productive establishments to the most productive ones.

Finally, in our benchmark analysis we include all establishments regardless of their size. As explained before our census covers all establishments employing 10 workers and a representative sample of firms were taken from establishments with less than 10 workers in Ethiopia and Ghana while the Kenyan census covers all manufacturing establishments in the country. One may argue that Kenyan census may not be comparable to the Ivorian, Ethiopian or Ghanaian censuses in covering the smallest establishments. To examine the extent to which this finding is an artifact of the Kenyan dataset that has sparse coverage of small firms, as a final robustness check, we redo the analysis by excluding establishments employing less than 10 workers. The result shows that the potential gains from reallocation decreases in all countries (29.4% vs. 31.4%) in Cˆote d’Ivoire, (60.9% vs. 66.6%) in Ethiopia, (66.9% vs. 75.7%) in Ghana and (141.5% vs. 162.6% ) in Kenya, implying that part of the gains from reallocation comes from small establishments but the qualitative result remains unchanged.

Table 7: Sensitivity Analysis: Potential Gains from Equalizing TFPR

||Baseline|_σ_=5|Trim 2%|L _>_ 10|WL=L|_αs_ = 1/3|Country _α_<sup>_′_</sup><br>_s_<sup>_s_</sup>|
|---|---|---|---|---|---|---|---|
|Cˆote d’Ivoire|31.4|44.7|25.8|29.4|33.2|21.8|44.9|
|Ethiopia|66.6|82.0|66.0|60.9|77.97|56.41|101.81|
|Ghana|75.7|85.1|56.1|66.9|66.88|59.17|93.06|
|Kenya|162.6|194.6|120.6|141.5|170.55|153.91|440.24|

Overall, the sensitivity analysis clearly implies that our finding that resource misallocation is the highest in Kenya and the least in Cˆote d’Ivoire is robust to alternative assumptions regarding the measures of labor inputs, elasticity of capital and outliers. However, the figure that emerges from comparing Ethiopia and Ghana is rather mixed, the relative ranking changes depending on the alternative assumptions.

# **10 Concluding Remarks**

This paper has examined the effects of resource misallocation induced by firm-specific distortions on aggregate manufacturing productivity using comprehensive and comparable data on manufacturing firms in four Sub-Saharan Africa countriess - Cˆote d’Ivoire, Ethiopia, Ghana and Kenya.

Our main results are as follows. First, we found evidence that firm-level distortions have a quantitatively sizable detrimental impact on aggregate productivity, through the misallocation of resources away from more efficient to less productive ones, in all four countries we study. The counterfactual exercises imply a substantial productivity gain from the removal of distortions in Kenya, with a range of estimates from 162.6 to 194.6, depending on the model assumptions. The potential productivity gains are also large for Ghana (with range from 66.6 to 82.0 percent) and Ethiopia (with a range from 75.7 to 85.1 percent). For Cˆote d’Ivoire the gains are from 31.4 to 44.7 percent, much smaller than the other three countries. These results are robust to alternative parameter values that we use to measure the distortions.

Our results also suggest that distortions are positively correlated with firm-level productivity in all 30