The first indicator is regulatory quality which is obtained from Worldwide Governance Indicators (WGI). This index captures the ability of a government to formulate and implement sound policies and regulations that permit and promote private sector development. Details of how this indicator is constructed are presented in Kaufman, Kraay, and Mastruzzi (2009). The indicator is standardized between -2.5 and 2.5 with high scores corresponding to better outcomes. The second indicator measures financial development. It is the log of the ratio of money and quasi money (M2) to GDP which is collected through World Bank’s World Development Indicators (WDI). This is a standard macro level indicator of financial development in the literature. The third indicator is an index of business entry regulations. It is collected through the Doing Business surveys of the World Bank and it measures the number of procedures it takes to start a business. The forth indicator is a measure of country’s customs efficiency. Time to export is measured as the log of the duration (in days) of the goods to be exported. It is also collected by Doing Business. The fifth indicator is the quality of overall infrastructure (e.g. transport, telephone, and energy). This indicator is collected by World Economic Forum’s Global Competitiveness report. The last indicator measures protection of property rights. It is obtained from Economic Freedom of the World database. However the original data is collected by World Economic Forum. The last two indicators vary between 1 and 7 where 1 corresponds to lowest rating.

In the estimation, in addition to the trade policy variables and the indicators for investment climate, I control for the size of the country with two measures: log of real GDP and log of its area. Real GDP is measured in constant 2005 US dollars at PPP. I follow Dollar and Kraay (2003) and Alcala and Ciccone (2004) to use the PPP for converting GDP values into US dollars. Presenting GDP in PPP is more appropriate than deflating with market exchange rates for cross-country analysis. As for the area of the country, studies like Rodrik (1998) and Frankel and Rose (2000) shows that area has a significantly negative impact on openness. Large countries are less likely to trade because of relatively higher domestic demand and higher transport costs of exporting abroad. Moreover small size limits the country’s possibilities to diversify production. Another explanatory variable is the remoteness of the country from the rest of the world. Anderson and Van Wincoop (2003) show that a country’s trade with any partner country depends on its average distance from the rest of the world. Following the method introduced in Head (2003) I define a remoteness index.<sup>6</sup> Finally, I control for the past export growth performance of the country. This variable can control for the positive steps taken in the past to spur export performance such as implementation of trade liberalization policies, expansion of trade into new markets, exports of new products, or establishing trade agreements.

Export performance of countries is measured by log of export sales in constant 2005 US dollars which is obtained from World Bank Development Indicators (WDI). By measuring exports in constant US dollars, I assume that exported goods’ prices are roughly equalized across countries. Endogeneity is a major concern in the estimation, especially for the relationship between GDP and export. Gravity models assume that the volume of trade between two trading countries is positively related to the GDPs

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> Remoteness is defined as _remotei_  1/  _jGDPj_ / _Distij_  where Distij is the distance between two countries _i_ and _j_ . Djankov et al. (2010) use this index in their estimation of a modified gravity equation. They argue that remoteness is correlated with factory-to-port time delays hence excluding it from the analysis would produce biased estimates of the impact of trade facilitation on export sales.

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