EPN 2013 3

North American Review of Finance

Volume 12, Number 4

Volume 13 (3)

Economic Papers and Notes

Economic Papers and Notes

Volume 13, Number 3

Articles

An Empirical Investigation of the Price Relationship between Open-End Mutual Funds and Amman Stock Exchange Index Mohamed Khaled Al-Jafari, Hussein Salameh and Khalid Al Asil ………………………………………………………….……………………..1 - 20 Investor overconfidence: An Examination Of Individual Traders On The Tunisian Stock Market Salma Zaiane ..……………………..…………………………………………………...….21 – 25 Foreign Direct Investment in the US: Externalities between the Two-Sector of the Economy Jean Emmanuel Fonkoua .…………………………………………………………….36 – 49 The Effect of Foreign Aid on Real Exchange Rate in Ghana Peter Arhenful ……………………………………………………...………………………50 - 68

Copyright © 2013 by North American Academic Journals

An Empirical Investigation of the Price Relationship

between Open-end Mutual Funds and Amman Stock

Exchange Index

Mohamed Khaled Al-Jafari1, Hussein Salameh2 and Khalid Al Asil3

Abstract This study examines the short and long-run price relationship between mutual funds (Jordinvest First Trust Fund, Growth Fund, Horizon Fund, and Jordan Securities Fund) and Amman Stock Exchange Index over the time period from March, 2005 till the end of November, 2009. The study findings are obtained with respect to various testing methods utilized, including Error Correction Model and Granger causality tests. These tests were applied on series of data for the monthly returns of mutual funds and Amman Stock Index. The empirical results show a long-run relationship of Amman Stock Index on mutual funds. However, the study also reveals no long-run relationship of mutual funds on Amman Stock Index. Furthermore, the empirical findings show that the relationship of Amman Stock Index on mutual funds is significantly more established than the relationship of mutual funds on Amman Stock Index. Finally, the results find a significant causal relationship in one way manner from mutual funds, with the exception of Jordinvest First Trust Fund, to Amman Stock Index. JEL classification numbers: C22, C32, G12, G23. Keywords: Mutual Funds, Amman Stock Exchange Index, Unit Root Test, Error Correction Model Test, Granger Causality Test.

1The Arab Academy for Banking and Financial Sciences. Banking and Finance Department. Dean of Post Graduate Studies. Damascus, Syria. 2An Associate Professor at Amman Arab University. Amman, Jordan. 3The Arab Academy for Banking and Financial Sciences. Damascus, Syria.

1 North American Academic Journals, 2013

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1 Introduction In an atmosphere of changing economic conditions, it is evident that mutual funds have been at the top of the agenda over the last decade. Nowadays an increasing number of investors are relying on mutual funds as investment and retirement vehicles. The mutual funds industry has registered a spectacular growth worldwide. The wider acceptance of equity investments by scholars paved the way to launch mutual funds. Thus, mutual funds have attracted the attention of academics and practitioners alike. A mutual fund is an indirect investment product created to serve as an alternative to direct stock market investment for investors. In Jordan mutual funds have experienced considerable growth over the last decade in terms of the volume of capital managed by them. For their successful operations and developments, it is logical to think that mutual funds prices have a good degree of responsiveness to the direct equity market. Mutual funds also require well-developed securities markets with a high level of market integrity and liquidity. The literature on the performance of mutual funds is extensive for the past several decades and many of these studies compare the fund’s return with that of the market. Early studies by Sharpe (1966) and Jensen (1968) confirm the inability of mutual funds to outperform the market benchmarks or indices. Mutual funds underperform the market, especially when fees are taken into account. Therefore, funds that heavily underperform have very high expense ratios, while funds that are successful do not increase revenues by raising their fees but benefit from the increased size of their funds (Elton et al., 1996). Mutual funds offer investors the advantages of portfolio diversification and professional management at low cost, and to perform as an alternative to direct stock market investment to investors when the cointegration presence as the relationship between mutual funds and the stock market index (Ben-Zion et al., 1996; Matallin and Nieto, 2002), which mean that the mutual funds are replicating the stock market index over the long-run. While the lack of cointegration suggests that mutual funds don’t show parallel movement with the market index over the long-run will provide a further evidence for the existence of active fund management activities among the fund manager. Therefore, investors will choose mutual funds that are consistent with their perspective of the market and upon their preferences. Unfortunately, there is a lack of attention to the contemporaneous mutual fund/index returns relationship specifically in the literatures that are related to Arab financial markets. Therefore, the main objective of this paper is to investigate the short and long-run price relationship between Jordanian mutual funds (Jordinvest First Trust Fund, Growth Fund, Horizon Fund, and Jordan Securities Fund) and Amman Stock Index. In addition, it provides further evidence for the existence of active fund management among the fund’s managers. The degree to which fund prices are related to the stock market index has several important implications for investors with regard to their investment strategies. This paper is organized into five sections as follows: Section two details the literature review. On the other hand, section three describes the methodology while section four discusses the empirical results. Finally, section five presents the concluding remarks.

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2 Literature Review Many previous studies make a comparison between the fund’s return with that of the market, which allows investors to gauge the differences in the performance between actively and passively managed portfolio. This section begins with the review of Sharpe (1966) as it forms the backbone of the following studies by developing a performance measure that leads other studies in this field. In his study, Sharpe (1966) found evidence of persistence for both high and low ranked mutual funds without considering that past performance is the best predictor. Also, he found that expense ratios explain the differences clearly and low expense ratios are related to superior performance. Finally, he found weak evidence that funds with larger assets base generate better performance. On the other hand, Jensen (1967) showed that mutual funds generally underperform a buy and hold portfolio, and funds do not perform well enough to cover even their brokerage expenses, which is in contrast with the findings from Swinkels and Rzezniczak (2009) and Elton et al., (1996) who point out the underperformance of passive strategies. Also, the results found that there is only very little evidence of fund managers with forecasting ability, which is in line with the findings from Comer et al., (2009) who found very weak evidence of timing ability (negative timing ability). On the other hand, Fama and French (1993) showed that common variation in stock returns is captured by overall market factor, size and B/M ratio, while in bond returns it is captured by term structure factors. In a complement study to Sharpe (1966), Malkiel (1995) showed that equity mutual funds perform worse than the market after deducting management expenses. In contrary evidence to Elton et al., (1996) and Swinkels and Rzezniczak (2009) and in line with Comer et al., (2009), they documented that actively managed mutual funds underperform passive investing (negative attribution returns). Also, in a contrast study to Sharpe (1966), Elton et al., (1996) showed that low ranked funds have a relatively higher expense ratio which is the reason why they fare significantly poorer. They showed that past performance is a good predictor of future performance in both short and long-run. Moreover, Jayadev (1996) indicated that the relationship between fund excess return and the market excess return is not linear and it is due to the reverse relationship of beta securities (high/low) between the portfolio and the market return. He concluded that passive management is the best. Providing a solid foundation for the concept of stock-picking ability, Kacperczyk et al., (2005) found evidence of size and momentum effects and that mutual funds managers give more weight to growth. They also found that mutual funds differ considerably in terms of their industry concentration and these concentrated mutual funds have a tendency to pursue distinct investment styles, and concentrated funds outperform diversified funds. Furthermore, Alexander et al., (2007) showed that managers are able to value stocks. When it comes to buying, they found that mutual fund managers are not able to beat the market since they are compelled to pump additional cash from inflows. However, when it comes to selling, they found that mutual fund managers are forced to sell their stocks to hold longer on valuation beliefs. On the other hand, Boudreaux et al., (2007) showed that the performance of nine out of ten of the international mutual funds was higher than the US market. In a comprehensive study that provides a solid foundation for the concept of market-timing ability, Cuthbertson et al., (2007) showed that only a low proportion of managers are able to time the market. As to fund age, they showed that better market timers tend to be shorter-lived funds in general since market timing ability is negatively


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correlated with fund age. On the other hand, with respect to fund size, they showed that small sized funds possess no significant positive timing. In a different study, Low and Ghazali (2007) showed that unit trust funds and the stock market index do not have a long-run equilibrium relationship. Their results also showed that index funds are found not to be cointegrated with the stock market index. With respect to causality tests, one-way Granger causality test showed that the prices of unit trust funds are related to the stock market index. On the other hand, Ainsworth et al., (2008) showed that managers put themselves in a unique position because they are remaining loyal to their self-stated investment style. However, they found that almost there is no relationship between performance persistence and fund drift. Another study by Arugaslan et al., (2008) did not find evidence of the momentum effect. They found that returns on international mutual funds with low level of risk can be boosted by means of financial leverage. On one hand, Cuthbertson et al., (2008) indicated that less than half of funds with significant alphas outperform their respective benchmarks, and the best performers are concentrated in the right tail of performance distribution. As for the poor performing funds, they seem to be relatively small, and that the worst performers are likely to be in the left tail. They concluded that the average performance of all UK funds represents overall neither underperformance nor over-performance. On the other hand, Mazumder et al., (2008) showed that equity mutual funds are the most predictable funds in comparison with international bond and hybrid funds. They found that developed trading strategies are more useful in international equity funds. However, due to the general market decline in the testing period, the second strategy leads to the highest returns which is consistent with the third strategy, whereas a buy and hold strategy possesses negative returns. Results related to the load/no-load funds found no significant difference in returns between them. Thanou (2008) found significant differences in rankings between up and down market conditions. However, as for the selection skills, he found evidence of the passive management while results related to market timing ability found that the timing ability of the fund managers is negative or non-existent. Similarly, Tower and Zheng (2008) showed that actively managed fund families have not performed well in general. However, when they are considered without loads, with low expenses in their least expensive class and with low average turnover, they beat the corresponding indexes. Finally, they conclude that indexing tends to provide superior returns to most managed mutual funds. From a different point of view, Comer et al., (2009) showed that managers possess neither market timing ability nor selectivity. As for the role of survivorship, they found that hybrid funds are not adding value. However, the results showed that an allocation shift during the bear market conditions would result in higher positive attribution returns, and returns in this period are reflective of a persistent pattern in performance. On the other hand, Hyde and Triguboff (2009) showed that there is no significant difference in results between the two specifications (basic and augmented models) and also the results for equal and value-weighted portfolios are the same. In addition, they found that value spread as a signal of style timing is considered to be a helpful and accurate predictor of value premium. Finally, their results found a positive relationship between value premium and value spread and that the short-side positions (growth stocks) are entirely responsible for the positive relationship, and it would serve a useful purpose as the value spread increases. Russian mutual funds were tested by Lukashin and Lukashin (2009). They found a


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positive correlation between the profitability of Russian mutual funds and stock indexes (RTS and MICEX). In addition, their findings showed that the higher the volatility, the more sensitive is mutual funds’ profitability to market fluctuations. Also, their results showed that the optimal portfolio of investment equities embraces 14 equities being mainly bond funds and silver, where profitable funds, bond and money market funds are the least sensitive and profitable funds and index funds and share mutual funds are the most sensitive. They conclude that the mutual funds market in Russia appears to be defensive. From another perspective, Sensoy (2009) showed that self-designated benchmarks of some funds do not represent the precise exposures of funds to size and growth/value factors. The results found that fund size and age are negatively correlated with fund flows. In addition, the finding showed that naive investors perceive performance of a mismatched benchmark as a guide to detect the patterns of flows. Finally, the results indicate that purchasing a fund with a matched benchmark provides investors with a better risk-return trade-off than purchasing a fund with a mismatched one. Furthermore, Swinkels and Rzezniczak (2009) showed that majority of the funds perform better than a passive stock market index for private investors. However, as for the bond mutual funds, they found that only a few of them outperform their benchmarks. On the other hand, the balanced funds showed a better results compared to their benchmarks, equity and bond indices. Finally their findings showed that timing coefficients are usually negative and managers do not possess equity and bond market timing skills. In another study by Jiang et al., (2011), evidence of active management was found, and that mutual funds acquire superior information that is not fully reflected in the prices of these stocks. Similarly, Soongswang and Sanohdontree (2011) found evidence of active management for 3-month time-period of investment. In addition, they found that the DEA technique can be used to assist investors in selecting appropriate funds, especially in the sense of robustness check. Contrary results were found by Hsu et al., (2012) where they found no statistical significant evidence of the momentum effect.

3 Data and Research Methodology 3.1 Description of the Data This paper investigates the relationship between Jordanian open-end mutual funds (Jordinvest First Trust Fund, Growth Fund, Horizon Fund, and Jordan Securities Fund) and Amman Stock Exchange Index (ASEI) over the time period between 2005 and 2009. The financial data comprised of the monthly Net Asset Value (NAV) of four Jordanian mutual funds and the monthly weighted closing prices of the stock market portfolio as proxy for ASEI. These time series data are obtained from a published monthly fund prospectus, annual reports of the fund management companies (http://www.jordinvest.com.jo, http://www.ajib.no.com.jo, http://www.hbtf.com, http://ww.capitalbank.jo/horizon_fund, http://www.zawaya.com) and from the website of Amman Stock Exchange (http://www.ase.com.jo) from the period of 31 March 2005 until 30 November 2009. Monthly data have been chosen to avoid a spurious correlation problem, often found in quarterly and annual data, while not compromising on the available degrees of freedom required in selecting appropriate lag structures.


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3.2 The Research Methodology Monthly returns for mutual funds were measured for each fund as following:

1

1

t

ttf NAV

NAVNAVR (1)

Where, Rf is the monthly return of the fund, NAVt is the Net Asset Value for time t, and NAVt-1 is the Net Asset Value for time t-1. Monthly returns for Market Stock Index were measured as following:

1

1

t

ttm GI

GIGIR (2)

Where, Rm is the monthly return of the market index, GIt is the General Index for time t, and GIt-1 is General Index for time t-1. The analytical framework of the study is based on Granger causality tests. It also based on two cases. The first case is the dependent variable of Amman Stock Index, and the second one, is the dependent variable of mutual funds. A prior condition for cointegration and causality tests is that the time series or variables are stationary. If a time series is stationary, any shock to the variable will temporarily or momentarily draw the variable away from its long-run mean values. However, if the series is non-stationary, the deviation from the long-run mean values will be permanent. By definition, a series or variable which is integrated at level I(0) is said to be stationary at the level form. The problem of non-stationarity can be eliminated by taking differences in the series. Therefore, if the series is characterized by I(d), that is integrated of order d, it means that the series need to be differenced d times before becoming a stationary series. If all variables under consideration are at level form, I(0), and they are not cointegrated, then we will need to implement the Granger tests using the first differences of the variables. 3.2.1 Unit Root Test

Before conducting estimation and in order to avoid possible spurious regression, it is necessary to distinguish stationary from non-stationary variables. The first step undertaken would be to establish the order of integration of variables used in the model. This is accomplished by applying first the Augmented Dickey-Fuller (ADF) test (Dickey and Fuller (1979; 1981)) on each of the series in the estimated equations, standard unit root tests. The well-known ADF test for a unit root in yt, omitting a linear deterministic trend is: Δyt = α + βyt-1 + ∑ δi Δyt-i + εt (3)

Where Δ is the difference operator, εt is a white noise disturbance term with variance ζ 2, and t = 1, …, T indexes time. The Δyt-i terms allow for serial correlation and are designed to ensure that εt is white noise. The empirical evidence suggests that there is no time trend in the data. The ADF test has a null hypothesis of non-stationarity against an alternative of stationarity. The appropriate number of lagged difference (k) is determined by Akaike Information Criteria (AIC) as in Akaike (1970). Optimal choice of lag length removes autocorrelations in the error term.


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3.2.2 Regression Model

Regression model is implemented in order to examine the relationship between mutual funds and stock market index. Regression of non-stationary time series on another non-stationary time series may cause a spurious regression or non-sense regression. The symptom of a spurious regression is R-Squared value would be greater than Durbin-Watson statistic, where R2 is practically zero, as it should be, where 0≤R2≤1. On the other hand, the Durbin–Watson (d) is about 2, where Durbin–Watson is used for detecting serial correlation (Gujarati, 2004). Since d≈ 2(1 - ), where is an estimator of r and which is the first-order coefficient of autocorrelation, and as -1≤ r ≤1, it can be implied that 0≤ d ≤4. The two values 0 and 4 are the bounds of d, and any estimated d value must lie within these limits. Where: If = 1, then d≈0, and one may assume that there is no first-order autocorrelation. If = 0, then d=2, indicating perfect positive serial correlation in the residuals. If =-1, then d≈4, indicating that there is perfect negative serial correlation in the residuals. The case of spurious and not spurious regression for the tests may be written as:

The first case: R-Squared > Durbin-Watson statistic The second case: R-Squared < Durbin-Watson statistic

Where under the first case, the model is spurious or non-sense regression, while under the second case; the model is not spurious or has sense regression. Engle and Granger (1987) note that even though economic or financial time series may be described as a random walk process, it is possible that the linear combinations of the series or variables would over time converge to equilibrium. If two series are non-stationary in their level forms, that is I(1) and the series are integrated of the same order (d) and if the error term from regressing one series on the other is stationary, then the series are said to be cointegrated. Thus, cointegration exists if two variables are individually I(1) and the error term from the linear regression between the two variables is I(0). We performed ADF test on the error term, εt from the following linear combinations between the mutual funds and the market index:

tt xy 21 (4) Where: yt: is the dependent variable. xt: is the independent variable. β1: is the intercept. β2: is the coefficient of the independent variable / or the long-run coefficient. εt: is the residual of the model /or equilibrium error. The residual of the model is found stationary by testing the t-statistic against Engle-Granger 5% and 10% critical value (equal to -3.34 and -3.04, respectively). The null and the alternative hypotheses for the tests may be written as:

H0: t-statistic < Engle-Granger critical value H1: t-statistic > Engle-Granger critical value

Where under the null hypothesis, there is a unit root, while under the alternative, there is no unit root. In order to test the validity of the model whether if the model is spurious or


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not, the R-Squared testing was implemented; where the symptom of a spurious regression is R-Squared value would be greater than Durbin-Watson statistic. The stationarity of the residual and the validity of its model, mean that stock market index and mutual funds in the model are cointegrated or they have long-run relationship between them. On the other hand, if the residual is found to be non-stationary, then there is an existence of no long-run equilibrium relationship between mutual funds and the stock market index. 3.2.3 Error Correction Model The Granger representation theorem, states that if two variables such as, stock market index and mutual fund return, are cointegrated, then the relationship between the two can be expressed as Error Correction Mechanism (ECM). Engle and Granger (1987) state that “For a two variable system a typical error correction model would relate the change in one variable to past equilibrium errors, as well as to past changes in both variables.” ECM states that the past change in dependent variable (yt−1) depends on past change in independent variable (xt−1) and also on the equilibrium error term. If the equilibrium error term is non-zero, then the model is out of equilibrium. When D(xt) is zero and Ut-1 is positive. This means D(yt) is above its equilibrium value of (β3 + β4 D(xt)). Since β5 is expected to be negative, the term β5Ut-1 is negative and, therefore, yt will be negative to restore the equilibrium. That is, if yt is above its equilibrium value, it will start falling in the next period to correct the equilibrium error. The absolute value of β5 will decide how quickly the equilibrium is restored. The Error Correction Model is calculated as following:

vUxDyD ttt 1543 )()( (5) Where: D: is the first difference operator. yt: is the dependent variable. xt: is the independent variable. β3: is the intercept. β4: is the short-run coefficient. β5: is the coefficient of the speed of adjustment, and it should be negative. Ut-1: is the error correction term (one-period lagged value of the error from the cointegrating regression). v: is the white noise (random) error term. Finally, in order to test the validity of the model and whether if the model is spurious or not, the R-Squared testing is implemented; where the symptom of a spurious regression is R-Squared value would be greater than Durbin-Watson statistics. Breusch(1978) and Godfrey (1978) known as Breusch-Godfrey Serial Correlation LM (Lagrange Multiplier) Test also conducted for testing whether the residual of the Error Correction Model is serially correlated. The null hypothesis is no serial correlation. In addition, Jarque-Bera (1980) test of normality was utilized for testing whether the residual of the Error Correction Model is normally distributed. This test is asymptotic or large-sample test, and it has a chi-square distribution. The null hypothesis is normal distribution.


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3.2.4 Causality Tests

The Granger (1969) approach to the question of whether x causes y is to see how much of the current y can be explained by past values of y, and then to see whether adding lagged values of x can improve the explanation. Y is said to be Granger-caused by x if x helps in the prediction of y, or equivalently if the coefficients on the lagged x's are statistically significant. Note that two-way causation is frequently the case; x Granger causes y and y Granger causes x. It is important to note that the statement "x Granger causes y" does not imply that y is the effect or the result of x. Granger causality measures precedence and information content but does not by itself indicate causality in the more common use of the term. In the causality test, the null hypothesis is that x does not Granger-cause y in the first regression and that y does not Granger-cause x in the second regression.

4 Results 4.1 Descriptive Statistics As shown in Table 1, the mean value of the normal and natural logarithmic for Amman Stock Exchange Index (ASEI) is 0.32%, and 0.02%, respectively, and the standard deviation is 7.9% and 7.8%, respectively. On the other hand, the mean value of the normal and natural logarithmic for First Trust Fund return is 0.23%, and 0.20%, respectively, and the standard deviation is 2.54% and 2.52%, respectively. Similarly, the mean value of the normal and natural logarithmic for Growth Fund return is 0.24%, and 0.12%, respectively, and the standard deviation is 4.8% and 5%, respectively. As for the Horizon Fund, the mean value of the normal and natural logarithmic return is 0.19%, and 0.27%, respectively, and the standard deviation is 4% and 4.1%, respectively. Finally, the mean value of the normal and natural logarithmic for Securities Fund return is 0.10%, and 0.01%, respectively, and the standard deviation is 4.32% for both series.

4.2 Results of Normality Tests The null hypothesis of normality in Amman Stock Exchange Index series and the natural logarithm of this series and the first difference for both are accepted and the series are normally distributed using Jarque-Bera test. On the other hand, the null hypothesis of normality in First Trust series and the natural logarithm of this series are rejected and the series are not normally distributed, while the first difference for both are normally distributed using the same test. As for the Growth Fund series, the null hypothesis of normality and the natural logarithm of this series and their first difference for both are rejected and the series are not normally distributed using Jarque-Bera normally distribution test. As for the Horizon Fund and Jordan Securities Fund, similar results were obtained. The null hypothesis of normality in the series and the natural logarithm series are rejected and the series are not normally distributed, while the first difference for both are normally distributed using Jarque-Bera test. This result was consistent with skewness and kurtosis statistical value.


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Table 1: Descriptive Statistics ASEI D(ASEI) Ln(ASEI) D(Ln(ASEI)) FST D(FST) Ln(FST) D(Ln(FST))

Mean 0.00327 -0.0045 0.00022 -0.00406 0.00235 -0.00156 0.00203 -0.00151

Stand. Dev. 0.07908 0.09538 0.07886 0.09473 0.02541 0.0324 0.02524 0.0321

Skewness 0.23824 -0.4658 -0.08801 -0.41781 0.45429 -0.10393 0.33621 -0.07545

Kurtosis 3.7737 2.73881 3.866 254.216 4.35628 4.13923 4.25945 4.00466

Jarque-Bera 1.9265 2.14524 1.82219 2.08051 6.21834 3.07321 4.75615 2.36527

Prob. 0.38165 0.34211 0.40208 0.35337 0.04464 0.21511 0.09273 0.30647

GRO D(GRO) Ln(GRO) D(Ln(GRO)) HOR D(HOR) Ln(HOR) D(Ln(HOR))

Mean 0.00242 -0.00101 0.00121 -0.00098 0.00195 -0.00208 0.00278 -0.002

Stand. Dev. 0.04801 0.05502 0.05039 0.05845 0.04058 0.04476 0.04155 0.04545

Skewness -172.326 0.488249 -2.22094 0.64956 -0.84547 -0.49086 -114.171 -0.41875

Kurtosis 10.19545 7.48876 12.88257 9.15435 6.42318 4.10653 7.17539 4.22934

Jarque-Bera 148.5236 48.36203 273.9229 90.66702 34.01408 5.01459 52.84506 5.07071

Prob. 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.07923

JOR D(JOR) Ln(JOR) D(Ln(JOR))

Mean 0.00108 -0.00241 0.00016 -0.00225

Stand. Dev. 0.0432 0.04978 0.04319 0.04956

Skewness 0.16553 -0.43411 -0.13136 -0.40863

Kurtosis 5.68235 354.262 5.62724 3.30714

Jarque-Bera 17.0441 2.40221 16.26661 1.7468

Prob. 0.0002 0.30086 0.00029 0.41753

4.3 Unit Root Test Results Table 2 depicts the results of the unit root test for both Amman Stock Index, Jordinvest First Trust Fund, Growth Fund, Horizon Fund, Jordan Securities Fund respectively. By using ADF test and applying Akaike Information Criterion with maximum lags of 10 and following the ordinary least square (OLS) estimation to test the unit root, ADF assumes individual unit root process with null hypothesis of unit root and an alternative hypothesis of no unit root. We reject the null hypothesis for tests which assumes individual unit root process and accept the alternative hypothesis of no unit root at the level, where the t-Statistic for both series (-6.47 and -7.05, -5.18, -5.39, -3.72 respectively) are larger than the critical value for 1%, 5% and 10%, respectively, and they are statistically significant at 1%, where P value equal to zero. Therefore, we can conclude that Amman Stock Index and Jordinvest First Trust Fund, Growth Fund, Horizon Fund, Jordan Securities Fund are individually integrated at the level I(0), and they are stationary series.

Table 2: ADF Unit Root Test Level

ASEI FST GRO HOR JOR -6.47*** -7.05*** -5.17972*** -5.393193*** -3.72178***

***Significance at 1%, * *Significance at 5% and *Significance at 10%


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The results in Table 3 indicate that the coefficient of Jordinvest First Trust Fund FST (the long-run coefficient) equal to 2.27 and that the coefficient of ASEI (the long-run coefficient) equal to 0.23 and both are statistically significant at 1%, where P value equal to zero, which is less than 1%. Granger and Newbold (1974) assume that the model is spurious, where the null hypothesis of the model is spurious and the alternative hypothesis of the model is non-spurious or sense regression. We reject the null hypothesis for test which assumes spurious regression and accept the alternative hypothesis of non-spurious or sense regression at the level, where R-Squared value (equal to 0.52) is less than Durbin-Watson statistic (equal to 1.95) for the first regression, while where R-Squared value (equal to 0.52) is less than Durbin-Watson statistics (equal to 2.12) for the second regression. Also in Table 3, the coefficient of Growth Fund (the long-run coefficient) equal to 1.23 and that the coefficient of ASEI (the long-run coefficient) equal to 0.45 and both of are statistically significant at 1%. We reject the null hypothesis for test which assumes spurious regression and accept the alternative hypothesis of non-spurious or sense regression at the level, where R-Squared value (equal to 0.55) is less than Durbin-Watson statistic (equal to 1.99) for the first regression, while where R-Squared value (equal to 0.56) is less than Durbin-Watson statistic (equal to 1.86) for the second regression. Similarly, the coefficient of Horizon Fund (the long-run coefficient) equal to 1.51 and that the coefficient of ASEI (the long-run coefficient) equal to 0.40 and both are statistically significant at 1%. We reject the null hypothesis for test which assumes spurious regression and accept the alternative hypothesis of non-spurious or sense regression at the level, where R-Squared value (equal to 0.59) is less than Durbin-Watson statistic (equal to 1.90) for the first regression, while where R-Squared value (equal to 0.59) is less than Durbin-Watson statistic (equal to 1.66) for the second regression. Finally, the coefficient of Jordan Securities Fund (the long-run coefficient) equal to 0.48 and that the coefficient of ASEI (the long-run coefficient) equal to 1.59 and both of them are statistically significant at 1%. We reject the null hypothesis for test which assumes spurious regression and accept the alternative hypothesis of nonspurious or sense regression at the level, where R-Squared value (equal to 0.76) is less than Durbin-Watson statistic (equal to 2.02) for the first regression, while where R-Squared value (equal to 0.75) is less than Durbin-Watson statistic (equal to 2.15) for the second regression. Since all the models are statistically significant and non-spurious or sense regression model, there is no need to the unit root test on the error term (residual) of any of the models at level form.


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Table 3: Regression Results Coeff. t value Sig. Coeff. t value Sig.

Const. -0.0021 -0.2804 0.7803 Const. 0.0016 0.6730 0.5038 FST 2.2705 7.8409 0.0000 ASEI 0.2345 7.8409 0.0000

Dependent Variable: ASEI Dependent Variable: FST R-Squared 0.532385 R-Squared 0.532385

Adjusted R- Squared 0.523725 Adjusted R- Squared 0.523725

Durbin-Watson 1.947542 Durbin-Watson 2.115462

Coeff. t value Sig. Coeff. t value Sig.

Const. 0.0003 0.0419 0.9667 Const. 0.0009 0.2165 0.8294 GRO 1.2313 8.2684 0.0000 ASEI 0.4537 8.2684 0.0000

Dependent Variable: ASEI Dependent Variable: GRO R-Squared 0.5587 R-Squared 0.5587

Adjusted R- Squared 0.5505 Adjusted R- Squared 0.550528 Durbin-Watson 1.9997 Durbin-Watson 1.858352


Const. 0.0062 0.9214 0.361 Const. -0.0033 -0.9390 0.3519 HOR 1.5106 9.0176 0.000 HOR 0.3978 9.0176 0.0000

Dependent Variable: ASEI Dependent Variable: ASEI R-Squared 0.6009 R-Squared 0.6009



Const. -0.0005 0.1668 0.8681 Const. 0.0016 0.2965 0.768 JOR 0.4752 12.962 0.0000 JOR 1.5926 12.9623 0.0000

Dependent Variable: ASEI Dependent Variable: ASEI R-Squared 0.7568 R-Squared 0.7568


4.4 Results of the Error Correction Model (ECM) Up to now we conclude that the series are stationary at level and there is a long-run relationship between Amman Stock Index and Jordinvest First Trust Fund. We employ Engle-Granger (1987) Error Correction Model to test cointegration between Amman Stock Index and any one of mutual funds and vice versa, where ECM would relate the change in one variable to past equilibrium errors, as well as to past changes in both variables. The results of ECM are depicted in Table 4. As shown in Table 4, the results of Amman Stock Index and Jordinvest First Trust Fund indicate that the coefficient of the speed of adjustment is statistically significant at 1%, where β5 is negative and equal to 0.99 and the P value equal to zero. In addition, there is a statistically significant short-run relationship at 1%, where β4 is positive and equal to 1.81 and the P value equal to zero. Finally, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at the level, where R-Squared value (equal to 0.71) is less than Durbin-Watson statistic (equal to 1.88). On the other hand, the results of Jordinvest First Trust Fund and Amman Stock Index indicate that the coefficient of the speed of adjustment is positive and equal to 0.30 and the P value equal to zero. Also, there is a statistically significant short-run relationship at


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1%, where β4 is positive and equal to 0.31 and the P value equal to zero. In addition, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at the level, where R-Squared value (equal to 0.56) is less than Durbin-Watson statistic (equal to 2.41). Similarly, the results of Amman Stock Index and Growth Fund as shown in Table 4 indicates that the coefficient of the speed of adjustment is statistically significant at 1%, where β5 is negative and equal to 0.77 and the P value equal to zero. Moreover, there is a statistically significant short-run relationship at 1%, where β4 is positive and equal to 0.89 and the P value equal to zero. Finally, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at the level, where R-Squared value (equal to 0.57) is less than Durbin-Watson statistics (equal to 2.52). As for the results of Growth Fund and Amman Stock Index, Table 4 shows that the coefficient of the speed of adjustment is not significant, where β5 is positive and equal to 0.15 and the P value equal to 25%. In addition, there is a statistically significant short-run relationship at 1%, where β4 is positive and equal to 0.41 and the P value equal to zero. Also, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at the level, where R-Squared value (equal to 0.41) is less than Durbin-Watson statistic (equal to 2.98). The results of Amman Stock Index and Horizon Fund indicate that the coefficient of the speed of adjustment is statistically significant at 1%, where β5 is negative and equal to 0.75 and the P value equal to zero. Also, there is a statistically significant short-run relationship at 1%, where β4 is positive and equal to 1.23 and the P value equal to zero. Finally, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at the level, where R-Squared value (equal to 0.64) is less than Durbin-Watson statistic (equal to 2.18). Moving to the results of Horizon Fund and Amman Stock Index, Table 4 displays that the coefficient of the speed of adjustment is not significant and statistical at 10%, where β5 is positive and equal to 0.17 and the P value equal to 9%. In addition, there is a statistically significant short-run relationship at 1%, where β4 is positive and equal to 0.38 and the P value equals to zero. Also, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at the level, where R-Squared value (equal to 0.50) is less than Durbin-Watson statistic (equal to 2.83). As for the results of Amman Stock Index and Jordan Securities Fund, Table 4 shows that the coefficient of the speed of adjustment is statistically significant at 1%, where β5 is negative and equal to 0.59 and the P value equal to zero. However, there is a statistically significant short-run relationship at 1%, where β4 is positive and equal to 1.30 and the P value equal to zero. In addition, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at the level, where R-Squared value (equal to 0.74) is less than Durbin-Watson statistic (equal to 2.47). Finally, the results of Jordan Securities Fund and Amman Stock Index indicate that the coefficient of the speed of adjustment is not significant but statistical at 10%, where β5 is positive and equal to 0.15 and the P value equal to 9%. In addition, there is a statistically significant short-run relationship at 1%, where β4 is positive and equal to 0.47 and the P value equal to zero. However, we reject the null hypothesis for test which assumes spurious model and accept the alternative hypothesis of non-spurious or sense model at


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the level, where R-Squared value (equal to 0.65) is less than Durbin-Watson statistic (equal to 2.86).

Table 4: Error Correction Model (ECM) Coeff. t value Sig. Coeff. t value Sig.

(Constant) -0.0018 -0.2591 0.7966 (Constant) -0.0001 -0.0383 0.9696 D(FST) 1.8063 8.1509 0.0000 D(ASEI) 0.3106 8.151 0.0000 U(-1) -0.9894 -7.5187 0.0000 U(-1) 0.3040 4.5645 0.0000

Dependent Variable: D(ASEI) Dependent Variable: D(FST) R-Squared 0.705406 R-Squared 0.561021

Adjusted R- Squared 0.694076 Adjusted R-Squared 0.544137



(Constant) -0.0037 -0.4356 0.6650 (Constant) 0.0009 0.1467 0.8839 D(GRO) 0.8857 5.4446 0.0000 D(ASEI) 0.4099 5.4446 0.0000 U(-1) -0.7721 -4.7068 0.0000 U(-1) 0.15069 1.1449 0.2575

Dependent Variable: D(ASEI) Dependent Variable: D(GRO) R-Squared 0.572643 R-Squared 0.405552




(Constant) -0.0021 -0.2629 0.7937 (Constant) -0.0004 -0.0807 0.936 D(HOR) 1.2302 6.7162 0.0000 D(ASEI) 0.3776 6.7162 0.0000 U(-1) -0.7457 -4.9628 0.0000 U(-1) 0.1697 1.726893 0.0901

Dependent Variable: D(ASEI) Dependent Variable: D(HOR) R-Squared 0.640693 R- Squared 0.49923




(Constant) -0.0015 -0.2171 0.829 (Constant) -0.0003 -0.0701 0.9444 D(JOR) 1.3036 8.9855 0.0000 D(ASEI) 0.4666 8.9855 0.0000 U(-1) -0.5934 -4.4831 0.0000 U(-1) 0.1539 1.6954 0.096

Dependent Variable: D(ASEI) Dependent Variable: D(JOR) R-Squared 0.737145 R-Squared 0.65464



Breusch-Godfrey Serial Correlation LM (Lagrange Multiplier) Test also conducted for testing whether the residual of the Error Correction Model is serially correlated. Table 5 depicts the result from Breusch-Godfrey Serial Correlation LM test. The test assumes that there is no serial correlation in the residual of ECM, where the null hypothesis of the test is no serial correlation and the alternative hypothesis of the test is serial correlation. Also, Jarque-Bera (JB) test of normality was also conducted for testing whether the residual of the ECM has normal distribution. Table 5 also depicts the results of Jarque-Bera test too. The test assumes that there is normal distribution in the residual of ECM, where the null hypothesis of the test is normal distribution and the alternative hypothesis of the test is no normal distribution. According to Table 5, the results of Amman Stock Index and Jordinvest First Trust Fund lead us to accept the null hypothesis for the test which assumes no serial correlation in the residual of the model and reject the alternative which assumes serial correlation in the


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residual of the model, where the P value of Obs. R-Squared 41.74% is greater than 5%. Also, we accept the null hypothesis for the test which assumes normal distribution in the residual of the model and reject the alternative which assumes no normal distribution in the residual of the model, where JB equal to 2.15 and the P value 34% is greater than 5%. On the other hand, the results of Jordinvest First Trust Fund and Amman Stock Index reject the null hypothesis for the test which assumes no serial correlation in the residual of the model and accept the alternative which assumes serial correlation in the residual of the model, where the P value of Obs. R-Squared 0.03% (almost zero) is less than 5%. In addition, we accept the null hypothesis for the test which assumes normal distribution in the residual of the model and reject the alternative which assumes no normal distribution in the residual of the model, where JB equal to 5.53 and the P value 6% is greater than 5%. As for the results of Amman Stock Index and Growth Fund, we reject the null hypothesis for the test which assumes no serial correlation in the residual of the model and accept the alternative which assumes serial correlation in the residual of the model, where the P value of Obs. R-Squared 2.7% is less than 5%. Also, we accept the null hypothesis for the test which assumes normal distribution in the residual of the model and reject the alternative which assumes no normal distribution in the residual of the model, where JB equal to 1.22 and the P value 54% is greater than 5%. Similarly, the results of Growth Fund and Amman Stock Index indicate reject the null hypothesis for the test which assumes no serial correlation in the residual of the model and accept the alternative which assumes serial correlation in the residual of the model, where the P value of Obs. R-Squared 0.04% is less than 5%. Moreover, we reject the null hypothesis for the test which assumes normal distribution in the residual of the model and accept the alternative which assumes no normal distribution in the residual of the model, where JB equal to 127.7 and the P value 0% is less than 5%. As for the results of Amman Stock Index and Horizon Fund, we accept the null hypothesis for the test which assumes no serial correlation in the residual of the model and reject the alternative which assumes serial correlation in the residual of the model, where the P value of Obs. R-Squared 8.58% is greater than 5%. Also, we accept the null hypothesis for the test which assumes normal distribution in the residual of the model and reject the alternative which assumes no normal distribution in the residual of the model, where JB equal to 0.65 and the P value 72.34% is greater than 5%. The results of Horizon Fund and Amman Stock Index as shown in Table 5 reject the null hypothesis for the test which assumes no serial correlation in the residual of the model and accept the alternative which assumes serial correlation in the residual of the model, where the P value of Obs. R-Squared 0.01% is less than 5%. Moreover, we reject the null hypothesis for the test which assumes normal distribution in the residual of the model and accept the alternative which assumes no normal distribution in the residual of the model, where JB equal to 9.48 and the P value 0.87% is less than 5%. On the other hand, the results of Amman Stock Index and Jordan Securities Fund In Table 5 sows that we should reject the null hypothesis for the test which assumes no serial correlation in the residual of the model and accept the alternative which assumes serial correlation in the residual of the model, where the P value of Obs. R-Squared 0.37% is less than 5%. Also, we must accept the null hypothesis for the test which assumes normal distribution in the residual of the model and reject the alternative which assumes no normal distribution in the residual of the model, where JB equal to 5.60 and the P value 6.08% is greater than 5%.


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Similarly, the results of Jordan Securities Fund and Amman Stock Index reject the null hypothesis for the test which assumes no serial correlation in the residual of the model and accept the alternative which assumes serial correlation in the residual of the model, where the P value of Obs. R-Squared is zero and it is less than 5%. In addition, we reject the null hypothesis for the test which assumes normal distribution in the residual of the model and accept the alternative which assumes no normal distribution in the residual of the model, where JB equal to 42.82 and the P value is zero, which is less than 5%.

Table 5: Breusch – Godfrey Serial Correlation LM Test ASEI on FST FST on ASEI

F value 0.8203 Pro.F(2.5) 0.4462 F value 10.571 Pro.F(2.5) 0.0001 Obs* R2 1.7472

Pro. 2

0.4174 Obs* R2 16.3445 Pro.

2 0.0003

R2 0.0318 Adj. R2 -0.0457 R2 0.2972 Adj. R2 0.2409 Jarque-Bera Test – Normal Distribution test Jarque-Bera Test – Normal Distribution test

Skewness 0.4376 Kurtosis 2.5828 Skewness -0.1668 Kurtosis 4.5178 Jarque-Bera 2.1547 Prob. 0.3405 Jarque-Bera 5.5346 Prob. 0.0628 Durbin - Watson stat 2.018441 Durbin -Watson stat 2.073924

ASEI on GRO GRO on ASEI F value 3.7576 Pro.F(2.5) 0.0302 F value 10.0386 Pro.F(2.5) 0.0002 Obs* R2 7.1865

Pro. 2

0.0275 Obs* R2 15.7576 Pro.

2 0.0004

R2 0.1307 Adj. R2 0.0611 R2 0.2865 Adj. R2 0.2294 Jarque-Bera Test – Normal Distribution test Jarque-Bera Test – Normal Distribution test

Skewness -0.3512 Kurtosis 3.2004 Skewness 1.591058 Kurtosis 9.752684 Jarque-Bera 1.2227 Prob. 0.5426 Jarque-Bera 127.7022 Prob. 0.0000

Durbin - Watson stat 1.878452 Durbin - Watson stat 2.002761

ASEI on HOR HOR on ASEI F value 2.451716 Pro.F(2.5) 0.0964 F value 12.34459 Pro.F(2.5) 0.0000 Obs* R2 4.9121

Pro. 2

0.0858 Obs* R2 18.18075 Pro.

2 0.0001


Skewness -0.0215 Kurtosis 2.4702 Skewness -0.45668 Kurtosis 4.817496 Jarque-Bera 0.6475 Prob. 0.7234 Jarque-Bera 9.481814 Prob. 0.008731 Durbin - Watson stat 1.867938 Durbin -Watson stat 1.90522

ASEI on JOR JOR on ASEI F value 6.4072 Pro.F(2.5) 0.0033 F value 14.72247 Pro.F(2.5) 0.0000 Obs* R2 11.2203

Pro. 2

0.0037 Obs* R2 20.38483 Pro.

2 0.0000


Skewness 0.4515 Kurtosis 4.2759 Skewness -1.1490 Kurtosis 6.661507 Jarque-Bera 5.5990 Prob. 0.0608 Jarque-Bera 42.8257 Prob. 0.0000 Durbin - Watson stat 2.044816 Durbin - Watson stat 2.05579

4.5 Granger Causality Test From the previous tests, we conclude that the series are stationary at level and there is a cointegration between ASEI and Jordinvest First Trust Fund, but it does not necessarily imply that a causality relationship exists. We employ Granger (1969) causality test to investigate the possible short-term relationship between ASEI and Jordinvest First Trust Fund, where the dependent variable can be said to be Granger-caused by the independent


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variable, if the independent variable helps in the prediction of the dependent variable, or if the coefficients of lagged independent variable are statistically significant. The results of Granger-Causality test are depicted in Table 6. By using 11 time lags (AIC lags), the F-Statistic (equal to 2.07) was statistically significant for the second equation at 10%, but not to the first equation. By using 1 time lag (AIC lags); the F-Statistic (equal to 5.94) was statistically significant for the first equation at 5%, but not to the second equation. Finally, by using 1 time lag (AIC lags); the F-Statistics (equal to 7.30) was statistically significant for the first equation at 1%, but not to the second equation. Therefore, we accept the null hypothesis of the first equation that is Jordinvest First Trust Fund does not Granger cause Amman Stock Index. Also, we accept the alternative hypothesis that is Amman Stock Index Granger causes Jordinvest First Trust Fund. On the other hand, we accept the alternative hypothesis that is Growth Fund Grange cause Amman Stock index, and we accept the null hypothesis of the second equation that is Amman Stock index does not Grange cause Growth Fund. Similarly, we accept the alternative hypothesis that is Horizon Fund Granger cause Amman Stock Index. However, we accept the null hypothesis of the second equation that is Amman Stock index does not Granger cause Horizon Fund. Finally, we accept the alternative hypothesis that is Jordan Securities Fund Granger cause Amman Stock Index, and we accept the null hypothesis of the second equation that is Amman Stock Index does not Granger cause Jordan Securities Fund.

Table 6: Pairwise Granger Causality Test Null Hypothesis F– Statistic FST does not Granger Cause ASEI (11) 0.55487 ASEI does not Granger Cause FST (11) 2.07446* GRO does not Granger Cause ASEI 5.94402** ASEI does not Granger Cause GRO 0.04501 HOR does not Granger Cause ASEI 7.29812*** ASEI does not Granger Cause HOR 1.92177 JOR does not Granger Cause ASEI 4.05493** ASEI does not Granger Cause JOR 0.44435 ***Significance at 1%, * *Significance at 5% and *Significance at 10%

5 Conclusion This study investigated the price relationship between Jordanian mutual funds and Amman Stock Exchange during the period from March, 2005 to November, 2009. The results of ADF unit root tests show that both the fund and index prices are stationary at level. The results show a positive significant impact of Amman Stock Index on Jordinvest First Trust Fund on the long-run, but not vice versa. As for the ECM test between Jordinvest First Trust Fund and Amman Stock Index, the findings reveals a positive significant short-run relationship. In addition, Jordinvest First Trust Fund manager caused by the past changes in the stock market index over the short-run (11 months period).


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On the other hand, the empirical results revealed a positive significant impact of Amman Stock Index on Growth Fund on the long-run, but not vice versa. Similarly, the ECM test for Amman Stock Index on Growth Fund showed a positive significant short-run relationship. However, the findings provided evidence of a positive significant short-run causality relationship between Amman Stock Index and Growth Fund in one way manner, from Growth Fund to Amman Stock Index. The results of Amman Stock Index on Horizon Fund show a positive significant impact of Amman stock index on Horizon Fund on the long-run, but not vice versa. On the other hand, the short-run relationship regarding to ECM test for Amman Stock Index on Horizon Fund revealed a positive significant short-run relationship. It is also concluded that there is a positive significant short-run causality relationship between Amman Stock Index and Horizon Fund in one way manner, from Horizon Fund to Amman Stock Index. As for the empirical tests of Amman stock index on Jordan Securities Fund, it was found that there is a positive significant impact of Amman Stock Index on Jordan Securities Fund on the long-run, but not vice versa. On the other hand, the short-run relationship regarding to ECM test for Amman Stock Index on Jordan Securities Fund revealed a positive significant short-run relationship. Also, the findings indicated to a positive significant short-run relationship between Amman Stock Index and Jordan Securities Fund in one way manner, from Jordan Securities Fund to Amman Stock Index. The results of this study provide a further evidence for the existence of active fund management activities in Jordinvest First Trust, Growth, Horizon and Jordan Securities Funds managers whose were able to outperform the market through market timing and securities selection. Therefore, Amman Stock Market would present a greater opportunities for active fund managers to find abnormal returns. This conclusion is consistent with the finding of Huij and Post (2011) where they found that emerging markets are less efficient than developed markets, and that active fund managers can achieve an excess returns.

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Investor overconfidence: An Examination Of Individual

Traders On The Tunisian Stock Market

Salma Zaiane1

Abstract The aim of this paper is to investigate individual overconfidence on the Tunisian stock market. This was achieved by administrating a questionnaire and by collecting empirical evidence about Tunisian individual investors. The survey is for exploratory purpose and it is based on multiple factorial correspondence analyses. The results reveal that Tunisian investors suffer from the overconfidence bias. In fact, they are confident about their intuition; they consider themselves lucky and trade aggressively. Besides, they use different sources of information when they choose their stocks. JEL Classification numbers: G11, G12. Keywords: behavioural finance, overconfidence bias, individual investors, questionnaire, multiple factorial correspondence analysis.

1 Introduction The scandals that have occurred in recent years and the crashes and successive financial crises that characterize modern economies, including the current financial meltdown from the subprimes, lead us to question the functioning of financial markets. Researchers try to understand the attitudes of investors, often influenced by mental routines, errors in judgments or even emotional factors. Obviously, this leads one to doubt the efficiency of financial markets, that is to say, their ability to control the policies of the firms and to allocate the capital optimally. Kahneman and Tversky (1979) propose an alternative study focusing on behavioral evidence in total opposition to the rationality of investors which follows the theory of financial markets. Indeed, investors are not fully rational and their demand for risky financial assets is affected by their beliefs or their feelings, which are clearly not justified by economic fundamentals. They are thus prey to several biases that affect their logical reasoning, and push them to commit errors in thinking.

1Assistant professor at the Faculty of Economics and Management Sciences of Tunis.


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Empirical work and recent experimental research have confirmed that the errors of judgments made by individuals affect the behavior of security prices on financial markets. In fact, investors do not necessarily follow objective notions of financial loss or gain calculated mathematically. A key way, in which investors are victims, is the overconfidence bias. Indeed, they are tempted to overestimate the quality of information they have and their ability to interpret it. These features give them an illusion of control over the evolution of markets and distort their perception of risk, sometimes even encouraging them to take more risks.

In this paper, we seek to better understand the human behavior that governs the dynamics of financial markets, studied through investor overconfidence on the Tunisian stock market. For that purpose, we use a questionnaire developed and administered to a Tunisian sample of individual investors. The rest of the paper is organized as follows: Section II presents a review of the literature of the overconfidence bias, and Section III presents the assumptions of our work. Empirical validation is described in Section IV and Section V is devoted to present the empirical results and their interpretation. Finally, Section VI contains the summary and the conclusion.

2 Literature Review Overconfidence bias is often regarded as the most prevalent judgment bias (Langer et al., 2010). It stems from the study of the calibration of subjective probabilities. This reflects how the confidence in an event corresponds to its actual probability of occurrence. In the psychological literature, there is no precise definition of overconfidence. In financial literature there are several findings that are often summarized under the concept of overconfidence: miscalibration, the better than average effect, illusion of control, and unrealistic optimism. - Miscalibration: It refers to the difference between the accuracy rate and the probability assigned (that a given answer is correct). This arises when the confidence interval around the investor’s private signal is tighter than it is in reality. This can be thought of as an irrational shift in perceived variance. According to Ben-David et al. (2010), miscalibrated people are those who overestimate the precision of their own forecasts, or underestimate the variance of risky processes; in other words, their subjective probability distributions are too narrow. Studies that analyze assessments of uncertain quantities using the fractile method usually find that people’s probability distributions are too tight (Lichtenstein et al., 1982), i.e. when subjects are asked to state a 90% confidence interval for some uncertain quantities, the percentage of true values that fall outside the interval, is higher than 10% (the percentage of surprises of a perfectly calibrated person). - Better than the average effect: Psychological research has established that, in general, people tend to have an unrealistically positive view of themselves. In fact, most of us, when comparing ourselves to a group (of co-student, co-workers, random participants), believe to be superior to an average representative of that group in various fields. A well known study of better than average effect carried out by Svenson (1981) demonstrated that, while comparing themselves with others, people generally believe themselves to be more skilful and less risky drivers than an average driver, without a prior definition or knowledge of the average driving skills. Taylor and Brown (1998) show that individuals


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feel they are better than others and this by taking into account the knowledge and the positive attributes of personality. In fact, the self serving bias2 makes people assign more responsibility for success and less for failure to themselves, while others are not given the same credit. - Illusion of control and unrealistic optimism: Langer (1975) defines the illusion of control as an expectancy of a personal success probability inappropriately higher than the objective probability would warrant. In fact, the existence of illusion of control in purely chance driven tasks has repeatedly been proven experimentally, with the participants convinced that their skill or past experience can influence the outcome of predicting the result of the task (Langer and Roth, 1975).Weinstein (1980) notes that this phenomenon is similar to the phenomenon of unrealistic optimism. According to this latter, people are particularly optimistic about future events to which they are personally in favor. Most people’s beliefs are biased in the direction of optimism (Kahneman and Riepe, 1998). In fact, Optimists underestimate the likelihood of bad outcomes over which they have no control. Several statistical studies have shown that individuals tend to overestimate the relevance of their knowledge (Alpert and Raiffa, 1982; Fischhoff, Slovic and Lichtenstein, 1977). Moreover, according to Griffin and Tversky (1992), 'experts' are more overconfident than inexperienced individuals. Odean (1998b) assumes that traders, insiders and market makers may unconsciously overestimate the precision of their information and rely on it more than is warranted, while traders display a better than average effect, evaluating their information as better than average than that of their peers. Such overconfidence of market participants may cause an increase in the trading volume. Daniel, Hirshleifer and Subrahmanyam (1998) show theoretically that investors are overconfident only towards private (and not public) signals. They propose a model of overconfidence and biased self-attribution of investors, i.e. people overestimate the degree to which they are responsible for their own success), where security market under and overreactions respectively follow public and private signals. This paper implies that volume should increase following positive returns when such returns build confidence. Moreover, researchers tend increasingly to study overconfidence using questionnaires or experimental studies. De Bondt (1998), for example, studied different measures of overconfidence (better than average effect, illusion of control and unrealistic optimism) using a large questionnaire. The author shows that investors are overly optimistic about the performance of shares that they themselves own but not about the level of the stock index in general. Maciejovsky and Kirchler (2002) note from an experimental study a greater overconfidence at the end of the experiment, when participants gain more experience and start to rely more heavily on their overestimated knowledge. Glaser, Langer and Weber (2010) show, from experimental studies related to the field of finance, that overconfidence of financial experts (professional traders and bankers) is higher than that of lay men (students). Bias et al. (2005) constructed an experimental asset market with varying private information and find that miscalibrated (overconfident) agents perform worse than their better calibrated counterparts. In addition, despite the fact that miscalibration itself is

2See Alicke et al. (2005) et Skala (2008) for further details about this bias.


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approximately the same for both men and women; it reduces trading performance in the experimental market, only for men who turn out to be more active traders than women. Glaser and Weber (2009), using data on 215 online investors who responded to a survey, find that “the better than average effect” is related to trading frequency. According to the authors, at the individual level, overconfident investors will trade more aggressively: the higher the degree of overconfidence of an investor, the higher his or her trading volume. Odean (1998b) calls this finding “the most robust effect of overconfidence”. Using experimental data, Deaves et al. (2008) observe that miscalibration-based overconfidence is positively related to trading activity, while Bias et al. (2002) find that miscalibration-based overconfidence reduces trading performance. Blavatskyy (2008), using an experimental study, shows that the subjects exhibit average confidence in their own knowledge. In addition, confidence does not depend on their attitudes towards risk or ambiguity. By contrast, Benoit et al. (2009) use a test as part of an experimental study to test the better than average effect. Their results do not reject the hypothesis that the data is provided by perfectly rational and confident agents. Using two analytic methods, Parker and Stone (2010) examine the implication of two common measures – labelled overconfidence and unjustified confidence- showing how and where they can lead to different conclusions when they are used to prediction. Ifcher and Zarghamee (2011) conduct a laboratory experiment to identify the effect of positive affect on overconfidence. They find that overconfidence may explain the effect of positive affect on trading volume and the persistence of speculative bubbles.

3 Hypothesis In order to examine the existence of the overconfidence bias on the Tunisian stock exchange, we will test the following hypothesis:

3.1 Hypothesis 1: Overconfident Investors have Confidence in their Intuition "The trust in intuition" was confirmed by the work of Griffin and Tversky (1992), Daniel, Hirshleifer and Subrahmanyam (1998) and Odean (1998). Indeed, the personal implications have an influence on achieving favorable but random events (Langer and Roth, 1975). This can also be explained by the optimism bias. Indeed, subjects are optimistic about their fates (Bernartzi, Kahneman and Tversky, 1999). Kahneman and Riepe (1998) summarize the motivation of overconfidence as a combination of overconfidence and optimism that makes people overestimate their knowledge, underestimate risks and exaggerate in their ability to control the events.

3.2 Hypothesis 2: Overconfident Investors Trade more on the Stock Market Overconfident investors tend to trade more than rational investors. According to De Bondt and Thaler (1995), "The key behavioral factor needed to understand the trading puzzle is overconfidence." Odean (1998) and Gervais and Odean (2001) consider changes in trading volume as the first testable hypothesis of the theory of overconfidence. Gervais and Odean (2001) assume that overconfident traders achieve, on average, lower gains as they increase both trading and volatility which, in turn, negatively affects their


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trading results. They show that greater overconfidence leads to a higher trading volume and that this suggests that trading volume will be greater after market gains and lower after market losses. Moreover, Barber and Odean (2002) analyze trading volume and performance of a group of 1,600 investors who switched from phone based to online trading during the sample period. They find that those who switch to online trading perform well prior to going online and beat the market. Furthermore, they find that trading volume increases and performance decreases after going online. Other studies (Statman, Thorley and Vorkink, 2006; Chuang and Lee, 2006; Glaser and Weber, 2007, 2009)), find that trading volume increases after a series of high returns, since the success of investors increases their degree of overconfidence. These authors conclude that a high level of overconfidence leads to a significant trading volume. Using experimental studies, Biais et al. (2005) and Deaves et al. (2008) confirm that overconfidence is positively related to trading volume.

3.3 Hypothesis 3: Overconfident Investors make little use of Available Information The amount of information and the strength of that information influences people’s confidence in their decisions (Koriat, Lichtenstein and Fischhoff, 1980). Peterson and Pitz (1986) theorized that when one piece of information is given, judgments become extreme and confident, whereas when several pieces of useful information are given they conflict with each other and the resulting prediction is close to the average but with low confidence, which reduces overconfidence. Overconfident investors tend to use a minimum of information sources when they select their assets. In fact, overconfidence often leads to the non-use of available information (Fishhoff, 1982; Wickens and Holland, 2000). Griffin and Tversky (1992) suggest that the less informed investors suffer from overconfidence. This result is confirmed by Bloomfield, Libby and Nelson (1996).

3.4 Hypothesis 4: Overconfident Investors consider themselves Lucky According to Camerer and Lovello (1999), subjects entering the game (or the market) tend to overestimate their chances of success. Moreover, Weinstein (1980) and Taylor and Brown (1988) show that most people consider themselves better than average. They have excessive confidence in their own abilities and are optimistic about their future. According to Cooper, Woo and Dunkelberg (1988), entrepreneurs systematically overestimate their chances of success. Indeed, they showed that 33% of entrepreneurs had total confidence in their project and in their chance of success.

4 Empirical Studies 4.1 Objective The aim of our empirical studies is to test the existence of the overconfidence bias on a sample of individual investors on the Tunisian stock market, to study if they are victims of this bias in making their decisions. For that, we conducted a questionnaire survey. Indeed, the psychology, which can be defined as "the science of behavior”, must be taken into account by a method of investigation which can well describe the characteristics of


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the investor. The questionnaire appears to be a useful tool in determining how individual errors affect aggregate behavior. We will particularly understand how the decisions of many individual investors are incorporated into prices on financial markets.

4.2 Data The subjects are targeted on the individual private stock investors in Tunis3. We addressed our questionnaire to 150 Tunisian investors4. We used two methods of data collection (face to face interviews and mail survey). We got a response rate of 83% and a final sample of 125 investors. The survey was conducted in July 2008. The face-to-face interviews5 allowed us to respond directly to questions that respondents were asked about the issue itself. It also allowed us to better control the representativeness of the sample. Furthermore, we avoided expressing any opinion or any form of approval or disapproval, to avoid influencing the respondent.

4.3 Profile of Respondents Table 1 reports summary statistics for our sample of investors grouped by gender, age, education and business position. 73.6% of the subjects who responded to the questionnaire were men. This is easily understood since the number of men is higher than the number of women investing in the Tunisian stock market6. A greater number of subjects (35.2%) were aged around 35~49 while 30.4% were aged between 25 and 34 years. 44% of the subjects have a bachelor degree while 44.8% have a master degree and above. We remark according to our sample, that the higher the degree of education, the more we invest in the stock market. Moreover, the proportion of executives is very high. In fact, they represent almost half of our sample (48%). Finally, most of the respondents belonged to the middle-income class with a monthly income between 600 and 2000 dinars7.

3We note that commercial agents working at the front offices in stock market intermediary houses help as to contact the investors. 4Several questionnaires were omitted since too many questions had been left unanswered. 5Face to face interviews represent 70% of total interviews. We chose to perform our investigation on the big Tunis (Tunis, Ben Arous, Ariana), because the population of the big Tunis is heterogeneous and diversified and therefore, it gives us a greater depth of information. 6See Dellagi et al. (2005, p. 5). 7100 Tunisian Dinars = 66.7074US Dollars as of 26/01/2012.


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Table 1: Profile of respondents Variables Response (in %) Gender Male

73.6 Female

26.4

Age <25 12.8

25-34 30.4

35-49 35.2

50-60 12.8

>60 8.8

Education* low 11.2

Middle 44.0

High 44.8

Income** Low 23.3

Middle 58.4

High 18.4

Business position

Merchant, Artisan,

Entrepreneur 6.4

Executive, Higher intellectual profession

48.0

Middle management

20.8

Employee

8.0

Student

9.6

Retired

7.2 *The education of low: high school or lower; middle: bachelor; high: master and above. **The income of low: < 600 dinars; middle: [600 dinars à 2000 dinars]; high: > 2000 dinars.

4.4 Methodology For our study, we used the “Sphinx” software (trial version, V5). This allowed us to design the questionnaire, to register the responses, and especially to process and analyze the data. We did not take missing data into consideration. Indeed, the terms "no answers" do not appear in the results: It could be either a deliberate refusal to answer certain questions or accidental omissions. The overconfidence bias is studied through the following four questions. For each question, one response modality is considered symptomatic of the psychological bias. If we accumulate three typical responses, we confirm the presence of the latter. We create a code for each question (variable) and each modality. This involves defining a label, that is to say an abstract in a smaller number of characters. Each theme is associated with a number. For example, the first question is associated with the code "Reason1. The coding variable is given in Table 2.

Table 2 : Coding Variable Reason

Why do you manage your portfolio by yourself?

Reason1 : it’s more amusing Reason2 : you trust your intuitions Reason3 : other

Duration How many months on average do you keep a line?

Duration1 : less than 3 months Duration2 : from 3 to 6 Duration3 : from 6 to 9 Duration4 : from 9 to 12 Duration5 : 12 and above

Information How many sources of information do you use to select your stocks?

Information1 : only one, we shouldn’t disperse Information2 : some of them, this is not fixed Information3 : many, because we can never be too informed

Chance Would you say that every day, you are

Chance1 : lucky Chance2 : unlucky Chance3 : no opinion


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After this coding, the data were entered on the Sphinx software. Finally, we presented the results of the analysis.

5 Results First, we will focus on the univariate analysis. Then, we will present the bivariate analysis. Finally, a multiple correspondence analysis will permit us to deepen our study and to represent, on the same graph, both active and status variables.

5.1 Univariate Analysis Tables 3, 4, 5 and 6 report the results of the univariate analysis of the various variables of the overconfidence bias. The symptomatic modality of the bias is set in gray.

Table 3 : « Reason » Table 4 : « Duration » Reason Number of

observations % Duration

(in months) Number of

observations %

Reason1 26 21.3% Duration1 35 32.4% Reason2 81 66.4% Duration2 25 23.1% Reason3 15 12.3% Duration3 19 17.6%

Total 122 100% Duration4 2 1.9% Duration5 27 25.0% Total 108 100

Table 5 : « Information » Table 6 : « Chance »

Information Number of observations

% Chance Number of observations

%

Information1 9 7.2% Chance1 40 36.0% Information2 47 37.6% Chance2 18 16.2% Information3 69 55.2% Chance3 53 47.7%

Total 125 100% Total 111 100% We observe from Table 3 that 66.4% of the respondents have confidence in their intuition against 21.3% who find amusement in managing their portfolios by themselves (Hypothesis 1 is thus confirmed). This was confirmed by the work of Langer and Roth (1975) and Daniel, Hirshleifer and Subrahmanyam (1998). Table 4 shows that 32.4% of the subjects retained, on average, their securities within 3 months (16.6% of them retain their stocks only one month). This is consistent with the studies of Odean (1998), Barber and Odean (2001), Gervais and Odean (2001), Chuang and Lee (2006) and Statman et al. (2006) (Hypothesis 2 is thus confirmed).

Table 5 shows that 7.2% of the respondents use a single source of information to choose their securities against 55.2% that use several sources of information in the selection of their securities. This can be explained by the large number of graduates (Master and above represent 44.8%) and senior intellectuals executives (48%) in our sample. In addition, apart from the advice of his broker, the investor can use more and more Internet and newspapers to decide on the choice of his securities. (Hypothesis 3 is rejected).


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However, intensive use of information can also lead to overconfidence. Oskamp (1965) find that more information increases overconfidence via increasing confidence and not increasing accuracy. According to Slovic et al. (1977), from a certain level of information, the accuracy of predictions decreases but confidence continues to grow. Guiso and Jappelli (2005) show that overconfident investors collect a lot of information and base their decisions on it. Confidence seems to increase with the magnitude of the available information. This result was confirmed by Tsai et al. (2008), who conclude from three experimental studies that the confidence level increases with the amount of the available information. We note from Table 6 that 36% of the respondents consider themselves lucky against 16.2% who consider themselves unlucky (Hypothesis 4 is confirmed). Thus, we can conclude that Tunisian individual investors suffer from the overconfidence bias. A further study using bivariate and multivariate analysis seems to be interesting. It will allow us to confirm the obtained results.

5.2 Bivariate Analysis We note from the histogram (Figure 1), crossing variables "reason" and "gender", that men tend to be more overconfident than women. Indeed, 76.5% of men have confidence in their intuitions against only 23.5% of women. This result confirms those of Beyer (1999), Biais et al. (2005) and Barber and Odean (2001). In addition, confidence seems more important for those having higher intellectual professions (55.6%) and those having a master's level and above (45.7%).

Figure 1: A cross between « Reason » and « Gender » variables

RAISON x Sexe

C'est plusamusant

30,8%

69,2%

Vous avezconfiancedans vosintuitions

23,5%

76,5%

Autre40,0% 60,0%

Féminin Masculin

0

81

It’s more amusingG

You trust your intuitions

Other

Female Male

Reason x Gender


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5.3 Multivariate Analysis The histogram of the eigenvalues is presented in Table 7. These represent the inertia (or variance) for each axis.

Table 7 : Histogram of eigenvalues

Number Eigenvalues % Explained Cumuative % 1 0.334 18.170 18.170 2 0.311 16.965 35.135 3 0.287 15.635 50.770 4 0.257 13.978 64.748 5 0.222 12.107 76.856 6 0.214 11.660 88.516 7 0.177 9.638 98.154 8 0.029 1.576 99.730 9 0.005 0.270 100.000

We can work with the first two axes as they render the maximum of the initial information (35.13%). Two sets of parameters are used to interpret the results, complementing the information given by the coordinates of the elements on the factorial axes: - The contributions (or absolute contributions) that describe the importance of the

modality for the interpretation of the axis. - The square cosine (or relative contributions) that describe the importance of the axis

for the interpretation of the modality. These settings are found in Table 8.

Table 8: Main parameters of the correspondence analysis Coordinates Contributions (%) Squared Cosine (%) axis1 axis2 axis3 axis1 axis2 axis3 axis1 axis2 axis3

Reason1 1.174 -0.751 -0.093 22.239 9.743 0.163 0.366 0.150 0.002 Reason2 -0.110 -0.149 0.344 41.208 1.114 6.379 0.022 0.040 0.212 Reason3 -1.322 -0.637 0.788 16.279 4.047 6.716 0.242 0.056 0.086

Information1 0.072 0.063 1.139 0.332 1.498 7.242 0.000 0.000 0.100 Information2 0.072 0.060 -0.308 1.756 7.007 2.773 0.003 0.002 0.056 Information3 0.082 0.059 0.063 3.361 10.179 0.169 0.008 0.004 0.005

Chance1 -0.500 0.087 -0.764 6.197 0.200 16.853 0.122 0.004 0.286 Chance2 1.759 -0.362 -0.610 34.556 1.564 4.830 0.530 0.022 0.064 Chance3 -0.198 0.059 0.803 1.288 0.124 24.644 0.030 0.003 0.499

Sex F -0.540 -1.191 0.081 5.977 31.123 0.156 0.104 0.505 0.002 Sex M 0.195 0.433 -0.037 2.166 11.476 0.089 0.101 0.501 0.004

Moreover, an interpretation of the first two factorial axes is possible by analyzing the positive and negative contributions of each axis (Table 9).


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Table 9: Contributions Table for the first two axes Axis1

(+19.14%) Axis 2

(+17.79%) Positive

Contributions

Chance2 +35.97% Female +27.45% Reason1 +25.64% Information1 +15.35% Information1 +8.99% Reason3 +14.67% Male +1.05% Reason1 +6.14% Information2 +4.46%

Negative Contributions

Raison3 -14.28% Male -10.23% Chance1 -6.72% Information3 -10.07% Female -2.90% Raison2 -10.06% Reason2 -1.64% Chance1 -0.79% Chance3 -1.43% Chance2 -0.04%

Reason1 : It’s more amusing- Reason2 : You trust your intuitions - Reason3 : Other - Information1 : Only one, we shouldn’t disperse - Information2 : Some of them, this is not fixed - Information3 : Many, because we can never be well informed – Chance1 : Lucky - Chance2 : Unlucky - Chance3 : No opinion. - Interpretation of axis 1 It can be seen from Exhibit 9 that the first factorial axis (comprising 19.14% of inertia, that is to say, of the total information in the analysis), is the most important axis of the analysis, regrouping on one side (negative side) lucky investors and on the other side (positive side) the unlucky ones. The lucky ones seem to trust their intuition.

- Interpretation of axis 2 Exhibit 9 informs as about the second factorial axis (comprising 17.79% of inertia). This area gathers on one side (the negative side) the confidents. These are men who have confidence in their intuitions, use multiple information and consider themselves lucky. On the other side (the positive side), this area includes the non-confidents that are women and use a single piece of information. Thus, this axis contrasts well the confidents with the non-confidents. - Interpretation of the factorial design The correspondence analysis allows us to represent graphically groupings of modalities involved in the analysis. Thus, we can have a graphic illustration of the individual investors (Figure 2). The modalities of the status variables are positioned closer to the modalities of opinion that resemble them the most, that is to say which are the most chosen by the same individuals.


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Figure 2: Factorial design -Axis1-Axis2-

The map shows the position of 11 modalities and coordinates of 108 observations. 39.93% of the variance is explained by two axes. The non-answers are ignored. R1 : It’s more amusing- R2 : You trust your intuitions - R3 : Other - I1 Only one, we shouldn’t disperse - I2 : Some of them, , it’s not fixed - I3 : Many, we can never be well informed- C1 : Lucky - C2 : Unlucky - C3 : No opinion - S1 : female - S2 : male. The factorial design formed by the first two axes shows interesting combinations between the modalities of the analysis. These are close if the individuals who take one or other of these modalities are not distinguishable for other variables: they form a group, and the distance involved in the distinction between these two modalities, do not disturb the cohesion of the group. We can see, from Figure 2, the formation of a homogeneous group (factorial cloud). This group consists of overconfident investors. Indeed, by projecting on this chart the four variables related to the overconfidence bias and the status variable (gender), we find that from the side of investors who trust their intuition, are placed in their majority, men that are lucky and use multiple information sources.

6 Conclusion Human decision making does not seem to conform to rationality and market efficiency, but exhibits certain behavioral biases that are clearly counter-productive from the financial perspective.

R1

R2

R3

I1

I2

I3

C1C2

C3

S1

S2Axe 1 (19.14%)

Axe 2 (17.79%)

Axis1 (19.14%)

Axis 2 (17.79%)


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In this paper, we tested the presence of the overconfidence bias on the Tunisian stock market. For that, we administered a questionnaire to a group of individual investors, to consider whether they are victims of this bias in their decision making. The results indicate that individual investors on the Tunisian stock exchange suffer from the overconfidence bias. In fact, they trust their intuition; they consider themselves lucky and trade their securities in an aggressive manner. Moreover, they use multiple information sources to select their stocks. Thus, these investors tend to overestimate the quality of information they have and their ability to interpret it. These features give them an illusion of control over the evolution of markets and distort their perception of risk. Besides, another interesting study could be made from the same research framework; it is to test the presence of other psychological biases such as herding, loss aversion, mental accounting and anchoring. Further research should further investigate overconfidence in the context of an experimental approach focusing on individual investment (Dittrich et al., 2001). Also, further research on the relation between overconfidence and personal traits, such as attribution styles or positive affects, is needed to learn how certain characteristics trigger overconfidence (Ifcher and Zarghamee, 2010).

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Foreign Direct Investment in the US: Externalities

between the Two-Sector of the Economy

Jean Emmanuel Fonkoua1

Abstract This paper empirically unfastens the spillover effects between the domestic-funded sector and the foreign-funded sector in the United States using inter-sectorial externalities. The study analyzes the spillover effect from domestic firms to foreign firms of the United States economy. Based on the two-sector analysis, the hypothesis that the domestic-funded sector plays a significant role in promoting the foreign-funded sector was tested in order to derive the externalities between the two sectors of the economy. The research provides support that a mean to supplement foreign investment for achieving a higher level of economic growth is possible through capital structure, transfer of technology, and managerial skills. Empirical evidence provided considerable support that the domestic-funded sector plays a significant role in promoting the foreign-funded sector in the United States. Indeed, the contribution of foreign direct investment is minimal. The empirical results also strengthen the view that multinationals concentrate their more capital-intensive or skill-intensive operations in the United States and allocate their more labor-intensive production to their affiliates in poor countries. JEL classification numbers: D620 Keywords: adaptive expectation, domestic-funded sector, externalities, foreign direct investment, foreign-funded sectors, investment demand, investment supply, moving averages, production function, spillover effect.

1 Introduction Studies on the spillovers of foreign technologies and skills, an issue of interest among researchers, have attempted to analyze the extent to which capital flow into the emerging economies has contributed to economic growth. Even though the perception seems to differ on ground of hypotheses, all the theories lead to the determination of the role of foreign direct investment in economic growth. In the United States, studies on foreign

1Faculty, Strayer University, USA.


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direct investment have focused on examining the home-country consequence of the foreign direct investment. By doing this, researchers failed to examine inward direct investment into the United States. Supposedly, the neglect of these issues is due to the role of the United States as dominant outward investor.

2 Methodological Analysis In this study, the use of investment incentives focuses on domestic supply and foreign demand in order to determine the externalities between the two sectors of the economy. On the expectation that some of the knowledge brought by the United States multinational corporations may spill over to foreign multinational corporations, the economy is dichotomized into the domestic-funded sector and the foreign-funded sector. The following analytical framework is developed as a way to use empirical analysis to investigate the theoretical motives for financial subsidies to foreign investments, and therefore examine the link between domestic and foreign investments. The study utilizes foreign investment supply and domestic investment demand for a quantitative assessment of the association between the foreign-funded sector and the domestic-funded sector in the United States. In the footsteps of Esq, and al. (2010), this research deeply looks into domestic investment supply and foreign investment demand on the basis that the factor productivity in the foreign sector is less than that in the domestic sector in industrialized countries open economies.

2.1 Domestic Investment Supply The spillover effect from the domestic-funded sector to the foreign-funded sector of the economy is derived from the supply guided model. Attention is paid to the neo-classical production function that ties economic growth to the factors of production. The production function of the domestic-funded sector is posited in the form: D t = f(L d

t , K dt ) (1)

D is the output of the domestic-funded sector; L represents the labor forces; K denotes the capital stocks; and the subscripts d and t stand for domestic sector and time indexes respectively. The output of the foreign-funded sector is a function of capital, labor, and the expected output of the domestic-funded sector due to the presence of spillover effect. In this respect, an equation of the foreign-funded sector that accounts for the spillover effect is reformulated as follow: F t = g(L f

t , K ft ; D *

t ) (2) F is the output of the foreign-funded sector; D * represents the expected output of the domestic-funded sector; and the subscript f denotes the foreign sector. Based on the adaptive expectations model in which expectations are revised in proportion to the error of the previous level of expectation (Koyck Geometric Lag Model), it is assumed that the production of the domestic-funded sector is expected; therefore,


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D *t - D *

1t = (D 1t -D *1t ) with 0< <1 (3)

The economy is composed of the foreign-funded sector and the domestic-funded sector because both sectors absorb the total capital stocks and labor forces. The output of the domestic-funded sector is the difference between the total output of the economy and the output of the foreign- funded sector. Output, capital, and labor of the domestic-funded sector are thus derived from the total output, labor employment, and the aggregate capital investment via the following functions: Y t = L t + D t (4)

L t = L ft + L d

t (5)

K t = K ft + K d

t (6) Y is the total production of the economy. The marginal factor productivities are not equal in the domestic-funded sector and the foreign-funded sector of the economy; the difference resulting from inter-sectorial beneficial externalities (Feder, 1983). In this respect, the model assumes that the marginal factor productivity in the domestic funded sector is higher than that in the foreign sector in industrialized countries. Based on this view, the model assumes that the ratio of the marginal factor productivity of the foreign labor to the domestic labor deviates from unity by a factor of which lead to express the partial derivatives of labor and capital as follows: D l /F l =D k /F k =1+ with >0 (7) Assuming that the two production functions (capital and labor) are homogeneous of degree one, differentiation of the production function of the domestic-funded sector (1) and the production function of the foreign-funded sector (2) with respect to time gives the following results respectively: dK t /Y t (8)

dL t /dL ft + /(1+ )+D *d

t (9) Under the assumption that a linear relationship exists between the marginal productivity in a given sector and the average output per labor in the economy (Bruno, 1968), the derivation of the above relationship in the foreign-funded sector gives the following result: F l = (Y/L) (10) The above result is then used to generate the growth equation as follows: dY t /Y t = (dK t /Y t )+ (dL t /dL f

t )+ *1/ DtD (dD t /D t )(D t /Y t ) (11)


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In this equation, dY t /Y t is the economic growth rate, dK t /Y t represents the investment-

output ratio, dL t /dL ft stands for the ratio of labor employment to labor forces employed

in the foreign-funded sector, *1/ DtD measures the amount by which the total

marginal productivity in the domestic-funded sector exceeds that in the whole economy, (dF t /F t )(F t /Y t ) denotes the foreign-funded sector weighted output growth rate, and

*DtD is the spillover effect of the domestic-funded sector to the foreign-funded sector,

which is measured as dF t /dD *t . The coefficients and are the marginal productivity

of capital in the domestic-funded sector, and a proportionality factor linking the marginal productivity of labor in the domestic-funded sector to the average labor output respectively. Estimation of the productivity differential reflects the difference in the factor productivities in both the foreign- and domestic-funded sectors. Under the assumption that the output of the domestic-funded sector affects the output of the foreign-funded sector at a constant exponential rate equation (2) is modified and the output of the foreign-funded sector is redefined as follow: F t =(D *

t ) (L ft , K f

t ) (12) Where is the rate, at which the domestic-funded sector influences the foreign-funded sector. The elasticity coefficient is considered as an indicator that evaluates the level of spillover effect, it follows that dF t /dD *

t = (F t /D *t ) (13)

The principle of adaptive expectation helps determine the production differential of equation 12 with respect to time. Substitution into the conventional growth of equation 11 results in the growth rate below: dY t /Y t = (dK t /Y t )+ (dL t /dL d

t )+ 1/ (dD t /D t )(F t /Y t )+ dD *t /D *

t (14) The output of the domestic-funded sector is expected, and therefore can be expressed as the weighted average of current and past values of the production of the domestic-funded sector. Therefore, the expected output of the domestic-funded sector (3) is re-arranged as follows:

D *t = D 1t +(1- )(D *

1t )=

0

1 u D ut where

0

1 u =1 (15)

Transformation into a dynamic function of economic growth is accomplished using a combination of equations (12), (14) and (15) as follows: dY t /Y t = (dK t /Y t )+ (dL t /dL f

t )+ 1/ (dD t /D t )(F t /Y t )

+ dD *t /D *

t +(1- )(dY 1t /Y 1t ) (16)


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The above equation associates economic growth with the growth of capital for each additional unit of output, the ratio of labor force for each additional worker in the foreign-funded sector, the weighted output growth rate of the domestic-funded sector, the ratio of the output of the domestic-funded sector to the aggregate output, and the economic growth of the previous period. The domestic investment supply guided model is determined using an empirical analysis that is accomplished through the transformation of equation 16 into a testable equation of the following form. dY t /Y t =α 0 +α 1 (dK/Y) t +α 2 (dL/dL f ) t +α 3 (dD/D) t (F/Y) t +α 4 (dD/D) t +α 5 (dY/Y) 1t

+u t (17) where u t is a random error term.

2.2 Foreign Investment Demand A derivation of the foreign investment demand driven model linking the spillover effect from the foreign-funded sector to the domestic-funded sector in the United States economy is derived from the demand guided model using the two sectors of the economy as follows. F t = g(L f

t , K ft ) (18)

As previously mentioned the output of the domestic-funded sector is a function of capital, labor, and the expected output of the foreign-funded sector due to the presence of spillover effect. In this respect, an equation of the foreign-funded sector that accounts for the spillover effect is reformulated as follow: D t = f(L d

t , K dt ; F *

t ) (19) As in the supply guided model, the theory assumes that the production of the foreign-funded sector is expected; therefore F *

t - F *1t = (F 1t -F *

1t ) with 0< <1 (20) F * is the expected output of the foreign-funded sector. Under the assumption that the difference of the marginal factor productivity of the domestic labor to the foreign labor deviates from unity by a factor of , couple with the logic that the marginal factor productivity in the foreign funded sector is less than that in the domestic sector in industrialized countries open economies, the partial derivatives of labor and capital are expressed as follow: D l /F l =D k /F k =1+with >0 (21)


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Estimation of the productivity differential reflects the difference in the factor productivities in both the domestic- and foreign-funded sectors. Under the assumption that the output of the foreign-funded sector affects the output of the domestic-funded sector at a constant exponential rate, equation 19 is reformulated and the output of the foreign-funded sector is as follow: D t =(F *

t ) (L dt , K d

t ) (22) The level of spillover effect is evaluated using the elasticity coefficient. It follows that dD t /dF *

t = (D t /F *t ) (23)

The economic growth rate is derived in the same way as in equation 14 dY t /Y t = (dK t /Y t )+ (dL t /dL f

t )+ 1/ (dF t /F t )(F t /Y t )+ dF *t /F *

t (24) Using the principle of adaptive expectations, a dynamic function is derived from the above equation as follows: dY t /Y t = (dK t /Y t )+ (dL t /dL d

t )+ 1/ (dF t /F t )(F t /Y t )+

dF *t /F *

t +(1- )(dY 1t /Y 1t ) (25) The above equation is the foreign investment demand-driven model that associates economic growth with the growth of capital for each additional unit of output, the ratio of labor force for each additional worker in the foreign-funded sector, the weighted output growth rate of the domestic-funded sector, the ratio of the output of the domestic-funded sector to the aggregate output, and the economic growth of the previous period. The domestic investment supply guided model is determined using an empirical analysis that is accomplished through the transformation of (25) into a testable equation of the following form. dY t /Y t =β 0 +β 1 (dK/Y) t +β 2 (dL/dL d ) t +β 3 (dF/F) t (D/Y) t +β 4 (dF/F) t +β 5 (dY/Y) 1t +v t (26)

where v t is a random error term

3 Previous Literature and Data Considerations Despite the significance of studies on outward foreign direct investment from the United States, there are questions that can be asked; one is about the spillover effects from the domestic-funded sector to the foreign-funded sector. Previous studies fail to examine inward direct investment in the United States. Shahmoradi and Najibzadehr (2010) analyze the relationship between the flow of foreign direct investment and economic growth in India. Using the gross domestic product as the measure of economic growth, the authors find a strong correlation, coupled with a unidirectional causality between foreign direct investment inflows and economic development. Using a two-steps


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procedure, Esq and al. (2010) also analyzed the interrelation between foreign direct investment and economic growth in China in terms of inter-sectorial externalities. The study employed the domestic-funded sector and the foreign-funded sector to analyze the externalities between the two sectors of the economy. The authors found that foreign capital contributed positively to China’s economic growth with a weakening spillover effect over time. Secondary data of the World Bank (World Development Indicators), ProQuest statistical datasets, and the United States Department of Commerce (Bureau of Economic Analysis) are essential components of the groundwork for the empirical testing of the theoretical analysis. All data are from 1970 to 2010. The foreign output, the total output, and the capital stocks variables are expressed in billions of dollars. The foreign and domestic labor employment is expressed in thousands of dollars. The output of the foreign-funded sector is the difference between the total output of the economy, expressed as the gross domestic product, and the output of the domestic sector; idem for labor. The unavailability of an index of foreign labor in the United States for the years 1970 to 1986 leads to update the missing foreign labor years using an index conversion procedure that consists of moving averages.

4 Empirical Development The basic problem with studies of externalities between the two sectors of the economy consists of examining the interaction between the domestic-funded sector and the foreign-funded sector. This research looks at the way to introduce and posit the domestic investment supply and the foreign investment demand in order to analyze the spillover effects from the domestic sector to the foreign sector or vice versa. The empirical investigation of the theoretical analysis is aimed at computing the contribution of one sector to another sector as a way to determine and analyze the flow direction in the economy.


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Table 1: Regression results: Values in parentheses are the t-statistics and values in brackets are probabilities

Domestic Supply-Guided Model Foreign Demand-Driven Model

Economic Growth of the previous period

0.01 (0.01) [0.99]

Economic Growth of the previous period

0.27 (3.13) [0.01]

Capital Stocks Growth Rate

0.09 (3.16) [0.01]

Capital Stocks Growth Rate

0.32 (6.62) [0.01]

Labor Growth Rate in the Domestic Sector

-0.09 (-0.88) [0.38]

Labor Growth Rate in the Foreign Sector

-0.04 (-0.83) [0.41]

Domestic Funded Sector Weighted Output Growth Rate

-2.14 (-1.16) [0.25]

Foreign Funded Sector Weighted Output Growth Rate

-0.84 (-1.12) [0.27]

Domestic Output Growth Rate

3.02 (1.65) [0.10]

Foreign Output Growth Rate

0.01 (1.21) [0.23]

R 2 0.83 R2

0.98 Based on the empirical results, it is evident that a mean to supplement foreign investment for achieving a higher level of economic growth can be done through capital structure, transfer of technology, and managerial skills. The above table infers that the economic output is particularly sensitive to the growth rate of the capital stocks. The coefficients of the capital stock are positive and statistically significant in both the domestic and foreign sectors of the economy. This result strengthens the view that multinationals concentrate their more capital-intensive or skill-intensive operations in the United States. The model refutes the theoretical argument that the labor force employed by both domestic- and foreign-capital enterprises contributes to economic growth, as the coefficients of the labor growth rate appear to be statistically insignificant in the two sectors of the economy. The result is consistent with the view that multinationals allocate their more labor-intensive production to their affiliates in poor countries. An important aspect of the economic growth resides in the statistically significant positive coefficient of the domestic output growth rate. This indicates a significant trend rate of growth and the estimated value traces out a pattern of three percent a year. Inconsistent with the financial logic is the lack of support of the apparent reality of domestic output determination in the United States. Economic evidence that past realizations of growth tend to have a positive effect in the short run does not find support in the domestic sector of the economy as the magnitude of the output coefficient is statistically insignificant. In the foreign sector however, the model supports the impact of the output observed in the previous period in affecting the current output. The silent nature of the weighted output growth rate in both the domestic- and the foreign-funded sectors is disappointing, as the empirical results minimize the influence of both the domestic- and foreign-funded sector on the total output of the economy. The hypothesis that there are externalities between the two sectors of the United States economy implies the determination of the constant exponential rates from the domestic to


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the foreign sector and from the foreign to the domestic sector in order to determine the magnitude of the spillover effect. Based on the insights of Esq, and al. (2010), the coefficients of the domestic- and foreign-output growth rates of the table above, in conjunction with equations (14) and (24) pertain to the determination of the estimated values of and respectively:

= 05.301.01

02.3

= 01.027.01

01.0

The purpose of this research is to evaluate the magnitude of the spillover effect from one sector of the economy to the other sector. The estimated values of the elastic coefficients and are thus employed to evaluate the spillover effect using the formula below: D t / D *

t = ( D t /F *t )

F t / F *t = (F t /D *

t ) The table below shows the summary of the results reported in appendix, which represent of the spillover for each year.

Table 2: Summary of the estimated spillover effect

From the Domestic to the Foreign Sector

From the Foreign to the Domestic Sector

Lowest Value 91.42529 0.00000773 Highest value 3947.226 0.000334

Range 3855.8008 0.00032627 First Quartile 242.9774 0.0000305

Median 388.2101 0.0000786 Third Quartile 1000.0181 0.0001255 Interquartile

Range 757.0407 0.000095

The above table supports the economic evidence that the factor productivity in the domestic-funded sector is higher than that in the foreign sector in industrialized countries open economies. There is strong support in this model that the contribution of foreign direct investment is minimal because of lower externalities in the foreign-funded sector. The higher externalities observed in the domestic-funded sector support the apparent reality of the low contribution of the foreign sector in promoting the domestic sector in developed economies; this is consistent with the financial literature that the domestic sector plays a role in promoting the foreign sector in industrialized countries open economies.


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5 Conclusion A look at the interaction between the domestic-funded sector and the foreign-funded sector is important in view of the reality of industrialized countries with open markets. The neo classical production function for the two sectors of the economy is used to determine the domestic investment supply and the foreign investment demand functions. The model added the estimated values of the elastic coefficients to evaluate the spillover effect using constant exponential rates from the domestic to the foreign sector and from the foreign to the domestic sector for an in-depth exploration of externalities between the two sectors of the economy. Previous studies on foreign direct investment in the United States focused on examining the home-country consequences of the foreign direct investment; these analyses failed to examine inward direct investment into the United States. Indeed, the model is specialized to conform to industrialized economies with open markets. In a dynamic environment that manifests itself with the extraverted nature of the United States economy, results of macroeconomic policies could be rendered irrelevant without a major investigation on spillover effects from the domestic-funded sector to the foreign-funded sector. The presented analysis put forth externalities between the two sectors of the economy due to their importance to open economies models. From the empirical standpoint, the contribution of foreign direct investment in the United States is minimal and the domestic-funded sector plays a significant role in promoting the foreign-funded sector. The results provided evidence that multinationals allocate their more labor-intensive production to their affiliates in poor countries and concentrate their more capital-intensive or skill-intensive operations in the United States. There is a strong relationship between foreign capital inflow into the United States and economic growth in the two sectors of the economy.

References [1] Barro, R. & Sala-i-Martin, X. (1995). Economic growth (2nd, Ed.). Cambridge, MA. [2] Bruno, M. (1968). Estimation of factors contribution to growth under structural

disequilibrium. International Economic Review, 86(4), 549-580. [3] Esq, P.Y., Chen, K.C. & Sun, K.C. (2010). Foreign direct investment and economic

growth in China: Evidence from a two-sector model. Journal of Financial Management and Analysis, 23(1), 1-9.

[4] Feder, G. (1983). On exports and economic growth. Journal of Development Economic, 12.

[5] Griliches, Z. (1967). Distributed lags: A survey. Econometrica, 16. [6] Lipsey, R. E. (2004). Home- and host-country effects of foreign direct investment.

University of Chicago Press, 332-382. [7] Shahmoradi, B. & Najibzadehr, E. (2010). Bivariate causality between FDI inflows

and economic growth in India since 1990. Retrieved: February 23, 2012, from: www.igi-global.com/chapter/pervasive-computing-business/41106


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http://www.igi-global.com/chapter/pervasive-computing-business/41106

Appendix Obs CAPITALSTOCKS TOTALOUTPUT DOMESTICOUTPUT FOREIGNOUTPUT TOTALLABOR DOMESTICLABOR FOREIGNLABOR 1970 194.0 1038.3 1037.040 1.26 78678 75414.60 3263.4 1971 211.7 1126.8 1125.930 0.87 79367 76174.30 3192.7 1972 241.1 1237.9 1236.550 1.35 82153 78869.10 3283.9 1973 275.5 1382.3 1380.180 2.12 85064 81646.10 3417.9 1974 291.7 1499.5 1496.170 3.33 86794 83175.50 3618.5 1975 299.6 1637.7 1635.140 2.56 85846 82399.10 3446.9 1976 341.2 1824.6 1821.350 3.25 88752 84940.20 3811.8 1977 406.5 2030.1 2027.200 2.90 92017 87862.10 4154.9 1978 489.2 2293.8 2287.950 5.85 96048 91470.10 4577.9 1979 563.4 2562.2 2553.500 8.70 98824 94300.20 4523.8 1980 585.5 2788.1 2771.170 16.93 99303 94991.90 4311.1 1981 649.5 3126.8 3101.610 25.19 100397 96232.70 4164.3 1982 644.5 3253.2 3240.730 12.47 99526 95522.30 4003.7 1983 692.9 3534.6 3524.130 10.47 100834 96803.30 4030.7 1984 809.6 3930.9 3906.140 24.76 105005 100785.9 4219.1 1985 873.2 4217.5 4197.490 20.01 107150 102682.9 4467.1 1986 913.2 4460.1 4424.680 35.42 109597 105196.5 4400.5 1987 942.1 4736.4 4677.930 58.47 112440 107610.9 4829.1 1988 989.2 5100.4 5042.660 57.74 114968 109776.7 5191.3 1989 1044.9 5482.1 5413.850 68.25 117342 111749.2 5592.8 1990 1062.2 5800.5 5752.010 48.49 118793 113355.1 5437.9 1991 1023.6 5992.1 5968.920 23.18 117718 112607.5 5110.5 1992 1071.6 6342.3 6322.490 19.81 118492 113577.1 4914.9 1993 1152.0 6667.4 6616.020 51.38 120259 115408.1 4850.9 1994 1254.4 7085.2 7039.070 46.13 123060 118140.8 4919.2 1995 1345.5 7414.7 7356.900 57.80 124900 119881.5 5018.5 1996 1453.7 7838.5 7751.980 86.52 126708 121425.5 5282.5 1997 1570.0 8332.4 8226.810 105.59 129558 124356.1 5201.9 1998 1709.9 8793.5 8614.470 179.03 131463 125816.9 5646.1 1999 1868.1 9353.5 9064.060 289.44 133488 127460.4 6027.6 2000 2022.0 9951.5 9630.230 321.27 136891 130366.4 6524.6 2001 2022.2 10266.2 10099.18 167.02 136933 130664.7 6268.3 2002 1978.4 10642.3 10557.93 84.37 136485 130559.8 5925.2


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2003 2069.1 11142.2 11078.45 63.75 137736 132022.8 5713.2 2004 2276.0 11853.3 11707.33 145.97 139252 133634.9 5617.1 2005 2514.3 12623.0 12510.36 112.64 141730 136064.5 5665.5 2006 2692.2 13377.2 13134.05 243.15 144427 138623.9 5803.1 2007 2722.6 14028.7 13807.53 221.17 146047 139958.3 6088.7 2008 2625.9 14291.5 13981.41 310.09 145362 139037.3 6324.7 2009 2213.0 13939.0 13786.90 152.10 139877 133906.9 5970.1 2010 2233.5 14526.5 14296.50 230.00 139064 133128.6 5935.4

Obs CAPITALSTOCKS TOTALOUTPUT DOMESTICOUTPUT TOTALLABOR DOMESTICLABOR DOMFOR FORDOM

1970 194.0 1038.3 1037.040 78678 75414.60 2510.295 1.21E-05 1971 211.7 1126.8 1125.930 79367 76174.30 3947.226 7.73E-06 1972 241.1 1237.9 1236.550 82153 78869.10 2793.687 1.09E-05 1973 275.5 1382.3 1380.180 85064 81646.10 1985.636 1.54E-05 1974 291.7 1499.5 1496.170 86794 83175.50 1370.366 2.23E-05 1975 299.6 1637.7 1635.140 85846 82399.10 1948.116 1.57E-05 1976 341.2 1824.6 1821.350 88752 84940.20 1709.267 1.78E-05 1977 406.5 2030.1 2027.200 92017 87862.10 2132.055 1.43E-05 1978 489.2 2293.8 2287.950 96048 91470.10 1192.863 2.56E-05 1979 563.4 2562.2 2553.500 98824 94300.20 895.1925 3.41E-05 1980 585.5 2788.1 2771.170 99303 94991.90 499.2362 6.11E-05 1981 649.5 3126.8 3101.610 100397 96232.70 375.5423 8.12E-05 1982 644.5 3253.2 3240.730 99526 95522.30 792.6405 3.85E-05 1983 692.9 3534.6 3524.130 100834 96803.30 1026.609 2.97E-05 1984 809.6 3930.9 3906.140 105005 100785.9 481.1683 6.34E-05 1985 873.2 4217.5 4197.490 107150 102682.9 639.7973 4.77E-05 1986 913.2 4460.1 4424.680 109597 105196.5 381.0072 8.01E-05 1987 942.1 4736.4 4677.930 112440 107610.9 244.0172 0.000125 1988 989.2 5100.4 5042.660 114968 109776.7 266.3684 0.000115 1989 1044.9 5482.1 5413.850 117342 111749.2 241.9376 0.000126 1990 1062.2 5800.5 5752.010 118793 113355.1 361.7989 8.43E-05 1991 1023.6 5992.1 5968.920 117718 112607.5 785.3842 3.88E-05 1992 1071.6 6342.3 6322.490 118492 113577.1 973.4273 3.13E-05 1993 1152.0 6667.4 6616.020 120259 115408.1 392.7377 7.77E-05 1994 1254.4 7085.2 7039.070 123060 118140.8 465.4057 6.55E-05


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1995 1345.5 7414.7 7356.900 124900 119881.5 388.2101 7.86E-05 1996 1453.7 7838.5 7751.980 126708 121425.5 273.2725 0.000112 1997 1570.0 8332.4 8226.810 129558 124356.1 237.6340 0.000128 1998 1709.9 8793.5 8614.470 131463 125816.9 146.7583 0.000208 1999 1868.1 9353.5 9064.060 133488 127460.4 95.51335 0.000319 2000 2022.0 9951.5 9630.230 136891 130366.4 91.42529 0.000334 2001 2022.2 10266.2 10099.18 136933 130664.7 184.4240 0.000165 2002 1978.4 10642.3 10557.93 136485 130559.8 381.6722 7.99E-05 2003 2069.1 11142.2 11078.45 137736 132022.8 530.0278 5.75E-05 2004 2276.0 11853.3 11707.33 139252 133634.9 244.6212 0.000125 2005 2514.3 12623.0 12510.36 141730 136064.5 338.7482 9.00E-05 2006 2692.2 13377.2 13134.05 144427 138623.9 164.7495 0.000185 2007 2722.6 14028.7 13807.53 146047 139958.3 190.4099 0.000160 2008 2625.9 14291.5 13981.41 145362 139037.3 137.5191 0.000222 2009 2213.0 13939.0 13786.90 139877 133906.9 276.4631 0.000110 2010 2233.5 14526.5 14296.50 139064 133128.6 189.5840 0.000161

domfor = spillover effect from the domestic-funded sector to the foreign-funded sector fordom = spillover effect from the foreign-funded sector to the domestic-funded sector

obs DYY DKY DDD DFF DLD DFL DDDDY DFFFY 1970 NA NA NA NA NA NA NA NA 1971 0.078541 0.015708 0.078948 -0.448276 0.009973 -0.022144 0.078948 -0.448276 1972 0.089749 0.023750 0.089459 0.355556 0.034168 0.027772 0.089459 0.355556 1973 0.104464 0.024886 0.104066 0.363208 0.034013 0.039205 0.104066 0.363208 1974 0.078159 0.010804 0.077525 0.363363 0.018388 0.055437 0.077525 0.363363 1975 0.084387 0.004824 0.084990 -0.300781 -0.009422 -0.049784 0.084990 -0.300781 1976 0.102433 0.022800 0.102237 0.212308 0.029916 0.095729 0.102237 0.212308 1977 0.101227 0.032166 0.101544 -0.120690 0.033256 0.082577 0.101544 -0.120690 1978 0.114962 0.036054 0.113967 0.504274 0.039445 0.092400 0.113967 0.504274 1979 0.104754 0.028959 0.103995 0.327586 0.030012 -0.011959 0.103995 0.327586 1980 0.081023 0.007927 0.078548 0.486119 0.007282 -0.049338 0.078548 0.486119 1981 0.108322 0.020468 0.106538 0.327908 0.012894 -0.035252 0.106538 0.327908 1982 0.038854 -0.001537 0.042929 -1.020048 -0.007437 -0.040113 0.042929 -1.020048 1983 0.079613 0.013693 0.080417 -0.191022 0.013233 0.006699 0.080417 -0.191022 1984 0.100817 0.029688 0.097797 0.577141 0.039515 0.044654 0.097797 0.577141


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1985 0.067955 0.015080 0.069411 -0.237381 0.018474 0.055517 0.069411 -0.237381 1986 0.054393 0.008968 0.051346 0.435065 0.023894 -0.015135 0.051346 0.435065 1987 0.058335 0.006102 0.054137 0.394219 0.022436 0.088754 0.054137 0.394219 1988 0.071367 0.009235 0.072329 -0.012643 0.019729 0.069771 0.072329 -0.012643 1989 0.069627 0.010160 0.068563 0.153993 0.017651 0.071789 0.068563 0.153993 1990 0.054892 0.002983 0.058790 -0.407507 0.014167 -0.028485 0.058790 -0.407507 1991 0.031975 -0.006442 0.036340 -1.091890 -0.006639 -0.064064 0.036340 -1.091890 1992 0.055217 0.007568 0.055923 -0.170116 0.008537 -0.039797 0.055923 -0.170116 1993 0.048760 0.012059 0.044367 0.614441 0.015865 -0.013193 0.044367 0.614441 1994 0.058968 0.014453 0.060100 -0.113809 0.023131 0.013884 0.060100 -0.113809 1995 0.044439 0.012286 0.043202 0.201903 0.014520 0.019787 0.043202 0.201903 1996 0.054066 0.013804 0.050965 0.331946 0.012716 0.049976 0.050965 0.331946 1997 0.059275 0.013958 0.057717 0.180604 0.023566 -0.015494 0.057717 0.180604 1998 0.052436 0.015909 0.045001 0.410211 0.011611 0.078674 0.045001 0.410211 1999 0.059871 0.016913 0.049601 0.381461 0.012894 0.063292 0.049601 0.381461 2000 0.060091 0.015465 0.058791 0.099076 0.022291 0.076173 0.058791 0.099076 2001 0.030654 1.95E-05 0.046434 -0.923542 0.002283 -0.040888 0.046434 -0.923542 2002 0.035340 -0.004116 0.043451 -0.979614 -0.000803 -0.057905 0.043451 -0.979614 2003 0.044865 0.008140 0.046985 -0.323451 0.011081 -0.037107 0.046985 -0.323451 2004 0.059992 0.017455 0.053717 0.563266 0.012063 -0.017108 0.053717 0.563266 2005 0.060976 0.018878 0.064189 -0.295898 0.017856 0.008543 0.064189 -0.295898 2006 0.056380 0.013299 0.047486 0.536747 0.018463 0.023711 0.047486 0.536747 2007 0.046441 0.002167 0.048776 -0.099381 0.009534 0.046907 0.048776 -0.099381 2008 0.018389 -0.006766 0.012437 0.286755 -0.006624 0.037314 0.012437 0.286755 2009 -0.025289 -0.029622 -0.014108 -1.038725 -0.038313 -0.059396 -0.014108 -1.038725 2010 0.040443 0.001411 0.035645 0.338696 -0.005846 -0.005846 0.035645 0.338696 dyy = economic growth dff = output growth rate in the foreign sector ddd = output growth rate in the domestic sector dky = growth rate in the capital stocks dld = labor growth rate in the domestic sector dfl = labor growth rate in the foreign sector dfffy = foreign funded sector weighted output growth rate ddddy = domestic funded sector weighted output growth rate


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The Effect of Foreign Aid on Real Exchange Rate in

Ghana

Peter Arhenful1

Abstract This paper assesses the effect of foreign aid inflows on real exchange rate in Ghana in order to test the hypothesis that large foreign aid inflows might lead to the appreciation of the real exchange rate of the recipient country and thus, impact negatively on its trade position, a case known as “The Dutch Disease” effect. Using the ordinary least squares method of estimation, the paper finds that although foreign aid inflows to Ghana are quite high, foreign aid inflows have positive impact on the real exchange rate. In other words, foreign aid inflows lead to the depreciation of the cedi, implying that “The Dutch Disease” hypothesis of large foreign aid inflows is rejected in the case of Ghana. In terms of policy recommendation, the results suggest that Ghana can still receive aid without fear of harming its exports competitiveness. JEL classification numbers: F310 Keywords: Foreign aid, Real Exchange Rate and Dutch Disease Effect.

1 Introduction Foreign aid, more commonly known as official development assistance (ODA) comprises medium and long term concessional and grants from bilateral (e.g. governments) and multilateral (e.g. International Monetary Fund, World Bank) sources (Moreira, 2002). Foreign aid has been transferred to developing countries in the form of project aid, commodity aid (including food aid), technical assistance, and programmed aid (balance of payments support and budget aid) (Cassen, 1994). A fundamental argument for aid, at least on economics, is that it contributes to economic growth in recipient countries. This has been the driving economic objective of aid for decades, formally established in the “two gap” model of Chenery and Strout (1966). In this approach investment is the cornerstone of growth and, at least initially, this requires imported capital goods.

1 Lecturer, Accra Polytechnic.


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However, low-income countries typically face fundamental constraints, or financing gaps. First domestic savings rates are insufficient to provide the resources to meet desired levels of investment. Second, export earnings are not adequate to finance the importation of capital goods. Consequently, such countries are constrained in their ability to achieve a target growth rates. In this approach, the contribution of aid is to finance investment, including imports and capital goods. Early empirical work on the impact of aid on growth was based on the “two-gap” model, often concentrating on the impact of aid on investment or savings rather than on growth per se. Recent studies of aid effectiveness have been based on some variant of neo-classical or endogenous growth models and assess the impact of aid on growth controlling for other variables, especially indicators of economic policy. One prominent view is that the correlation between aid and growth is, at best weak (Burnside and Dollar, 1997). Aid only appears to be effective in countries with appropriate economic policies, that is, “Aid works in a good environment” (World Bank, 1998). From this perspective, good policy is a necessary condition for aid effectiveness. During the 1980s, several African countries experienced negative economic growth despite a substantial increase of aid inflow to these countries (White, 1992). “A large number of countries became more aid-dependent in the 1990s than they were in the late 1970s” (Tsikata, 1998). This grim reality has raised many concerns over the effectiveness of aid. Questions such as “What is effective aid?”, “What is ineffective aid?”, and whether aid works or not have become a substantial source of debate among academic researchers and aid practitioners over the past few decades. These questions raised are directly applicable to Ghana.

1.1 Statement of the Problem Concerns that large aid inflows will induce an appreciation of the real exchange rate and discourage the expansion of exports, particularly non-traditional exports, thereby damaging growth prospects in the recipient economy are rarely far the center of contemporary debates on the macroeconomics of aid to low-income countries. The Ghanaian economy, with support from the World Bank and International Monetary Fund (IMF), has since September 1980 witnessed the introduction of mechanisms to halt the downturn of the economy and to move on a path of sustained growth and development. This change elicited tremendous donor assistance in the form of grants, concessional loans and technical assistance. Net official development assistance (ODA), which constituted about 4% of GDP in 1980, rose to 10% in 1990 and has been in that neighborhood ever since. The overwhelming dependence on external aid inflows from developed countries for the supply of basic import commodities has made the Ghanaian economy vulnerable to policy conditionality that might accompany such assistance (Sackey, 2001). Depending on whether these aid inflows have been temporary or permanent, and whether they were spent on imports or domestically produced goods and services, they have had various repercussions. Throughout the economic adjustment agenda, exchange rate and trade reform occupied a core position. The real exchange rate, by virtue of its impact on the international competitiveness of an economy, assumed an overriding importance among the cohorts of policy variables. Surges in aid inflows are believed to be causing “Dutch disease” problems for the macroeconomic management of the economy. The management of aid has been


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characterized by a combination of foreign exchange accumulation (both building reserves and eliminating arrears), credit to the banking system, and increased public spending especially on development projects. Efforts to maintain the real exchange rate in an area of increased aid inflows have kept inflation high (Younger, 1992). Yet, arguably, in the absence of aid inflows Ghana’s growth and development efforts would have been stifled.

1.2 Objectives of the Study In broad terms, the study sought to determine whether foreign aid inflows have generated “Dutch Disease” effect in Ghana or not. In order to achieve this broad objective, the following specific objectives were set: To find out whether foreign aid inflows have led to the depreciation or appreciation of

the real exchange rate in Ghana. To determine whether foreign aid inflows have positively or negatively affected

exports in Ghana. To make recommendations from the findings for macroeconomic management.

1.3 Justification of the Study Both Ghanaians and donors should ask themselves, has the aid done any good? Thus, a study in this area is justified because it will: Let the general public realize the effect of foreign aid on the Ghanaian economy. Serve as an effective source to strengthen aid management measures to policy makers. Assist both donors and recipient governments to address the policy implications for

making foreign aid more effective. Serve as basis for further research. 2 Review of Related Literature 2.1 Theoretical Review Theoretically, there are two principal definitions of real exchange. In internal terms, real exchange rate has been defined as the ratio of the domestic price of tradable (exportable and importable) goods to non-tradable (domestic) goods within a single economy. That is: RER = Price of tradable goods / price of non- tradable goods. Where tradable goods refer to goods which are traded across national boundaries and non-tradable refer to goods which are not traded across national boundaries (Van Wijnbergen, 1985 and 1986). In internal terms, Lansdsburg and Feinstone (1997) defined real exchange rate as the quantity of domestic goods required to buy one foreign good. This is expressed in terms of the price levels as: Real Exchange Rate (RER) = eP’ / P Where e = nominal exchange rate. P = the consumer price index of the domestic country. P’ = the consumer price index of a country. The term “Dutch Disease” refers to the deindustrialization of a nation’s economy that occurs when the discovery of a natural resource raises the value of that nation’s currency, making manufactured goods less competitive with other nations, increasing imports and decreasing exports. The term was devised to describe the adverse impact on Dutch manufacturing of the increase in income associated with the discovery of natural gas in


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the Netherlands in the 1960s, essentially through the appreciation of the Dutch real exchange rate (RER). The focal point of the theory on aid inflows and Dutch Disease has been the impact exerted by aid on the relative prices of non-tradable goods (Van Wijnbergen 1985 and 1986). This theory holds that part of foreign aid will be channeled to the non-tradable sector of the economy causing a possible increase in the demand for non-tradable goods, thereby raising their price. Given that the real exchange (RER) is defined as the relative price of tradable goods to that of non-tradable goods (i.e., RER = Price of tradable goods / price of non- tradable goods), a rise in the price of the latter would result in a decline in the real exchange rate.

2.2 Empirical Review Analysis of countries’ experiences with sectoral booms has revealed varied results. The windfall gains from diamond exports in Botswana have not been associated with the Dutch disease (Harvey, 1992). Benjamin, Devarajan, and Weiner (1989) conduct a simulation with a computable general equilibrium (CGE) model of Cameroon and find that as an result of a boom in the oil sector, the agricultural sector is most likely to be hurt, whereas some components of the manufacturing sector will benefit. On balance, the non-oil tradable sector may not necessarily shrink. In their analysis of the macroeconomic impact of aid in Nicaragua, Vos and Johanasson (1994) find that aid is weakly but negatively correlated with export volumes. They indicate that the simple negative correlation, which they find to be stronger during years of small aid inflows (the 1970s) than during the period of large aid inflows (the 1980s and 1900s), does not seem to make the case of a typical aid-associated Dutch disease. Ogun (1995) also carried out a research on the relation between foreign aid and real exchange rate in Nigeria and found that aid inflows led to depreciation of the currency. Using the newly developed technique to cointegration, the autoregressive distributed lag approach, Outtara and Issah (2003), used time series data from Syria to test the hypothesis that foreign aid inflows generate “Dutch disease” in the recipient country. They found that foreign aid inflows are associated with depreciation of real exchange rate. In a model of the RER for Tanzania during 1967-93, Nyoni (1998) finds that aid was associated with RER depreciation. He presents figures indicating that the RER depreciated more sharply over the period 1985-93 than in the earlier nine-year period, despite a significant increase in ODA flows. This contrasts with the predications of the Dutch disease model since RER appreciation, the main channel through which aid is conjectured to affect the tradable sector adversely, did not materialize. However, Falck (1997) also undertakes an assessment of aid-induced real exchange rate appreciation in Tanzania. The model for the determination of the real exchange rate specifies among other variables the real exchange rate lagged one period, rate of change of the nominal exchange rate, foreign aid, macroeconomic policy proxied by the growth of excess domestic credit, international terms of trade and investment. He computes twelve different real exchange rates indexes for Tanzania, applies a three-stage selection procedure to each one of them and estimates the model by the use of ordinary lest squares. Falck finds that foreign aid inflows cause the real exchange rate to appreciate which in sharp contrast to the findings of Nyoni (1998). Van Wijnbergen (1986) applies a single regression equation to estimate the aid-real exchange rate nexus model for Africa countries. He finds a significantly negative


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relationship between aid and the real exchange rate in four out of six African countries. He also demonstrates that the effect of the “aid boom” permanently lowers the total productivity in the export sector. Despite the real exchange rate being allowed to depreciate after the effect of the “aid boom”, productivity does not return to the level before the “aid boom”. Nevertheless, he argues that if capital markets were perfect, there should have been no problem after the effect of the “aid boom” as the private sector can re-borrow and re-invest after the economic recovery from this effect. Analyzing the link between aid and “Dutch disease”, Edwards (1989) estimate an empirical model specifying explanatory variable like international terms of trade, government consumption of non-tradable, measure of extent of controls over external aid inflows, index of severity of trade restrictions and exchange controls, measure of technological progress and ratio of investment to GDP. Ordinary least squares and instrumental variables techniques were used. He found that excessive aid inflows put pressure on the real exchange rate and causes it appreciate in the short run. Using the CGE model, Weisman (1990) investigates the impact of aid inflows to Papua New Guinea. He finds that aid inflows increased government spending, which in turn increased the prices of non-traded goods and services. Producers responded to the increase in prices of non-traded goods by increasing supply in this sector and shifting resource from the production of traded goods. Therefore, aid inflows brought about the “Dutch disease” effect that threatened the export earning of Papua New Guinea. Collier and Gunning (1992) also apply the CGE model to examine “Dutch disease” effects in African economies. They find that aid supported government spending that raised aggregate demand and exerted upward pressure on the prices of non-tradable sectors. As a result of the booming of non-tradable sectors, labour and capital were drawn away from the tradable sector. They illustrated that devaluation does reduces this inverse effect on tradable sectors. White (1992a) points out that aid will lead to real exchange rate appreciation so long as part of the aid inflows is spent on non-tradable goods. The upward pressure on the real exchange rate is greater, the higher is the marginal propensity to spend on traded goods, the lower is the responsiveness of supply of non-traded goods, and the higher is the responsiveness of demand to price changes. The impact of previous aid inflows is that the real exchange rate has to depreciate when aid flows cease (White, 1992c). On his part, Vos (1993) indicates that if the aid boom is temporary, there may be an inclination to consume the additional wealth or accumulate reserves to safeguard the economy against future losses. Where aid is of a permanent nature, the rational choice would seem to be to invest the “windfall gain” in order to maximize future consumption. Analyzing the macroeconomic aspects of the effectiveness of foreign aid, Van Wijbergen (1986) points out that temporary aid flows will lead to temporary appreciation of the real exchange rate and will lead to a decline in the production of traded goods as well as exports. Collier and Gunning (1992), on the other hand, writing on aid and exchange rate adjustment in African trade liberalization is export promotion. In a simple exchange rate model, a higher export price is the only effect of liberalization. Aid-only liberalizations, although technically feasible, produce perverse resource shifts and require massive rapid nominal wage flexibility to avoid unemployment. In an empirical analysis of the impact of aid on the RER in four CFA countries – Burkina Faso, Cote d’Ivoire, Senegal, and Togo during 1980 -1993, Adenauer and Vagassky (1998) find evidence of a direct relationship between aid flows and RER appreciation. They suggest that, during the period when the four countries received large aid flows,


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their government deficits increased through high wage bills and para-public spending and their trade balances widened. The developments appear to lead support to the idea of Dutch disease, Nevertheless, as economic performance in the four countries was affected by adverse developments in the world prices of their primary exports and the appreciation of the French franc against the dollar during the latter part of the 1980s, it would have been useful to ascertain the role played by the CFA frances per U.S. dollar or French francs per U.S dollar exchange rates in the development of RER. Also, real export figures could help ascertain whether the deteriorating trade balances were driven by declining world prices, declining trade volumes, or both. In an econometric model of RER behavior for Sri Lanka during 1974-88, White and Wignaraja (1992) find a direct relationship between total aid and remittances and RER appreciation. They suggest that increased aid flows, among other factors, played an important role in the failure of the RER to depreciate, despite depreciations of the nominal rate. Also, they associate the RER behavior with a disappointing performance of the manufacturing sector, lending support to the Dutch disease theory. In contrast, Bandara (1995) does not find support for the Dutch disease theory in an analysis of the impact. He indicates that despite the RER appreciation associated with foreign capital inflows, some tradable sectors may expand a result inline that of Benjamin, Devarajan, and Weiner (1989).

2.3 Review of Studies on Ghana Assessing the impact of aid on macroeconomic management in Ghana, Younger (1992) finds that the increase in foreign aid to Ghana from an annual average of 3 percent of GDP during 1981-83 to 6 percent of GDP during 1984-87 gave rise to macroeconomic management problems that were associated with high inflation, an appreciating RER, and tight credit to the non-bank private sector. First, the increased availability of foreign exchange in the economy did not come from aid alone. The rise in aid flows was accompanied by a significant increase in private transfer and capital, consistent with the idea of pro-cyclicality between private capital, such a foreign direct investment (FDI) and foreign aid associated with policy reforms, while Younger suggests that the private sector was crowed out; the evidence to support such a claim is, best, very weak. He indicates that the Ghana government’s response to aid inflows was a combination of foreign exchange accumulation, provision of credit to the banking sector, and increased public spending, especially on development projects. At the same times although private investment remained low, as the author indicates, the figures the presents indicates that the private investment-to-GDP ratio doubled to 5 percent during 1984-89, compared with 2.5 percent during 1980-83. Third, not only did Ghana’s overall economic performance improve as compared with the period preceding the aid increase, but it also compared favorably with the average for low income countries in the sub-Saharan African region on many indicators, including growth of total and sectoral GDP, exports, and goes domestic investment. Sackey (2001) a adopts a cointegration technique to examine the aid-real exchange rate relationship using annual time series data for the period 1962-1996 and found that although aid inflows are quite high, aid inflows have led to depreciations in the real exchange rate. He also estimated an export performance model for Ghana and found that aid inflows have also had a positive impact on export performance. He concluded his paper by emphasizing that for external aid to be an effective investment, policy


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management needs to focus on ensuring the prevalence of sound macroeconomic fundamentals, among others. The results of their estimations appear to be conflicting. Whilst some of the like Falck (1997) for Tanzania, White and Wignaraja (1992) for Sri Lanka and Younger (1992) for Ghana found that aid inflows caused the real exchange rate to appreciate, other such as Ogun (1995) for Nigeria, Nyoni (1998) for Tanzania and Sackey (2001) for Ghana found no evidence of “Dutch Disease”. Thus, this study attempts to contribute to the aid –real exchange rate nexus by using a more targeted approach. In the next chapter, we outline the detailed methodology for the study.

3 Methodology 3.1 Model Specification In order to estimate the effect of foreign aid inflows on the real exchange rate in Ghana, we establish a model in which real exchange rate is a function of foreign aid. However, since foreign aid is not the only determinant of real exchange rate, we include some other de like government consumption, GDP per capital, openness, terms of trade, growth of money supply as other explanatory variables. Thus, based on the works of Ouatarra and Strobl (1989), the baseline regression equation is assumed to take the form: RERt = a0 +a1Aid t +a2Gt + a3GDPPCt +a4 Opent + a5TOTt+a6GMt+εt where: RER = Real exchange rate Aid = Official development assistant G = Real government consumption GDPPC = Real per capita income Open = Openness of the economy

TOT = Terms of trade GM = Growth of money ε = Error term Real exchange rate is defined as the quantity of domestic goods required to buy one foreign good. This is expressed in terms of the price levels as: Real Exchange Rate (RER)=eP1 / P where e = nominal exchange rate. P = the consumer price index of a good in a foreign country. P = the consumer price index of a good in the domestic country. Foreign aid specifically refers to official development assistance (ODA) such as loans and grants. Ratios are computed using values in U.S. dollars converted at official exchange rates. Real government consumption includes all government current expenditures for purchases of goods and services (including compensation of employees). It also includes most expenditure on national defense and security, but exclude government military expenditure that are part of government capital formation. Real income per capita (Gross Domestic Product per capita) is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in


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the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant U.S. dollars. Openness of the economy is given by the sum of exports and imports of goods and services measured as a share of gross domestic product. The terms of trade refer to the ratio of the export price to the ratio of the import price. That is TOT = Px / Pm.

The growth of money refers to the average annual growth rate in money and quasi money. Money and quasi money comprise the sum of currency outside banks, demand deposits other than those of the central government, and the time, savings, and foreign currency deposits of resident sectors other than the central government. This definition is frequently called M2; it corresponds to lines 34 and 35 in the International Monetary Fund’s (IMF) International Financial Statistics (IFS). The change in the money supply is measured as the difference in end-of-year totals relative to the level of M2 in the preceding year. The expected theoretical impacts of the respective variables included in our model are as follows: Aid (-) Tends to cause real appreciation by changing the

composition of the demand for traded and non-traded goods, according to the “Dutch disease” theory of foreign aid.

GDPPC (-) The expected effect of this variable on RER is to be

negative. This is because as development takes place, the productivity improvement in the tradable goods sector exceeds that of non-tradable goods sector. This implies that the decreased in the price of the former is relatively bigger than that in the later, thus, causes appreciation of the RER.

(?) The effect depends on the composition of government of consumption. Consumption of non-tradable tends to appreciate the RER, while that tradable leads to real depreciation.

OPEN (?) Openness of the economy would cause real depreciation (appreciation) if it reduces (increases) the demand for non tradables.

TOT (?) The effect of the terms of trade on the real exchange

rate depends on whether the substitution or the income effect dominates. If the income (substitution) effect dominates then a deterioration of the TOT tends to cause real depreciation (appreciation).


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GM (-) Changes in the money supply (expansionary monetary policies) would tend to raise the general price level and thus lead to an appreciation of the RER.

NB: Following our definition for the real exchange rate, a negative sign represents an appreciation of the real exchange rate whilst a positive sign represents a deprecation of the real exchange rate.

3.2 The Exports Equation In order to estimate the relationship between export performance and real exchange rate, a simple export performance model abstracted from Vos (1998) is used. In this model, growth of real exports (Exp) is assumed to be a function of real exchange rate (RER, foreign aid inflows (Aid) and price of exports. That is: EXP = β0 + β1 RER + β2Aid + β3Px+εt

Exports of goods and services represent the value of all goods and other market services to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government, services. They exclude labor and property income (formerly called factor services) as well as transfer payments. Data are in constant 1995 U.S. Dollars. The price of exports is a weighted average of the prices in U.S. dollars of goods and services exported with their respective share in the total exports of goods service as weights. It can be recalled that real exchange rate and foreign aid have been defined already under section 3.2.1. The expected theoretical impacts are as follows:

RER (+) Increase in the real exchange rate are expected to result

in exports expansion. AID (?) A good policy environment (proxied by real net ODA

toGhana) tends to elicit positive response from the export sector. Aid inflows, by providing some sort of assistance to the export sector tend to encourage export competitiveness and output enhancement.

Px (+) A rise in the price of exports, all other things being equal, will lead to an increase in the supply of exports.

3.3 Sources of Data The study employs annual time series data from Ghana over the period of 1970-2002. The data used to estimate the models are obtained from a number of the sources. The real exchange rates are obtained from the IMF International Financial Statistics Yearbook 1995. All the other variables were obtained from the World Bank World Development Indicators (WDI), 2004, CD Rom version except the series on price of exports that were extracted from the African Development Indicators, (2004).


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3.4 Estimation Techniques In order to estimate equations (1) and (2), we employ the ordinary least squares (OLS) methods of estimation. Thus, based on the classical assumptions, some relevant residual and specification tests are rigorously carried out. Since the presence of serial correlation in the residuals reduces the efficiency and forecasting powers of the estimators based on OLS estimates, the Durbin-Watson test for first order serial correlation in the residuals is conducted to ensure that there is no autocorrelation in the residuals. The variance inflation factors test for checking the extent of collinearity between the explanatory variables will also conducted to ensure that the extent of collinearity between the explanatory variable is not severe. For, if the inter correlation between the explanatory variables is high, the estimates are indeterminate and the standard errors of these estimates become infinitely large (Koutsoyiansnis, 1973). The White’s test for heteroscedasticity will also be performed. This test is motivated by the observation that in many economic time series, the magnitude of the residuals appears to be related to the magnitude of the recent residuals. The presence of heteroscedasitcity itself does not invalidate standards least squares. However, ignoring it may result in loss of efficiency in the estimated parameters. The null hypothesis is that hetorscedasticity is not present. The Ramsey RESET test is a general test for model specification errors resulting from omitted variable, incorrect functional from and correlation between the independent variable and the residuals, which may be due to errors in measurements, simultaneity and serially correlated disturbances. Under such specification errors, least square estimates will be biased and inconsistent and for that matter conventional inference procedures will be invalidated. The model is correctly specified if the F-statistic is insignificant at the given error level (mostly 5%). The Jarque-Bera statistic is for testing whether the residuals are normally distributed. If the residuals normally distributed, the Jarque-Bera statistic, which has a chi-square distribution under the null hypothesis of normally distributed errors, should be insignificant.

4 Ordinary Least Squares Estimation 4.1 Results of the Real Exchange Rate Equation The result of the real exchange rate equation (equation 1) estimated with OLS are presented in Table 4.2. The results of all the diagnostic tests performed are very satisfactory. The results of the F-test show that the F-statistic (F (6,26) = 45.0279) is statistically significant at 1 present error level. This implies that we can reject the null hypothesis that all the parameters are zero at one percent error level this further implies that the overall regression is statistically significant. The R2 of 0.912212 (Adjusted R21 = 0.891953) shows that approximately, 91 percent of the variations in real exchange rate can be explained by foreign aid, real government consumption, real per capita income, the degree openness of the economy, terms of trade and growth of money. This high value of the R2 shows that the overall model is statistically significant.


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The results also show that there is absence of autocorrelation in the residuals. The Durbin-Watson statistic of 1.75 is closer to 2 (no autocorrelation) than to zero (perfect autocorrelation). Also, the first order autocorrelation coefficient which is 01131 is closer to zero (no autocorrelation) than 1 (perfect autocorrelation). This is therefore a confirmation that serial correlation between the error terms is not a serious problem in our model. The Ramsey RESET test for the regression specification revealed that the model is correctly specified. The null hypothesis HO: specification is adequate is tested against the alternative hypothesis H1: specification is not adequate. The F-statistic F (2, 24) = 3.02827 has a probability value of 0.067184. This implies that the null hypothesis cannot be rejected at 5 percent error level. This confirms that the model is correctly specified. The White test for hetorosecedasticity is employed to test the presence or otherwise of hetoeroscedasitctiy. The null hypothesis “Heteroscedasticity is not present” is tested against the alternaive hypothesis “Heteroscedasticity is present”. The Chi-square value of 30.913 is significant only at 27.5 percent error level. This means that we accept the null hypothesis of no heteroscedasticity implying that the model is free from heteroscedasticity. By employing the variance inflation factors (VIF) technique of determining the presence of or absence of multitcollinearity among the variables, we found that there is a less problem of collinearity between the variables in the model. The VIF (j) = 1 / (1-R (j)2), where R (j) is the multiple correlation coefficient between variable j and the other independent variables. Minimum posisbel value = 1.0 Values greater than 10.0 may indicates a collinearity problem. The results of the VIF test is shown in table 1

Table 1: Results of the Variance Inflation Factors Test Variable VIF

Aid 3.413 G 1.628

GDPPC 4.540 Open 3.756 TOT 2.988 GM2 1.457

From the table above, since all the values are far less than 10, it can categorically be concluded that multicollinearity is not a serious problem in the model. Thus, the model passes all assumption of the OLS estimates. Table 2 presents the results of the parameters.

Table 2: Results of the Real Exchange Rate Equation Variable Coefficient Stand. Error T-statistic P-value Constant -1351.91 440.964 -3.0658 0.005014

Aid 3172.91 1694.43 1.8726 0.072418 G -800.624 2353.14 -0.3402 0.736412

GDPPC 5.99041 1.52254 3.9345 0.000554 Open 1127.63 251.699 4.4801 0.000133 TOT -704.172 191.271 -3.6815 0.001067 GM2 521.944 265.745 1.9641 0.060296


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R2 = 0.912212 Adjusted R2 = 0.891953 F-Statistic = 45.0279 DW = 1.74873 The estimated coefficient of the aid variable is positive (3,172.91) and is statistically significant at 10 percent error level. This means that aid is statistically important determination of real exchange rate. The coefficient implies that an increase in aid by one million U.S. dollars will cause the real exchange rate (measured as cedis per dollar) to increase by 3172.91. In other words, an increase in aid causes the price of the dollar in terms of the cedis to rise; implying a deprecation of the cedi. This result is contrary to the “Dutch disease” theory of foreign aid which states that an increase in foreign aid tends to cause real appreciation of the local currency. Thus, as far as Ghana is concerned, surges in foreign aid inflows causes depreciation of the cedi instead of appreciation. However, this result confirms that of Isaa and Quattara (2004) who found that increases in aid to Syria causes depreciation of the local of Syeria. His result was also significant at 10 percent. This same result corroborates the findings by Ogun (1995) for Nigeria, Nyoni (1998) for Tanzania, Sackey (2001) for Ghana and Quattara and Strobol (2003) for panel of CFA franc counties. Therefore, the potential “Dutch disease” effect associated with foreign aid inflows is not supported by this study. That is; aid does not generate “Dutch disease” in Ghana. The coefficient of real government consumption is negative. This implies that increases in real government consumption cause the cedi to appreciate. The coefficient implies that an increase in government spending by one million US dollars causes the cedi to appreciate by 800.64 cedis all other things being equal. As argued earlier, this scenario could occur if government consumption is dominated by non-tradable goods. However, the coefficient is not statistically significant. In other words, the coefficient of real government of Ghana spends equally on tradable and non tradable goods. The coefficient of real per capita income is positive (5.99041) and is statistically significant at 1 percent error level. This implies that higher income levels tend to increase the real exchange rate and hence depreciate the cedi. This is contrary to the prediction made in chapter three that, higher levels of income causes an appreciation of real exchange rate in a sense that, increases in GDP per capita will lead to a productivity improvement in the tradable goods sector and hence cause the prices of the tradable goods to fall and thus appreciation of the currency. On the other hand, in a country where the marginal propensity is high, an increase in income will lead to an increase in imports and thus create demand for foreign currency. This will increase the supply of the domestic currency at the foreign exchange market. The combined effect will therefore be the depreciation of the currency. Hence, the contradictory result could be explained that the marginal propensity to import for most Ghanaian is directory related to level of income. The coefficient of degree of openness of the economy, measured as the sum of exports and imports as a ratio of GDP is positive (1127.63) and is significant at 1 percent error level. This result suggests that openness leads to a depreciation of the cedi in Ghana. This could mean that the degree of openness tend to reduce the demand for non tradable goods in Ghana and increase that of tradable goods. The positive relationship between the degrees of openness of the economy variable might have resulted from the lifting of tariffs and other barriers by the Ghanaian government and its trading partners to encourage trade with each other. The terms of trade variable is negative (-704.172) and is statistically significant at 1 percent level of significance. This means that terms of trade negatively affects real


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exchange rate. This may be due to the fact that the substitution effect associated with changes in the terms of trade appears to be greater than the income effect. Finally, the coefficient of growth of money variable is positive (521.944) and is significant at 10 percent error level. Implying that, increases money supply lead to the depreciation of the cedi. The direct relationship between money supply and the real exchange rate could still be explained by the fact that the marginal propensity to import in Ghana is very high. Therefore an increase in money supply without a corresponding increase in output causes people to import more and hence put pressure on the foreign currency leading to a depreciation of the local currency.

4.2 Results of the Export Equation The exports equation stated in chapter three was also estimated with OLS method and the results are presented in Table 4.3. The model passes all the diagnostic tests except autocorrelation. The results of the F-tests show that the F-statistic at (F (3,29) = 18.6658) is statistically significant at one percent error level. The Durbin Watson Statistic of 1.00881 and the first order autocorrelation coefficient of .494206 indicate that there is a serious problem of autocorrelation. The results are shown on the table below:

Table 3: Results of the Exports Equation Variable Coefficient Stand. Error T-statistic P-value

Const 974854 193241 5.0448 0.000022*** PX -2739.07 4624.37 -0.5923 0.558232

RER 1056.74 153.032 6.9053 <0.00001*** Aid -4.55664e+06 3.36467e+06 -1.3543 0.186112

R2 = 0.658813 Adjusted R2 = 0.62353518 F-statistic = 18.6658 DW = 1.00881 The estimated coefficient of real exchange rate is positive (1056.74) and is statistically significant at 10 percent error level. The coefficient implies that an increase in real exchange rate will lead to an increase in exports. This is consistent with the predication made earlier on in chapter three. The positive relationship between exports and real exchange rate is also a confirmation that the “Dutch disease” hypothesis of aid is not validated in Ghana, owing to the fact increases in real exchange rate or depreciations of the cedi, positively affect export performance. The coefficient of aid is negative (-4.55664e+06). This implies that aid inflows are negatively related to exports performance. However, the estimated coefficient is not statically significant implying that there is no direct meaningful relationship between foreign aid and export performance in Ghana. Finally, the price of exports variable is negative implying that the price of exports is negatively related to the volume of exports. This is consistent with a prior theoretical expectation that a rise in the price of exports, all things being equal will lead to a reduction in the demand for exports. However, due to the problem of autocorrelation, the results are not good for analysis, inferences and forecasting. Thus, we change the estimation technique by using the


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Cochrane-Orcutt Interactive procedure in order to overcome the problem of autocorrelation to ensure efficiency in our predictions. The results of the Cochrane-Orcutt Interactive estimation are presented on the table 4:

Table 4: Results of the Cochrane-Orcutt Iterative Estimation Variable Coefficient Stand. Error T-statistic P-value

Const 6.82969e+06 4.45864e+06 1.5318 0.136796 PX -7001.07 3156.75 -2.2178 0.034854

RER -64.1907 119.604 -0.5367 0.595719 Aid 1.42396e+06 1.78517e+06 0.7977 0.431781

R2 = 0.919036 F-statistic = 1.77315 Adjusted R2 = 0.910361 DW = 1.42374 It can be seen clearly from the table the Durban-Watson statistic and the R square have significantly improved. This is an indication of the null hypothesis of no autocorrelation in the residuals. All the other diagnostic tests with regard to heteroscedasticity, normality of residuals, parameter stability and correct functional form are all satisfiactory. The results from the Cochrane-Orcutt estimation are therefore very good and reliable for analysis, inferences and forecasting. The estimated coefficient of real exchange rate is negative (-64.1907). This implies that an appreciation or a full in the real exchange rate will lead to an increase in the volume of exports, and vice versa. The estimated coefficient of aid is now positive (1.42396e+06) implying the foreign aid has a direct relationship with exports. This supports our finding that foreign aid has caused the depreciation of the cedi rather than appreciation since in general, a depreciation of a currency leads to an increase in the volume of exports. Finally, the price of exports variable is negative implying that the price of exports is negatively related to the volume of exports. This is consistent with the prediction made in chapter three that a rise in the price of exports, all things being equal will lead to a reduction in the demand for exports.

4.3 Two-Stage Least Square Estimation The estimation of these two equations, that is, equation (1) and (2), without any consideration of possible simultaneity bias can generate misleading results. Thus, estimation was performed by using the two-stage least square (2SLS) method to determine whether the results would be consistent with the OLS results or not. The two-stage least squares (2SLS), like other simultaneous-equation techniques, aims at the elimination as far as possible of the simultaneous-equation bias (Koutsoyiannis, 1973). The two-stage least squares (2SLS) method of estimation boils down to the application of two-stage least squares (2SLS) method of estimation in two stages. In the first stage we apply OLS to the reduced-form equation in order to obtain an estimate of the exact and random components of the endogenous variables. In the second stage, we replace the endogenous variables appearing in the right-hand side of the equation with their estimated value, and we apply OLS to the transformed original equation to obtain estimates of the structural parameters. The formulae for the two-stage least squares (2SLS) method of estimation are the same as those of the ordinary least squares (OLS) method of estimation


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(Koutsoyiannis, 1973). Hence the assumptions underlying the two-stage least squares (2SLS) method of estimation are almost the same as those of the ordinary least squares (OLS) method of estimation. The results of both the real exchange rate equation (equation 1) and the exports equation (equation 2), estimated with 2SLS are presented in Table 4.5 and Table 4.6 respectively.

Table 5: 2SLS Results of the Real Exchange Rate Equation Variable Coefficient Stand. Error T-statistic P-value Constant -1603.059 984.7429 -1.627896 0.1161

Aid 23770.560 3545.512 0.668609 0.5099 G -3950.057 4015.817 -0.983625 0.3347

GDPPC 8.022621 3.033296 2.644853 0.1039 Open 1019.871 458.2757 2.225453 0.0353 TOT -907.9635 303.7557 -2.989125 0.0062 GM2 952.4069 775.2282 1.228550 0.2307

R2 = 0.900344 F-statistic = 34.07998 Adjusted R2 = 0.876426 DW = 1.72

Table 6: 2SLS Results of the Real Exchange Rate Equation Variable Coefficient Stand. Error T-statistic P-value

Const 724.4808 317.4628 2.282097 0.0303 RER 1.483757 0.223991 6.624168 0.0000 Aid -13339.64 7898.561 -1.688870 0.1024 PX 3.683078 10.96604 0.335862 0.7395

R2 = 0.521831 F-statistic = 18.6658 Adjusted R2 = 0.470599 DW = 1.23747

4.4 Comparative Analysis of OLS and 2SLS Results The results obtained from the 2SLS method of estimation are almost the same as those of the OLS method of estimation. With regard to the signs, all the variables (parameters) as well as the constant term had the same signs in both estimations. Real Government Consumption (G) was not statistically significant in both cases whilst Real per capita income (GDPPC), Opennes (Open) and Terms of Trade (TOT) were all highly statistically significant in both cases. Perhaps, the only difference observed is that in the first estimation, both Aid and Growth of money (GM2) were significant only at 10 percent error level but in the second estimation, both variable were not significant at all, even at 10 percent error level; meaning that both Aid and Growth of money have no significant impact on real exchange rate in Ghana. In terms of the various diagnostic tests conducted, the R2 for the OLS estimation 0.912212 whilst that of the 2SLS estimation was 0.9000344. Also, the Adjusted R2 for the OLS estimation 0.891953 whilst that of a 2SLS estimation was 0.876426. The F-statistic obtained in the case of the OLS estimation was 45.0279 whilst that of the 2SLS estimation was 18.6658. It should be noted that both F-values are highly statistically significant. Again, the DW statistic obtained in the case of the OLS estimation was 1.74873 whilst that of the 2SLS estimation was 1.72. It can clearly be seen that the two


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values are almost the same and can be concluded that autocorrelation was absent in both approaches. With regard to the export equation, it was also observed that the results obtained from the 2SLS method of estimation are almost the same as the results obtained from the OLS same signs in both estimations. Just as it was in the first case, it was only the real exchange rate variable that was highly statistically significant in 2SLS estimations. From the above discussions, it can be seen that the results of the two estimations performed, that is, the OLS estimation and the 2SLS estimation are similar. We see that the coefficients differ only in terms of magnitudes but not in terms of signs. This implies that the OLS results obtained earlier are without simultaneity bias and can therefore be used for analysis, inferences and forecasting, as far as the Ghanaian economy is concerned.

5 Summary of Major Findings The study found that foreign aid inflows lead to real depreciation of the cedi rather

than appreciation of the cedi. Hence, the hypothesis that foreign aid inflows generate “Dutch disease” is rejected in the context of Ghana. The coefficient of foreign aid was positive and statistically significant at 10 percent error level.

The coefficient of real government consumption is negative implying that increase in real government consumption causes the exchange rate to fall. However, the coefficient is not statistically significant. That is government spending has no significant impact on real exchange rate in Ghana.

The impact of real per capita income is positive and is statistically significant at 1 percent error level, thus implying that higher income levels tend to increase the real exchange rate and hence depreciate the cedi.

The coefficient of degree of openness of the economy is positive and is highly significant at 1 percent error level. This result suggests that openness leads to a depreciation of the cedi in Ghana.

The coefficient of the terms of trade variable in negative and is statistically significant at 1 percent error level. This means that terms of trade negatively affects real exchange rate in Ghana.

Finally, the coefficient of growth of money variable is positive and is significant at 10 percent error level. This implies that increases in the growth of money causes the real exchange rate to increase.

With regard to the export equation, using the Cochrane-Orcutt Iterative procedure; the following findings were made:

The estimated coefficient of real exchange rate is negative implying that real exchange rate and exports are negatively related in Ghana. However, this variable was no found to be significant even at 10 percent error level. This means that the real exchange rate is not a major determinant of exports in Ghana.

The study also found that foreign aid inflows are positively related to exports performance. However, the estimated coefficient is not statistically significant implying that there is no direct meaningful relationship between foreign aid and export performance in Ghana. In other words there are more relevant factors than these. Further researches can therefore these factors.


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Finally, the price of exports variable is negative implying that the price of exports is negatively related to the volume of exports. This is consistent with the prediction made in chapter three that a rise in the price of exports, all things being equal will lead to a reduction in the demand for exports.

5.1 Recommendations The main policy recommendation to be drawn from this study is that because aid

inflows are associated with the depreciation of the real exchange rate, the Ghana government can continue to receive aid without fear of harming its export competitiveness. Aid can be used to finance supply sides improvement which would sustain higher exporter values and quality too.

Based on the results related to the openness of the economy, we suggest that the government of Ghana should reexamine the concept of over-liberalization of the economy. There is the need to check the volume of imposts so that it will not lead to over-depreciation of the cedi.

Finally, the fact that government consumption appreciates the real exchange rate implies the public sector has to introduce some fiscal discipline by curtailing its consumption or composition of tradable goods.

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[50] World Bank (1998), Assessing Aid, Washington D.C.: Oxford University Press.Younger, S. (1992), Aid and the Dutch Diseases: Macroeconomic Management When Everybody Loves You. World Development, Vol. 20.


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