category is all employees). From this regression we obtain estimates of the earnings differentials between non-professional own-account workers vs. employees, employers and professionals vs. employees, and non-professional own-account workers vs. employers and professionals. In a third set of estimates we separate employees into formal and informal sector employees and estimate the earnings differentials between formal vs. informal sector employees, all self-employed workers vs. informal employees, and all selfemployed workers vs. formal employees. Finally, we re-estimate equation 1 and interact the two self-employed and formal/informal dummy variables to obtain estimates of the earnings differentials between non-professional own-account workers vs. formal sector workers, non-professional own-account workers vs. informal employees, and employers and professionals vs. non-professional own-account workers.

Equation 1 is estimated separately for every county (c) and year (t) for which we have the appropriate variables in the I2D2 data set. This results in estimates of the earnings premium for each country (c) and year (t) combination in the I2D2 data set, ����. ���� is that percent by which the earnings of the self-employed differ from the earnings of wage and salary workers. If EPct is positive, that indicates that there is an earnings premium for selfemployment; if ���� is negative, which indicates an earnings penalty for self-employment.

Tables 3 and Tables 4 summarize the results of these estimations for all countries in our sample by income level and regions. In calculating these means across surveys we weight the estimates in three ways. First, in calculating means across countries, the estimates of the earnings differentials in each country are weighted by total employment for that country and year. In addition, because the number of surveys in the data base for each country are different, the results from each survey (country/year combination) are weighted by the inverse of the number of surveys for each country. Weighted this way, each country is effectively counted once even if there are more than one survey for that country. Finally, to take into account that the earnings differentials estimated in each country are estimates with different standard errors due to varying sample sizes, we also weight the results from each survey by the inverse of the estimated standard error on ����.

# **V. The Extent of Self-Employment and Informal Employment around the World**

Table 1 presents the mean proportion of workers in each employment category across all surveys in the sample and for countries at different income levels. Specifically, Table 1 presents the proportion of workers who are: self-employed (divided between nonprofessional own-account vs. employers and own-account professionals) and employees (divided between informal employees and formal employees).<sup>7</sup> Approximately 50% of workers in low income countries are non-professional own account workers (Table 1b).

> 7 To calculate the means across countries, the proportion of workers in each employment category from each survey (country/year combination) are weighted by total employment for that country and year.  In addition, because the number of surveys in the data base for each country are different, the results from each survey (country/year combination) are weighted by the inverse of the number of surveys for that country.  Weighted this way, each country is effectively counted only once even if there is more than one survey for that country.

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