by the different labor supplies at deciles level; otherwise we would always have had returns on participation higher in the bottom deciles than the top ones, and this is not the case.<sup>14</sup> For the sake of brevity, only the results of the RIF-IV regression analysis will be presented. These results look at both the impact on the two selected measures of inequality (P80/P20 and P90/P10 ratios), and the impact on individual deciles.<sup>15</sup> As mentioned, for MENA countries the selected instrument was not available. These results are equally presented in Table 4, with the caveat that they are not fully comparable with the rest but as it appears, they show the expected signs and significance.

Looking at the RIF results in Table 4, and starting from the left to right, India, Morocco and Jordan all show a positive (inegalitarian) impact of FLFP on inequality. If we look at India, for example, the FLFP rate of return of the last two deciles is almost double that of the bottom two, indicating that if not compensated with measures that favor women in the bottom deciles, the FLFP increase would dramatically raise inequalities. At the same time, the magnitude of the decile regression coefficients indicates that the gains obtained from an increase in FLFP on households’ income are huge for all deciles as participation is low – a similar pattern is also valid for Morocco and Jordan.

When LAC countries are compared to India, the magnitude of coefficients decreases both in the inequality and deciles of household income regressions. FLFP is higher than in the SA and MENA countries, and marginal increases would not have such a big impact on household incomes. At the same time, inequality is already high, and if countries want to further increase FLFP, they should consider the distributional impact; as in the previous case, targeted policies towards women in

> 14 Other factors beyond the scope of this paper may be at play in determining this difference in returns to participation at decile level. First, discrimination against women, particularly in developing countries, is much stronger in the bottom deciles than in the top ones (World Bank, 2012); therefore, even if there is less competition, job opportunities and remuneration for women tend to be worse than those of women in the top deciles . Second, as shown in Appendix, education gaps, particularly in developing countries, are higher in the bottom than in the top deciles. Related to the previous point, even if we remove the hypothesis of discrimination in the jobs market, there has been ex ante discrimination in access to education, which has particularly affected poorer women. Hence while women in top deciles can at least aspire to jobs and remunerations like men, those in the bottom decile have few job opportunities because of their low level of education.

> 15 The corresponding RIF-OLS estimates are not reported here but are available on request. For an estimation of RIFregressions under both OLS and IV specifications, we first obtained the corresponding RIF for each observation in the samples of interest for the selected distributional statistics using the Stata command `rifvar` (Rios-Avila, 2019). We have then used the RIFs as the dependent variables and estimated the OLS and IV models using the Stata official commands given by, respectively, `regress` and `ivregress` (the latter using generalized method of moments estimation).

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