The impact of exports on economic growth: It s the market. form

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1 The impact of exports on economic growth: It s the market form Michael Jetter May 23, 2014 Abstract This paper unites theories about growth effects from various trade characteristics, proposing the market form into which a country exports as the main driver. Exporting into concentrated world markets (oligopolies or monopolies) leads to higher profit margins, comparable to fundamental microeconomic logic. I develop an index measuring the average market form of a country s exports (the AEC). The index produces a positive and statistically significant coefficient in conventional growth regressions. A one standard deviation increase in the AEC is suggested to raise growth by percentage points. This result holds for numerous extensions and seems particularly important for developing economics. JEL Classification: D40, F43, F60, O40 Keywords: International Trade, Exports, Growth, Export Diversification, Export Markets Universidad EAFIT and IZA (Bonn), Medellin, Colombia; web:

2 1 Introduction Several characteristics of international trade have been proposd to be directly related to economic growth. Explanations of exporting primary or secondary products, traditional or non-traditional goods, and other denominations have been used to determine which forms of exports should be preferable over others. The natural resource curse (Sachs and Warner, 2001) here is standing out in particular. However, we continue to the see exceptions to these rules, such as the outstanding performance of oil producing countries. Is there a common denominator under which we can identify growth-promoting and -harming exports? This paper proposes such a common denominator by focusing on the global market form of goods and services. Exporting a good, which none (or very few) other countries export on the world market may create substantial margins of profit. Reasons for this could be any of the generic arguments for monopolies or oligopolies: entry barriers due to high setup costs, rare natural resource abundance etc. Exporting into more competitive markets does not provide this opportunity, everything else equal. 2 The Index Consider country i (with 1 i n) and the US$ value of its total exports in one period, X i. Further classify the world production into m tradable goods and services (with 1 j m). Finally, define x ij as country i s export value of good j. First, I create a global index of concentration for each good with HHI j = n ( xij X i=1 j ) 2, (1) where X j represents total worldwide exports of good j and 0 HHI j 1. The HHI j is an application of the Herfindahl-Hirschman index, only applied to countries. The closer the value is to 1, the less competition exists among countries exporting good j. We can then derive an 1

3 index measuring country i s average export concentration (AEC) degree by AEC i = m x ij HHI j j=1 (2) m x ij j=1 with 0 AEC i 1. If AEC i approaches 1, the country is exporting mostly in oligopolistic or even monopolistic markets. If AEC i approaches 0, the country exports mostly in perfectly competitive markets. As in basic microeconomic theory, the following pages argue that the higher AEC i, the more exports contribute to profits, which on the country level translates to economic growth. 3 Empirical Framework To derive the AEC i, choosing data becomes crucial. If global exporting data is incomplete, for example missing a significant number of goods and services traded on the world market, then the AEC i can be severely biased, not capable of capturing the entire export basket of a country in a given year. On the other hand, if a substantial number of countries are absent in the data set, such a bias can occur for the calculation of the world market form of particular goods and services. For example, were the database to miss exports from Saudi Arabia and Russia, then the calculation of the world market for oil would become severely biased, missing the two biggest exporters. The data source used here is the United Nation s Commodity Trade Statistics Database (ComTrade), which contains data on exports on the 6-digit level. The number of countries included in this database steadily rises throughout the 1990s and eventually jumps from 138 in the year 1999 to 157 in From there on it peaks in 2004 with 166 observations and remains relatively stable. Thus, I choose the years as the main empirical framework for this analysis. To get an idea of what the AEC index measures, consider the values for 2010 in table 1, displaying the countries with the five most and the five least concentrated export markets for which growth rates are available. Here, it might be helpful to look at the conventional HHI 2

4 definitions of market forms: an AEC i < 0.01 indicates highly competitive markets, AEC i < 0.15 marks unconcentrated markets, 0.15 < AEC i < 0.25 suggests moderate concentration, and AEC i > 0.25 stands for high concentration. We can see that only 2 countries export in highly concentrated markets on average the Niger and the Philippines. Interestingly, both countries show high growth rates for 2010 (8.8 and 7.6 percent) and the remaining leaders on the top of this list display strong growth as well. However, countries exporting in highly competitive markets mostly show weaker growth performances. Overall, growth rates and the AEC i in 2010 return a correlation coefficient of almost 40 percent, as displayed in figure 1. Table 1: Index of the Average Concentration (AEC) in most concentrated export markets 5 least concentrated export markets Country AEC growth Country AEC growth Niger Samoa Philippines Kyrgyz Republic Paraguay Venezuela Zimbabwe Bahamas Australia Mali To test whether the average export concentration index (AEC) is related to a country s growth rate, I set up a basic growth regression. Given the abundant growth literature and the so-called openendedness of growth determinants, I follow two benchmark papers Levine and Renelt (1992) and Mirestean and Tsangarides (2009) in terms of control variables. This list includes trade openness (Lopen), GDP per capita (Lgdp), the inflation rate (Linfl), government size (Lgov), the exchange rate (Lexch), life expectancy (Llif e), the investment level (Linv), and population growth (popgr). All variables beginning with an L indicate using the natural logarithm following the conventional growth literature and facilitating comparability across countries. Further, given the recent findings surrounding the institutional framework and its relationship to growth, I incorporate the Polity IV index to measure the degree of democracy. 1 1 Specifically, I choose the variable polity2, ranging from -10 (totally autocratic) to +10 (total democracy). 3

5 Figure 1: Correlation between the AEC and the growth rate of GDP/capita in 2010 Unless otherwise noted, variables are taken from the World Development Indicators (World Bank). 4 Empirical Results Table 2 displays the main results, where control variables are added moving from left to right. To facilitate the comparison of coefficients, columns (1) through (5) only use observations for which all variables are available. Column (1) begins by simply regressing a country s growth rate on the AEC and trade openness. Notice that both variables return positive estimates, yet the coefficient on AEC is also significant on the 10 percent level. Columns (2) to (4) include country and time fixed effects to control for any potential unobservable heterogeneity across nations and years. Given vast differences across countries, but also 4

6 Table 2: Main OLS regressions estimating annual growth rates. Means of variables are displayed in parentheses after variable names. (1) (2) (3) (4) (5) (6) Dependent variable: gr it (3.92) AEC it (0.15) (6.591) (5.886) (6.323) (5.092) (3.622) (4.373) Lopen it (0.55) (0.626) (2.664) (0.547) (2.518) (2.096) (1.895) Country fixed effects yes yes yes yes Time fixed effects yes yes yes yes Control variables a yes N ,519 # of countries R Standard errors in parentheses. p < 0.10, p < 0.05, p < 0.01 a Includes the following control variables: lgdp it, linfl it, lgov it, lexch it, llife it, polity it, linv it, and popgr it. 5

7 common global events, such as the financial crisis in 2007/2008, incorporating two-way fixed effects may be of particular importance here, as previously shown in a variety of literatures (e.g. Islam, 1995, for growth). Note that the AEC remains a strong positive predictor of growth rates as statistical significance increases, as well as the magnitudes of the estimated coefficient. Column (5) incorporates the control variables described in section 3. The AEC remains statistically significant on the one percent level with a magnitude of In this most complete specification, a one standard deviation increase of the average world market in which a country exports to is suggested to raise growth by percentage points. Notice also that this most complete model is able to explain over 64 percent of the variation in growth rates. Finally, column (6) re-estimates the basic model including two-way fixed effects with all available observations. As the inclusion of the control variables substantially limits the sample size, this estimation tests whether the derived result is also valid for the entire sample. In fact, the general interpretation remains consistent. 5 Extensions Table 3 presents several extensions to this main finding. Column (1) increases the timeframe of observations back to 1995, whereas column (2) addresses the latent endogeneity issues for growth equations in a GMM framework, where all control variables are instrumented with their lagged values by one year. Columns (3) to (5) include other variables, which are closely related to international trade: the traditional Herfindahl-Hirschman index measuring the diversification of a country s export basket in general, the share of natural resources in GDP, and a measurement for regulatory trade freedom (from the Economic Freedom Index). Note that the importance of the AEC does not appear to be driven by basic export diversification (the hhi), natural resource importance (natres), or trade regulations (tradef reedom). Further, column (6) uses country averages, thereby addressing the long-term aspect of the link between the AEC and growth. All of these estimations lead to the same conclusion: countries exporting in more concentrated markets experience higher growth rates. Finally, columns (7) and (8) split the sample into OECD and non-oecd countries checking for potential endogeneities regarding the proposed relationship. Indeed, we find that the effect 6

8 Table 3: Extensions. Means of variables are displayed in parentheses after variable names. GMM b OECD non-oecd (1) (2) (3) (4) (5) (6) (7) (8) Dependent variable: gr it (3.92) AEC it (0.15) (4.029) (2.760) (3.608) (3.611) (4.255) (3.711) (16.020) (3.558) lopen it (0.55) (2.330) (0.597) (2.108) (2.144) (2.145) (0.414) (5.093) (2.422) hhi it (0.13) (1.470) natres it (8.20) (0.035) tradefreedom it (67.75) (0.022) Country & time fixed effects yes yes yes yes yes yes yes Control variables a yes yes yes yes yes yes yes yes N # of countries R Standard errors in parentheses. p < 0.10, p < 0.05, p < 0.01 a Includes the following control variables: lgdp it, linfl it, lgov it, lexch it, llife it, polity it, linv it, and popgr it. b Instrumenting for lgdp it, linfl it, lgov it, lexch it, llife it, polity it, linv it, and popgr it with their respective values of the previous year. Using an unadjusted independent weight matrix. does not exist in OECD nations, but prevails with force for non-oecd economies. Thus, the market form of exports might be of particular importance for emerging countries. 6 Conclusion This paper tries to put trade aspects and their link to economic growth on one common denominator. I find that it is not export diversification, distinctions between traditional versus non-traditional goods, primary versus secondary products, or other categorizations of goods and services that drive whether a country grows faster. In fact, it appears to be a simple market power story, where exporting in less competitive global markets may allow for bigger profits. Implementing this measurement of market concentration into a conventional growth regression then suggests that a one standard deviation increase towards exporting in more concentrated markets would lead to a percentage point increase in the country s rate of economic growth. These results remain statistically significant and robust throughout a variety of alternative estimations. 7

9 References Islam, N. (1995). Growth empirics: a panel data approach. The Quarterly Journal of Economics, 110(4): Levine, R. and Renelt, D. (1992). A sensitivity analysis of cross-country growth regressions. American Economic Review, 82(4): Mirestean, A. and Tsangarides, C. (2009). Growth Determinants Revisited, volume 9. International Monetary Fund. Sachs, J. D. and Warner, A. M. (2001). The curse of natural resources. European Economic Review, 45(4):

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