# 5.1 IDENTIFICATION OF FISP ELIGIBLE HOUSEHOLDS

As noted above, the FISP attempts to target resource poor smallholder households. Although the IHS3 data provide a comprehensive household consumption aggregate that could be utilized with the official national poverty line to deduce household poverty status, we are concerned that the observed household consumption outcome could be directly affected by program participation; as such, it cannot directly be used as an objective identifier to gauge FISP eligibility. In an attempt to recover an objective resource poverty indicator in the absence of program participation, we rely on survey-to-survey imputation, as presented in Stifel and Christiaensen (2007).

The survey-to-survey imputation method has its origins in the small area estimation (SAE) method developed by Elbers et al. (2003). We start out by estimating a model of annual household consumption per capita as a function of non-monetary explanatory variables using the _rural_ sample of the nationally-representative Second Integrated Household Survey (IHS2) 2004/05, which was conducted prior to the first year of FISP implementation during the 2005/06 agricultural season. The estimation takes into account household sampling weights and allows for intra-cluster correlation in the regression residuals to avert underestimation of standard errors. The estimated variance-covariance matrix is then used to obtain Generalized Least Squares estimates of the parameters and their variance, which are in turn combined with the same set of explanatory variables from the IHS3 data to obtain the annual rural household consumption per capita predictions.

This procedure is repeated 100 times, drawing different sets of random terms to inform predictions, and the point estimate is computed as the average of these 100 simulations. Once we have the point estimate, the official IHS2 2004/05 poverty line (16,615 Malawi Kwacha per capita per year) is used to classify whether a rural IHS3 household is poor (i.e. FISP-eligible) or not.<sup>15</sup> The non-monetary explanatory variables underlying the predictions are constructed based on comparable questionnaire instruments and implementation across surveys, which ensures an identical definition of the household consumption outcome over time. The critical assumption underlying the method is that the relationship between the household consumption outcome and the explanatory variables is stable over time.

The implicit assumption in using a consumption-based poverty measure to identify FISP-eligible households is that the indicator is a reasonable proxy for being resource poor. Although consumption should be a product of household resources, an argument can be made that in a

> 15 The survey-to-survey imputation exercise was conducted using the PovMap2 software, which can be downloaded from http://iresearch.worldbank.org. The list of explanatory variables and the model parameter estimates underlying the predictions are provided in Table 2. The regression coefficients for the same set of covariates stemming from the model estimated with the IHS3 data were stable with respect to their counterparts in Table 2. Those results are available upon request.

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