The relationship between trade openness and economic growth: Some new insights on the openness measurement issue

Reviewing the existing literature on openness and growth shows that there is not a clear definition of trade openness. For many authors trade openness implicitly refers to trade policy orientation and what they are interested in is to assess the impact of trade policy or trade liberalization on economic growth. For other authors however, trade openness is a more complex notion covering not only the trade policy orientation of countries but also a set of other domestic policies (such as macroeconomic policies or policies related to law and institutions for instance) which altogether make the country more or less outward oriented. In such a case, what the authors are interested in is to measure the impact of global policy orientation on economic growth. Finally, one may adopt an even more global view of trade openness covering not only the policy dimension but also all other non-policy factors that clearly have an impact on trade and on the outward orientation of countries. Factors such as geography and infrastructures, for instance, do affect trade and the outward orientation of countries, whatever their policy orientation. Many different measures of trade openness have been proposed and used in empirical analyses of the relationship between openness and growth. They more or less relate to the three alternative definitions of openness mentioned above. In line with the trade policy orientation definition, some authors have retained measures based on trade restrictions/distortions, such as average tariff rates , average coverage of quantitative barriers, frequency of non-tariff barriers or collected tariff ratios (see, e.g., Pritchett, 1996; Harrison, 1996; Edwards, 1998, Yanikkaya, 2003). Obviously these indicators are very imperfect partial measures of the overall restrictions/distortions induced by trade policies. Furthermore, data required to compute such indicators are most often available for only a limited set of countries and years. Corresponding to the global policy orientation definition, various “qualitative” indices allowing for classifying countries according to their trade and global policy regime have been proposed (see, e.g., the 1987 World Development Report outward orientation index, the Sachs and Warner, 1995, openness index or the Wacziarg and Welch, 2003). Such indices unfortunately provide only a very rough classification of countries (from rather closed to rather open). Also many of the data required to construct these indices are available only for a few countries and at one point in time. Finally, measures based on trade volumes, which have been very commonly used in empirical analyses, rather relate to the most global definition of trade openness. Trade dependency ratios are the most popular of these measures (see, e.g., Frankel and Romer, 1999; Irwin and Tervio, 2002; Frankel and Rose, 2002; Dollar and Kraay, 2004 and Squalli and Wilson (2011) for a recent contribution). Their main advantage is that the data required to compute them are available for nearly all countries and over a rather long period. Their main weakness is that they are outcome-based measures and as such are the result of very complex interactions between numerous factors so that it is never clear finally what such measures empirically capture exactly. Another limitation of these trade dependency ratios lies in their endogeneity in growth regressions, which requires specific estimation techniques (such as instrumental variables techniques as in Frankel and Romer, 1999, and Irwin and Tervio, 2002, or identification through heteroskedasticity techniques as in Lee et al., 2004). This last limitation may in fact be extended to all trade openness measures and constitutes the second shortcomings in existing empirical evidence on openness and growth that has been pointed out by Rodriguez and Rodrik (2001). As argued by Lee et al. (2004), all measures of openness are generally closely linked to the growth rate. Hence, this is likely that all measures of openness are jointly endogenous with economic growth, which may cause biases in estimation resulting from simultaneous or reverse causation. Various methods have been used to remedy this problem and there is still a debate among scientists about which method is the most appropriate (see, e.g., Dollar and Kray, 2004, and Lee et al., 2004). In this paper, we adopt the most global definition of trade openness and our aim is to contribute to the on-going debate on the growth effect of trade openness. Relative to the existing literature, our contribution is the following. Firstly, we argue that trade openness is a multidimensional concept that cannot be summarized to a single measure such as the most commonly used trade share (calculated as the sum of imports and exports over GDP). Secondly, recent developments in growth theory and in international economics have provided new insights on the relationship between trade and growth, which call for additional measures of trade openness. Hence, we propose a more elaborated way of measuring openness taking into account two additional dimensions of countries’ integration in world trade: quality and diversification. Endogenous growth theory has provided results on the positive growth effect of trade through innovation incentives, technology diffusion and knowledge dissemination (see, e.g., Young, 1991; Grossman and Helpman, 1991). Inspired from these theoretical developments, Hausmann et al. (2007) proposed an analytical framework linking the type of goods (as defined in terms of productivity level) a country specializes in to its rate of economic growth. In order to test empirically for this relationship, they then defined an index aimed at capturing the productivity level (or the quality) of the basket of goods exported by each country. Their growth regression results showed that countries exporting goods with higher productivity levels (or higher quality goods) have higher growth performances. These results suggest that what countries export matter as regards the growth effect of trade. Hence our measurement of trade openness should consider this quality dimension as a complement to the volume (or the dependency) dimension. Monopolistic competition trade models with heterogenous firm and endogenous productivity provide theoretical results supporting the positive growth effect of trade through both increased variety of products and improved productivity due to the exit of less efficient firms (e.g., Melitz, 2003). Based on this literature, Feenstra and Kee (2008) developed a model allowing to link, for each country, relative export variety to average productivity and then to GDP growth. Using Feenstra (1994)’s index of export variety, they tested this relationship on the basis of exports to the US for a panel of 48 countries over the period 1980-2000. Their empirical results indicated that there is a positive and significant relationship between export variety and average productivity. Once again these results suggest that the structure of countries’ exports matter regarding the growth effect of trade. Hence, our measurement of trade openness should also consider this variety/diversification dimension. Our empirical application draws on the Barro and Lee (1994)’s model, which has been extended to take into account our set of three indicators of trade openness: trade dependency ratio, quality index and diversification index. Barro and Lee (1994) study the empirical determinants of growth. They are in line with the endogenous growth theory which calls into question diminishing returns. Unlike the usual neoclassical growth model for a closed economy (Solow (1956)), endogenous growth model questioned the source of technology (human capital, role of government for instance). Openness may play an important role for developing countries in particular: it may help them to import technology from developed countries and thus increase human capital in these countries. We include some proxy for openness in our empirical model. Estimations are performed on annual data over the period 1980-2004 for an unbalanced panel of 170 countries. We use a generalized method of moments (GMM) estimation approach developed for dynamic panel data models in order to deal with potential endogeneity biases due to omitted variables, simultaneity and measurement error. Our results confirm that countries trading higher quality products grow more rapidly. The preliminary results of the impact of diversification are less obvious. They seem to depend on the indicator of diversification that is used. It is expected that countries trading more diversified products could grow more rapidly.