SSM - Population Health 4 (2018) 117–125
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SSM - Population Health
journal homepage: www.elsevier.com/locate/ssmph
Article
Food insecurity and family structure in Nigeria T
Nkechi S. Owoo
University of Ghana, Department of Economics, P. O. Box LG 57, Accra, Ghana
A R T I C L E I N F O A B S T R A C T
Keywords: The article explores a series of questions and hypotheses related to polygynous family structures and both
Polygyny household and individual-level food security outcomes, using the World Bank Living Standards Measurement
Food security Survey data from Nigeria, collected in 2011, 2013 and 2015. A Correlated Random Effects (CRE) model is used
Child health to examine the relationship between polygyny and household-level food security, and the degree to which it is
Nutrition mediated by household wealth, size, and livelihood. A Household Fixed Effect model is employed to explore
Nigeria
whether a mother’s status as monogamous versus polygynous relates systematically to her child’s health, and
also whether child outcomes of senior wives are better than outcomes of junior wives within polygynous
households. At the household level, polygynous households are found to have better food security outcomes than
monogamous households with differences in household composition and agricultural livelihood as potential
explanatory mechanisms. At the individual level, however, children of polygynous mothers have worse nutrition
outcomes than children of monogamous mothers in the long run. Within polygynous households, children of
junior wives appear to have better nutritional outcomes in the long run, compared to children of more senior
wives.
Introduction vary within the households and as a direct function of intra-household
characteristics, such as household structure and decision-making pro-
Progress toward achieving food security is often cited, with focus cesses. Family structure in Nigeria is complex and varied, with potential
typically on global progress toward the Millennium Development and implications for resource distribution and bargaining power that are
World Food Summit goals, that estimate the proportion and numbers likely to be important determinants of food security at the household
(respectively) of the population that is undernourished (State of Food and individual levels (Nazli & Hamid, nd).
Security and Nutrition-SOFI, 2015). Nonetheless, not only have the This paper explores the relationship between polygyny (the still
numbers of the estimates of those globally affected actually increased in common practice of a man marrying more than one wife) and food
some areas, but progress is uneven. Existing indicators mask the un- security, as measured by both household-level dietary diversity and
derlying distribution, including both regional variation within countries coping strategies indicators, and individual level child anthropometric
and variation within households (Barrett, 2010). Among the most dif- outcomes. Polygyny is hypothesized to have a significant relationship
ficult issues to understand and measure is that food insecurity is an with food security outcomes at the household level, after controlling for
individual concept, and different members of specific households can household structure, wealth and other relevant factors. In turn, children
experience different outcomes—men versus women, adults versus of mothers in polygynous unions have different individual health out-
children, and potentially even different children within the same comes than children of mothers in monogamous unions. Finally, the
household. mother’s status within a polygynous union can also be important and, in
Nigeria is of particular interest given that the numbers of in- particular, children of senior wife mothers in polygynous settings are
dividuals experiencing food insecurity is rising. According to a Food likely to have different individual health outcomes from children of
and Agriculture Organization, FAO (2015) report, despite Nigeria junior wives.
having achieved the reduction of undernourishment of the population The question of how polygyny affects the distribution of power and
by more than half, from 19.3% in 1990 to 8.5% in 2010 to 2012, the subsequent household welfare has been explored. Some studies find a
number of people who are undernourished in Nigeria increased from positive association between polygyny and household welfare
roughly 10 million to almost 13 million from 2010 to 2012. Ad- (Anderson, Reynolds, Biscaye, Greenaway & Merfeld, 2016; Akresh,
ditionally, there is regional, rural, urban, and cultural variation in food Chen & Moore,2012). Akresh et al. (2012) use a game theoretic ap-
security across the country. Food insecurity in Nigeria is also likely to proach and show that there is greater efficiency in agricultural
E-mail address: nowoo@ug.edu.gh.
https://doi.org/10.1016/j.ssmph.2017.12.004
Received 12 October 2017; Received in revised form 27 November 2017; Accepted 18 December 2017
2352-8273/ © 2017 The Author. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
N.S. Owoo SSM - Population Health 4 (2018) 117–125
production in polygamous households in West Africa, compared to analysis.
monogamous households, largely attributable to co-operation among The survey defines a household as a social unit consisting of one or
co-wives in this setting. Co-operative outcomes are not always by more people who are or are not related, and who live in the same
choice, however. According to Dauphin (2016), a wife may be forced to household unit; that is, live under the same roof, and who eat together;
cooperate under a husband’s threat to take an additional wife if she that is “eat from the same pot”. This definition and its application in
does not. Dauphin (2016) found a negative correlation between poly- practice have implications for the nature of the responses to food se-
gyny and efficiency, as measured by agricultural production in Benin, curity questions, in particular for polygynous households. First, while in
Burkina Faso and Senegal. Other studies also find a negative relation- principle a respondent is to be a knowledgeable person answering on
ship between polygyny and efficiency. For example, Kazianga and behalf of all household members, a potential limitation lies in that it is
Klonner (2009) point to co-wife rivalry as a driver of inefficient out- difficult to be certain that a respondent in a polygynous setting is in fact
comes, namely health disparities between wives in rural Mali. Other answering for all co-wives and children, as opposed to for his or her
studies find that efficiency in polygynous households tends to be con- specific family unit within the household. The child-level analysis,
text-specific. For instance, Han and Foltz (2015) found that the degree however, overcomes this limitation, as it addresses specific children of a
of co-wife competition or cooperation in Mali depends on the cultural certain age regardless of their mothers’ status. Second, this definition of
characteristics of polygyny. Using ethnic groups as a proxy, the authors a household also has implications for how polygyny is handled in this
found that among the Dogon, Fulani, and Bambara, there were differ- paper; some polygynous households may have wives who would not be
ences in child health outcomes as a result of unobserved characteristics considered as family members if they live in different locations and
linked to ethnicity. Munro, Kebede, Tarazona-Gomez, and Verschoor therefore do not “eat from the safe pot”. Households are classified as
(2010), however, found no difference in household efficiency between being polygynous if co-wives are listed in the household roster, there-
monogamous and polygynous households in their experimental study fore, and just by the husband reporting that he is married to multiple
conducted in northern Nigeria. Here, the total endowment invested in a women.
common pool by monogamous and polygynous wives did not differ, For household-level outcome variables, two indices of food security
indicating an absence of efficiency loss from polygyny. Where husbands are constructed, in order to reflect different aspects of the availability of
controlled the allocations however, there was higher investments of and access to food. First, dietary diversity is examined through the Food
household resources under monogamous unions; and polygynous hus- Consumption Score (FCS), following the World Food Programme ap-
bands’ investments tended to favour first wives. Husbands were the proach put forward by Weismann, Bassett, Benson & Hoddinott (2009).
ultimate gainers from the household allocation of resources. All of these The FCS uses information on the frequency of consumption in the week
findings point to ambiguous effects of polygyny on household level prior of cereals, tubers, pulses, vegetables, fruits, meats and fish, milk,
measures of food security. sugar and oil. Higher scores are indicative of better food security. To
Food security is best considered individually, since different mem- reflect other dimensions of food security, such as economic and social
bers of the same households can experience different outcomes based access to food, the Reduced Coping Strategies Index (RCSI) is con-
on gender, age, or other factors. Different children within the same structed, following Maxwell, Vaitla, Tesfay and Abadi (2013). The RCSI
household may have different food security outcomes (Sellen, 1999; provides information on household behaviour or coping strategies in
Wagner and Rieger, 2015). The relationship between polygyny and the presence of food deficits. It is constructed from self-reported prac-
individual children’s health outcomes most likely operates through ef- tices, including relying on less preferred foods, limiting portion sizes
ficiency channels, while at the same time depending on characteristics and the number of meals eaten, and reducing meals so as to give
of the child’s mother. Polygyny is generally negatively correlated with priority to children.
female bargaining power; co-wives in polygynous households wield less For child-level food security, child anthropometric measures are
bargaining power than their monogamous counterparts because the used. The height-for-age z-score (HAZ) compares children’s height
value of individual wives’ assets in the latter, on which bargaining against global averages for that age (in months). Children’s skeletal
power may be based, is smaller, given that multiple wives contribute to (linear) growth may be compromised due to constraints to nutrition or
household welfare (Anderson et al., 2016). health, making HAZ a good indicator of stunting, resulting from long-
These relationships are examined using the nationally-re- term or chronic nutritional deprivation. The weight-for-height z-score
presentative Nigeria General Household Survey, collected as part of the (WHZ) is also considered. As children suffer thinness resulting from
Living Standards Measurement Survey – Integrated Surveys on energy deficit and disease-induced poor appetite, or loss of nutrients,
Agriculture (LSMS-ISA) project of the World Bank. Three waves of the the WHZ is a fitting indicator for wasting, or more transitory nutritional
data are used to run correlated random effects (CRE) and Household deprivation.
fixed effects (FE) estimators, in order to convincingly examine re-
lationships and mechanisms. The present research contributes to the Summary statistics
existing literature in the following ways. First, appropriate and na-
tionally representative data is employed in carrying out micro-level Summary statistics of variables from wave 1 (2010/2011) are pro-
analyses of food security in Nigeria. Second, the study builds on lit- vided in the table below. It is noted in the descriptions where averages
erature on both intra-household bargaining and the nature and im- differ greatly between waves. About 23% of households in the data
plications of the practice of polygyny, with the specific application of its were in polygynous unions. While the rate of polygyny has been on the
implications for food security in Nigeria. decline in recent years, it remains a defining feature of household
structure in the Nigerian context (Fenske, 2011). Polygynous and
Materials and methods monogamous households differ significantly with respect to participa-
tion in formal education and the highest education level attained by any
The study employs nationally-representative data from the Nigerian household member, with education levels higher in monogamous
General Household Survey (GHS), containing information collected households. While only 12% of household members in monogamous
from 5000 households. The data consists of three waves, 2010/2011, households report having no formal education, 21% of members in
2012/2013 and 2014/15, and each wave consists of two seasons, post- polygynous households had no education. Additionally, in 33% of
planting and post-harvest. Post-harvest data is primarily relied on, only monogamous households, the highest educational qualification among
updating missing information using the post-planting rounds, as the members was a secondary school education, compared to only 20% in
data in this season included information on both household-level food polygynous households. Across all households, roughly 89% of heads in
security and child anthropometric outcomes that were necessary for the the sample are employed.
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N.S. Owoo SSM - Population Health 4 (2018) 117–125
The study sample is predominantly rural, with only about 29% of Theory/ calculation
respondents based in urban areas. Consistent with existing literature,
polygyny is predominantly a rural phenomenon; only 16% of poly- Building directly on the diverse—and often conflicting—findings in
gynous households were based in urban areas, compared with 33% of the literature, a series of questions and hypotheses related to poly-
monogamous households. gynous family structures and household-level food security outcomes
Religious dummies were constructed for household heads and it is are explored, as well as child-level health outcomes in Nigeria.
observed that a majority of polygynous households reported being
Muslim; 77% of household heads in polygynous unions are Muslim. The Estimation strategy
higher proportion of Muslims among polygynous households is not
surprising, as Muslim men’s right to marry multiple wives is rooted in As mentioned above, it is difficult to make causal claims about the
the Koran. There is, however, a reasonably high incidence of polygyny nature of the relationship between polygyny and child health or nu-
among Christians also (21% of polygynous households are Christian). trition outcomes. Descriptive analyses of these relationships are there-
With respect to household composition, the dependency ratio, that fore provided, in addition to a series of robust correlations, so as to test
is the ratio of children and the elderly to total household members, is the hypotheses about the relationship between food security and
higher in polygynous households, as is the number of children below 5 polygyny, and elucidate the underlying mechanisms that may be at
and 15 years of age. Polygynous households have dependency ratios play.
and the number of children under 5 years and under 15 years to be It may be expected that unobservable household characteristics si-
0.52, 1.74 and 4.72 on average, respectively. Monogamous households multaneously influence a household’s propensity to have multiple wives
have smaller numbers of 0.46, 0.94 and 2.54, respectively. The average and a household’s food security status. That is, there is selection into
household size for polygynous households is 9.43 members, compared polygyny on unobservables. A common approach in this case would be
to 5.66 members for monogamous households. Finally, polygynous to include a household-level fixed effect, since a household fixed-effect
households in the sample were characterized by a higher share of fe- may account for these omitted variables, to the extent that these un-
males in the household of 0.53, compared to 0.48 for monogamous observables are time-invariant. However, a fixed-effects model cannot
households, and the former also had a higher number of adult women address inter-temporal selection into polygyny based on time-varying
in the household, compared to the latter. Thus, while more labor is unobservables at the household level, nor is it useful for identifying the
available in polygynous households, each worker still has on average coefficient of interest on polygyny, which is for the most part time in-
more members to support. variant. A random effects model may allow for identification of the
With respect to household wealth, results indicate that a greater coefficient on polygyny, but the essential assumption of a random ef-
proportion of monogamous households were found in the higher wealth fects model, that the household-specific random effect is uncorrelated
quintiles, compared to polygynous households. Twelve percent of with selection into polygyny and other control variables, is unlikely to
polygynous household belonged to the richest wealth quintile, com- hold.
pared to 24% of monogamous households. Although food and total Due to polygyny’s limited variation over time, a correlated random
household expenditures were higher in polygynous, compared to effects model (CRE) is estimated at the household level, as an approx-
monogamous households, the reverse is true once per capita measure- imation of a fixed effects model that allows the identification of coef-
ments are employed. In per capita terms, monogamous households had ficients on time-invariant characteristics. For child-level outcomes,
annual food and total household expenditures of $304 and $404, while however, a household fixed effects model is employed. Given intra-
polygynous households had lower food and total household ex- household variation, coefficients of interest such as mothers’ char-
penditures of $227 and $277. acteristics can be examined, while controlling for all time-invariant
There does not appear to be significant differences in livestock household-level traits with the fixed effect.
ownership, as measured by Tropical Livestock Units (TLUs), or in total Hypotheses and specific empirical models are developed below, first
land size between polygynous and monogamous households. Using a for the household-level, and then for the child-level.
dummy variable for household experiences of idiosyncratic shocks,1 it
is observed that there were no differences between polygynous and Household-level analysis
monogamous households in the incidence of shock experience. Finally,
polygynous and monogamous households’ geographical distribution Four hypotheses are developed regarding the relationship between
indicate a prominence of polygynous unions in the northern parts of the polygyny and household-level food security:
country, versus the south, particularly in the north-western zone.
It is observed that polygynous households reported resorting to 1. Polygyny has a relationship with food security independently of
fewer coping strategies than monogamous households did. As men- wealth, household structure, and agricultural livelihood strategy.
tioned earlier, these indicators may be limited when it comes to poly- 2. While household-level wealth should, on average, relate positively
gynous households, as it is difficult to be certain that any given re- to food security as it improves access to food, for polygynous
spondent reports the food security situation for his or her own sub- households, the effect of wealth on food security is different than for
family unit, or for all members of the household.2 monogamous households due to different bargaining structures.
T-tests comparing child nutrition outcomes between monogamous 3. In polygynous households, the effect of household structure on food
and polygynous mothers, and between senior and junior wives indicate security is different than in monogamous households.
that while children of monogamous mothers had better HAZ outcomes 4. In polygynous households, the effect of an agricultural livelihood
than children of polygynous mothers, within polygynous households, strategy on food security is different than in monogamous house-
children of junior wives fared better than children of senior wives holds.
(Table 1).
To test these hypotheses, a basic CRE model is set out as follows:
FSht = αPht + γ1Xht + γ2Xh + δTt + τh + εht (1)
1 These include the following shocks; death/disability/ illness/ departure of a working In this model, FS refers to food security (as measured by FCS and
adult, death of someone who sends remittances, loss of an important contact, job loss, ht
nonfarm business failure, theft of crops, cash or livestock, destruction of harvest, de- RCSI) for household h at time t and Pht is a dummy variable for whether
struction of dwelling. a household is polygynous (Pht = 1) or not (Pht = 0). The set of control
2 FCS and RCSIs for other waves shown in Appendix A. variables is represented as as Xht , all of which vary across households
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Table 1
Summary statistics of household-level variables, by Polygyny: Nigerian general household survey, baseline data, 2011.
Aggregate sample Monogamous Polygynous T-tests
Mean SD Mean SD Mean SD Mono- Poly
Polygyny 0.225 0.42 – – – – – –
Gender of household head (male) 0.999 0.03 0.999 0.04 1 0 -0.00135 (-1.08)
Age of household head 48.813 14.58 48.146 14.8 51.108 13.57 -2.962*** (-5.27)
Highest educational qualification among household members
No education 0.135 0.34 0.116 0.32 0.207 0.41 -0.0910*** (-6.36)
Basic education 0.337 0.47 0.33 0.47 0.365 0.48 -0.0353 (-1.77)
Secondary education 0.301 0.46 0.325 0.47 0.215 0.41 0.110*** (5.72)
Post-secondary education 0.226 0.42 0.23 0.42 0.214 0.41 0.0163 (0.92)
Household head is employed 0.888 0.32 0.887 0.32 0.891 0.31 -0.00392 (-0.32)
Urban locality 0.294 0.46 0.334 0.47 0.156 0.36 0.177*** (10.22)
Religion
Household head is Christian 0.481 0.5 0.559 0.5 0.21 0.41 0.349*** (18.89)
Household head is Muslim 0.501 0.5 0.421 0.49 0.777 0.42 -0.356*** (-19.27)
Household Composition
Dependency ratio 0.479 0.21 0.468 0.21 0.516 0.17 -0.0481*** (-6.08)
Household size 6.511 2.94 5.659 2.21 9.438 3.22 -3.779*** (-39.54)
# household members< 5yrs 1.122 1.17 0.942 0.97 1.739 1.53 -0.797*** (-18.35)
# household members< 15 yrs 3.028 2.23 2.536 1.81 4.719 2.67 -2.183*** (-27.76)
Ratio of female to hh members 0.494 0.16 0.484 0.16 0.53 0.14 -0.0467*** (-7.68)
Adult women (15–65) 1.707 1.03 1.438 0.84 2.63 1.07 -1.192*** (-34.43)
Adult women (>=15) 1.779 1.03 1.506 0.85 2.717 1.07 -1.211*** (-34.75)
Wealth Quintiles
Poorest wealth quintile 0.208 0.41 0.199 0.4 0.24 0.43 -0.0409** (-2.61)
Poorer wealth quintile 0.199 0.4 0.182 0.39 0.26 0.44 -0.0789*** (-5.12)
Middle wealth quintile 0.188 0.39 0.177 0.38 0.227 0.42 -0.0497** (-3.29)
Richer wealth quintile 0.194 0.4 0.206 0.4 0.149 0.36 0.0572*** (3.75)
Richest wealth quintile 0.211 0.41 0.236 0.42 0.124 0.33 0.112*** (7.17)
Per capita food consumption expenditures ($) 286.68 217.03 304.19 230.18 227.07 150 77.12*** (9.14)
Per capita household expenditure ($) 375.35 278.84 404.15 294.72 277.28 185.37 126.9*** (11.79)
Tropical livestock units 24.837 946.44 33.576 1142 5.676 22.31 27.9 (0.60)
Land size (meters square) 441.44 5430 559.30 6337.2 132.59 1180.3 426.7 (1.79)
Idiosyncratic shocks 0.201 0.4 0.198 0.4 0.212 0.41 -0.0135 (-0.87)
Geographical Zones
North central zone 0.172 0.38 0.161 0.37 0.208 0.41 -0.0467** (-3.21)
North east zone 0.187 0.39 0.155 0.36 0.297 0.46 -0.142*** (-9.54)
North west zone 0.219 0.41 0.186 0.39 0.333 0.47 -0.147*** (-9.28)
South east zone 0.132 0.34 0.159 0.37 0.038 0.19 0.121*** (9.34)
South west zone 0.154 0.36 0.178 0.38 0.074 0.26 0.104*** (7.47)
South south zone 0.136 0.34 0.161 0.37 0.05 0.22 0.111*** (8.49)
Reduced Coping Strategies Index (0 - 56) 2.14 5.1 2.43 5.5 1.01 2.9 1.198*** (6.63)
Food Consumption Score 53.24 20.19 53.1 19.96 53.77 21.03 -0.0244 (-0.03)
Observations 3839 2974 865 3839
t-statistics in parenthesis: *p< 0.10,
** p< 0.05.
*** p< 0.01.
and some of which vary across time. Included in this vector are urban composition (number of adult women in the household, Fht , de-
locality dummy, household wealth scores, religion dummy for religion pendency ratio indicator, Dht); agricultural livelihood, Aht; and their
of household head, TLU, education, sex and age of household head. This within-household means are each added in three subsequent specifi-
model also includes a vector of within-household averages of all time- cations. The magnitude or direction of any changes in α are then in-
varying covariates, Xh. To the extent that Xh is correlated with un- terpreted.
observable household characteristics, a fixed-effect control is approxi-
mated. Tt , a term containing the year and region indicator variables and Child-level analysis
their interactions are added, to account for factors common to all
households in a given location and year, such as ecological, economic, In this section, hypotheses are developed, each building on the
or political shocks, or other region-specific time trends. A household previous, related to the relationship between child-level health out-
random effect τh, is included, as well as εht , as the idiosyncratic error comes and the existing family structure. While it is recognized that
term for each household and time period. selection into polygyny is non-random, it is posited that the key features
To test the first hypothesis, (1) is estimated. The coefficient of in- of selection that would be likely to affect child heath, including
terest is α, and the anticipated direction of effect is ambiguous. To test household, parent, and child-level characteristics, are captured in this
the other hypotheses, per capita total consumption Wht ; household formulation. What unobservable factors may remain manifest as
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N.S. Owoo SSM - Population Health 4 (2018) 117–125
differences in bargaining power and cooperation, and as such allow us with a column for the core regression and each step-wise change, and
to test the hypotheses. A household fixed effects model is employed in panels for each child nutritional outcome indicator. It is observed first
order to explore within-variations at the household level. Regressions that children of polygynous mothers had poorer health than children of
are run first, for children of monogamous versus polygamous mothers, monogamous mothers with respect to long-term measures of child nu-
and then run for children of senior and junior wives within polygynous tritional outcomes. This finding contradicts Becker (1981)’s hypothesis
households. that women’s welfare might be better when polygyny is practiced. The
To test the first hypothesis, the following is run: findings are more in line with Han and Foltz (2015). This confirms the
m initial hypothesis, that women in monogamous households may enjoyYiht = α1Piht + γ1Miht + γ2Fiht + γ3Ciht + γ4Xht + δTt + μh + εiht (2) higher bargaining power, which allows them to allocate sufficient re-
In this formulation, Y is the health status (HAZ or WHZ) of child i, sources to their children. In terms of identifying mechanisms, there isiht
in household h, at time t. Pm is a binary variable indicating whether the little supportive evidence for the posited pathways of wealth, house-iht
child’s mother is in a polygynous union. (In a second set of specifica- hold structure, and agricultural livelihoods.
tions for within polygynous household variations, this represents the In Table 3b, results are presented of the effect of wife rank or se-
senior or junior rank of wives). M th niority within polygynous households, on child nutrition outcomes. Iniht contains the i child’s mother’s age,
education, and employment status. F contains the ith child’s father’s order to investigate the effect of wife rank on child nutritional status iniht
age, education, and employment status; C contains child character- polygynous households, observations are limited to only children iniht
istics such as age and birth order. Xht contains other time-varying polygynous households. Consistent with preliminary statistics in
household characteristics (dependency ratio, household size, asset Table 3b, children of junior wives are healthier in the long run, com-
index, TLU and idiosyncratic shocks). T , is a term containing the year pared to children of senior wives. This is consistent with findings byt
and region indicator variables. Han and Folt (2015) and Bove, Vala-Haynes and Valeggia (2014) on
To test the other hypotheses, per capita total consumption W ; their examination of polygyny on child nutrition among children iniht
household composition (number of adult women in the household, F , Mali. The finding contradicts Strauss (1990), who found that children ofiht
dependency ratio indicator, Diht); agricultural livelihood, Aiht ; and their junior wives are more likely to experience stunting and wasting. The
within-household means are each added in three subsequent specifi- finding of better nutritional outcomes for children of junior wives may
cations. The magnitude or direction of any changes in α are then in- hint that junior wives may not be as weak, with respect to their bar-
terpreted. gaining position, as may be expected (Strassmann, 1997).
A reason for this finding of better nutritional outcomes among ju-
nior wives may be that junior wives are younger than senior wives. As
Results and discussion noted above, younger mothers might have better child birth, and sub-
sequently health outcomes (Rutstein & Winter, 2014). Another ex-
Household-level regressions planation proposed by Han and Foltz (2015) is that marriage to a first
wife is usually arranged by the parents, with men having greater in-
The results at the household level are presented in Table 2, with a
fluence in choosing additional wives. Therefore, polygynous husbands
column for the core regression and each step-wise change, and panels may prefer, and thus allocate, more resources to (more favoured) junior
for each household-level food security outcome indicator. It is observed wives of their own choosing. Furthermore, since senior wives and their
first that polygynous households performed better than monogamous offspring had a period of time to enjoy household resources exclusively
households with respect to food security as measured by dietary di- until such a time as an additional wife is brought into the household by
versity, with dietary diversity scores on average 2 to 3 points higher for her husband, junior wives might be able to persuade their husbands
polygynous households with statistical significance at the 1% level. This that it is “their turn” to benefit from household resources. Sellen (1999)
confirms the initial hypothesis, that there is a relationship between also proposes that children of lower ranking ranks may be better off if
polygyny and household-level food security. these women are entering the marriage under more favorable or pros-
In terms of identifying mechanisms, there are some, though weak, perous circumstances.
supportive evidence for the posited pathways of wealth, household
structure, and agricultural livelihoods. In model two, after inclusion of Conclusions
per capita food expenditures, the coefficient on polygynous household
increases and remains significant, indicating that this is not a potential In this study, a series of questions and hypotheses related to poly-
channel of explanation for better food security performance among gynous family structures and both household and individual-level food
polygynous households. Controlling for household structure, however, security outcomes were explored. These questions were examined using
reduced the magnitude of the difference in food security outcomes three rounds of World Bank Living Standards Measurement Survey data
between monogamous and polygynous households. The implication from Nigeria, collected from 2011 to 2014. Analyses at the household
here is that the household make-up of polygynous households differs level involved the use of a correlated random effects model while a
from the composition of monogamous households, and those differ- household fixed effects model was employed for the individual level
ences at least in small part explain the better dietary diversity outcomes analyses.
in the former. Polygynous households, for example, have a larger Although the results of household-level regressions suggested that
number of adult females, which may serve as useful labor on farms. The polygyny has better implications for food security, the results of the
inclusion of land size as a proxy for agricultural participation further household level regressions should be interpreted with caution, given
reduces the magnitude of the polygyny variable, indicating that agri- the noted limitation of the data collection process on food security.
cultural participation may also be part of the relationship between food Individual level regressions indicated that better child nutrition out-
security outcomes and polygyny. comes were found in monogamous households, compared to poly-
gamous households. Within polygynous households, it was found that
Child -level regressions children of junior wives had better long term nutritional outcomes,
compared to children of senior wives.
The results at the child level are presented in Tables 3a and 3b There are other important results from the analyses- wealth and
below. In Table 3a, the results are presented at the individual level, livestock ownership are positively associated with food security at the
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N.S. Owoo SSM - Population Health 4 (2018) 117–125
Table 2
Testing the various hypotheses- regression estimates of polygyny in Nigeria (2010/11, 2012/13 and 2014/15 waves).
Dependent Variables: Food Consumption Scores Reduced-Coping Strategies Index
Hypotheses: 1 2 3 4 1 2 3 4
Per Capita Expenditure 0.00 0.00 0.00 -0.00 -0.00 -0.00
(0.30) (0.09) (1.09) (-0.63) (-0.44) (-0.84)
Dependency ratio 2.12 4.06* 1.21* 1.08
(1.00) (1.74) (1.93) (1.53)
# Adult women -0.39 0.10 0.14 0.06
(-0.93) (0.22) (1.21) (0.47)
Household size -0.03 -0.41 0.01 0.02
(-0.11) (-1.20) (0.16) (0.19)
Land size (logged) -0.19* 0.10**
(-1.67) (2.37)
Polygyny 2.85*** 3.23*** 2.90*** 2.94*** 0.11 0.11 -0.07 0.04
(4.73) (5.28) (4.07) (3.86) (0.73) (0.73) (-0.39) (0.22)
Male head 1.90 0.82 0.91 -0.51 -1.21 -1.25 -1.24 -1.04
(0.89) (0.36) (0.40) (-0.22) (-1.17) (-1.15) (-1.14) (-0.71)
Age of Head -0.06 -0.06 -0.06 -0.09* -0.01 -0.01 -0.01 -0.01
(-1.34) (-1.30) (-1.35) (-1.65) (-0.97) (-0.46) (-0.71) (-0.46)
Education (no education is base)
Basic -0.64 -0.90 -0.82 -1.12 0.18 0.20 0.22 0.03
(-0.68) (-0.95) (-0.87) (-1.14) (0.66) (0.76) (0.81) (0.11)
Secondary 0.13 -0.22 0.01 -0.57 0.30 0.40 0.43 0.33
(0.12) (-0.21) (0.01) (-0.50) (0.96) (1.24) (1.33) (0.96)
Post-secondary 0.10 -0.38 -0.09 -0.18 -0.18 -0.10 -0.07 -0.18
(0.08) (-0.32) (-0.07) (-0.14) (-0.55) (-0.30) (-0.20) (-0.54)
Muslim head -45.01*** -43.17*** -42.96*** -42.81*** -1.12 -1.28 -1.33 0.99
(-7.01) (-5.88) (-5.91) (-5.72) (-0.55) (-0.63) (-0.63) (0.43)
Urban 6.05 8.17* 7.91* 4.93 -1.48* -1.26 -1.29 1.16*
(1.51) (1.86) (1.80) (0.69) (-1.65) (-1.30) (-1.36) (1.86)
Wealth scores 0.70*** 0.58** 0.60** 0.49* -0.35*** -0.31*** -0.31*** -0.25***
(2.97) (2.38) (2.45) (1.73) (-5.06) (-4.36) (-4.32) (-2.94)
Tropical livestock Units 0.00 0.00 0.00 0.00 -0.00*** -0.00*** -0.00*** -0.00***
(0.36) (0.35) (0.34) (0.31) (-4.81) (-4.23) (-4.13) (-3.75)
Shocks 0.48 0.55 0.52 0.92 0.81*** 0.85*** 0.85*** 1.14***
(0.62) (0.71) (0.66) (1.09) (3.41) (3.48) (3.48) (4.24)
Zonal Controls YES YES YES YES YES YES YES YES
Wave control YES YES YES YES YES YES YES YES
Zone*Wave Interactions YES YES YES YES YES YES YES YES
R2- Within 0.02 0.02 0.02 0.03 0.12 0.12 0.12 0.13
R2- Between 0.18 0.18 0.19 0.19 0.19 0.20 0.20 0.27
R2- Overall 0.14 0.15 0.15 0.14 0.20 0.20 0.20 0.23
# of Observations 6793.00 6568.00 6568.00 5437.00 6796.00 6569.00 6569.00 5449.00
t-statistics in parentheses:
* p<0.10.
** p< 0.05.
*** p< 0.01.
household level, while the presence of economic shocks is negatively protect women’s rights, or to foster a country’s development (Tertilt,
correlated. At the child level, the age of mothers, higher mothers’ 2005; Gould, Moav & Simhon, 2008). Indeed, polygyny is banned in a
education and livestock ownership are all positively associated with number of developed and developed countries, although the practice
child nutrition. From a policy perspective, these findings may indicate a still exists. The present study sought to provide empirical evidence of
need for greater emphasis on higher education, in addition to the the correlation between this practice and household and child welfare
creation of employment opportunities, in order to improve the wealth outcomes. Although positive effects of polygyny were initially observed
status of households in the country and increase nutrition status of at the household level, these findings are not observed at the more
children. The presence of economic shocks is also associated with critical individual level (better child nutrition outcomes were found in
poorer food security outcomes at the household level, indicating the monogamous households, compared to polygamous households),
need for social interventions and safety-net programmes to mitigate the raising some concerns about potential negative effects of polygyny on
adverse effects of shocks on food security of families in Nigeria. child welfare outcomes in Nigeria.
There are a number of calls to ban the practice of polygyny either to
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N.S. Owoo SSM - Population Health 4 (2018) 117–125
Table 3a
Household fixed effects regressions of child health outcomes on polygyny- Nigerian general household survey (2010/11, 2012/13 and 2014/15).
Hypotheses (1) (2) (3) (4) (1) (2) (3) (4)
Height-for-Age Weight-for-Height
Mother is polygynous -1.66* -1.87** -2.49*** -2.42** 0.68 0.69 0.76 0.96
(-1.93) (-2.10) (-2.64) (-2.48) (0.88) (0.87) (0.90) (1.10)
Mother’s age 0.08** 0.07** 0.08** 0.08** -0.01 -0.01 -0.01 -0.03
(2.49) (2.46) (2.51) (2.42) (-0.53) (-0.54) (-0.56) (-0.92)
Education (no education is base)
Basic -0.83 -0.86* -0.96* -0.97* 1.00** 1.00** 0.97** 1.00**
(-1.63) (-1.69) (-1.88) (-1.86) (2.29) (2.28) (2.19) (2.21)
Secondary -1.71*** -1.76*** -1.79*** -1.70*** 0.91* 0.91* 0.92* 0.88
(-2.79) (-2.86) (-2.91) (-2.67) (1.73) (1.72) (1.73) (1.62)
Post-secondary -0.92 -1.02 -1.16 -1.21 1.20* 1.20* 1.14* 1.13
(-1.17) (-1.28) (-1.47) (-1.48) (1.82) (1.81) (1.70) (1.64)
Mother is employed 0.05 0.07 0.04 0.11 0.27 0.27 0.30 0.31
(0.13) (0.19) (0.12) (0.29) (0.92) (0.91) (0.99) (0.99)
Fathers age 0.03 0.03 0.02 0.01 -0.08** -0.08** -0.08** -0.06*
(0.67) (0.73) (0.37) (0.26) (-2.27) (-2.26) (-2.20) (-1.65)
Education (no education is base)
Basic 0.08 0.10 0.11 0.16 0.02 0.02 0.05 0.06
(0.18) (0.23) (0.25) (0.34) (0.06) (0.06) (0.14) (0.16)
Secondary 0.19 0.26 0.31 0.25 -0.01 -0.01 0.02 0.07
(0.31) (0.41) (0.49) (0.37) (-0.02) (-0.02) (0.03) (0.12)
Post-secondary 0.50 0.57 0.65 0.83 -0.76 -0.76 -0.68 -0.82
(0.58) (0.66) (0.75) (0.90) (-1.01) (-1.01) (-0.89) (-1.02)
Father is employed 0.77 0.69 0.68 0.75 0.79 0.80 0.84 0.77
(1.16) (1.04) (1.02) (1.04) (1.45) (1.45) (1.51) (1.30)
Muslim head 1.14 0.94 0.81 0.54 -3.23 -3.22 -3.03 -4.63*
(0.51) (0.42) (0.36) (0.20) (-1.65) (-1.63) (-1.51) (-1.93)
Child age -0.12* -0.13* -0.15** -0.12 0.06 0.05 0.04 0.02
(-1.66) (-1.75) (-2.01) (-1.59) (0.88) (0.84) (0.63) (0.26)
Child birth order -0.13 -0.14 -0.17* -0.13 -0.05 -0.05 -0.07 -0.09
(-1.41) (-1.45) (-1.67) (-1.26) (-0.61) (-0.59) (-0.80) (-1.03)
Urban -3.27 -1.69 -1.68 1.79 1.77 1.69 1.79 0.40
(-1.28) (-0.56) (-0.56) (0.73) (0.79) (0.63) (0.67) (0.18)
Shocks 0.26 0.27 0.31 0.32 -0.22 -0.22 -0.25 -0.37
(0.65) (0.68) (0.79) (0.77) (-0.68) (-0.68) (-0.77) (-1.08)
Tropical Livestock 0.03 0.03 0.01 0.02 -0.02 -0.02 -0.03 -0.03
(0.75) (0.68) (0.36) (0.53) (-0.64) (-0.63) (-0.75) (-0.92)
Wealth scores -0.01 -0.00 -0.01 -0.01 -0.01 -0.01 -0.02 -0.01
(-0.12) (-0.04) (-0.13) (-0.14) (-0.15) (-0.15) (-0.21) (-0.12)
Per capita expenditure -0.73 -0.56 -0.73 0.04 -0.14 -0.18
(-0.95) (-0.72) (-0.89) (0.06) (-0.21) (-0.25)
Dependency ratio -2.50 -2.04 -2.16 -2.40
(-1.30) (-1.01) (-1.27) (-1.35)
Household size 0.21 0.11 -0.02 -0.05
(1.11) (0.53) (-0.14) (-0.27)
# of Adult women 0.30 0.25 -0.19 -0.23
(0.92) (0.74) (-0.67) (-0.79)
Land size (logged) -0.03 -0.02
(-0.52) (-0.40)
Zonal controls YES YES YES YES YES YES YES YES
Wave control YES YES YES YES YES YES YES YES
Zone*Wave controls YES YES YES YES YES YES YES YES
Household dummies YES YES YES YES YES YES YES YES
R2 0.74 0.74 0.74 0.73 0.70 0.70 0.70 0.68
N 1078.00 1065.00 1065.00 919.00 1112.00 1100.00 1100.00 952.00
t-statistics in parentheses:
* p<0.10.
** p< 0.05.
*** p< 0.01.
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N.S. Owoo SSM - Population Health 4 (2018) 117–125
Table 3b
Household fixed effects regressions of child health outcomes on wife seniority- Nigerian general household survey (2010/1, 2012/13 and 2015/16).
Hypotheses (1) (2) (3) (4) (1) (2) (3) (4)
Height-for-Age Weight-for-Height
Mother is Senior wife -0.72** -0.76** -0.78** -0.86** 0.07 0.05 0.05 0.12
(-2.03) (-2.12) (-2.17) (-2.15) (0.29) (0.19) (0.21) (0.42)
Mother’s age 0.06* 0.06** 0.06** 0.07** -0.00 -0.00 -0.00 -0.03
(1.96) (2.00) (2.04) (2.09) (-0.03) (-0.02) (-0.15) (-0.97)
Education (no education is base)
Basic 0.06 0.06 0.01 0.12 0.29 0.32 0.30 0.45
(0.12) (0.12) (0.02) (0.20) (0.82) (0.89) (0.84) (1.08)
Secondary 0.50 0.53 0.52 1.35 0.13 0.14 0.07 -0.29
(0.74) (0.78) (0.75) (1.57) (0.28) (0.29) (0.13) (-0.49)
Post-secondary 2.00** 1.98** 1.97** 2.36** -0.58 -0.56 -0.68 -0.67
(2.37) (2.33) (2.26) (2.31) (-0.97) (-0.94) (-1.12) (-0.96)
Mother is employed 0.14 0.22 0.29 0.10 0.07 0.04 0.01 0.11
(0.32) (0.48) (0.63) (0.20) (0.21) (0.13) (0.03) (0.32)
Fathers age 0.01 0.01 0.03 0.04 0.00 0.00 -0.01 -0.02
(0.23) (0.20) (0.58) (0.65) (0.06) (0.05) (-0.32) (-0.51)
Education (no education is base)
Basic 0.32 0.42 0.20 -0.22 -0.29 -0.34 -0.14 -0.22
(0.50) (0.64) (0.29) (-0.29) (-0.66) (-0.74) (-0.31) (-0.43)
Secondary -0.63 -0.65 -0.59 -1.19 2.15*** 2.15*** 2.17*** 2.68***
(-0.59) (-0.60) (-0.54) (-1.00) (2.80) (2.79) (2.81) (3.23)
Post-secondary -0.31 -0.50 -0.85 -0.66 -0.03 0.05 0.01 -0.40
(-0.24) (-0.39) (-0.64) (-0.42) (-0.04) (0.05) (0.01) (-0.37)
Father is employed 1.98* 1.94 1.82 2.34 0.76 0.73 0.32 0.38
(1.65) (1.61) (1.34) (1.59) (1.10) (1.05) (0.40) (0.46)
Muslim head 0.40 1.24 -0.58 -0.17 -0.72 -1.06 0.79 1.06
(0.15) (0.44) (-0.16) (-0.04) (-0.37) (-0.52) (0.31) (0.39)
Child age 0.04 0.03 0.01 -0.08 0.01 0.01 0.03 -0.02
(0.29) (0.23) (0.08) (-0.48) (0.08) (0.08) (0.33) (-0.18)
Child birth order -0.13 -0.13 -0.18 -0.23 -0.10 -0.10 -0.04 -0.10
(-0.64) (-0.66) (-0.88) (-0.98) (-0.70) (-0.69) (-0.24) (-0.64)
Urban -0.53 -2.05 -2.24 -1.62 0.44 1.02 1.42 1.01
(-0.21) (-0.70) (-0.76) (-0.52) (0.28) (0.54) (0.75) (0.51)
Shocks 0.82 0.70 0.69 0.95 0.26 0.30 0.42 0.22
(1.45) (1.19) (1.15) (1.43) (0.64) (0.73) (0.99) (0.49)
Tropical Livestock 0.22* 0.20* 0.24** 0.28** 0.22** 0.23** 0.22** 0.18
(1.95) (1.72) (2.00) (2.07) (2.18) (2.24) (2.13) (1.56)
Wealth scores -0.36** -0.33* -0.36** -0.36* -0.23* -0.25* -0.26** -0.13
(-2.12) (-1.93) (-2.03) (-1.87) (-1.87) (-1.93) (-2.00) (-0.93)
Per capita expenditure -1.35 -1.15 -0.81 0.54 0.29 0.16
(-0.99) (-0.83) (-0.55) (0.58) (0.31) (0.16)
Dependency ratio 2.54 1.26 -2.97 -3.80
(0.73) (0.34) (-1.22) (-1.49)
Household size 0.11 0.16 -0.25 -0.10
(0.40) (0.46) (-1.27) (-0.40)
# of Adult women 0.49 0.47 -0.00 0.07
(0.89) (0.78) (-0.01) (0.17)
Land size (logged) -0.01 0.02
(-0.05) (0.23)
Zonal controls YES YES YES YES YES YES YES YES
Wave control YES YES YES YES YES YES YES YES
Zone*Wave controls YES YES YES YES YES YES YES YES
Household dummies YES YES YES YES YES YES YES YES
R2 0.69 0.69 0.69 0.71 0.76 0.76 0.77 0.77
N 531.00 524.00 524.00 454.00 552.00 545.00 545.00 473.00
t-statistics in parentheses:
* p<0.10.
** p< 0.05.
*** p< 0.01
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N.S. Owoo SSM - Population Health 4 (2018) 117–125
Conflicts of interest
None.
Appendix A. Measures of household food security indicators, by Wave (Nigerian General Household Survey)
Wave 1 (Full Sample) Wave 2 (Full Sample) Wave 3 (Full Sample)
Mean SD Difference Mean SD Difference Mean SD Difference
(T-tests) (T-tests) (T-tests)
Reduced coping strategies index
2.14 5.1 1.198*** 3.21 6.05 1.47*** 3.85 5.97 0.82***
(6.63) (6.53) (3.62)
Food consumption score (dietary diversity)
53.24 20.19 0.0244 51.89 20.01 -2.71*** 52.35 19.92 0.37
(0.03) (-3.61) (0.49)
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