gap.This ratio reflects the fraction of the actual dispersion of productivity that can be accounted for by the dispersion in technology across firms. Table F.12 in the Appendix reports the predicted productivity levels at the 10<sup>_th_</sup> and 90<sup>_th_</sup> percentiles and the ratio of predicted over actual productivity gaps for each of the specifications we have estimated of regression 6. This ratio ranges from 31% to 37%. Therefore, we conclude that differences in firm-level measures of productivity account for one third of the gap in productivity between the firms at the 10<sup>_th_</sup> and 90<sup>_th_</sup> percentiles of the distribution.

# **7.2 Cross-country productivity gap in agriculture**

One of the big puzzles in the productivity literature is the large sectoral variation we observe in cross-country productivity differences. In particular, Caselli (2005) shows that crosscountry differences in productivity are ten times larger in agriculture than in non-agricultural sectors.<sup>48</sup> Table 10 shows that the sample of firms covered by the FAT survey also displays a larger cross-country productivity gap in agriculture than in non-agriculture. Using employment weights to compute sectoral productivity in each country, we observe that the ratio of labor productivity between Brazil and Senegal in agriculture in FAT is 15.3, while in nonagricultural sectors it is 2.5. Therefore, the relative productivity gap between the Brazilian State of Cear ́a and Senegal in FAT is 6.1 times greater in agriculture than in non-agricultural sectors.<sup>49</sup> Part of these sectoral differences in productivity reflect the larger cross-country differences in firm size in agriculture than non-agricultural sectors. As smaller production units tend to be less productive, the greater difference in average size between rich and poor countries in agriculture vs. non-agriculture explains some of the cross-sector difference in the relative productivity gap, in productivity. To filter the effect of firm-size variation we focus on raw (rather than employment weighted) firm productivity measures. The first two columns of Table 10 report the average productivity of FAT firms in each country and broad sector without using employment weights. In this case, the gap in firm productivity between Brazil and Senegal in agriculture is 11.4, while in non-agriculture is 2.9. Therefore, the ratio of the cross-country firm-productivity gaps in agriculture vs. non-agriculture is 4.

Lagakos and Waugh (2013) show that selection of labor across sectors coupled with a minimum subsistence requirement for agricultural products may generate cross-country differences in productivity that are twice larger in agriculture than in non-agricultural sectors.

> 48Unlike our data, Caselli, uses PPP adjustments to compute sectoral productivity which may induce additional discrepancies in cross-country productivity gaps across sectors if the PPP price index differs more across countries in agriculture than in non-agricultural sectors.

> 49Using World Bank Data on value added per worker by sector, the productivity ratios between these two countries in 2019 are 4.6 for agriculture and 3.1 for non-agriculture, resulting in a significantly smaller ratio than what we find in our survey (1.5 vs. 6.1).

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