IFPRI Discussion Paper 01423
March 2015
Agriculture, Nutrition, and the Green Revolution in
Bangladesh
Derek D. Headey
John Hoddinott
Poverty, Health, and Nutrition Division
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
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AUTHORS
Derek D. Headey ([email protected]) is a senior research fellow in the Poverty, Health, and Nutrition
Division of the International Food Policy Research Institute (IFPRI), Washington, DC.
John Hoddinott ([email protected]) is deputy division director of the Poverty, Health, and Nutrition
Division of IFPRI, Washington, DC and H.E. Babcock Professor of Food and Nutrition Economics and
Policy at the Charles H. Dyson School of Economics and Management at Cornell University, Ithaca, NY.
Notices
1.
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Contents
Abstract
v
Acknowledgments
vi
1. Introduction
1
2. Agricultural Production and Nutrition in Bangladesh
3
3. Conceptual Linkages between Rice Productivity Growth and Nutritional Outcomes in
Bangladesh
6
4. Data and Methods
8
5. Results
11
6. Conclusions
16
References
17
iii
Tables
2.1 Sources of growth in Bangladeshi agriculture, 1997–2011 (in percentages)
5
4.1 Descriptive statistics
9
5.1 Fixed-effects estimates of the impact of rice yields on chronic and acute child nutrition indicators
11
5.2 Adding control variables to the WHZ and mild wasting fixed-effects models
12
5.3 Fixed-effects estimates of the impact of yield growth on child-feeding indicators (in percentages)
13
5.4 Fixed-effects estimates of the associations between child feeding indicators and child nutrition
outcomes
14
5.5 Assessing the robustness of the impact of yields on child weight: Fixed-effects and Blundell-Bond
generalized methods of moments estimates
15
5.6 Assessing the robustness of effects of yields on child-feeding indicators: Fixed-effects and
Blundell-Bond generalized methods of moments estimates (in percentages)
15
Figures
1.1 Trends in child undernutrition and rice yields, 1997–2011
2
2.1 A late bloomer catching up: Rice yields in Bangladesh relative to Indonesia
4
2.2 Trends in irrigation, modern variety uptake, and rice yields
4
iv
ABSTRACT
Although agriculture is widely thought to be an important sector for influencing nutrition outcomes, there
is a remarkable dearth of rigorous evidence on the role of large scale agricultural programs, such as Asia’s
Green Revolution. This paper therefore analyzes agriculture and nutrition linkages in Bangladesh, a
country that achieved rapid growth in rice productivity at a relatively late stage in Asia’s Green
Revolution, as well as unheralded progress against undernutrition. To do so the authors create a synthetic
panel that aggregates nutritional data from five rounds of the Demographic Health Surveys (1997 to
2011) with district-level estimates of rice yields. Using various panel estimators, they find rice yields
significantly explain weight gain in young children but not linear growth. The authors further show that
rice yields have large and positive effects on the timely introduction of complementary foods for young
children but not on dietary diversity indicators and that this complementary feeding indicator is positively
associated with child weight gain but not with linear growth. The reverse holds for dietary diversity
indicators, which influence linear growth but not weight gain. The results therefore suggest that
Bangladesh’s Green Revolution has made a large contribution to improvements in weight gain but has
had little effect on postnatal linear growth. Achieving the latter would appear to require greater efforts to
diversify diets out of cereals and into more micronutrient-rich foods.
Keywords: undernutrition; Green Revolution; agricultural productivity; rice; Bangladesh
v
ACKNOWLEDGMENTS
We would especially like to thank Wahid Quabili and Aminul Islam Khandaker for invaluable research
assistance and Akhter Ahmed and Purnima Menon for comments and advice. This work has partly been
supported by the Department for International Development (United Kingdom) through its funding of the
Transform Nutrition Consortium and the Leveraging Agriculture for Nutrition in South Asia Consortium
as well as the Bill & Melinda Gates Foundation through its funding of the Advancing Research on
Nutrition and Agriculture project.
vi
1. INTRODUCTION
Despite a surge of recent interest in identifying the impact of agricultural interventions on maternal and
child nutrition, the existing scientific literature has brought little light to bear on the core question of how
large-scale agricultural programs and policies influence nutrition outcomes (Ruel and Alderman 2013).
Much of the literature has been confined to cross-sectional studies for which even indirect policy
attribution is very difficult (Bhagowalia, Headey, and Kadiyala 2012; Dillon, McGee, and Oseni 2014;
Hoddinott, Headey, and Dereje 2014) or to more experimental studies of small-scale livestock or
homestead gardening interventions (Berti, Krasevec, and FitzGerald 2004; Leroy and Frongillo 2007;
Masset et al. 2012).
Yet advocates of agriculture-led development typically have in mind much larger-scale
agricultural programs and policies in the spirit of Asia’s “Green Revolutions” (Bezemer and Headey
2008; Diao et al. 2008; Diao, Hazell, and Thurlow 2010; Hazell 2009; Mellor 1976; Pinstrup-Andersen
2013). These Green Revolutions were led by the research and development of improved rice, wheat, and
maize varieties, which—along with associated policies to promote the expansion of irrigation, fertilizers,
and other inputs—have contributed to rapid growth in Asian food production during the past 40 years.
Rice yields, for example, have increased by around 150 percent in Bangladesh, northern India, Indonesia,
and Pakistan since the 1960s, while wheat yields in these countries increased by some 250 percent (Food
and Agriculture Organization of the United Nations [FAO] 2014).
Despite its fundamental contribution to poverty reduction, surprisingly little is known about the
impact of Asia’s Green Revolution on nutrition, and much of what has been written is speculative at best
(see Hazell 2009 for a review). Optimists have focused on the contributions of Green Revolution
investments to calorie consumption and national food security (Pinstrup-Andersen and Jaramillo 1991),
while pessimists point to the adverse micronutrient consequences of reduced biodiversity in
monocropping systems, particularly lower consumption of pulses, coarse grains, and fish (Bouis 2000),
and to the harmful health and nutritional impacts of excessive use of fertilizers and pesticides (Brainerd
and Menon 2014). Yet the majority of this evidence has not examined the impacts of agricultural growth
on changes in nutrition outcomes. This knowledge gap exists because the Green Revolutions of the 1960s,
1970s, and 1980s largely preceded the kinds of large, multitopic surveys that are typically a prerequisite
for identifying the welfare impacts of large-scale interventions (Elbers and Gunning 2013; Barrett and
Carter 2010).
This paper seeks to fill this knowledge gap by studying the nutritional impacts of rice
productivity growth in Bangladesh. Bangladesh is an ideal case study for several reasons.
First, for political reasons, Bangladesh was a relatively late adopter of Green Revolution
technologies (Evenson and Gollin 2003), meaning that much of its productivity growth occurred during
more recent periods of improved statistical surveillance. From 1997 to 2011 (the period of our analysis)
yield growth for rice averaged 3.6 percent per year (Figure 1.1) on the back of increased adoption of
improved varieties and the rapid expansion of the irrigated dry season rice crop. Second, productivity
growth in Bangladesh coincided with substantial improvements in preschooler nutritional status. In
1996/1997, fully half of preschooler children in Bangladesh were stunted (height for age z (HAZ) score
< –2) or mildly wasted (weight for height (WHZ) z score < –1). These very high rates of undernutrition
were among the highest in the world at that time, although from 1996/1997 to 2011 all three indicators
fell dramatically (Figure 1.1). Third, Bangladesh has a relatively rich array of nutritional and agricultural
data, the dearth of which has undoubtedly been a constraint in this literature.
1
Figure 1.1 Trends in child undernutrition and rice yields, 1997–2011
4.5
4.0
50%
Mild wasting
40%
30%
3.5
3.0
Stunting
Rice yields
2.5
2.0
20%
1.5
Rice yields (mt/ha)
Undernutrition prevalence (%)
60%
1.0
10%
0.5
0%
0.0
1997
2000
2004
2007
2011
Source: FAO (2014).
Note: mt/ha = metric ton per hectare. Stunting refers to HAZ scores of –2 or less, for children 0 to 24 months of age. Mild
wasting refers to WHZ scores of –1 or less, for children 0 to 24 months of age measured from October to December of
the calendar year in question, to control for seasonality of wasting and the different survey timings of the various
Demographic Health Surveys rounds. Wasting values for 2004 and 2007 are imputed with a linear trend.
In this paper, we exploit these rich data sources to construct a synthetic panel dataset comprising
nutritional indicators from five rounds of the Bangladesh Demographic Health Surveys (DHS) (during
1996/1997 to 2011) and district-level data on rice yields from the Bangladesh Bureau of Statistics. With
these data, we examine the impact of rice productivity growth on preschool nutritional status, controlling
for district-level fixed effects and time period effects as well as other time-varying factors that might also
explain nutritional improvements in Bangladesh, such as the rapid expansions in women’s education,
sanitation, health and family planning services, women’s empowerment, and nongovernmental
organization (NGO) services. We assess the robustness of our time and location fixed-effects models
through the use of panel generalized methods of moments (GMM) estimation techniques that address
endogeneity concerns regarding the estimated impacts of rice yields. Hence we are able to purge our
regression models of factors that potentially confound the inferences drawn from the cross-sectional
econometric literature referenced above.
The remainder of this paper is structured as follows. Section 2 provides an overview of the
nutritional and agricultural situation in Bangladesh. Section 3 describes a conceptual model used to
motivate our empirics. Section 4 describes the construction of the synthetic dataset and the empirical
models we use to analyze that data. Section 5 presents our results, and Section 6 summarizes our findings.
2
2. AGRICULTURAL PRODUCTION AND NUTRITION IN BANGLADESH
Bangladesh is characterized by a uniquely intensive agricultural production system that largely takes
place on very small family farms engaged in multiple cropping seasons. Most of the poor are landless
farm or nonfarm laborers, or smallholders (Balagtas et al. 2014; Hossain 2004). Traditionally, rice
production used relatively low levels of irrigation and other modern inputs. Production was a highly
seasonal affair, with the vast majority of production taking place in the monsoonal aman season, with
production in the dry season—particularly the boro season—constrained by lack of water. But like other
Asian countries, Bangladesh was to benefit substantially from the research and development of highyielding varieties (HYVs) and the associated adoption of irrigation and other modern inputs: in short, the
so-called Green Revolution package (Ahmed, Haggblade, and Chowdhury 2000; Hossain, Bose, and
Mustafi 2006; Naher 1997).
Bangladesh’s Green Revolution got off to a sluggish start (Figure 2.1). In 1967 the Bangladesh
Academy of Rural Development imported the IR8 variety from the International Rice Research Institute
in the Philippines and introduced it in the dry season, while IR20 was introduced in 1970 for the wet
season. The spread of these varieties was slow in the 1970s, delayed by the war of independence and the
rebuilding process as well as the vulnerability of new varieties to pests and disease (Hossain, Bose, and
Mustafi 2006). By the mid-1980s only around 27 percent of the rice area was planted with modern
varieties, and yield growth was averaging around 2.2 percent per year (FAO 2014). The 1990s saw more
dramatic changes, however. First, there was an acceleration of HYVs adoption (Figure 2.2) driven via
improved linkages between agricultural extension and research (including increased tolerance of disease
and local farming cycles) and better collaboration between the public and private sectors (including
influential domestic non-government organizations (NGOs) such as BRAC). By the late 1990s around
two-thirds of the rice area was planted with HYVs. Second, the government progressively liberalized
agricultural inputs, particularly the imports of small-scale irrigation equipment, including diesel pumps
and shallow tubewells (Nazneen et al. 2007). As a result, the irrigated area doubled from 1990 to 2010,
and the share of the once minor boro crop in total production increased from around 15 percent in the
1970s to 58 percent in 2010 (Figure 2.2).1 In 1997, rice accounted for almost two-thirds of total
production value (Table 2.1). From 1997 to 2011 rice production grew by 80.7 percent, or 5.8 percent per
year, and accounted for 61.4 percent of total production growth. In contrast, growth of other cereals was
immaterial. While growth in livestock and “other food products” was relatively rapid, it started from a
low base; Hossain, Bose, and Mustafi (2006) argue that the development and adoption of fast-maturing
rice varieties allowed farmers to plant more non-rice crops, suggesting that some of the growth in other
food products might have resulted from rice productivity gains.
1
Note, however, that fertilizer application per cropped area changed only modestly during the same period.
3
Figure 2.1A late bloomer catching up: Rice yields in Bangladesh relative to Indonesia
Rice yields (mt per hectare)
6.0
5.0
_ _ _ DHS rounds
4.0
3.0
Bangladesh
Indonesia
2.0
1.0
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
0.0
Source: FAO (2014).
Note: mt = metric ton; DHS = Demographic Health Surveys.
Figure 2.2 Trends in irrigation, modern variety uptake, and rice yields
1.4
80%
1.2
70%
1
60%
50%
0.8
40%
0.6
30%
0.4
20%
0.2
10%
0
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
0%
MV area (%)
Irrigated area
Yield (MT/Ha)
Source: Data are from the Bangladesh Bureau of Statistics (various years).
Note: mt = metric ton; MT/Ha = metric tons per hectare; MV = modern variety.
4
Rice yields (mt/acre)
Area under irrigation & MVs (%)
90%
Table 2.1 Sources of growth in Bangladeshi agriculture, 1997–2011 (in percentages)
Initial share of
production value,
1997
Variable
Growth rate of
production
1997–2011
Share of total
agricultural
growth
Food
Rice
5.8
63.5
Other cereals
3.1
Livestock
15.3
Other food products
14.3
Nonfood
6.1
4.0
9.7
3.2
3.7
All agriculture
Source: FAO (2014).
61.4
3.2
10.2
23.3
2.0
5.8
100.0
This combination of rapid growth in yields combined with a more even spread of production
across the wet and dry seasons is plausibly linked to substantial improvements in food security. FAO
(2014) estimates suggest that from 1990 to 2010, the percentage of the population that was calorie
deprived fell by half, from 35 percent to 17 percent. An additional benefit of the rapid emergence of the
boro crop was reduced intra-annual rice price variation (Dorosh and Shahabuddin 2002), which may have
reduced seasonal hunger, especially for landless laborers, who are the poorest group of net food
consumers.
However, there is thus far no evidence of any impact on rice productivity growth on nutritional
change, and there are numerous other potential explanations of Bangladesh’s rapid reductions in maternal
and child undernutrition. Bangladesh is arguably better known for its microfinance revolution (Khandker
2005), focus on women’s empowerment and secondary school education for girls in particular (World
Bank 2005), significant improvements in community-led health and family planning initiatives
(Chowdhury et al. 2013; World Bank 2005), pioneering Community-led Total Sanitation campaigns (Kar
2003), sustained growth in labor-intensive manufacturing (Zhang et al. 2013), and massive surge in
overseas remittances during the past ten years (Balagtas et al. 2014; Orr et al. 2009). The various rounds
of the DHS also show fairly substantial improvements in several important infant and young child feeding
indicators, particularly immediate postnatal breast-feeding and timely introduction of complementary
foods (Hanif 2013), suggesting that nutritional programs in Bangladesh might have been more effective
than is commonly thought (World Bank 2005).
Consistent with the hypothesis that nutritional change was a multisectoral process, Headey et al.
(2015) conduct statistical decompositions of changes in child HAZ scores and stunting during the same
five DHS rounds (1996/1997 to 2011) and find that asset accumulation and gains in parental education
were the two largest drivers of change, followed by significant improvements in health, sanitation, and
family planning outcomes. However, their model—which notably excludes any agricultural indicators—
accounts for only slightly more than half of the actual nutritional change in rural areas.2
2 Their
preferred model explains around 70 percent of the improvement in urban areas.
5
3. CONCEPTUAL LINKAGES BETWEEN RICE PRODUCTIVITY GROWTH AND
NUTRITIONAL OUTCOMES IN BANGLADESH
The agricultural household model developed by Singh, Squire, and Strauss (1986) and extended by
Behrman and Deolalikar (1988) of nutrient intake and anthropometric outcomes provides a basis for
linking rice productivity growth to nutrition.3 These models conceptualize the household decisionmaking
process as one wherein parents are concerned about the nutritional status of preschool children and their
nutrient intakes. Households command various resources—their labor, time, and agricultural assets—and
make utility-maximizing production and consumption decisions in the context of standard budget
constraints. The resulting models cast nutrition outcomes and nutrient intakes as a function of farm
income, off-farm wage earnings, food and nonfood prices, time resources devoted to child care,
community characteristics, and exogenous consumption preferences (tastes).
These models posit a number of important linkages between exogenous technological shocks—
such as the availability of modern varieties and irrigation technologies—and individual nutrition
outcomes. A first-order effect of a technology shock will be on farm income, with farm households
making adoption, planting, input, and marketing decisions based on the relative profitability of the
technology. As stressed by Singh, Squire, and Strauss (1986), under the strong assumptions of complete
markets farmers do not consider household nutrition requirements in these production decisions.
However, various market failures might motivate farmers to make production decisions for nonincome
reasons. For example, farmers might specialize in a staple food like rice because it is less perishable than
fruits or vegetables, generally less susceptible to flooding, and hence less vulnerable to marketing failures
(Shahabuddin and Dorosh 2002). In the absence of other effective means of securing household food
security, it is possible that farmers allocate more resources to rice production than would be optimal under
pure profit incentives in the context of complete well-functioning markets.
For the large number of nonfarm households in rural Bangladesh, however, it is the second-order
effects of the technology shock that matter most. The availability of HYVs in Bangladesh and the
liberalization of input markets resulted in pervasive multiple cropping. This, in turn, translated into large
increases in the demand for farm labor, which is expected to benefit landless households through higher
wages, increased working hours, or a combination of both (Ravallion 1990). Similarly, the income gains
accruing to rice farmers increase their demand for local nonfarm goods and services (such as house
construction), which also serves to increase wages. Another second-order effect pertains to reductions in
real rice prices. Since the bulk of the poor are landless net food consumers, lower real rice prices are
expected to substantially improve real incomes for the poorest segments of society.
However, a key question facing the agriculture-nutrition literature is whether these household
welfare gains also translate into nutritional improvements in young children. A well-established
nutritional literature has shown that child undernutrition largely manifests itself in the first 1,000 days of
life (in utero and the first 24 months of childhood), after which there is substantial stabilization (Victora et
al. 2009). Child nutrition in this period is affected by maternal nutrition insofar as the latter affects birth
size and weight (both of which are low in Bangladesh), but thereafter the growth-faltering process is
largely influenced by inappropriate infant and young child care practices and the disease environment.
Among the former, poor feeding practices are widely recognized as a critical constraint in South
Asia (Senarath et al. 2012; Kabir et al. 2012; Menon 2012). While the introduction of complementary
foods (solid or semisolid) should be almost universal by six to eight months of age, only 20 percent of
such children were given complementary foods in the 1996/1997 round of the DHS, though there was
rapid improvement by 2004, with almost 70 percent of children six to eight months of age being given
solid foods. In extremely food-insecure settings it is possible that parents may delay the introduction of
complementary foods and instead rely on prolonged breast-feeding (we hereafter term this the rationing
hypothesis). Since rice is easily the most common complementary food for infants and young children in
3 For
a recent discussion, with new extensions, see LaFave and Thomas (2012).
6
Bangladesh—with more than 60 percent of children in the six to eight month age group consuming rice in
2007, compared to much smaller proportions consuming micronutrient-rich foods—it is possible that rice
productivity growth contributed to observed improvements in feeding practices.
While rice productivity growth should substantially improve basic household food security—and
perhaps also food availability for young children—it is less obvious that these gains have translated into
significant dietary diversity. Theoretically, the real income effects of lower rice prices will lead to dietary
diversification (according to Bennett’s law), although reductions in the price of rice relative to other
calories could lead to substitution (in both production and consumption) out of more nutritious foods
(pulses, fish) in favor of rice production and consumption (Bouis 2000). Using multiple rounds of
nutrition and price data collected in high-frequency surveillance data, Torlesse, Kiess, and Bloem (2003)
find that reductions in rice prices increase the share of household food expenditure devoted to nonrice
foods (that is, dietary diversity), which suggests that income effects dominate. However, at the aggregate
level the FAO Food Balance Sheets show very modest dietary diversification in Bangladesh during the
past two decades (FAO 2014), while the various rounds of DHS used herein also show very limited
dietary diversification among young children, as do other surveys.4
Productivity growth in agriculture might also have some negative impacts on nutrition. New
agricultural technologies can lead to more demands on women’s time use (Headey, Chiu, and Kadiyala
2012), though rice production in Bangladesh is primarily a male occupation. More generally,
discrimination against women and girls is widely thought to be a significant constraint on nutrition in
Asia (Jayachandran and Pande 2013; Smith et al. 2003; Molini, Nubé, and van den Boom 2010), implying
that girls might benefit less from productivity growth than do boys. However, as we noted above,
Bangladesh is internationally renowned for the promotion of greater empowerment of women, so in this
particular instance it is possible that the benefits of agricultural productivity growth were actually
enhanced by the simultaneous improvement in women’s status. Finally, the overuse and inappropriate use
of fertilizers and pesticides is a significant concern in South Asian agriculture. A study on fertilizer use in
India used a natural experiment to gauge the nutritional impact of excessive fertilizer use and found
significant negative effects on child growth outcomes (Brainerd and Menon 2014).5 There may well be
similar concerns in Bangladesh, where a World Bank study found that in 2005 around half of farmers
were overusing pesticides, while more than 87 percent admitted to using little or no protective measures
while applying pesticides (Dasgupta et al. 2005). On the other hand, since Bangladeshi women tend to
work less in rice production and more in the homestead, it is not obviously the case that the effects are
equally severe in Bangladesh. Thus, while we still have relatively strong expectations of observing a
significant and positive effect of rice productivity growth on maternal and child nutrition, there are also
mechanisms that might weaken these effects or lead to impacts on some nutrition indicators but not on
others.
4 The Demographic Health Surveys (DHS), unfortunately, measure different food groups during different periods, and for
some specific groups, like dairy, they phrase the questions inconsistently. While we have made every effort to ensure
consistency, it is possible that the different phrasing of the questions across rounds creates some systematic inconsistencies. In
our regression models these inconsistencies are addressed through time period (round) effects.
5
One study conjectures that rice-based diets may constrain nutritional improvement. In a very detailed intrahousehold study
in very poor villages in northern Bangladesh, Ahmed (1993) found that preschooler children often struggled to eat the rice
allocated to them by their parents. He conjectured that this stemmed partly from poor health environments but also from the
starchiness of rice and the tendency toward eating large, twice-daily meals that overfill small stomachs rather than snacking more
frequently on more diverse food preparations.
7
4. DATA AND METHODS
Empirical research into agriculture-nutrition linkages has been greatly constrained by lack of data, with
nutritional surveys rarely collecting agricultural data and long-running economic surveys rarely collecting
anthropometric data. For these reasons we create a synthetic panel by merging five rounds of data on rural
households and children from the nationally representative Bangladesh DHS collected in the years
1996/1997, 2000, 2004, 2007, and 2011 with district-level agricultural data (Bangladesh Bureau of
Statistics 2014). We focus on rural areas only. While rice productivity growth might benefit urban areas
through lower rice prices, high levels of market integration across urban areas would presumably result in
very little spatial variation in rice prices. Moreover, the urban sample in DHS is relatively small, leaving
some districts with very few urban observations.
Each observation in our rural panel is a year-district mean. There are 23 rural districts and five
observations per district, but because we are missing data for several districts in several rounds, our
sample has 109 observations. When constructing this sample, we were concerned that we had a sufficient
number of children in each district. In our main sample the median number of children per district-year is
352, though there are a handful of districts where many fewer children were measured (the minimum
being 17 children), mostly in the Chittagong Hill districts. We test the robustness of our results to the
exclusion of such districts.
Our outcome variables reflect two dimensions of undernutrition in children: acute undernutrition
(weight for height) and chronic undernutrition (height for age). We also apply cutoffs to create indicators
of undernutrition prevalence at the district level. For weight-for-height z scores we use a threshold of –1
standard deviations to identify mild wasting prevalence and –2 to identify moderate wasting. For heightfor-age z scores we use –2 standard deviations for moderate stunting and –3 for severe stunting. We focus
on weight gain and linear growth in the first 24 months largely because anthropometric processes tend to
stabilize beyond 24 months (Victora et al. 2009) but also because we lack dietary indicators for children
older than 24 months.
We also test whether rice productivity growth explains changes in some basic DHS indicators of
child-feeding practices among children aged 4 to 18 months.6 The first such indicator is the timely
introduction of complementary foods, which refers to the proportion consuming any solid or semisolid
foods. A second child-feeding indicator is minimum dietary diversity (as defined by the World Health
Organization), which is the percentage of children consuming more than four food groups out of a
maximum of seven. Dietary diversity indicators have previously been shown to be a robust predictor of
linear growth in children (Arimond and Ruel 2006). Last, we look at dairy consumption (specifically,
milk and yogurt consumption) as a diversity indicator. This has previously been shown to be a strong
predictor of child growth outcomes in rural areas of other developing countries (Hoddinott et al. 2015;
Rawlins et al. 2014) and, in a number of more experimental studies, in both developed and developing
countries (Hoppe et al. 2006; Iannotti, Muehlhoff, and Mcmahon 2013; de Beer 2012).
In terms of rice productivity, we focus on rice yields in the 12 months just prior to the start date
of each DHS round. Yields are likely the best summary indicator of productivity in rice production,
reflecting the benefits of HYVs, irrigation, and other modern inputs such as fertilizers, pesticides, and
6 The timely introduction of complementary foods is normally measured in the 6 to 8 month window per
World Health Organization guidelines. However, restricting the sample to 6 to 8 months would not
provide us with enough children at the district level to ensure sufficiently minimal measurement error.
Moreover, earlier rounds of the Bangladesh DHS suggest that reasonably large numbers of children were
not even given solid or semisolid foods in late infancy (12–18 months). In the past, some nutritionists
have also advocated earlier introduction of solid or semisolid foods (4–5 months) in conjunction with
continued breast-feeding. Hence, our window of measurement for this indicator is somewhat
unconventionally large. In any event our results are qualitatively robust to marginally reducing this
window, although they are inevitably somewhat less precise.
8
herbicides. But because our weight-based indicators are potentially quite sensitive to short-term
fluctuations, we pay particular attention to the timing of measurement. We measure yields in the 12
months prior to each survey since current household rice stocks and local rice prices depend largely on
production in recent seasons rather than the still-ongoing current season.7 Given Bangladesh’s multiple
seasons, rice yields are the sum of production in different seasons divided by the total crop area harvested
in those seasons. This indicator is available at only the district level and is based on agricultural surveys
carried out by the Bangladesh Bureau of Statistics (Bangladesh, Bureau of Statistics 2014). Table 4.1
shows descriptive statistics for our key agricultural and nutrition indicators for the 109 district-years in
our sample. There is substantial variation in our productivity and nutrition indicators across district-time
periods.
Table 4.1 Descriptive statistics
Variable
Number of districtyear observations
Mean
Standard
deviation
109
0.93
0.21
HAZ score
109
–1.56
0.33
Moderate stunting, HAZ < –2 (0–1)
109
0.39
0.11
Severe stunting, HAZ < –3 (0–1)
109
0.16
0.08
WHZ score
109
–1.02
0.29
Mild wasting, WHZ < –1 (0–1)
109
0.51
0.10
Severe wasting, WHZ < –2 (0–1)
109
0.19
0.08
109
0.68
0.18
Dietary diversity (1–7)
89
2.17
0.78
Dairy consumption (0–1)
109
0.31
0.15
Rice productivity
Yields (metric ton/acre)
Child nutritiona
Child feedingb
Complementary feeding (0–1)
Source: Authors’ estimates from various DHS rounds.
Note: HAZ = height-for-age z score; WHZ = weight-for-height z score. a. Child nutrition indicators are measured for children
aged 0 to 24 months. b. Child feeding indicators are measured for children aged 4 to 18 months. Note that the
dietary diversity indicator is not available for the 1996/1997 round.
We model the relationship between nutrition outcomes and yields as
𝑁𝑖,𝑡 = 𝛼 + 𝛽. 𝑦𝑖,𝑡 𝜸𝑫𝑖,𝑡 + 𝜹𝑿𝑖,𝑡 + 𝑻𝑡 + 𝝁𝑖 + 𝜀𝑖,𝑡 ,
(1)
where N refers to child nutrition outcomes for district i and time t; y to rice yields; 𝛽 to the impact of
yields on nutrition; D to child age and its square; X to various other determinants of nutrition outcomes
such as education, health, sanitation, and demographic variables; T to time dummies for each survey
round; 𝝁 to district fixed effects; and 𝜀 to an error term. As such, we control for time-invariant factors that
could be correlated with both yields and nutritional quality (for example, water availability, soil quality,
7
The availability of seasonal production data at the district level allows us to adjust our agricultural measurement dates to
the DHS survey timing. For example, in the 1996/1997 DHS round the survey dates ranged from October 1996 to April 1997.
Thus we measured yields in the 12 months from July 1995 to June 1996, which includes the 1995 aman yield (harvested mostly
in November and December 1995) and the boro and aus yields from April to June 1996. In another round (2004) the DHS began
in January and finished in May, so we measured yields in the previous calendar year (2003).
9
climate, cultural practices), aggregate temporal effects that might simultaneously influence agricultural
productivity and nutrition (for example, changes in international food prices), and a number of nutritionrelevant control variables that could also potentially be correlated with rice productivity/technology
growth.
A limitation of equation 1 is that yields, though predetermined, may not be strictly exogenous.
Unobservable shocks that cause yields to vary may be correlated with other factors that influence
nutrition outcomes, including both positive unobservable shocks (such as transitory increases in nonfarm
employment) and negative unobservable shocks (such as localized flooding). Past realizations of these
shocks might also have persistent effects on both yields and nutrition outcomes. This can be addressed by
including lagged nutrition outcomes as an additional explanatory variable, but of course this will be
correlated with the error term to yield a dynamic panel bias (Arellano and Bond 1991). The solution to
both concerns is to use lagged levels or differences of the endogenous or predetermined variables as
instruments. Hence, in addition to the conventional fixed-effects model in equation 1, we estimate a firstdifferenced version of equation 1 with lagged nutrition and yields dynamically instrumented using the
GMM estimator proposed by Blundell and Bond (1998). Note also that we use a collapsed instrument set
to prevent overidentification of the endogenous/predetermined variables, as recommended by Roodman
(2009).
10
5. RESULTS
Table 5.1 reports the results of regressing the various chronic and acute child nutrition outcomes against
rice yields in models that control for fixed effects and time period effects as well as child age and its
square. In regressions 1, 2, and 3 we find that rice yields have no significant effect on the three chronic
nutrition indicators. One explanation may be that current stunting rates are in fact related to cumulative
nutrition processes and hence to several years of lagged rice productivity outcomes. Another explanation
may be that linear growth benefits more from dietary diversification than calorie availability. We explore
these explanations below.
Table 5.1 Fixed-effects estimates of the impact of rice yields on chronic and acute child nutrition
indicators
1
HAZ
score
Variable
Rice yields
2
Moderate
stunting
(HAZ < –2)
3
Severe
stunting
(HAZ < –3)
4
WHZ
score
5
Mild
wasting
(WHZ < –1)
6
Moderate
wasting
(WHZ < –2)
–0.16
0.15
–0.03
0.83**
–0.26**
–0.14
(0.34)
(0.16)
(0.10)
(0.31)
(0.12)
(0.14)
Age control
Yes
Yes
Yes
Yes
Yes
Yes
Time effects
Yes
Yes
Yes
Yes
Yes
Yes
Fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
Sample size
.42
.33
.42
.28
.33
.39
109
109
109
109
109
109
Source: Authors’ estimates.
Note: HAZ = height for age z score; WHZ = weight for height z score. Robust standard errors are included in parentheses. All
indicators are measured for children 0 to 24 months of age. *Significant at the 10 percent level. **Significant at the 5
percent level. ***Significant at the 1 percent level.
In contrast, rice yields have a large, well-measured impact on acute undernutrition. The effects of
yields on the WHZ score are significant at the 5 percent level and are meaningful in magnitude. The 0.5
metric ton increase in yields per acre from 1997 to 2011 predicts a 0.41 standard deviation improvement
in WHZ scores. The same increase in yields predicts a 13 percentage point decline in mild wasting (WHZ
< –1). But in regression 6 we observe much more modest effects on moderate wasting, with the
coefficient’s being insignificant though still negative and reasonably large in magnitude.
Our results could be confounded by location-specific time-varying characteristics. Fortunately,
DHS also provide a wealth of nutrition-relevant control variables, such as education, sanitation, access to
health services, fertility rates and household demographics, and proxies for women’s empowerment. In
Table 5.2 we assess the robustness of our results on WHZ scores and mild wasting by adding a set of
control variables to the relevant models from Table 5.1. Our selection of variables is guided by the
existing literature on nutritional change in Bangladesh, particularly Headey et al. (2015). That study
showed that parental education was one of the main drivers of nutritional change from 1997 to 2011,
particularly maternal education, though several other important factors were also identified, including
availability of toilets, basic health services, and reductions in fertility rates. We also add an admittedly
crude indicator of women’s decisionmaking (whether a woman can go to a health clinic alone) on the
basis of Bangladesh’s pervasive emphasis on female empowerment initiatives as well as the percentage of
women surveyed who said they participated in NGO programs. The latter control could be important
because several NGOs, particularly BRAC, engage in both agricultural programs and nutrition and health
initiatives.
11
Table 5.2 Adding control variables to the WHZ and mild wasting fixed-effects models
1
Variable
Rice yields
2
WHZ
3
4
Mild wasting (WHZ < –1)
0.83**
0.70***
–0.26**
–0.22*
(0.31)
(0.24)
(0.12)
(0.12)
Maternal education (years)
0.18**
–0.03
(0.09)
(0.02)
Paternal education (years)
–0.01
0.01
(0.05)
(0.02)
Households with toilets (%)
0.80**
–0.21*
(0.36)
(0.12)
Prenatal care (%)
Fertility rates
Women can visit clinics (%)
Nongovernmental organization
members (%)
0.29
–0.16
(0.35)
(0.11)
0.13
–0.02
(0.09)
(0.03)
0.11
–0.08
(0.28)
(0.09)
0.26
–0.07
(0.56)
(0.16)
Age controls
Yes
Yes
Yes
Yes
Time effects
Yes
Yes
Yes
Yes
Fixed effects
Yes
Yes
Yes
Yes
R-squared
.28
.44
.33
.40
Sample size
109
109
109
109
Source: Authors’ estimates.
Note: WHZ = Weight for height z scores. Robust standard errors are included in parentheses. All indicators are measured for
children 0 to 24 months of age. *Significant at the 10 percent level. **Significant at the 5 percent level. ***Significant
at the 1 percent level.
Table 5.3 shows that adding time-varying control variables to the models reduces the effects of
yield growth only very slightly. In the case of WHZ scores the coefficient drops from 0.83 to 0.70, while
in the case of mild wasting it falls from –0.26 to –0.22. The results from Table 5.1 therefore appear quite
robust to incorporating some of the alternative explanations of nutritional change in Bangladesh.
Yield-induced improvements in child weight gain suggest that children are given more food or
possibly also better food (since micronutrients can also build up immunity to diseases, which can affect
weight gain). The amount of food given to children is not measured in DHS, though as noted above, we
do have an indicator of the timely introduction of solid or semisolid foods (complementary feeding). We
also have a dietary diversity indicator from the 2000 DHS round onwards, as well as an indicator of dairy
consumption. As we noted above, however, only the complementary feeding indicator has significantly
improved over time. Specifically, the proportion of children in the 4 to 18 month age bracket who were
given any solid or semisolid foods rose from a paltry 42 percent in 1996/1997 to 74 percent in 2011,
while dietary diversity and milk consumption improved marginally up to 2007 but declined significantly
from 2007 to 2011. A priori, it seems unlikely that yield growth has contributed much to the dietary
diversification of children.
12
In Table 5.3 we regress these three indicators against rice yields while controlling for a larger set
of time-varying variables discussed in the previous section. Consistent with expectations we observe a
large and significant effect of rice yields on complementary feeding but no significant effects on dietary
diversity or milk consumption. The results for complementary feeding in regression 1 imply that the 0.5
metric ton per acre increase in yields from 1997 to 2011 increased the prevalence of complementary
feeding by around 10 percentage points, which amounts to about one-third of the actual change observed
during that period. This suggests that—in the past, at least—poor rural Bangladeshi households would
delay the introduction of complementary foods and instead extend their reliance of breast-feeding as the
principal source of nutrition for young children.
Table 5.3 Fixed-effects estimates of the impact of yield growth on child-feeding indicators (in
percentages)
1
Complementary foods
2
Minimum dietary
diversity
3
Dairy consumption
0.21**
–0.04
–0.17
(0.09)
(0.13)
(0.17)
Child age controls
Yes
Yes
Yes
Additional time-varying controls
Yes
Yes
Yes
Time effects
Yes
Yes
Yes
Fixed effects
Yes
Yes
Yes
R-squared
.87
.88
.44
Sample size
109
89
109
Variable
Rice yields
Source: Authors’ estimates.
Note: Robust standard errors are included in parentheses. Complementary feeding is the proportion of children aged 4 to 18
months who were given any solid or semisolid foods in the past 24 hours. Minimum dietary diversity is the proportion of
children given more than four food groups. Dairy consumption refers to milk and yogurt consumption. Additional timevarying controls are maternal education (years), percentage of women with access to prenatal care, fertility rates,
whether women can visit health clinics unaccompanied (percentage), and whether women are nongovernmental
organization members (percentage). **Significant at the 5 percent level.
Finally, Table 5.4 reports associations between regressions of mild wasting and moderate stunting
rates against the three indicators of feeding practices used in Table 5.3, with fixed effects, time period
effects, and child age controls again included. Regression 1 shows that the introduction of complementary
foods is strongly associated with child weight outcomes, with a coefficient of –0.19. A 30-point increase
in complementary feeding—as observed from 1996/1997 to 2011—is associated with a decrease in
wasting of almost 6 percentage points. Minimum dietary diversity and milk consumption have highly
insignificant coefficients in the wasting model (regressions 2 and 3, respectively). In regression 4 we
observe no significant association between complementary feeding and stunting, consistent with the lack
of a significant impact of yields on stunting. However, minimum dietary diversity and dairy consumption
are strongly and negatively associated with stunting rates, with comparable point estimates (–0.29).
13
Table 5.4 Fixed-effects estimates of the associations between child feeding indicators and child
nutrition outcomes
1
Variable
Complementary feeding (%)
2
3
Mild wasting (WHZ < –1)
–0.20**
(0.09)
Minimum diet diversity (%)
4
5
6
Moderate stunting (HAZ < –2)
0.08
(0.14)
0.06
–0.29*
(0.11)
(0.15)
Dairy consumption (%)
0.07
–0.29**
(0.09)
(0.14)
Age controls
Yes
Yes
Yes
Yes
Yes
Yes
Time effects
Yes
Yes
Yes
Yes
Yes
Yes
Fixed effects
Yes
Yes
Yes
Yes
Yes
Yes
R-squared
.28
.27
.26
.31
.24
.36
Sample size
107
88
107
109
89
109
Source: Authors’ estimates.
Note: Robust standard errors are included in parentheses. Complementary feeding is the proportion of children aged 4 to 18
months who were given any solid or semisolid foods in the past 24 hours. Minimum dietary diversity is the proportion of
children given more than four food groups. Dairy consumption refers to milk and yogurt consumption. In these
regressions anthropometrics are also measured for children aged 4 to 18 months. Also note that two outliers were
removed for both the WHZ and HAZ scores. However, the results are qualitatively robust to inclusion of these outliers.
*Significant at the 10 percent level. **Significant at the 5 percent level.
Together, the results in Tables 5.3 and 5.4 suggest that rice productivity growth has significantly
influenced child weight gain through the earlier introduction of solid and semisolid food and perhaps also
greater quantities of calories. However, the insignificant effects of yield gains on linear growth in children
seem related to the limited impacts of these gains on dietary diversification.
Our results are qualitatively robust to inclusion of outliers, to the exclusion of the Chittagong Hill
districts (which have smaller DHS samples and somewhat less dependence on rice production), and to the
inclusion of additional control variables (such as alternative indicators of access to health services).
However, the results in Table 5.2 do not extend to older children aged 24 to 59 months (results available
on request). This likely stems from the widely observed stabilization of anthropometric outcomes from 24
months of age onwards, including—perhaps—reduced sensitivity to household food insecurity for older
children (certainly infants and younger children are especially vulnerable to combinations of poor care
practices, low nutrient intake, and a harmful disease environment). Another explanation may be that older
children are universally given complementary foods, such that substitution between prolonged breastfeeding and solid food consumption (our rationing hypothesis) is not a relevant mechanism linking
agricultural production and weight gain.
Our final set of robustness checks are the Blundell-Bond GMM estimates reported in Tables 5.5
and 5.6. The Blundell-Bond estimates do not change the core conclusions of the fixed-effects models;
specifically, they are never smaller than the fixed-effects estimates. In many cases, they are much larger
in magnitude. The point estimate of the coefficient on yields in the WHZ regression, for example, rises
from 0.83 in the fixed-effects model to 2.55 in the Blundell-Bond model. The analogous point estimates
in the mild wasting model more than double, and unlike the fixed-effects estimate, the impact of yields on
moderate wasting is statistically significant in the Blundell-Bond model. We note, however, that because
these point estimates are more imprecisely measured in the Blundell-Bond model, we cannot reject the
null hypothesis that they equal the fixed-effects estimates.
14
Table 5.5 Assessing the robustness of the impact of yields on child weight: Fixed-effects and
Blundell-Bond generalized methods of moments estimates
1
2
3
4
Mild wasting
(WHZ < –1)
FE
Blundell-Bond
–0.26**
–0.62**
WHZ
Variable
Rice yields
FE
0.83**
Blundell-Bond
2.55***
(0.31)
(0.90)
Lag-dependent variable
R-squared
.28
Sample size
109
(0.12)
(0.30)
5
6
Moderate wasting
(WHZ < –2)
FE
Blundell-Bond
–0.14
–0.47*
(0.14)
(0.27)
–0.82***
–0.46**
–0.25
(0.26)
(0.18)
(0.23)
.33
84
109
.39
84
109
84
Arellano-Bond test
0.8
0.26
0.47
Sargan overid test
0.36
0.47
0.08**
Hansen overid test
0.64
0.84
0.74
Number of instruments
27
27
27
Source: Authors’ estimates.
Note: FE = fixed effects. *Significant at the 10 percent level. **Significant at the 5 percent level. ***Significant at
the 1 percent level.
In Table 5.6, the Blundell-Bond estimate of the effects of yields on complementary feeding is
unchanged though marginally insignificant at the 10 percent level. Unlike the fixed-effects estimates, the
Blundell-Bond estimate of yields on milk consumption is significant and positive, though in this case the
model does not pass the Sargan overidentification test, so we treat this result cautiously. Overall,
however, the Blundell-Bond models suggest that our fixed-effects results are lower-bound estimates of
the impact of rice yields on child weight.
Table 5.6 Assessing the robustness of effects of yields on child-feeding indicators: Fixed-effects and
Blundell-Bond generalized methods of moments estimates (in percentages)
1
2
Complementary feeding
Variable
Rice yields
FE
0.28**
(0.13)
Lag-dependent variable
R-squared
.28
Sample size
109
3
4
Minimum diet diversity
5
6
Dairy consumption
Blundell-Bond
0.27
FE
0.06
Blundell-Bond
0.05
FE
–0.02
Blundell-Bond
0.57*
(0.22)
(0.12)
(0.21)
(0.16)
(0.30)
–0.14*
–0.24
–0.06
(0.08)
(0.16)
(0.13)
.33
84
109
.39
84
109
84
Arellano-Bond test
0.80
0.26
0.47
Sargan overid test
0.36
0.47
0.08**
Hansen overid test
0.64
0.84
0.74
Number of instruments
27
27
27
Source: Authors’ estimates.
Note: FE = fixed effects. Robust standard errors are included in parentheses. Complementary feeding is the proportion of
children aged 4 to 18 months who were given any solid or semisolid foods in the past 24 hours. In these regressions
anthropometrics are also measured for children aged 4 to 18 months. Also note that two outliers were removed for both
the regressions. *Significant at the 10 percent level. **Significant at the 5 percent level.
15
6. CONCLUSIONS
In this paper, we assess the impact of productivity growth in staple food production on preschool
children’s nutritional status. We find that increases in rice yields have large and statistically significant
impacts on child weight gain. The result appears to be explained by increased food consumption for
young children, particularly the timelier introduction of solid and semisolid foods in the critical early
window of child development. Conversely, we find no substantial evidence that growth in rice
productivity has significantly improved linear growth of young children or the dietary diversification
processes that are significantly associated with linear growth. Thus, while the estimated effects of yield
growth on WHZ scores are large enough to account for much of the improvement in weight gain observed
in rural areas of Bangladesh from 1997 to 2011, we find no evidence that yield growth explains much of
the rapid improvement in stunting observed during this period. Of course, this does not mean that no such
effects exist. Rice productivity growth might have had general equilibrium price effects—which our
model is not suited to identifying—that favorably affected linear growth in children. Another possibility
is that rice productivity growth had improvements on maternal nutrition, which heavily determines a
child’s birth size. While there are some indirect indications that birth size improved quite substantially
from 1997 to 2011 (Headey et al. 2015), the DHS do not directly measure birth size, meaning that we are
unable to directly test this hypothesis.
Bearing these caveats in mind, our findings yield some important policy implications. In
particular, while agricultural productivity growth in Bangladesh has made a significant contribution to
alleviating food insecurity and acute undernutrition, its contribution to reducing chronic undernutrition
has been more limited because of the limited effects of rice productivity on the dietary diversification of
young children. A major challenge going forward, then, is to understand the determinants of dietary
diversification in Bangladesh and policy options for promoting diversification. Examples include a
rebalancing of Bangladesh’s agricultural research and development portfolio toward more micronutrientrich crops and livestock products, expansion or improvements in homestead gardening programs
(Iannotti, Cunningham, and Ruel 2009), and more effective use of trade mechanisms to bolster domestic
production with imports. Linking these to improvements in women’s schooling and autonomy, with
improved hygiene, sanitation, and child care practices (particularly around the introduction of more
diverse diets), may well have especially large impacts. Further research, however, is needed in this area.
16
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