income should therefore reduce the effect of inter-annual income fluctuations, and provide a more appropriate picture of the household’s poverty status.

An alternative interpretation of the discrepancy between predicted and observed income is the following: There might be elements that affect household income that we have not included in the regression model. First, there are relevant factors that we do not have data for (unobservables). For example, some of the _income poor &asset rich_ may have characteristics that make them unable to use productively the assets they own, e.g., they have a chronic disease that makes them unable to use fully the agricultural land that they possess. Second, there are potentially relevant variables that we have some data for but that are not included, such as weather anomalies or access to environmental resources. For example, some of the asset poor household may have (under-predicted) high environmental income that brings them above the income poverty line (i.e. make them belong to the category of _income rich &asset poor_ ). Such hypotheses are addressed in the paper. For this reason, variables such as access to forest resources, weather anomalies and shocks are excluded from the regression of predicted income, as we test whether these can explain why a household falls in a particular poverty category.<sup>11</sup>

# **3.3.2 Classification into poverty categories**

The combination of observed and predicted income is used to classify households into the four poverty categories. Summary statistics for the variables included in the regression models are presented in the Appendix (Table A2-A7). Agricultural land, livestock and financial assets are key predictors of household income, and these vary greatly across the regions. The mean total income among the households in Latin America is more than three times higher than mean income among the households in Africa, and the value of assets is more than five times higher. The households in Latin America also hold more livestock and have more agricultural land.

The regression model results that underlie our income predictions are given in the Appendix Table A8. Most results are in line with expectations. The range of predicted income is much smaller than for observed income, as would be expected (Table 2).<sup>12</sup> The difference in mean observed and mean predicted income is due to the smearing estimate.<sup>13</sup> Also, we note that the median observed income is well below the median predicted income. Income distributions typically has a long right tail, resulting in the mean being well above the median income (only about one third of the households have an income above the mean). The predicted incomes have a more normal distribution, with the mean and median being closer.

> 11 Alternatively, we could have included indicators for shock, weather anomalies and access to environmental resources in the regressions of predicted income, which would then test to what extent these variables are relevant to predict income. The approach chosen, to test for systematic differences in, for example, exposure to shock and weather anomalies across the groups, gives, in our view, a more transparent and clearer analysis.

> 12 Some of this difference in number of poor could also be explained by measurement errors, i.e., some households are classified as poor based on the observed income simply because of incomplete income recording. While we cannot exclude that this explains some (unknown) proportion of the difference, the PEN studies did – more than most household surveys – put much efforts into carefully recording all income sources in the four quarterly income surveys done over the one year study period.

> 13 When transforming the income variable back from log to the level variable to estimate the predicted income, we adapt the smearing estimate developed by Duan (1983) to avoid the retransformation bias and underestimation of predicted income.

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