Market access and nutrition smoothing: Access to towns and cities protects farm children against...

Market access and nutrition smoothing: Access to towns and cities protects farm childrenagainst poor health conditions at birth in the DRC

William A. Mastersab

http://sites.tufts.edu/willmasters

Amelia F. Darrouzet-Nardia

http://sites.tufts.edu/ameliadarrouzetnardi

a Friedman School of Nutrition Science and Policyb Department of Economics (by courtesy)

Tufts University

Seminar at the Delhi School of Economics3 December 2014

http://sites.tufts.edu/willmasters

http://sites.tufts.edu/ameliadarrouzetnardi/

http://sites.tufts.edu/ameliadarrouzetnardi/

How important is access to towns and cities for child nutrition and health on farms?

1. Access to markets could improve or worsen child nutrition :– could raise purchasing power, but also raise cost of caregivers’ time – and alter relative prices of nutritious foods

2. Market access also changes the ag-nutrition relationship– can separate decision-making between farm and household,– and creates opportunities for consumption smoothing

3. This paper focuses on resilience to seasonal health shocks– loosely inspired by Burgess & Donaldson (2010), "Can Openness Mitigate

the Effects of Weather Shocks? Evidence from India's Famine Era." American Economic Review, 100(2): 449-53.

– farmers’ vulnerability to shocks may be increasingly important over time– towns and cities offer diverse channels for consumption smoothing

• labor markets, migration and remittances• product and asset markets• public services and insurance networks

Market access and farm household nutrition motivation | method | results | robustness

This paper is about separability

Qty. of nutritious foods (kg/yr)

Qty. of farm household’s labor time (hrs/yr)

Qty. of farm household’s other goods (kg/yr)

Other employment(allows sale of labor to buy food)

Can towns and cities help rural childrenovercome shocks to nutrition and health production?

Qty. of nutritious foods(kg/yr)

Once farmers are actively trading, production decisions are “separable”

from consumption choices,linked only through purchasing power

Rural food markets(allows sale of other goods to buy food)

In self-sufficiency, production =consumption

Consumption

Production

Consumption

Production

That same separability applies whether households are buying or selling,

and allows consumption smoothing over time


• Farmers’ access to towns and cities is a major focus for public investment and is increasing rapidly around the world, but causal impact is unclear– Markets arise and grow where people have something to sell– And people who have things to sell move towards markets

• How might one identify (some of) urbanization’s effects on rural people?– Randomized trials isolate specific interventions, and cannot reveal combined

effects of transport, communications, investment and trade– Many surveys occur around natural experiments, but access to towns and cities

varies only slowly and predictably• Here, we focus on spatial and temporal variation in seasonal risks

– Our natural experiment is the timing of conception and birth• Relative to spatial and temporal variation in weather shocks• In a country that offers “placebo” regions with little seasonality


…and about identification strategy

What can cross-sectional survey data reveal about nutrition and health behavior?

Birth timing relative to seasonal variation creates a recurring natural experiment

• The “treatment” is having the worse season (if there is one) occur during the period of greatest vulnerability– late pregnancy and early infancy are highly sensitive for child growth– wet seasons often bring both hunger and disease exposure

• Market access may be protective– Households can trade to smooth consumption– Households can access health and other services

• We expect less effect of birth timing, and less protection from market access, in regions with less seasonal fluctuation in rural conditions


The D.R. Congo is the size of India, but much poorer


Source: http://www.ifitweremyhome.com/compare/IN/CD

The D.R. Congo has low density and straddles the equator


equator

Towns and cities depend on mining etc.; seasonality depends on latitude

Within each cross-sectional survey, we have a triple difference-in-difference design

Household location and child birth timing

Region has a distinct wet season? (= farther from the equator)

Yes No (placebo region)

Child was born in or after wet season? (=Jan.-Jun. if lat.<0, Jul.-Dec. otherwise)

Yes(at risk)

No(control) Yes No

Household is closer to town? (=closer to major town)

Yes(protected?) No Yes No Yes No Yes No

Hypothesized effect of birth timing: Neg. None

Note: To test our hypothesis that market access protects against seasonality, the identifying assumptions are that birth timing occurs randomly between seasons (tested), and that seasonal risk factors would have been similar in the absence of towns (untestable).


Seasons depend on rainfall and temperature

equator

At the equator, average monthly rainfall fluctuates from 100 to 200 mm, and average monthly temperature fluctuates from 24 to 26 degrees Celsius.


“Winter” is a drier period, farther from the equator

equator

Away from the equator, there is a drier, colder winter, here May through August.

Latitude -6


In the other hemisphere, winter is 6 months later

equator

Here in the Northern Hemisphere, the drier season occurs from November through February.

Latitude +4


The data are... • Demographic and Health Surveys (DHS), in 2007 and 2013, for

– Height and weight of the index child (N=8,435 children)– Mortality of children ever born to the respondent (N=69,641 births)m which

permits us to control for mother fixed effects– Demographic controls (age, sex, whether firstborn or a short birth interval)– Wealth quintile (relative to other DHS respondents)

• The Armed Conflict Location and Event Dataset (ACLED) for– Exposure to armed conflict near the child’s home during their birth year

• The FAO’s Multipurpose Africover Database on Environmental Resources, for

– Proximity to the nearest of 160 towns and cities– Latitude (and hence exposure to seasonality)



Birth timing:

Presence of seasons:

Jan.-June

No

N=18,009

Jan.-June

Yes

N=18,973

July-Dec.

No

N=16,724

July-Dec.

Yes

N=15,935

All Births

N=69,641

Child status

Children Alive (%) 84.6% 84.5% 83.7% 85.2% 84.5%

HAZ -1.51 (1.68) -1.51 (1.62) -1.61 (1.92) -1.26 (1.80) -1.47 (1.86)

WHZ -0.31 (1.25) -0.47 (1.12) -0.24 (1.41) -0.45 (1.31) -0.38 (1.33)

Age (months) 28.24 (17.57) 28.00 (17.29)

29.70 (17.10)

29.88 (16.69)

29.16 (16.53)

Firstborn (%) 23.8% 24.9% 23.8% 23.5% 24.5%

Short interval (%) 28.2% 27.9% 26.1% 19.74% 25.6%

Boys (%) 50.5% 51.2% 50.4% 50.2% 50.6%

Household

Wealth (quintile) 2.61 (1.27) 3.20 (1.46) 2.60 (1.26) 3.25 (1.45) 2.92 (1.40)

Proximity (km-1) 0.11 (0.23) 0.16 (0.27) 0.10 (0.23) 0.15 (0.27) 0.13 (0.26)

Environment

Conflicts 108.72 (716.5)

15.03 (65.7) 93.52 (596.8)

15.95 (69.7) 31.28 (66.9)

Latitude (abs val) 1.91 (1.36) 6.14 (2.01) 1.98 (1.17) 5.99 (2.02) 4.31 (2.64)

We split the population into groups by risk exposure

Note: Data shown are means and standard deviations (in parentheses). Births labeled as Jan.-June occurred in calendar months July-December for children born in the Northern hemisphere (N=418). Conflicts are number of fatalities during the child’s birth year in the respondent’s 1-degree square grid-cell of residence.


(1) (2) (3)Variables Unit/type Child is alive HAZ WHZ

Age spline 1 Linear spline -0.017*** -0.074** -0.107***Age spline 2 Linear spline -0.002** -0.072*** 0.011***Age spline 3 Linear spline -0.006 Child is male Binary -0.115* -0.133** -0.108**Child is firstborn Binary -0.288*** 0.021 -0.026Short preceding birth interval Binary -0.594*** -0.148* -0.020Ln(fatalities during birth year) Continuous -0.062*** -0.114*** 0.031**Household Wealth index Categorical 0.145*** 0.250*** 0.053***Absolute value (latitude) Continuous -0.046*** -0.015 -0.017Proximity to town km-1 0.281** -0.022 0.162Born Jan.-June Binary 0.134** -0.107 0.075Constant Constant 2.940*** -0.256 0.407***Observations N 18,845 3,405 3,473R2 R2 0.179 0.073

An exploratory regression with continuous variables describes the relationships between them


(1) (2) (3)Variable Unit/type Child is alive HAZ WHZ Age spline 1 Linear spline -0.016*** -0.080*** -0.100***Age spline 2 Linear spline -0.002*** -0.067*** 0.010***Age spline 3 Linear spline -0.009*** Short preceding birth interval Binary -0.510*** -0.187*** -0.039Child is male Binary -0.149*** -0.164*** -0.116***Ln(fatalities during birth year) Continuous -0.057*** -0.087*** 0.018Proximity to town km-1 0.744*** 0.369 0.144Born Jan.-June Binary 0.080 -0.097 -0.022Absolute value(latitude) Continuous -0.004 0.045*** -0.019Born Jan.-June*Proximity Interaction 0.104 0.877** 0.232Born Jan.-June*Abs(lat) Interaction -0.002 0.018 0.007Abs(lat)*Proximity Interaction -0.053 0.038 -0.014Born Jan.-June*Proximity*Abs(lat) Interaction -0.021 -0.201*** -0.000Constant Constant 3.081*** 0.200 0.627***Observations N 18,845 3,405 3,473R2 R2 0.144 0.056

Splitting each variable into categories, we can run a triple difference-in-difference regression

Notes: The linear age splines are actually ‘time elapsed in months since birth’ for the mortality regressions. Age splines control for child’s age at observation. Born Jan.-June is actually born July-Dec. in Northern hemisphere to account for inversion of seasons at the equator. Age splines control for child’s age at observation. Conflicts are the cumulative count in the child’s cluster of residence during the child’s birth year. Errors clustered by DHS survey cluster (v001), * p<.10, ** p<.05, *** p<.01.


Our preferred specification is to split the sample, taking advantage of relatively large sample size

(1) (2) (3) (4) (5) (6)Variable Unit/type Alive

SeasonsAlive

No SeasonsHAZ

SeasonsHAZ

No SeasonsWHZ

SeasonsWHZ

No Seasons

Age spline 1 Spline -0.021*** -0.022*** -0.051 -0.135*** -0.098*** -0.101***Age spline 2 Spline -0.003*** -0.002*** -0.086*** -0.090*** 0.010*** 0.012***Age spline 3 Spline -0.005 -0.003 Short interval Binary -0.284*** -0.302*** -0.385*** -0.449*** -0.172*** -0.062Male Binary -0.117*** -0.126*** -0.029 -0.293*** -0.104* -0.038Conflict exposed Binary -0.043 0.036 0.139 0.249** -0.074 -0.062Jan.-June Binary -0.127** 0.079 -0.097 0.063 0.051 -0.093Jan.-June*Remote Interaction 0.128* -0.025 -0.329** -0.188 -0.034 0.132

Constant Constant 0.158 0.537** 0.524*** 0.624***Observations N 17217 17297 4224 4211 4312 4319R2 R2 0.290 0.299 0.083 0.077

Note: The mortality tests (col. 1 and 2) include mother fixed effects, and the linear age splines are actually time elapsed since birth, in months. Born Jan.-June is actually born July-Dec. in Northern hemisphere to account for inversion of seasons at the equator. Age splines control for child’s age at observation. Conflict exposure is a binary indicator of whether there was civil conflict in a 1-degree square of the child’s residence during the child’s year of birth. Errors clustered by DHS-cluster (v001), * p<.10, ** p<.05, *** p<.01


jan feb mar apr may jun jul aug sep oct nov dec0

1000

2000

3000

4000

5000

6000

7000

8000

total births, by calendar month total births, shifted for hemispheres

nu

mb

er o

f ch

ildre

n b

orn

tota

l: N

=69

,641

Note: Data shown are the number of children ever born in each month, as recorded across each DHS survey in the DRC. The solid line refers to calendar months, and the dashed line uses a seasonal adjustment by hemisphere, where dates north of the equator are recorded as “January” for births in June, “February” for July, etc. In our regressions, these “rain months” are aggregated into six-month periods, since as children in higher latitudes who are born in the January-June period are more exposed to heavy rains and subsequently poor health outcomes than those born in the rest of the year.

Could the correlations we see be driven by selection into healthier birth timing?

This turns out to be the less

healthy season in which to be

born, suggesting no attempt at

selection into healthier timing of

conception and birth

Factors other than the health of the

child must be driving seasonality in

conception and birth


Note: Dependent variable is a binary indicator of birth during the Jan.-June wet season. Regression estimated using fixed-effects logit. All results include fixed effects for survey clusters (N=840), with notation and variable definitions as in Table 6. p-values in parentheses ; * p<.10, ** p<.05, *** p<.01.

Could the correlations we see be driven by selection into healthier birth timing?

(1) (2) (3)Variable Units/type Born Jan.-June Born Jan.-June

SeasonsBorn Jan.- June

No seasons

Child is Male Binary 0.009 0.023 0.005 (0.762) (0.632) (0.895)Wealth index Categorical -0.015 -0.057 0.002 (0.384) (0.106) (0.919)Ln(fatalities) Continuous 0.014 0.003 0.018 (0.125) (0.830) (0.152)Proximity to town km-1 0.319* 0.538 -0.047 (0.069) (0.227) (0.875)Abs val (latitude) Continuous 0.021 (0.138) Observations 18804 7060 11728

The only correlation we see is with proximity to town,

e.g. from a seasonal migration effect


Child is

Aliv

e*

HAZ**W

HZ

moth

er's e

ducatio

n (yrs)

moth

er's h

eight

(

m)

father'

s educa

tion (y

rs)

years

lived

in in

terview

loca

tion

altitu

de of c

luste

r (m

)-1.5

-0.5

0.5

1.5

In areas with seasons: estimated average treatment effects for various "placebo" dependent variables compared with Mortality, HAZ, and WHZ estimates

Note: Data shown are coefficient estimates and 95% confidence intervals for “average treatment effects” in our preferred specification (Table 5), for our three dependent variables of interest followed by five ‘placebo’ variables for which no effect is expected of our ‘treatment’, due to the absence of any plausible mechanism of action.

Among our robustness checks, we do “placebo” tests for desirable outcomes that could not be caused by birth timing

Hypothesized effects on

survival, heights, weights No significance where no

effect is expected

Conclusions and implications

• In the DRC, farm households that are closer to towns use it to protect themselves from seasonal shocks to nutritional status

• Possible mechanisms underlying this effect include:– Specialization and trade, to overcome diminishing returns on the farm– Consumption smoothing, via separability of production & consumption– Access to public services

• Future work may be able to distinguish among mechanisms– But all of them provide opportunity for farm households to exploit or respond to

their own idiosyncratic, diverse circumstances– Policies and programs based on markets cannot prescribe what households will do,

only that they can do it more easily!


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Market access and nutrition smoothing: Access to towns and cities protects farm children against...

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