# iii. Using census data to establish economic and nonmonetary indicators

To study the effect of the FAIS on nonmonetary dimensions of welfare, the analysis relies on population censuses and inter-census surveys for 2005, 2010, and 2015. The relevant indicators include (a) the share of the population with access to basic services in their dwellings (electricity, drinking water, sewerage), (b) share of the population with adequate quality floors and bathrooms in their dwellings, (c) share of the population with access to health care services, (d) share of the population ages 15 or more that have completed primary education, and (e) illiteracy rates among the population age 15 or more and among children ages 5–14.

Municipality-level data were compiled to measure the stock of income earner assets and the rate at which these assets are utilized. This involved measures of physical capital, labor, and productivity. The information on these factors was taken from the economic censuses in 2004, 2009, and 2014. Data on assets, employment, and value added were used as proxies for municipality-level physical capital, skilled and unskilled labor, and productivity.

# **5. Identification strategy**

To estimate the effect of the FAIS on welfare, the analysis uses a reduced-form specification. This includes three sets of controls. The first set considers the production factors and technology available at the local level that account for the generation of private income. In particular, the analysis controls according to the stock of physical capital, labor (by skill categories), and a measure of total factor productivity. The second set includes the fiscal resources to account generally for public income. The third set exploits the panel structure of the data to absorb any unobserved heterogeneity across municipalities and over time to control for general income shocks. The reduce form specification is thus defined as follows: shocks. The reduce form specification is thus defined as follows: L m 0 1L F m 2L m 3 m 3 m m (1) mm mm where m is the outcome of interest used to proxy for the welfare of municipality LL WW = ββ + ββ LL F + ββ LL F FF + ββ XX + ββ DD + δδ + γγ _m_ , and this is only observed + εε in each population census indexed by year _t_ . Welfare is measured using household per capita income and other WW measures of well-being, such as the poverty headcount and inequality and nonmonetary deprivation measures. The coefficient of interest is 1, which captures the effect of the FAIS on welfare once standard orthogonality conditions are satisfied. F F m is measured as the monthly average per capita FAIS received by municipality ββ over the three years before the census year t. Similarly, mm F m controls for all the fiscal resources other than the FAIS. It is measured as the monthly average of per capita revenues over the three years preceding the census year t. m is a vector of variables that account FF for the level and rate of utilization of production factors: (a) number of blue-collar and white-collar workers, XX (b) total value of the assets owned by the economic units, and (c) productivity (output per worker). m are dummies that control for population size to consider scale effects that may affect local welfare.<sup>14</sup> Finally, is DD a set of time fixed effects to control for macroeconomic shocks and national policies, and is a set of mm δδ municipality fixed effects that absorbs any unobserved heterogeneity affecting both the FAIS allocation and mm γγ local economic welfare over the long run.

The FAIS is defined using the three-year average before the census year utilized for poverty maps for two reasons: First, this allows for a control by the local political cycle because mayors are elected every three years.

> 14 Two dummies are used: one for municipalities with more than 15,000 inhabitants and another for municipalities with more than 2,500 inhabitants.

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