metadata: authors: 0: author: Dasgupta,Susmita
1: author: Mustafa,Mohammed Golam
2: author: Paul,Tapas
3: author: Wheeler,David
count: South Asia
docna: 0: docna: The socioeconomics of fish consumption and
            child health in Bangladesh
docty: Policy Research Working Paper
theme: Environmental policies and institutions,Water resource management,Rural services and infrastructure,Climate change,Biodiversity
majtheme: Rural development,Environment and natural resource management
subtopic: Educational Sciences,Health Care Services Industry,Nutrition,Law and Justice Institutions,Reproductive Health,Early Child and Children&#39,Early Child and Children's Health
teratopic: Industry,Health, Nutrition and Population,Education,Law and Development
abstracts: cdata!: Child malnutrition in Bangladesh exceeds
            WHO's threshold for public health emergencies. Using
            more than 36,000 records from several waves of the
            Bangladesh Demographic and Health Survey, the research
            focuses on the socioeconomic determinants of household
            consumption of all animal-source foods; the socioeconomic
            determinants of fish consumption, given its importance in
            the Bangladeshi diet; and the impact of observed consumption
            patterns on mortality and resistance to infectious diseases
            for children in their first years of life. Better maternal
            education and family economic status significantly increase
            the level of animal-source food intake, but they decrease
            the consumption share of fish. This suggests that increased
            income and education impart a "status bias" toward
            eggs and meat, even though they are more expensive and less
            beneficial than fish for child health. In addition,
            mothers' individual preferences for different
            animal-source foods, and the seasonal availability of fish
            during the pre- and post-partum periods have large effects
            on child mortality and significant effects on resistance to
            several common childhood illnesses. These findings highlight
            the importance of programs to increase supply of fish,
            maternal nutrition education and more public health programs
            to promote fish consumption.
repnme: repnme: The socioeconomics of fish consumption and
            child health in Bangladesh
display_title: The socioeconomics of fish
            consumption and child health in Bangladesh
listing_relative_url: /research/2017/10/28077636/socioeconomics-fish-consumption-child-health-bangladesh
topicv3: Infant and Child Health m1326775 642,Nutrition m1326779 644,Education m1327048 470,Mortality m1504365 3661,Health m1504661 3480
publishtoextweb_dt: 2017-10-13T00:00:00Z
historic_topic: Industry,Health, Nutrition and Population,Education,Law and Development
no_of_pages: 33
author_keys: 0: author_key: 95558
1: author_key: 539111
model_extractions: WPS8217 Policy Research Working Paper 8217 The Socioeconomics of Fish Consumption and Child Health in Bangladesh _Susmita Dasgupta Mohammed Golam Mustafa Tapas Paul David Wheeler_ Development Research Group Environment and Energy Team & Environment and Natural Resources Global Practice Group October 2017
Policy Research Working Paper 8217

# **Abstract**

Child malnutrition in Bangladesh exceeds WHO’s threshold for public health emergencies. Using more than 36,000 records from several waves of the Bangladesh Demographic and Health Survey, the research focuses on the socioeconomic determinants of household consumption of all animal-source foods; the socioeconomic determinants of fish consumption, given its importance in the Bangladeshi diet; and the impact of observed consumption patterns on mortality and resistance to infectious diseases for children in their first years of life. Better maternal education and family economic status significantly increase the level of animal-source food intake, but they decrease the consumption share of fish. This suggests that increased income and education impart a “status bias” toward eggs and meat, even though they are more expensive and less beneficial than fish for child health. In addition, mothers’ individual preferences for different animal-source foods, and the seasonal availability of fish during the pre- and post-partum periods have large effects on child mortality and significant effects on resistance to several common childhood illnesses. These findings highlight the importance of programs to increase supply of fish, maternal nutrition education and more public health programs to promote fish consumption.

This paper is a product of the Environment and Energy Team, Development Research Group and the Environment and Natural Resources Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Socioeconomics of Fish Consumption and Child Health in Bangladesh**

Susmita Dasgupta* Lead Environmental Economist, Development Research Group, World Bank, United States Mohammed Golam Mustafa Scientist, World Fish, South Asia Tapas Paul Lead Environmental Specialist, World Bank, India David Wheeler Senior Fellow, World Resources Institute

## **This research was conducted under the South Asia Water Initiative - Sundarbans Landscape.**

* Authors’ names in alphabetical order. Corresponding Author: Susmita Dasgupta. Email: sdasgupta@worldbank.org; Tele phone: 1-202-473-2679; Fax: 1-202-522-2714

We are thankful to Dr. Shakuntala Thilsted and Dr. Randall Brummett for their review of the paper.

**Keywords:** animal-source foods; fish; maternal nutrition; child mortality; child health; Bangladesh

**JEL Codes** : I120; I140; Q220
# **1.  Introduction**

Malnutrition is a primary source of health damage for children in poor countries, accounting for 45% of under-five mortality and 50% of years lived with disability for children aged four and under (Golden et al. 2013). Many children and adults in Bangladesh suffer from malnutrition, which is also responsible for estimated economic losses of around $US one billion per year (Howlader et al. 2012). Bangladeshi fish consumption is critical in this context, because fish are the largest source of high-quality protein (Roos et al. 2007, Kent 1987), as well as micronutrients that are difficult to obtain from plant-based foods in sufficient amounts (Murphy and Allen 2003, Michaelsen et al. 2009, Sandström et al. 1989).

Micronutrient intakes are enhanced by whole consumption of small indigenous fish, which is common for poor households (Bogard et al. 2015b). This is particularly true for young children who eat small meals (Bogard et al. 2015a). Protein from fish and other animals also increases absorption of nutrients from dietary plant consumption (Neumann et al. 2001).

Extensive research has documented the health benefits of fish-intensive diets. Rimm and Mozaffarian (2006) find a 36% reduction in mortality risk from heart disease; Zhao et al. (2015) show that individuals who consume at least 60 grams of fish per day have 12% lower mortality than their counterparts. In a global study, Lim et al. (2012) find that 1.4 million deaths in 2010 can be attributed to deficiency in omega-3 fatty acids from fish. From the same database, Ezzati and Riboli (2013) calculate that diets low in fish and seafood account for about 1% of total disability-adjusted life years (DALYs). The stakes for children are particularly high, since micronutrient deficiencies can lead to increased mortality risk, damaged cognitive development and reduced immunity to disease (Golden et al. 2013).

Fish consumption plays multiple roles in health improvement, including higher concentrations of bioavailable minerals and vitamins; essential fatty acids and animal protein (Bogard et al. 2015b, Wheal et al. 2016); and vitamin B12, found only in animal food sources, which supports physical development, 2
various brain functions, and nervous system maintenance (FAO and WHO 2004). Small fish, commonly consumed by the poor in Bangladesh, are a particularly important source of calcium in diets that are often deficient in milk and milk products (Hansen et al. 1998), as well as zinc and iron (Thilsted et al. 2016). Pre-partum consumption of omega-3 fatty acids from fish is associated with lower risk of pre-term delivery (Imhoff-Kunsch et al. 2012), as well as higher likelihood of children’s normal cognitive development and fine motor skills (Hibbeln et al. 2007).

Several assessments for Bangladesh have established statistical associations between fish market conditions, household socioeconomic characteristics, fish consumption and measures of child undernutrition.<sup>1</sup> In aggregative cross-section and time-series studies, Dey et al. (2010) and Toufique (2015) find that fish consumption is highly responsive to income and fish prices. Grant (2016 - Figures 8.5 and 8.6) finds a higher incidence of stunting in poor households, and for children with less-educated mothers. However, it also finds significant stunting in wealthy households. NIPORT (2016) reports a similar finding: Infant and young child feeding (IYCF) practices improve with maternal education and wealth status, but remain unsatisfactory for 1/3 of children in the highest wealth quintile. After investigating traditional beliefs in very poor households, Choudhury and Ahmed (2011) suggest that improved IYCF practices may require targeted health behavior education.

In summary, extensive nutritional research has established a significant link between fish consumption and child health, and numerous aggregative studies have indicated that both fish consumption and child health vary with maternal education and household economic status. However, even relatively well-educated and wealthy mothers can have deficient IYCF practices. And more precise

> 1 These studies typically use WHO-recommended comparative physical measures based on reference populations or international standards : stunting (low height/length  for  age); wasting (low weight-for-height/length); and malnutrition (circumference of a child's upper arm, or a measure of weight for height).

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impact assessment in Bangladesh has been hindered by the relative scarcity of individual-level multivariate analyses.

In this paper, we attempt a more precise quantification using a household panel database constructed from five Bangladesh Demographic and Health Surveys (2000, 2004, 2007, 2011, 2014). We also revisit the role of maternal beliefs in child nutrition and health first investigated by Choudhury and Ahmed (2011), extending the research to potentially-perverse status beliefs associated with income and higher education.

The remainder of the paper is organized as follows. Section 2 introduces our panel database, with a particular focus on its temporal and spatial components. Sections 3, 4 and 5 develop and estimate models for assessing children’s intake of animal-source foods, the fish share of that intake, and the impact of fish consumption on health status. We summarize and conclude the paper in Section 6.

# **2.  Data and Estimation Strategy**

We construct our panel from georeferenced survey data collected by the Bangladesh Demographic and Health Surveys for 2000, 2004, 2007, 2011 and 2014 (NIPORT 2001, 2005, 2009, 2011, 2016). We include data for 2,263 DHS clusters, 27,827 households and 36,491 individuals, distributed across years as indicated in Table 1.

# **Table 1:  DHS survey statistics by year, 2000-2014**

|**Year**|**DHS**<br>**Clusters**|**Households**|**Individuals**|
|---|---|---|---|
|**2000**|**341**|**4,847**|**6,813**|
|**2004**|**361**|**4,999**|**6,900**|
|**2007**|**361**|**4,626**|**6,150**|
|**2011**|**600**|**6,882**|**8,742**|
|**2014**|**600**|**6,473**|**7,886**|
|**Total**|**2,263**|**27,827**|**36,491**|

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# **2.2 The Temporal Dimension**

De Graff (2003) documents the annual “pulse” of Bangladesh’s inland fisheries, with a sharplyhigher catch during September - December as monsoon floods reach their peak and begin receding. By implication, a complete analysis of fish consumption and child health should incorporate seasonal fluctuations in fish supply. Single-year DHS surveys do not permit this, since they are implemented in a subset of months. However, as Table 2 shows, full monthly effects can be incorporated into the five-year composite panel. Survey periods are November-April (DHS 1999-2000); January-May (DHS 2004); March-August (DHS 2007); July-December (DHS 2011); and June-November (DHS 2014). As the Total column shows, combining the surveys provides ample observations for all 12 months. This permits a full representation of seasonal effects in our exercise, combined with yearly controls to account for annual fluctuations in flood extent.<sup>2</sup>

**Table 2:  Distribution of DHS surveys by month and year**

|||<br>|**Year**<br>||||
|---|---|---|---|---|---|---|
|**Month**|**1999-2000**|**2004**|**2007**|**2011**|**2014**|**Total**|
|**January**|**1,596**|**1,860**|**0**|**0**|**0**|**3,456**|
|**February**|**1,521**|**1,226**|**0**|**0**|**0**|**2,747**|
|**March**|**694**|**1,389**|**479**|**0**|**0**|**2,562**|
|**April**|**3**|**1,416**|**1,501**|**0**|**0**|**2,920**|
|**May**|**0**|**1,009**|**1,448**|**0**|**0**|**2,457**|
|**June**|**0**|**0**|**1,126**|**0**|**127**|**1,253**|
|**July**|**0**|**0**|**1,206**|**1,786**|**1,914**|**4,906**|
|**August**|**0**|**0**|**390**|**1,201**|**2,066**|**3,657**|
|**September**|**0**|**0**|**0**|**1,388**|**2,063**|**3,451**|
|**October**|**0**|**0**|**0**|**1,797**|**1,315**|**3,112**|
|**November**|**1,551**|**0**|**0**|**1,411**|**401**|**3,363**|
|**December**|**1,448**|**0**|**0**|**1,159**|**0**|**2,607**|
|**Total**|**6,813**|**6,900**|**6,150**|**8,742**|**7,886**|**36,491**|

> 2 While the rationale for incorporating monthly effects seems compelling, we acknowledge the possibility of countervailing factors.  For example, recent research has studied the impact of flooding in Dhaka on diarrheal disease (Schwartz et al. 2006) and mortality more generally (Thiele-Eich et al. 2015).  Our assessment of disease incidence in Section 5 controls for this factor.

6
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εi = Random error term

# **3.2 Estimation**

We estimate (1) by probit, using the 13,997 observations with non-missing values for all model variables. Table 3 reports their joint distribution by quintile for household asset ownership and mother’s years of education. The sample populates all cells of the table, although observations are relatively sparse for extreme pairs (e.g. [asset quintile 5, mother’s education none]; [asset quintile 1, mother’s education post-secondary]).

**Table 3:  Intake of** animal-source foods **regression sample, DHS 2000-2014**

|**Household**<br>**Asset**<br>**Quintile**|**0**|**M**<br>**Primary**<br>**(1-6)**|**other’s Y**<br>**Middle**<br>**(7-9)**|**ears of Educ**<br>**Secondary**<br>**(10-12)**|**ation**<br>**Post-**<br>**Secondary**|**Total**|
|---|---|---|---|---|---|---|
|**1**|**1,351**|**749**|**135**|**28**|**7**|**2,270**|
|**2**|**1,736**|**2,623**|**1,260**|**290**|**28**|**5,937**|
|**3**|**404**|**1,106**|**1,092**|**444**|**67**|**3,113**|
|**4**|**128**|**456**|**706**|**496**|**164**|**1,950**|
|**5**|**38**|**85**|**222**|**268**|**114**|**727**|
|**Total**|**3,657**|**5,019**|**3,415**|**1,526**|**380**|**13,997**|

We report our results in Table 4, excluding estimates for the 191 grid cell dummy variables. To prevent total collinearity, our specification drops dummy variables for mother’s education [0 years], household asset quintile 1, survey year 2000 and survey month January. For each category (education, etc.), estimates should therefore be interpreted as increments from partial effects for the excluded variable levels.

We find high significance for the variables in each category. The effect of mother’s education increases steadily by schooling level, while the impact of asset ownership increases rapidly for quintiles 2 and 3 and more slowly for quintiles 4 and 5. We approximate the age effect with a log-cubic 8
**Table 4:  Probit results - child animal-source food intake**<sup>**a**</sup>

|**Mother's Education**|**Parameter**|**Month**|**Parameter**|
|---|---|---|---|
|**(0 Excluded)**|**Estimates**|**(January Excluded)**|**Estimates**|
|**Primary**|**0.153**|**February**|**-0.047**|
||**(4.24)****||**(0.66)**|
|**Middle**|**0.422**|**March**|**0.048**|
||**(10.05)****||**(0.64)**|
|**Secondary**|**0.496**|**April**|**-0.021**|
||**(9.18)****||**(0.27)**|
|**Post-Secondary**|**0.637**|**May**|**-0.045**|
||**(7.02)****||**(0.55)**|
|||**June**|**0.091**|
|**Asset Quintile (Q1 Excluded)**|||**(0.87)**|
|**Q2 **|**0.142**|**July**|**0.176**|
||**(3.09)****||**(2.17)* **|
|**Q3 **|**0.366**|**August**|**0.267**|
||**(6.80)****||**(3.06)****|
|**Q4 **|**0.407**|**September**|**0.361**|
||**(6.42)****||**(3.95)****|
|**Q5 **|**0.415**|**October**|**0.257**|
||**(4.99)****||**(2.82)****|
|||**November**|**0.334**|
|**Log Age **|**-3.331**||**(4.21)****|
||**(8.82)****|**December**|**0.279**|
|**[Log Age]**2**|**3.061**||**(3.34)****|
||**(13.99)****|||
|**[Log Age]**3**|**-0.539**|||
||**(14.12)****|**Constant**|**-3.459**|
||||**(11.39)****|
|**Year (2000 Excluded)**||||
|**2004**|**0.435**|||
||**(7.77)****|||
|**2007**|**0.187**|||
||**(2.76)****|||
|**2011**|**0.108**|||
||**-1.73**|||
|**2014**|**0.145**|||
||**(2.08)* **|||

**a Grid location results excluded for brevity**

**Observations: 13,987 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

9
specification, which indicates that, ceteris paribus, intake probability of animal-source foods increases rapidly after the first months of life and then passes an inflection point toward an asymptote around 24 months. Our monthly results are consistent with significantly-expanded fish supplies in the period September - December (per De Graaf (2003)). Controlling for monthly effects, we find that fish supply was particularly ample in 2004 and somewhat greater than the 2000 supply in the other survey years.

# **3.3 Temporal Impacts:  Mother’s Education and Economic Status**

We use the probit results to predict monthly animal-source food intake probabilities for 23-monthold children in 2014. Figure 2 presents monthly results by mother’s education group for a household in the lowest asset quintile. Figure 3 presents monthly results by asset quintile for a mother with zero years of education.

The figures highlight two particularly-striking features of the results. First, as previous research has suggested, both mother’s education and household economic status have large impacts on the probability of child intake of animal-source foods. For households in the lowest asset quintile (Figure 2), the median difference in intake probability of animal-source foods for children of the most highly-educated and leasteducated mothers is 24%.<sup>4</sup> For mothers with no education (Figure 3), the median difference in intake probability of animal-source foods for children in the lowest and highest asset quintiles is 16%.<sup>5</sup> Table 5 provides evidence on the joint impact of mothers’ education and economic status. In January, child animal-source food intake probability for mothers with no education in the lowest asset quintile is 38%. In contrast, the probability is 78% for mothers with post-secondary education in the highest asset quintile - - an increase of 40%.

> 4 This is the median probability difference over 12 months for mothers with no education and those with post-secondary education.

> 5 This is the median probability difference over 12 months for mothers with no education in the lowest-asset quintile and those in the highest-asset quintile.

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**Table 5:  Mother’s education, household asset ownership and child animal-source food intake probability**<sup>**a**</sup>

|**Household**<br>**Asset**<br>**Quintile**|**0**|**Mothe**<br>**Primary**<br>**(1-6)**|**r’s Educat**<br>**Middle**<br>**(7-9)**|**ion (Years)**<br>**Secondary**<br>**(10-12)**|**Post-**<br>**Secondary**|
|---|---|---|---|---|---|
|**1**|**0.38**|**0.44**|**0.55**|**0.58**|**0.63**|
|**2**|**0.44**|**0.50**|**0.61**|**0.63**|**0.69**|
|**3**|**0.53**|**0.59**|**0.69**|**0.72**|**0.76**|
|**4**|**0.55**|**0.60**|**0.70**|**0.73**|**0.77**|
|**5**|**0.55**|**0.61**|**0.71**|**0.73**|**0.78**|

# **a Predictions computed for January**

The second striking feature of the results is the seasonal impact of fish supply on child nutrition. Table 6 displays predicted animal-source food intake probabilities by education and asset group for September, a peak catch month, and February, after the floods have receded. For mothers with no education, children’s September animal-source food intake probability in the lowest asset quintile (53%) matches the February probability for children in the highest asset quintile. In the lowest asset quintile, children’s September intake probability for mothers with no education (53%) matches the February probability for mothers with middle school education (7-9 years). Taken together, the results in Table 5 highlight the critical importance of expanded fish supply for children’s animal-source food intake in the poorest, least-educated households.

**Table 6: Economic and education status vs** animal-source food **intake probability: Seasonal comparison**

|||**Mother**<br>**Hous**<br>|**’s Educati**<br>**ehold Asse**<br>|**on: 0 Years**<br>**t Quintile**<br>||
|---|---|---|---|---|---|
|**Month**|**1**|**2**|**3**|**4**|**5**|
|**February**|**0.37**|**0.42**|**0.51**|**0.53**|**0.53**|
|**September**|**0.53**|**0.58**|**0.67**|**0.68**|**0.69**|
||**Mo**|**ther’s Hous**<br>**Mothe**<br>|**ehold Asse**<br>**r’s Educat**<br>|**t Status: Qui**<br>**ion (Years)**<br>|**ntile 1**|
|**Month**|**0**|**Primary**<br>**(1-6)**|**Middle**<br>**(7-9)**|**Secondary**<br>**(10-12)**|**Post-**<br>**Secondary**|

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<!-- Start of picture text --> 5 0.40 |<br>& 035 ;— a E ee eS<br>Fal= 0.30 + ——p90p<br>0.25 - p95<br>012 3 4 5 6 7 8 9 10 11 12 13<br>Month<br><!-- End of picture text -->
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# **4.  The Share of Fish in Consumption of Animal-Source Foods**

All five DHS surveys ask mothers whether, during the previous 24 hours, their children have consumed any animal-source food -- eggs, poultry, fish, and other meats. In Section 3, we have used the full data set to explore the determinants of total consumption of animal-source foods. In this section, we extend the analysis to fish consumption using the source-specific information in DHS 2011 and 2014.

As Table 7 shows, understanding the determinants of fish consumption is particularly important for the poor in Bangladesh. The table summarizes DHS survey responses on children’s consumption of fish, eggs and meat during the 24 hours prior to the survey date. For each child, we code each animal-source food as 1 if it was consumed and 0 otherwise. We focus on cases with positive consumption (total coded value > 0) and compute the fish share of animal sources by dividing the fish consumption code [0,1] by total coded value. The computed ratio can be interpreted as the share of fish in animal-source foods, conditional on non-zero consumption.

Table 7 reports mean and median shares by asset quintile. The results highlight the particular importance of fish as a animal-source food for the poorest children. In household asset quintile 1, the mean and median fish shares are 66.7% and 100%, respectively.

# **Table  7:  Share of fish in children’s animal-source foods by household asset class**

|**Asset**<br>**Quintile**|**Mean**<br>**Fish Share**<br>**(%)**|**Median**<br>**Fish Share**<br>**(%)**|
|---|---|---|
|**1**|**66.7**|**100.0**|
|**2**|**57.5**|**50.0**|
|**3**|**52.6**|**50.0**|
|**4**|**41.3**|**50.0**|
|**5**|**35.0**|**33.3**|

15
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Taken together, the seasonal results in Tables 4 and 8 suggest that children’s fish intake, and therefore animal-source food intake, increase with the pulse in fish supply during September - December.

And, as Tables 6 and 7 show, this increase is particularly important for poor households. Figures 6 and 7 highlight two important facets of these results: the impact of the peak-season fish catch (Figure 6) and the joint impact of economic status and mother’s education (Figure 7).

**Table 8:  Fractional logit results - share of fish in animal-source foods**<sup>**a**</sup>

|**Mother's**<br>**Education**<br>**(0 Excluded)**|**Parameter**<br>**Estimates**|**Month**<br>**(June Excluded)**|**Parameter**<br>**Estimates**|
|---|---|---|---|
|**Primary**|**-0.319**|**July**|**1.385**|
||**(2.71)****||**(3.14)****|
|**Middle**|**-0.518**|**August**|**1.815**|
||**(4.19)****||**(4.15)****|
|**Secondary**|**-0.726**|**September**|**1.777**|
||**(5.23)****||**(4.04)****|
|**Post-Secondary**|**-1.053**|**October**|**1.899**|
||**(5.28)****||**(4.24)****|
|||**November**|**1.565**|
|**Asset Quintile**<br>**(Q1 Excluded)**|||**(3.48)****|
|**Q2 **|**-0.666**|**December**|**1.48**|
||**(2.14)* **||**(3.22)****|
|**Q3 **|**-0.707**|||
||**(2.25)* **|**Constant**|**-0.308**|
|**Q4 **|**-1.081**||**(0.46)**|
||**(3.38)****|||
|**Q5 **|**-1.175**|||
||**(3.54)****|||
|**Year**<br>**(2011 Excluded)**||||
|**2014**|**-0.203**|||
||**(2.59)****|||

**a Grid location results excluded for brevity**

**Observations:  3,201 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

17
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extensive research has shown that fish consumption conveys more health benefits than consumption of other animal sources. Nevertheless, our results for Bangladesh clearly indicate a “status bias” favoring non-fish animal-source food consumption as mothers’ education and household economic status increase. The education result seems particularly unfortunate, with two possible interpretations: Either current school instruction pays little attention to the health benefits of fish consumption, or the effect of this instruction is overwhelmed by the higher status assigned to non-fish protein sources. In any case, our results for both education and economic status suggest a powerful and perverse “status goods” effect on fish consumption. We believe that these findings have potentially-important implications for health education policy in Bangladesh.

# **5.  Child Health Impacts**

Using DHS 2011 and 2014, we investigate the incremental health impacts of consuming fish and other animal-source foods (eggs, meat) using an estimation model that includes the following elements: (i) Secular declines in mortality and morbidity risk from improved public health services.

(ii) The socioeconomic, temporal and spatial determinants of consumption of animal-source foods incorporated in models (1) and (2): mother’s education; household economic status, seasonal variations in fish supply, and spatial variations in fish supply and consumption norms. After incorporating these variables (and gender) into the estimation exercise, we also include the animal-food sources themselves. Ceteris paribus, the direct estimates for these sources can be interpreted as indicators of individual preferences. Our approach assumes that these preferences are constant through time.

(iii) Maternal fish consumption in the immediate pre-partum and post-partum periods. Although we do not have direct evidence<sup>9</sup> , basic economics suggests that mothers’ fish consumption should also

> 9 We are unable to include direct survey information on maternal fish consumption because the relevant questions were excluded from DHS 2011 and 2014.

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value and fail to meet the standard criteria for statistical significance. Per our prior expectation, we also find a large, negative, highly-significant impact for birth during the peak-catch period.

We find that female children have lower mortality rates than male children, ceteris paribus.

Mother’s education, family asset ownership and child size at birth do not have significant effects. Reported child deaths yield a higher mortality rate for the 2014 survey, but survey month has no impact on the results. In contrast, the birth-year results are strong and declining as public health services improve.

To assess their quantitative significance, we use our probit results to estimate differential mortality rates within a centrally-located grid square with typical population density (Figure 8, square outlined in light blue). We perform the prediction for a mother with no education who resides in a household whose asset count is zero; the mean child size at birth for this education and asset ownership group; survey year 2014; birth year 2012; the first six survey months (dummy values 0 for the survey months included in the result); and zero consumption preference for eggs and meat.

We produce four predictions: with and without fish consumption preference and peak-catch-season birth, respectively. Table 10 presents our results, which suggest substantial impacts. The estimated mortality rate is 72.3 per 1,000 for children born in this locale during the off-peak-catch season to mothers with no fish preference (beyond the structurally-determined factors, per model (1)). With no change in fish preference, the estimated mortality rate declines to 32.4 per 1,000 for a child born during the peakcatch season. In contrast, children born to mothers with fish preference have predicted mortality rates of 31.5 per 1,000 in the off-peak season and 12.3 in the peak-catch season.

In summary, our results suggest a remarkable 83% decrease in the child mortality rate (from 72.3 to 12.3) produced by two elements related to fish consumption. We believe that this should be viewed as an outer-bound estimate, because our interpretation depends on the following assumptions: (1) the fish consumption result reflects individual preferences, not other factors that cannot be observed; 21
**Table 9:  Probit results - child mortality rate**<sup>**a**</sup>

|**Protein Intake**|**Parameter**<br>**Estimates**|**Survey Month**|**Parameter**<br>**Estimates**|
|---|---|---|---|
|**Fish**|**-0.400**|**July**|**0.12**|
||**(3.62)****||**(0.24)**|
|**Eggs **|**-0.186**|**August**|**-0.022**|
||**(1.44)**||**(0.04)**|
|**Meat**|**-0.244**|**September**|**0.173**|
||**(1.48)**||**(0.33)**|
|||**October**|**0.217**|
|**Characteristics**|||**(0.41)**|
|**Female**|**-0.278**|**November**|**0.262**|
||**(3.10)****||**(0.48)**|
|**Mother’s Education**|**0.021**|**December**|**0.576**|
||**(1.45)**||**(1.01)**|
|**Household Assets**|**-0.021**|||
||**(0.69)**|**Survey Year 2014**|**2.809**|
|**Size at Birth**|**0.081**||**(7.98)****|
||**(1.42)**|||
|||**Birth Year**||
|**Peak-Catch Birth**|**-0.388**|**2006**|**5.347**|
||**(3.96)****||**(9.33)****|
|||**2007**|**5.032**|
|**Constant**|**-6.389**||**(9.54)****|
||**(6.92)****|**2008**|**5.081**|
||||**(9.66)****|
|||**2009**|**4.806**|
||||**(9.01)****|
|||**2010**|**3.963**|
||||**(7.63)****|
|||**2011**|**2.927**|
||||**(6.71)****|
|||**2012**|**2.127**|
||||**(5.47)****|
|||**2013**|**0.836**|
||||**(2.15)* **|

# **a Grid location results excluded for brevity**

**Observations:  4,761 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

22
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# **5.2 Childhood Illness**

We also consider the impact of animal-source food intake on the incidence of childhood illness, as reported in the DHS surveys. Our exercise tests the impact of consumption of animal-source foods, particularly fish, on resistance to common childhood infections. We focus on the reported occurrence of diarrhea, fever and coughing immediately before the survey date.

As before, we consider the roles of fish consumption preference and fish supply during the birth period, after controlling for the socioeconomic, temporal and spatial determinants of fish intake during the survey period. We perform probit estimation on a stacked panel data set drawn from DHS 2011 and 2014. The dependent variable is the occurrence of illness, defined as diarrhea, fever or coughing. Our independent variables replicate those in the child mortality model (Table 9). We also introduce dummy variables to allow for variation in mean incidence rates across illness categories.

Our results (Table 11) indicate that child health status is strongly affected by socioeconomic status: Both mother’s education and household asset ownership have negative, highly-significant effects on illness incidence. Our results also suggest that female children have lower illness incidence, ceteris paribus. After controlling for the socioeconomic, temporal and spatial determinants of consumption of animal-source foods, we find that fish consumption preference has a negative, significant impact on illness. As before (Table 9), we find no significant impact for the intake of other animal-source foods. And again, we find a lower incidence rate for children born during the peak-catch period (September - December).

To assess the quantitative significance of these results, we replicate our simulation strategy for child mortality. We use the probit results reported in Table 11 to predict incidence rates for children with birth year 2012 whose mothers reside in the grid square identified in Figure 8; have no formal education; belong to a household with zero tabulated assets; and have zero consumption preference for eggs and 24
meat. We produce four predictions: with and without fish consumption preference and peak-catch season birth, respectively. Table 12 presents the results.

**Table 11:  Probit results - child illness rate**<sup>**a**</sup>

|**Protein Intake**|**Parameter**<br>**Estimates**|**Survey Month**|**Parameter**<br>**Estimates**|
|---|---|---|---|
|**Fish**|**-0.066**|**July**|**-0.18**|
||**(2.48)* **||**(1.46)**|
|**Eggs **|**0.005**|**August**|**-0.303**|
||**(0.19)**||**(2.45)* **|
|**Meat**|**-0.042**|**September**|**-0.218**|
||**(1.17)**||**(1.74)**|
|||**October**|**-0.147**|
|**Characteristics**|||**(1.16)**|
|**Female**|**-0.084**|**November**|**-0.283**|
||**(3.86)****||**(2.19)* **|
|**Mother’s Education**|**-0.018**|**December**|**-0.483**|
||**(5.26)****||**(3.53)****|
|**Household Assets**|**-0.016**|||
||**(2.09)* **|**Birth Year**||
|||**2009**|**0.241**|
|**Peak-Catch Birth**|**-0.071**||**(3.82)****|
||**(2.63)****|**2010**|**0.292**|
||||**(7.19)****|
|**Constant**|**0.036**|**2011**|**0.146**|
||**(0.19)**||**(3.65)****|
|**Diarrhea Adjustment**|**-1.27**|**2012**|**0.093**|
||**(41.54)****||**(1.57)**|
|**Fever Adjustment**|**0.084**|**2013**|**0.177**|
||**(3.60)****||**(4.60)****|

**a Grid location results excluded for brevity**

**Observations:  18,008 Absolute value of t statistics in parentheses [* significant at 5%; ** significant at 1%]**

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# **Table 12:  Child illness incidence by fish preference and birth season (%)**

||**Birth**|**Month**||
|---|---|---|---|
||**Off-**<br>**Peak**|**Peak-**<br>**Catch**|**Max**<br>**Change**<br>**(%)**|
|**Diarrhea**||||
|**Fish**<br>**Preference**||||
|**0**|**8.7**|**7.6**||
|**1**|**7.7**|**6.7**|**-23.0%**|
|**Fever**||||
|**Fish**<br>**Preference**||||
|**0**|**49.8**|**46.9**||
|**1**|**47.1**|**44.3**|**-11.0%**|
|**Cough**||||
|**Fish**<br>**Preference**||||
|**0**|**46.4**|**43.6**||
|**1**|**43.8**|**41.0**|**-11,6%**|

For diarrhea, the predicted incidence rate is 8.7% for children born during the off-peak season to mothers with no fish preference (beyond the structurally-determined factors, per model (1)). With no change in fish preference, the rate declines to 7.6% for a child born during the peak-catch season. For mothers with fish preference, the incidence rates are 7.7% and 6.7% respectively for children born in the off-peak and peak-catch seasons. In comparison with the worst case (no fish preference, off-peak birth), the best case (fish preference, peak-catch birth) has an incidence rate 23.7% lower.

Fever and cough have much higher mean incidence rates than diarrhea, but similar patterns of variation with fish preference and season of birth. From worst to best case, the incidence rates for fever and cough fall by 11.0% and 11.6% respectively.

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# **6.  Summary and Conclusions**

In this paper, we have analyzed 36,491 case histories drawn from individual and household records in the Bangladesh Demographic and Health Surveys for 2000, 2004, 2007, 2011 and 2014. We address three related questions: What are the socioeconomic determinants of consumption of animal-source foods? Among animal-source foods, what are the socioeconomic determinants of the fish share? What are the impacts of consumption of animal-source foods (fish, meat, eggs) on child mortality and resistance to infectious disease? The paper focuses particularly on fish consumption and its health effects for children in their first years of life. Critical factors examined include maternal education, family economic status, child gender, and temporal/spatial variations in fish supply.

We find that maternal education and family economic status are highly-significant determinants of animal-source food intake, along with annual, seasonal and local-area fish supply conditions. After controlling for these factors, we find that children’s animal-source food intake is gender-neutral. Our results for the fish share of animal-source food consumption assign significant roles to family economic status, maternal education and seasonal and spatial variations in fish supply. The importance of fish supply for poverty alleviation is highlighted by another finding: The share of fish in consumption of animal-source foods is highest for the poorest households, and declines significantly with income. By implication, programs that increase fish supply and lower fish prices will have a significant impact on intake of animal-source foods, particularly for the poorest households.

We also find a highly-significant, negative relationship between maternal education and the share of fish in animal-source food consumption. In the lowest income groups, mothers shift strongly toward meat and eggs as their education level increases, suggesting a perverse “status bias” against fish despite their lower cost and health advantages.

27
We assess the implications for child health by estimating the impact of fish consumption on the child mortality rate and the incidence of three common disease symptoms: diarrhea, fever and coughing. After controlling for socioeconomic, temporal and spatial determinants of animal-source foods and fish consumption, we focus on two additional factors: (1) mothers’ individual preference for fish consumption; and (2) maternal fish consumption during the immediate pre- and post-partum periods. Our results suggest that both factors have large effects on child mortality risk and significant effects on childhood disease symptoms.<sup>10</sup> In summary, our results highlight the critical role played by fish supply in maternal and child animal-source food consumption, the fish share of that consumption, and children’s health status in Bangladesh. They suggest that programs which promote fish supplies during the off-peak season may significantly improve child health outcomes, particularly in the poorest, least-educated households. Even for those households, however, we find that schooling has a strong and perverse “status bias” effect: As mothers’ formal education increases, they shift away from fish toward other sources of animal-source foods that are both more costly and less healthy. This suggests a potentially-important role for public education about the benefits of fish as an animal-source food.

> 10 Identification of components in fish with desired effects on child nutrition and health is a subject for future research.

28