# **Introduction**

Economists have long been interested in measuring the poverty and distributional impacts of macroeconomic projections based on structural reforms, macroeconomic shocks, and other events. A standard solution is to extrapolate the welfare impact of these projections from the historical responses of income (consumption) poverty to changes in output by estimating an elasticity of poverty to output or gross domestic product (GDP). Although this approach is easy and rapid to implement, it is limited in its predictive capability since it cannot estimate distributional impacts (i.e., poverty gap, inequality, vulnerability, etc.).

To estimate distributional impacts, microsimulation models allow accounting for several transmission channels through which macroeconomic projections could impact individuals and households. Thus, it helps evaluate the consequences of a change in the economic environment induced by a macroeconomic scenario on the welfare of each individual or household and identifies those likely to be losers and winners. The more sophisticated microsimulation models are based on computable general equilibrium (CGE) or general equilibrium macroeconomic models that demand substantial information (for constructing social accounting matrices or time series of macroeconomic data) to create the ""linkage aggregate variables"" (LAVs) that are fed into the microsimulation model (Bourguignon et al., 2008). At the same time, most of these models do not allow for changes in some key features of the population, such as gender or age composition, except for the Maquette for MDG [Millennium Development Goal] Simulations (MAMS).<sup>3</sup> Further, the relationship between the CGE model and the microsimulation model can be sequential (top-down approach), in which case the outputs of the CGE model are used in the microsimulation as inputs (Bourguignon et al. 2003; Heraoult, 2010); or iterative (top-down/bottom-up approach) in which case the results of the microsimulation model are reincorporated in the CGE model as inputs until an equilibrium between micro and macroeconomic estimations is achieved (Savard, 2003; Colombo, 2010). The main advantages of the iterative models relate to the improved accuracy of the counterfactual and consistency of the analysis. However, the information demands of these models make them difficult to apply in most developing countries, thus calling for an approach that is workable with the available data and macroeconomic projections.

Following the top-down approach, there are multiple types of microsimulation models, from those which ignore behavioral 'agents' responses to changes in the economic environment (arithmetical or accounting approaches) as in Buddelmeyer et al. (2008) and Ferreira and Horridge (2006), to those which include a detailed representation of behavioral responses of individuals or households in aspects such as occupation (Olivieri, S. et al., 2014) or savings behavior (Van Ruijven et al., 2015). There is also a differentiation between static and dynamic micro-simulation models. The former does not consider changes in the baseline sociodemographic characteristics of individuals and households, such as the level of education, the household composition, demographic change, etc.; the latter introduces changes in individuals’ and households' sociodemographic characteristics and behavior over time derived from

> 3 <mark>Ferreira et al. 2008.</mark>

2