part because of this, recent research has explored the use of satellite and phone data as an alternative to identify poor households.

A recent paper (Aiken et al, 2022) evaluates an innovative two-step approach to identify the poorest households in Togo, which was applied to the Novissi cash transfer program. The first step entailed using the Meta relative wealth index from Chi et al. (2021) to identify the poorest hundred Cantons, which are the third administrative level in Togo, out of 397 total Cantons. Within these identified Cantons, the team used CDR data to identify poor households, using a model trained against per capita consumption collected as part of a phone survey in September 2020. Targeting accuracy was then evaluated against a Proxy Means Test constructed from an independent phone survey representative of all cell phone subscribers in the country.

The headline result is that this two-step targeting approach outperformed two hypothetical feasible alternatives based on geographic targeting, as shown in Table 6. The first feasible alternative considered is a transfer of equal value to all persons within the poorest prefectures, which is the second administrative level in Togo, out of 40 total prefectures in Togo. The second alternative provided a transfer of equal value to all individuals within the poorest Cantons. Both of these hypothetical alternatives simulated transferring cash to all households in the poorest geographic areas, Prefectures in the first case and Cantons in the second, until 29 percent of the population was covered. This 29 percent threshold selected to cover the same percentage as the two-step approach that combined the relative wealth index with CDR data.

The Meta relative wealth index, however, is a measure of asset wealth rather than a measure of household-size adjusted consumption. This raises the question of whether targeting could be further improved if the 100 poorest cantons were identified using small area estimates of poverty, derived from combining survey and geospatial auxiliary data along the lines of the studies discussed above, instead of small area estimates of wealth. The paper does not address this question directly, because Canton-level poverty estimates derived from combining survey data on per capita consumption with geospatial data was not considered in the set of feasible options.

The first feasible alternative considered simulated the provision of a uniform transfer to all individuals within the poorest prefectures. There are only 40 prefectures in Togo, however, as opposed to 397 cantons. This means that the simulated prefecture transfer differs in two ways from the simulated Canton procedure. First, it is based on many fewer prefectures, which reduces targeting accuracy because no attempt is made to distinguish hypothetical recipients within prefectures. Secondly, the prefecture estimates are estimates of predicted per capita consumption instead of wealth, which all else equal should improve targeting, since a measure of predicted per capita consumption is used for evaluation. Comparing the bottom two rows in Table 6 indicates that in this case, targeting based on less wealthy Cantons is more accurate than targeting based on poor prefectures, as the benefits of more disaggregated targeting outweighs the disadvantage of targeting based on predicted wealth. However, this difference is not large, given the much smaller number of prefectures, as the difference in rank correlation is only 0.04. This suggests that geographic targeting could be further improved if the 100 poorest cantons were determined based on estimates of per capita consumption rather than wealth.