Table 5: Potential TFP Gains with Different Industrial Weights

||Cen|sus||Enterprise|Surveys|
|---|---|---|---|---|---|
||Baseline|Partial|ES|Full Census|Partial Census|
|Ghana|75.7|71.1|146.6|32.8|54.3|
|Kenya|162.6|184.2|86.5|16.7|51.4|

Note: The TFP gains are calculated based on different industrial weights. The TFP gains under the column ‘Partial’ are calculated by restricting to the subset of industries that overlap with the ES. The columns “Full Census” and “Partial Census” refer to TFP gains across firms in the ES where industries are weighted based on the full and partial censuses, respectively.

Finally, table 5 reports the counterfactual gains in aggregate manufacturing resulting from reversing distortions in the the three types of data that we have been comparing: census, ES with census weights, and ES with survey weights. In the case of the Census, we report the baseline results introduced above together with the gains corresponding to the case where we restrict to the subset of industries that overlap with the industries in the ES.

The table conveys a clear message: once industries are weighted as in the census, and hence once we correct for biases in the sectoral distribution of production in the survey, gains from withdrawing distortions are significantly muted (column 4 vs column 2). They are less than half the census-based gains in the case of Kenya, and are reduced by 25% in the case of Ghana. The gains from the ES using its own implied weights are expectedly larger.

# **8 The Underlying Sources of Misallocation**

The results presented above clearly revealed that resource misallocation is quite large in our sample countries. Thus improving the allocation of resources is a critical element in order to boost manufacturing productivity in these countries. To offer specific suggestions on how these countries can improve their productivity by reducing allocative inefficiency, it is important to identify the specific factors that have contributed to the observed misallocation. Nevertheless, identifying the specific policies or institutions that may have contributed to such misallocation is notoriously difficult. In principle, many factors – observable and unobservable – may reasonably contribute to resource misallocation across firms. For example access to finance, tax rates and regulatory requirements may favor particular firms because of political connections or family ties.<sup>22</sup> Identifying specific distortions is perhaps more difficult in Africa since the region is often characterized by a high cost of doing business. While our census data do not allow us to pin down the reasons behind the substantial level of resource misallocation in our sample countries, we believe that this pattern is associated to the heterogeneous business environment faced by firms. Our goal in this section is to uncover the potential underlying sources of misallocation by examining the role of different business environment bottlenecks experienced by firms. To this end we merge our data with the ES data, which provide various measures of business climate indicators.

> 22Hsieh and Klenow (2009) argue that the substantial domination of SOEs and cumbersome licensing requirements contribute to misallocation of resources in China and India, respectively.

26