months or younger.<sup>3</sup> After removing trends from GDP in various ways, we compare the probability of infant death in years in which GDP was below trend with other years. This approach closely follows Baird Friedman and Schady (2007) (henceforth BFS; see also Paxson and Schady 2005 on a crisis in Peru and Bhalotra 2009 on cyclicality in infant mortality in India).<sup>4</sup> Table 1 gives the list of countries (30) and DHS (62) in Africa. These surveys collect information on approximately 260,000 women and 640,000 live births, and are the basis for our calculations. Yearly GDP per capita numbers are taken from the IMF; for every country, Table 2 gives the average growth rate between 1993 and 2008, the growth rate in 2008, and the April 2009 IMF projection of the growth rate for 2009; the last column, finally, gives the “growth shortfall”—the difference between the 2008 growth rate and the 2009 projected growth rate.<sup>5</sup> This difference is a (simple) measure of how much lower growth rates in 2009 are expected to be than they would have been in the absence of the financial crisis, and are important for our calculations of “excess deaths”. We discuss this in more detail below.

The basic approach we take involves pooling data from all of the surveys and running regressions of the following form: (1) Dimct   c  f c ( _t_ )   logGDPct   _imct_ 3 We use this measure of infant mortality, rather than the standard definition of mortality for children _younger than_ 12 months, because of age heaping in reports of mortality. Further, we discard information for children born within 12 months of the survey when calculating mortality rates to avoid complications with censored data. Results are largely unchanged if we restrict our mortality measure to those infants who died at an age _younger than_ 12 months.

4 Although the DHS are a rich source of data, they also have some limitations for our analysis. We briefly discuss two of these limitations, both of which are related to the use of _retrospective_ information in the DHS to construct birth and death histories. First, recall bias may be a concern if women are less likely to accurately remember more distant births and deaths. To minimize recall errors, we do not use information on births that occurred more than 11 years prior to the date of the survey. However, our results are robust to different cut-offs for the recall period. Second, any given survey is representative of women ages 15-49 at the time of the survey, but is not representative of all births and child deaths in earlier years. To see this, note that a woman aged 49 in a survey carried out in 2000 would have been 39 in 1990. If no surveys were carried out between 1990 and 2000 in this country, no data would be available on births to women aged 40 or older in 1990. Children born to older women may respond to economic fluctuations differently than those born to younger women. To avoid this problem, we discard from the sample births to women age 40 or older. Our analysis therefore provides meaningful estimates of the relationship between income fluctuations and infant mortality for women aged 15 to 39. (We note, however, that a very small fraction, less than 3 percent, of births in our sample of DHS countries occur to women age 40 or older.) This retrospective construction of births and infant deaths to women aged 15-39 results in series of varying lengths and with varying start periods depending on the number and dates of DHS surveys in each country. 5 For four countries in the table – Djibouti, Eritrea, Lineria, and Zimbabwe – growth information was missing for either 2008 or 2009 (or both). In these cases, we replace the missing value with the Africa-wide average. These four countries represent 2.9% of the SSA population and 2.5% of births and so any mismeasurement as a result of these imputations is highly unlikely to bias the aggregate results for the continent.

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