# **4. Data**

# Data on Income, Poverty and Inequality at the Municipal Level

To construct income and inequality measures at the municipal level, we employ the small-area estimation methodology proposed by Elbers et al. 2003. The basic idea is to impute income to households in the Population Census (and Population Counts), using a model that predicts income from a household survey. Empirical evidence based on this method has proven to be precise when applied to data from nations like Ecuador, South Africa, Brazil, Panama, Madagascar and Nicaragua (see Elbers et al. 2003, Alderman et al. 2002, and Elbers et al. 2001). In addition, the small-area estimation methodology has key advantages as it benefits from the strengths of both household surveys and census and avoids their weaknesses. More specifically, whereas most household surveys are only representative at high levels of aggregation (e.g., national, regional, urban/rural), census and count data provide total coverage (universality).<sup>5</sup> Typically, census data provide the inputs when welfare indicators at low levels of aggregation, such as municipalities, are needed. In Mexico, both the Census and the Population Counts are representative at the municipality level, which is the unit of interest in this study.

However, the census has its limits. First, fewer variables are available compared to the more comprehensive household surveys. Second, one of the main weaknesses of this data and the most relevant for this analysis is the lack of information on income. Census data, not designed to comprehensively measure household income, provides an incomplete picture of the household’s monetary circumstances, usually underreporting total income. Alternatively, household surveys such as the National Survey on Household Income and Expenditures (ENIGH), while representative only at the national and urban/rural level, are nevertheless designed to measure more precisely household income and expenditures.

The method consists of taking the household survey as a random sample of the total population (found in the census databases) and choosing the common variables between these sources. The distribution of the chosen variables is compared, looking for variables in which the sample mean is statistically equivalent to the population mean. The variables that are not rejected are used to model income with ordinary least-squares (OLS) regressions using household survey data. It is important to note that the coefficients obtained from the model cannot be economically interpreted—as some of them are endogenous—but they are still included to reduce prediction error. Finally, the parameters obtained from these income regressions are employed as predictors to generate the household income distribution in the census and count data.<sup>_6_</sup>

> 5 Strictly speaking, Population Count data does not provide universal coverage as it consists in fact, of surveys not censuses. However, the sample size is large enough such that the data can be disaggregated to the municipal level and the level of precision of estimates is extremely high.

> 6 In order to construct poverty maps for a twenty-year period, the analysis identified fifteen common variables between the ENIGH and the Census and Population Counts, which can be used to generate around 35 indicators to construct the necessary income models. These variables include dwelling characteristics, socio-demographic characteristics and asset ownership. Moreover, to increase precision in the estimators, around 50 municipality-specific indicators were

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