The Impact of Population on Bilateral Trade Flows in the Case of OIC Elif Nuroglu, International University of Sarajevo
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Abstract
The aim of this paper is to investigate bilateral trade flows and their determinants among six big OIC (Organization of the Islamic Conference) economies by using panel data analysis and cross sectional data. This paper extends the original gravity model of bilateral trade with population and volatility of exchange rates, and then uses this modified gravity model in panel data analysis. It shows how income and population of a country, distances between two countries and volatility of exchange rates affect bilateral trade flows among six big countries of OIC. The paper gives special emphasis on the influence of the population on a country`s trade flows and approaches to the issue of population size from a scientific perspective. It is shown that the impact of population on bilateral trade flows is positive for the exporter country, while it is negative for the importer country.
Keywords: OIC, gravity model, bilateral trade, volatility of exchange rates, panel data
1-Introduction and Literature Review on Gravity Model of Total Trade
This study investigates the determinants of bilateral trade flows across six big OIC economies, which are Indonesia, Malaysia, Turkey, Saudi Arabia, Iran and the United Arab Emirates, from 1985 to 2009 by employing panel data analysis. We use a modified gravity model of total trade that is extended by including population of exporting and importing countries and exchange rate volatility as explanatory variables of bilateral trade flows. This model is estimated for a data set of six big OIC countries from 1985 to 2009. The basic gravity model is developed by Tinbergen in the 1960s explaining bilateral trade between two countries depending positively on their economic sizes and negatively distances between them (Tinbergen 1962: 263-264). The gravity model claims that higher income tends to support trade by leading to more production, higher exports and also higher demand for imports (Dell`Ariccia: 1999; De Grauwe and De Bellefroid: 1986; Cushman: 1983, Balogun: 2007; Clark et al.: 2004; Matyas: 1997). Furthermore, larger distances between countries are expected to decrease bilateral trade (Clark et al.: 2004; Glick and Rose: 2002; Rose et al.:2000) by leading to higher transportation costs and some other difficulties to trade such as informational and
psychological frictions (Huang: 2007). It is well known that transport costs are an important barrier to trade and therefore they tend to reduce the level of international trade (Jacquemin and Sapir: 1988; Neven and Röller: 1991). It is possible to extend the basic gravity model by including the populations of exporting and importing countries to see what is the effect of population on bilateral trade flows between two countries. Matyas (1997) finds that population has a tendency to increase trade and the level of specialization by producing gains from specialization. On the other hand, Dell`Ariccia (1999) finds a negative population coefficient. Moreover, Bergstrand (1989) reports a positive GDP per capita coefficient, which means a negative relationship between population and trade flows, suggesting that imports and exports are capital intensive in production. He interprets a negative GDP per capita coefficient in a way that the product group which is subject to the estimation is not capital intensive but labor intensive. Moreover, the impact of population on trade may also differ depending on the length of the estimation period (short-term vs. long-term). . Population may have a positive impact on trade flows in the short-run, since it may increase the amount of labor force, the level of specialization and more products to export as a result. However, in the long run higher population has a tendency to decrease income per capita, making every individual poorer, and therefore it may cause production and exports to decrease. In addition to that, lower income per capita tends to decrease the demand for imports as well. Another variable that is supposed to affect the level of international trade is the exchange rates. Therefore, the effects of exchange rate volatility on international trade have long been studied. There are two approaches in the literature. According to the first approach, exchange rate uncertainty or volatility does not affect trade while the other side tries to prove the opposite. Hooper and Kohlhagen (1978) analyze the impact of exchange rate uncertainty on the volume of the US – German trade for the years 1965 - 1975, and find no statistically significant effect. Gotur (1985) reports a similar result after analyzing the volume of trade among the US, Germany, France, Japan and the UK. An IMF study (1984) summarizes that the large majority of empirical studies could not find a significant relationship between exchange rate variability and the volume of trade although this does not prove that the relationship does not exist. This view was supported recently by Bacchetta and van Wincoop (2000) who report that exchange rate uncertainty does not affect trade significantly.
On the other hand, Ethier (1983) finds that uncertainty in future exchange rates reduces level of trade. Cushman (1983) investigates fourteen bilateral trade flows among industrialized countries and finds a significant negative effect of exchange risk on trade. Akhtar and Hilton (1984) report a significant negative effect of nominal exchange rate uncertainty on bilateral trade between Germany and the US. Kenen and Rodrik (1986) analyze the effects of volatility in real exchange rates on the volume of trade and conclude that volatility depresses volume of trade. De Grauwe and De Bellefroid (1986) use cross sectional techniques for the European Economic Community countries for the years 1960-1969 and 1973-1984, and investigate the impact of variability in real exchange rates on trade. They also find significant negative effects. Lanea and Milesi-Ferretti (2002) examine the effects of appreciation and depreciation of exchange rates on trade and conclude that in the long run, larger trade surpluses are to be expected with more depreciated real exchange rates. Viane and de Vries (1992) analyze the effects of exchange rate volatility on exports and imports separately and find that exporters and importers are affected differently by the changes in exchange rates, because they are on opposite sides of the forward market. In addition to the above mentioned variables, international or bilateral trade is affected by many other factors such as common language, common border, colonial ties, being in the same trade union or free trade area, sharing a common culture and religion and so on. For example, Mehanna (2003) investigates the effects of politics, as represented by political freedom and corruption, and culture, as represented by religion and language affiliation, on intra – Middle East trade for the period 1996-1999 for a sample of 33 countries, 13 of which are middle-east countries and 20 are their major trading partners. The results show that culture has a statistically significant effect on the Middle-east trade. Moreover, Mehanna (2003) finds that the level of political freedom in these countries does not statistically affect Middle-east trade. However, corruption is shown to have a highly statistically negative effect on both exports and imports in the Middle-east. The structure of the paper is as follows. Section 2 gives some recent statistics of OIC countries and mentions about the studies related to intra-OIC trade. Section 3 introduces the modified gravity model of total trade used in this study. Section 4 informs about the data set and reports the regression results that explain the determinants of bilateral trade flows among OIC countries. Finally, Section 5 concludes.
2- Some Statistics of OIC Countries and Literature on Intra-OIC Trade According to 2008 figures, the GDP at current prices of the OIC member countries has increased almost three times from 1998 to 2007. In 1998, the GDP of the OIC countries as a group was 4.4% of the world GDP, while it amounted to 6.5% of the world GDP in 2007. Moreover, almost 50% of the total GDP of all OIC countries is produced by five countries which are Turkey, Indonesia, Saudi Arabia, Iran and the United Arab Emirates. Among these countries Saudi Arabia and United Arab Emirates are classified as High-income countries. In addition to these, when we look at the growth rate of GDPs of OIC countries and of the world from 2000 on, it is seen that GDPs of OIC countries has grown more than the world GDP. GDP per capita of the OIC countries shows a similar trend as well. The GDP per capita of OIC countries in 2007 is more than double the GDP per capita of OIC countries in 1998 (Statistical Yearbook OIC Member Countries 2008, 2009) In this study six big economies of OIC have been chosen and bilateral trade flows among these six countries have been analyzed. According to the latest figures these six countries, namely Turkey, Indonesia, Malaysia, Saudi Arabia, Iran and the United Arab Emirates, produce more than 50% of the total GDP of all OIC countries. So far many studies have been conducted to analyze bilateral trade flows among OIC member countries and also non-members. One of the questions that researchers seek to answer is whether OIC is trade creating or trade diverting. Hassan et al. (2010) investigate economic performance of the OIC countries within the framework of the gravity model and find that D8 which includes eight bigger OIC countries is trade creating. They claim that two countries in D8 block would trade 4.28 times more among themselves than two otherwise-similar country in outside of the block. Gundogdu (2009) estimates intra-OIC trade for the period of 1995-2007 and finds that OIC member countries have started to trade more with each other and also with the rest of the world as a result of their individual efforts and requirements of being members to free trade areas to remove trade barriers and reduce tariffs. He thinks that there are still many trade facilitation improvement opportunities which will support intra-OIC trade as well as OIC countries` trade with non-members. Furthermore, Karimi-Hosnijeh (2008) analyzes bilateral trade flows between Iran and OIC countries for the years 1998-2005 and shows that economic and cultural
similarities among OIC countries have a significant positive impact on their bilateral trade flows of agricultural products. Moreover, he claims that there is still a high potential of OIC countries to increase their exports to non-members up to 36% and imports from them up to 28%. On the other hand, researchers propose some ways to improve intra-OIC trade. For example, Ariff (1998) suggests that to increase intra-OIC trade, intra-OIC private sector investments should be encouraged. It is a fact that major trading partners may also be the major sources of foreign direct investment. According to him, through foreign direct investment raw materials and intermediate inputs will be imported and final products will be exported, and the level of total trade will increase as a consequence. He thinks that increased intra-OIC trade should not be the objective, but just a means to the end goal of making Islamic economies internationally competitive. The same issue is pointed out by Hassan (2002) as well. He thinks that much intraOIC trade can be created, not through preferential trading arrangements which will cause trade distortions and also be very costly to manage, but through intra-OIC private sector investment activities. He states in his paper that the OIC countries still trade more with the outside world than with their own partners. Because most of the OIC countries are producers of primary products, their foreign trade is mainly characterized with the export of raw materials and import of manufactured goods. Therefore, it is not surprising that their trade partners are mostly developed countries. Removal of tariff barriers may increase intra-OIC trade but more importantly, Hassan (2002) suggests, the production structure of OIC countries should be enhanced and developed through private sector investment. Moreover, Hassan (2002) counts a number of impediments to trade among the OIC member countries. First, most of the OIC countries are poor. Second, reliable and updated trade information among these countries is missing. Third, opportunities for business contacts among the private bodies of the OIC countries are limited. Fourth, there is a lack of marketing and distribution skills among business people of the OIC countries. Finally, the exportables of many OIC countries are not diversified. Bendjilali (2000) applies the gravity model to a sample of OIC countries that represent various geographical regions and levels of economic development, and examines the main determinants of intra-OIC bilateral trade in 1994 with reference to the characteristics of these countries. The results show that the larger the population, the larger the domestic market and the less are the exports. A larger population can also be interpreted as a bigger market for imports. The effect on total trade depends on which effect overcomes the other. On the other hand, higher GDP per
capita means enhanced demand for differentiated products as well, which has a tendency to increase the level of imports. Bendjilali (2000) also says that the volume of intra-trade among OIC members is low and the size of economic cooperation is limited. He shows that the size of the economy, the IDB financing and joint participation in regional integration schemes affect bilateral trade among OIC members in a positive way. Cernat (2003) makes an an ex-ante analysis of the Framework Agreement on Trade Preferential System (FATPS) among the member States of the OIC and the results indicate that FATPS has a significant potential for overall trade expansion, increasing the potential intraregional trade among its members. Although there is some potential for trade diversion, the net effect is trade creation.
3- A Modified Gravity Model of Total Trade
The Gravity Model says that trade flows between two countries depend positively on their income and negatively on the distances between them as shown in Equation 1 Tij C
GDPi GDPj Dij
( Eq. 1)
where c is a constant term, Tij is the value of trade between country i and country j, GDPi is country i`s income, GDPj is the country j`s income and Dij is the distance between two countries (Krugman and Obstfeld, 2006). According to the gravity model, large economies spend more on imports and exports. Therefore, higher GDP means more trade for a country. After the introduction of this basic gravity model, it was extended to catch the effects of population, exchange rates, having a common language and common border or being in the same trade union and so on. In this study, the gravity model is extended with the population of exporting and importing country and volatility of bilateral exchange rates. The proposed model that is used to explain the determinants of bilateral trade flows among six big OIC countries is: ln𝑇𝑖𝑗𝑡 = 𝛼 + 𝛽1 ln𝐷𝑖𝑗 + 𝛽2 ln𝑌𝑖𝑡 + 𝛽3 ln𝑌𝑗𝑡 + 𝛽4 ln𝑃𝑜𝑝𝑖𝑡 + 𝛽5 ln𝑃𝑜𝑝𝑗𝑡 + 𝛽6 𝑉𝑜𝑙𝑋𝑅𝑖𝑗𝑡 + 𝜀𝑖𝑗𝑡 (Eq. 2)
where Tijt is total bilateral trade between country i (exporter) and country j (importer) during time t. Tijt is calculated as the sum of exports from country i to country j and imports from country j to country i. Exports and imports are measured in current US dollars. Dij is the distance between capital cities of country i and country j that is measured in kilometers. Yit and Y jt are real GDP of country i and j respectively. Pop it and Pop jt are the populations of country i
and country j in time t. VolXR ijt is the 5-year (“t-4,...,t”) average of standard deviations from the average quarter-on-
quarter percentage change in bilateral nominal exchange rates calculated over the last 4 quarters, given by the following formula:
Volxrijt
1 q 19 q , 20 q
Eq. 3
where q is the last quarter in year t and
q
q 3 1 1 de q 3 q q 4 deq q 3
2
.
Eq. 4
q is a standard deviation from the average quarter-on-quarter percentage change in bilateral nominal exchange rate calculated over the last 4 quarters where deq eq eq 1 and e q is a logarithm of bilateral exchange rate at the end of quarter q (Kowalski, 2006). We expect that volatility in exchange rates will affect bilateral trade flows negatively. The reason behind is that an unstable economic environment will cause the prices to be very changeable depending on the fluctuations in exchange rates. This will result in less predictable profits for the exporters and therefore bilateral trade will be discouraged (Frank and Bernanke, 2007: 889).
4- Data and Results of the Modified Gravity Model
The data used in this study is obtained from IMF`s International Financial Statistics and Direction of Trade Statistics, World Bank`s World Development Indicators and Global
Economic Monitor. The sample period covers 25 years from 1985 to 2009. Countries included are Indonesia, Malaysia, Turkey, Iran, Saudi Arabia and the United Arab Emirates that are selected among the biggest economies of OIC countries. The model is estimated using bilateral trade flows across these six OIC countries from 1985 to 2009. From the data set of 6 countries, 15 bilateral trade flows are obtained and equation 2 is estimated by using these bilateral trade flows. The results are shown in Table 1.
Coefficient
t-Statistic
Prob.
LOG(Distance)
-0.63
-9.72
0.00
LOG(Exporter GDP)
0.59
4.19
0.00
LOG(Importer GDP)
0.32
2.13
0.03
LOG(Exporter Population)
0.18
2.68
0.01
LOG(Importer Population)
-0.26
-4.85
0.00
Exchange Rate Volatility
-0.71
-2.10
0.036
R-squared
0.66
Adjusted R-squared
0.63
Akaike info criterion
2.77
Schwarz criterion
3.10
Number of observations
370
Sample (adjusted)
1985-2009
Periods included
25
Cross-sections included
15
Table 1: Balanced panel estimates with period fixed effects, dependent variable: log of total bilateral trade
According to the gravity theory, the income of a country affects its trade in a positive way. Table 1 indicates that both income terms for exporter and importer country have the expected positive sign. We find that a 1 percent increase in the income of country i (exporting country) leads to a 0.59% higher bilateral trade. On the other hand, a 1% increase in the income of
country j (importing country) results in a 0.32% increase in bilateral trade. The difference from the previous studies should be emphasized by the discrepancy in the two coefficients. The contributions by the income terms of each country to the bilateral trade are quite different. Therefore, we do not take the product of exporter and importer GDPs, but include them to the model separately. Population of the exporting country has a positive impact on bilateral trade flows which shows that the higher the population the higher the production and exports as a result. Additionally, higher population may increase the need for the imported goods as well. On the other hand, population has a negative sign for the importing country. It can be interpreted in a way that higher population is expected to decrease income per capita which may lower the need for imports and also the level of exports. In the literature on intra-OIC trade flows, there are different results showing the effects population on bilateral trade flows. Hassan et al. (2010), Hassan (2002), Mehanna (2003) find positive income per capita coefficients supporting the idea that higher income per capita leads to more trade. According to Mehanna (2003) it is usual to find a positive impact of GDP per capita on bilateral trade flows in the intra-industry trade models while the comparative advantage theory predicts a negative link because it is based on different factor endowments. Following this interpretation, we can expect a positive link between income per capita and trade flows for the intra-OIC trade due to similar factor endowments of many OIC countries. On the other hand, Karimi Hosnijeh (2008) finds negative population coefficients for the exporter and importer countries. He interprets the negative exporter population coefficient in a way that if the population in a country is higher, people have a less tendency to export because they need more of the products for the domestic use. Another essential element of the gravity model is the distance between countries, which is on the denominator of the gravity equation (Equation 1). It is assumed that distance should have a negative sign because higher distances decrease international trade by increasing transportation costs and creating some additional difficulties to trade. The results show that distance has already the expected negative sign. Lastly, exchange rate volatility has a negative impact on bilateral trade flows among six big OIC countries. As the results indicate, when volatility increases by 1%, the percentage change in bilateral trade is -0.71%.
5- Conclusion In this study a modified gravity model of total trade is used to analyze bilateral trade flows among six big countries of OIC which approximately produce more than 50% of the total GDP of OIC countries. The adjusted R-squared is 63% which shows that our model can explain 63% of the variation in bilateral trade flows among six big OIC countries. Moreover, the coefficients of income, distance and volatility of exchange rates are in accordance with the international trade theory. The variable of interest for this study is population which has a positive sign for the exporting country suggesting that higher population is good to increase bilateral trade flows. This positive effect can be interpreted in a way that more people in an OIC country mean more production, better specialization opportunities, also more goods to be exported and a higher demand for the imports. However, population of the importer country has a negative sign. Here it can be interpreted that higher population decreases GDP per capita in a country, making everyone less rich and therefore it decreases the level of exports and also the need for imports.
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