath of a financial bubble.

The structure of the paper is the following. Section 2 presents the main stylized facts of the labour market performance. Section 3 discusses the jobless recovery and the necessity of a correct benchmark of


reference. Section 4 illustrates different transmission mechanisms for the downturn. Section 5 stresses the role of uncertainty, while Section 6 examines the role and the meaning of capacity at the extensive margin. Section 7 stresses the asymmetry that characterize the so called jobless recovery. Section 8 investigates the thesis of wage rigidity. Section 9 considers alternative explanations of the jobless recovery. Section 10 concludes. STYLIZED FACTS

The “Great Recession” seems to be more severe than the average of the past business cycle experiences that occurred after World War II. The interaction between financial and real aspects on a world scale has certainly contributed to this result. At the same time, the “Great Recession” has been less severe than the “Great Depression”.

Let us start from some of the stylized facts that characterize the Great Recession that are worth stressing. The emphasis is on the experience of the US.1

a) In the recession of 2007-09 the rate of unemployment peaked at 10% and remained above 8% for 41 consecutive months. During the recession 1982-83, the maximum rate of unemployment was higher, but it decreased more rapidly.

b) The pattern of the Employment/Population ratio is almost the mirror image of the rate of unemployment. According to Lazear and Spletzer (2012), the correlation between the two variables during the period 1972-2012 is negative and equal to -0.87. This implies that the dynamics of employment is fundamental to understand also that of unemployment, a variable that is at the core of policy reactions.

c) The Employment/Population ratio presents two patterns that are worth stressing. First of all, the fall has been unprecedented: 4 percentage points, even though the maximum of the ratio was achieved before the 2001 recession. In the second place, after the end of the recession it has remained almost constant. In this sense, there is a problem of recovery. In particular, the number of workers employed is about 5 million down from its peak.

d) Finally, Table 1 shows how manufacturing, constructions and the service sector have contributed with the same amount to cause the employment fall of the “Great Recession”. Employment at the end of 2011 has diminished by a couple of million in each sector.

TABLE 1

EMPLOYMENT CHANGES BY SECTOR SINCE 2007 (TOTAL NON-AGRICULTURAL. THOUSANDS OF PERSONS)

Years Total Private Goods-producing Construction Manufacturing Services

2007 137.598 115.380 22.233 7.630 13.879 93.147 2011 131.159 109.080 18.037 5.526 11.723 91.043 Diff. -6.439 -6.300 -4.196 -2.104 -2.156 -2.104

Source: Economic Report of the President 2012, Table B-46

The impression is that the fall in employment, although concentrated in the building sector as the epicentre of the “Great Recession”, has been widespread. THE JOBLESS RECOVERY AND THE BENCHMARK

These stylized facts have been interpreted as supporting the thesis of “jobless recovery”. However, in order to confirm this thesis an appropriate benchmark must be chosen. The problem is that the particular nature of the “Great Recession” makes this choice rather difficult. In fact, if one compares the present


situation with the previous business cycles experiences, as in Figure 1, one realizes that that the present employment recovery is in line with the 1991 business cycle experience and fares much better than the 2001 experience.

From this perspective, one cannot talk of a jobless recovery as a peculiarity of the present situation. On the contrary, the peculiarity is represented by the five million people missing from the peak reached before the crisis. However, also this benchmark is debatable because it refers to an unsustainable state of the economy.

FIGURE 1 PRIVATE NONFARM EMPLOYMENT DURING RECOVERIES

(INDEXED TO 100 AT NBER-DEFINED TROUGH)

Source: US Economic Report of the President, 2012. Data from Bureau of Labor Statistics, Current Employment Statistics; NBER; CEA calculations

This aspect is strengthened if one consider also the performance of the product market. As appears from Figure 2, the peculiarity of the present situation lies more in aggregate demand. Furthermore, it is decisive to consider that during the Great Recession, the economy lost about twelve percentage points of output relative to trend. Both aspects can be better understood within a financial bubble framework (see Reinhart and Reinhart, 2010). In this perspective, one can understand why demand remains low with respect to previous business cycle experiences and why it cannot reach the previous maximum, when the credit cycle was at its peak. COMPETING EXPLANATIONS OF THE CONTRACTION

At this stage of the analysis, it is important to consider the main explanations put forward to understand both the contraction and the jobless recovery. As far as the former is concerned, one must find some forces that contributed to shift the demand for labour downward (see Figure 3).


FIGURE 2 REAL GDP DURING RECOVERIES

(INDEXED TO 100 AT NBER-DEFINED TROUGH)

Source: Bureau of Economic Analysis, National Income and Product Accounts; NBER; CEA Calculations.

According to Real Business Cycle (RBC) theories productivity shocks should be at the root of the shift in the demand for labour. However, the difficulty in identifying what would be a technical regress has stimulated the so called DSGE models to look for other driving forces. In particular, demand shocks can add two further mechanisms of transmission: income distribution and capacity utilization, respectively. According to the first interpretation, which has been enriched by the supplementary hypotheses of imperfect competition (on this point, see Ferri and Variato, 2010) and price (nominal) rigidity, there is a countercyclical movement in the mark-up (a relative price) shifting down the labour demand (see Basu and Bundick, 2011 and Bils, Klenow and Malin, 2012). This shift might explain, at least partially, why the fall in employment in the Great Recession has been so substantial. However, a close look at Figure 4 shows that, at least for the more recent period, the mark-up has moved pro-cyclically.

FIGURE 3 THE LABOUR MARKET IN THE CYCLE


Another explanation stresses the role of a decrease in capital utilization that also may shift the demand for labour downwards. In effect, if one considers Figure 5, one realizes the big fall in capacity utilization during the “Great Recession”.

This is certainly a variable that deserves to be deepened, both to understand contraction and the problems of an unsatisfactory recovery.

In the literature (see Wen, 1998 and Eusepi and Preston, 2011), a particular definition of capital utilization has been adopted. It refers to a varying degree of intense utilization and implies incurring different depreciation costs. The theoretical framework is that based on the presence of adjustment costs that make it difficult to inputs to be always optimally employed. Adjustment costs are assumed to be convex and therefore they are compatible with the usual maximization conditions. It is this change in adjusting costs that shift the demand for labour.

FIGURE 4 PRICE MARK-UP OVER UNIT LABOUR COSTS

(NONFARM BUSINESS, 1947-2011; RATIO OF PRICES TO UNIT LABOUR COSTS)

Source: US Economic Report of the President, 2012. Bureau of Economic Analysis, National Income and Product Accounts; Bureau of Labor Statistics, Productivity and Costs Current Employment Statistics; NBER; CEA calculations. Note: Shading denotes recession.

UNCERTAINTY AND CAPACITY UTILIZATION

A rediscovery of the role of uncertainty as a prime driver of business cycle has taken place in recent times (see, for instance, Bloom and alia, 2009 and 2012). What we want to stress is its impact on the nature of adjusting costs, in particular, and that of capital, in general. In this context of uncertainty, also the concept of capacity utilization can undergo a change. More specifically, three relevant aspects seem to emerge. The first is that adjustment costs can become more complex than the usual convex costs and this allows the presence of more sophisticated patterns. For instance, inaction can become an optimal


strategy in the presence of uncertainty. In the second place, uncertainty favours the irreversible nature of capital (see Bloom, 2009) that must be considered within a putty-clay perspective.

FIGURE 5

CAPACITY UTILIZATION (MANUFACTURING NAICS MCUMFN)

Source: Board of Governors of the Federal Reserve System. Note: Shading denotes recession.

In this context, and this is the final aspect to be stressed, changes in capital utilization does not necessarily mean variations in the intensity of the utilization, which is like supposing a different quantity of shifts on the same plant. On the contrary, capital utilization can vary along different routes, for instance the “extensive margin” (see Cooley et alia, 1995). In particular, it is not the intensity or the period of time with which a unit of capital is utilized that varies only, but more importantly it is the fraction of the capital stock used for production that changes at the extensive margin. Some plants are simply not working. CAPACITY UTILIZATION AND THE EXTENSIVE MARGIN

In order to better understand the role of the extensive margin, let us consider the technology of the firm in a more detailed way. To this purpose, let us suppose that ex-ante firms design production under some expectations of aggregate demand. There is flexibility, capital is putty and production is organized both within plants and between plants. Overall, there are constant returns to scale. In this context, the demand for labour can be expressed in the following way:

),,(*ttttt wkAfML = (1)

where M* is the optimal number of plants, representing the extensive margin, while the remaining component represents the marginal productivity of labour, given the relative prices, capital (k) and technical change (A).


Ex-post the situation can be different. In fact, a fall in demand is faced by a putty-clay technology. Capital and labour can be substituted within each plant, but the number of plants is given. They can only be shut down. In this perspective, the demand for labour can be represented in the following way:

),,(*tttttt wkAfML κ= (2)

where

*t

tt M

M=κ (3)

represents the percentage of utilized plants. In other words, it measures capacity from an extensive point of view.

In this context, there is a strict link between changes in demand, and movements in capacity and employment.2 The necessary conditions in order to obtain this result are the following: firms must be demand constrained and capital is of the putty-clay type. Both these conditions seem to be respected the more the contraction is stronger. It is for this reason that they can offer a good interpretation of the more acute phase of the “Great Recession”. AN ASYMMETRY

There are two differences with respect to the current literature that insists on the role of capacity utilization. First of all, κ is determined by aggregate demand and therefore its value depends on the nature of the model into which the above equations are inserted (for a dynamic version, see Cristini, Ferri and Variato, 2012). In the second place, it does not depends on relative price adjustment carried out through changes in the depreciation rate. It is simply a quantity adjustment that can create problems to a general equilibrium model only based upon relative price adjustments. In this case, the labour market is not self-contained because of the presence of κ in the demand for labour.

It follows that while the presence of capital utilization in the new synthesis literature (see, for instance, Rotemberg and Woodford,1999, Jaimovich and Rebelo, 2009) emphasizes the possibility that firms can choose an optimal level by means of modifying depreciations costs in a convex cost structure, in the present case, κ refers to an extensive margin, where firms are in an inertia mood and aggregate demand determine its macro dimension.3

These results become more blurred when a different time horizon is considered. In fact, the number of plants (and the number of firms) can change and this implies that κ may increase without an increasing productive capacity. In this case κ and employment become less correlated.

This drives the discussion into the recovery phase. If one compares Figure 5 with the formula (2), one should expect an expansion in labour demand since κt has increased.

The problem is that the increase in κt has been produced by a fall in M*, the optimal number of plants, and this has implied a contained employment expansion. If one tries to identify the forces behind this behaviour, one is compelled to face the various interpretations of the jobless recovery. A WAGE PROBLEM?

A recurrent issue is that insisting on the role of wage rigidity. That wage rigidity can cause an increase in unemployment within a neoclassical paradigm, both in a frictionless world and version with frictions, it is undoubtedly true. As shown by Shimer (2012) also within the neoclassical paradigm there are differences. In fact, within a frictionless world, the presence of wage rigidity generates a permanent loss in employment, once the capital process of an economy has been hit by a negative shock. On the contrary, if one refers to a model with frictions, such a search model, the same shock can only generate a persistent loss of employment.


What is at stake, however, is not so much a methodological problem concerning the adequacy of the neoclassical model, at least at this stage of the analysis. The problems, in fact, are different, the first one is empirical, while the second one is more analytical. Let us start from the empirical aspect. For instance, if one considers the decade 2000-10, one realizes that the real compensations (in the business sector) have not changed in a substantial way: 1.4 % per year. Can one conclude that real wages have been rigid? In a stationary world, the answer would be affirmative. But in a growing world, one must compare real wages (W/P) with the dynamics of productivity (A). In this perspective, they have been flexible. In fact, from the formula

µω 1/

===PYWN

APW (4)

one realizes that this ratio is just equal to the labour share (ω), which is just the inverse of the mark-up µ, which is defined as the ratio between prices and unitary labour cost. However, as Figure 4 has shown, the mark-up has been pro-cyclical, while labour share has been countercyclical. It follows that there cannot be a macroeconomic problem concerning wages when their share is falling. This does not exclude the possibility of micro rigidities that are also very important in determining the allocation of labour.

Secondly, there is a theoretical problem. In our story, wages may have a short-run impact on labour substitution within the plant, but a rather indirect role on the number of plants, where it is just one component of medium-run profitability. In this perspective, aggregate demand and productivity are by far more important in order to understand medium-run dynamics. THE AFTERMATH OF A FINANCIAL CRISIS

In order to understand both contraction and recovery, it is important to consider the identity: (see also, Fazzari, Ferri, Greenberg and Variato (2013):

ttt alg +≡ (5) where g is the rate of growth of output, l is the rate of growth of employment and a is the rate of growth of productivity.

If one looks at the data of the Great Recession, it turns out that the unsatisfactory behaviour of l is due to the unsatisfactory dynamics of g. Due to the presence of floors to nominal interest rate, presence of uncertainty and high debt ratio that prevented a stronger use of fiscal policy, the dynamics of g has been below the trend.4 In this situation, the dynamics of productivity is anchored to the maximum previously reached and does not change a lot.

There are of course cyclical deviations. In fact, the increase in productivity quarters after the trough has been reached is a common feature of almost every post-war recovery. According to Gordon (2003), this behaviour is due to two tendencies: the “end-of-expansion effect” which implies firms’ tendency to over-hire, and the “early recovery productivity bubble”, which is the tendency to under-hire in the early stages of recoveries. However, since the economic downturn of 1990-91 a new era of productivity-led recoveries started where the length of the phase of high productivity growth and stalled growth in employment has increased in a substantial way. This implies that “productivity growth” cannot be “the deus ex-machina” of the explanation of employment in the “Great Recession”.

The present situation can be better understood as representing the aftermath of a speculative bubble (see also Reinhart and Rogoff, 2012) and therefore does not simply represents a normal recovery. This consideration is not only important to understand the behaviour of employment in construction, but also the overall pattern. Our thesis is that while it is not true that one can characterizes the present economic situation as a case of jobless recovery, it is, on the contrary, true that one can talk about a jobless economy. The main explanation is due to the fact that aggregate demand, and therefore output, are still far


away from the previous trend. In this perspective, it decisive to consider that during the Great Recession, the economy lost about twelve percentage points of output relative to trend. Aggregate demand has a pivotal role in order to understand both contraction and expansion. From the latter point of view, it is unlikely that it can reach the crisis levels when was fuelled by a credit boom. CONCLUDING REMARKS

Even though present events differ from those of the “Great Depression”, they call into question the impact of aggregate demand on labour market either during recession and recovery. The paper has referred to an economy with slack capacity. While normally, capacity utilization refers to an intensive use of factors of production, in the present papers it is grounded on an extensive concept, as the ratio of operating plants on the total. In this perspective, a fall in aggregate demand implies a fall in employment by means of a reduction of capacity utilized. For these reasons, the presence of spare capacity is accompanied by quantity adjustment and therefore strengthen the relationship between aggregate demand and the labour market.

In order to understand the unsatisfactory job creation during the aftermath of the “Great Recession”, one has mainly to choose an appropriate benchmark. In fact, the present situation is better defined as the aftermath of a financial bubble than as a typical recovery phase of a normal business cycle. In this perspective the unsatisfactory growth of aggregate demand, along with credit conditions, play a strategic role.

The analysis can be extended into different directions. The first would consist in considering other variables of the labor markets, both in terms of stocks and flows, in order to have a more complete picture of the situation. The second would imply to better characterize the present situation by considering the implications of financial distress (see Calvo et alia, 2012). Finally, one has to deepen the link between stagnating growth and sustained productivity. ENDNOTES

1. For a comparison with the Euro Area, see ECB (2012). 2. The putty-clay models can obtain the same relationship even though with a different causal order. See

Gilchrist and Williams (2005). 3. It also different from those approaches that stress the role of shocks on capital accumulation, see Gertler

and Kiyotaki, 2011. 4. On the role of technical change in a fix price model, see Basu and Kimball (2006) and also Bils et alia

(2012). REFERENCES Basu, S., J.G., Fernald and M.S., Kimball (2006). Are technology improvements contractionary? American Economic Review, 96, 1418-1446. Basu S., B., Bundick (2012). Uncertainty shocks in a model of effective demand, NBER Working Paper, 18420, Cambridge (MA). Bils, M., P.J., Klenow, B.A., Malin (2012). Testing for Keynesian labor demand, NBER Working Paper, 18149, Cambridge (MA). Bloom, N. (2009). The impact of uncertainty shocks, Econometrica, 77, 623-685. Bloom, N., M., Floetotto, N., Jaimovich, I., Saporta-Eksten (2012). Really uncertain business cycles, NBER Working Paper, 18245, Cambridge (MA).


Calvo, G. A., F., Coricelli, P., Ottonello (2012). The labor market consequences of financial crises with or without inflation: jobless and wageless recoveries, NBER Working Paper, 18480, Cambridge (MA). Cooley, T.F, G. D., Hansen, E. C., Prescott (1995). Equilibrium business cycles with idle resources and variable capacity utilization, Economic Theory, 6, 35-49. Cristini A., P., Ferri, A., Variato (2012). The dynamics of an economy with slack resources, paper presented at MDEF 2012, Department of Economics, Society, Politics, University of Urbino, September 20-22. ECB (2012): Euro area labour markets and the crisis, Occasional Paper Series, 138, Frankfurt. Eusepi, S., B., Preston (2011). Expectations, learning, and business cycles fluctuations, American Economic Review, 101, 2844-2872. Fazzari, S., P.,Ferri, E., Greenberg, A.,Variato (2013). Aggregate demand, instability and growth, Review of Keynesian Economics, 1, 153-173. Ferri, P., A., Variato (2010). Imperfect Competition, learning and fluctuations. In Bischi, G. I., C., Chiarella, L., Gardini (eds.). Nonlinear Dynamics in Economies, Finance and Social Sciences, New York, Springer-Verlag, 215-28. Gertler, M., N., Kiyotaki (2011). Financial intermediation and credit policy in business cycle analysis. In B., Friedman and M. Woodford (eds), Handbook of Monetary Economics, Ch. 11. Gilchrist, S., J. C., Williams (2005). Investment, capacity and uncertainty: a putty-clay approach, Review of Economic Dynamics, 9, 1-27. Gordon, R. J., (2003). Exploding productivity growth: context, causes and implications, Brookings Papers on Economic Activity, 2, 207-79. Hall, R. E. (2011). The long slump, American Economic Review, 101, 431-489. Jaimovich, N., S., Rebelo (2009). Can news about the future drive the business cycle?, American Economic Review, 99, 1097-1118. Lazear, E. P., J. R. Spletzer (2012). The United States Labor market: status quo or a new normal? NBER Working Paper, 18386, Cambridge (MA). Reinhart, C., V., Reinhart (2010). After the fall, NBER Working Paper, 16334, Cambridge (MA). Reinhart, C., K., Rogoff (2012). This Time is Different: Eight Centuries of Folly, Princeton University Press, Princeton (NJ). Rotemberg, J. J., M., Woodford (1999). The cyclical behaviour of prices and costs. In Taylor, J. M., M., Woodford (eds), Handbook of Macroeconomics, Elsevier, Amsterdam, pp. 1051-1135. Shimer, R. (2012). Wage rigidities and jobless recoveries, Journal of Monetary Economics, 69, (supplement), S66-S77.


Wen, Y. (1998). Capacity utilization under increasing returns to scale, Journal of Economic Theory, 81, 7-36. Woodford, M. (2009). Convergence in macroeconomics: elements of the new synthesis, American Economic Journal of Macroeconomics, 1, 242-266.


The Day-of-the-Week Effect: The CIVETS Stock Markets Case

Julio César Alonso Cifuentes Icesi University

Beatriz Eugenia Gallo Córdoba

Icesi University

Finding patterns in the behavior or performance of financial markets has been a subject of interest for both analysts and academics. We use GARCH and IGARCH models with covariates to estimate the day-of-the-week (DOW) effect on both volatility and daily returns of the stock exchange markets for the CIVETS. We found a DOW effect on the daily returns for all of the CIVETS’ stock markets. DOW effect was also found for the daily returns’ volatility of some of the stock markets. Finally, there is evidence of lags in the DOW effect for the stock markets we analyze. INTRODUCTION

Finding patterns in the behavior or performance of financial markets has been a subject of interest for

both analysts and academics. Since the 1980s, the search for predictable patterns in the fluctuations of prices (and returns) in the stock and exchange markets has been the focus of attention of a plethora of investigators. For instance, a number of regularities have been reported in the literature such as the "January effect"1, the monthly effect2, the “firm size effect”3, the “end-of-the-week effect”, and the “day-of-the-week effect”.

Cross (1973), French (1980), Gibbons (1981), Lakonishok(1982), Keim (1984) and Rogalski (1984) were the first to provide sufficient evidence to show the existence of the day-of-the-week effect (also known as DOW effect). Victoria (2005) and Jarrett (2006) recently provided new evidence to support the DOW effect. These authors have particularly shown that there are statistical differences in the distribution of returns for each day of the week in the U.S. stock market.

These regular patterns have also been found in the market of bonds issued by the U.S. Federal Government –Flannary & Protopapadakis (1988)– and the foreign exchange market –Corhay, Fatemi, & Rad (1995)–. Other authors including Balvers (1990), Breen (1990), Campbell (1987), Fama (1989), and Pesaran (1995), have applied different approaches that also support the existence of this effect in the United States.

Based on the prices of a considerable number of shares traded in the New York Stock Exchange (NYSE), Jarrett (2006) found evidence of these daily effects which allow forecasting the daily returns on each kind of share with a certain level of accuracy. In general, the most frequently reported finding is that a lower return is expected on a Monday or close to the weekend (i.e. end-of-the-week effect). Studies such as those by Clare (1995), Black (1995), and Pesaran (1995) use similar approaches for England. Jaffe (1985) reported the presence of this effect in Australia, Canada, England, and Japan.


Pettway & Tapley (1984) reported a DOW effect in the Japanese stock market using three different indexes and information about the five largest firms in the period from 1979 to 1982. They also found the lowest returns to occur on Tuesdays and the highest returns on Wednesdays.

Following those studies conducted for the United States, Ikeda (1988) used the Tokyo stock exchange index and found similar daily effects to those reported by Pettway (1984). He also proved this behavior based on the kurtosis and the asymmetric rate for daily returns. For an extended time series, Kato (1990) found the same weekly patterns of behavior in the returns on stock in Japan. However, he included in his research tests to determine returns on each day of the week. He came to the conclusion that in most cases returns have a significant increase during the time when the stock exchange market is closed.

Like any other pattern, the “day-of-the-week effect” (DOW) on the returns of an asset would enable agents to profit from behavior patterns of the markets by designing trading strategies. The existence of a behavior pattern in the returns associated with the days of the week could suggest predictable characteristics of the time series. This could indicate the existence of conditional returns depending on the day of the week, thus providing investors with opportunities for arbitration. The existence of these long-term valid negotiating rules would imply a conflict with the Efficient Market Hypothesis (also known as EMH) as discussed by Granger (1992). In general, if there are foreseeable and openly available patterns that allow generating profit, then there will be evidence against the EMH.

The efficiency of financial markets has been the subject of important research studies since Fama (1955) and Fama (1970) explained that the foundations of his EMH are based on the impossibility to predict the behavior of price series of financial assets.

In recent years, market annalists and market agents have been given major attention to emerging markets’ performance. For example, Goldman Sachs popularized an acronym for a group of emerging markets: BRICS (Brazil, Russia, India and China). The BRICS aggrupation dates from 2001, when Goldman Sachs began to use the term (O’Neill, 2001). More recently, a “second generation” of emerging markets became popular: the CIVETS (Colombia, Indonesia, Vietnam, Egypt, Turkey and South Africa) (Greenwood, 2011). The Economist popularized this acronym in 2009.

In the case of the CIVETS, not too many studies have investigated the existence of calendar effects in their financial markets. Furthermore, Rivera (2009), for Colombia; Basher & Sadorsky (2006) for Colombia, Indonesia, South Africa and Turkey; Yalcin & Yucel (2006) for Colombia, Indonesia, South Africa and Turkey; Kamaly & Tooma (2009) and Aly, Mehdian & Perry (2004) for Egypt; Alagidede (2008) for Egypt and South Africa; Lean, Smyth & Wong (2007) for Indonesia; Brounen & Ben-Hamo (2009) for South Africa; Aksoy & Dastan (2011), Kamath and Liu (2010), Cinko & Avci (2009) and Berument, Coskun & Sahin (2007) for Turkey; and Hau (2010) for Vietnam provide mixed evidence on the hypothesis of a DOW effect for both the mean and the volatility of the returns for the CIVETS.

Maps 1 and 2 show the results of different studies about the DOW effect around the globe since 2004. Countries for which no day-of-the-week effect evidence is found are shaded in dark color. The lightest color corresponds to those countries for which no previous study was found. GRAPH 1 focuses on DOW effect in returns, while GRAPH 2 corresponds to evidence regarding this anomaly in volatility.


GRAPH 1 DAY-OF-THE-WEEK-EFFECT ON RETURNS EVIDENCE AROUND THE GLOBE

GRAPH 2 DAY-OF-THE-WEEK-EFFECT ON VOLATILITY: EVIDENCE AROUND THE GLOBE


This paper looks into the weekly behavior patterns of returns on the CIVETS’s stock to determine whether there is a DOW effect that would refute the validity of the EMH. This examination is not limited to the regular behavior patterns of the first moment of returns. Following Berument (2003), Harvinder (2004), and Galai (2005), this paper also includes a review of possible patterns at the second moment of returns.

The paper is organized as follows: i) the first section is a brief introduction; ii) the second section discusses the models and data used for demonstrating the existence of the DOW effect; iii) the third section discuses the estimation of the GARCH family models; and iv) the last section deals with the final remarks.

METHODOLOGY AND DATA

In order to assess the existence or not of the DOW effect in the CIVETS we will use daily returns of the Colombian, Indonesian, Vietnamese, Egyptian Turkish and South African stock markets. All samples end in the last trading-day of July 2012 for the six countries. The source of our data was Reuters.

Finding patterns in the behavior of the daily returns implies to determine the distribution of daily returns on stock indexes on each day of the week. The histogram and the associated normal distribution (based on the unconditional mean and variance of the sample) are shown in GRAPH 3, GRAPH 4 and GRAPH 5.


GRAPH 3 DAILY RETURNS’ HISTOGRAMS: COLOMBIA AND EGYPT


GRAPH 4 DAILY RETURNS’ HISTOGRAMS: INDONESIA AND SOUTH AFRICA


GRAPH 5 DAILY RETURNS’ HISTOGRAMS: TURKEY AND VIETNAM

Monday

Den

sity

-0.1 0.0 0.1 0.2

05

15

Turkey

Tuesday

Den

sity

-0.1 0.0 0.1 0.2

05

15

Wednesday

Den

sity

-0.1 0.0 0.1 0.2

05

15

Thursday

Den

sity

-0.1 0.0 0.1 0.2

010

20

Friday

Den

sity

-0.1 0.0 0.1 0.2

010

20Monday

Den

sity

-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06

020

40

Vietnam

Wednesday

Den

sity

-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06

020

40

Friday

Den

sity

-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06

020

40


The difference between the empirical distribution of the data and the normal distribution is evident. The returns show a rather peaky or leptokurtic distribution for each of the days considered in the analysis. Heavier tails are obtained simultaneously which entails a higher likelihood of obtaining extreme values versus what one would expect to attain in a normal distribution.

In this respect, the results are similar to those obtained by Alonso C. & Arcos (2006) whose study did not make any distinction among the different days of the week. On the other hand, those histograms provided some indication of a possible DOW effect for each country. Of course, this approach is only descriptive.

The most common approach to determine the day-of-the-week effect on daily returns ( ) is based on the OLS estimation of the following model:

FIGURE 1

MODEL FOR OLS ESTIMATION WITH DUMMY VARIABLES

where stands for an error term with a zero mean and constant variance. represents the dummy variables depending on the day of the week. In this respect, if t corresponds to a Tuesday, and

otherwise. Similarly, if t corresponds to a Wednesday, and so forth. Thus, the Monday effect is gathered by the constant , and the estimated coefficients in FIGURE 1 represent the difference in the average returns in any given day with respect to Monday. It is worth noting that this kind of approach is the same as that used in most of the above-mentioned studies for the CIVETS.

It is well known that inefficient estimators will be obtained for the parameters in FIGURE 1 if (the error term) is heteroscedastic and self-correlated. Consequently, a better approach to model the returns on stock indexes implies to take into account the fact that returns have a non-constant behavior.

Thus, it is interesting to be able to capture the periods of steadiness and high volatility in each series and model the variance, because model in FIGURE 1 only considers the case of a conditional mean. The GARCH-M model allows not only modeling an appropriate conditional mean return (based on unbiased efficient estimators), but also modeling day-of-the-week effects on the variance. Special consideration was given to the following GARCH-M model:

FIGURE 2

GARCH-M MODEL

where stands for a random error term with a mean equal to zero and heteroscedastic variance that reflects the behavior described in the second equation in FIGURE 2. It is worth noting that a similar specification was suggested by Berument (2003).

The coefficients in the second equation in FIGURE 2 capture the day-of-the-week effect on the volatility of returns with respect to Mondays. The model described FIGURE 2 above would also allow verifying whether empirical results are consistent with classical financial theory, i.e. the higher the

tR

4

1t i it t

iR D uδ β

=

= + +∑

tu itD

1 1tD =

1 0tD = 2 1tD =δ

tu

4

1 t i it t t

iR Dδ β λσ ε

=

= + + +∑4

2 2 2

1 1 1

p q

t i it i t i i t i ti i i

Dσ κ α φσ θ ε υ− −= = =

= + + + +∑ ∑ ∑

tε

iα


conditional variance of returns (risk), the higher the necessary compensation must be (return). This statement is confirmed by coefficient .

On the other hand, a special case of the GARCH-M model is the restricted case when

. In this case, the model is known as IGARCH-M (Integrated Generalized

Autoregressive Conditional Heteroskedasticity). The IGARCH model implies a unit root in the GARCH process, i.e. persistence in the volatility process.

EMPIRICAL EVIDENCE

Estimations derived from the GARCH-M model (FIGURE 2) using Bollerslev's and Wooldridge's quasi-maximum likelihood method (QMLE) (Bollerslev & Wooldridge., 1992) and assuming normally distributed errors are shown in TABLE 1 and TABLE 24.

It is worth highlighting several results with respect to the conditional mean. As far as the Colombian stock exchange index is concerned, the mean return is different from zero only on Thursday and Friday. For Indonesia, returns on Mondays and Tuesdays are on average negative. Wednesdays, Thursdays and Fridays’ returns are positive. For South Africa, the results show that Thursdays’ returns are similar to returns on Mondays, and the other days of the week present a different average. Estimations for Turkey imply that the mean return is different from zero on Wednesdays, Thursdays and Fridays. In the case of Egypt and Vietnam the mean return is different from zero only on Thursdays and Fridays, respectively

With regard to the performance of the variance, for Colombia, Indonesia, and Egypt there is not a day-of-the-week effect. The returns of the stock market index for South Africa, Turkey and Vietnam do present a DOW effect in volatility.

λ


TABLE 1 GARCH MODELS

GARCH-M (QMLE) Dependent variable: Returns (z- statistic in parenthesis) Eq. for the mean COLOMBIA INDONESIA SOUTH AFRICA TURKEY Constant 0.0004

-0.0007

0.0015

0.0000

(0.717)

(2.393) ** (4.130) *** (0.055) Tuesday -0.0002

0.0003

-0.0010

-0.0005

(0.306)

(0.990)

(2.513) ** (0.555) Wednesday 0.0009

0.0015

-0.0014

0.0019

(1.512)

(3.984) *** (2.940) *** (1.920) *

Thursday 0.0013

0.0014

-0.0005

0.0032

(2.166) ** (3.559) *** (1.086)

(3.012) ***

Friday 0.0023

0.0020

-0.0010

0.0024

(3.944) *** (5.781) *** (2.317) ** (2.463) **

σ2t 1.6470

0.1214

1.0523

0.5882

(0.740)

(0.109)

(0.568)

(0.630) AR(1) 0.2633 *** 0.2918 *** -0.4293 ** 1.2673 ***

(6.532)

(13.095)

(2.215)

(11.317)

AR(1) × Tuesday -0.1065

-0.2214

-0.2186

-0.2506

(1.875) * (6.456) *** (4.491) *** (5.615) ***

AR(1) × Wednesday -0.0941

-0.0743

-0.0246

-0.1679

(1.704) * (2.077) ** (0.454)

(3.428) ***

AR(1) × Thursday 0.0107

-0.0880

-0.0877

-0.0917

(0.174)

(2.437) ** (1.685) * (1.829) *

AR(1) × Friday -0.1250

-0.0948

-0.0896

-0.1104

(2.136) ** (2.694) *** (1.783) ** (2.356) **

Eq. for the variance

Constant 0.0000

0.0000

0.0000

0.0001

(1.643)

(0.987)

(0.435)

(4.063) ***

Tuesday 0.0000 0.0000

0.0000

-0.0001

(0.679)

(0.083)

(0.008)

(3.299) ***

Wednesday 0.0000 0.0000

0.0000

-0.0001

(1.625)

(0.179)

(1.911) * (1.691) *

Thursday 0.0000 0.1837

0.0000

-0.0001

(0.547)

(0.854)

(0.236)

(1.398) Friday 0.0000 -0.6325

0.0000

-0.0002

(0.464)

(0.527)

(1.933) * (4.166) ***

σ2t - 1 0.6824 0.8268

0.8955

0.8478

(17.477) *** (70.011) *** (108.058) *** (64.148) ***

ε2t - 1 0.1670 0.1261

0.0477

0.0950

(4.244) *** (8.440) *** (5.403) *** (7.692) ***

ε2t - 1 × (εt - 1 < 0) 0.1249 0.0968

0.0909

0.0737

(2.569) ** (4.476) *** (6.676) *** (3.930) ***

AR(p) 1 1

1

5 MA(q) 0 0

2

5

GED PARAMETER 1.2352

1.2329

1.5886

1.4243 (28.460) *** (44.391) *** (36.480) *** (33.803) ***

R2 0.0475 0.0437

0.0083

0.0228

Durbin Watson 2.0558 2.0178

2.0373

2.0023

Number of Obs. 2225 4986 3792 3624


TABLE 2 IGARCH MODELS

IGARCH-M (QMLE) Dependent variable: Returns (z- statistic in parenthesis) Eq. for the mean EGYPT VIETNAM Constant 0.0007

-3.3583

(1.322)

(0.001) Monday -0.0008

-

(1.337)

- Tuesday -0.0008

-

(1.186)

- Wednesday -0.0004

-0.0001

(0.575)

(0.194) Thursday 0.0011

-

(1.833) * - Friday -

0.0009

-

(2.619) *** σ2

t 0.3388

-0.8635 (0.239)

(0.518)

AR(1) 1.17768 *** 0.9999 *** 11.311

(607.834)

AR(1) × Monday -0.0977

- (2.139) ** - AR(1) × Tuesday -0.1255

-

(2.444) ** - AR(1) × Wednesday -0.1495

-0.1251

(3.008) *** (3.669) *** AR(1) × Thursday -0.1713

-

(3.592) *** - AR(1) × Friday -

-0.0390

-

(0.991)

Eq. for the variance Monday 0.0000 - (0.203)

-

Tuesday 0.0000

- (1.474)

-

Wednesday 0.0000 0.0000 (0.294)

(1.092)

Thursday 0.0000 - (1.320)

-

Friday -

0.0000 -

(5.165) ***

σ2t - 1 0.8571 0.7164

(67.100) *** (38.067) *** ε2

t - 1 0.1429 0.2836 (11.188) *** (15.073) ***

AR(p) 2

1 MA(q) 3

2

GED PARAMETER 1.3175

1.4450 (39.797) *** (28.943) ***

R2 0.0443

0.1298 Durbin Watson 2.0287

1.9492

Number of Obs. 3136 2363


FINAL REMARKS

We used a GARCH-M model, which, unlike an estimation based on a conventional linear model that uses OLS, allows determining a consistent efficient approach to estimate the DOW effect on mean returns. This approach also allows estimating the DOW effect on volatility.

In our case, the weekly behavior patterns of financial markets (DOW effect) are identified for the stock-exchange-market indexes of the CIVETS. All five countries present day-of-the-week effect on mean returns. On the other hand, DOW effect was not found in the returns’ volatility for Colombia, Indonesia and Egypt.

Likewise it must also be noted that volatility of returns does not have any effect on the mean return on the CIVETS’ stock-market indexes. These results contradict classical financial theory which states that the higher the conditional variance (risk) of returns, the higher the necessary compensation (return).

Finally, the behavior pattern of mean returns in the stock exchange market implies that there is a possibility to establish negotiating rules and, therefore, provides some sort of evidence that these markets is inefficient. This finding should be discussed and examined in more detail in further studies.

ENDNOTES

1. See Rozeff & Kinney(1976) for one of the first studies of this subject matter. 2. Ariel (1987) released one of the first papers on this subject 3. Keim (1983) was one of the pioneers in this subject. 4. The models selected in all cases conform to a GARCH(1,1). These models were selected based on modified

criteria of AIC and SBC as suggested by Enders (2004). On the other hand, in the case were the sum of the ARCH and GARCH terms is not statistical different from 1, an IGARCH-M model is estimated.

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Dynamic Behavior of Inflation in Nigeria: A P-Star Approach

Ikechukwu Kelikume Lagos Business School

The P-star model performance in predicting inflation renders it a valuable tool in analyzing dynamic behavior of prices. This study tests the performance of the P-star model in Nigeria with a view to ascertaining its usefulness in predicting price movement. Using quarterly data obtained from the Central Bank of Nigeria statistical Bulletin over the period 1970 to 2011, we obtained estimates of the price-gap, velocity-gap and output-gap model. The result obtained shows the usefulness of the price-gap model in explaining and predicting inflation in Nigeria. INTRODUCTION

One of the most frequently debated issues in contemporary economics, especially in developing and emerging markets is the issue of inflation forecasting given the role of price movement in the business cycles. Usually, the Central Bank or the monetary authorities periodically set or forecast base inflation as a strategy in defining inflation target for the given period. The ability of monetary authorities to predict the movement of the general price levels guarantees to a large extent the relative effectiveness of monetary policy in pursuing either a tight or loose monetary policy as a veritable tool in checking deflationary and inflationary pressures in the economy. Between the periods, 1970 to 2000, high and persistent inflation was a characteristic feature of the Nigerian economy (See Figure 1), but the trend in inflation between 2000 and 2010 showed the general price level has been receding towards a single digit level. 12 month moving inflation averaged 13.22 percent in the period 1970-1979 but rose to an average of 22.88 percent and 30.65 percent in the periods 1980-1989 and 1990-1999 respectively. The period 2000 to 2009 showed a significant decrease in average inflation to 12.31 percent marking a step towards single digit inflation. (See figure 1)

The word inflation is a steady increase in the general price level of goods and services ''in an economy''. When rise in the price level is a result of excess demand over supply, Inflation is a monetary phenomenon (Friedman 1963). However, inflation can be described as cost-push where the main driver is a steady increase in production cost driven primarily by an increase in; exchange rate, interest rate, salaries and wages, taxes, profits, commodity price, external shocks, depletion of natural resources imported input prices and international oil price trend. This latter phenomenon-the supply-side factors are studied in the literature extensively (Bernake 2005, Haque and Qayyum 2006). For effective forecasting of the general price trend, it is expedient for the monetary authorities to understand factors that drive the long-term price changes and short term determinants of inflation.

Two key variables that have played the crucial role in inflation targeting and forecasting in most research studies is the money supply variable narrowly defined (M1) and the money supply broadly defined (M2). It is certified that these variables with the output gap and price gap model to forecast


inflation successfully in different countries. Following the framework developed by Hallman, Porter and Small (1991) we build on the reputed P-star model of inflationary pressure based on the classical equation of exchange in which changes in the money stock determines the equilibrium path of price level to which the actual price level has to adjust. The forecasting abilities of the P-star model in predicting long term determinant of inflation and short term changes in the current price level; renders the model a helpful tool in predicting the dynamic behavior of prices changes in a developing country like Nigeria.

Given the role of price movement in the business cycles, inflation forecasting has become one of the most debated issues in modern economics and emerging markets in particular. Usually, the Central Bank or the monetary authorities periodically set or forecast base inflation as a strategy in defining inflation target for the given period. The ability of monetary authorities to predict the movement of the general price levels guarantees the effectiveness of monetary policy in checking deflationary and inflationary pressures in the economy. Between the periods, 1970 to 2000, high and persistent inflation was a characteristic feature of the Nigerian economy (See Figure 1), but the trend in inflation between 2000 and 2010 showed the general price level has been receding towards a single digit level. 12 month moving inflation averaged 13.22 percent in the period 1970-1979 but rose to an average of 22.88 percent and 30.65 percent in the periods 1980-1989 and 1990-1999 respectively. The period 2000 to 2009 showed a significant decrease in average inflation to 12.31 percent marking a step towards single digit inflation. (See figure 1)

FIGURE 1 TREND IN INFLATION IN NIGERIA (1970-2010)

Figure 1 shows significant volatility in the general price level over the periods 1970-2010 with inflation attaining the highest level in 1995. Inflation has been less volatile since 2000

The basic objective of this study is to evaluate the performance of the P-star model for the Nigerian economy. Furthermore, the study anticipates reliable prediction of long and short run determinants of inflation as well as generating recursive forecast from the p-star model.

Following the introductory section, the rest of this paper is structured as follows; section two provides a brief review of the literature while section three sets out the basic methodology underpinning the P-star model. In section four, we present the empirical analysis and forecasting performance of the model while section five concludes the paper.

0

10

20

30

40

50

60

70

80

(%)

INFLATION 12 Month Moving Average


LITERATURE REVIEW

Debate over the causes of inflation in economics is still on till date leading to the development of various theories and inflation models which are both competing and complementing. Overtime, the causes of inflation have evolved to include; monetary shocks; fiscal shocks; inflation expectation; exchange rate; wage rate; and import prices grouped into supply side shocks; demand side shocks; political and structural shocks.

Decomposing an inflation model into it causal component has been the workhorse of economist. While the monetarist are of the view that inflation is always and everywhere caused by monetary phenomenon (Friedman 1968, 1970, 1971), the Keynesians believe that inflation is a result of a pull in demand and the Structuralist emphasizes increases in cost of production as the cause of inflation (Thirwell, 1974; Aghevei and Khan 1977).

In the past decade, shift to inflation targeting has herald all economies of the world and the success hinges on the ability to forecast inflation with considerable certainty. This has led to the development of many forecasting model ranging from basic regression to Vector Auto regression (VAR) to Autoregressive Integrated Moving Average to P-star model.

Based on the classical quantity theory of money, the P-star model was introduces by Hallman, Porter and Small (1981) as an inflation forecasting model which suggest that price level gravitate towards equilibrium. The conclusion of Hallman et al (1981) that the price gap is a better anchor for inflationary pressure has drawn attention to the idea of P-star model. Following the success of their work in developing a strong measure of inflation pressure in the US economy, numerous studies have applied this same model in predicting and forecasting inflation in both developed and developing countries.

While some researchers applauded the P-star model for it informative and predictive ability (Hallman, Porter and Small 1981; Qayyum and Bilquees 2005). Some have shown that the model outperforms other elementary inflation model and forecast (Hallman et al 1989, 1991; Christiano 1989; Hoeller and Poret 1991; Qayyam and Bilquees 2005; Anglingkusumo 2005; Mujeri, Shahiduzzaman and Islam 2009). Others have criticized it as having excessive dynamic behaviour and low predictability in forecasting performance (Pecchemi and Rasche 1990; Hoeller and Poret 1991; Funke and Hall 1994; Hall and Milne 1994; Vicente and Vicente 1999; Nachame and Lakshmi 2002).

Although, the P-star model has the problem of measuring potential variable of output and velocity of money circulation since they are unobservable series (Tatom, 1992; Muzafer 1997). However, it is satisfactory for long run inflation forecasting and analysis (Kool and Tatom 1994; Gerlash and Svensson 2003). Evidence from the P-star model has shown the existence of long run relationship between inflation and money growth (Tatom 1992; Todter and Reimers 1994; Kool and Tatom 1994; Svensson 1999; Frait, Komarek and Kulhane 2000; Belke and Polleit 2004; Azim and Mesut 2008) - this gives credence the proposition of the classical quantity theory of money that inflation is always and everywhere a monetary phenomenon (Friedman, 1956).

Empirical evidence from the p-star model has shown that it is satisfactory for short-run forecast although the conclusions are mixed. While some conclude that the approach yields a reasonable estimate and forecast for developed countries, others have found evidence that the estimates of the model for developing countries are poor METHODOLOGY

The theoretical framework of this study is an outcome of the prestigious P-star model which has a substantial degree of success in both developed and developing countries. (Hallman, Porter and Small (1989, 1991), Allen and Hall (1990), Bordes, Girardin and Marimoutou (1993), Todter and Reimers (1994), Hoeller and Poret (1991), Nachane and Laxmi (2002), Todter (2002), Qayyum and Bilquees (2005) and Mujeri, Shahiduzzaman and Islam (2009)). The classical quantity theory of money is the foundation of the P-star model as demonstrated in pioneering works of Hallman et al (1989, 1991). We begin with the basic classical equation of exchange.


MV=PY (3.1)

Where M is the total stock of money (Notably the broad money supply M2), V is the velocity of money circulation, P is the general price and Y is real output.

The P-star model combines the long-term and the short-term determinants of changes in the price level in the economy. It assumes that real GDP fluctuates around its full employment level and the existence of equilibrium level for income velocity of money, which can be obtained directly from the equilibrium price level in the classical equation of exchange as follows;

***

YMVP = (3.2)

Where; P* is the equilibrium price level, Y* is equilibrium output and V* is the equilibrium level of income velocity. By simply letting the lower case letters denote logarithms, we can rewrite equations (3.1) and (3.2) as follows;

m + v = p + y (3.3) m + v* = p* + y* (3.4)

By subtracting equation (3.4) from equation (3.3), we obtain the price gap, the output gap and the velocity gap, which is usually the proxy for liquidity overhang in the economy.

v - v* = (p - p*) + (y - y*) (3.5) By simply transposing equation (3.5) we obtain the price gap model as;

(p - p*) = (v - v*) - (y - y*) (3.6)

Where; (p - p*) is the price gap, (v - v*) is the income velocity gap or the proxy for liquidity overhang in the economy and (y - y*) is the output gap.

Equation 3.6 represents the price-gap model which is useful in predicting the movement of the inflation rate. However, before the model can be used we have to define potential GDP y* and the equilibrium velocity v*. The definition of equilibrium velocity (v*) is carried out in a way that v (t) is stationary (that is I (0)) either around a constant v0 or around a linear trend (a + bt). In the former case, the equilibrium (log) velocity is taken as v* = v0 while the in the latter case, v* is time varying with v*(t) = (a + bt). The potential real GDP can be obtained directly if we assume that a reliable series y1* exist. However, we may obtain the potential output by using the same process applied in obtaining the equilibrium velocity (v*). An alternative way of obtaining the potential output is to use the Hodrick-Prescort filter approach (Hodrick and Prescot 1980). The Hodrick-Prescort filter uses the long-run symmetric moving average to de-trend the particular series-in this case the output series. It is essentially a two-sided linear filter that minimizes output from its trend;

( )2

1

*lnln.∑=

−T

ttYYMin (3.7)

Subject to ( ) ( )[ ] eYYYYT

tttt ≤−−−∑

−

=−

1

2

21

** lnlnlnln

Specifically, the HP method chooses lnY* to


Minimize ( ) ( ) ( )[ ]∑∑−

=−

=

−−−+−1

2

21

**2

1

* lnlnlnlnlnlnT

tttt

T

tt YYYYYY λ (3.8)

Where; Y is the actual output, Y* is a trend or potential output (GDP) at constant market price, Yt-1, is

one period lag values of actual output and λ is the Lagrange multiplier. The parameter λ controls the smoothness of the series variance and asλ approaches infinity, lnY* approaches a linear trend.

After obtaining the equilibrium velocity (v*) and the potential output level (y*), the value of the equilibrium price p*, is obtained by simply substituting v* and y* into equation (3.6). Depending on whether the equilibrium v* and y* is around a constant or around a linear trend we will obtain two versions of equilibrium price level P1* and P2*. After obtaining the equilibrium price level, we proceed to checking whether P and P* are co integrated (Engle and Granger 1987, Johansen and Juselius 1990). If cointegration is established, the equilibrium price (P*) in excess of the current price (P) indicates a future rise in inflation. However, if the equilibrium price (P*) is less than the current price (P), this indicates future deceleration of inflation. Co integration between P and P* also implies stationarity of both velocity and output gap. The Model

The precise dynamic inflation model adopted for this study draws from the pioneering works of Hallman et al. (1991) generalizing on the earlier works of McCallum (1980) and Mussa (1981). Using Πt to denote inflation, they postulate a general model of the form;

( ) ( ) )()1(1

)1()1()1(2)1()1(1)( ** tt

q

jjtttttot yyvv επφβπαααπ +∆++−+−+=∆ −

=−−−−− ∑ (3.9)

Where, ∆ operator denotes one period difference operator, q is the appropriate lag length and )(tεsatisfies the white noise properties. Equation (3.9) includes a lagged inflation variable to allow for incomplete adjustment and is specified recognizing the possibility that the velocity and output gap may impinge differently on inflation changes.

From the specification in equation (3.9), we proceed to testing the coefficient of the lagged inflation variable (β ), to test ifβ =0 using the Dickey-Fuller non-standard statistic (Dickey and Fuller 1979, Fuller 1976). The test gives rise to two separate models;

( ) ( ) )()(1

/)1()1(

/2)1()1(

/11)( *)* tjt

q

jjttttt yyvv επφαααπ +∆+−+−+=∆ −

=−−−− ∑ (3.10)

Equation (3.10) arises if β is insignificant. However, if β is significant we switch to the model in

equation (3.9) which is presented as follows;

( ) ( ) )()(1

//)1()1(

//2)1()1(

//12)( ** tjt

q

jjttttt yyvv επφαααπ +∆+−+−+= −

=−−−− ∑ (3.11)

Equations (3.9), (3.10) and (3.11) allows for the use of different lags chosen based on the lag length

criteria-Akaike information criteria or the Schwarz criterion.


EMPIRICAL ANALYSIS

The P-Star model is designed for analyzing and forecasting long term behavior of inflation while taking account of short term changes in inflation. However, success in the use of the P* model depends largely on the quality of the frequency data used in generating the result. This poses a serious limitation for a developing country like Nigeria where in most cases some of the reported data are spliced. This limitation, notwithstanding, we utilized quarterly time series data spanning over forty years 1970-2011. The data used in generating the series was sourced directly from the Central bank of Nigeria Statistical Bulletin (CBN). The variable used includes; money supply narrowly defined (M1), nominal GDP, real GDP and implicit GDP price deflator was sourced directly from the Central Bank of Nigeria (CBN) Statistical Bulletin and Nigeria National Bureau of Statistics (NBS). We calculated the income velocity of money directly by simply taking the ratio of nominal GDP to narrow money supply (GDP/M1) given that narrow money supply is the closest proxy for measuring liquidity.

The potential output y* and equilibrium velocity v* are obtained using the Hodrick-Prescort filter approach (Hodrick and Prescot 1980) while inflation is generated by simply taking the log difference of the GDP deflator from the last quarter GDP deflator.

The first step in the empirical analysis is the construction of equilibrium velocity (V*) and potential output (Y*). We start by checking the stationarity properties of V by conducting unit root tests of the Augmented Dickey-Fuller (ADF) to establish whether V is I (0) or I (1) series. The standard procedure conducting the unit root test is the use of augmented Dickey-Fuller test (Dickey and Fuller 1979). This test requires regressing ∆Vt on a constant, a time trend, lagged previous value of Vt, (Vt-1) and several lag dependent variables. This is a procedure that has been set by Holden and Perron (1994).

The result of the unit root test is reported in Table 1. From the result, V (t) is stationary at levels at the 5 percent and 10 percent level of significant. Similarly, the unit root test of the output variable Y (t) was stationary at levels.

The next step after establishing the stationary properties of the variables is to test the tendency of the actual price (p) to move to its equilibrium value (P*). This is done by testing the co integrating relationship between P and P* by applying the Engel and Granger (1987) procedure.

TABLE 1 UNIT ROOT TEST FOR THE VARIABLE V(t)

Note: The table is the result of the unit root test for the variable V(t). * and ** indicate significance at 10% and 5% level respectively.

The result of the co integrating relationship between actual P and equilibrium P (P*) is reported in Table 3. The result shows there is a tendency for P to gravitate to its equilibrium value. Having established the co integrating relationships between the actual P value and the equilibrium P value, we continue to estimating the dynamic inflation model. First, we need to establish whether the model should be estimated in inflation levels (Πt) or inflation change (∆Πt).

Augmented Dickey-Fuller Test Variables V(t) 5% critical value 10% critical value

Levels Status -3.2155 I(1) -2.8787** -2.5760*


TABLE 2 UNIT ROOT TEST FOR THE VARIABLE Y (t)

Note: The table is the result of the Augmented Dickey-Fuller (ADF) unit root test for the variable Y (t). * and ** indicate significance at the 10% and 5% level respectively.

This last step requires estimating equation (3.9) and testing the coefficient of the lagged inflation variable (Πt-1) ( β ), to test the null hypothesis that β =0 against the alternative hypothesis using the Dickey-Fuller non-standard statistic (Dickey and Fuller 1979, Fuller 1976).

TABLE 3 RESULT OF CO INTEGRATION (P AND P*)

Variables Augmented Dickey Fuller (ADF) Actual Value Critical Value 5 (%) 1 (%) P* and P -5.7880*** -2.8792 -3.4709

Note: The table is the result of the Augmented Dickey-Fuller (ADF) which indicates that the series is stationary at levels. *** indicates significance at 1% level.

The result of the estimation of equation (3.9) is reported in Table 4. The result rejects the hypothesis that β =0 given the significance of the coefficient of the lag inflation variable (Πt-1). The significance of the coefficient means we adopt equation (3.11) and drop the model in its change form (∆Πt). This is seen in the result reported in Table 4 where it the coefficient of the lag inflation variables passes the test of significance at the 5 percent levels.

TABLE 4 P* MODEL OF EQUATION DYNAMICS. DEPENDENT VARIABLE (∆Πt)

Independent Variables Coefficients T-Stat

V(t-1) –V*(t-1) -19.5598 -2.1539** Y(t-1) –Y*(t-1) 35.6375 5.0758***

(Πt-1) -0.4371 -2.4350** (∆Πt-1) -0.4381 -3.0135 (∆Πt-2) -0.3340 -3.0109*** (∆Πt-3) -0.2591 -3.4696***

Constant 1.0615 0.7255 R2 (adjusted) 0.5876

Durbin-Watson 2.0716 Note: The table is the result of dynamic change in inflation model. Where *** and ** indicate significance at 1% and 5% levels of significance.

Augmented Dickey-Fuller Test Variables Y(t) 5 % critical value 10% critical value

Levels Status -3.2639 I(1) -3.4369** -3.1426*


So far, we have been able to establish and generate the P* model. The model has three parts: the price-gap (p - p*), the velocity gap (v - v*) and the output gap (y - y*). A positive velocity gap simply implies that current velocity (V) is greater than equilibrium velocity (V*) and hence the tendency is for V to fall. In the case of output–gap, a positive output gap (Y > Y*) simply indicates a decline in output to it equilibrium level.

Given the adopted model, equation (3.11), a positive velocity gap and a negative output gap implies that inflation is decelerating while a negative velocity gap and a positive output gap indicate a strong expectation for prices to rise.

The result of the price-gap, velocity-gap and output-gap models is reported in Table 5. On the basis of apriori signs, the velocity-gap and the output-gap is expected to be negatively and positively signed respectively. The result reported in Table 5 shows that velocity gap and the output gap have the correct sign, both passing the test at 5 and 1 percent levels respectively base on the t-test. From the result, the value of the coefficient of the output gap in the price-gap model is greater than the value of the coefficient of the velocity-gap variable in absolute terms.

The implication of this result is that inflation in Nigeria is not purely a monetary phenomenon but rather driven by output and structural rigidities. Although, the adjusted coefficient of determination performed poorly, but the F-statistic passed the test of significance at the 1 percent levels of significance an indication that the overall price-gap model has a good fit. The success in the price-gap model in explaining inflation dynamics simply shows solving the challenges of rising prices in Nigeria requires a hybrid of the neo-classical output-gap framework and the monetarist velocity-gap framework.

TABLE 5

P* MODEL OF EQUATION DYNAMICS. DEPENDENT VARIABLE (ΠT)

Independent Variables Price-gap Model Velocity-gap Model Output-gap Model V(t-1) –V*(t-1) -18.4322

(-1.9766)** -13.6035 (-1.4089)

Y(t-1) –Y*(t-1) 24.0980 (3.9218)***

22.4944 (3.6596)***

(∆Πt-1) -0.0264 (-0.4122)

-0.0513 (-0.7711)

-0.0329 (-0.5097)

(∆Πt-2) -0.0705 (-0.9481)

-0.1085 (-1.4076)

-0.0729 (-0.9625)

(∆Πt-3) -0.1311 (-2.0404)**

-0.1547 (-2.3147)**

-0.1253 (0.0549)

Constant 2.2456 (0.7255)

2.1264 (-0.1547)

2.2059 (1.5049)

R2 (adjusted) 0.1000 0.0181 0.0835 Durbin-Watson 1.8252 1.9642 1.8245 F-Statistic 39.4708*** 1.7490 4.6903*** Note: The table is the result of price-gap, velocity-gap and the output-gap model. The t-values are reported in parenthesis. *** and ** indicate significance at 1% and 5% levels of significance.

To check the validity of the Output-gap and the velocity-gap model we estimated the models separately with both results reported in Table 5. The result shows the output-gap model performed significantly better than the velocity gap-model. The implication of this result for Nigeria is that inflation is not purely a monetary phenomenon in Nigeria and as such the use of interest rate and money supply to check volatility in the general price level may not produce the desired result.


CONCLUSION

The P* model though a tremendously powerful forecasting tool has been used successfully in modeling price movement in developing and developed countries alike. Success in the use of the P* star model depends on the quality of the data and the frequency of the data used. The result obtained from the study shows the price-gap model is useful for analyzing and predicting price movement in Nigeria. This position strongly supports a combination of the neo-classical output-gap framework and the monetarist velocity-gap framework. The study strongly refutes the notion that inflation is purely a monetary phenomenon as is evident in the poor performance of the velocity gap model.

The study question the rationale for the Central Bank continuous use of monetary aggregates (interest rate and money supply) to check volatility in the general price level. REFERENCES Aghevei, B. & Khan, M. S. (1977). Inflationary Finance and the Dynamics of Inflation: Anwkan Economic Review, 67. Allen C. & Hall, S.G., (1990). Money as a Potential Anchor for the Price Level: A critique of the P* Approach. Mimeo London Business School, London. Anglingkusumo, R. (2005). Money-Inflation Nexus in Indonesia: Evidence from a P-Star Analysis. Tinbergen Institute Discussion Papers Series 054, No. 4. Azim, K.O & Mesut, S. (2008). Monetary Pressures and Inflation Dynamics in Turkey: Evidence from P-Star Model. Research and Monetary Policy Department, Central Bank of the Republic of Turkey Working Papers. Belke A. & Polleit, T. (2004). A model for Forecasting Swedish Inflation. University of Hohenheim, discussion paper No. 246. Bernanke, B.S (2005). Inflation in Latin America-A New Era. Remarks at the Stanford Institute for Economic Policy Research Economic summit, February 11 2005. Bordes, C.E., Girardin & Marimoutou, V. (1993). An Evaluation of the Performance of the P-star as an Indicator of Monetary Conditions in the perspectives of EMU: The Case of France. In P. Arestis (ed), Money and Banking Issues for The Twenty-first Century, St. Martin’s Press, New York. Christiano, L.J. (1989). P*: Not the Inflation Forecaster’s Holy Grail. Federal Reserve Bank of Minneapolis, Quarterly Review 13, 3–18. Dickey D.A. & Fuller, W. A., (1979). Distribution of the Estimator for Autoregressive Time Series with a Unit Root. Journal of American Statistical Association, 75, (1), 427-431. Frait, J., Komárek, L., & Kulhánek, L. (2000). P-star-model based analysis of inflation dynamics in the Czech Republic. University of Warwick Department of Economics, Warwick economics research papers. Friedman, M. (1956). The Quantity Theory of Money: A Restatement” in Studies in the Quantity Theory of Money. University of Chicago Press. Friedman, M. (1963). Inflation: Causes and consequences. New York: Asia Publishing House.


Friedman, M. (1968). The Role of Monetary Policy. American Economic Review, 58, 1-17. Friedman, M. (1970). A Theoretical Framework of Monetary Analysis. Journal of Political Economy, 78, 193-238. Friedman, M. (1971). A Monetary Theory of Nominal Income. Journal of Political Economy, 79, 323-337. Fuller, W.A. (1976). Introduction to Statistical Time Series. Wesley, New York. Funke, M. & Hall, S. (1994). Is the Bundesbank Different from Other Central Banks: A Study Based on P*. Empirical Economics, 19, 691-707. Gerlach, S. & Svensson, L.E.O (2003). Money and Inflation in the Euro Area: A Case for Monetary Indicators. Journal of Monetary Economics, 50, 1649-1672. Hall, S. & Milne, A. (1994). The Relevance of P-Star Analysis to UK Monetary Policy. Economic Journal, 104, 597-604. Hallman, J. J., Porter, R. D. & Small, D. H. (1989). M2 per Unit of Potential GNP as an Anchor for the Price Level. Federal Reserve System, Staff Study No. 157. Hallman, J. J., R. D. Porter, & D. H. Small, (1991). Is the Price Level Tied to the Stock of M2 in the Long-run? American Economic Review 81, 841–858. Haque, U.N. & Qayyum, A. (2006). Inflation Everywhere a Monetary Phenomenon: An introductory Note. The Pakistan Development Review, 45, (2), 179-183. Hodrick, R.J. & Prescott, E.C. (1980). Post-War US Business Cycles: An Empirical investigation. Evanston, Illinois: Centre for Mathematical studies in economics and management Science, Northwestern University, Discussion paper 451. Hoeller, P. & Poret, P. (1991). Is P-Star a Good Indicator of Inflationary Pressure in OECD Countries? OECD Economic Studies 17. Kool, C., & Tatom, J. (1994). The P-Star Model in Five Economies. Federal Reserve Bank of St. Louis Review, 3. Mujeri, M. K., Shahiduzzaman, MD. & Islaam MD. E. (2009). Application of the P-star Model for Measuring Inflationary Pressure in Bangladesh, The Bangladesh development studies, 32, (1), retrieved from http://mpra.ub.uni-muenchen.de/2058/ Muzafer Shah Habibullah (1997). Can the P-star Approach be used to model inflation in Indonesia? Some empirical evidence, Ekonomidankeuangan Indonesia, 14, (3). Nachane, D.M. & Lakshmi, R. (2002). Dynamics of Inflation in India- A P-Star Approach. Journal of Applied Economics, 34, (1), 101-110. Pecchenino, R. A., & Rasche, R. H. (1990). P* Type Models: Evaluation and Forecasts. National Bureau of Economic Research, Working Papers Series, 3406.


Qayyum, A. & Bilquees, F. (2005). P-Star Model: A Leading Indicator of Inflation for Pakistan. Pakistan Development Review, 44, (2), 117-129. Svensson, L.E.O. (1999). Does the P* Model Provide any Rationale for Monetary Targeting? National Bureau of Economic Research, Working Paper Series, 7178 http://ideas.repec.org/a/fip/fedlrv/y1994imayp11-29.html Tatom A. John (1992). The P-Star Model and Austrian Prices. The Federal Reserve Bank of St. Louis working paper series 001. Retrieved from http://www.medwellonline.net/ref.php?doi=javaa.2009.2461.2467 Thirwall, A. P. (1 974). Inflation, Saving and Growth in Developing Economics. London, Macmillan. Tödter, K. H., & Reimers, H. E. (1994). P-star as a Link between Money and Prices in Germany. Review of World Economics, 130, (2), 273-289. Todter, K.H. (2002). Monetary Indicators and Policy Rules in the P-Star Model. Deutsche Bundesbank, Discussion papers series 18. Vicente, J. P., & Vicente, E. (1999). The P-Star Model and Its Performance for the Spanish Economy. University of Valencia, Department of Economic Application, working paper 11. Http://en.wikipedia.org/wiki/Monetarism


Exchange Rate Pass-Through to External and Internal Prices: A Developing Country Perspective

M. Nusrate Aziz

Multimedia University

Muhammad Sabbir Rahman Multimedia University

Md. Alauddin Majumder

Middle Tennessee State University

Somnath Sen University of Birmingham

We estimate the exchange rate pass-through to external and internal prices of a developing country, specifically, Bangladesh. The study also examines whether the tradition view that exchange rate pass-through should be ‘full’ for developing countries. We construct some variables which are not readily available in existing databases. A full sample estimation indicates that exchange rate pass-through to external prices is ‘full’, however, pass-through to internal prices is ‘partial’. Rolling regressions indicate that the response of external prices to exchange rate movement has been constantly around unity until 2003, however, it has fallen rapidly in subsequent years. Response to internal prices has been found unstable and relatively small. INTRODUCTION

Although there is a growing body of literature on exchange rate pass-through (ERPT), few studies have investigated this issue for developing countries. Nonetheless, testing ERPT to external and internal prices for developing countries are extremely useful for policy implications. It is worth clarifying here that external prices include both import and export prices and internal price means domestic price level, which is measured by either consumer price index (CPI) or producer price index (PPI).

Estimating ERPT to export price is necessary to apply an effective exchange rate policy in those economies which have been following an export-led growth strategy. Generally, the developing countries follow an export-led growth policy and they therefore frequently adjust the exchange rate to find competitiveness in export markets. However, if the exporting industries of those countries import their capital goods, then any increased demand for exports immediately increases the demand for capital goods, thereby increasing the expenditure on imports. So ERPT to import price of these economies also need to be investigated. These lead us to estimate ERPT to both import and export prices.


Secondly, Olivei (2002), Marazzi et al. (2005) and Mumtaz et al. (2006) indicate that import price is one of the main channels through which the exchange rate affects domestic prices. Subsequently, ERPT to domestic prices is worth estimating to test whether the 2nd stage pass-through1 is significant for the country. This gives an opportunity to examine whether the exchange rate is one of the significant determinants of inflation. Besides, Mishkin (2008) and McCarthy (1999) suggest that the price level of an economy may be affected by import price pass-through. Moreover, inflation has become a great concern for Bangladesh economy in recent years. Subsequently, testing the 2nd stage pass-through may be useful for inflation-forecasting and monetary policy targeting (Taylor 2000, Marazzi et al. 2005) for the country.

IMF Annual Report (2008) indicates that Bangladesh has been following the de facto managed floating exchange rate regime. Hence, assuming the managed floating exchange rate regime in Bangladesh, we investigate ERPT to both external and internal prices for the country. It is worth noting that even if a country follows the free floating exchange rate regime, the exchange rate can be affected by the country’s monetary policy (Mishkin, 2008), which in turn may influence external and internal prices.

Bangladesh has been pursuing an active exchange rate policy since its independence in 1971, which is reflected in its frequent exchange rate regime shifts and frequently announced exchange rate devaluations (Islam, 2003; Younus et al., 2006; Aziz, 2012). The key objectives of these policies are to reduce the extra pressure of imports, accelerate exports and improve the balance of trade (Hossain and Alauddin, 2005; Financial Sector Review, 2006; Aziz, 2012). Subsequently, examining the effectiveness of the exchange rate is required to apply appropriate policy. However, Hoque and Razzaque (2004) and Chowdhury and Siddique (2006) are only studies which estimate ERPT to export price and domestic prices of Bangladesh, respectively. The earlier literature estimates ERPT to disaggregated export prices. Hoque and Razzaque (2004) find a partial evidence of significant pass-through to export prices, while ERPT to domestic prices is found to be an insignificant phenomenon in Chowdhury and Siddique (2006). To the best of our knowledge, none of the studies in the literature tests ERPT to import price of Bangladesh. This study attempts to fulfil that vacuum. We estimate ERPT to both external and internal prices at aggregate level. THE LITERATURE

Yang (1997), Anderton (2003), Campa and Goldberg (2005), Campa and Minguez (2006), Mumtaz et al. (2006), Zorzi et al. (2007) find evidences of incomplete ERPT to import price in developed countries. Mallick and Marques (2006) and Zorzi et al. (2007) also indicate that there is an incomplete ERPT to import prices in developing countries and emerging markets, respectively.

Some studies, for instance, Dornbusch (1987), Olivei (2002), Marazzi et al. (2005), Mumtaz et al. (2006), Ihrig et al. (2006), Mallick and Marques (2006) suggests that there is a sustainable fall in ERPT to import prices over time.

Other studies including Froot and Klemperer (1989), Anderton (2003), Marazzi et al. (2005), Campa and Goldberg (2005), and Mishkin (2008) indicate that the size of pass-through coefficient depend on firms’ price setting behaviour. That means, the magnitude of ERPT coefficient depends on whether exporting firms set their product prices in local/consumer currency (LCP) or they follow the producer currency pricing (PCP). Consequently, the success of exchange rate policy (currency devaluation) to increase exports depends on whether the export price is rigid in PCP or LCP. If the export price follows LCP, then devaluation cannot increase exports.

The evidence of ERPT to export price is mixed. Bussière (2007), Gagnon and Knetter (1995), Knetter (1993) and Ohno (1989) find a partial ERPT to export price in developed countries. Vigfusson et al. (2007) and Mallick and Marques (2006) also find a partial support of ERPT to export price in middle income countries and developing countries, respectively. On the contrary, Haque and Razzaque (2004) find an evidence of full-ERPT to export prices in Bangladesh.

Literature on ERPT to domestic prices has also found a mixed result. Using the VAR approach, McCarthy (1999) finds that ERPT to domestic prices is partial in industrial economies. Zorzi et al. (2007) also find a partial support of ERPT to domestic prices in emerging markets. Zorzi et al. (2007),


therefore, reject the hypothesis that – ERPT is always higher in emerging economies compared to their developed country counterpart. Leigh and Rossi (2002) find that ERPT is ‘full’ in Turkey. However, Chowdhury and Siddique (2006) find no significant evidences of ERPT to domestic prices in Bangladesh. We, in light of the existing research, estimate ERPT to external and internal prices and test whether the pass-through is ‘partial’ or ‘full’ in developing countries, particularly in Bangladesh. Secondly, we examine whether ERPT has been falling over time. Thirdly, we investigate whether there is any evidence of pricing-to-market (PTM). Finally, we test whether ERPT to consumer prices and producer prices are different from each other.

The organisation of the following sections is as follows. Section 3 discusses data and variables. Section 4 gives theoretical basis of empirical models and methodology of the study. Section 5 analyses the estimated results. Section 6 concludes the paper. DATA AND VARIABLES

This study uses data from various local and international sources, namely, ‘Monthly Statistical Bulletin’ (MSB) and ‘Foreign Trade Statistics’ (FTS) of ‘Bangladesh Bureau of Statistics’ (BBS), International Financial Statistics (IFS) of the International Monetary Fund (IMF), and the World Development Indicators (WDI) of the World Bank.

We used ‘unit price index’ (UPI) for external prices. UPI of exports and UPI of imports are collected from various volumes (1972 - 2008) of FTS of BBS. The producer/wholesale price index (PPI/WPI) and consumer price index (CPI), which are proxies for domestic prices, have been collected from MSB of BBS. It is worth noting that BBS has developed the CPI by using data of the middle income group from Dhaka city. Exchange rate data are found in the IFS (2010) of the IMF, and the constant GDP and world WPI are found in the WDI (2010) of the WB.

Data for domestic prices are used from 1973-2007. However, data for external prices are used from 1978-2007. It is worth mentioning that the quarterly data for external prices are available up to 1991q4 only. We therefore have failed to use quarterly data in our study.

The Nominal Effective Exchange Rates of Import (NEERm) and Export (NEERx) for Bangladesh were constructed by using the following formula:

∑=

=k

iititit EwNEER

1

,

where, t is time and itw is trade-weight of ith trade partners at time t (see Appendix IA and Appendix IB).

Figure 1 shows NEERm, NEERx, the nominal exchange rate (NEER) and the exchange rate with US-dollar (E). We have included data of all major trade partners to construct the exchange rate variable for Bangladesh. NEERx and NEERm explain 77.78% (19 countries) and 76.66% (24 countries) of total export and import share of the country, respectively. Bangladesh mainly imports from India and China, followed by Japan, Singapore and the USA. The USA is the largest importer from Bangladesh which is followed by Germany and the UK. We have not considered countries which have less than 0.5% individual trade share with Bangladesh, in this regard.


FIGURE 1 TIME-SERIES OF E, NEER, NEERM AND NEERX

MODEL AND METHODOLOGY

Three different models are estimated in this paper which are explained as follows. Import Price

The study employs the benchmark model for import price which is suggested by Campa and Goldberg (2002, 2005) and Goldberg and Knetter (1997). However, we include one additional variable (the trade openness) with the existing model. The following model is estimated empirically.

tuOPENtYtPCtEtP XM ++∆+∆+∆+=∆ 4ln3ln2ln10ln ααααα , (1) where M

tP is the import price at time t, E is NEERm in this model, XPC is production cost by foreign firm; Y is home GDP (constant); and OPEN is the ratio of trade to GDP. 1α is the coefficient of interest, ERPT. First-difference of variables in the model possibly overcomes the non-stationarity problem for relatively small sample size. Export Price

ERPT to export price is tested by adjusting the model presented in Knetter (1995) and Gagnon and Knetter (1995). Our benchmark model for export prices is derived as follows.

We know that the difference between revenue and cost determines firm’s profit. Firm maximizes its profit, which is indicated in the following equation-

( )∑=

∑=

×−=∏

n

i

n

i iwEjwMCE

xjP

iQE

xjP

iQxjPMax

1 1, ,


where iQ is demand for j from country i;

E

xjP

is product price for consumer in their own currency

(say, miP ); x

jP is export price; i stands for importer, j for exporter, and MC is marginal cost of

production. MC depends on jw which is the domestic input cost, and iw is cost of imported capital goods. We assume that export demand changes positively when E changes. Similarly, MC changes with E in the positive direction. Consequently, any change in input prices (imported capital goods) due to change in exchange rate is reflected in the production cost of firms. The necessary condition for profit maximization gives us:

−×=

1)(

)(

ExjPi

ExjPiMCjP

η

η,

where, iη is elasticity of demand for export. Raising price is not an option for the exporter because if exporter does so, he/she may lose market share. However, depreciation may give that opportunity. Logarithm in both side and the first order Taylor series approximation give,

( )

−

−+=

1

lnln

ln

lnlnln

i

EdxjPd

ExjPd

idMCdx

jPdη

η.

By re-organizing, we find the testable model for export prices which can be written as:

tjjtjixjt EdMCdPd lnln)1(ln ββµ +−+= ,

where, ( ) ( )1

ln

ln1

ln

ln−

∂

∂+−

∂

∂=

ExjP

iiEx

jPi

jη

ηη

β , and iµ is a constant. If the price of imported capital

goods iw is not affected by the movement of the exchange rate, then jβ gives the magnitude of ERPT to export prices. This is in line of the findings in Ohno (1989) and Mallick and Marques (2006). In fact, the effect of exchange rate movements is implied in MC. β would indicate whether there is PTM behaviour by exporter. If 01 >> jβ , then ERPT is partial, which means that exporting firms adjust prices in local

currency term. If, however, 1=jβ , it means that ERPT is ‘full’ and no adjustment is taken place in LCP. Hence, the testable model for ERPT to export prices is given as follows.

tOPENtPCtEtP X ελδβµ ++∆+∆+=∆ lnlnln (2) where X

tP is price of exports at time t; tE is xNEER for Model (2), and β captures the exchange rate pass-through; tPC is production cost.


Domestic Prices The theoretical setting of ERPT to consumer and producer prices emerged from the law of one price

(LOP) and the purchasing power parity (PPP) concepts. Additionally, a constant term and an openness variable are included in the model. The model is as follows:

tOPENtPtEdtP ελγβµ ++∆+∆′+=∆ *lnln0ln , (3)

where d

tP is domestic price; tE is NEER; and *tP is foreign prices. β ′ is the pass-through coefficient. It

is worth mentioning that if the pass-through is complete, (i.e., 1=′β ) and all other coefficients become zero then it will indicate that the PPP holds between Bangladesh and its trade partners.

This study estimates all empirical models (Model 1, Model 2 and Model 3) using OLS. We also employ the rolling regressions technique to capture any significant variation in ERPT over time. ESTIMATED RESULTS

Estimated results are reported in Table 1. This table shows that ERPT to import price is +0.73, which is statistically significant. This indicates that a one percent devaluation increases the import price by 0.73 percent. Consequently, import falls. The 2R is not large because we have used some proxy variables such as, world-WPI and ‘domestic real GDP’ in Model 1. These variables are not found statistically significant. Moreover, PPI of some major trade partners of Bangladesh such as Germany, China, UAE, France, Malaysia, Hong Kong, Italy, and Saudi Arabia are not available in existing datasets. It is worth mentioning here that we have used the weighted foreign price index (WFPI) as a proxy for foreign production cost. However, it cannot make any significant difference in estimated results. Besides, a large number of imported goods of Bangladesh are necessary goods and they are inelastic to income (Aziz, 2012). It can also be noted here that Vigfusson et al. (2007) and Marazzi et al. (2005) have also experienced small 2R values in their ERPT model.

ERPT to export price is -0.91, which is found to be significant. The negative sign indicates that if there is a devaluation of currency, export price of Bangladeshi products significantly decreases. Consequently, the demand for export increases. Hence, devaluation is found to be an effective policy for export competitiveness of Bangladeshi products.

ERPT to domestic prices are +0.59 (CPI) and +0.62 (PPI), respectively. They are found to be positive and significant. These indicate that if there is a devaluation of currency, inflation increases. This pass-through is also called second-stage pass-through. Hence, the exchange rate is found to be a significant determinant of inflation in Bangladesh.


TABLE 1 EXCHANGE RATE PASS-THROUGH TO EXTERNAL AND INTERNAL PRICES

Variables Coefficients (standard error in parenthesis)

Import Prices (1978 – 2007)

Export Prices (1978 – 2007)

Consumer Prices (1974-

2006)

Producer Prices (1974-

2006)

tEln∆ 0.73** (0.345)

-0.912*** (0.297)

0.59*** (0.094)

0.62*** (0.097)

tYln∆ -0.032 (1.328) - - -

tPln∆ - 1.35*** (0.39) - -

*ln tP∆ 0.33 (0.247) - 0.47**

(0.172) 0.47** (0.178)

OPEN -0.01 (0.007)

0.010 (0.007)

-0.012*** (0.003)

-0.014*** (0.003)

.Cons -0.007 (0.085)

0.017 (0.032)

0.018 (0.022)

0.01 (0.023)

2R 0.25 0.46 0.70 0.70 testF − 1.91 6.70*** 22.23*** 22.66***

DW 2.42 1.97 1.72 1.56 Note: ***, **, and * indicate significance at the 1%, 5% and 10% levels, respectively. tE is NEERm for import, NEERx for export and NEER for domestic prices. The Breusch-Godfrey Serial Correlation LM Test confirms that there is no autocorrelation. The White Heteroskedasticity Test suggests that there is no heteroskedasticity. The CUSUM test indicates that there is no structural break in data. The Jarque-bera test affirms that the error is normally distributed.

Complete or Partial Pass-Through

We test whether ERPT to import price is ‘full’ or ‘partial’ (i.e., the unit coefficient of the exchange rate). The test result (TABLE 2) indicates that ERPT to import price is ‘full’. It indicates that there is no PTM behaviour in Bangladeshi imports. This may be because exporters are not much concerned about their market share in Bangladesh.

We also test whether ERPT to export price is ‘full’. We could not reject the hypothesis. This implies that ERPT to export price is also ‘full’. Hoque and Razzaque (2004) also find similar results at commodity specific ERPT to export prices.

Finally, we test whether ERPT to internal prices are ‘full’. Unlike external prices pass-through, we find that ERPT to internal prices are only ‘partial’. Hence, the conventional wisdom that ERPT should be complete in developing countries is found appropriate for external prices but not for internal prices. The rejection of full-ERPT to domestic prices also indicates that the PPP, between Bangladesh and its trade partners, does not hold.


TABLE 2 SUMMARY OF ‘FULL’ AND ‘PARTIAL’ EXCHANGE RATE PASS-THROUGH

Exchange Rate Pass-

through to Null Hypothesis T-Statistic Critical Values

Import Prices

1: 10 =αH

-0.791

2.467 (1%) 2.048 (5%)

Export Prices

1:0 −=βH

0.296

2.467 (1%) 2.048 (5%)

Consumer Prices

1:0 =′βH

-4.36

2.457 (1%) 2.042 (5%)

Producer Prices 1:0 =′βH

-3.96

2.467 (1%) 2.048 (5%)

The Rolling Regressions

We then run ‘rolling regressions’ to examine whether there is any significant variation in ERPT over time. The study employs the baseline Model (2) for export and Model (3) for consumer prices. The study, however, has not employed the entire Model (1) for import price pass-through. This is because firstly, most of Bangladeshi imports are inelastic to income (Aziz, 2012). Secondly, although ‘world-WPI is a proxy for cost of production (for foreign firm) in our model, the majority imports of Bangladesh come from a small set of trade partners. An eighteen-year window for each regression is used.

Figure 2 indicates that response of export price to exchange rate movement has been negative and significant until 2003. Pass-through is consistently around one till 2003 and it has fallen in later years. It can be noted that we use both 95 percent and 90 percent confidence intervals.

FIGURE 2A ROLLING REGRESSIONS FOR ERPT TO EXPORT PRICES

(CI = CONFIDENCE INTERVAL)

Rolling Regressions for the NEERx Pass-through to Export Prices (95% CI)

-2

-1.5

-1

-0.5

0

0.5

19791996

19801997

19811998

19821999

19832000

19842001

19852002

19862003

19872004

19882005

19892006


FIGURE 2B ROLLING REGRESSIONS FOR ERPT TO EXPORT PRICES

Figure 3 shows the rolling regressions for ERPT to import price which indicate that ERPT has been complete until 2001 and it has fallen gradually after 2001.

FIGURE 3A

ROLLING REGRESSIONS RESULTS FOR ERPT TO IMPORT PRICES

Rolling Regressions for the NEERx Pass-through to Export Prices (90% CI)

-2

-1.5

-1

-0.5

0

0.5

19791996

19801997

19811998

19821999

19832000

19842001

19852002

19862003

19872004

19882005

19892006

Rolling Regressions for the NEERm Pass-through to Import Prices (95% CI)

-1.5-1

-0.50

0.51

1.52

2.53

1979

1996

1980

1997

1981

1998

1982

1999

1983

2000

1984

2001

1985

2002

1986

2003

1987

2004

1988

2005

1989

2006

1990

2007


FIGURE 3B ROLLING REGRESSIONS RESULTS FOR ERPT TO IMPORT PRICES

Unlike ERPT to external prices, ERPT to internal prices are found unstable over time. Figure 4 shows that ERPT is partial for internal prices which has been around 0.6 until 1993, and it has fallen to about 0.3 (Figure 4a, Figure 4b, Figure 4c and Figure 4d) after 1993. We also find that trade openness is a significant determinant of internal prices in Bangladesh.

FIGURE 4A

ROLLING REGRESSIONS FOR ERPT TO CONSUMER PRICES

Rolling Regression for the NEERm Pass-through to Import Prices (90% CI)

-1

-0.5

0

0.5

1

1.5

2

2.5

1979

1996

1980

1997

1981

1998

1982

1999

1983

2000

1984

2001

1985

2002

1986

2003

1987

2004

1988

2005

1989

2006

1990

2007

Rolling Regressions for the NEER Pass-through to Consumer Prices (95% CI)

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1974

1991

1976

1993

1978

1995

1980

1997

1982

1999

1984

2001

1986

2003

1988

2005

1990

2006


FIGURE 4B ROLLING REGRESSIONS FOR ERPT TO CONSUMER PRICES

FIGURE 4C ROLLING REGRESSIONS FOR ERPT TO PRODUCER PRICES

Rolling Regressions for the NEER Pass-through to Consumer Prices (90% CI)

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1974

1991

1976

1993

1978

1995

1980

1997

1982

1999

1984

2001

1986

2003

1988

2005

1990

2006

Rolling Regressions for NEER Pass-through to Producer Prices (95% CI)

-0.2

0

0.2

0.4

0.6

0.8

1

1974

1991

1976

1993

1978

1995

1980

1997

1982

1999

1984

2001

1986

2003

1988

2005

1990

2006


FIGURE 4D ROLLING REGRESSIONS FOR ERPT TO PPI

CONCLUSION

This study estimates the exchange rate pass-through to external and internal prices of Bangladesh. Nominal effective exchange rate variables, which are not readily available in existing databases, are constructed for the estimation. We test the hypothesis that unlike developed countries, ERPT should be ‘full’ for developing countries. We also examine whether trade liberalization has a significant effect on external and internal prices. Finally, we estimate rolling regressions to depict the responses of external and internal prices to exchange rate movements over time.

Estimated results indicate that ERPT to external prices (i.e., import and export prices) are statistically significant and ‘full’. However, ERPT to internal prices (i.e., consumer and producer prices) are found to be ‘partial’. Trade liberalization is found to be a significant determinant of internal prices.

Rolling regressions demonstrate that responses of external prices to exchange rate movement have been significant and consistently one until 2003. However, this pass-through has fallen considerably in subsequent years. This finding is in accordance with the evidence reported by Marazzi et al. (2005) and Frankel et al. (2005). The responses of internal prices to exchange rate movement, however, are relatively small and unstable in Bangladesh.

We have not found any evidence of pricing-to-market behaviour in import and export of Bangladesh. There is no evidence of PPP between Bangladesh and its trade partners either. The theoretical prediction about the complete exchange rate pass-through in developing countries cannot be rejected when we take the external prices into consideration. However, we reject this theoretical prediction when we take the internal prices into account. No significant difference is found between exchange rate pass-through to consumer price and producer price. ENDNOTE

1. Exchange rate pass-through to import price is considered as the first stage pass-through because the exchange rate directly affects the import prices. We know that CPI (basket) includes both domestic and

Rolling Regressions for NEER pass-through to Producer Prices (90% CI)

-0.2

0

0.2

0.4

0.6

0.8

1

1974

1991

1976

1993

1978

1995

1980

1997

1982

1999

1984

2001

1986

2003

1988

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1990

2006

Year


imported products. Hence, domestic price may be affected by the import prices. Exchange rate pass-through to domestic prices are therefore called the second stage pass-through.

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APPENDIX IA TRADE-SHARE AND TRADE-WEIGHT (%) FOR IMPORT OF BANGLADESH

Countries Weight (%) for each country Bangladeshi import-share (%)

Australia 2.59 1.99 Canada 1.95 1.495 China 12.25 9.39 Denmark 0.87 0.66 France 1.22 0.935 Germany 3.21 2.46 Hong Kong 6.10 4.68 India 14.89 11.41 Indonesia 2.36 1.81 Italy 1.15 0.89 Japan 9.84 7.54 Korea 5.03 3.85 Kuwait 3.91 3.00 Malaysia 2.24 1.72 Netherlands 1.71 1.31 Pakistan 1.57 1.21 Saudi Arabia 3.19 2.45 Singapore 8.99 6.89 Sweden 1.08 0.83 Switzerland 1.52 1.16 Thailand 2.42 1.86 UAE 2.99 2.29 UK 3.50 2.69 USA 5.40 4.14 Total 100 76.66


APPENDIX IB TRADE-SHARE AND TRADE-WEIGHT (%) FOR EXPORT OF BANGLADESH

Countries Weights (%)for each country Bangladeshi Export-share (%)

Australia 0.69 0.54 Belgium 3.18 2.47 Canada 3.06 2.38 China 0.91 0.71 Denmark 1.17 0.91 France 6.99 5.43 Germany 14.01 10.89 Hong Kong 1.99 1.55 India 1.50 1.17 Italy 5.89 4.58 Japan 2.35 1.83 Netherlands 4.52 3.52 Pakistan 1.34 1.045 Singapore 1.34 1.05 Spain 2.75 2.14 Sweden 1.64 1.28 Switzerland 0.67 0.52 UK 11.49 8.94 USA 34.49 26.82 Total 100 77.78


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