exercise suggests is that, again, selection plays an important role in explaining the differences in productivity, sales and exports. However, in contrast to what one would have expected on the basis of the comparison of firms that have at least one formal employment contract and those that have none (Table 3 above), selection seems to play a smaller role in the case of log employment. This suggests that the difference in employment levels between informal and formal firms may be more closely related to the status of informality itself rather than selection. Put differently, if the evidence were taken at face value, one would conclude that that informality does not cause low productivity, it causes lower sales to some extent, and it seriously constrains jobs. This suggests that there may be factors in the business environment associated with informality that discourage or prevent informal firms from employing more people relative to formal firms; it suggests that informal firms may prefer to keep a low profile and stay below the enforcement radar, to prevent the hiring of additional workers possibly jeopardizing the informality status of these firms. It might also indicate that firms may face more pressure to formalize if they hire full-time employees: with weighted data, the share of formal firms with self-employed owners and no full-time employees is only 33 percent, whereas it rises to 38 and 48 percent for firms with 1-2 fulltime employees and 3 or more employees, respectively. It could also suggest high costs of formality, especially associated with labor regulations (e.g., restrictions on firing). As indicated above, these insights, especially the suggestion that selection may be playing a limited role in the case of employment, should be treated as tentative conclusions that need to be further explored with better data.<sup>17</sup>

# **_4.2 Correlates of (in)formality_**

What types of firms choose to be formal? With a single cross section, it is only possible to carry out an exploratory analysis to identify variables that are corelated with formality since inference about causal effects is not possible. Table 7 reports the results of probit regressions where the dependent variable is a dummy that takes the value 1 if the firm is legally registered either at the national registrar general or country-level revenue authority, or at the local authority or municipality, or both. The table reports average marginal effects. The baseline model is reported in column 1: log productivity, having electricity and having a young woman owner (relative to having an older woman owner) are positively correlated with formality, with positive coefficients significant at the 1 percent level. In the baseline specification, having a young woman owner is associated with a 22 percentage points higher probability of being formal, relative to having an older woman owner. These variables remain significant when additional covariates are included in the model. Being in an urban area is positively associated with formality but only at 10 percent level of significance. The second column adds having customers abroad as a covariate. Its coefficient is positive but again statistically significant only at the 10 percent level, and, as can be seen in

> 17 The results discussed in the text were obtained using the sampling weights in the RIA data set. The IPW approach was also used on the data without sampling weights and the results were overall similar. The main differences were that the formal-informal gap for log productivity was reduced less, to 0.38, which is significantly different from zero only at the 10 percent level. Also, the final ATET for employment of 2.31 is a bit lower than the gap in the unweighted data. A further robustness check was carried out by using a propensity score matching approach rather than inverse probability weighting, using the “teffects psmatch” command of Stata. The results are as follows: For log productivity, the gap is reduced to 0.41 log points, and is still significant at the 5 percent level. The gap in log sales is reduced to 0.37. The gap in log employment does not change much (is reduced to 0.62). The gap in customers abroad is reduced to 0.08 when log productivity is not included in the estimation of the propensity score and 0.03 when log productivity is included in the estimation of the propensity score (not significantly different from zero). We conclude that qualitatively the results are similar to those in Table 6: results suggest that selection is an important driver in the observed performance differences between informal and formal firms for productivity, sales and exports, but not for employment, where it seems to play a smaller role. If the evidence were taken at face value, one would conclude that informality has a negative effect on employment and exports, but the effect on productivity seems smaller or even non-existent, depending on the methodology.

17