excludes all other costs borne by the firm such as inputs or raw materials, utilities, and salaries paid to employees. The use of self-reported profitability as the main variable of interest follows the critique of revenue-based productivity measures by Katayama et al (2009) and the arguments of De Mel et al (2008) for using profits measured through a direct question.<sup>8</sup> In particular, De Mel et al (2008) find that direct reports of profits tend to be less noisy and at least as reliable as asking firms for revenue and expenditure details; moreover, even if the true level of profits is likely to be understated, the ranking across firms is likely to be reasonable.

**Figure 7: Self-reported profits vs. calculated profits and labor productivity**

<!-- Start of picture text --> 30,000 12<br>10<br>20,000<br>8<br>10,000<br>6<br>0<br>4<br>-10000<br>2<br>-10000 0 10,000 20,000 30,000 4 6 8 10 12 14<br>Monthly revenues less expenses, US$ Revenue per worker, log US$<br>Self-reported profits, log US$<br>Self-reported monthly profits, US$<br><!-- End of picture text --> _Source_ : Authors’ calculations with EMES data.

Following the suggestions of De Mel et al (2008), the EMES did not contain very detailed questions on the structure of firms’ costs. However, we did ask firms about their revenues as well as their expenditures on salaries, inputs or raw materials, and electricity.<sup>9</sup> The correlation between reported profits and the difference between revenues and expenditure on all of the above categories is very high: 87 percent for monthly data and 97 percent for annual data. Moreover, in the majority of cases the reported profits are higher than revenue less expenses on the above categories, as expected (see the left panel of Figure 7, where most observations lie below the 45 degree line). Additionally, there is a significant positive relationship between firms with higher reported profits and firms with greater output per worker (right panel of Figure 7). These results, therefore, give us additional confidence in using self-reported profits as a measure of profitability and firm performance.

The distribution of profits among the sampled firms is quite wide, ranging from zero to over US$10,000 a month. The distribution of profits is approximately log-normal with half of the firms reporting profits of 8 Katayama et al (2009) argue that most firm-level productivity measures (i.e., output per a specific unit of input) have little to do with measuring technical efficiency or the likelihood of firm survival, unless data on physical quantities are available and the firms are relatively homogeneous in their input and output characteristics. The authors show that commonly-used revenue-based proxies to productivity indices (e.g., firm revenue deflated by an appropriate price index less the cost of inputs) are contaminated by variation in factor prices and demand elasticities and are therefore likely to give rise to spurious results regarding firm efficiency or performance.

9 For each of these questions, the respondents were asked to provide numbers for the last month, the last full year (2010), and expected outturn for the current year (2011). The length of recall period did not seem to affect the responses very much: for profits and revenues, respectively, their annualized values (12*the monthly outturn of May/June) were quite close to the values reported for 2010 and the values expected for 2011 (around 80 percent for profits and 90 percent for revenues).

9