# **1. Introduction**

Low female labor force participation can be an important barrier to economic growth. In Mexico, for example, women’s labor force participation is considerably lower than that of men and is also low in comparison to other countries. The female labor force participation rate was just 45 percent in 2019, compared to 77 percent for men, a gap of 32 percentage points. In the OECD, only Turkey and Italy have a lower rate of female labor force participation, while in LAC only Guatemala has a lower value than Mexico. This has important economic impacts. A recent World Bank analysis suggests that if women participated at the same rate as men, per capita income could be as much as 22 percent higher (Inchauste et al., 2021). The same analysis suggests that if Mexico could implement policies to increase the female labor force participation rate by 0.6 percentage points a year – in line with that observed in Spain, Ireland, and Chile – this would help eliminate the gender gap in labor force participation and increase economic growth by 0.4 percent a year, highlighting how increases in female labor force participation are not just an output of the development process, but a driver of it, as well.

Trends in female labor force participation are determined by several key factors, including the relationship between households’ economic conditions and female participation, the supply and demand for jobs deemed appropriate for more educated women, national growth strategies, and occupational gender segregation (Klasen, 2018). To better understand from a spatial perspective how policy can address these factors, it is useful to understand where pockets of low rates of female labor participation exist. However, most labor force surveys can only provide adequate estimates for larger areas of aggregation. Mexico’s labor force survey is no exception, as it can only provide estimates for urban and rural areas within states, as well as for select larger municipalities. Since Mexico has 32 states and 2,450 municipalities, reliable estimates of labor force participation – for both women and men – at the level of the municipality could greatly inform the geographic targeting of education, labor market programs, and other measures designed to improve female labor force participation. This is also true of other developing countries.

Small area estimation is a branch of statistics that combines survey data with richer auxiliary data to generate more precise and accurate estimates of statistics. It has frequently been applied to measures of poverty and labor market outcomes. An extensive literature has documented that auxiliary data, typically taken from census or other administrative sources, can improve estimates of labor force participation and unemployment (Esteban et al., 2020, Chambers et al., 2016, LópezVizcaino et al., 2015, Ugarte et al., 2009, Molina et al., 2007, Datta et al, 1999). This literature employs a variety of methods, including hierarchical Bayes, empirical Best Predictors, and multinomial logit mixed models, as well as area level models. Intuitively, small area estimation allows surveys to “borrow strength” from auxiliary data that is more geographically comprehensive, and therefore covers unsampled areas. Combining the survey data with model predictions for unsampled areas, derived from the auxiliary data, makes the estimates more accurate and precise. In addition, by modeling the average relationship between the survey and auxiliary data across the entire sample, small area estimation can help correct sampling error even in sampled areas. This method can therefore produce more precise estimates for geographic levels, such as Mexican municipalities, where the labor force survey is too small to generate reliable estimates.

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