Table 6: Idiosyncratic distortions and business environment

||(1<br>TFP|)<br>R|(2<br>_τ_<br>|)<br>_~~k~~_|(3<br>_τ_<br>|)<br>_~~y~~_|
|---|---|---|---|---|---|---|
|Finance|0.90<sup>_∗∗∗_</sup>|(0.04)|1.88<sup>_∗∗∗_</sup>|(0.05)|0.20<sup>_∗∗∗_</sup>|(0.03)|
|Electricity|-0.05<sup>_∗∗∗_</sup>|(0.00)|-0.02<sup>_∗∗∗_</sup>|(0.00)|-0.04<sup>_∗∗∗_</sup>|(0.00)|
|Corruption|0.06<sup>_∗∗∗_</sup>|(0.01)|0.01|(0.01)|0.05<sup>_∗∗∗_</sup>|(0.01)|
|Red-tape|0.03<sup>_∗∗∗_</sup>|(0.00)|0.05<sup>_∗∗∗_</sup>|(0.01)|0.01<sup>_∗_</sup>|(0.00)|
|Industry FE|Yes||Yes||Yes||
|Firm controls|Yes||Yes||Yes||
|Observations|8037||8037||8037||
|_R_<sup>2</sup>|0.631||0.648||0.237||

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 05, _p <_ 0 _._ 01, _p <_ 0 _._ 001

# **9 Sensitivity Analysis**

This section provides a sensitivity analysis to assess the robustness of our main findings reported in Table 3. To start with, in our baseline analysis, labor input is measured using wage bill. However, one can also argue that wages reflect rent sharing between the establishment and its workers, resulting in the underestimation of TFPR dispersion across establishments since the most profitable establishments have to pay better wages (Hsieh and Klenow, 2009). As a first robustness check, we test our results using the number of people engaged as our measure of labor input instead of using wage bill. The reallocation gains would be modestly larger in Cˆote d’Ivoire (33.2 vs 31.4), Ethiopia (77.97 vs. 66.6) and Kenya (170.6 vs. 162.6) but smaller in Ghana (66.9 vs. 75.7). This reflects that wage differences can lead to a decrease in TFPR dispersion across establishments in Cˆote d’Ivoire, Ethiopia and Kenya, but increases in Ghana. Overall, our results are robust to this change, the potential gains from reallocation continue to be the largest in Kenya.

In our baseline computation, _αs_ was set to correspond to the capital share in the U.S. One may argue that the characteristics of the U.S. industries can be different from those in SSA countries, due to the differences in technology and other factors. As a robustness check, we recalculate the potential TFP gains under two different assumptions of _αs_ . First, we set _αs_ = 1 _/_ 3 for all sub-sectors _s_ . Second, we repeat our computation by setting _αs_ on the basis of industry-specific capital share in each country instead of capital share in the US, assuming that the industry is, on average, undistorted in these countries. The result reveals that using country-specific elasticity of capital leads to a much larger potential gains from reallocation in all four countries.

The extent of misallocation is sensitive to the treatment of outliers. To ensure that our results are not affected by outliers, we also estimate the potential aggregate TFP gains by trimming the top and bottom 2 % tails of TFPR and TFPQ. The potential gains from reallocation decrease from 31.4% to 25.8% in Cˆote d’Ivoire, 75.7% to 56.1% in Ghana and from 162.6 % to 120.6 % in Kenya but remains unchanged in Ethiopia. The large drop in the TFP gains in Kenya (by about 40%) after trimming the 2% outliers highlights that large gains can be obtained through reallocation of resources from the least 29