# **IV. Final remarks**

This paper assesses the predictive capacity of specific microsimulation models using a minimum set of perfect macroeconomic input data and under different assumptions on how family income is estimated. Using actual input data allows for isolating the analysis from any possible bias from macroeconomic inputs, so the predictive capacity of the model variations relies only on the changes in the assumptions made in the proposed microsimulation methodology. The study makes two contributions: first, it extends previous microsimulation methodologies by accounting for labor income movements within each economic sector given by informality, and second, it tests three versions of the model (i.e., the old model, the new model with rescaling, and the new model without rescaling) to identify the one that fits better the actual income distribution. The _old model_ corresponds to the first option in which labor income varies only at the macroeconomic sectoral and total output growth rates; the _new model with rescaling_ is the second, which incorporates within-sector variation in labor earnings using the formal (informal) changes in average labor income in the main activity, provided by the labor income-pseudo-labor productivity elasticities for each employment sector, and rescales for sectoral and total output growth. The last option is the _new model without rescaling_ , which only considers changes in average income earnings given by changes in average labor earnings by employment sector. The proposed alternatives are tested using the SEDLAC harmonized household data for 2016 or the closest year to predict poverty, inequality, and the GICs in the short (2017 – 2018) and medium run (2019 – 2020) for 15 LAC countries.

Overall, results suggest that incorporating more flexibility in the labor income estimation by adding labor market attributes such as informality produces a smaller bias across time and a better estimation of the changes along the income distribution. For poverty, results indicate that the three model variations generally have outstanding performances in the short run, irrespective of the country's poverty level. Hence, in the short run, these alternative models do not differ largely in performance gains; even the _old model_ brings reliable results. However, the _new model without rescaling_ is the best at identifying poor people in the medium run, meaning that accounting for labor market aspects such as informality produces more accurate estimates in contexts of high uncertainty. On the other hand, results indicate that the _old model_ is best at estimating inequality, except for atypical years like 2020, but the advantage over the other model variations is slight. Moreover, the assessed model's variations overestimate inequality for several countries (i.e., Bolivia, Costa Rica, El Salvador, Honduras, Mexico, Panama, Peru, and Paraguay) and underestimate it in a few others (like the Dominican Republic). Thus, results might diverge, and the same micro-simulation model option is not be the best for poverty and inequality.

This document extends the analysis along the income distribution using GICs. In this case, the _new model without rescaling_ outperforms the other two options, and the difference is significant in atypical years like 2020. Therefore, introducing intra-sectoral variation through differentiated growth by informality status helps translate macroeconomic movements into more precise growth at every level of the income distribution. Three lessons can be drawn from this exercise: first, when the proposed micro-simulation model performs exceptionally well, _the new model without rescaling_ variation is remarkably superior to the others. Second, all variations find it challenging to estimate income changes in the tails, possibly due to limited variation in the labor market setting that does not allow to account for other specificities 29