Weather and Infant Mortality in Africa

  

Weather and Infant Mortality in Africa

∗

  

Masayuki Kudamatsu, Torsten Persson, and David Str¨omberg

August 16, 2016

Abstract

Using 50 retrospective fertility surveys from 28 countries and global me-

teorological reanalysis data for 1957-2002, we estimate the causal impacts of

malarious weather and growing-season rainfall on infant mortality across a

considerably wide set of African locations. Relying on year-to-year devia-

tions from the local monthly average pattern, we find that mortality rose by

at least a quarter of the sample mean for infants born after droughts in arid

climates, or after unusually long spells of malarious weather in low malaria-

transmission areas. Our estimates imply that malarious weather explains at

least 1.8% of infant mortality in Africa’s low malaria-transmission areas.

  ∗ : OSIPP at Osaka University, 1-31 Machikaneyama Toyonaka, Osaka, 560-0043 Kudamatsu

  

Japan, m.kudamatsu@gmail.com. Persson: IIES at Stockholm University, SE-106 91 Stockholm,

Sweden, torsten.persson@iies.su.se. Str¨omberg: IIES at Stockholm University, SE-106 91 Stock-

holm, Sweden, david.stromberg@iies.su.se. Acknowledgements: We are grateful to Daron Ace-

moglu, Sandra Black, Robin Burgess, Angus Deaton, Colin Jones, Dean Karlan, Heiner K¨ornich,

Ben Smith, Peter Svedberg, Jakob Svensson, John van Reenen, and participants in seminars at the

  

IIES, Amsterdam, UPF, UCLA, UBC, Chicago, LSE, Princeton, Berkeley, Oslo, LSHTM, Edin-

burgh, Nottingham, Houston, Stockholm School of Economics, Gothenburg, Harvard/MIT, Bris-

tol, Leicester, Ume˚a, Zurich, Toulouse, SEA Annual Meeting, CIFAR Meeting, and EEA Annual

Congress for helpful comments; to Heiner K¨ornich, Lars Eklundh, and Johannes Karlsson for assis-

tance with data; to Pamela Campa for research assistance; and to Mistra, the ERC, and the Torsten

and Ragnar S¨oderberg Foundations for financial support.

1 Introduction

  To evaluate global policy responses to climate change, we must learn more about the impact of weather on important socioeconomic outcomes. Infant mortality in Africa is one such outcome. Despite a declining trend, the mortality rate of newborns in the sub-Saharan part of the continent still stands around 60 per 1,000 live births in 2013, ten times higher than prevailing rates in high-income countries (World Bank 2015).

  Although mortality impacts of climate change have been widely discussed (Costello et al. 2009, Patz et al. 2005, Stern 2007), credible impact estimates to inform this discussion are very difficult to obtain for low-income, data-poor regions (Parry et al. 2007). This applies particularly to Africa, a region thought to be very vulnerable to climate change. Africa’s presumed vulnerability reflects that its adverse weather changes are likely to be substantial (IPCC 2014) and that its major production sec- tors are highly weather-dependent, as are its major diseases like malaria.

  In this paper, we provide the estimates of how weather affects infant mortality in Africa by using micro data, and use these estimates to predict areas at risk for high infant mortality, 100 years down the line, under two different emission scenarios.

  Our empirical strategy has three distinctive features, which concern scale, structure, and source of identification.

  Scale We use a dataset with a massive number of observations at wide geograph- ical coverage and high spatial resolution. For weather, we use data for all of Africa from meteorological reanalysis by a global atmospheric weather-forecasting model, on a

  1.25 × 1.25 degree earth grid (139 × 139 kilometers, at the equator) with a six-hour frequency from 1957 to 2002. For infant mortality, we use micro data from 50 nationally representative retrospective fertility surveys, known as Demo- graphic and Health Surveys (DHS). These cover nearly one million live births, ob- served monthly, at more than 17,500 geo-referenced locations in 28 countries across Africa.

  The large number of observations in our dataset helps us estimate precisely the impacts on outcomes as rare as infant death. The spatial richness allows us to observe a large number of rare weather events, including droughts and unusually long spells of malarious weather in areas where malaria is not endemic.

  Structure To use the data efficiently, we exploit prior structural knowledge on how weather patterns link to infant mortality in Africa. The IPCC Report (Niang et al. 2014) and the Stern Review (Nkomo et al. 2011) list Africa’s main weather- related vulnerabilities: (i) droughts and floods, (ii) safe drinking water and sanita- tion, (iii) agriculture and food security, (iv) malaria, and (v) environmental conflicts and migration driven by food scarcity. These vulnerabilities are directly related to the most common death causes among children on the continent: malnutrition, malaria, and diarrhoea (Black et al. 2008, Black et al. 2010). While we know little about how weather affects drinking water, sanitation, or diarrhoea incidence, the previous research in public health and agricultural science inform us how specific weather conditions shape malaria transmission and crop yields.

  In particular, we follow Tanser et al. (2003) in measuring malarious weather by four local temperature and rainfall conditions, which are necessary for the survival and growth of malaria vectors and parasites. We also measure the amount of rainfall in each location during the growing season, the weather condition most relevant for food security and scarcity. To isolate the impacts of malarious weather and growing- season rainfall from other weather impacts, we follow the recent climate-economy literature (see below) by controlling for the frequencies at which temperature and precipitation fall into different bins.

  We believe that using structural indices of malarious weather and growing- season rainfall have two clear advantages over non-parametric or agnostic approaches. First, since our indices are guided by pre-existing research on weather-patterns linked to key mortality factors, they are likely to predict infant mortality in an effi- cient way. Second, since our indices capture structural mechanisms, they allow us to extrapolate the findings to out-of-sample areas or time periods.

  Studying the effects of climate change is plagued by en- Source of identification dogeneity concerns. This is well-acknowledged by Patz et al. (2006, p. 314), one of the most cited survey articles on the impacts of climate change: “all previous continental or global models of malaria-climate relationships ... fail to account for non-climatic determinants or the variation of specific climate-disease relationships among locations.” It is not feasible to control for all relevant drivers of infant mor- tality because average geographic and seasonal climate differences are correlated with numerous determinants of infant mortality. To tackle this problem, we follow the same approach as studies in the new climate-economy literature (see below), namely to identify the causal effects of weather shocks on infant mortality from ex- ogeneous deviations of local weather outcomes from their average monthly pattern.

  We uncover a set of new results. We find statistically and quantitatively Findings significant effects of malarious weather in areas with low malaria transmission, where epidemiology studies have been very scarce (Desai et al. 2007). Compared to a typical year in these epidemic areas, malarious weather for more than six months in the year before birth makes infants 2.8 to 3.7 percentage points more likely to die in their first year of life. This additional death toll amounts to a quarter to a third of mean mortality in the sample. As for growing-season rainfall, infants in arid-climate regions of Africa face a risk of death 2.5 percentage points higher than in a typical year if they are born within a year after a drought. This effect is just over a quarter of the sample average. Even though these extreme weather events are very rare, these numbers are precisely estimated thanks to the large number of observations and the wide spatial coverage of the data.

  By multiplying the sample frequencies of malarious weather spells with the es- timates of their impact (relative to the level of mortality in the absence of malarious weather), we obtain the average number of infant deaths due to these spells. Our estimates suggest that at least 1.8% of infant deaths can be attributed to malari- ous weather in Africa’s low malaria-transmission areas. A comparable estimate is absent in the epidemiology literature, as most malaria transmission surveys are conducted in high malaria-transmission areas (Desai et al. 2007).

  Using these historical estimates together with future weather predictions from the climate model similar to the one that supplies the historical reanalysis data, we pinpoint the areas most at risk for additional infant deaths due to droughts and malaria epidemics 100 years down the line.

  Our study is in line with the new climate-economy literature, sur- Related work veyed by Dell et al. (2014), in that we rely on random weather fluctuations around locality-specific average patterns. The impact of weather on mortality has recently been investigated by Deschˆenes and Greenstone (2011) and Barreca et al. (2016) for the United States, and by Burgess et al. (2013) for India. Deschˆenes et al. (2009)

  1

  also look at the impact on birth weight. These studies non-parametrically estimate the impact of binned temperature realizations, and find that extremely high temper-

  2 atures raise general mortality, and reduce the birth weight of infants, respectively.

  The use of reanalysis data for weather – and growing-season rainfall as the condition relevant for crop yield – makes our paper related to Guiteras (2009), who studies how weather affects agriculture in India. The use of weather to predict disease environments echoes the approach taken by Alsan (2015).

  Our study is also related to a large literature in public health and epidemiology, which investigates either how malaria and/or malnutrition responds to weather (e.g., Hay et al. 2009), or how infant health responds to malaria or malnutrition (e.g., Steketee et al. 2001, Black et al. 2008, Black et al. 2010). Unlike these studies, we estimate a direct causal link from weather conditions to infant mortality. Therefore, our impact estimates not only reflect pregnant women’s health conditions (the focus of these earlier studies). They also reflect health conditions among other members in the households of newborn babies, as well as behavioral changes by mothers and other household members in response to weather outcomes. This way, our estimated parameters may be of more immediate interest for the debate on the adverse impacts of climate change.

  Roadmap In the following, Section 2 gives a general background on our data sources and how we put the data together. Section 3 describes the details of our empirical approach, our measurement of malarious weather and growing-season rainfall, and our econometric specifications. Section 4 discusses our empirical find- _{1 2} See Deschˆenes (2012) for a review of the literature on the impact of temperature on health.

  Artadi (2006) estimates the impact of being born in rainy seasons and hungry seasons on infant

mortality in African countries. But her interest is to measure the impact of average monthly weather

patterns, while our focus is to estimate the weather impact of deviations from the average seasonal pattern. ings and robustness checks when it comes to the recent history of weather and infant mortality in Africa. Section 5 extrapolates these findings to the whole of Africa and to the future under two different scenarios for carbon-dioxide emissions. Section 6 concludes. The Appendix spells out some additional details on data construction, econometric strategy, and auxiliary evidence.

2 Data This section describes our data and their sources.

  Infant deaths To measure infant deaths, we pool together the retrospective fertility- survey component of 50 nationally representative Demographic and Health Surveys (DHS) (Corsi et al. 2012) in 28 African countries. Appendix Table A.1 lists the sur- veys used in the analysis. Women aged 15 to 49 in sampled households are asked about the month and year of each live birth they gave in the past, whether the child died after birth and, if so, the age at death in months. From this information, we

  3 define a binary indicator whether the child died at an age of 12 months or less.

  These 50 surveys cover nearly 1.2 million live births to about 300,000 mothers in 1957-2002, the period for our weather data. Since we exploit year-to-year varia- tion by calendar month and location, we drop observations with imputed birth dates, leaving us with 962,471 live births by 269,754 mothers in 17,568 geo-referenced

  

4

survey clusters (villages or town districts).

  Note that our measure of infant mortality does not include stillbirths. Appendix Section A.1 provides further detail on the DHS surveys. The section also discusses possible biases due to data accuracy and finds that these are likely to be small.

  Section 4.5 discusses possible sample-selection bias due to endogenous fertility and maternal mortality. ₃ We include reported deaths at 12 months because of a peak at that age in the distribution of ages at death. Excluding deaths at 12 months does not qualitatively change our estimation results. ₄ One could argue that mortality of babies with missing birth date may be lower because mothers

may remember the birth date of dead babies better. In the data, however, babies with imputed birth

  

dates die more likely, by about 96 deaths per 1,000, than babies with non-imputed dates. This

suggests that babies dropped from our sample are more vulnerable to weather shocks, and that our

estimates are likely to be a lower bound of the true impact.

  To gauge local temperature and precipitation, we use the ERA-40 data Weather

  5

  archive (Uppala et al. 2005), obtained from meteorological re-analysis. The re- analysis was carried out by a global atmospheric weather-forecasting model on a 1.25 × 1.25 degree earth grid (about 139 x 139 km at the equator) with a six-hour frequency for the period from 1 September 1957 to 31 August 2002. We aggregate the original six-hourly data to monthly data on temperature and total precipitation. Then, we match each DHS survey cluster to its corresponding ERA-40 grid cell by ArcGIS’s Spatial Join tool, resulting in 743 grid cells covering 17,568 DHS clusters. Figure 1 illustrates the geographical coverage of these data and shows that a wide range of African regions are included in our sample.

  We expect ERA-40 to contain among the best weather data for Africa, especially for its arid and semi-arid areas. The climate model makes observations from data- sparse regions more realistic and reliable, as weather follows physical laws almost linearly at a six-hour time scale. This advantage is larger from the time when global satellite data (fed into the climate model to predict the atmospheric state) becomes available: in 1973 and, at higher frequency, in 1979. About 88% of the births in our sample occur after 1978.

  Most importantly, the precipitation data in ERA-40 do not depend on rainfall gauge data, which is particularly coarse and low-quality in Africa, but instead on

  6

  the climate model and the estimated state of the atmosphere. A comparison of rainfall data from ERA-40 and gauge data (where available) by Zhang et al. (2013) suggests that the ERA-40 data have less bias in the arid and semi-arid areas of Africa, where the departures from regular seasonal fluctuations – our main source of identification – are the largest. ₅ See Appendix Section A.2 for how re-analysis in ERA-40 is conducted. Also see Auffhammer et al. (2013) for an account of re-analysis directed to economists. ₆ Due to the well-known difficulty of predicting the precise location of convective rainfall (i.e.

  

thunderstorms), the forecast may fail to predict the exact amount of rainfall in a precise location in

a particular six-hour period. Aggregation in time (to a month) and space (to 1.25 × 1.25 degrees),

however, resolves much of this problem.

3 Structural Measurement and Estimation

  This section first explains how we exploit prior knowledge on weather patterns that are likely to impact infant mortality in Africa to produce indices measuring malarious weather condition and growing-season rainfall. Then, it discusses our econometric specification.

3.1 Malarious Weather Conditions

  The monthly malaria index Our goal is to construct a binary indicator for weather conditions in a particular month being suitable for malaria transmission. We do so by adapting a parsimonious weather-based index of malarious conditions for Africa proposed by Tanser et al. (2003), which is in turn based on the work by the Mapping

  7 Malaria Risk in Africa (MARA) project (Craig et al. 1999). These authors validate

  their index against 3,791 laboratory-confirmed parasite-ratio surveys of one-month duration across Africa conducted between 1929 and 1994. They find that, if the index indicates malarious weather, then 99% of the surveys indeed report malaria prevalence. If the index indicates the absence of malarious weather, 67% of the surveys report malaria prevalence.

  Specifically, we define our binary monthly index as follows. Let τ be the (run- ning) month of birth and g the ERA-40 grid cell. Our index for location g in birth month , takes a value of one if the following four conditions are

  τ , denoted by Z g,τ satisfied: (a) Average monthly rainfall during the past 3 months (the birth month and the two proceeding months) is at least 60mm;

  (b) Rainfall in at least one of these months is at least 80 mm; (c) None of the past 12 months (the birth month or the previous 11 months) has _{◦ 7} an average temperature below

5 C; and

  Appendix Section A.3 discusses the original index of Tanser et al. (2003) in detail and how our index adapts it.

  ◦

  (d) The average temperature in the past 3 months exceeds the sum of C and

  19.5 the standard deviation of monthly average temperature in the past 12 months. If any of these conditions fails, our malaria index turns to zero. Conditions (a) and (b) ensure the availability of breeding sites for the vector and sufficient soil moisture for vectors to survive; (c) rules out the death of vectors, as this happens quickly at lower temperatures; and (d) allows parasites to become infectious inside

  

8

  the vector’s body before the vector dies. The required threshold of temperature in condition (d) goes up with the standard deviation of monthly temperature because, after a cold winter, the populations of parasites and vectors need to be quickly regenerated to a level sufficient for malaria transmission. Using the ERA-40 data,

  9 we then calculate the malaria index for each grid cell and month.

  The year prior to birth Infants are known to have a reduced sensitivity to malaria during the first few months of life (Maegraith 1984). On the other hand, malaria in

  10

  pregnancy is known to raise the likelihood of low birth-weight, a major risk factor

  11 for infant death (McCormick 1985).

  For these reasons, we focus on malarious weather in the year prior to birth as a determinant of infant death. For an infant born in cell g in month t, we define the key independent variable in our estimation of the impact of malarious weather:

  

t

  X , (1) z g,t = Z g,τ

  τ ₈ =t−11 The vector obtains a parasite by biting a malaria-infected person. But it takes time for the

parasite to become infectious and thus for the vector to transmit malaria by biting another person.

  

Higher temperature both shortens the time required for the parasite to become infectious and helps the vector survive long enough. ₉ Dropping separately each of the four conditions, we find conditions (a) and (d) to be the most relevant ones to predict infant death. ₁₀ See Desai et al. (2007) for a recent and extensive review of the medical literature on malaria in pregnancy. ₁₁ On top of a higher likelihood of low birth-weight, babies born to mothers with a malaria-infected

placenta are reported to be more likely to develop a malaria infection during the first year of life (Le

Hesran et al. 1997). They may also become susceptible to measles (accounting for 12% of under-5

child mortality in sub-Saharan Africa in 1990, according to Murray and Lopez, 1996, Appendix

Table 6f) earlier than other babies due to reduced placental transfer of maternal antibody (Owens et

al., 2006). Snow et al. (2004) argue that looking only at the direct cause of death would significantly underestimate the impact of malaria on child death. where is the monthly malaria index in ERA-40 cell Z g,τ g at (running) month τ , defined above. In words, measures how many months in the year before birth z g,t weather conditions are conducive to malaria transmission.

  Although we expect maternal malaria infection to be the major channel through which malarious weather affects infant death, our 12-month index of malarious weather, , may also capture the effect of malaria infection by other members of z g,t the mother’s household or their behavioral change in response to malarious weather. Consequently, our estimates should be interpreted as the full impact of malarious weather, not as the impact of maternal malaria infection.

  Appendix Section A.4 discusses whether malarious weather after birth matters for infant mortality. We find no significant effects on infant mortality of in-life shocks. Malaria zones We strongly expect the impact of variations in malarious weather conditions on infant mortality to be larger in areas where malaria transmission is low. Where malaria is endemic, adults develop partial immunity from repeated in- fections since childhood and thus avoid symptoms such as fever and anemia. Where malaria is seasonal or epidemic-prone, however, most adults lack such partial im- munity. In these areas, people of all ages remain susceptible to the full range of clinical effects. Hence we expect weather shocks to matter more in these malaria-

  12 epidemic areas.

  We use our monthly index of malarious weather to identify epidemic and en- demic areas. If the average annual number of malarious months in each ERA-40 grid-cell during 1957-2002 is more than zero and up to four, we call the grid-cell

  13

  . If it is larger than four, we call it endemic. Finally, if it is zero, we call

  epidemic

  it non-malarious. Figure 1 shows the distribution of these three malaria zones by color. This map corresponds well to the distribution of parasite infection in malaria ₁₂ Due to the lack of partial immunity, pregnant women get sicker once infected with malaria in

  

epidemic areas than in endemic areas. One of the symptoms of malaria, fever, is known to increase

the chance of premature delivery and of infant death (Luxemburger et al. 2001). Since malaria

mortality in general is known to be much higher in epidemic areas (Kiszewski and Teklehaimanot

2004), the death of a pregnant mother’s household members may be more likely, possibly affecting

her baby’s survival through income loss or the mother’s psychological stress. ₁₃ We have also set the epidemic-endemic split at 6 months. But estimation results are similar.

  maps based on cross-sectional clinical observations (Hay et al. 2009).

3.2 Growing-Season Rainfall

  Measuring growing seasons Alongside malaria, crop yields are the major chan- nel through which weather outcomes shape human life in Africa. Most African countries are agricultural economies – in 2004, some 55% of people on the conti- nent were employed in agriculture (Frenken 2005, Table 2), and many more depend on agriculture in other indirect ways. As transportation infrastructure in Africa is poorly developed, most people are largely dependent on the local yields of subsis-

  14 tence crops, or on cash crops for income to buy food.

  Crop yields in Africa’s non-tropical areas are crucially dependent on the sea- sonal rains in the growing season. Irrigation plays a minor role, especially in Sub- Saharan Africa – only 6.4% of cultivated land was irrigated in 2004 (Frenken 2005, Table 12).

  We therefore use total precipitation during the location-specific growing season to capture the weather impact on infant mortality via crop yields. We first determine annual growing seasons in each ERA-40 grid cell. To do so, we use a satellite measure of plant growth derived by the TIMESAT program (J¨onsson and Eklundh 2004) to process the bi-weekly NDVI index (Tucker et al. 2005) at a resolution of 8 × 8 kilometers from 1982 onwards (see Appendix Section A.5 for more detail). We then average the start and end dates of the annual growing seasons in each location, to avoid the endogeneity of each year’s growing season to socioeconomic determinants of infant mortality. Finally, using the precipitation data in ERA-40, we calculate the total annual amounts of rainfall during the location-specific average

  15 growing season.

  To validate total rainfall during the growing season as a measure of food avail- ability, we compare it to monthly crop prices in the following 12 months for se- lected African locations. We find that low growing-season rainfall indeed predicts ₁₄ Herbst (2000, Table 5.3) reports that the road density for the median African country around the year of 1997 is merely 0.07 kilometers per square kilometers of land. ₁₅ In areas where there are two growing seasons per year, we use every odd growing season in our

calculation of the fixed growing season. This does not affect our main results in Table 2 as the arid

  climate zone does not have two growing seasons per year. high crop prices in the following year quite well (see Appendix Section A.6 for detail).

  The year prior to birth Food availability prior to birth is more important than availability after birth for infant survival. On the one hand, maternal malnutrition poses a major risk for infant health (Black et al. 2008). A lack of food during pregnancy diminishes the intake of calories and important micro-nutrients, which negatively affects the growth of the fetus in utero. This raises the risk of low birth- weight, which in turn raises the risk of infant death through birth asphyxia and infections (McCormick 1985). On the other hand, most African infants are breast- fed, and thus have lower mortality risk than those who obtain non-breast milk liquid or solid food during the first six months of life (see, e.g., Black et al. 2008, Table

  16 4).

  We therefore focus on food availability during the 12-month period prior to birth, in an analogous fashion to our analysis on malarious weather. Since rainfall during the growing season does not affect crop yields until the end (harvest) of the season, we match growing-season rainfall to each birth month in the following way.

  First, we divide total rainfall in the growing season equally into the following 12 months. Then, we sum the monthly allocations of growing-season rainfall over the 12 months before each birth, to approximate available crop yields for the mother and her household in this period.

  The above procedure effectively produces the weighted sum of total rainfall during the two previous completed growing seasons. Putting it mathematically, let

  g,t g,t

  and be total rainfall during the last and second-to-last completed growing r r

  1

  2

  seasons before running month t in grid cell g. For infants born in that month and grid cell, we assign the following growing-season rainfall index:

  g,t g,t

  r r ) r , (2)

  g,t g,t + (1 − ω g,t

  ≡ ω ₁₆

  1

  2 One might think that food availability after the birth of a child is important for his or her mother

to produce breast milk. However, as long as it is not very severe, maternal malnutrition is known to

have little impact on the volume and composition of breast milk (see Brown and Dewey 1992 for a review). g,t g,t

  with the weight given by is the running month of ω g,t )/12, where h

  = (t − h the last harvest. Growing-season rainfall is likely to have a nonlinear impact on infant mortality. Susser (1991), for example, reviews studies on the relationship between maternal nutrition and birth weight and concludes that nutritional intake by mothers signif- icantly affects birth weight only in famine conditions. To capture this non-linear impact, we create a drought index from in the following way. For each ERA-40 r g,t grid cell, we calculate the mean and standard deviation of r . Based on these mo-

  g,t

  ments, we create the drought index as a dummy variable, when r is more than two

  g,t

  standard deviations below its mean. This index is similar to the Standardized Pre- cipitation Index (McKee et al. 1993), a widely-used measure of drought, but based

  17 on r rather than monthly rainfall. g,t

  It is important to note that this growing-season rainfall index captures not only maternal nutritional intake but also other channels through which crop yields influ- ence the survival of newborns. For example, crop yields affect the mother’s house- hold income, which may affect prenatal health-care access. Low crop yields may cause conflicts (e.g., Miguel et al. 2004), which may in turn affect the survival of infants. Our purpose in this paper is to estimate the total impact of growing-season rainfall on infant mortality. Climate zones We allow one unit of growing-season rainfall to translate into dif- ferent amounts of crop yields by splitting the sample according to climate, to which people adapt by crop choice (e.g., drought-resistant millet and sorghum in arid ar- eas). We rely on the K¨oppen classification, which distinguishes climate types by monthly mean temperature and precipitation, as well as the latitude (Peel et al. 2007). Using these criteria and our ERA-40 data, we subdivide all DHS clusters into two climate zones: rainy (rainforest, monsoon, savannah and temperate cli- mates), and arid (steppe and desert climates) areas. This classification is shown in Figure 2. Arid-climate zones largely overlap with epidemic-malaria zones in Figure 1. ₁₇ We also consider an analogous flood index (i.e. two standard deviations above the mean). How- ever, we do not find its significant impact on infant mortality.

  3.3 Summary Statistics

  Table 1 reports summary statistics for infant mortality and the two weather vari- ables, by malaria zones and climate zones.

  Panel A shows that average infant mortality in the sample is 108.5 and 107.1 per 1,000 live births in malaria endemic and epidemic areas, respectively. These numbers are much higher than in non-malarious areas (72.5 per 1,000). The higher death toll in malarious areas may reflect differences in socioeconomic determinants of infant survival from non-malarious areas. Our estimation results help us disen- tangle the mortality by malarious weather from that due to other causes (see Section 4.4).

  Panel B of Table 1 reports summary statistics on the number of malarious months in the year before birth, by malaria zone. Mothers in endemic areas are on average exposed to 7.9 months of malarious weather conditions, with the stan- dard deviation of 1.0 months. In epidemic areas, the corresponding numbers are 1.8 and 1.0 months. Mean-adjusted variability is thus much higher in epidemic areas.

  Panel B also shows the mean and standard deviation of the growing-season rain- fall index: 122.7 and 28.5 for the rainy climate zone, and 17.3 and 5.9 for the arid climate zone. Mean-adjusted variability is clearly larger for the arid climate zone.

  3.4 Econometric Specification

  Identifying variations To identify the causal effects of weather outcomes, we will only use temporary deviations in weather outcomes from their average monthly pattern in each location. On top of the regular seasonal cycle, temperature and rain- fall in Africa fluctuate considerably from year to year, especially in arid and semi- arid areas. The fluctuations partly reflect chaotic weather dynamics over horizons beyond a couple of weeks. They also reflect hard-to-predict, medium-term fluc-

  18

  tuations in air pressure associated with the Southern Oscillation. The warming phase (El Nino) is generally associated with wetter-than-normal weather in East Africa during March-May, but less rainfall than normal in parts of South and Cen- ₁₈ The time series pattern of these fluctuations during the past century are analyzed and discussed in Zhang, Wallace and Battisti (1997). tral Africa during December-February, with opposite patterns during the cooling phase (La Nina).

  Estimation equation We estimate the impact of malarious weather and growing- season rainfall on infant death by versions of the following equation for infant i born in cluster c (located in grid cell g and country x) in calendar month s of calendar year y: _{′ ′}

  α β y i,c,s,y + R ∗ 1000 = M

  

g,s,y g,s,y

_{′ ′} T P

  γ γ

• T + P

  g,s,y g,s,y g

  . (3)

• µ c,s + η x,y + δ G s,y + ε i,c,s,y In this expression, the dependent variable y is the infant-death indicator. This is

  i,c,s,y

  multiplied by 1,000 to allow the estimated coefficients to be interpreted as changes in the number of death per 1,000 live births (the conventional unit for infant-mortality statistics). As we control for cluster-by-month fixed effects, µ , the source of iden-

  c,s

  tification is the deviation from the average seasonal pattern in each location, i.e., the random component of weather outcomes. Year-fixed effects are allowed to differ by country ( ), to control for non-parametric trends in national health systems, poli-

  η

  x,y

  cies, or economic conditions, which could conceivably be related to local weather

  19 g

  outcomes. In addition, we control for grid-cell specific linear trends, , to δ G s,y allow for sub-national trends.

  Independent variables The key independent variables are M and R . M

  g,s,y g,s,y g,s,y

  is the vector of malarious weather variables (constructed from our 12-month index, z , defined in equation (1)). R is a vector of growing-season rainfall variables

  g,s,y g,s,y

  (defined in Section 3.2) that capture the weather impact on crop yields, including

  20

  the (log) growing season rainfall index ( the drought index, and their ₁₉ ln(r g,t + 1)),

  

For example, Kudamatsu (2012) finds democratization has reduced infant mortality in sub-

Saharan Africa while Bruckner and Ciccone (2011) find negative rainfall shocks led to democrati- zation in Africa. ₂₀ The growing season rainfall index is transformed in the logarithm term after adding one. The

index takes the value of zero for 17,423 births, and has a skewed distribution with a very long upper interactions with the rainy climate-zone indicator.

  To isolate the impacts of malarious weather conditions and growing-season rain- fall from other weather impacts, we control for two additional sets of weather vari- ables. First, T is a vector of the number of days in the past 12 months in which

  g,s,y _{◦ ◦}

  daily average temperature falls in various categories (below

  C,

  C, 16 16-18 18- _{◦ ◦ ◦}

  21 C, ...,

  C, and above

  C). These variables capture the impact of daily 20 34-36 36 temperatures in the year up to birth in a flexible fashion, following the approach in

  Deschenes and Greenstone (2011), Barreca et al. (2016), and Burgess et al. (2013) among others.

  Second, P is a vector of dummies for the past 12-month total precipitation

  g,s,y

  falling in various categories (less than 500mm, 500-550mm, 550-600mm, ..., 950-

  22

  1000mm, and above 1000mm). These variables capture the potentially nonlinear

  23 impact of total rainfall in the year up to birth.

  For inference, the error term, ε , is clustered at the ERA-40 grid-cell level to

  i,c,s,y

  compute the standard errors, because weather variables are measured at the grid-cell level and likely to be serially correlated. To allow for spatial correlations across time (i.e., the correlation of weather at one location in the current month and at another location in the subsequent months), we also adopt an alternative scheme where we cluster standard errors at the country by malaria zone (endemic, epidemic, and non- malarious).

  To summarize, our parameters of interest α and β measure how many more infants per 1,000 live births die by one unit change in the related weather variables. Since we control for cluster-month fixed effects, we are identifying these parameters from the deviation within each cluster from its site-specific monthly mean.

  tail. _{21 ◦ 22} The most frequent category 26-28 C is omitted to avoid multicollinearlity.

  The category in which the sample average 12-month total precipitation falls is omitted to avoid multi-collinearity. ₂₃ Following Burgess et al. (2013), we also specify the impact of the past 12 month total precipita-

tion as location-specific percentile dummies. This specification, however, does not yield statistically

significant coefficients on these dummies.

4 Past Weather and Infant Mortality

  This section presents our results regarding the effects on infant mortality of malari- ous weather conditions and growing-season rainfall.

4.1 Full-Sample Results

  Columns (1) to (3) of Table 2 report the full sample results. For the impact of malarious weather, we estimate its linear impact by using our 12-month index in this table. Column (1) controls for year fixed effects and cluster-month fixed ef- fects. One additional month of malarious weather within the year before birth is estimated to increase infant mortality by 0.76 per 1,000 live births. The coefficients on growing season rainfall variables are both imprecisely estimated.

  In column (2), we allow year fixed effects to differ across the three malaria zones (endemic, epidemic, and non-malarious) and interact the 12-month malaria weather index with the indicator for malaria-endemic areas, to see if partial im- munity developed in these areas mitigates the impact of malarious weather shocks. The coefficient on the interaction term is negative and significant at the 10% level. It suggests that one additional month of malarious weather has a nearly zero effect on infant death in endemic areas while 1.49 more infants per 1,000 live births die in epidemic areas due to one extra month of malarious weather (significant at the 1% level).

  In column (2), we also interact the growing season rainfall variables with the indicator for rainy climate zones (tropical and temperate climates), to allow for differential impacts by crop choice in response to the climate. The drought in arid areas is estimated to increase infant mortality by 24.5 per 1,000 live births, and this estimate is statistically significant at the 1% level. In rainy areas, this effect appears to be muted, although the estimate is noisy.

  Column (3) allows the year-by-malaria-zone fixed effects to differ by country. The estimated coefficients on the malarious weather variables are smaller, but the result is qualitatively similar to column (2). This specification absorbs all country- by-year malaria shocks in the fixed effect. The smaller coefficient may make sense if country-wide malaria shocks have more severe consequences than purely local shocks because, for example, infections spread between neighboring areas. For growing-season rainfall variables, the estimated impact of droughts in arid areas is comparable to column (2), now amounting to 25.3 extra infant deaths per 1,000 live births. The size of the impact is equivalent of more than a quarter of the sample mean infant mortality in arid climate zones. On the other hand, such a large impact is absent in rainy areas.

  These full-sample results show that infants in epidemic areas are more likely to die if they are born after the longer spell of malarious weather than usual while those in endemic areas are not. Given this finding, we focus on the epidemic areas in more detailed analysis in Section 4.3 below. However, it is important to note that our result does not imply that in malaria-endemic areas malarious weather is not a large risk factor for infant death. Our identification of the impact hinges on the deviation from the average seasonal pattern of malaria transmission. As year- to-year variation in seasonal malaria transmission for endemic areas is not very large (as discussed in Section 3.3), most malaria-induced infant deaths are likely absorbed by the cluster-month fixed effects.

  Another takeaway message from the full-sample results is that growing-season rainfall affects infant survival in arid climate zones when its amount is severely smaller than the normal year. In the next subsection, we check the robustness of this finding and discuss its implications.

4.2 Impacts of Growing-Season Rainfall

  In columns (4) to (6) of Table 2, we restrict the sample to arid climate zones and check the robustness of our finding that the severe lack of growing-season rainfall raises infant mortality by as much as a quarter of the sample mean.

  Column (4) controls for T (temperature bin variables) and P (rainfall

  g,s,y g,s,y

  bin variables) in equation (3), to isolate the impact of growing season rainfall from other types of weather effects. The estimated coefficient on the drought index is slightly smaller than column (3), but it is significant at the 5% level. F-tests show that, while the temperature-bin variables are not jointly significant, the rainfall- bin variables are jointly significant at the 1% level. Droughts in terms of growing season rainfall thus affect infant mortality over and above the statistically significant impact of the past 12-month rainfall.

  Column (5) further controls for the ERA-40 cell-specific linear trends. The estimated coefficient on the drought index changes little. Column (6) uses the same specification as in column (5) except that standard errors are clustered at the country-by-malaria-zone level, to allow spatial correlations in the error term across ERA-40 cells. The standard error for the drought coefficient is smaller than in col- umn (5).

  By definition, the drought is a rare event. Therefore, our finding could be driven by a few outliers. However, the incidence of these droughts is not concentrated in a particular period or in a specific area. Out of 69,303 months in which we observe at least one birth in the arid areas, droughts occurred in 181 months. Figure 3 shows the spatial distribution of the number of such drought months across arid areas. As the figure shows, the droughts from which we identify our estimates are quite evenly spread over the various African regions in the arid climate zone. Consequently, the

  24 estimated impact of droughts on infant mortality is not driven by an outlier.

  In summary, these arid-sample results show that infants in arid areas are more likely to die, by as much as 22% of the sample mean mortality, if they are born after a severe lack of growing-season rainfall in the previous two completed growing seasons. In Appendix Section A.6, we show that the severe lack of growing-season rainfall raises staple crop prices by 9.5% in the arid areas. This finding suggests that the shortage of nutritional intake due to drought is one mechanism through which droughts raise infant mortality. Taken together, our finding is consistent with the argument by Susser (1991), as discussed in Section 3.2, that it is only in famine conditions that nutritional intake by mothers significantly affects birth weight (and consequently the survival of newborns). ₂₄ In comparison, it is noteworthy that the vast majority (99%) of deaths (of all ages) due to

  

droughts recorded by the well-known EM-DAT data base (Centre for Research on the Epidemiology

of Disasters 2013) are associated with a mere three droughts in the mid 1980s (Ethiopia, Sudan

and Mozambique). Thanks to the large spatial coverage of our data, the estimates capture extreme

deviations from local rainfall conditions in arid areas that typically do not attract the attention of the

large aid donors.