may be a crude measure since it does not account for differences between cohorts at birth with growth of a cohort over its life cycle. It is obviously of interest to reexamine this relationship using a panel dataset in Africa.<sup>16</sup>

# **7 A Comparison with Survey Data**

Our strategy to measure and quantify the costs of misallocation was taken directly from the literature. In return to relying on strong assumptions, the methodology provides a clear benchmark of efficiency with which to confront the data and is relatively simple to apply. The real limitation is the availability of adequate data sources. In this section we perform the same exercise of measuring and computing the costs of misallocation from an alternative data source, the World Bank’s Enterprise Surveys.

First, we diagnose the differences between the two data sources exploring their implications for the firm size distribution. We show that there are significant deviations in the coverage of the full spectrum of firms in the ES for Ghana, where we find that large firms are over-represented relative to the Census. In Kenya, on the other hand, we find that the two distributions match up relatively closely.

Then, we investigate the properties of the distribution of TFPQ and TFPR and compute counterfactual gains in productivity from the ES, in order to highlight differences with the results from the Census. In this case, we find that accounting for the true distribution of industrial value added share across manufacturing industries is essential for the patterns of misallocation and the aggregate gains in productivity from its reversal. Because the ES does not attempt to ensure representativeness at the 4-digit level in these countries, we use industrial weights from the Census. We find that when doing so, the ES depict a much less distorted economy with significantly lower gains from resource reallocation. We interpret this finding as indicative of the importance of counting with a database that properly accounts for the real distribution of value added shares in the economy.

## **7.1 Firm-Size Distribution: Survey vs Census**

To give some background to the analysis, the ES is briefly described as follows. The ES is an ongoing project of the World Bank to collect establishment-level data from several countries, particularly from low and middle income countries.<sup>17</sup> The dataset contains firm-level information including output and input measures in a harmonized fashion for 135 countries for at least one year since 2002. Two rounds of surveys were conducted in each of our sample countries.

To ensure the proper sample representation, the surveys rely on a stratified sampling technique. Three levels of stratification were used: sector of activity, firm size, and geographical location. In each country, regions are selected based on the extent of economic activity. The population of firms is stratified into three size strata: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). The degree of industry stratification depends on the size of the economy. In Ethiopia, sectors are classified into two strata: manufacturing and service, whereas in Ghana and Kenya the manufacturing is subdivided further into selected 2-digit industries according to their contribution

> 16Although there are some studies in Africa that examine productivity dynamics over the life cycle of firms using panel data Van Biesebroeck (2005), they rely on revenue-based measures of productivity. As a result, the dynamics of “productivity” over the life cycle may be misleading.

> 17The data are freely available from http://www.enterprisesurveys.org.

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