shows a clear difference in firm size distribution between the two datasets. For the case of Kenya, a comparison of the ES (for the years 2007 and 2013) against the 2010 Census of Business Establishments (CBE) reveals that the size distributions of firms in the two datasets track each other quite closely. This can be explained by the fact that, unlike in the other countries, the CBE is actually being used to create the sampling frame in Kenya.<sup>20</sup> Even though there is no unique mechanism through which biases in the size distribution could be conveying biases in the distribution of distortions, one would be, in principle, more reassured about Kenya’s distribution being close to the Census-based one, assuming the latter is the best representation of the true distribution of firm sizes. We will see below, however, that one also needs to worry about the representativeness of the distribution of sectoral value added shares.

# **7.2 Extent and Cost of Misallocation: Survey vs Census**

We turn now to evaluating the degree and costs of misallocation as measured from the ES. The goal is to see whether the divergence in the size distribution in the case of Ghana, and its similarity in the case of Kenya, are informative about differences or similarities in the extent of misallocation in these economies when compared to the calculations based on the Census.<sup>21</sup> There are a number of challenges involved in making the analysis of misallocation in the ES comparable to that in the Census. First, the surveys for the four countries under study are not built to ensure representativeness of in terms of disaggregated sectoral coverage. As mentioned above, there is stratification across two broad categories, manufacturing and services, in the case of Ethiopia, while representativeness is captured only at the two digit level in Ghana and Kenya. Adequate sectoral representativeness is important for our calculation because aggregate sufficient statistics of misallocation and the aggregate counterfactual gains in productivity are all constructed based on 4-digit weighted averages. Thus, ensuring that an industry is properly represented in the aggregate is essential for the validity of the results.

To illustrate the importance of the adequate weighting of sectors, we perform two versions of our calculations. In the first one we weight 4-digit industries according to the value added shares implied by the firms sampled in the surveys. In the second one we adopt the value added shares from the Census. Under the assumption that Census-based weights are closer to the true ones, the experiments allow us to assess the quantitative significance of the weak sectoral representativeness of the enterprise surveys.

A second challenge that we face, which is related to the same limitations in the construction of the ES that we highlighted above, is that some 4-digit industries may receive no coverage at all in the surveys. To gain comparability in face of this difficulty, we restrict the sample of firms in the Census to those belonging to industries that are covered both in the Census and the ES, re-weighting valued added shares accordingly.

> 20Obviously, we do not expect the sample to resemble our census due to the structure of the sampling procedure. Large establishments are in typically oversampled in the ES relative to the random sampling of firms in each country. To further explore the comparability of the two datasets, we compare the size distribution in our census data with the sampling frames used to draw samples in the ES. We find that the sampling frame significantly underestimates small firms in Ethiopia and Ghana.

> 21Note that Cˆote d’Ivoire and Ethiopia are excluded in this exercise due to the large number of missing values of capital in the ES.

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