Hidden Hidden Hunger:
Invisible Heterogeneity in Soil Zinc,
Crop Zinc, and Zinc Deficiency in Rural Uganda
Leah EM Bevis⇤
Cornell University
Preliminary Draft
Comments Very Welcome
Please Do Not Cite or Circulate
March 12, 2015
Acknowledgements
Special thanks to Clark Gray, PI on the NSF-funded survey that made this data
collection possible, co-PIs Ephraim Nkonya, Darrell Shultze, Christopher B. Barrett and
Leah VanWay, the Kampala IFPRI and HarvestPlus offices for housing me during the
survey, and the entire group of Ugandan surveyors. Thanks to HarvestPlus DC, the
Cornell International Institute for Food, Agriculture and Development (CIIFAD), and
Cornell’s NSF-sponsored Food Systems and Poverty Reduction IGERT program for the
funding that made this research possible. Also thanks to Ross Welch, Christine Hotz,
Daniel Gilligan, Raymond Glahn and Anna-Marie Ball for their aid and guidance, to
Tembi Williams for her research assistance, to Maia Call for her collaboration, and to
Agaba Choice and Sentumbwe George for their aid in data cleaning. Thanks to Mike
Rutzke, the Cornell Nutrient Analysis Laboratory and the USDA for their careful
analysis of soil, crop and food samples.
⇤
Email: [email protected], Phone: 1.614.288.4008
1
Abstract
Over two billion individuals worldwide are a↵ected by micronutrient deficiencies, or
“hidden hunger,” with consequences including sustained loss of productivity, irreparably
reduced physical and cognitive capacity, blindness, and increased infant and maternal
mortality. Population estimates of micronutrient deficiencies, crucial for targeted
interventions, commonly rely on individual food recalls or food supply data paired with
Food Composition Tables (FCTs) that list “standard” food micronutrient contents. Yet
in rural Uganda, FCT zinc standards ignore vast heterogeneity in food zinc content, and
misrepresent even the mean zinc content of many crops. Using a unique dataset with
food recall data for children under 5 and the mineral contents of household- and
market-procured crop samples and household soil samples, as well as a much larger,
country-representative panel dataset on agricultural production and food consumption,
I examine how unobserved heterogeneity in food zinc content biases individual and
population-wide estimates of zinc deficiency. Specifically, because market crops much
lower in zinc than home-produced crops, families who rely on the market for staples are
more likely to be under-counted as zinc-deficient. Observable household characteristics
such as non-farm income, access to transportation and isolation are highly predictive of
market reliance for staples. These associations may be useful when considering which
families are likely be under-counted by standard zinc intake measures.
Keywords: zinc deficiency, micronutrient malnutrition, markets, soils, childhood health
2
1
Introduction
Beginning around the 1960s, scientists, nutritionists and policy-makers have gradually
come to understand of the importance of vitamins and minerals — collectively,
micronutrients — to human health. Micronutrient deficiencies, now known to a↵ect
over 2 billion people, are increasingly viewed as a primary constraint to health and
development worldwide (Kennedy, Nantel and Shetty, 2003; Black, 2003). While
capable of irreversibly eroding human health and productive capacity, micronutrient
deficiencies are difficult to diagnose and to treat because they often present with few or
no clinical symptoms. For this reason, they are collectively termed “Hidden Hunger.”
With a new, global awareness of micronutrient deficiencies came a realization that
“hunger” or malnutrition was distributed far more widely and heterogeneously than
previously understood. In the context of rural Uganda, I show that an additional layer
of invisible heterogeneity, so far ignored in both the nutrition and economics literature,
further complicates detection of zinc deficiency. Population estimates of micronutrient
deficiencies commonly rely on individual food recalls or food supply data paired with
Food Composition Tables (FCTs) that list “standard” food micronutrient contents. Yet
in rural Uganda, FCT zinc standards ignore vast heterogeneity in food zinc content, and
misrepresent even the mean zinc content of many crops. In particular,
market-purchased crops are far lower in zinc than standards would suggest, while
household-purchased crops can be up to fifty or a hundred times higher in zinc content
than standards suggest. Such variation in food micronutrient content is generally
ignored, as it is assumed to be an insignificant driver of intake. Yet in this context,
accounting for heterogeneous food zinc content significantly shifts the distribution of
zinc intake and the rate of zinc deficiency.
Further, heterogeneity of food zinc content is not random — it is correlated with
observable household characteristics such as geographic isolation, farm size, non-farm
income, and distance to market. These patterns have the potential to bias standard
methods of zinc intake calculation, such that zinc deficiency is systematically under- or
over-estimated for specific types of children.
In this paper, I begin by illustrating the substantial heterogeneity that exists in the zinc
content of Ugandan crops. Particularly notable is the di↵erence between
home-produced and market-purchased crops: market crops are far lower in zinc, on
average, than home-produced crops, suggesting that children who rely on the market for
their food intake may be at greater risk for zinc deficiency than other children, even if
they consume identical foods. Knowing which children rely on the market, and which
children rely on home-produced crops, is therefore potentially important. A nationally
representative data-set is used to show that families who are closer to roads and
markets, poorer families, and families who are reliant on non-farm income are most
likely to source their foods from the market rather than from home-production.
In the last section of the paper I construct expected zinc intake according to market
crop zinc intake, home-produced crop zinc intake, and estimated reliance on
home-sourced foods. I then compare the zinc intake calculated in this way with the
standard zinc intake calculated via an FCT. While the sample size is too small to make
strong claims the di↵erential between these intake variables, it does appear that
3
children who are particularly reliant on the market are more likely to be counted as
“zinc adequate” when in fact they are zinc inadequate. Similarly, children who are
particularly reliant on home-produced foods are more likely to be counted as zinc
inadequate when they are in fact zinc adequate.
This paper proceeds as follows. Section 2 provides background on the importance of
childhood nutrition for later health and productivity, on childhood zinc deficiency, and
on zinc deficiency in Uganda. Section 3 presents a model that illustrates how farmer
cropping choices and marketing practices impact household zinc intake and household
health, and highlights the di↵erential in crop zinc content between household-grown
crops and market-purchased crops. Section 4 gives an overview of the two datasets used
in this paper. Section 5 illustrates the heterogeneity found in crop zinc content, and
shows that market-purchased crops are much lower in zinc than household-produced
crops. Section 6 constructs child zinc intake using crop samples from households and
markets, and predicted ratios of produced to purchased consumption for each relevant
crop. (These ratio predictions are estimated using LSMS-ISA panel data.) It
additionally explores which children are likely to be under-counted in terms of zinc
deficiency. Section 7 concludes.
2
Background
Childhood health is a primary determinant of adult productivity. This is especially true
in a developing context where income depends on physical as well as cognitive capacity,
and a growing literature documents the later-life health and productivity consequences
of early childhood malnutrition in poor countries. Maluccio et al. (2009), for instance,
examine the impact of childhood protein supplements on wages in Guatemala, and find
that improved nutritional status below the age of two increases wages by 46 percent.
Chen and Zhou (2007) estimate that early life exposure to famine is associated with
decreased longevity and lower income in China. Victora et al. (2008) find that
indicators for childhood malnutrition are associated with lower income in Brazil and
lower assets in India.
Yet, in many of these studies the mechanism connecting childhood diet (or even clinical
indicators for childhood malnutrition) to later life outcomes is unclear. For instance,
Maccini and Yang (2009) show that early life rainfall shocks in Indonesia lead to poor
health and reduced levels of education, a causal association presumably but not
obviously acting through reduced calorie intake. In such cases, it might be protein
deficiency rather than reduced calorie intake driving later life impacts, or micronutrient
deficiencies such as iron and/or zinc deficiency.
The irreversibility of micronutrient malnutrition makes it a plausible potential
mechanism connecting early childhood diet and later life productivity in many contexts.
E↵ectively, early childhood micronutrient malnutrition is capable of degrading an
individual’s human capital so much that it changes the production technology available
to them as adults (Barrett, 2010; Carter and Barrett, 2006), essentially becoming a
poverty trap mechanism (Azariadis and Stachurski, 2005). Severe iodine deficiency, for
instance, causes mental retardation, and even mild iodine deficiency reduces cognitive
abilities (Hetzel, 1990). Severe selenium deficiency in utero is associated with cretinism
4
(a condition of severely stunted physical and mental growth), and even mild selenium
deficiency in pregnant women can have lifelong health impacts for their unborn children
through miscarriage, preeclampsia and pre-term labor (Mistry et al., 2012). Vitamin A
deficiency is a leading cause of acquired blindness in children (WHO, 2009).
Zinc deficiency is one of the most common micronutrient deficiencies,1 with an
estimated 2.6-3 billion people at risk (Hotz and Brown, 2004). It is also one of the most
dangerous. Zinc deficiency causes abnormal labor and fetal abnormalities in pregnant
women, retards physical growth and cognitive capacity in children, is associated with
diarrhea and acute lower respiratory infections in children, and delays sexual maturity
in adolescents (Prasad, 2003; Hotz and Brown, 2004). In fact, because zinc interacts
with a vast number of human proteins (over 900 proteins, 11 times more than does
iron), the symptoms of zinc deficiency are many and varied (Graham et al., 2007). Even
adults lose muscle mass under zinc deficiency in order to release zinc for maintenance of
vital organs, and adult zinc deficiency is associated with a number of
diseases/conditions including chronic liver disease, diabetes, and macular degeneration
(Prasad, 2003). Zinc deficiency is also associated with a loss of appetite, therefore
contributing to deficiency in other nutrients (Hotz and Brown, 2004). Thus, zinc
deficiency in children is capable of irreversibly degrading future productivity, while zinc
deficiency in adults can decrease current productivity.
Moreover, mild or moderate zinc deficiency is almost impossible to diagnose clinically,
as it presents with few observable symptoms besides stunting. Zinc is so closely
regulated by the body that it is difficult to diagnose even with blood analysis. (Ideally
bone tissue is analyzed). For this reason, and because of the diverse functions in the
human body that depend on zinc, Graham et al. (2007) call zinc deficiency the
“ultimate hidden hunger.”
Zinc deficiency is generally accepted to be common in Uganda, and particularly in
Ugandan children (Bitarakwate, Mworozi and Kekitiinwa, 2004). Ndeezi et al. (2010)
found that, in a group of 247 HIV positive children in Kampala, 54 percent had low
serum zinc, defined as zinc less than 10 µmol/liter. Using that same cut-o↵,
Bitarakwate, Mworozi and Kekitiinwa (2004) found that half of even healthy children in
Kampala displayed low serum zinc status, and that low zinc status was even more
prevalent in children with persistent diarrhea. A study by Bachou (1998) found that up
to 90 percent of of adolescents in the West Nile region of Uganda had low hair zinc
levels. Ndeezi et al. (2010) found that zinc supplementations reduced mortality by two
thirds in children with severe pneumonia in Kampala, and Kikafunda et al. (1998)
found that zinc supplementation improved child weight gain in preschool children from
3 low-income nursery schools Kampala.
While no country-wide estimate of prevalence exists, zinc deficiency in Ugandan
children has been found at rates of 50 to 90 percent (Ndeezi et al., 2010; Bachou, 1998),
and has been associated with diseases ranging from diarrhea to HIV/AIDs.
Tidemann-Andersen et al. (2011) find that dietary zinc intake in Uganda is low, and
1
Its prevalence surpasses that of iodine deficiency, su↵ered by slightly under 2 billion people (WHO,
2004), may be on part with that of vitamin A deficiency, impacting a third of preschool age children
and fifteen percent of pregnant women (WHO, 2009), and approaches that of iron deficiency, estimated
to impact between 4 and 5 billion people, with 2 billion severely deficient, or anemic (WHO, 2008).
5
work by Ecker, Weinberger and Qaim (2010) suggests that approximately half of all
dietary zinc in Uganda intake stems from cereals. Dependence on cereals and other
staples for zinc intake is common in semi-subsistence societies that consume primarily
plant-based foods (Graham et al., 2007), and leaves families and children particularly at
risk of zinc deficiency.
In much of rural East Africa, zinc is consumed largely through staples rather than
though animal-source foods (ASFs), (Ecker, Weinberger and Qaim, 2010). Work by
Ecker, Weinberger and Qaim (2010) suggests that approximately half of all dietary zinc
intake in Uganda stems from cereals, and Tidemann-Andersen et al. (2011) find that
dietary zinc intake in the Kumi District of Uganda is low in part due to the low zinc
content of staples. The authors compare sorghum, millet, maize, groundnut, soy bean
and brown bean samples taken from local markets in Kumi to the same crops sampled
in Kenya and Mali. They find that crops in Kumi are consistently lower in zinc than
crops in Kenya and Mali.
Tidemann-Andersen et al. (2011) also find that the zinc content of crops varies across
counties, within Kumi District. A number of factors might account for such variation —
crop varieties, methods of processing, or variation in soil zinc across counties. It is well
known that, because crops uptake minerals from the soils in which they grow, variation
in soil minerals is often transmitted to variation in crop minerals. Zinc is strongly
transmitted from soils to crops, a fact that has been shown experimentally (Peck et al.,
1980; Hipp and Cowley, 1971; Cakmuk and Erdal, 1996; Moraghan, 1994; Shivay,
Kumar and Prasad, 2008) and observationally (Singh, 2009; Mayer et al., 2007). Such
transmission has been shown to e↵ect the zinc status of rats (Welch, House and
Allaway, 1974; House and Welch, 1989), and Mayer et al. (2007) show that the soil zinc
concentration of rice paddies in rural Bangladesh e↵ects not only rice zinc content, but
the zinc status of farming families who consume the rice.
Risk of zinc deficiency is commonly gauged through estimations of national-level or
individual-level zinc intake (Joy et al., 2014; Ecker, Weinberger and Qaim, 2010; Gibson
and Heath, 2011). While serum zinc status is also measured in many cases, it is far
more expense to collect blood samples than to conduct a food recall. Serum zinc is also
considered a poor biomarker for individual-level zinc deficiency, as it reflects very recent
zinc consumption rather than long-term zinc status (Gibson et al., 2008; Hess et al.,
2007). (It does, however, provide a population-level estimate of zinc deficiency.)
These estimations of zinc intake rely on FCTs to provide the zinc content of foods
consumed. Yet evidence suggests that Uganda’s crops may be low in zinc, and that zinc
content varies highly within crops (Tidemann-Andersen et al., 2011). This, combined
with a heavy reliance on plant-based foods (Ecker, Weinberger and Qaim, 2010),
suggests that zinc intake (and status) in Uganda might be lower and more varied than a
it would appear when intake is constructed according to standard FCT zinc content
values.
6
3
Model
In this model, farmer i seeks to maximize household utility over each day t within
agricultural season {0, T }. A household-specific utility function Ui takes the
consumption vector Cit as its argument, as in equation 1, where Cit holds the
consumption quantities of maize Citmz , sorghum/millet Citsg , sweet potato Citsw , cassava
Citcs , beans Citbn , gnuts Citgn , and non-crop foods such as animal-source foods Cita . Farmers
maximize utility by choosing optimal quantities of crop sold Mit = {Mitmz , Mitsg , Mitsw ,
Mitcs , Mitbn , Mitgn }, and purchased Pit = {Pitmz , Pitsg , Pitsw , Pitcs , Pitbn , Pitgn , Pita }.2
max Ui =
f
f
Mit ,Pit
t=T
X
t
Ui (Cit )
(1)
t=0
For each f 2 {mz, sg, sw, cs, bn, gn}, Citf is the consumption of crop f in time t.
(Similarly, Cita is the consumption of non-crop foods in time t). Crop production occurs
only once at the start of the season (i.e., we define t = 0 as the moment of harvest),
realized as Fi0f for each crop f . No production occurs within the period {1, T }.
Conversely, on any day t farmers may choose crop f purchasing quantity Pitf and crop f
f
selling quantity Mitf . If we allow Si0
to be the initial quantity of crop f in storage, at
f
t = 0, and if we allow SiT to be the closing quantity of crop f in storage, at t = T , then
the maximization problem is constrained according to equation 2.
t=T
X
Citf
=
t=0
T n
X
f
Si0
+
Fi0f
+
Pitf
Mitf
f
SiT
t=1
o
8f 2 {mz, sg, sw, cs, bn, gn}
(2)
In the Ugandan context however, storage from one season to another is rare.
Additionally, storage data is unreliable to work with. In this model I therefore assume
that storage from one season to the next is zero, and the farmer’s maximization
problem is constrained according to equation 3. Equation 4 additionally requires that
selling quantities are no larger than production quantities.3
t=T
X
Citf
=
t=0
t=T
X
t=0
t=T n
X
Fi0f + Pitf
Mitf
t=0
o
8f 2 {mz, sg, sw, cs, bn, gn}
Mitf  Fi0f 8f 2 {mz, sg, sw, cs, bn, gn}
(3)
(4)
Equation 5 displays the household budget constraint for time t, where pit is the vector
of crop purchasing prices in time t, and rit is the vector of crop selling prices in time t.
Non-farm income gained at time t is given by N Fit .
0
pit Pit 
s=t n
X
s=0
0
N Fis + ris Mis
0
pis Pis
o
2
(5)
Superscripts indicate identical crops across consumption, selling and purchasing variables. I assume
that farmers do not sell non-crop foods, i.e., no Mita exists.
3
This constraint implicitly rules out trading.
7
Given equations 1-5, optimal selling and purchasing quantities for each crop f will be
defined as in equations 6 and 7, where pi and ri are defined as price vectors across both
crops/non-crop food and time, N Fi is defined as the vector of non-farm incomes over
time, and Xi includes household characteristics that help to define household
preferences over food, i.e. characteristics that impact the form of Ui .
Pitf = Pitf (Fi0f , pi , ri , N Fi , Xi )
(6)
Mitf = Mitf (Fi0f , pi , ri , N Fi , Xi )
(7)
While the farmer does not observe the zinc content of crops produced, sold and
purchased, production and marketing decisions do impact the supply of zinc available to
the household. Zinc intake depends on both food choice (consumption of maize vs.
cassava) and also the zinc density of foods (consumption of low-zinc maize vs. high-zinc
f
maize), which we know to be heterogeneous. I define zm
as the zinc density of food f at
f
market m, and zi as the zinc density of food f produced by household i.
I assume that at any given time t, household i draws its consumption of crop f from
either the market or the household — but not from both sources at once.4 The home
consumption dummy variable HCitf defines the source of crop f at time t in the
following manner:
(
1 if crop f is sourced from the home
f
HCit =
0 if crop f is sourced from the market
Household zinc intake via crop f , Zitf , is therefore defined as in equation 8, relying on
f
market zinc density zm
if HCitf = 1, and relying on household zinc density zif if
HCitf = 0.
f
Zitf = zif ⇤ HCitf ⇤ Citf + zm
⇤ (1
HCitf ) ⇤ Citf
(8)
If HCitf is not observed at time t, expected Zitf might be defined as in equation 9, where
f
d
HCif is the probability that HCit = 1.
d
f
E[Zitf ] = zif ⇤ HCitf ⇤ Citf + zm
⇤ (1
d
HCitf ) ⇤ Citf
(9)
Expected total zinc intake at time t, E[Zit ], is therefore defined as in equation 10.
o
X n f df
d
f
a
E[Zit ] =
zi ⇤ HCit ⇤ Citf + zm
⇤ (1 HCitf ) ⇤ Citf + zm
⇤ Cita
(10)
f
The key components of equation 10 have already been defined; Citf is the consumption
of crop f in time t, Citf is the consumption of non-crop food a in time t, zif is the
f
household-specific zinc density of home-produced crop f , zm
is the zinc density of crop
f
df
f at market m. The only variable left to be explicitly defined is HC
i = P rob(HCit = 1).
4
This assumption is supported by the LSMS-ISA data, where only 5 percent of consumed crop observations were sourced simultaneously from the home and the market. And even in these cases, the vast
majority of the crop was usually consumed from one source.
8
If we assume that families source crop f from home production until they run out of all
home-produced crop, then HCitf is defined as in equation 11. Even without this
assumption, HCitf must necessarily rely on the quantities Fi0f , Mis , Cisf , and Pisf for all
s < t.5,6 By substituting for Misf and Pisf according to equations 6 and 7, and because
Cisf is itself a function of Fi0f , Mis , and Pisf for all s < t, a reduced form definition of
df
HCitf may be written as in equation 12. The probability HC
i may be obtained as the
prediction from this estimated model.
HCitf
= 1 i↵
s=t
X
(Fi0f
Misf
Cisf ) > 0, 0 otherwise
(11)
s=1
HCitf = HCitf (Fi0f , pi , ri , N Fi , Xi )
4
(12)
Data
This paper utilizes two sets of data. The first set of data covers rural households across
much of Uganda, and was collected by the University of North Carolina (UNC) and the
International Food Policy Research Institute (IFPRI). The second set of data is a
four-wave panel dataset from the Living Standard Measurement Study-Integrated
Surveys on Agriculture Initiative (LSMS-ISA). The LSMS-ISA datasets — nationally
representative, agriculturally intensive panel data sets — cover a number of countries in
sub-Saharan Africa, but this paper utilizes only the panel dataset from Uganda.
UNC-IFPRI Dataset
The UNC-IFPRI data were collected during the summer of 2013 in nine districts of
rural, agrarian Uganda.7 This paper utilizes five types of data from the survey:
household survey data, plot-level soil samples, plot-level crop samples, market-level food
and crop samples, and child survey and food recall data. While 424 households are
represented in the household survey, soil samples are drawn from only 318 of these
households, and crop samples from 282. Child data was gathered at 237 households, one
child per household.
Soil and crop samples were collected on all plots growing maize, sorghum, cassava,
sweet potato, beans, or groundnuts. A total of 791 soil samples and 556 crop samples
(of those 6 crops only) were gathered. In most cases crops were taken directly from the
5
While it is possible that HCitf indicates the position of some latent variable with respect to a defined
threshold, as in equation 11, this is not necessarily true. If, for instance, families may freely choose to
consume either home-produced crops or market-produced crops directly after harvest, then during this
period HCitf might equal either 1 or 0, regardless of the value of the latent variable.
6
It may also be true that other variables determine HCitf . However, production, selling and purchasing
quantities necessarily impact HCitf even if they do not define HCitf , given that HCitf = 1 when Fi0f = 0 or
P f
P f
f
Pis = 0.
when Fi0
=
Mis , and HCitf = 0 when
7
s<t
s<t
These districts are: Kabale, Iganga, Soroti, Lira, Luuka, Busiki, Amolatar, Dokolo and Serere. Data
from these nine districts was collected alongside a larger survey that covered 21 districts. This larger
survey was a follow-up round to a previous survey run in 2003. The data utilized in this paper, however,
are cross-sectional. Information on the sampling strategy utilized in 2003 can be found in Nkonya et al.
(2008). Essentially, rural households were randomly chosen within survey districts, but the survey
districts themselves were chosen to represent various agro-ecological zones across Uganda.
9
field; if already harvested, they were sampled from storage.8 Both soil and crop
sampling were conducted according to standard protocols for in-field, representative
sampling. (See Appendix 1 for details.) For soils, 12-20 subsamples were taken from
each plot, evenly distributed. Crop samples were collected similarly. Thus, soil and crop
nutrient content can be viewed as plot-representative, and variation in nutrient content
across plots is largely related to systematic factors (such as soil texture or pH) rather
than random variation within plots.
Certain crops and foods, not commonly grown at the household but important to zinc
intake, were purchased at local, sub-county markets for later micronutrient analysis.
One sample of each food item was taken at each of 32 markets, subject to availability,
for a total of 357 samples of 13 food items.9
Soils, crops and foods were analyzed for total micronutrients, and soils were additionally
analyzed for “available” micronutrients. Total nutrient quantity, obtained via a Vulcan
84 Digestion, reflects the total mineral quantity (measured in parts per million) in each
soil, crop, or food sample. “Available” nutrient quantity, obtained via a Modified
Morgan’s extraction, reflects the quantity of soil minerals (measured in parts per million
or billion) actually available for plants to uptake. Minerals that are bound tightly in
complexes or rock formations may not, for instance, be available to plants. Soil pH is
one of the primary factors driving the availability of soil minerals.
A Food Frequency Recall (FFQ) was conducted for a maximum of one child per
household, and covered all food intake in the preceding week.10 Qualifying children were
under 5 years of age, present at the time of the survey, and not exclusively
breastfeeding.11,12 During the FFQ child care-takers stated how many times their child
had consumed each of 53 selected, zinc-rich foods over the course of the last seven days,
and the average portion size for each food consumed. More information on the FFQ can
be found in Appendix 3. While the child health module was conducted at only 237
households, in 99 cases a second food recall was conducted.13 This extends the food
intake dataset to 336 observations.
Table 1 displays descriptive statistics for the children in the UNC-IFPRI sample. Of the
237 children in our sample, all but 4 were under 60 months (5 years) of age, all were
8
Analysis of nutrient content in field-crops vs. storage-crops shows no di↵erence between the two groups.
I control for this factor in all relevant analysis, but it has no appreciable impact.
9
Sample food items were matooke (a variety of banana), fish (primarily dried Nile Perch and Tilapia),
mukene (small dried fish), cowpeas, rice, cassava flour, maize flour, millet flour, eggs, avocado, milk,
cooking oil, and chicken.
10
Food Frequency Recalls are a standard tool for capturing the dietary intake of one or two select
micronutrients.
11
If a household had multiple children present who fit these criteria (e.g. a son, a niece, and a cousin),
surveyors chose the biological child of the household head. If the household head had multiple biological
children present, all fitting the necessary criteria, the surveyors chose the oldest child.
12
Selection on having a young child means that these households are not representative of their survey
district. Appendix 2 explores this selection.
13
These 99 children were revisited partway through the survey, as their heights had been improperly
recorded. (In theses cases, the digital survey tool was failing to record decimals. This technical
problem was not endogenous to enumerator skill.) When enumerators returned to these households to
re-measure the children, they also conducted a second food frequency survey. Some of these children
were re-measured within a few days of the initial measurement, others over a month or so later.
10
under 66 months of age, and exactly half were males. Calorie intake averaged around
1,000 calories per day (with a median intake of 870 calories/day), generally adequate for
children of this age. The median child ate 15 distinct food items or ingredients over the
course of the week-long recall period, and during that same period ate meat or fish 2
times, ate milk or eggs 2 times, ate some form of cereal 14 times, ate some type of
legume 11 times, and ate some sort of tuber 12 times. Because the UNC-IFPRI survey
was conducted during one of Uganda’s two yearly harvest periods, the dietary diversity
displayed in this sample likely represents the upper bound of dietary diversity for
children in these areas of Uganda.
LSMS-ISA Data
Uganda’s LSMS-ISA panel includes four rounds of data, collected in 2005/6, 2009/10,
2010/11, and 2011/12. Because Uganda has two agricultural seasons, each family in the
Ugandan panel is visited twice during each round, and the agricultural survey, including
extensive measurement of agricultural production and sales, is conducted during each
visit. The household survey, including a detailed section on household-level food
consumption, is conducted only once per round — during the first visit for half of the
families in each enumeration area, and during the second visit for the other half. Thus,
over the course of 4 rounds, each taking about a year to complete, and each o↵set from
the last round by about a month, consumption data has been collected during every
week of the year.14
The food consumption module consists of a 7-day recall. For each of 56 common food
items listed, families report whether that food item was consumed in the household
during the last 7 days, if so how much of it was consumed in total, how much was
consumed from purchases, and how much was consumed out of home production. The
dummy variable HCitf is assigned a 1 if consumption of crop f was from home
production, and a 0 if consumption of crop f was from market purchases.15
A total of 1,756 rural families are included in the Ugandan panel dataset, each observed
4 times.16 (Data from urban areas is discarded.) Because I use LSMS data to model
HCitf for each of six crops, and because HCitf is specific to household i and crop f , the
the panel dataset constructed is also unique by household, round, and crop consumed.
This provides a total of 42,144 possible observations, or 7,024 possible observations per
crop. Not every crop is visible in every round, however — if a family did not happen to
consume that crop during the 7-day recall window of a particular round, HCitf will not
be visible for that round.17
14
Round one was conducted from May 2004 - November 2006, round 2 from September 2009 - October
2010, round 3 from October 2010 - Sept 2011, and round 4 from November 2011 - October 2012.
15
A very small proportion of food is drawn from transfers rather than home production or purchases.
f
For these observations, HCit
= 0.
16
Currently I keep only household observed in all four rounds. I could expand the data-set to include
families viewed in only 2 or 3 rounds, and may do so in the future.
17
This selection into the data-set is largely driven by seasonal timing. If this is the only variable driving
f
f
visibility of HCit
, then any estimation of HCit
is consistent, given that survey timing was chosen
randomly by the LSMS team, and is therefore uncorrelated with other independent variables that
f
might drive HCit
. If other variables drive selection into the data-set, such as wealth or tribe, and
f
these variables are correlated with the independent variables that drive HCit
, then estimation is not
consistent without adjustment for selection.
11
It is important to note that HCitf refers to the consumption of crop f for household i
during week t. The subscript t is defined by the day of interview, and di↵ers across but
is not defined by the panel round. For some households, t varies a great deal across
rounds, while for others t is similar for all rounds.
For a very small number of observations (less than 5 percent), the consumption of crop
f is drawn both from market and household production simultaneously. In a few cases,
households do truly source a single food item, e.g. maize flour, from both the market
and home production within the given, 7-day recall period. However, because multiple
forms of certain crops are observed (e.g. cassava tubers and cassava flour), these
quantities are first aggregated into total kilograms per crop before HCitf is defined.
Most of the apparent double-sourced crops are actually due to this aggregation —
cassava tubers may have been drawn from home production, for instance, while cassava
flour was purchased. In these cases, HCitf is assigned a 1 if the majority of consumption,
by kilograms, was drawn from home production. Because this occurs for so few
observations it is unlikely to change results, and is preferably to throwing away
potentially non-random observations.
df
Table 2 displays variables used to predict HC
i , from both the LSMS-ISA and the
UNC-IFPRI datasets. While the probability of producing any particular crop is similar
across the two data-sets, production quantity conditional on production di↵ers in a
statistically significant manner. Given the di↵erences in both sampling area and year,
this is to be expected. In fact, significant di↵erences in production exist within the
LSMS-ISA panel, if production quantities are compared across rounds (results available
upon request). It is also clear that the LSMS-ISA datasets are, on average, more
isolated than the IFPRI-UNC households. They are both further from the nearest cities
and further from the nearest all-weather roads.18 They are also less likely to own a
bicycle. Households are larger in the IFPRI-UNC data, in part due to selection on
having a child under 5 years old. Non-farm income is substantially higher in the
LSMS-ISA data, which is curious given the more isolated nature of these households.
However, the di↵erence is largely due to higher wage earnings in the LSMS-ISA data,
and these earnings are particularly large in the central region, where no IFPRI-UNC
households exist. [[I worry that this di↵erence is due to the survey instrument itself,
and may in future exclude this variable from analysis.]]
5
Heterogeneity in Crop Zinc Content
The nutrient density of food is important only if zinc content is significantly
heterogeneous within particular foods. In our dataset, 76 percent of zinc is consumed
through plant-based foods, primarily cereals and legumes. It is therefore important to
examine the heterogeneity of zinc content in plant-based foods. Figures 1-5 do so,
di↵erentiating between crops gathered at households and crops gathered at market, and
marking zinc content values from the HarvestPlus FCT for the sake of comparison.
These figures illustrate three points. First, the zinc content of crops is indeed highly
18
Distances to nearest city is measured by euclidean distance, created in both datasets using household
and city GPS coordinates. Distance to nearest all-weather road is reported by IFPRI-UNC farmers,
and created using GPS coordinates in the LSMS-ISA data.
12
heterogeneous. The highest-zinc sorghum samples contained 164 and 185 mg of zinc per
gram of crop, over 100 times the HarvestPlus FCT estimate of 1.6. The highest-zinc
maize samples contained 96 and 128 mg of zinc per gram of crop, 50-75 times the FCT
estimate for unrefined maize, and 130-180 times the FCT estimates for refined maize.
Second, market zinc content is far lower and less variable than household zinc content,
for every crop. Median cassava zinc content drops by 40 percent at market (Figure 1),
and median maize content drops by 83 percent (Figure 2). Figure 3 illustrates the zinc
content of millet purchased at market and sorghum sampled at households, two cereals
generally considered to have almost identical zinc content.19 The median zinc content of
millet purchased from market is only 56 percent of the median zinc content of sorghum
sampled from homes. Figure 4 illustrates the zinc content distribution of cowpeas
purchased at market and beans sampled at households. While not strictly comparable,
these crops are again considered to have similar zinc content, and the observed pattern
is the same: market-purchased cowpeas are lower and less variable in zinc than
home-produced beans.
What drives the observed heterogeneity in household and market crops? As shown in
Appendix 4, variation in the zinc content of household crop samples can be largely
explained by soil zinc concentration and other soil conditions. While the precise model
may vary across soil types, this type of soil-to-crop mineral transmission is commonly
observed (Singh, 2009; Mayer et al., 2007; Chilimba et al., 2011). In these data
interactions between total zinc concentration, pH, and extractable cadmium and
manganese are the primary soil-level predictors of crop zinc.
The di↵erence between household and market samples is more puzzling, and a novel
finding. While investigation of this phenomenon is outside the scope of this paper,
Appendix 5 holds some details on potential mechanisms. It is unlikely that processing,
nutrient degradation over time, or plot-level selection explains the di↵erential. Selection
on variety and farmer selection into marketing are the most plausible explanations.
Third, these figures illustrate that HarvestPlus FCT standards are often poor proxies
for median crop zinc content. For instance, the FCT zinc content standard is close to
the median value for cassava grown at home. It dramatically over-estimates the zinc
content of cassava purchased at market, however. The FCT zinc content standard
over-states the median content of sweet potato grown at home by two thirds — and
market purchased sweet potato may be even lower in zinc than home-produced sweet
potato, given the trends observed in Figures 1-4. The median zinc content of
home-produced maize is far higher than the FCT value for refined or unrefined maize
flour, where-as the median zinc content of market-produced maize is significantly lower.
The first point might lead us to expect that zinc intake estimates for any particular
individual may often be wrong. The second two points suggest, further, that estimates
of population-wide zinc deficiency rates might also be biased, either for entire countries
or for entire subsets of individuals within a given country.
19
The HarvestPlus FCT lists millet and sorghum as having 1.7 and 1.6 mg of zinc per 100 grams of crop,
respectively.
13
6
Constructing Zinc Intake
Calculating expected zinc intake E[Zit ], as given in Equation 10, requires knowledge of
crops consumed, Cit , and non-crop foods consumed Cita . These variables can be
constructed directly from the UNC-IFPRI child food recall data. It is also necessary to
a
calculate or estimate the zinc density of non-crop foods consumed zm
, the
f
market-specific zinc density of purchased crops zm , the household-specific zinc density
df
of produced crops z̄if , and the probability of home consumption for each crop f , HC
i .
The zinc density variables can be calculated or estimated using the UNC-IFPRI dataset
df
only, while predicting HC
i requires the LSMS-ISA data.
Zinc Density Variables
a
Zinc density of non-crop foods, zm
, is calculated as the district-median zinc content of
of market-sampled foods. These foods were chosen for sampling because they were (i)
potentially important to zinc intake, but (ii) rarely produced at the home, e.g., fish, oil,
milk, or chicken. For foods that are not important to zinc intake (most fruit and
vegetables), market samples were not taken for zinc analysis. For these non-crop foods,
a
zm
is given by the HarvestPlus FCT zinc content. For two other items (beef and goat),
market samples were not legal to import and analyze, even though these items may
a
contribute substantially to zinc intake. For these non-crop foods also, zm
is given by the
HarvestPlus FCT zinc content.
f
Market-specific zinc density of purchased crops, zm
, should ideally be calculated as the
district-median zinc content of market-based crop samples. For maize, cassava, and
sorghum/millet this is done, but market samples were not taken for the other three
crops. Thus, HarvestPlus FCT values are used for the crops where market samples are
not available. If anything, this likely over-estimates the zinc contribution of these
market crops.
Household-specific crop zinc density, z̄if , is predicted by a soils-based, household-specific
model of crop zinc content. The model is parameterized by estimating — by crop, for
hundreds of crop samples — the impact of soil zinc concentration and other soil
characteristics on crop zinc content. This model is presented in panel 3 of Appendix 4.
Predictions, as oppose to true crop sample values, were utilized because it was rare for
all six crops — maize, sorghum, sweet potato, cassava, beans and groundnuts — to be
sampled at the household level. However, if the relevant crop was sampled at a given
household, then this sample zinc value is substituted for the predicted value.
Estimating Home Consumption (HCiit )
Because data on food source is not gathered in the IFPRI-UNC data, LSMS-ISA data
are used to parameterize a logit model for HCitf , defined by Equation 18. This model is
df
used to predict HC
it for each household in the IFPRI-UNC data, using a technique
similar to small area estimation. However, I estimate HCitf in the larger data-set and
df
predict HC
it into the smaller data-set, rather than estimating in the smaller data-set to
predict into the larger one as described by Elbers, Lanjouw and Lanjouw (2003)
Table 3 estimates the impact of farm and household characteristics on HCitf , by crop,
using a random e↵ects logit model. The production of crop f significantly, positively
14
influences HCitf , non-farm income negatively influences HCitf , bike ownership positively
influences HCitf , and distance to road increases seasonal fluctuations in HCitf .
Other estimation methods are under consideration. For instance, Appendix 6
additionally displays a conditional (household fixed e↵ects) logit model,20 and a probit
model that adjusts for non-random selection. For the probit estimation, a binary
production variable is used to identify selection into the data-set. While an admittedly
less than perfect instrument, it is true that production dummies are insignificant in the
intensity (home production ratio) equation, conditional on the log production variables
already controlled for. Outcome predictions from these estimations strategies are poor,
however, as explained in Appendix 6. The random e↵ects logit model is chosen to
f
df
predict HC
it within the IFPRI-UNC data, because it mostly closely predicts HCit in the
LSMS-ISA data.
df
Thus, using the model displayed in Table 3, HC
it is predicted for each crop f and each
household i in the IFPRI-UNC dataset, according to interview date t. Expected child
zinc intake E[Zit ] can therefore be constructed according to equation 10.
Examining Zinc Intake
Figure 6 illustrates the importance of correctly estimating P rob(HCitf ) by illustrating
the distribution of expected zinc intake under three assumptions: (1) P rob(HCitf ) = 0
for all crops (making households totally dependent on market-purchased crops), (2)
P rob(HCitf ) = 1 for all crops (making households totally dependent on home-produced
df
crops) and (3) P rob(HCitf )=HC
it for all crops, as estimated via the LSMS-ISA model.
Zinc intake stemming from the first assumption is henceforth referred to as
E[Zit |HCitf = 0], and from the second assumption as E[Zit |HCitf = 1]. The third
assumption leads to E[Zit ], as defined in equation 10.
These various assumptions shift the zinc intake distribution dramatically. When zinc
intake is defined by E[Zit |HCitf = 0], 56 percent of children are zinc inadequate, and
mean zinc intake is 3.9 mg/day.21 When zinc intake is defined by E[Zit |HCitf = 1], only
35 percent of children are zinc deficient, and mean zinc intake rises to 6.1 mg/day.
df
When estimated HC
it is used to define E[Zit ], 41 percent of children are zinc
inadequate, and mean zinc intake is 5.4 mg/day.
Generally, however, zinc intake is calculated under the assumption that crop zinc density
is defined by a single value, procured from a Food Composition Table. In this case,
di↵erentiating consumption from market and consumption from home is unnecessary.
Zinc intake calculated in this manner is henceforth referred to as E[Zit |F CT ].
Figure 7 contrasts the probability distribution of E[Zit |F CT ] with the probability
distribution of E[Zit ]. The distributions are similar, though not identical. According to
20
The conditional logit model maximizes likelihood by group, in this case household, conditional on the
number of positive outcomes in that group. In this way it conditions on fixed household means without
including dummy variables in the model, which are well known to bias coefficients in non-linear models.
21
“Zinc adequacy” is calculated by dividing zinc intake by the child-specific Recommended Daily Allowance (RDA) for zinc — i.e., the intake necessary to meet the needs of 95 percent of the relevant
population. Children with adequacy ratios of less than 1 are intaking inadequate levels of zinc. The
RDA for zinc ranges from 2 mg/day (children under 6 months) to 5 mg/day (children 4-8 years).
15
E[Zit |F CT ], 46 percent of children are zinc inadequate (rather than 41 percent), and
mean zinc intake is 4.6 mg/day (rather than 5.4 mg/day). Figure 8 shows that, while
the cumulative distributions of these two intake variables are again similar, E[Zit ] first
order stochastically dominates E[Zit |F CT ]. It seems, therefore, that the standard
method of calculating zinc intake leads to slight over-estimates of zinc deficiency in
Uganda as a whole.
These distributional similarities hide ordinal di↵erences between E[Zit ] and
E[Zit |F CT ]. While it is true that for most children E[Zit |F CT ] < E[Zit ], for some
children the opposite is true. The di↵erence between E[Zit ] and E[Zit |F CT ] increases
df
with probability of home consumption HC
it , as seen in Figure 9. This is logical, given
that average zif is generally higher than the FCT crop f zinc density (Figures 1-5). The
di↵erence is also increasing in cereal consumption, again logical given that zif is
particularly high, as compared to the FCT measures, for maize and sorghum/millet
(Figures 2-3).
Thirty-four children (9 percent of the sample) are zinc adequate according to E[Zit ],
but zinc inadequate according to E[Zit |F CT ]. For these children, the standard FCT
method of zinc intake calculation appears to under-estimate intake and over-estimate
risk of deficiency. The majority of these children are in the Eastern region of Uganda.22
Why is zinc intake in Eastern Uganda most likely to be under-estimated by standard
calculations? Household-produced crops tend to have heterogeneous zinc density in the
Eastern region, largely due to heterogeneous concentrations of soil zinc (Figure 11) and
heterogenous pH levels. This means that zinc intake from home produced crops is also
particularly heterogenous in the Eastern region. Figures 12 illustrates this phenomenon
by graphing the probability distribution of household-specific zinc intake less
market-specific zinc intake (i.e., graphing the probability distribution of E[Zit |HCitf = 1]
E[Zit |HCitf = 0]) for each region. The di↵erence between these two intake calculations
is most variable in Eastern Uganda, where sourcing from home production rather than
market might either increase or decrease zinc intake by up to 10 mg per day. Because
auto-consumption is most prevalent in the Eastern region (Figure 13),23 E[Zit ] ends up
particularly variable in the Eastern region. And while this leads to some
over-estimation of zinc intake, it leads primarily to under-estimation of zinc intake for
those children who are depending on extremely high-zinc, home-produced crops.
Eighteen children (5 percent of the sample) are zinc adequate according to E[Zit |F CT ],
but zinc inadequate according to E[Zit ]. For these children, the standard FCT method
of zinc intake calculation appears to over-estimate zinc intake, and under-estimate risk
of deficiency. The majority of these children are in the Northern region of Uganda; none
of them are in the South-West district of Kabale.24
22
Twelve percent of children in the Eastern region are adequate according to E[Zit ], and inadequate
according to E[Zit |F CT ]. This figure is 7 percent in the Northern region and 5.5 percent in the
South-Western province of Kabale.
df
23
While this figure illustrates average HCit
across cereals and legumes, the same pattern is true for
tubers, and for all individual crops except beans.
24
Eight percent of children in the Northern region are adequate according to E[Zit |F CT ], and inadequate
according to E[Zit ]. This figure is 4.5 percent in the Eastern region and 0 percent in the South-Western
district of Kabale.
16
Why is E[Zit |F CT ] particularly likely to under-estimate zinc intake in the Northern
region, and why does it estimate zinc deficiency rates so well in Kabale? Figures 13 and
14 illustrates that auto-consumption is particularly low in the Northern region, making
f
Northern children highly dependent on market crops. Because average zm
is lower than
the FCT zinc density for most crops f , E[Zit |F CT ] is likely to under-estimate child zinc
intake in areas like the North where consumption is highly dependent on market crops.
Conversely, E[Zit |F CT ] will be unlikely to under-estimate zinc intake in areas where
zinc intake is largely drawn from home production. When zinc deficiency is calculated
according to E[Zit |F CT ], 23, 63, and 66 percent of children from Kabale, the North,
and the East respectively are at risk of zinc deficiency. In Kabale, zinc intake is
particularly dependent on beans,25 and Figure 14 shows that bean consumption is more
likely to be sourced from home-production in Kabale than in any other region.
In summary, E[Zit |F CT ] is likely to under-estimate zinc intake for children who are
particularly dependent on market crops for zinc intake, and likely to over-estimate zinc
intake for children who are particularly dependent on home-produced crops for zinc
intake. Variation in soil zinc may marginally impact the accuracy of E[Zit |F CT ], but if
so the e↵ect is hidden by the much larger di↵erential between children who rely on the
market and children who rely on auto-consumption.
7
Conclusion
Significant heterogeneity in food and crop zinc content exists in Uganda. Crop zinc
content varies, first, with the characteristics of the soils in which it is grown. Even more
significant, however, is the zinc content di↵erence between market crops and
home-produced crops. While such heterogeneity is commonly ignored during the
calculation of zinc intake, I show that zinc intake distributions and zinc deficiency
estimates shift when heterogeneity is accounted for.
Zinc intake in Uganda appears to be slightly higher would be apparent if zinc intake
was calculated in the standard manner, via a Food Composition Table. The di↵erence
is not huge, however — in this sample, zinc deficiency decreases by five percent when
heterogeneity of crop zinc and food sourcing patterns are accounted for. More
significant is how children switch from appearing zinc deficient to zinc adequate, or visa
versa, between the two calculation methods. Children highly reliant on home-produced
crops for their zinc intake, and particularly children who are reliant on home-produced
crops and also live in an area with zinc-rich soils and zinc-rich crops, are likely to
consume greater quantities of zinc than suggested by the standard calculation method.
This appears to be the case with children in the Eastern region of Uganda. Conversely,
children highly reliant on market-produced crops for their zinc intake are likely to
consume less zinc than suggested by the standard calculation method. This appears to
be the case with children in the Northern region of Uganda.
These findings hinge largely on the di↵erential between home-produced crop zinc
content and market-purchased crop zinc content. While this di↵erential appears across
25
The average child in Kable consumed beans X times during the recall period, where-as the average
child from other regions consumed beans only Y times.
17
regions and crops in Uganda, such a comparison has never to my knowledge been made
in other sub-Saharan African countries.26
Calculating zinc intake as I do in this paper requires detailed data on crop zinc content
at least, at the household level and at the market level, and if possible soil zinc content
and other soil characteristics. This type of data collection is surely prohibitively
expensive for most organizations that wish to gauge zinc deficiency in poor, agricultural
areas.
This paper points, however, to large-scale patterns that might help to inform
interpretation of standard zinc intake, at the very least. That is, standard zinc intake
measures may consistently overestimate zinc deficiency in areas where zinc intake stems
largely from home-produced crops, particularly if soils in that area are fertile (rich in
nutrients) and neutral in pH (which increases zinc availability). On the other hand,
standard zinc intake measures may consistently underestimate zinc deficiency in areas
where zinc intake stems largely from market-produced crops. While reliance on
home-produced crops vs. market-purchased crops varies by area, it also varies by
observable household characteristics: in Uganda, families with greater levels of non-farm
income and families close to markets and roads are likely to depend greatly on
market-produced crops. Even in areas with generally high zinc intake, it may be these
families who are “invisibly” zinc deficient.
26
However, initial investigation into the mechanism behind that di↵erential suggests that farmer selection
into the market plays a role. If this is true, the di↵erential is likely to exist across sub-Saharan Africa,
as a similar selection process exists in most countries. That is, in most countries it is the largest,
wealthiest farmers who supply the majority of crops to market, and in low-input settings these farmers
are also likely to have some of the lowest-nutrient soils.
18
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rice–wheat cropping system.” Nutrient Cycling in Agroecosystems, 81(3): 229–243.
Sillanpää, Mikko. 1972. Trace elements in soils and agriculture. Food & Agriculture
Org.
Singh, MV. 2009. “Micronutrient Nutritional Problems in Soils of India and
Improvement for Human and Animal Health.” Indian Journal of Fertilizer,
5(4): 11–16, 19–26, 56.
Tidemann-Andersen, Ida, Hedwig Acham, Amund Maage, and Marian K
Malde. 2011. “Iron and zinc content of selected foods in the diet of school children in
Kumi district, East of Uganda: a cross-sectional study.” Nutr J, 10: 81.
Victora, Cesar G, Linda Adair, Caroline Fall, Pedro C Hallal, Reynaldo
Martorell, Linda Richter, and Harshpal Singh Sachdev. 2008. “Maternal and
child undernutrition: consequences for adult health and human capital.” The Lancet,
371(9609): 340–357.
Welch, Ross M, William A House, and William H Allaway. 1974. “Availability
of zinc from pea seeds to rats.” Journal of Nutrition, 104(733): 999–1010.
WHO. 2004. “Iodine status worldwide: WHO global database on iodine deficiency.”
21
WHO. 2008. “Worldwide prevalence of anaemia 1993-2005: WHO global database on
anaemia.”
WHO. 2009. “Global prevalence of vitamin A deficiency in populations at risk
1995-2005: WHO global database on vitamin A deficiency.”
22
Figures
Figure 1: Heterogeneity in Cassava
Figure 2: Heterogeneity in Maize
Zinc Concentration
Zinc Concentration
Figure 3: Heterogeneity in Millet and
Figure 4: Heterogeneity in Bean and
Sorghum Zinc Concentration
Cowpea Zinc Concentration
Figure 5: Heterogeneity in Sweet
Figure 6: Child Zinc Intake PDFs:
Potato Zinc Concentration
Three Estimations
23
Figure 7: Child Zinc Intake PDFs:
Figure 8: Child Zinc Intake CDFs:
FCT vs Estimated HC
FCT vs Estimated HC
Figure 9: Calculation Di↵erentials
Figure 10: Calculation Di↵erentials
by Estimated HCR
by Cereal Consumption
Figure 11: Soil Zn Concentration
Figure 12: Household vs Market
by Region
Zinc Intake
24
f
f
Figure 13: Prob(HCi =1) by Region,
Figure 14: Prob(HCi =1) by Region,
Average over Grains & Legumes Only
for Beans
25
Tables
Table 1: Child and Household Descriptive Statistics (IFPRI-UNC)
Median
St Dev
By Child
Male (%)
Age (months)
Breastfeeding (%)
51
37
17
15
-
By Intake Obs
Dietary Diversity (distinct items/week)
Calorie Intake (kcal/day)
Consumption Meat & Fish (items/week)
Consumption Eggs & Milk (items/week)
Consumption Cereals (items/week)
Consumption Legumes (items/week)
Consumption Tubers (items/week)
15
870
2
2
14
11
12
7
697
4.1
5.5
8.9
9.8
8.6
Table 2: Household Descriptive Statistics (LSMS-ISA, IFPRI-UNC)
Produced Maize (%)
Maize Production (kg)
Produced Sorghum/Millet (%)
Sorghum/Millet Production (kg)
Produced Sweet Potato (%)
Sweet Potato Production (kg)
Produced Cassava (%)
Cassava Production (kg)
Produced Beans (%)
Beans Production (kg)
Produced Groundnuts (%)
Groundnuts Production (kg)
Distance to Nearest All-Weather Road (km)
Distance to Nearest City (km)
Non-Farm Income (dollars)
Household size (people)
Bike (%)
LSMS-ISA
Mean*
IFPRI-UNC
Mean*
t-stat
(of equal means**)
p-value
(di↵=0)
0.61
226
0.33
120
0.49
400
0.48
400
0.58
130
0.28
90
0.63
275
0.54
188
0.46
390
0.45
500
0.65
90
0.26
120
0.65
2.86
4.34
20
0.76
6.84
0.92
10.49
1.92
2.78
.0.51
22.55
0.52
0.00
0.00
0.00
0.45
0.00
0.36
0.00
0.06
0.01
0.61
0.00
8.62
16.10
725.27
5.82
0.47
6.59
11.74
253.06
7.92
0.63
3.29
10.86
10.31
10.80
5.04
0.00
0.00
0.00
0.00
0.00
*Because production quantity is distributed log normally, medians are reported.
**T-test of equal means, unequal variance — for production quantities log (kg) is tested
26
Table 3: Explaining Household Consumption Ratio by Random E↵ects Logit
Household Production
Maize (log kg)
Sorghum/Millet (log kg)
Sweet Potato (log kg)
Cassava (log kg)
Beans (log kg)
Groundnuts (log kg)
House Characteristics
Household Size
Non-farm income (log)
Bike (dummy)
Geography/Time
Distance to City (km)
(Distance to City)2
Distance to Road (km)
(Distance to Road)2
(Dist to City)*Weeks
(Dist to City)*Weeks2
(Dist to Road)*Weeks
(Dist to Road)*Weeks2
Observations
Number of hhid
(1)
Maize
(2)
Sorghum/Millet
(3)
Sweet Potato
(4)
Cassava
(5)
Beans
(6)
Groundnuts
0.260***
(0.0184)
0.0185
(0.0209)
0.0106
(0.0171)
0.0737***
(0.0151)
-0.0390*
(0.0209)
0.0621***
(0.0210)
0.0569***
(0.0217)
0.253***
(0.0233)
0.0647***
(0.0218)
0.0224
(0.0191)
0.0487*
(0.0276)
0.108***
(0.0255)
0.0448**
(0.0220)
0.0199
(0.0261)
0.173***
(0.0218)
0.0383**
(0.0194)
0.116***
(0.0265)
0.0338
(0.0282)
0.0449**
(0.0184)
0.0435**
(0.0216)
0.0802***
(0.0180)
0.173***
(0.0156)
0.0904***
(0.0223)
0.0823***
(0.0222)
0.0466***
(0.0160)
-0.0177
(0.0185)
0.0111
(0.0157)
0.0169
(0.0139)
0.314***
(0.0191)
0.0283
(0.0197)
0.0321
(0.0215)
0.0681***
(0.0249)
0.00999
(0.0206)
0.0484***
(0.0182)
0.00730
(0.0258)
0.373***
(0.0249)
0.0225
(0.0169)
-0.0557***
(0.0144)
0.560***
(0.0944)
-0.0255
(0.0213)
-0.0572***
(0.0197)
0.342***
(0.120)
0.0672***
(0.0219)
-0.0574***
(0.0194)
0.625***
(0.121)
-0.0238
(0.0177)
-0.0273*
(0.0159)
0.485***
(0.0995)
0.0126
(0.0162)
-0.0468***
(0.0136)
0.219**
(0.0893)
-0.0144
(0.0198)
-0.0344**
(0.0175)
0.288**
(0.113)
0.0313
(0.0283)
-0.000307
(0.000516)
0.0625**
(0.0285)
-0.00280***
(0.000723)
-0.00371**
(0.00166)
7.68e-05**
(2.99e-05)
0.00297
(0.00184)
-5.02e-05
(3.37e-05)
0.0491
(0.0339)
-5.60e-05
(0.000629)
0.0841**
(0.0375)
-0.00317***
(0.000914)
-0.00341
(0.00222)
5.42e-05
(4.20e-05)
0.00103
(0.00244)
-4.80e-06
(4.54e-05)
-0.0126
(0.0314)
-0.000544
(0.000571)
0.0764**
(0.0328)
-0.00253***
(0.000789)
0.00287
(0.00205)
-4.84e-05
(3.75e-05)
-0.000750
(0.00221)
1.80e-05
(4.08e-05)
-0.0261
(0.0279)
0.000412
(0.000484)
0.104***
(0.0301)
-0.00322***
(0.000704)
0.000718
(0.00166)
-1.55e-05
(3.05e-05)
-0.000984
(0.00193)
1.84e-05
(3.52e-05)
0.0526**
(0.0246)
-0.00103**
(0.000453)
0.0333
(0.0271)
-0.00190***
(0.000682)
-0.00119
(0.00141)
1.65e-05
(2.59e-05)
0.00212
(0.00174)
-3.33e-05
(3.16e-05)
0.0681**
(0.0309)
-0.00113*
(0.000581)
0.00823
(0.0333)
-0.00116
(0.000835)
-0.00250
(0.00191)
4.49e-05
(3.65e-05)
0.00352
(0.00220)
-6.88e-05*
(4.14e-05)
3,687
1,548
2,090
1,039
2,862
1,392
3,736
1,486
4,829
1,636
2,436
1,228
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
All estimations control for the producer price of all 6 crops (averaged by enumeration area),
for round, and for weeks and weeks squared by region
Appendix 1
Soil sampling was conducted according to standard protocols for in-field, representative
soil sampling. Twelve to twenty sub-samples were taken from each plot, with a thin soil
probe that reached down to 20 cm. In plots with very hard soil, occasionally an auger
or a hoe was used to collect soil samples, rather than a soil probe. In such cases e↵ort
was still made to gather soil down to 20 cm.
Sub-samples were taken from randomly distributed locations around the plot, roughly
following zig-zag patterns, but avoiding any “odd” patches of ground such as termite
mounds or compost piles. After mixing all sub-samples together in a bucket, a
representative quantity of 500 grams of soil was gathered for subsequent drying,
grinding and micronutrient analysis.
Crop samples were conducted in a similar manner. For grains and legumes, enumerators
sub-sampled crops (e.g. bunches of beans, a kernel of maize) from 10 locations around
the plot. As it was feared that farmers might object to 10 cassava or sweet potato
plants being dug up, enumerators were told to sub-sample these crops from 5 or 6
plants across the plot. (It is possible that sub-samples were sometimes taken from an
even smaller number of plants.) The total weight of each sample, once compiled from all
sub-samples across the plot, was usually 1 kilogram or more. This large quantity was
chosen in order to obtain a representative sample of the crop micronutrient status
within each plot. This was important, because plant-to-plant variation in
micronutrients can be large (Blair et al. 2013), and we hoped to capture plot-average
micronutrient density for each crop sampled. A kilogram of crop matter, gathered
across each plot, should suffice for this purpose (Glahn 2013 personal communication).
28
Appendix 2
Households with a child under 5 years of age were on average younger, more educated,
and larger, with household heads more often male and more often married. They also
appear to be slightly better o↵ in terms of assets. In Table A1, means are given
according to the variable as measured. For variables distributed normally, the t-test was
also conducted using the measured variable. For those variables distributed log
normally, the t-test was instead conducted using the log of that variable. Log normal
variables include cattle ownership, sheep/goat ownership, land hectares and land value,
and all income variables.
Table A1: Household Selection on Having a Child less than 5 Years of Age
Head Married (%)
Head Male (%)
Head Age (years)
Head Education (years)
Spouse Education (years)
Household Size
Cattle (#)
Sheep & Goats (#)
Household owns Motorcycle (%)
Household owns Bicycle (%)
Agricultural Land (hectares)
Agricultural Land Value ($)
Household Income ($)
Income from Livestock Products ($)
Income from Selling Livestock ($)
Crop Income
Has Child
Mean
SD
No Child
Mean
SD
0.84
0.82
45.83
6.08
4.35
7.92
1.88
1.86
0.09
0.64
3.21
2549
2131
268
303
1714
0.64
0.68
53.39
5.36
4.16
4.95
1.81
1.48
0.07
0.48
2.96
5900
2450
367
312
3805
29
0.37
0.39
12.72
3.61
3.52
2.29
3.98
2.74
0.29
0.48
3.49
3290
5178
532
728
1066
0.48
0.47
15.54
4.34
3.57
2.54
6.15
3.5
0.26
0.5
3.94
23432
5564
693
645
22267
T-stat
-4.42***
-3.48***
5.56***
-1.87**
-0.48
-11.07***
0.40
1.74**
-0.77
-3.32***
-1.08
-0.21
-0.78
2.08**
1.20
-0.54
Appendix 3
Food Frequency Questionnaires (FFQs) are a common tool for procuring the dietary
intake of a select group of vitamins or minerals, rather than attempting a to gather
comprehensive data on all dietary intake. In an FFQ, an individual (or caretaker) gives
the number of times that each item on a list of foods was eaten during a particular
recall period — in our case one week. In some cases, and in our survey, an average
portion size is also chosen for each food item. We illustrated portion sizes (small,
medium and large) with a book of pictures, as shown in figures A2 and A3. Caretakers
were also shown the physical plate on which the pictures were taken.
Foods listen on an FFQ are chosen to represent the major dietary sources of the
nutrients of interest, according to prior and more comprehensive food recall data. Our
nutrients of interest were zinc and selenium, and the foods listened on our FFQ capture
98 percent of all zinc consumption in Ugandan children under five, according to detailed
food recall data gathered in central and eastern Uganda by the Reaching End Users
(REU) project, a HarvestPlus initiative introducing orange-fleshed sweet potato to
farmers in rural Uganda (Hotz et al. 2012).27
It was not possible to easily gauge the proportion of selenium intake captured by our
FFQ, because the food consumption table put together by HarvestPlus for the REU
project did not list selenium density of foods. However, the major sources of selenium
are quite similar to the major sources of zinc (Figures A4 and A5), and additional food
items (dishes with mushrooms, dairy and fish) were added to the FFQ in order to
capture intake of foods particularly rich in selenium. Thus, it seems likely that over 90
percent of selenium intake, at least, was also captured by our FFQ.
Most food items listed in the FFQ were dishes (e.g., cooked cassava and beans), rather
than food items (e.g., mango or groundnuts). Recipes for each dish were chosen from
the HarvestPlus recipe list, a comprehensive list of all common recipes used in the REU
project areas in central & eastern Uganda.28 Portion sizes were for each food item and
dish were weighed out in grams, with weights having been chosen as quintiles in the
FEU food recall data.
27
In using the REU food recall data to construct a list of foods for the FFQ, I followed protocols laid out
by a technical document written by Christine Hotz, the primary architect of the REU Food Frequency
Recall. That food recall was focused on vitamin A consumption, and so the recall itself was not useful
for our purposes, but the methodology — and data to conduct it — was highly useful.
28
If multiple recipes were listed then the recipe most common in the REU food recall data was chosen.
If a recipe was not listed (e.g. sorghum porridge, common in our survey district of Kabale but not in
central or eastern Uganda), then I modified the closest recipe to it (e.g. maize porridge).
30
Figure A1: Portion Size Picture of
Figure A2: Portion Size Picture of
Mukene (Small Fish)
Katogo (Cassava & Beans)
Figure A3: Sources of Dietary Zinc
Figure A4: Sources of Dietary Selenium
Intake, by 2003 District
Intake, by 2003 District
31
Appendix 4
Equation 10 models zijf , the zinc density of crop f grown on plot j by household i. It
shows zijf to be a function of soil zinc, zij , and other soil characteristics that e↵ect the
availability of soil zinc to the plant, availij . A reduced form is adopted for the
estimation of equation 10, including interactions between soil zinc, crop variety, and the
key soil characteristics that impact soil zinc availability. These characteristics are pH
and pH2 , manganese (Mn) and cadmium (Cd). Soil zinc is also interacted with county
fixed e↵ects, to capture environmental or unobserved soil characteristics impacting zinc
availability.
Included soil characteristics were chosen according to soil science literature on zinc
availability. Soil pH is a primary determinant of soil zinc availability to plants.
Sillanpää (1972) writes that the range of lowest zinc availability is pH 6-7, because in
this range soil zinc tends to form insoluble calcium zincates which are less available to
plants. Manganese also impacts zinc availability, as zinc and manganese form
unavailable complexes within certain pH ranges (Havlin et al., 2005). Zinc often
competes with cadmium for plant uptake, likely because the two cations share the same
carrier site (Duxbury personal communication, Oct 2 2014 ).
Other minerals and soil characteristics are also known to impact soil zinc availability
(e.g., iron, sand content, calcium), and a number of extended model specifications were
examined. However, it was found that once zinc interactions with pH, manganese and
cadmium were included, additional soil characteristics add very little to the model R2 .
Given a fairly small sample for most crops, these extraneous variables were dropped
from the model.
Table A2 summarizes this core model (panel 2), along with a model including only crop
variety fixed e↵ects (panel 1), and a model including only soil characteristics (panel 3).
In each case, column 1 pools all crops, while columns 2-7 give crop-by-crop estimations.
Panels 2 and 3 give the elasticity of crop zinc with respect to soil zinc, ✏z , along with
the confidence interval around this marginal e↵ect. All three panels give the associated
R2 and observation number N.
Panel 2 of Table A2 is the preferred specification, as it has the highest R2 values.
However, if crop variety is not known, the model in panel 3 predicts crop zinc quite
accurately using only soil characteristics and zinc-county interactions, the latter of
which captures some regional variation in common crop varieties. Figure A5 illustrates
crop zinc estimates derived from panel 3 model.
32
Table A2: Marginal E↵ect of (Log) Soil Zinc Concentration on (Log) Crop Zinc Content
(1)
All Crops
(2)
Maize
(3)
Sorghum
(4)
Sw. Potato
(5)
Cassava
(6)
Beans
(7) )
Gnuts
1.
Varieties
R2 =0.587
N=586
R2 =0.058
N=235
R2 =0.025
N=228
R2 =0.222
N=119
R2 =0.126
N=235
R2 =0.086
N=228
R2 =0.049
N=119
2.
Varieties &
Soil Traits
✏z =0.221
[.061 .38]
R2 =0.635
N=586
✏z =0.138
[-.501 .778]
R2 =0.505
N=235
✏z =-0.107
[-.533 .318]
R2 =0.354
N=228
✏z =0.105
[-.432 .641]
R2 =0.492
N=119
✏z =0.283
[-.252 .819]
R2 =0.526
N=235
✏z =0.071
[-.239 .098]
R2 =0.426
N=228
NA
NA
NA
N=119
3.
Soil
Traits
✏z =0.285
[.1 .469]
R2 =0.165
N=586
✏z =0.127
[-.341 .595]
R2 =0.421
N=235
✏z =0.065
[-.195 .325]
R2 =0.228
N=228
✏z =0.146
[-.496 .788]
R2 =0.280
N=119
✏z =0.375
[-.219 .969]
R2 =0.453
N=235
✏z =-0.092
[-.22 .035]
R2 =0.350
N=228
✏z =0.371
[-.958 1.7]
0.973
N=119
Panel 1 Regressors: variety
Panel 2 Regressors: Zn, variety, variety*Zn, pH, pH*Zn, pH2 , pH2 *Zn,
county*Zn, Mn, Mn*Zn, Mn*Zn*pH, Cd, Cd*Zn, Cd*Zn*pH
Panel 3 Regressors: Zn, pH, pH*Zn, pH2 , pH2 *Zn, county*Zn,
Mn, Mn*Zn, Mn*Zn*pH, Cd, Cd*Zn, Cd*Zn*pH
Figure A5: Crop Zinc Content Predictions)
Appendix 5
It is unlikely that processing accounts for the di↵erence between household and market
zinc content, as there was no di↵erence between the market vs. home processing of
cassava, millet and sorghum, or beans and cowpeas. There was a slight di↵erence for
maize; household-sampled maize was ground into unrefined maize flour, while maize
flour purchased at market had been ground at local mills, and often refined or partially
refined. But while refinement might lead to 10-50 percent decrease in maize zinc
content, it is unlikely to account for an 83 percent decrease. HarvestPlus FCT values
indicate that maize processing decreases zinc content by 60 percent, substantially less
than the 83 percent di↵erential observed in our data. And even this value may be
higher than appropriate, given that the HarvestPlus value for refined flour is derived
from the USDA FCT value for refined, de-germed cornmeal. (This is NDB No 20022 in
the most recent USDA table, Nutrient Database Release 26.) Locally-owned Ugandan
mills de-husk, but do not de-germ, cereals, therefore preserving a greater proportion of
zinc and other nutrients. In a study of such locally-owned mills in Benin, Gre↵euille
et al. (2011) finds that processing dry maize grain (as is done in Uganda) decreases zinc
content by only 11 percent, and processing wet, washed maize grain decreases zinc
content by 54 percent.
Degradation of nutrients is also unlikely to account for the di↵erential, given that
metals do not degrade significantly over time. It is possible that farmers are more likely
to sell larger crops to market, and keep smaller crops at home. Because cereal size is
often negatively associated with cereal nutrient content, such selection might lower the
mean nutrient content of market crops. Crop variety is similarly correlated with crop
zinc (as well as crop size), and it is also possible that farmers sell particular varieties of
crop to market, and keep other varieties at home.
Given the soil-to-crop zinc transmission examined in Appendix 4, it seems plausible
that farmers might sell crops di↵erentially according to their beliefs about plot-level soil
nutrients. That is, they might be more likely to sell crops from less fertile (and lower
zinc) plots, and to consume crops from fertile (and higher zinc) plots. This would
mirror the findings of Ho↵man et al. (2015), who observed that farmers in Kenya were
more likely to sell high-aflatoxin maize, and to keep low-aflatoxin maize for home
consumption. However, farmers in Uganda generally mix crops from all fields during the
drying process, which occurs before selling, and so it seems unlikely that they are
di↵erentially selling by plot. It also seems unlikely that they can pick out low zinc or
high zinc crops by eye after drying, except insofar as zinc is correlated with crop size.
There is no other observable characteristic correlated with zinc, unless crops are highly
zinc deficient, which they are not in this context.
Soil macro- and micronutrients are lower on large plots and on large firms. Because
land size is highly correlated with crop quantity sold to market, this suggests that crops
at market are largely sourced from farms on the lower end of the fertility spectrum.
Along with crop size and crop variety, this farm-level selection may help to explain the
low zinc content of market crops.
34
Appendix 6
While Table 3 display random e↵ects logit estimation of home consumption ratio, other
estimation strategies were considered.
For instance, table A3 displays a conditional (fixed e↵ect) logit estimation of home
consumption ratio, for each crop. Because household characteristics and location do not
change substantially over time for the majority of households in our dataset, the
coefficient estimates in these sections are imprecisely estimated. It is clear, however,
that the production of a crop (a factor that does shift over time) is significantly,
positively associated with the home consumption ratio of that crop. The o↵-diagonal
coefficients in the top panel of Table A3 appear smaller and less significant than they do
in Table 3 — suggesting that cross-crop associations in this table is biased by fixed
household characteristics such as land quality or location.
Table A4 displays a Heckman-adjusted probit with estimation of home consumption
ratio, adjusting for selection into the dataset. The selection equation estimates the
visibility of home consumption ratio via probit, with a dummy for production of the
relevant crop being used to identify selection. This identifying variation may not be
perfect, but it does hold that, conditional on log production being included in the
intensity (home consumption ratio) equation, binary production is an insignificant
predictor for most crops.
Ultimately the random e↵ects logit was chosen over either of these models, as it
predicted outcomes most accurately. Predictions from a conditional logit require strict
assumptions about outcomes within groups — generally assuming either an identical
mean for every group, or one and only one positive outcome per group. Thus, while the
the conditional logit model is useful for regression analysis, it is very poor at prediction.
The Heckman-adjusted probit model predicts probabilities similar to those of the
random e↵ects logit model, but with slightly less accuracy.
35
Table A3: Explaining Household Consumption Ratio by Heckman-Adjusted Probit with District FE
Household Production
Maize (log kg)
Sorghum/Millet (log kg)
Sweet Potato (log kg)
Cassava (log kg)
Beans (log kg)
Groundnuts (log kg)
House Characteristics
Household Size
Non-farm income (log)
Bike (dummy)
Geography/Time
(Dist to City)*Weeks
(Dist to City)*Weeks2
(Dist to Road)*Weeks
(Dist to Road)*Weeks2
Observations
Number of hhid
(1)
Maize
(2)
Sorghum/Millet
(3)
Sweet Potato
(4)
Cassava
(5)
Beans
(6)
Groundnuts
0.119***
(0.0267)
0.0505
(0.0331)
0.0248
(0.0248)
0.0399*
(0.0210)
-0.0261
(0.0315)
0.0284
(0.0316)
0.00735
(0.0384)
0.168***
(0.0363)
0.0990***
(0.0354)
0.0230
(0.0317)
-0.0189
(0.0563)
0.0395
(0.0498)
-0.0116
(0.0448)
0.0228
(0.0461)
0.134***
(0.0395)
0.0447
(0.0341)
0.0779
(0.0531)
-0.00513
(0.0576)
-0.0170
(0.0283)
0.0291
(0.0332)
0.0740***
(0.0264)
0.121***
(0.0210)
-0.0120
(0.0368)
0.0207
(0.0334)
-0.00114
(0.0229)
0.0474*
(0.0263)
-0.000437
(0.0214)
-0.0254
(0.0187)
0.125***
(0.0256)
0.000747
(0.0291)
-0.0251
(0.0399)
0.0334
(0.0440)
-0.0571*
(0.0347)
0.0232
(0.0278)
0.0198
(0.0424)
0.180***
(0.0373)
0.0272
(0.0329)
-0.0156
(0.0209)
0.410***
(0.154)
0.00841
(0.0603)
-0.0497
(0.0329)
0.135
(0.265)
0.0728
(0.0501)
-0.00736
(0.0338)
0.601**
(0.242)
-0.0165
(0.0372)
0.0313
(0.0227)
0.212
(0.163)
0.0169
(0.0303)
-0.00151
(0.0179)
0.251*
(0.137)
0.0224
(0.0428)
-0.0288
(0.0266)
-0.0862
(0.222)
-0.00279
(0.00245)
5.71e-05
(4.37e-05)
0.000594
(0.00260)
-1.45e-05
(4.81e-05)
-0.00166
(0.00381)
3.11e-05
(6.73e-05)
0.000904
(0.00415)
1.62e-05
(7.74e-05)
0.00640*
(0.00364)
-0.000111*
(6.31e-05)
0.000567
(0.00402)
-3.13e-05
(7.51e-05)
0.00265
(0.00250)
-5.23e-05
(4.42e-05)
0.00614*
(0.00315)
-0.000115**
(5.78e-05)
-0.00153
(0.00190)
1.87e-05
(3.45e-05)
0.000256
(0.00212)
-3.67e-06
(3.92e-05)
-0.000753
(0.00310)
2.73e-05
(5.74e-05)
0.00667*
(0.00379)
-0.000139*
(7.27e-05)
1,664
554
831
291
701
270
1,528
492
2,244
674
932
350
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
All estimations control for the producer price of all 6 crops (averaged by enumeration area),
for round, and for weeks and weeks squared by region
Table A4: Explaining Household Consumption Ratio by Heckman-Adjusted Probit
Household Production
Maize (log kg)
Sorghum/Millet (log kg)
Sweet Potato (log kg)
Cassava (log kg)
Beans (log kg)
Groundnuts (log kg)
House Characteristics
Household Size
Non-farm income (dollars)
Bike (dummy)
Geography/Time
Distance to City (km)
(Distance to City)2
Distance to Road (km)
(Distance to Road)2
(Dist to City)*Weeks
(Dist to City)*Weeks2
(Dist to Road)*Weeks
(Dist to Road)*Weeks2
Observations
(1)
Maize
(2)
Sorghum/Millet
(3)
Sweet Potato
(4)
Cassava
(5)
Beans
(6)
Groundnuts
0.0891***
(0.0174)
0.0257***
(0.00941)
0.00136
(0.00756)
0.0481***
(0.00671)
-0.0341***
(0.00921)
0.0346***
(0.00937)
0.0402***
(0.00954)
0.0187
(0.0161)
0.0266***
(0.00970)
0.0115
(0.00847)
0.0363***
(0.0119)
0.0281**
(0.0121)
0.0302**
(0.0144)
0.00920
(0.0144)
0.119**
(0.0594)
0.0187*
(0.0110)
0.0597**
(0.0298)
0.0174
(0.0143)
0.0426***
(0.00751)
0.00503
(0.00955)
0.0322***
(0.00824)
0.115***
(0.00666)
0.0129
(0.0127)
0.0789***
(0.00936)
0.0214***
(0.00676)
-0.00736
(0.00773)
0.000600
(0.00673)
0.00469
(0.00594)
0.112***
(0.00831)
0.00670
(0.00847)
0.0126
(0.0157)
0.0397***
(0.0119)
0.00409
(0.0106)
0.0214*
(0.0116)
-0.00249
(0.0138)
0.144
(0.0893)
0.000480
(0.00732)
-3.56e-05***
(9.10e-06)
0.218***
(0.0464)
-0.0237***
(0.00920)
-4.39e-05***
(1.18e-05)
0.0945*
(0.0539)
0.0378***
(0.0114)
-2.58e-05**
(1.30e-05)
0.364***
(0.0650)
-0.00296
(0.00747)
-1.50e-05
(9.42e-06)
0.305***
(0.0406)
-0.0108
(0.00668)
-3.39e-05***
(8.01e-06)
-0.0135
(0.0377)
-0.00795
(0.0101)
-8.47e-06
(1.07e-05)
0.0693
(0.112)
0.0143
(0.0119)
-4.46e-07
(0.000211)
0.0363***
(0.0121)
-0.00159***
(0.000297)
-0.00181**
(0.000727)
3.41e-05**
(1.33e-05)
0.00123
(0.000821)
-1.97e-05
(1.52e-05)
0.00998
(0.0150)
0.000140
(0.000265)
0.0185
(0.0165)
-0.000859**
(0.000406)
-0.00105
(0.000970)
1.73e-05
(1.81e-05)
0.00135
(0.00106)
-2.07e-05
(1.96e-05)
-0.0102
(0.0169)
-0.000323
(0.000342)
0.0408**
(0.0205)
-0.00127*
(0.000683)
0.00189
(0.00124)
-3.26e-05
(2.16e-05)
-0.000577
(0.00119)
1.42e-05
(2.32e-05)
-0.0187*
(0.0111)
0.000495***
(0.000190)
0.0523***
(0.0125)
-0.00126***
(0.000309)
0.000214
(0.000696)
-5.31e-06
(1.28e-05)
-0.00120
(0.000790)
2.02e-05
(1.45e-05)
0.0286***
(0.00997)
-0.000556***
(0.000174)
0.0144
(0.0105)
-0.000403*
(0.000241)
-0.000473
(0.000613)
7.00e-06
(1.14e-05)
6.96e-05
(0.000716)
1.31e-06
(1.31e-05)
0.0414***
(0.0141)
-0.000648**
(0.000256)
0.000843
(0.0159)
-0.000785**
(0.000381)
-0.00134
(0.000931)
2.30e-05
(1.76e-05)
0.00254**
(0.00106)
-4.88e-05**
(1.99e-05)
6,135
6,135
6,135
6,135
6,135
6,135
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
All estimations control for the producer price of all 6 crops (averaged by enumeration area),
for round, for weeks and weeks squared by region, and for district dummies