Analyzing the Distributive Effects of Fiscal Policies
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result of the global financial crisis. What will be the distributional impact of such slashed financing without additional domestic compensation? In addressing this question, a traditional benefit incidence analysis would show how the new (and reduced) monetized educational transfers accrue to the distribution of households ordered by income, consumption, or wealth. It has been argued that these analyses have a very-short-term perspective, require stringent assumptions about household behavior (see also Van de Walle and Nead 1995), and focus on immediate welfare outcomes rather than on longer-term investment consequences in terms of human capital. By contrast, using a logit technique, the proposed tool will, first, determine the probabilities that different groups of individuals who share the same set of circumstances (“types” in Roemer’s words) will access a specific opportunity. Second, the exercise isolates the impact of public educational spending on the individual’s chance of attending school. Third, the exercise simulates the impact of a crisis-specific shock (for example, public education expenditure cuts following an aid freeze) across population types (defined by sets of circumstances). Fourth, the exercise reestimates the new distribution of probabilities for each type after the financing shock is introduced. By comparing the distribution of probabilities before and after the shock, the exercise identifies winning and losing types (again, based on common sets of circumstances) of individuals and an approximate magnitude of the impact on their chances to attend school. Before we present the results of the simulation conducted for Liberia (figure 5.1), a few points are in order. First, by focusing on opportunities that will have impacts on individuals’ future capacities to lead a decent life, the analysis is able to expand the very-short-run, crisis-specific perspective over a longer-term horizon. Second, results are sensitive to the choice of opportunity, the relevant age group, and the set of circumstances chosen. These are country-specific decisions, but Narayan and Hoyos (2011) reassuringly show that key messages are generally robust to such decisions. Third, the analysis is not intended to provide normative conclusions. Rather, it is conceived as a “constraint” model that assesses the relevance of a number of constraints (the set of circumstances considered) in determining the probability of enjoying an opportunity. In fact, the logit estimation in the first step of the microsimulation does not distinguish among different policy alternatives that are compatible with a given public expenditure; instead, it focuses on the total fiscal cost. Nonetheless, circumstances included in the logit model refer to both demand and supply