specification requires a sound understanding of the rules that govern budget allocation and program placement. This understanding is also crucial to deal effectively with the potential problem of _endogeneity_ that can confound the causal interpretation of the results<sup>29</sup> .

Behavioral approaches are also relevant in the context of marginal incidence analysis which seeks to determine who benefits from the expansion of an expenditure program (or who loses from a cut in public spending). The standard benefit incidence method predicts that people gain or lose in proportion to the benefits they are currently receiving under the program. This might be the case for a policy (e.g. a subsidy) that changes the price of a commodity proportionately. In general, one should not expect the additional benefits from an expanded program to go to current beneficiaries. Even if they do flow to them, it is not necessarily in proportion to their current benefits. Indeed, the socioeconomic composition of participants can change dramatically as the program expands or contracts (Lanjouw and Ravallion 1999).

Several methods have been proposed to deal with this issue. Younger (2003) notes the following three. The first method relies on _spatial variations_ in program coverage. The second is based on changes induced by program _expansion over time_ . The last approach relies on _econometric analysis_ to estimate either compensating variations or changes in the probability of participation. The first method emerged from a study by Lanjouw and Ravallion (1999) of the relationship between program size and the composition of program participation<sup>30</sup> . These authors argue that the distributional 29 Endogeneity would arise if the policy variable (this could be a dummy variable indicating program participation) is correlated with unobservable characteristics of the household as represented by the random disturbances in the regression equation. Under these circumstances, the use of standard OLS would lead to a biased and inconsistent estimate of policy impact. This correlation can stem from at least three sources (van de Walle 1998). Placement into the program could be determined in part by the welfare indicator used to measure program performance, for instance assigning a nutrition program on the basis of nutritional status. (This would lead to simultaneity bias). Trouble could also stem from the fact that there is a variable that explains both placement and outcomes, yet this in not included in the model. The existence of regional or community level fixed effects or time varying effects that determine both outcomes and placement illustrates this case. Finally, self-selection can cause endogeneity. Statistical solutions to this problem include the use of panel data and fixed effects models, instrumental variable techniques and randomization. 30 Lanjouw and Ravallion (1999) explain that program participation is homogeneous of degree one if group-specific participation rates stay the same as the program expands. For instance if 100p percent of the participants are poor when the program operates at level 1 with a given number of participants, then 100p percent of participants are also poor when the program expands to level 2, covering more people. Thus, standard benefit incidence analysis assumes homogeneity. If the poor are able to capture program benefits at certain points in time and not others, then participation is said to be non-homogeneous. This would be 35