# **2. Data and Methodology**

# **a. Census and Survey Data**

The primary source of data on labor market outcomes is the 2020 Census of Population and Housing, carried out in March 2020 by the Mexican National Institute of Statistics, Geography, and Informatics (INEGI).<sup>3</sup> The census was collected the month before the COVID-19 pandemic led to widespread shutdowns. INEGI publishes census statistics publicly at different geographic levels, with the lowest level being the Área Geoestadistica Básica (AGEB). INEGI publishes aggregate statistics only for urban AGEBs. We use these to generate municipality statistics that are weighted by the population of urban AGEBs. Because only urban AGEBs are included in the analysis, the results in this paper pertain to urban state and municipality-level rates. Urban AGEBs total to around 96.4 million people, or more than 75% of Mexico’s estimated 127.6 million people.

We are primarily interested in examining the feasibility and effectiveness of combining geospatial data with survey data to improve municipal estimates of labor force participation rates and unemployment rates, separately for women and men. We also examine the ability of the procedure to improve state-level estimates of these four labor market outcomes. We restrict the analysis to urban AGEBs in order to utilize the census data as a credible benchmark against which to assess the performance of the small area estimates.

From the available urban census data, we construct a data set with six different variables: the total number of women in the labor force, the total number of women employed, the total number of women 12 years or older, plus the same three variables for men.<sup>4</sup> Since this is census data, we take these values to be the true values for each AGEB. We then randomly sample AGEBs from the full set of urban AGEBs to form a pseudo survey. We select AGEBs with probability proportional to size within states, which serve as the strata. We then choose the number of AGEBs per stratum (state) using sample sizes similar to INEGI’s labor force surveys.<sup>5</sup> Like INEGI, we assume an average of 2.7 people 12 years of age and older per household when selecting AGEBs.<sup>6</sup> Out of 50,942 AGEBs with non-missing geospatial auxiliary features, 7,642 – or 15.0 percent – are included in the pseudo sample.

After selecting AGEBs, we simulate sampling individuals within each selected AGEB. The census data indicate the number of men and women the number of people 12+ living in each AGEB, as well as the number of people 12+ who are active labor force participants as well as the number employed. Since being employed is conditional on being in the labor force, we can use this information to simulate a sample of men and women within each AGEB based on these variables. We essentially construct the total population and randomly select 40 individuals within each AGEB – or all individuals if there are fewer than 40 men and women 12+ in the AGEB. These are then be used to construct gender-specific labor force participation and unemployment rates in the sample.

> 3 The data are available online <u>here.</u>

> 4 The labor data are collected for those 12 years of age and older, hence why we use 12+ as our population. 5 More information is available here. One difference between our methodology and theirs – apart from the fact that we do not have access to actual enumeration areas – is that INEGI also includes some larger municipalities as strata. For simplicity, we only use states.

> 6 This number comes from INEGI’s labor force survey methodology manual in footnote 5.

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