Table 1.

Targeting simulations in Togo and Nigeria

	1)	2)	3)	4)	5)
	No. of spatial units	No. of units with estimates	$R^2$	Targeting accuracy, poorest 25%	Targeting accuracy poorest 50%
Togo
A) High-resolution estimates
Tile targeting	10,187	10,187	0.60	0.73	0.79
Canton targeting	387	387	0.56	0.73	0.77
B) Imputation based on DHS data
Prefecture targeting	40	40	0.49	0.70	0.70
Canton targeting	387	185	0.52	0.76	0.80
Nigeria
C) High-resolution estimates
Tile targeting	159,147	159,147	0.53	0.79	0.79
Ward targeting	8,808	8,808	0.51	0.78	0.78
D) Imputation based on DHS data
State targeting	37	37	0.37	0.75	0.74
LGA targeting	774	631	0.47	0.78	0.76
Ward targeting	8,808	1,218	0.54	0.83	0.79

A and C simulate the performance of antipoverty programs that geographically target households using the ML estimates of tile wealth, under scenarios where the program is implemented at the tile level (first row) or smallest administrative unit in the country (second row). B and D simulate geographic targeting based on the most recent DHS survey, using administrative units of different sizes. For B and D, when an admin-unit has no surveyed households, the wealth of the unit is imputed based on the wealth of the geographic unit closest to the household. Column 1 indicates the number of units in the country; column 2 indicates the number of units where data exist—see also SI Appendix, Fig. S19 for maps highlighting the regions in Togo and Nigeria that were surveyed in the most recent DHS. Column 3 indicates the R² from a weighted least-squares regression, at the household level, of the ground-truth wealth of each household (from the EHCVM or NLSS) and the estimate of the wealth of the spatial unit in which that household is located, weighted using the EHCVM or NLSS household weight. Columns 4 and 5 assume that the government has a fixed budget, sufficient to cover 25% (column 4) or 50% (column 5) of the population, and provides benefits to all households in the poorest administrative units; we then report the accuracy at targeting the 25% or 50% of the poorest households (in the EHCVM or NLSS).