importance of sector effects in the ANOVA (Table 3).<sup>20</sup> The results indicate that, within sectors, larger countries and richer countries have more exporters, larger-sized exporters (on average), as well as higher concentration in the top 5 percent of firms.<sup>21</sup> The economic magnitude of these effects is such that if GDP per capita increased from the value for the country in the 25<sup>th</sup> percentile (Cameroon) to the value for the country in the 75<sup>th</sup> percentile (Lebanon) the number of exporters would increase by 80 percent, average exports per firm would increase by 71 percent, and the share of the top 5% exporters would increase by 8 percentage points.<sup>22</sup> Columns (5)-(8) in Table 4 use the country-destination-year level data for years 2004 to 2008 and results are qualitatively similar. In addition to destination fixed effects these specifications control for traditional determinants of bilateral trade in gravity equations: distance, contiguity, common language, and common colonizer. The estimates show that controlling for year and market-specific characteristics, such as trade costs, more developed countries and larger countries have more exporters that on average export larger volumes and have higher export concentration at the top of the size distribution.<sup>23</sup>

> 20 The results remain similar if we use country-HS 4-digit-year level data and control for 4-digit sector fixed effects.

> 21 Total exports are equal to the product between the number of exporters and average exporter size. Since our regressions use the log of each of those variables as dependent variable and are estimated linearly, the coefficient on GDP (or GDP per capita) in the total exports regression is exactly equal to the sum of the coefficients on GDP (or GDP per capita) in the number of exporters regression and in the average exporter size regression, provided that the samples used in the three regressions are similar. Therefore in Tables 4, 5, 6 and Appendix Table A2.2 we restrict the sample in the number of exporters’ regression to be the same as that used in the total exports or average exporter size regressions, that is, it excludes observations with the number of exporters equal to 1. However, note that in unreported results where we estimate number of exporter regressions including those observations the effects of GDP per capita and GDP are stronger in magnitude and significance that those shown.

> 22 These magnitudes are obtained as the product of the difference in logs GDP per capita, 1.704, and the corresponding coefficient in columns (2)-(4) of Table 4: 0.471, 0.419, and 0.047.

> 23 As robustness check to the result on average exporter size, in Appendix Table A2.1, we re-estimate the specifications for average exporter size using data on incumbent exporters only, in order to exclude possible biases due to the use of “partial year” data as discussed in Bernard et al. (2013). In their analysis, they show that the entry and exit of firms into and from export markets at different points in a calendar year means that the use of calendar annual data on export values to measure the average size of exporter entrants or exiters may introduce a bias - mainly their size being under-estimated during the first year - which they refer to as “partial year” effects. Our consideration of the average size of incumbent exporters addresses this concern to the extent that bias should not permeate the calculation of average size for exporters that have not interrupted their exporting activity. We find that the results from Table 4 remain qualitatively similar under this alternative sample.

17