to using cross-sectional data for analysing the effects of family planning programs.<sup>1</sup> To address program placement challenges in such contexts, one approach is to use variables that influence program placement, but that are unrelated to individual fertility as done for Tanzania (Angeles et al. 1998).<sup>2</sup> A woman in Tanzania exposed to family planning throughout her fertile lifespan is found to have 4.13 children compared with 4.71 children in the absence of family planning programs.<sup>3</sup> Lingering concerns remain, however, that some of the variables used to identify placement (such as child mortality levels and the presence of other family planning services) may also be correlated with unobservable variables that influence both placement and fertility decisions.

To address non-random program placement when evaluating the effect of family planning programs in Ethiopia, this study exploits detailed information on area characteristics and the geographical allocation decisions of the family planning programs. The identification strategy is novel in that it draws on the insight that areas compete for limited resources and that ordinal rankings (as opposed to cardinal scores of the ranking criteria) are often used to discern between competing demand.<sup>4</sup> To fix ideas, assume that there are only three areas, A, B and C, that compete for resources from the government. Using the extent of urbanization as an example, we expect that the degree of urbanization of area A will affect fertility in area A, but that the degree of urbanization of areas B and C will have little or no effect on fertility in area A.<sup>5</sup> Because the three areas compete for resources the _relative_ degree of urbanization may, however, affect the program placement decision.<sup>6</sup> This opens up the opportunity to use rankings as identifying variables (as opposed to levels). Imagine that urbanization is highest in area A, followed by B and C, and that the more urbanised an area is, the more likely it is to receive a program. Identification is achieved because the _rank_ of an area primarily depends on other areas’ absolute value of the ranked variable. Specifically, assume that

> 1There are also additional problems with using fixed effects, such as measurement error bias. For a discussion of this and other problems in the study of family planning see, for example, Angeles, Guilkey and Mroz (1998). 2Also using cross-sectional information, Miller (2010) found that Columbia’s family planning program, Profamilia, reduced lifetime fertility by around half a child, equivalent to only 10 percent of the the sharp decline in fertility over the period the program was implemented. As the process of allocating programs were assumed to be arbitrary, these results may, however, be under- or over-estimates.

> 3For Indonesia, Angeles, Guilkey and Mroz (2005) report using the same approach, but found no evidence of non-random placement of family planning programs.

> 4The approach closest to ours is by Pitt and Menon (2010), who used average characteristics of other areas, such as education level, for their instruments. A potential issue with their approach is that if network effects are important these averages might not serve as valid instruments.

> 5The cost of children may be higher in more urbanized areas reducing fertility.

> 6It may, for example, be less costly for the government to place programs in areas that are more urban because of easier access.

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