responses are recorded in a five-point scale, i.e., absolutely satisfied, mostly satisfied, neutral, not very satisfied, and absolutely unsatisfied. These were in turn used to construct a binary dependent variable equal to one if the response fell in the bottom two out of five categories, i.e., if the person was either dissatisfied or strongly dissatisfied.

Descriptive statistics show that a higher share of informal workers is dissatisfied with their jobs and the difference with formal workers is statistically significant **.** 14.9 percent of informal workers report to be either dissatisfied or strongly dissatisfied with their job, compared to just 9.2 percent of formal workers (Figure 11).

However, informal workers are not more likely to be dissatisfied with their jobs than formal workers, after controlling for work conditions. Table 9 presents estimated coefficients from logit regressions of job dissatisfaction and satisfaction. The coefficient on informal employment is positive -negative in the case of satisfaction- and statistically significant at the 1 percent level in specifications only controlling for personal and job market characteristics (columns 1 and 4). However, once work conditions such as wages, concern about job loss and envelop wages (as well as paid vacation and hazardous job in the case of a sample salaried workers only) are included, the coefficients lose significance (columns 2, 3). In particular, hourly wages are highly significant in explaining job dissatisfaction. Hazardous job conditions also explain job dissatisfaction among salaried informals. In the case of job satisfaction, the coefficient of informal employment is unstable: it changes sign and significance to changes in sample and specification. This evidence seems to confirm that it is work conditions, rather than type of job contract (formal vs informal), that explains job dissatisfaction. No conclusive evidence is derived regarding job satisfaction.

# **Informal employment and health**

Next, we explore the relationship between informal employment health outcomes. We are able to define a measure of a worker’s health status based on information on self-assessed overall health which is captured through a question that asks how the respondent would rate her general health on a five-point scale. Population health is by definition complex and difficult to measure, and the reliability of one’s self-assessment of could vary by population subgroup -see (Lundberg, 1996); (Zajacova, 2011). Nevertheless, self-assessed health status is widely used to study empirical trends in population health. In the RLMS, respondents are asked how they would evaluate their health. Possible responses include very good, good, average, bad and very bad. We define a binary measure of “poor health” that includes workers that rate their health as bad or very bad. We also consider another binary variable that defines “good health” which includes the top two responses, i.e., good or very good health. With this, we attempt to capture whether there are health inequities arising from differences in one’s informality status.

The distribution of original responses shows that formal and informal workers are in fact not very different in their self-assessed health measure. The distribution is fairly similar between formal Informal Employment and Worker’s Wellbeing in Russia 22/67