# **II. Input data**

The best predicting model assessment consists of evaluating which of the previously proposed model variations best capture the observed changes in the income distribution for 15 LAC countries from 2017 to 2020, given the availability of perfect inputs. In other words, this paper attempts to identify the model with the lower bias to estimate changes in the income distribution under the availability of perfect information about the sectoral and total output growth, the total population growth, and the changes in labor market structure and earnings; and to test if allowing for more flexibility in the simulation of labor income results in a more accurate estimation. For this purpose, this paper uses as inputs the World Bank's Macro Poverty Outlooks (MPOs) actual growth in sectorial and total GDP rates and remittances growth rate for the years 2015 – 2020.<sup>19</sup> It also uses the harmonized SEDLAC dataset<sup>20</sup> to compute actual changes between the baseline year and the estimated year in total population growth, labor market structure, and average formal and informal labor income.<sup>21</sup> The simulations performed to test the model's variations use 2016 SEDLAC household survey data for each country (or the most recent household survey data available before 2017) as the baseline for the estimation. It is important to note that formality (informality) has been defined as contributing (not contributing) to work-related retirement insurance for most countries. Table 1 presents the countries considered, the baseline year used for each country, the simulated years, and the informality definition used. It is important to note that, for this work, all inputs are in real terms in 2017 USD PPP, so they already account for inflation changes.

**Table 1 SEDLAC Country Data Used in the Simulations**

|**Country**|**Baseline**<br>**Survey**|**Simulated**<br>**Years**|**Informality Definition**|
|---|---|---|---|
|Argentina|2016|2017 – 2020|- Salaried workers who do not receive work-related<br>pension insurance.<br>- Non-salaried workers without complete tertiary<br>education.|
|Bolivia|2016|2017 – 2020|Workers who do not receive work-related pension<br>insurance.|
|Brazil|2016|2017 – 2020|- Salaried workers without the work-registry book<br>("carteira").<br>- Non-salaried workers who do not contribute to the social<br>security system.|

> 19 Available at <u>https://www.worldbank.org/en/publication/macro-poverty-outlook.</u>

> 20 SEDLAC is a database of harmonized socio-economic statistics constructed from Latin American and Caribbean (LAC) household surveys. The SEDLAC database and project were jointly developed and are jointly maintained by CEDLAS (Universidad Nacional de La Plata) and The World Bank’s LAC Team for Statistical Development (LAC TSD) in the Poverty and Equity Global Practice. SEDLAC includes information from over 300 household surveys carried out primarily in 18 LAC countries for which a comparable income aggregate (for welfare analysis) can be created: Argentina, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, and Uruguay.

> 21 Specifically, the growth rates between the baseline and simulated year are calculated using actual data for participation, informality, employment levels, and earnings by labor sector, inputs which otherwise would be projected using elasticities as in Braga. C., et al. (2023).

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