1. INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Cows, missing milk markets and
nutrition in rural Ethiopia
John Hoddinott, Derek Headey and Mekdim Dereje

2. Introduction
 In rural areas, is child nutrition affected by which goods
the household produces?
 Conceptually, if there are complete markets, production
and consumption decisions are separable
=> production decisions do not affect consumption
=> agricultural assets & production decisions only affect
nutrition via their contribution to income
 But if markets are missing, this will no longer be true
production decisions can directly affect nutrition
There is an opportunity for agricultural policies to
influence nutrition outcomes directly, not just via income
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3. Introduction, cont’d
 Milk production is an instructive good to consider
 Markets often missing since milk is naturally perishable
• In Ethiopia, 85% of all milk produced is consumed by
the producing household
• Another 7% seems to be trade/bartered locally
• Domestically processed milk only started about a
decade ago and largely confined to urban areas
 Milk is important for growth in early life
• Cow’s milk contains and stimulates insulin-like growth
factor I (IGF-I) which plays a key role in child growth
• Also important source of animal-sourced protein,
amino acids, iron, B-12 and other micronutrients
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4. Introduction, cont’d
 Milk may also be important for other reasons:
1. Relative to solids, it’s easily digestible for young children
2. Dairy cattle often the responsibility of women, so more
maternal control of milk resources than other nutrient-
rich crops
 Factors above may imply more opportunities for snacking
 Milk consumption may also explain a long-noted paradox
in Ethiopia:
some areas of high agricultural potential have relatively
high rates of malnutrition (e.g SNNP); some areas of low
potential have relatively low rates of stunting (e.g. Somali
region) >>> could cow ownership explain this?
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5. Introduction, cont’d
 Nutrition literature finds evidence of milk impacting
linear growth and other nutrition outcomes (Marquis, et al.
1997, McLean, et al. 2007, Murphy & Allen 2003, Neumann et al. 2002, Randolph, et al. 2007)
 But not much evidence on the issue at the farm level:
 Small sample farm surveys from Kenya
(Hoorweg, Leegwater and Veerman 2000, Nicholson, et
al. 2003), and Uganda (Vella, et al. 1995)
 More experimental design from Rwanda (Pimkina et al.
2013) uses cow allocation to HHs in an NGO program
 Sadler et al. (2012) use an RCT with supplementary feed
in pastoralist Ethiopia (during drought)
 Nearly all find some impact on child growth
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6. Data
 Primarily use a very new and large (n=7,930 HHs) baseline
survey for GoE’s Agricultural Growth Programme (AGP)
 Sample is drawn from 94 high agricultural potential
woredas, with 304 villages (EAs) , 2011
 Most of the sample pertains to highlands
 Although “high potential”, there is actually a lot of
variation in agroecologies and market access
 This variation should, in turn, produce the necessary
variation in farm sizes, asset ownership and access to
markets
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7. 7
F1: AGP enumeration areas , major markets and woreda level population density
Source: http://www.gafspfund.org/content/ethiopia. Market towns (light circles) are from FEWSNET, and population density at the woreda level is
from the 2007 National Census of Ethiopia.
Notes: Population density categories (in persons per square kilometer) from lightest to darkest are 0-31, 31-101, 101-139, 139-195, 195-537, 537 and
above.

8. Data
 Data collected on agricultural assets, production, foods
consumed by children under 2 years in previous seven
days, anthropometry of children under 5 years
• Around one quarter of children consumed milk in the
last 7 days;
• one of the most important sources of protein for young
children, in a highly undiversified diet
• About 64% of households own at least one cow
• High level of stunting (47%)
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9. Data
 Cattle and other livestock ownership are also available in
the Demographic Health Surveys (DHS) of 2000 and 2010
 However, 2011 only includes 24 hr recall, and both
surveys don’t distinguish between sex of cattle
 Nevertheless, they are nationally representative high-
quality surveys, so we test robustness to these data
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10. Estimation strategy – very simple
 Outcome variables:
• Did child consume milk in last 7 days
• Number of days child consumed milk in last 7 days
• Anthropometry (HAZ, stunting, WHZ, wasting)
 Use linear probability models and OLS. More complicated
estimators (probit, count models) produce similar results
 Include as controls: child sex and age; care givers
education and age; characteristics of the head (age,
education, sex); region dummy variables; and capital goods
for agriculture - land operated by the household for
cultivation and the ownership of at least one cow.
 Standard errors are clustered at the woreda level.
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11. First some descriptives . . .
11
-2.07
-2.00
-2.03
-1.73
-2.10
-2.00
-1.90
-1.80
-1.70
-1.60
-1.50
No milk 1 to 3 times 4 to 6 times Daily
ChildHazscores
Consumption milk, last 7 days

12. 12
0.55
0.52
0.55
0.45
0.00
0.10
0.20
0.30
0.40
0.50
0.60
No milk 1 to 3 times 4 to 6 times Daily
Probabilityofbeingstunted
Consumption milk, last 7 days

13. Basic results
Household owns at least one cow: Impact on:
(se’s in parentheses)
Marginal impacts on Anthropometry:
6-24m
Anthropometry:
12-24m
Any milk in last 7
days
# milk days last
week
HAZ Stunted HAZ Stunted
0.225*** 1.263*** 0.214* -0.055* 0.324*** -0.099***
(0.024) (0.140) (0.112) (0.028) (0.117) (0.028)
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14. Extensions and robustness checks (1): Including additional controls
Impact of cow ownership on # days milk consumed in last 7 days
1.263*** 1.005*** 1.212*** 0.983*** 1.289*** 0.818***
Other assets N Y N Y Y Y
Ag income N N Y Y Y Y
Village factors N N N N Y Y
Woreda dummy N N N N N Y
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Impact of cow ownership on stunting, children 12-24m
-0.099*** -0.088*** -0.102*** -0.091*** -0.086*** -0.069***
Other assets N Y N Y Y Y
Ag income N N Y Y Y Y
Village factors N N N N Y Y
Woreda dummy N N N N N Y

15. Extensions and robustness checks (2): “Placebo test”
Marginal effect on any cow ownership on other types of food consumption in last
seven days
Pulses Vegetables Leafy
vegetables
Fruit Meat Meat
organs
Eggs
0.002 -0.015 0.022 0.012 0.000 0.003 0.006
(0.027) (0.019) (0.024) (0.018) (0.016) (0.005) (0.023)
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 If cow ownership represents wealth rather than dairy
consumption, we would expect it to have a significant
impact on consumption of other high value foods
 This placebo test suggests no evidence of this

16. Extensions and robustness checks (3): Looking for the missing markets
Food Market in village No food market in village
Any milk
consumption
# days milk
consumed
Stunted
12-24m
Any milk
consumpti
on
# days milk
consumed
Stunted
12-24m
HH owns cow 0.189*** 0.702** 0.114 0.222*** 1.275*** -0.119***
(0.050) (0.309) (0.106) (0.026) (0.152) (0.033)
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Any milk
consumption
# days milk
consumed
HAZ
12 – 24m
Stunted
12-24m
HH owns cow 0.225*** 0.20*** 1.26*** 1.10*** 0.32*** 0.23* -0.099 -
0.08***
(0.024) (0.024) (0.140) (0.126) (0.117) (0.130) (0.023) (0.029)
Village cows - 0.058** - 0.362**
*
- 0.22 -0.032
(log) (0.028) (0.152) (0.150) (0.030)

17. Extension to 2000 Ethiopian DHS
Any milk
consumption
Milk
every day
Stunted
12-24m
Child
HAZ
Stunted
12-24m
Child
HAZ
HH owns
cow
0.20*** 0.20** -0.06*** 0.22***
or camel (0.02) (0.02) (0.02) (0.06)
Milk daily 0.10*** 0.28***
(0.02) (0.06)
Controls
Maternal Yes Yes Yes Yes Yes Yes
Wealth Yes Yes Yes Yes Yes Yes
Region Yes Yes Yes Yes Yes Yes
N 6150 6150 5430 5430 5430 5430
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18. Extension to 2000 Ethiopian DHS
Any milk
consumption
Milk
every day
Stunted
12-24m
Child
HAZ
Stunted
12-24m
Child
HAZ
HH owns
cow
0.20*** 0.20** -0.06*** 0.22***
or camel (0.02) (0.02) (0.02) (0.06)
Milk daily 0.10*** 0.28***
(0.02) (0.06)
Controls
Maternal Yes Yes Yes Yes Yes Yes
Wealth Yes Yes Yes Yes Yes Yes
Region Yes Yes Yes Yes Yes Yes
N 6150 6150 5430 5430 5430 5430
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19. Conclusions
 Ownership of cows is associated with higher milk
consumption by children 6-24m (especially children 12-
24m), improvements in HAZ and reductions in stunting
 Magnitudes of effects are large – reduction in stunting of
approximately 7-10 percentage points
 Impact is much larger than many other variables
 Some evidence that the existence of food markets can
partially substitute for own production
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20. Conclusions
 Need to be cautious; not an experimental study, though
results are robust to a number of checks and alternative
model specifications, and even very different data
 Main concern is that cows are not randomly allocated;
unobservables could be associated with cattle
 Particular concern is that cattle represents wealth
 In future work we are exploring instrumentation
strategies. Conceivably, livestock feed constraints might
impact cow ownership but not nutrition directly
 May also try propensity score matching
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21. Conclusions
 Interesting and important policy implications
 In subsistence settings, ownership of cows at household
level is key, though some externalities to ownership at
village level too
 But ultimately cow ownership may decline with
increasing feed and water constraints, and mechanization
 This decline is substituted, however, by greater market
access
 For GoE and partners, there may be large returns to
increasing investment in dairy sector, which currently
gets a tiny fraction of agricultural budget
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22. Conclusions
 What sorts of investments?
 Ethiopia has large livestock herd, but very little use of
improved breeds
 Yields are very low: about half of neighbouring Kenya
 Modern processing of dairy products very limited and
confined to urban areas
 Potentially, important lessons to be learned from
international dairy success stories, such as India
 Perhaps also potential for dairy supplements, though
these need to target young children
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