changes in the macroeconomic environment. Examples of these changes are decisions on training, child conception, etc. (Bourguignon and Spadaro, 2006). The microsimulation model studied in this document belongs to those static with behavioral 'agents' responses.

Among the behavioral models, Olivieri et al. (2014) present a microsimulation model that evaluates the distributional impacts of a macroeconomic shock with low data and computational requirements. It allows accounting for labor and non-labor income mechanisms and captures impacts at the micro level for the entire income distribution. The model focuses on labor market adjustments in employment and earnings and changes in non-labor income and prices (with a view to the variation in food and non-food prices). However, it does not allow for capturing labor informality, an important feature for Latin American countries. Moreover, Olivieri (2020) adapts the previous version of the microsimulation model to assess the effects of the triple crisis in Ecuador. This version incorporates labor informality only in estimating the labor market structure in the three main sectors of economic activity. Yet, its predictive capacity is limited at the labor income level.

In the absence of CGE models, an alternative way to feed microsimulation models under the top-down approach is using macroeconomic projections of output that are almost always available. However, since employment and labor income estimations are usually unavailable, the output growth estimates must be translated into employment and labor income changes at the sector level. These estimates are typically made using sectoral output-employment and productivity-labor income elasticities based on aggregate output and labor market past data, which are then applied to the output growth projections to generate changes in employment and labor income by sector (Braga, C. et al., 2023). The predicted sectoral employment and income, along with the macroeconomic output projections, are the final inputs for the microsimulation model. In this paper, these essential inputs come from actual data to isolate biases from the model and the macroeconomic inputs.

This paper considers the model proposed by Olivieri (2020) and assesses the gains in the predictive capacity of microsimulation models when different assumptions are applied in the way family income components are estimated between 2017 and 2020. This work isolates the effect of changes in the microsimulation model's assumptions by considering the use of actual macroeconomic and labor market input data for the period of analysis. Thus, the study first contributes to extending previous microsimulation methodologies by introducing informality to estimate how labor income moves within each sector of economic activity. Second, it tests the performance of three versions of the model (i.e., the _old model_ , the _new model with rescaling_ , and the _new model without rescaling_ ) in fitting the actual income distribution in four consecutive years. The results indicate that overall, the methodology proposed successfully identifies the poor and estimates the intensity of poverty in the most immediate years, indistinctively of how labor income is simulated. However, allowing for more intra-sectoral variation in labor income results in more accurate projections of poverty and changes along the income distribution, with gains in its performance in the middle term, especially in atypical years such as 2020.

The rest of the paper is organized as follows. Section I lays out the methodological approach, which differs from traditional techniques (i.e., the elasticity of poverty to output or GDP) and micro-simulation 3