accurate modeling. Instead, the model relies on a simple, non-parametric assignment rule consistent with the existing evidence.

Equations (1) to (14) complete the model. Total household income is a nonlinear function of the observed characteristics of the household and its members and of unobserved characteristics of household members. This function depends on two main sets of parameters: those of the occupational choice model for each skill level; and those in the earning functions for each employment sector and skill level. It is assumed that no variation exists in the composition of the household. In other words, the number, age, and gender of the members of a household remain constant over time. The demographic change is incorporated via calibration of the survey weights. For further details on the estimation strategy of these parameters, see Olivieri, S. et al. (2014).

# **Simulation**

The second step consists of _replicating_ the projected macroeconomic changes (i.e., sector of employment, total output, or public and private transfers) between the baseline and each projected year. These projections derive from various possible changes in different components of the household income generation model (i.e., labor, and non-labor income). This process is divided into three sub-steps ordered in the following sequence: population growth, labor market status and income, and non-labor income.

The population growth adjustment is particularly important in countries with high fertility rates or significant immigration flows, or in cases where the last available national household survey is relatively distant from the projection year. In the first of these instances, the number of labor market entrants rises faster than the overall population. In practical terms, this allows us to explicitly consider changes in the size of the working-age population and hence to distinguish between employment growth driven (or rather absorbed) by demographic trends and net (or additional) employment growth. A simple approach is adopted in this paper to account for population growth with low computational requirement. The estimation weights for all observations are adjusted using neutral distribution to account only for the growth in total population between the baseline survey and the simulated year, maintaining the demographic structure.

One of the transmission channels on which the model focuses is labor markets. This is modeled considering the employment structure and labor earnings projected changes between the baseline and the simulated year. Thus, it allows for changes in employment or earnings or a combination of both. The first stage of the labor market model consists in the allocation of labor status. This step reassigns working-age individuals (i.e., between 15 and 64 years old) between the employment status and across economic sectors by informal and formal status to match the projected aggregate changes in total and sectoral employment. The reallocation method follows Habib et al. (2010).<sup>13</sup>

> 13 Briefly, the procedure starts with activity and calculates the total number of individuals that need to be reassigned between activity and inactivity statuses. The process continues assigning unemployed and employed population among the actives, and then the employees are allocated across employment sectors to match aggregated changes. The estimated probabilities from the multinomial model are used to select candidates for reassignment in all stages. Error terms are included to represent the unobserved heterogeneity of agents’ labor supply behavior. These lead to some disparateness in responses to a change in the

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