Does Inflation Kill? Exposure to Food Price Inflation and Child Mortality

Daniel Kidane* and Andinet Woldemichael¥

January, 2019

Abstract

Unlike extreme malnutrition shocks, such as famine and drought which grab the attention of the media, international aid organizations and policymakers, malnutrition due to food price hikes are often neglected and their impacts on children are not well known. It is well established that malnutrition during the critical periods of early life—between inception and the first 1000 days after birth—have lasting consequences on health and mortality. In this paper, using a uniquely constructed data from Ethiopia that takes advantage of high-frequency local retail food prices, we examine the impacts of early life exposure to food price inflation on child mortality. Following survival events since inception, we estimate the causal impacts of exposure to inflation during in-utero and infancy. The results show that exposure to 10 percent inflation in staple food prices during in-utero decreases the survival of children under the age of five by about 5.4 percent. We also find that the impacts are non-linear depending on the specific month of exposure and substantially vary by observable characteristics and the type of staple food.

JEL Classification: E31, I10, I15, Q18

Keywords: Survival; Under-five; Malnutrition; Ethiopia; In-utero; Micronutrients.

_________________________

* Department of Economics, Salem State University, 352 Lafayette, St. Salem, MA 01970 USA: Email: dkidane@salemstate.edu.¥ Development Research Department, African Development Bank, Abidjan, Cote d’Ivoire: Email: a.woldemichael@afdb.org.

1

1. Introduction

In the wake of high and rising food prices in developing countries children are particularly a risk

of malnutrition. Unlike extreme malnutrition shocks, such as famine and drought that grab the

attention of the media, international aid organizations and policymakers, malnutrition due to food

price hikes are often neglected and their impacts on children are not well known. Households

typically respond to food price hikes by substituting expensive food items with cheaper

alternatives, probably with lower nutritional quality, resulting in micronutrient deficiencies.

Malnutrition and hidden hunger during the critical window of “early life”— in-utero and the first

1,000 days after birth—has been linked to elevated risks of impaired physical and cognitive

development that may lead to irreversible long-term impacts. The most pernicious impact of

exposure to malnutrition during early life is child mortality. Although numerous studies

document the impacts of rising food prices on children health (Brinkman et al., 2010; Christian,

2010; Woldemichael et al., 2017), the full implication on child mortality is yet to be understood.

This study fills the gap in the literature by investigating the impact of exposure to food price

inflation on child mortality in Ethiopia. We use the Demographic and Health Survey (DHS) data,

which covers more than 21,000 cases of live and deceased children who were born between 2000

and 2010. We carefully linked each child’s early life months since inception with the

corresponding monthly local staple food inflation and estimate the impacts of exposure to food

price inflation during each month since inception on under-five survival. Our data construct and

empirical approach allow us to iron out the causal impacts and possible heterogeneity arising

from the complicated biological mechanisms through which nutrition affects fetal growth,

depending on the different stages of the embryo. To the best of our knowledge, our paper is the

2

first to quantify the causal impacts of exposure to food price inflation during each month of early

life on child mortality.

Biomedical literature shows that maternal undernutrition before and during pregnancy affects

fetal growth as well as alters fetal genome which dictates subsequent growth trajectories in

what is referred to as “fetal programing” (Barker and Clark, 1997; Wu et al. 2004). Fetal

nutrition status is determined by the mother’s dietary intake and nutrient stores, nutrient

delivery to the placenta, and placental transfer capabilities (Barker and Clark, 1997; Owens et

al, 1989).1 Moreover, intrauterine environment in general and fetal nutrition and oxygen in

particular change the concentration of growth-influencing fetal and placental hormones such as

insulin and insulin-like growth factors (IGFs) which play key role in fetal growth. Therefore,

fetal growth is especially vulnerable to maternal dietary deficiencies, such as protein and

micronutrients, during the pre-implantation and during the period of rapid placental

development (Wu et al., 2004). Regardless, fetal malnutrition results in various complications

even after birth resulting in, for instance, preterm birth, low birth weight and predisposing the

child to certain diseases and ultimately death.

It is also well established that the impacts of maternal malnutrition during pregnancy vary

depending on the specific stages of fetal growth. Maternal undernutrition around conception

time influences the growth trajectory of the fetus as it affects the sensitivity of early embryonic

growth to nutrients. Whereas undernutrition in the last trimester affects fetal development,

resulting in fetal wasting and consumption of fetal amino acids by the placenta putting a

downward pressure on fetal growth trajectory during early gestation period and altering the

subsequent need for nutrition in late gestation (Leese, 1990 in Barker and Clark, 1997).

1 Some of these factors, such as maternal nutrient stores and anemia level, are determined well before conception.

3

One of the innovations in our paper is that our empirical model explicitly accounts for non-

linearity of the impacts arising from the biological mechanism through which malnutrition

affects fetal growth and ultimately mortality. Moreover, our approach explicitly accounts for

the cumulative effects of malnutrition experienced throughout early life periods since inception

and control for it to net out the causal impacts of exposure to food price inflation during each

month of early life.

A number of empirical studies show that shocks experienced during in-utero and early childhood

have lasting consequences on many later-life outcomes, such as health, education and skills

formation, labor market outcomes, and social behavior (Godfrey and Barker, 2000; Behrman and

Rosenzweig, 2004; Cunha and Heckman, 2007; Case and Paxson, 2008; Hoddinott et al., 2008;

Currie, 2009; Case and Paxson, 2010; Currie and Almond, 2011; Hoddinott et al., 2013). In

times of high food price inflation, the resulting loss in purchasing power adversely affects

welfare through several channels including lower consumption levels, lower investments in

education and worse health and nutrition outcomes. Children are particularly at risk during high

food price inflation as reduction in real income forces households to not only reduce their

spending on food but also to switch from relatively more expensive sources of protein such as

meat, fish and eggs to cheaper cereals with inferior nutritional values (Akinlo and Odusanya,

2016). The resulting nutritional deficiencies due to poor dietary quantity and quality leads to

malnutrition, concomitant increases in infectious diseases and thereby mortality. Moreover, prior

studies (Wodon and Zaman, 2008; Brinkman et al, 2010; Christian, 2010) indicate that declines

in nutritional status as a result of a surge in food prices may cause poor birth outcomes, like fetal

growth restriction and preterm birth, which are also associated with infant and child mortality.

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There is a dearth of evidence examining the association between exposure to high food prices

and child mortality. In the context of developing countries, the consensus is that economic

downturns and exposure to high food prices adversely affect the survival of children (Baird et al.,

2011; Friedman and Schady, 2009; Lee et al., 2013). Baird et al. (2011) and Friedman and

Schady (2009) also noted that mortality of infant girls is significantly sensitive to such shocks

than that of boys. A number of nutritional pathways have been illustrated in the literature in that

economic crisis and hikes in food prices may increase infant and child mortality. A decrease in

food availability due to such crisis can lead to a reduction in dietary quality (Fledderjohann et al.,

2016; Darnton-Hill and Cogill, 2010) which increases childhood wasting and stunting,

intrauterine growth restriction, and micronutrient deficiencies (Christian, 2010) that leads to

higher prevalence of morbid diseases and mortality.2

Our paper contributes to the existing literature in a couple of dimensions. First, unlike prior

studies, which rely mostly on annual mortality rates, we use a unique dataset with high frequency

local market prices to estimate the effects of exposure to food price inflation on child survival.

Second, we show that survival does not only depend on what happens to children after birth but

also in-utero circumstances. To our knowledge this issue has not been documented and this study

will attempt to answer this very important question.

Our focus on Ethiopia is motivated by two important factors. First, it experienced historically

higher levels of food price inflation for sustained period of time between 2005 and 2010.

2 High food prices may also reduce mortality rates of infants and children. During times of high food prices, the real income of the household could be depressed. Baird et al. (2011) pointed out that this reduces the opportunity cost of parental time such as taking children for health visits, breastfeeding, cooking and collecting clean water which may improve child health outcomes. In fact, Miller and Urdinola (2010) find that exogenous declines in the price of coffee in Colombia was associated with lower infant mortality due to the fact that the relative price of health decreases as the value of time declines with falling prices of coffee.

5

Although the country had mild inflationary episodes in the past, the food price inflation

experienced in 2008—2009 were unprecedented and among the highest in Africa.3 The extent to

which this sustained price surge impacted childhood mortality is not well documented. Second,

the country is one of the countries with high under-five child mortality in the world.4 In this

regard, it is imperative to understand and quantify how malnutrition and hidden hunger due to

food price hikes exacerbate the prevailing high under-five child mortality rate.

The findings in this study show that exposure to 1 percent inflation in staple food prices during

in-utero and the first 6 months after birth decrease childhood survival by about 5.4 percent and

8.6 percent, respectively. We also find that the impacts are non-linear, depending on the specific

month of exposure and substantially vary by observable characteristics and the type of staple

food.

The remainder of the paper is organized as follows: Section 2 presents the data. Section 3

outlines the empirical strategy and Section 4 discusses the findings of the study. Finally, Section

5 concludes the paper.

3 Studies suggest that expansionary monetary policy, higher domestic demand, structural reforms and the rising international prices of food and oil as key driving factors behind the high level of inflation (Alem and Söderbom, 2012). At the peak of the global food crisis, in July 2008, annual food price inflation in Ethiopia surpassed 90% (Durevall et al., 2013). Between 2007 and 2008, food price inflation, which accounts for more than 50% of the CPI consumption basket, rose from 18.2 percent to 91.7 percent (CSA, 2009). The level of inflation during this period was even higher for major staple crops; teff, wheat, maize, barley, sorghum and enset which account for more than 50 percent of the total daily calorie intake among Ethiopian households. For instance, between 2008 and 2009, the month-on-month inflation levels of some of these staple particularly that of maize and teff, were well above 100% (see Figure A1 in Appendix A).

4 The level of child mortality rate still high compared to the global average. In 2011, according to a report by Central Statistical Agency of Ethiopia (CSA, 2012), infant mortality rate was 59 deaths per 1000 live births, while an estimated 88 children in every 1,000 live births died before the age of five. This indicates that a total of 67% of all deaths of under-five children in Ethiopia take place before a child’s first birthday, which was one of the highest in the world. The high infant and under-five mortality rates in Ethiopia are primarily due to a wide range of diseases, such as acute respiratory infection, diarrhea, prematurity and newborn infection that children suffer at their early life, could be attributed to exposure to malnutrition due to high food price inflation during the critical periods of their early-life growth. A recent study by Woldemichael et al. (2017) indicated that exposure to food price inflation while in-utero and during infancy has detrimental and long-term impacts on malnutrition and the incidence of stunting and wasting among Ethiopian children which is strongly associated with high child mortality.

6

2. The Data

This study uses the Ethiopian Demographic and Health Survey (EDHS) data collected in two

rounds in 2005 and 2010. The EDHS survey collects detailed information on children’s vital

statistics such as date of birth, date of death, if deceased. Women were also asked if they were

pregnant at the time of survey or ever had a pregnancy that was miscarried, aborted or ended in a

stillbirth. We use this information to construct retrospective birth and death histories since

inception. The survey also has comprehensive information on parental and household

characteristics. The two waves cover about 21,366 live and dead children under the age of five

and 10,987 cases of live and terminated pregnancies. Moreover, the survey covers all regions in

the country, making it one of the few nationally representative and detailed household surveys on

children’s health.

Panels (a) and (b) in Table (1) show the proportion of children under the age of five according to

their survival status since inception and since birth, respectively. As shown in panel (a), under-

five mortality has declined slightly from 7.4% in 2005 to 7.2% in 2010, underscoring some

improvement in reducing child mortality. In panel (b) that includes all events since inception,

while the proportion of live pregnancy cases declined slightly from 23.5 % in 2005 to 21.7 % in

2010, terminated pregnancies have spiked from about 9% in 2005 to 14% in 2010. Such high

increase in pregnancy termination could be attributed to a host of factors including severe

malnutrition. Moreover, increased access to family planning services, including abortion clinics

could be part of the story, which is beyond the scope this paper.

Table 1: Survival Events

  2005 2010 Pooled(a): Events since birth

7

Child alive (%) 92.60 92.80 92.70Child dead (%) 7.38 7.20 7.28No. of obs. 9,719 11,647 21,366

(b): Events since inceptionPregnancy unborn: alive (%) 23.50 21.70 22.50Pregnancy terminated (%) 8.84 13.60 11.50Child alive (%) 62.60 60.10 61.20Child dead (%) 4.99 4.67 4.81No. of obs. 14,371 17,982 32,353

Source: Authors’ computations

One of the key challenges in the quality of the DHS data is recall bias. Survey respondents in

countries like Ethiopia where there is low level of literacy and numeracy with limited practice of

keeping vital records, they are less likely to accurately remember distant births and deaths. This

may lead to a recall bias and potentially underestimate or overestimate the impact depending on

the direction of the bias. Recall bias is also an important source of measurement error in both the

dependent variable and the corresponding levels of inflation that a child is exposed to. In

addition to the bias on the direction and magnitude of the coefficients, measurement error due to

recall bias could inflate standard errors and hence make the coefficients statistically

insignificance. However, we do not believe this to significantly affect our results as our focus is

on relatively recent periods where we consider children who were in-utero, died while in-utero,

born alive or died after they were born in the last five years between 2001 and 2010.

Another data quality problem that can potentially lead to underestimation of mortality rates is if

mothers selectively omit to report non-surviving cases, such as miscarriages, stillborn, and

neonatal death. However, according to the EDHS, misreporting of such deaths are less severe

and are found to be within the normal range for deaths occurring during 0—4 years preceding the

survey (CSA and ORC Macro, 2006; CSA and IFC-Int, 2012). This to some extent validates our

8

focus to use recent birth history which is less likely to be contaminated by recall bias and

selective omission.

For food price information, we use monthly retail food prices data collected by the Ethiopian

Central Statistical Agency (CSA) between July 2001 and December 2010. The monthly price

survey collects data on prices of thousands of goods and services from 119 representative urban

markets and the CSA uses these data to analyze prices and construct monthly Consumer Price

Index (CPI) at the national and regional levels. We have information on inflation for every

under-five birth history covered in the 2010 survey and majority of the cases covered in the 2005

survey. For older children covered in the 2005 survey, we do not have the corresponding price

data prior to July 2001 as shown in Figure 1 below. Hence, we drop observations out of our

analysis.

Figure 1: Kernel distribution of inception months by survey year Source: Authors’ computations

9

We then spatially linked the DHS clusters with the high-frequency local market prices, using a

GIS-based, Nearest Neighbor Matching algorithm (see Figure 2). We use the reported date of

birth information in the DHS survey for each live birth, and duration of pregnancy at the time of

survey for live unborn cases and month of termination for terminated cases. We then use month

of inception as an anchor to match each live and dead case (fetus, infant, child) month of “early

life” with the corresponding monthly inflation rates. We do this all the way from inception to the

first 15 months after birth. Such an approach allows us to identify the exact month of “early life”

during which exposure to inflation has the most impact on mortality.

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Figure 2: Spatial Nearest Neighbor Match between Local Market Hubs and DHS Clusters

Note: The Red dots correspond to market centers; the Green dots correspond to the 2010 DHS cluster areas, the Yellow dots corresponds to the 2005 DHS cluster areas, the outer shaded buffer corresponds to 50 km radius, and inner shaded buffer circles indicates the 25 km radius from the market centers.

For our analysis, we use a composite price which is constructed by calculating the weighted

average price of six major staple foods which are commonly consumed among Ethiopian

households--Teff, Wheat, Maize, Barely, Sorghum, and Enset.5 The weights are calculated based

on each staple’s share out of total calorie consumed in the household. The weights are obtained

from the Ethiopian Household, Income and Consumption (HICE) surveys conducted in 2000 and

2005. As shown in Table (2) there is a substantial variation in terms of both calorie and

expenditure shares of each staple across regions. For instance, in Addis Ababa Teff constitutes

about one fourth of the total calorie intake, whereas Maize is the dominant source of calorie in

Gambella region. There are slight changes in consumption expenditure share of the staples

5 These staples provide important micronutrients and carbohydrate that are essential for the mother, the fetus, and the growth of the infant. For instance, teff, a gluten-free grain is a good source of essential minerals (iron, magnesium, and calcium), carbohydrate/fiber, vitamin B-12, protein (Baye, 2014).

11

between the two survey periods. Given the fact that consumption patterns have changed slightly

between the two survey periods, we also constructed two separate calorie weights for the period

before and after 2005. This makes the level of food price inflation spatially comparable.

12

Table 2: Share in Total Expenditure and Total Calorie (2005)

Region

Teff   Maize   Wheat   Barley   Sorghum   Enset   Total Staples

Exp Cal   Exp Cal   Exp Cal   Exp Cal   Exp Cal   Exp Cal   Exp Cal

Tigray 8.7% 7.0% 7.1% 1.9%

8.1

%

8.7

%

2.3

%

3.0

% 6.8% 8.9% 0.0% 0.0%

33.0

%

29.5

%

Affar 7.9% 8.0% 7.7% 5.5%

2.1

%

7.3

%

0.2

%

0.1

% 6.4% 1.0% 0.0% 0.0%

24.2

%

21.9

%

Amhara

11.4

% 8.7% 3.9% 5.0%

6.2

%

7.5

%

3.1

%

4.4

% 5.0% 5.8% 0.0% 0.0%

29.6

%

31.4

%

Oromiya 6.0% 4.7%

11.0

% 7.5%

4.9

%

6.0

%

1.9

%

2.8

% 4.6% 4.1% 2.5% 5.3%

30.9

%

30.4

%

Somalie 0.8% 0.8% 9.8% 7.0%

8.2

%

6.9

%

0.4

%

0.1

% 4.6% 7.1% 0.0% 0.0%

23.8

%

21.9

%

Bemshang

ul 2.8% 4.9% 4.8% 6.0%

0.4

%

0.5

%

0.1

%

0.0

%

18.3

%

11.7

% 0.0% 0.0%

26.4

%

23.1

%

SNNPR 1.1% 1.7% 7.6%

11.2

%

1.6

%

2.4

%

0.7

%

0.5

% 1.3% 1.9%

18.2

%

14.5

%

30.7

%

32.2

%

Gambela 1.7% 6.6%

16.6

%

46.0

%

1.2

%

3.2

%

0.0

%

0.1

% 4.3%

12.5

% 1.0% 3.2%

24.8

%

71.4

%

Harari 4.8% 9.8% 3.2% 1.5%

3.7

%

5.0

%

0.1

%

0.3

%

10.3

% 7.5% 0.0% 0.0%

22.0

%

24.1

%

Addis 13.3 23.7 0.5% 1.3% 1.6 4.0 0.1 0.4 0.1% 0.0% 0.2% 0.0% 15.7 29.5

13

Ababa % % % % % % % %

Dire Dawa 5.7% 6.8% 3.7% 0.4%

2.7

%

4.3

%

0.1

%

0.3

% 8.3% 5.9% 0.0% 0.0%

20.6

%

27.1

%

Average 5.8% 7.5%   6.9% 8.5%  

3.7

%

5.1

%  

0.8

%

1.1

%   6.4% 6.0%   2.0% 2.1%  

25.6

%

31.1

%

Source: Authors’ computations using the Household, Income and Consumption (HICE) surveys conducted in Ethiopia in 2005.

14

As shown in Figure (3) and Table (3), there are considerable differences in the level of inflation

that children were exposed to while they were in-utero and during infancy. Deceased children

were exposed to a higher level of food price inflation compared to those who were alive,

regardless of when they were exposed to the inflation. For instance, for those children who were

born before 2005, the average food price inflation (the composite price inflation) sustained by

dead children during in-utero and during infancy were, respectively, 3.4 and 7.8 percentage

points higher than that of alive children. This shows the importance of investigating the link

between exposure to food price inflation and child mortality.

0.2.4.6.81

Pro

porti

on

-100 -50 0 50 100Inflation (%)

Dead Alive

In-Utero

0.2.4.6.81

Pro

porti

on

-100 -50 0 50 100 150Inflation (%)

Dead Alive

During Infancy

Figure 3: Exposure to Food Price Inflation and Survival Status

Source: Authors’ computations.

15

Table 3: Average Food Price Inflation (%) by Survival Status

  Whole Sample   Inception: Before 2005   Inception: 2005-2010

 

Aliv

e Dead

Poole

d   Alive Dead

Poole

d  

Aliv

e Dead

Poole

d

Exposure during In-Utero

Teff

18.6

5

21.2

8 18.76 13.35 14.79 13.42

21.2

0

25.2

2 21.35

Wheat

16.9

4

21.4

5 17.12 15.01 20.15 15.25

17.8

7

22.2

4 18.03

Maize

17.9

6

25.2

3 18.25 23.92 32.26 24.30

15.1

0

20.9

7 15.32

Barley

17.2

7

21.0

4 17.43 15.37 18.40 15.51

18.1

9

22.6

4 18.35

Sorghum

16.9

2

21.7

5 17.11 17.68 24.21 17.99

16.5

5

20.2

6 16.68

Enset

14.8

1

17.5

9 14.92 1.523 4.658 1.669

21.1

8

25.4

1 21.34

Staple (Composite)

16.7

5

20.5

0 16.90 13.71 16.97 13.86

18.2

1

22.6

4 18.37

No. of obs.    

16,55

7       5,429      

11,12

8

Exposure during Infancy (0-6)

Teff

13.2

9

19.7

9 13.55 10.86 14.58 11.03

14.4

5

22.9

5 14.77

Wheat

14.7

0

19.0

1 14.87 10.13 14.03 10.32

16.8

8

22.0

2 17.08

Maize

14.2

7

23.7

3 14.65 16.56 19.30 16.69

13.1

8

26.4

2 13.67

Barley

14.0

5

20.1

4 14.30 9.545 13.82 9.743

16.2

2

23.9

7 16.51

Sorghum 12.9 20.1 13.23 12.96 16.81 13.14 12.9 22.1 13.27

16

4 0 3 0

Enset

8.90

0

14.1

4 9.111

-

2.714 -0.964 -2.633

14.4

8

23.2

7 14.80

Staple (Composite)

12.3

6

19.5

7 12.65 8.239 13.09 8.465

14.3

4

23.5

0 14.68

No. of obs.    

15,76

0       5,145      

10,61

5

Source: Authors’ computationsNote: Composite price of the six staple crops is weighted average using calorie shares as weights.

Control variables

In our formal econometric analysis in the next section we control for a number factors. Table (4)

provides a descriptive summary of control variables including household, parental and

geographic characteristics. Household characteristics include household size, household head’s

gender and age, the number of under-five children in the household, wealth quartiles and

religion. Maternal variables included in the model are marital status, age, height, years of

education, age at first birth, age at first cohabitation, body-mass-index (BMI), adjusted

hemoglobin, and dummy variable indicating whether the mother was anemic or not. In order to

capture intra-household allocation of resources and the bargaining power of mothers within the

household, we also control for the husband’s age, education and occupation.

In addition to individual and household-level characteristics, we control for geographic factors

such as the location of residence, altitude, “geodesic” distance to the market hub, travel time (in

minutes) to the nearest town/city of 50,000 or more inhabitants. Furthermore, we control for

population density of the cluster and the number of drought episodes between 1981 and 2000. It

17

also captures factors that affect both food price inflation and child mortality including nutritional

interventions by government and non-governmental organizations in response to malnutrition

shocks.

Table 4: Descriptive Summary of Control Variables  Dead Alive Pooled   Dead   Alive   Pooled(i) Household (iii) HusbandHousehold Size 5.32 6.16 6.10 Age 37.18 37.41 37.39

(2.41) (2.31) (2.33) (11.69) (10.95) (11.01)HH: Male 83.0% 83.7% 83.6% Years of Education 0.56 0.68 0.67HH: Age 37.66 37.91 37.89 (0.99) (1.08) (1.07)

(12.13) (11.80) (11.83) Occ. Farming 79.4% 74.6% 74.9%No. of U5 Children 1.09 1.84 1.78

(0.95) (0.81) (0.84) (iv) GeographicWealth Qnt: 1st 31.2% 28.5% 28.7% Urban 12.1% 15.9% 15.6%Wealth Qnt: 2nd 30.3% 26.6% 26.8% Altitude 1,722 1,722Wealth Qnt: 3rd 24.3% 25.2% 25.2% (676) (676)Wealth Qnt: 4th 14.2% 19.7% 19.3% Distance (Km) 35.33 33.75 33.86Orthodox Christian 33.9% 34.9% 34.9% (28.64) (28.98) 28.95Muslim 44.6% 43.2% 43.3% Travel time (min.) 422.20 402.68 404.11

(327.05) (339.85) (338.96)

(ii) Maternal Malaria 0.08 0.07 0.07

18

Married 89.2% 89.6% 89.5% (0.04) (0.04) (0.04)Age 28.84 29.03 29.02 Population density 274 671 642

(7.37) (6.72) (6.77) (2,645) (3,980) (3,890)Height (cm) 156.56 157.45 157.38 Drought episodes 6.52 6.53 6.53

(6.59) (6.58) (6.58) (2.51) (2.52) (2.52)Years of Education 0.95 1.41 1.37

(2.32) (3.00) (2.96)Age at first birth 18.64 18.81 18.80

(3.68) (3.66) (3.66)Age at first cohabitation 16.10 16.51 16.48

(3.58) (3.63) (3.63)BMI 21.53 21.92 21.89

(10.26) 11.61 11.51Adjusted hemoglobin 123.63 126.72 126.49

(21.11) (19.36) (19.52)Anemic 24.4% 19.5% 19.8%No. of obs. 1,556 19,810 21,366     1,556 19,810 21,366

Source: Authors’ computations

3. Empirical Model

Our focus is on estimating the impact of exposure to food price inflation both during in-utero and

during the first 6 months after birth on infant and child survival rates. We use simple Cox

proportional hazard model to estimate the effects of exposure to food price inflation on the

survival probabilities of infants and children. The Cox proportional hazard model is one of the

most commonly used survival model because of its flexibility and no distributional assumptions

on the baseline hazard function (Cox, 1972). In our baseline specification, we estimate the

effects of exposure to average food price inflation during in-utero on survival. In the baseline

19

model, the distribution of hazard of child i at age a conditional on observed characteristics can

be written as

H i (a∨X i )=h ( a∨0 ) . exp (γ π i ,(¿−utero)+β X i ) , (1 )

where h (a∨0 ) is the baseline hazard rate (mortality rate)6 at age airrespective of exposure to

food price inflation or other factors, π i ,(¿−utero)is the average food price inflation that child i was

exposed to during in-utero, γis the coefficient of interest measuring the impact of exposure to

food price inflation while in-utero, X i is a vector of control variables including child, parental,

household, geographic, and temporal characteristics as well as survey year dummies which

potentially affect children’s survival, and β is the corresponding vector of coefficients to be

estimated.

Child-level factors include birth order and month of birth. Parental characteristics include

variables that capture demographic and socioeconomic factors, which enter the child health

production directly and indirectly, including mother’s age, marital status, years of education,

occupation, age at first birth and age at first cohabitation (or marriage), and religion. In addition,

we control for husband’s age, years of education and occupation. The vector also includes

parental health factors that could predispose a child to certain life-threatening diseases or death.

In order to control for such factors, the regression includes mother’s height, hemoglobin level,

BMI, and whether she is anemic or not. Household-level factors include age of household head,

household size, the number of under-five children in the household, religion, wealth index, etc.

Geographic characteristics include location of residence, altitude, dummies for regional states,

dummies for survey year, and interaction terms of survey year and regions. Moreover, given the

6 The baseline hazard rates are unknown but could be retrieved after estimation of the full model.

20

fact that there is considerable variation in the availability of nutrition depending on crop

plantation and harvest seasons, we control for month of birth.

It is also important to note that in the context of rural Ethiopia, the impacts of food price inflation

could vary by households' status as net-sellers or net-buyers. Net-selling households could

benefit from the rise in price of the crop that they produce and sell at the market and hence

increase in their crop income. On the contrary, for a constant level of income, net-buyers could

suffer from such hikes in food prices as they will be forced to reduce the quantity and quality of

food (calorie) they purchase. The effects of food price inflation on calorie intake could,

therefore, vary by households’ net-seller and net buyer status. One of the disadvantage in our

data is that we do not have information on which household is net-buyer or net-seller. We

attempt to address this issue in two different ways. First, instead of including inflation of each

staple crop separately in our model, we use a composite staple food price inflation consisting of

five staple crops using their calorie weights for each region. In this case, even if a household is

net-seller of one crop it could be net-buyer of the other which minimizes the potential bias.

Second, we include location of residence (urban versus rural) dummy in our regressions as a

proxy for net-buyer/net-seller status, albeit a strong assumption.

Non-linearity, heterogeneous exposure time and cumulative effects

Another important issue is non-linear effects depending on exposure during the specific stage of

fetal growth. For instance, exposure to malnutrition during the first trimester could have different

impact on survival as compared to exposure to the same level of food price inflation in the third

trimester during which fetal growth is accelerated. We address this issue by running the model

for each month of exposure. Specifically, we estimate the following model

21

H i (a∨X i )=h ( a∨0 ) . exp ( γk π i ,(k)+β X i) ,∀ k∈ [−9 ,⋯ , Birth ,⋯ ,+6 ] , (2 )

where k is month of exposure and γkcaptures the effects of exposure to food price inflation during

the k thmonth. However, one of the shortcomings of Equation (2) is that it does not account for

the cumulative impacts of exposure during months prior to monthk . As such, the coefficient γk

could be picking up not only the effects of exposure to food price inflation during thek thmonth

but also the effects from all k−1months. In order to account for this, we include the cumulative

average food price inflation of the previous J=k−1 months in the regression. We calculate the

cumulative average food price inflation asΠ i , J<m<a= ∑j=inception

J ( π ij

J ), where Π i , J<m<ais the

cumulative average inflation of the previous Jmonths. Then the model that accounts for

heterogeneous exposure time and cumulative effects is given by

H i (a∨X i )=h ( a∨0 ) . exp ( γk π i ,(k)+γ J Π i ,J <m<a+β X i) ,∀ J , m∈[−9 , …, Birth ,…,+6] , (3 )

where γJ is a coefficient on the cumulative average staple food price inflation for months prior.

4. Results and Discussion

Table (5) presents the results from the Cox proportional hazard estimates. Specification (1)

shows the coefficients without including controls. Specifications (2) - (6) incrementally add

vectors of controls. As shown in Panel (a) in Specification (1), the exposure to food price

inflation during in-utero reduces under-five survival rate by about 0.002 percentage points,

which is statistically significant at 5% confidence level. When we control for household and

parental characteristics, the coefficient doubles and become statistically significant at 1%.

Controlling for geographic characteristics, survey year and region-year interaction dummies,

further increases the magnitude of the coefficient to 0.005. This implies that exposure to 10%

22

higher level of month-on-month staple food price inflation during in-utero decreases under-five

survival rate by 5.4%. For children who were in-utero during period of high food price inflation,

this is a large magnitude to be concerned of.

Table 5: Cox Proportional Hazard Estimates: The Impact of Exposure to Staple Food Price Inflation (1) (2) (3) (4) (5) (6)

Panel (a)In-utero -0.002** -0.003*** -0.004*** -0.005*** -0.005*** -0.005***

(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Observations 16,667 16,667 16,567 16,567 16,567 16,567

Panel (b)Infancy 0.001 0.001 0.000 -0.007*** -0.007*** -0.008***

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002)

Observations 18,418 18,418 18,308 18,308 18,308 18,308Household Char. -- X X X X X

Parental Char. -- -- X X X XGeographic -- -- -- X X XSurvey Year -- -- -- -- X X

23

Interaction Terms -- -- -- -- -- XRobust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Panel (b) shows the estimated coefficients on the impacts of exposure to staple food price

inflation during the first six months after birth. The coefficients in specifications (1) - (3) are

positive but statistically insignificant. However, when we control for geographic characteristics,

survey year, and region specific time-varying shocks, the coefficients become negative and

significant at 1%. The results show that exposure to 1% higher staple food price inflation during

the first 6 months after birth, reduces under-five survival by 0.008 probability points.

Table (6) shows the results for restricted sample of cases. Columns (1) - (2) show the estimates

for cases located within 50 and 25 km radius from the centroid. The coefficients on the in-utero

exposure are -0.005 and -0.006, respectively, for the subsamples within 50 and 25 km radius.

Similarly, coefficients on infancy exposure to inflation are negative and statistically significant.

Table 6: Cox Proportional Hazard Estimates: The Impacts of Exposure to Staple Food Price Inflation by Distance from Market and Survey Year

(1) (2) (3) (4)50 Km Radius

25 km Radius

2010 Survey

2005 Survey

Panel (a)In-utero -0.005*** -0.006*** -0.005*** -0.007**

(0.002) (0.002) (0.002) (0.003)

Observations 13,005 7,378 11,303 5,264

Panel (b)Infancy -0.008*** -0.009*** -0.010*** -0.006

(0.002) (0.003) (0.002) (0.004)

Observations 14,527 8,242 11,047 7,261Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

24

In order to address non-linearity and heterogeneity in the impacts, we estimate the model in

equation (1) for each month of exposure since inception and the results are shown in Figure (4).

While the coefficients and the 95% confidence interval are shown on the y-axis, month of

exposure are shown in the x-axis. Exposure to staple food price inflation during different in-

utero months decreases survival significantly. Although there are some non-linearity in the

impacts, exposure to 1% higher level of food price inflation during any one of in-utero month

decreases child survival by up to 0.01 percentage points. However, the effects of exposure after

birth decrease and become insignificant, especially after the 6th month.

Figure (5) shows the results after accounting for the cumulative average inflation during prior

months. For comparison, the figure also shows the baseline estimates without accounting for the

cumulative effects. Interestingly, after we control for the history of price shocks, the magnitude

of the impacts increased for any given month of exposure. -.02

-.01

0.01

.02

beta

coe

ffici

ent

- -2 -4 -6 -8 Birth +2 +4 +6 +8 +10 +12Month of exposure

Figure 4: Impacts of Exposure to Staple Food Price Inflation during Each Month of Early Life

25

-.01

-.005

0.005

beta

coe

ffici

ent

- -2 -4 -6 -8 Birth +2 +4 +6 +8 +10 +12Month of exposure

Baseline Estimates Controlling for cumulative effects

Figure 5: Impacts of Exposure to Staple Food Price Inflation during Each Month of Early Life Controlling for The Cumulative Effects

We also find considerable level of heterogeneity in the impacts depending on the type of staple

food. Figure (6) show the estimates for each of the six cereals that are staples in different parts of

the country—teff, wheat, maize, barley, sorghum, and enset. The results show that in-utero

exposure to teff price inflation has the highest impact on child survival followed by maize. For

instance, the impacts of exposure to 1% higher level of teff price inflation on child survival range

from -0.005 to -0.012 probability points. Children who were exposed to 1% higher level of teff

price inflation during the 8--9th months of pregnancy have a 0.012 probability point lower chance

of survival.

26

-.015

-.01

-.005

0.005

beta

coe

ffici

ent

- -2 -4 -6 -8 Birth +2 +4 +6 +8 +10 +12Month of exposure

Teff WheatMaize BarleySorghum Enset

The effects of Food Price Inflation on Infant and Child Survival

Figure 6: Impacts of Exposure to Staple Food Price Inflation during Each Month of Early Life Controlling for the Cumulative Effects

6. Conclusion

The medium- and long-term consequences of malnutrition during early life on children’s health

are well understood, with the impacts having potentially determining future health, educational

achievement, job market, family formation, social behavior, and many other life-course

outcomes during adulthood. But there is limited knowledge on the causal impacts of hidden

hunger and malnutrition of pregnant women and children on mortality. This paper investigates

the impacts of exposure to malnutrition and hidden hunger, due to food price inflation, during

the critical periods of early life on child mortality. We use data from the Ethiopian

27

Demographic and Health Survey and high frequency local retail food price data, matching each

child’s months of early life since conception with the corresponding levels of inflation. We

carefully linked the two datasets using a GIS-based Nearest Neighbor Matching algorithm. Our

focus is on children aged 6–59 months and six major staple foods in Ethiopia.

We find that exposure to high food price inflation during the critical window of early life

significantly increase under-five mortality. Owing to the complicated biological mechanisms

that govern the link between nutrition and human growth, the impacts are non-linear and vary

by the specific month of exposure. The results are heterogeneous over some observables and

the type of cereals but robust to different empirical specifications.

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Additional Tables and Figures

Figure A1: Staple Food Price Inflation: 2001-2011

33