Strengths and Weaknesses

While other poverty impact analysis methodologies like cross-section regressions show the impact of policy reforms on general poverty, their results hide the specific gainers and losers of such reforms. Applying this paper's method, GTAP can utilize multi-poverty elasticities which capture the shape of income distribution for specific subgroups from the survey data, and thus determine the mobility and distributional welfare of these subgroups in different simulations (Hertel, Keeney, and Winters 2007b).

Compared to national accounts data, survey data better reflect labor force classification without significant sampling bias. However, the real size of economic activities may be understated; some households may underreport their incomes, and it is likely that the richest households are underrepresented. To address this issue, Ivanic (2004) added the residual of the GTAP totals and the survey factor returns to the survey data. It is important that surveys are properly designed in order to minimize the discrepancy.

Imputation of labor income to self-employed individuals decreases the variance, as values in this variable tend to lump together towards the mean. In order to better capture the true variability across labor income values, future household surveys can include questions on the actual income of household members who participate in the household business. Also, this methodology can be improved by utilizing surveys that have better coverage than the Cambodia (6.3%) and Lao PDR (5%) surveys. In sum, the methodology can be readily extended to other developing countries' survey data, provided that they have representative coverage, sound sampling design, good quality, and timely data.

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