considered in the analysis (i.e., each income percentile). Except for the immediately following year to the baseline, the _new model without rescaling_ performs better overall than the other variations of the microsimulation model. The difference is prominent in atypical years like 2020. In addition, there is not a clear trend in the errors like in the previous cases since the errors have an inverted-U shape - a decrease in the first years and then a remarkable increase-. However, the exponential growth in the presence of shocks is salient for the less flexible variations.

At the country level, Table 3 presents the share of percentiles that fall inside the actual GIC Confidence Interval of 95% for each model's variation. This is the number of percentiles with an estimated growth rate that falls inside the confidence interval of the respective actual growth rate, divided by the total number of percentiles. In the table, columns with numbers "1", "2", and "3" contain the share of percentiles within the confidence interval for the _old model_ , the _new model with rescaling_ , and the _new model without rescaling_ , respectively. In the best case, all percentiles fall inside the confidence interval of 95%, which means a share of 100. Results indicate that there is still high variation across countries and years. Yet, the _new model without rescaling_ estimates the income growth along the distribution more accurately since the average share for all countries is higher for all the years included in the analysis.

# _Good fit of the models' variations_

In addition, Table 3 shows cases where the model's variations, especially the most flexible one, perform exceptionally well (shares over 80 for the _new model without rescaling_ ), such as Bolivia 2019 and Dominican Republic 2018. In these cases, the estimated growth of income along the whole distribution is very close to the actual growth for the _new model without rescaling_ , as shown in Figure 8. In contrast, the less flexible variations are more distant from the actual distribution and even out of the confidence interval, as in the case of Bolivia 2019. The latter is an interesting case since, as shown before, Bolivia is one of the countries with the poorest fit in poverty estimation. The explanation for this discrepancy relies on the measure of analysis. Poverty measures the number of people whose income falls below a determined poverty line. In this sense, this measure depends on the selected threshold and the income growth for the population most likely to have income below that threshold. In the case of Bolivia, the GICs presented in Figure 8 show a good level of adjustment overall but insufficient income growth below the 31 percentile, where the poverty lines probably fall. This example highlights the importance of considering the models concerning the target measure and the measured object of analysis (e.g., poverty, inequality, income distribution).

_Divergence of distribution tails_ In other cases, the fitting is not so good along the whole distribution. Argentina 2019 and Panama 2019 are examples of these cases (Figure 9). Notably, these countries show that the three assessed model variations might have difficulties estimating income variation in the distribution's tails. A possible explanation might be due to the limited employment sector classification. The micro-simulation model relies on a 6-sectors classification (formal agriculture, informal agriculture, formal industry, informal industry, formal services, and informal services), and movements of labor earnings according to aggregate output or average earnings are not enough to capture intra-sectoral variations. That said, 23