Testing the Export-Led Growth Hypothesis for less developed countries

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Testing the Export-Led Growth Hypothesis

for less developed countries

The Netherlands, 2016

Bachelor of Science

Written by

Erik Timmer

10545948 Supervised by

Rui Zhuo

University of Amsterdam Faculty of Economics and Business

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Statement of Originality

This document is written by Student Erik Timmer who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents

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To study this question, a cross-country data set has been developed with 36 less developed countries from over the world in the time period 1975-1985. I took the averages over the data period so I get one observation for each country. The OLS estimation is used to come to my results. After I did this, there was a significant positive relation between the growth rate of the export and economic growth in less developed countries at the 10 percent level.

1.Introduction

The Worldbank (1987) did a study on trade strategies and came with some evidence that an export promotion policy is the best option for a less developed country trying to become a more developed country. This view is also supported by the US Agency of Development and the International Monetary Fund (Giles and Williams, 1985). In 1993 the Worldbank came with another statement to give their previous statement more power. They stated that export is one of the primary sources of economic growth. In this study I want to find out if I can find support for these statements of the Worldbank in less developed countries. So my research question will be: Is there a significant positive relation between export and

economic growth in less developed countries?

The literature is divided in two groups, the studies that found support for the statements of the Worldbank and the studies that didn’t found support for the statements of the Worldbank. So does Dodaro (1993) find evidence for the statements in slightly more than half of the 86 countries he investigated using a time-series analysis, also Ram (1987) uses this method and found significant evidence for 38 out of the 88 countries he

investigates. Then there is the study of Bahmani-Oskooee and Alse (1993) who find evidence for the statements in eight out of the nine countries they investigate using an error

correction model. Then there are also studies that don’t support the statements of the Worldbank. Ahmed, Butt and Alam (2000) don’t support the statements in all but one country they investigate. Also Dutt and Gosh (1996) don’t find much evidence for the statements of the Worldbank, in just 5 out of the 26 countries they could find support for these statements. Both the studies of De Gregorio (1992) and Yaghmaian and Ghorashi (1995) don’t find evidence for the statements of the Worldbank and Yaghmaian and Ghorashi even find that the export doesn’t increase the model’s goodness of fit.

In this study, I will do a cross-sectional analysis between several less developed countries. I will choose countries from over the world to have a good variation of countries. I do research in the time period 1975-1985 and take the averages of these years. My

dependent variable will be the growth rate of the real GDP. My explanatory variable will be the growth rate of export, and the first control variable is the growth rate of gross capital

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formation. These 2 variables are divided by GDP to correct for country sizes. Then my last 2 control variables will be the inflation rate and the growth rate of the population. To come to my results I will use the OLS method.

In this study three equations will be tested. In the first equation where all the variables have been included, the growth rate of the export isn’t statistically significant. After this I excluded the growth rate of the population, but still the growth rate of the export wasn’t statistically significant. In the last equation, the growth rate of the gross capital formation has also been excluded. The only variables that are left are the growth rate of the export and the inflation rate. The result from this regression is that the growth rate of the export is statistically significant at the 10 percent level, so the export-led growth hypothesis will be accepted at the 10 percent level.

This paper is organized as follows: The next section of this paper will review the theoretical and empirical studies on the subject of an export-growth relationship. The section after that will provide the methodology I will use. Then the results of the study will be showed. Finally there will be a conclusion based on this study combined with the results of other studies.

2. Literature review

2.1. Theory on export-led growth hypothesis.

The export-led growth hypothesis states that economic growth is caused by the growth of the export of a country. Within this export-led growth hypothesis there are several reasons mentioned by other studies that support this hypothesis. So does Kónya (2006) support the export-led growth hypothesis by arguing that an increase in productivity level can lead to growth. An increase in export may cause specialization in the field of the product they are exporting. The increase in productivity level can in turn lead to output growth. He argues also that there can be another factor in favour of the increase in productivity level, this is that the increase in export may permit the imports of high quality products and technology. This could have a positive impact on technological change, capital efficiency and the

productivity level, which can in turn benefits the country’s production.

Another study that supports the export-led growth hypothesis is the study of Giles and Williams (1985). They argue that it is possible that an increase in export represents an increase in demand for the products made in their own country. When the demand

increases and so the export increases, there will be more job opportunities for the people in the home countries. More job opportunities means more income and this in turn leads to higher economic growth.

Grossmann and Helpman (1990) support the export-led growth hypothesis in their study by arguing that comparative advantages could increase the export of a country. Because of the low labour costs in developing countries or less developed countries the production is relatively cheap. Because it is relatively cheap to produce you can start

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producing more and through this you can make use of economies of scale which makes the production of a product even cheaper than it already was. Because of the cheap products, the price can decrease which in turn can led to higher exports. Also Chenery and Strout (1966) support the export-led growth hypothesis through the comparative advantage channel. They argue that because of the low wages, many investors wants to invest in the low-wages countries. These investors from abroad have more advanced technology and know-how. These investors could transfer this technology and know-how to improve the production process. This lead to lower costs and in turn to more exports and higher growth.

But not everyone supported the export-led growth theory based on theoretical arguments. So does Ramos (1990) not support the findings of Kónya (2006) based on the fact that the arguments he found are based on a short-run macro model. This model of the Keynesian type is orientated on the demand side and are not suitable to explain long-term growth. He argues that if you want to find support for the export-led growth hypothesis, you should look at models that are based on the supply side.

Bailey, Tavlas and Ulan (1987) reject the export-led growth hypothesis in their study because of the exchange rate-volatility. They say in their study that trade is hampered by exchange-rate risk. Their analysis goes as follows: If a firm sells in a forward market, so the firm knows the future price of his product at the time it incurs its costs of production. However, the firm doesn’t know the exchange rate at the moment they will sell in the future, there is an exchange-rate risk. If a firm is risk-averse they want to be compensated for this risk, which in turn leads to higher prices. These higher prices leads to less exports and that leads to lower increase in GDP than in absence of the exchange-rate risk.

Also Jaffee (1985) don’t find support for the export-led growth hypothesis. He argues that deteriorations in the terms of trade, as well as the fluctuations in the market price can have a negative effect on the long-term growth of a country that heavily depends on their exports. This questions whether a reliance on export can guarantee a stable revenue to allow sustained long-term growth. For Adelman (1984), this is also a reason for not

supporting the export-led growth hypothesis. In his paper he includes one extra reason for not supporting the export-led growth hypothesis, he says that trade barriers can become a big problem for the exporting countries. When exporting countries have to deal with trade barriers, the price of their products will increase or there will be a quota which leads to lower export volume. This decreases the export and decreases the growth of the country. So in theory they don’t think that you should focus on the export, so there is no support for the export-led growth hypothesis.

2.2 Empirical results

In this part I will review the empirical results of previous studies on the export-led growth hypothesis in less developed countries and developing countries. I split this part up in 2 sections, the proponents of the export-led growth hypothesis and the opponents of this hypothesis.

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2.2.1 Proponents of export-led growth hypothesis

Dodaro (1993) uses a very simple model which just includes the growth rate of the export as determinant for GDP growth. The data he employed are taken from the Worldbank’s World Tables. In total he collects data for 86 less developed countries and newly industrializing countries. He uses annual data from 1967 to 1986 if the data was available. Dodaro chooses to do a time-series analysis for each country individually, because he argues that a cross-sectional analysis will cause a biased effect due to the vast heterogeneity between the less developed countries. For slightly more than half of the countries he investigates, the coefficient of the growth rate of the export differs from zero at the 10 percent significant level. So he does find support for the export-led growth hypothesis in more than half of the countries. In his research you can see that almost all newly industrializing countries have a significant coefficient for the growth rate of the export, but that this is less for the less developed countries. He concludes from this that the export-led growth hypothesis holds more for newly industrialized countries than that it holds for less developed countries. As final remark on his study he says that a substantial number of countries is just significant at the 10 percent level. It is reasonable to expect that many of these coefficients may turn out to be insignificant when including additional variables.

The second study I will review is the study from Bahmani-Oskooee and Alse (1993). They use the error-correction model to test the export-led growth hypothesis. They collect their data from the International Financial Statistics of the International Monetary Fund. They are able to find data for only nine less developed countries for which quarterly export price indexes are available. These indexes are used to deflate the export values (in domestic currency) to obtain real exports. They use quarterly data from 1973 to 1988. They come to the result that there is a positive long-run relation between export and output in eight out of the nine countries they investigate at the five percent significant level. So they find strong evidence for the export-led growth hypothesis.

Kravis (1970) uses another approach to find a significant relation between export and output growth. He is one of the first to look for a relation using the spearman rank

correlation. He collects data of 37 non-oil exporting countries from 1950-1952 and 1963-1965 and took the averages in both periods to get one result per country each period. He argues that there is a tendency that successful exporters have had more rapid growth in real product. For these 37 non-oil exporting countries, the spearman's coefficients of rank

correlation between increases in exports and increases in real G.N.P. in both the sub-periods

1950-1952 and 1963-1965 was 0,51.This is significant at the one percent significant level. So

he finds evidence for the export-led growth hypothesis. This doesn’t match completely with the results of Michaely (1977). He does a test on 41 less developed countries on the average of the period 1950-1973 for each country. He finds a spearman rank coefficient of 0,38, this is also statistically significant at the one percent significant level. But he notes that the correlation of the two variables is in particularly strong in countries that are in the top of the list of the countries with rapid growth. So he decides to divide the countries by income per

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highest income per capita which is significant at the one percent significant level, but in the group with the lowest income per capita he finds a spearman rank correlation of -0,04 which is not statistically significant. So he finds evidence for the export-led growth hypothesis when a certain benchmark has been reached.

Balassa (1978) finds support for the significant relation between export and output using OLS. He uses data from 1960-1973 on 11 semi-industrial developing countries. He chooses countries that adopted export-oriented policies, countries that started import oriented but that became export oriented from the mid-sixties and countries that pursued import oriented policies the whole period. He uses domestic investment, foreign investment and labour as control variables and uses exports as the explanatory variable. For every country he takes the average of these variables and used these averages to run the OLS. Balassa finds a coefficient of 0,04 for exports, which is significant at the one percent significant level.

Another researcher that finds evidence for a significant relation between export and output is Ram (1987). He does a study on 88 less developed countries in the period 1960-1982. First he does a time-series analysis on the 88 countries individually. In his model he uses growth rate of labour, the investment-output ratio and growth rate of export as

independent variables and the growth rate of output as dependent variable. In 38 out of the 88 countries he finds a significant positive relation between the growth rate of export and the growth rate of output at the 10 percent significant level. He also finds evidence that the relation is more statistically significant among the middle-income less developed countries than among the low-income less developed countries, the same result as Dodaro (1993) found. After this he does also a cross-sectional analyses on these 88 countries for the sub-period 1960-1972 which is before the oil-crisis and the sub-sub-period 1973-1982 which is after the oil-crisis. In this case he uses the averages of the two sub-periods separately to come to his results. In both the sub-periods he finds evidence for a significant relation between the growth rate of export and the growth rate of output at the five percent significant level. It seems that the impact of the export on growth of the output has increased in the latter period, especially for the low-income less developed countries. He also looks at the

difference in low-income and middle-income less developed countries, and he finds that for the low-income less developed countries the growth rate of export isn’t statistically

significant in the first sub-period but becomes statistically significant at the five percent significant level in the second sub-period. For the middle-income less developed countries the relation is statistically significant at the five percent level in both the sub-periods.

2.2.2 Opponents of export-led growth hypothesis

Contrary to the studies that found evidence for the export-led growth hypothesis, there are also studies that rejects this hypothesis. So do Ahmed, Butt and Alam (2000) reject the export-led growth hypothesis in all but one countries they investigate using a trivariate causality framework. They do a study on eight Asian countries in the time period 1970-1977. They use export revenue and external debt servicing to explain economic growth. They use

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annual data on real GDP and real exports of goods and services from the World Development Indicators and for the external debt servicing they collect data from Global Development Finance. In their study they don’t find evidence of a long-run equilibrium relationship

between export and output growth in this time period, except for one country (Bangladesh). So they have to reject the export-led growth hypothesis.

Another study that didn’t find much evidence for the export-led growth hypothesis is a causality analysis by Dutt and Ghosh (1996). They did a study on 26 low-middle and high-income countries individually in the time period 1953-1991. First they test for cointegration between export and output growth. For some countries they don’t find cointegration results at all. This is also the case for Japan, which is really surprising. Because they argue that for Japan the export-led growth hypothesis is generally accepted. Now they have to look in which direction the cointegration goes for the countries where the cointegration results hold. And they come to the conclusion that just in 5 out of the 26 countries, that export-growth led to output export-growth at the five percent significant level. So there is not much

evidence in this study, so they have to reject the export-led growth hypothesis in most of the countries they investigated.

Also Colombatto (1990) don’t find evidence for the export-led growth hypothesis in his study on 47 less developed countries. It is a cross-sectional study, so he groups the countries together. In his study he looks at 3 different years, 1971, 1978 and 1985. This study uses a little bit a different approach as the others use, because he uses openness of trade as approximation for export and uses this as his dependent variable, the GDP he uses as independent variable. Then he looks at the spearman rank correlation and he finds no statistical relation between openness of trade and GDP per capita. From this he concludes that higher growth rates in export don’t necessarily benefit economic growth. So he rejects the export-led growth hypothesis with this conclusion.

De Gregorio (1992) does a study on 12 Latin American countries during the period 1980-1985. He uses a panel data analysis to come to his results. His dependent variable is the growth rate of the GDP and his independent variables are investment, variation of inflation, average inflation, foreign investment, literacy, government spending, initial GDP, terms of trade and a political variable. In this case the terms of trade is an approximation of the exports. He uses data found at the Worldbank and at the International Monetary Fund. After doing his regression on this data, he finds a significant effect for private investment, foreign investment, the variance of the average rate of inflation and the initial level of per-capita GDP, but the terms of trade don’t significantly differ from zero. His general conclusion is that the terms of trade don’t have an effect on the growth rate of GDP, so he rejects the export-led growth hypothesis based on these findings. He argues that Latin American countries are highly vulnerable to the external environment. Because of this it is not

surprising that the terms of trade highly correlates with the growth rate of GDP. But he finds that in the long-run this correlation seems to disappear and that it cannot be argued that improving terms of trade will increase the growth rate of the economy in the long-run.

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cross-country study on 30 developing countries in the time period 1980-1990. They took the averages of the variables in this period. Their sample includes countries in all of the four categories of trade orientation that are used in the 1987 World Report of the Worldbank. These categories are strongly outward oriented, moderately outward oriented, moderately inward oriented and strongly inward oriented. They argue that most of the previous studies used labour-force or population for the labour variable in the growth equations, but given the long structural unemployment in most developing countries this won’t be an appropriate proxy. They use only the employed section of a country in their model. But the lack of

available data on employment is a major factor in scaling the study down to 30 countries. As dependent variable they use the average annual growth rate of GNP, for their independent variables they use the investment-income ratio, the average annual growth rate of total employment and the average annual growth rate of exports. They find that the export variable is not only statistically insignificant, but that it also fails to improve the model’s goodness of fit. From this they conclude that there is no evidence to support the export-led growth hypothesis. They give as possible explanation that this could be due to

multicollinearity between export and the investment-income ratio. They test for this and they find a high correlation between these variables. So they test the first regression again, but exclude the investment-income ratio. Even without these variable they find that the coefficient of the export still isn’t statistically significant at the 10 percent level, so they reject the export-led growth hypothesis again.

3. Methodology

First of all the variables I want to include in the model. For my dependent variable I will use the growth rate of the real GDP of the countries, because I want to find out if the growth in exports has a significant positive effect on the growth of the GDP. So my explanatory variable will be the growth rate of the exports/GDP of the countries. I divide it by GDP to correct for different country sizes. I also will include some control variables. The first variable will be the growth rate of gross capital formation/GDP, import won’t be included because of the high correlation between export and import, which is explained by Chenery and Strout (1966). Balassa (1978, 1985) and Kavoussi (1984) included both the average labour force growth and in all these studies this was a significant variable for low income countries, but since the lack of data I will use population growth as an approximation for this just like Feder (1983) did. I will also include inflation in the model like De Gregorio(1992) did and the last control variable I want to include is the growth rate of broad money/GDP like Coppin (1994) used in his study, but because of the lack on data on this variable for several countries I

won’t include this control variable. So the regression formula will be:

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Where:

∆GDP = growth rate of the real GDP ∆EXP = growth rate of the export/GDP INF = inflation rate

∆CAPF = growth rate of gross capital formation/GDP ∆POP = population growth

Secondly the data set I will use in this research. I will choose 36 less developed

countries from the whole world. This because in the Asian countries there have been a rapid increase in exports relative to the output in the period I want to investigate, while on the other hand many African countries are typical examples of countries that have experienced inwardly orientated policies (Coppin, 1994). So to find out if the export-led growth

hypothesis holds for less developed countries in general I need to include different countries. The Asian countries I will include are Bangladesh, Hong-Kong, India, Indonesia, Korea, Malaysia, Nepal, Pakistan, Philippines, Singapore, Sri Lanka and Thailand. The African countries I will include are Cameroon, Central African Republic, Cote d’Ivoire, Gabon, Ghana, Kenya, Madagascar, Mauritius, Morocco, Senegal, South Africa, Togo. Then there are also countries from South- and Middle-America I want to include. These countries are Bolivia, Chile, Colombia, Costa-Rica, Ecuador, El Salvador, Guatemala, Honduras, Panama and Peru. From Europe I will use Greece and Portugal in my dataset. With these countries I have a good variation of countries around the world. These countries are a combination of countries that are used in other researches. So did for example Ram(1987), Bahmani-Oskooee et al. (1991) and Coppin (1994) used some of these countries as well. The time period I want to investigate is 1975-1985. I chose this period, because this is the period after the oil crisis in 1973, so they had time to recover from that. In this period no rare events like a crisis took place. I didn’t choose a more recent period, because then some countries didn’t be in the category less developed anymore. The reason I didn´t choose one specific year is because of the possibility that, that year wasn´t illustrative for the whole period. I will take the average over this time period to get one observation for each country and combine all the countries in one dataset.

The data I will use comes from the database World Development Indicators from the Worldbank. In my research I will use annual data. I will use the OLS method to come to my results. There are some potential drawbacks when using OLS. One of the problems in my regression model can be that there is an omitted variable bias, since I just use one explanatory variable and three control variables. It will be possible that there are more control variables that should be used and when these variables are not included you can get a biased result. Another potential problem in this research is that I don’t have a large data set. Because you don’t get many observations, your standard errors will increase and you get less significant results. A way to solve this problem is using a panel data analysis. When using a panel data analysis, you can extend your data set since then you will have 10 observation for one country instead of 1. Another problem that could occur is multi-collinearity between

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the independent variables. If two or more independent variables correlate high with each other, it can cause biased results. To test for this you should look at the correlations. This correlations can be found in table 1:

Table 1:

correlations GDP EXP INF CAPF POP

GDP 1

EXP 0,257442 1

INF -0,48461 -0,01588 1

CAPF 0,315307 0,240749 -0,10301 1

POP 0,203242 0,498162 -0,03025 0,194769 1

As you can see, the correlation between the growth rate of the export and the growth rate of the population is very high. There could be multi-collinearity between these variables, which can cause biased effects. Also the growth rate of the capital formation has a relatively high correlation with the growth rate of the export. The inflation has a high

correlation with the GDP. This suggests that there will be a significant effect between those variables. For the other variables, there is a relative high correlation so for these variables you could expect an effect on GDP. You can see a negative correlation between the growth rate of the export and the inflation rate, this make sense because inflation causes higher prices and higher prices decreases the export. The positive correlation between the growth rate of capital formation and the growth rate of the export can be explained by the fact that investments in a company increases the output of a company, which in turn can increase the export of a company.

4. Results

My hypothesis for this research is that the growth rate of export/GDP has a statistical positive relation with the growth rate of the GDP. This holds with the export-led growth hypothesis. So the regression will be tested with

𝐻0: 𝛽1 = 0 𝐻1: 𝛽1 > 0

First I tested the whole regression model:

(1) ∆𝐺𝐷𝑃𝑖 = 𝛼 + 𝛽1 ∗ ∆𝐸𝑋𝑃𝑖+ 𝛽2 ∗ 𝐼𝑁𝐹𝑖+ 𝛽3 ∗ ∆𝐶𝐴𝑃𝐹𝑖+ 𝛽4 ∗ ∆𝑃𝑂𝑃𝑖+ 𝜀

The results can be seen in table 2. For Bolivia and Chile 2 years have been leaved out the model, because of the extreme inflation in that period. This extreme inflation indicates that there was something happening in the economy that was not illustrative for the group of less developed countries. For Mauritius a time period from 1977-1985 has been taken, because data from before this period wasn’t available for the growth rate of the GDP and

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the inflation rate. From the results of this model can be concluded that inflation rate is the only significant variable in the model at the one percent significant level. This corresponds to what we expected when looking at the correlations. So the H0 hypothesis should be

accepted and the export-led growth theory doesn’t hold in this case.

To see if the growth rate of the export will become a significant variable we need to exclude some of the insignificant variables. The first variable I will exclude is the growth rate of the population. This variable is the less significant of all variables and has the highest correlation with the growth rate of the export. This high correlation can lead to higher standard errors and more biased results. The insignificancy of the population as

approximation for labour force growth contradicts the findings of Feder (1983), who found a significant effect, but corresponds to the findings of Copper (1994) and Burney (1996). When you looked at the data I found, this insignificancy wasn’t much of a surprise. There wasn’t a high difference in population growth between the countries I investigated, while there were differences in the growth rate of the GDP between the countries. So you could predict from this that the growth rate of the population wouldn’t have a significant effect on the growth rate of the GDP. Now we are left with the following equation:

(2) ∆𝐺𝐷𝑃𝑖 = 𝛼 + 𝛽1 ∗ ∆𝐸𝑋𝑃𝑖+ 𝛽2 ∗ 𝐼𝑁𝐹𝑖+ 𝛽3 ∗ ∆𝐶𝐴𝑃𝐹𝑖+ 𝜀

The results can be found in table 2. As you can see the R-squared of the model didn’t decrease a lot when the growth rate of the population has been excluded. This is also what you could expect, since the growth rate of the population wasn’t statistically significant at all. Inflation is still the only variable that is statistically significant at the one percent significant level. You can also see that the growth rate of export became much more significant than before. This could be due the high correlation between the growth rate of the population and the growth rate of the export. But this variable still isn’t statistically significant, so the export-led growth hypothesis still will be rejected.

Also the growth rate of the capital formation still isn’t statistically significant. So this variable should also be excluded from the model. The insignificancy of the growth rate of the capital formation corresponds to the findings of Jaffee (1985), who found that capital

formation isn’t statistically significant for less developed countries, but that it is statistically significant for more developed countries. When this variable is excluded we are left with the following equation:

(3) ∆𝐺𝐷𝑃𝑖 = 𝛼 + 𝛽1 ∗ ∆𝐸𝑋𝑃𝑖+ 𝛽2 ∗ 𝐼𝑁𝐹𝑖+ 𝜀

The results can also be found in table 2. The inflation rate still is the only variable that is significant at the one percent level, but now the growth rate of the export is also statistically significant at the 10 percent significant level. So now the growth rate of the capital

formation is excluded from the model we are left with two significant variables. So in this case the H0 hypothesis will be rejected and the export-led growth hypothesis will be accepted at the 10 percent significant level. This corresponds to the findings of Dodaro (1993) and Ram (1987) who also found a significant positive relation between the growth

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rate of the export and the growth rate of the GDP at the 10 percent significant level. The standard errors still are relatively high, this is because I don’t have a large dataset. If the dataset will be larger these standard errors probably will go down and the result will become much more significant.

Table 2: regression results

Eq. Constant EXP INF CAPF POP 𝑅2

(1) 0,060** 0,130 -0,234** 0,141 0,070 0,3457 [6,65] [0,98] [3,13] [1,43] [0,38] (2) 0,061** 0,155 -0,234** 0,144 0,3426 [7,16] [1,33] [3,18] [1,49] (3) 0,061** 0,196* -0,245** 0,2972 [7,02] [1,71] [3,29]

This table gives the values of the coefficients in the regression models. Between brackets the absolute t-value has been given. * is statistically significant at the 10 percent level, ** is statistically significant at the 1 percent level.

5. Conclusion

In this paper I presented an empirical analysis to find an answer on the question: Is there a significant positive relation between the growth rate of the export and economic growth? In my research I used a cross-section analysis to come to my results. I collected data about the growth rate of the GDP, the growth rate of the export, the growth rate of the capital

formation, the inflation rate and the growth rate of the population for 36 less developed countries from over the world. I collected these data from the period 1975-1985 an took for every country the averages for these variables. In the first regression the growth rate of the export wasn’t statistically significant at the ten percent level. Then I tested the regression again, but excluded the population variable. I excluded this variable because this variable had the highest correlation with the growth rate of the export and this variable wasn’t statistically significant. Also in this regression the growth rate of the export wasn’t statistically significant at the ten percent level and the export-led growth hypothesis was rejected again. Then I tested the regression again, but excluded the growth rate of the capital formation as well because of the insignificancy of this variable. Now the growth rate of the export became statistically significant at the 10 percent level. This result confirms the results of Dodaro (1993), Bahmani-Oskooee and Alse (1993), Kravis (1970), Balassa (1978) and Ram (1987). Then there are some downsides about the method I used in this research. The cross-sectional analyses method may not be the best method to use for testing the export-led growth hypothesis. Because of the fact that when you group countries together, you assume that these countries have the same economic structure and production

technology which isn’t probably the case. Another downside is that a cross-section analysis ignores the shifts in relationship between variables over time. Export and economic growth is a long-run relationship that can’t be fully captured by this kind of analysis. A way to solve

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these problems is using a panel data analysis. With a panel data analysis you get much more data in your data set. You don’t need to take the average for a period but you can use every year separately in your data set. Because of this you can find long-run relationships between variables which solves the problem of the cross-section analysis. Another benefit of a panel data analysis is that you get more observations per country, the more observations the more reliable your results will be. The standard errors of your variable will become lower and the variables will become more significant.

6. References:

Adelman, I. (1984). Beyond export-led growth. World Development, 12(9), 937-949.

Ahmed, Q. M., Butt, M. S., Alam, S., & Kazmi, A. A. (2000). Economic Growth, Export, and External Debt Causality: The Case of Asian Countries. The Pakistan Development Review, 591-608. Bahmani-Oskooee, M., & Alse, J. (1993). Export Growth and Economic Growth: An Application of

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Testing the Export-Led Growth Hypothesis for less developed countries