groups. The regional and income distribution of the countries included in the dataset for 2005 is presented in Table 2.<sup>9</sup> For the analysis, I combine data from several sources which are explained in the previous section. In Table 3, I present the list of variables included in the analysis. The export data is obtained from 2006 to 2008 whereas the explanatory variables are from 2005 to 2007 when possible. Although some of the variables are available for a longer time span, the analysis is limited by the availability of the data for TTRI and MA-TTRI from WTI which are only available for 2005-2007. Data from global competitiveness report on the quality of infrastructure is only available for 2008. The last column in Table 3 shows the expected signs of the relationships between the explanatory variables and export sales. Descriptive statistics for the variables used in the analysis are presented in Table 4. In the table, I present both within and between country variations for each variable. The data shows that for most of the variables within variation is quite small compared to the overall variation which is due to the few number of time periods. This also shows why using fixed effects method in panel regressions would give imprecise results.

# **4. Analysis and Results**

Collinearity between the explanatory variables could make the estimation results imprecise and make it difficult to identify the relationship between each explanatory variable and export sales. I present the correlation matrix between the variables in Table 5. The table shows that investment climate indicators are highly correlated with each other. Thus in each estimation, I include only one investment climate indicator.

The first group of estimation results with the pooled OLS method is presented in Table 6. GDP is a strong correlate of high export performance which is a common finding of gravity models. On the other hand, large and remote countries export less. Ten percent increase in the remoteness of the country decreases export sales by 4-5 percentage points. These findings are in according with the results of Djankov et al (2010) and Iwanow and Kirkpatrick (2009). Past export growth also contributes significantly to the current export performance. Lastly, trade restrictiveness index which measures the stringency of domestic trade policies on imports negatively affect exports. This shows that there is complementarity between importing and exporting activities which could be caused by exporters’ extensive usage of imported intermediate goods.

Redding and Venables (2003) emphasize the importance of foreign market access as a factor of export growth. In their theoretical model, they define foreign market access of an exporting country as the sum of market capacities of all partner countries. They find that a substantial part of the differential export growth of numerous countries since 1970 can be attributed to variations in the rate at which their foreign market access has grown. In Table 6, I use the market access restrictiveness index (MATTRI) as a proxy for foreign market access. The table shows that difficulties in market access significantly decrease export performance. In four of the six specifications, its coefficient is negative and significant. Ten percent increase in market access lead to 2 to 8 percentage points increase in exports. Among the

> 9 In 2005 there are 136 countries. Data for Namibia is available for 2006 and 2007.

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