# **5. The Potential of Geographic and Additional Targeting Variables**

Given knowledge about where poor people/households live, a natural question to ask is how effective geographical variables are in identifying the poor. Experience in other countries indicates that the ability to target poor households typically improves with greater geographical disaggregation (Baker and Grosh, 1984; Bigman and Fofack, 2000). Since many of Vietnam's antipoverty programs use highly disaggregated listings of "poor and remote communes", one would expect the efficiency of its geographically targeting programs to be quite high.l<sup>5</sup> It would also be interesting to know whether the poor can be identified more accurately if additional information other than place of residence is available. Implicitly, this is what the commune/district level staff of Vietnamese Ministry of Labor, Invalids and Social Assistance does when determining whether a household is classified as poor. If a household is classified as poor, it is eligible to receive various benefits such as health cards, free or subsidized primary schooling for children, and sometimes exemption from local taxes. Put differently, can the geographic targeting of transfers to the poor be improved by the use of the type of additional socio-economic variables that can be collected easily in a "quick-and-dirty" enumeration of households?

We assess the efficacy of different targeting variables using a relatively novel technique: Receiver Operating Characteristic (ROC) curves, a graphic way of portraying the accuracy of a diagnostics test originally developed for use in electrical engineering and signal-processing (Stata Corporation, 2001 a). An ROC curve shows the ability of a diagnostic test to correctly distinguish between two states or conditions. In the context of poverty targeting, an ROC curve plots the probability of a test correctly classifying a poor person as poor (known as the test's "sensitivity") on the vertical axis against 1 minus the probability of the same test correctly classifying a non-poor person as non-poor on the horizontal access (known as the test's "specificity").'<sup>6</sup> When the diagnostic test (here the 15 Vietnam has two official lists for identifying poor and remote communes. The Ministry of Labour, Invalids and Social Assistance (MOLISA) maintains a list of "poor communes", most of which are located in coastal and lowland areas under Programme 133. In addition, the Committee for Ethnic Minorities in Mountainous Areas (CEMMA) is responsible for identifying "especially difficult mountainous and remote communes" under Programme 135. Since the geographic areas in which these two programmes operate are reasonably distinct, we have combined the two into one list of "poor and remote communes" for our analysis of targeting. This list was then matched to commune information in the VLSS to identify households living in areas identified as poor by MOLISA or CEMMA. **16** ROC curves can be linked to the occurrence of Type I and Type II errors familiar from conventional statistical hypothesis testing (known as "false positives" and "false negatives" in epidemilogy and medicine and F and E errors in the targeting literature) as follows. Sensitivity is 1 minus the probability of a Type I error (incorrectly classifying a poor 30