Appendix Table A 1 shows the actual post-lasso model coefficients for the random sample results. In general, areas that are more urban on average are associated with higher female LFP, as mean vegetation intensity is negatively associated with participation, while the correlation is positive for the number of residential roads and street crossings. Consistent with this, areas that have been urbanized more recently, as indicated by the mean year of switching to impervious surfaces, have lower participation rates all else equal. Furthermore, smaller municipalities (by area), many of which are in Southern Mexico, are correlated with lower female labor force participation all else equal.

The relationship between other indicators of urban density and female labor force participation is more complex, however. Areas that are more heterogeneous in terms of vegetation intensity, such as the suburbs, are other things equal associated with higher labor force participation. The relationship between night-time lights and female LFP is another example. The median value of nighttime lights is positively associated with female LFP while the mean value is negatively associated, although the latter is not statistically significant. This nonetheless suggesting that AGEBs with a large number of very bright pixels, perhaps due to the presence of highways, have lower female labor force participation rates.

Some of the infrastructure variables are more easily interpretable, as well, but these are rarely significant. The number of schools is significantly positively correlated with higher male LFP but does not show up for any of the other three indicators.

Appendix Table A2 decomposes model R<sup>2</sup> across different categories of variables. The latter shows that the determinants of male and female LFP differ significantly. For example, the geographic size of the AGEB and municipality, nighttime light luminosity, the mean of the vegetation index, the number of different points of interest recorded in Open StreetMap, and the year that pixels changed to being an impervious surface are important predictors of female LFP. Municipal area is positively associated with female LFP, which makes sense in the Mexican context because many of the smaller municipalities are located in the poorer south of the country where female LFP tends to be lower. Median nighttime luminosity in the municipality is positively associated with female LFP, consistent with nighttime lights indicating economic activity. Mean nighttime lights in the municipality, which conditional on median is a measure of right-skewness or inequality in luminosity, is negatively associated with LFP. This is consistent with women in more developed areas being able to afford not to work. Areas with a greater concentration of vegetation, as proxied by mean NDVI, have lower female LFP, perhaps also reflecting greater wealth in suburban areas with more vegetation. The correlates of male LFP are more difficult to interpret. The share of bare land in the municipality is positively and significantly correlated with male LFP, as is the number of untagged highway points, and the latter may be positively correlated with economic activity.

<u>Table 2 - Model diagnostics by dependent variable and sample type</u>

||Female<br>LFP|Male<br>LFP|Female<br>Unemp|Male<br>Unemp|
|---|---|---|---|---|
|**State - Simple Random Sample within Selected A**|**GEBs**||||
|Number of (non-state) variables selected|41|22|19|10|
|<br>Marginal R<sup>2</sup>|0.127|0.061|0.025|0.028|

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