DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2013 No. 98

Samuel Kobina Annim

Kofi Awusabo-Asare

Joshua Amo-Adjei 

August 2013

This document was produced for review by the United States Agency for

International Development.

Household Nucleation, Dependency and 

Child Health Outcomes in Ghana

Household Nucleation, Dependency and Child Health Outcomes in Ghana 

Samuel Kobina Annim 1

Kofi Awusabo-Asare 2

Joshua Amo-Adjei 2

ICF International 

Calverton, Maryland, USA 

August 2013 

1

 Department of Economics, University of Cape Coast, Ghana 

2

 Department of Population and Health, University of Cape Coast, Ghana 

Corresponding author: Joshua Amo-Adjei, Department of Population and Health, University of Cape Coast, Ghana; 

Email: kjamoadjei@yahoo.com
ACKNOWLEDGEMENTS 

The authors are grateful to ICF International and USAID for their financial and technical 

support in the Demographic and Health Surveys (DHS) Fellowship program, which has made 

this publication possible. We are most grateful for the valuable insights received from Sarah 

Staveteig, Wenjuan Wang, Ambrose Akinlo, Samwel Mbugua and Kerry MacQuarrie during and 

after the Fellowship workshops in Kampala, Uganda, and Nairobi, Kenya. Their facilitation and 

support contributed immensely to the development and completion of the paper. 

We also acknowledge Fred Arnold for his insights and comments on the various drafts of 

the paper, as well as the contributions of Cathy Johnson and Bryant Robey as editors and Yuan 

Cheng as formatter. We appreciate the collegiality of the 2013 DHS Fellows and the support of 

colleagues at the Faculty of Social Sciences, University of Cape Coast, Ghana. Finally we wish 

to thank management of the University of Cape Coast for making it possible for us to participate 

in the DHS Fellowship Program. 
ABSTRACT 

The Millennium Development Goals have increased international attention to child 

malnutrition in the developing world. Goal 1 aims to reduce hunger, and Goal 4 aims to reduce 

under-five child mortality. The study uses three key anthropometric measures of nutritional 

status among children—stunting (low height for age), wasting (low weight for height), and 

underweight (low weight for age)—to explore the dual effects of household composition and 

dependency on nutritional outcomes of children under age 5. The objectives are to examine 

changes in household living arrangements of under-five children and to explore the interaction of 

dependency and nucleation on child health outcomes. 

The concept of nucleation, which is at the core of this paper, refers to the changing 

structure and composition of household living arrangements, from highly extended with its 

associated socioeconomic system of production and reproduction, social behavior and values, 

toward single-family households—especially the nuclear family, containing a husband and wife 

and their children alone. A negative relationship between levels of dependency, as measured by 

the number of children in the household, and child health outcomes is premised on the grounds 

that high dependency depletes resources, both tangible and intangible, to the disadvantage of 

young children. 

Data for the study were drawn from the last four rounds of the Ghana Demographic and 

Health Surveys (GDHS), from 1993 to 2008, for the first objective—to explore changes in 

household composition. For the second objective, to explore how dependency affects health 

outcomes of children under age 5, in the context of household composition, the study used data 

from the 2008 GDHS. 

The results show that, over time, households in Ghana have been changing toward 

nucleation. The main finding is that in households with the same number of dependent children, 

in nucleated households children under age 5 have better health outcomes compared with 

children under age 5 in non-nucleated households. The results also indicate that the effect of 

dependency on child health outcomes is mediated by household nucleation and wealth status and 

that, as such, high levels of dependency do not necessarily translate into negative health 

outcomes for children under age 5, based on anthropometric measures. 

Keywords: nucleation, dependency, child health, stunting, underweight, wasting 

1 

INTRODUCTION 

Child health outcomes, as an important index for measuring socioeconomic development, 

are captured in Millennium Development Goal (MDG) 4, which seeks to reduce mortality of 

under-five children as part of overall human development (Black et al. 2008; Liu et al. 2012; 

WHO, UNICEF and The World Bank, 2012). One measure that has become of interest in the 

discourse on child health outcomes is nutritional status of under-five children, since poor 

nutrition among such children manifests itself in underweight, stunting and wasting. These 

dimensions of nutrition and childhood diseases have become global public health concerns, 

especially in low-income countries. 

The proportions of children stunted, wasted and underweight, as well as suffering from 

some preventable childhood environmental diseases, have also emerged as important markers for 

measuring Target 1C in MDG 1, which aims to halve the incidence of extreme hunger (Black et 

al. 2008). Of the 165 million stunted, 101 million underweight, and 52 million wasted children 

globally in 2010, between 70 percent and 90 percent live in Africa and Asia, two continents with 

high prevalence of poverty (WHO et al. 2012). Available evidence suggests that about one-third 

of child deaths in low-income countries can be prevented through adequate nutrition and 

improved sanitation (Black et al. 2008; Liu et al. 2012; Black et al. 2013). Therefore, an 

understanding of the linkages between health outcomes among children and other underlying 

factors is important for policy and planning. 

This paper examines levels of stunting, wasting and underweight among children under 

age 60 months (5 years), in various household arrangements, based on data from the 2008 Ghana 

Demographic and Health Survey (GDHS). Specifically, the study explores the implications of 

the dual effects of household composition and dependency on health outcomes of children under 

age 5. The view is that as societies undergo socioeconomic transformation, changes also occur in 

sociodemographic dynamics such as levels of fertility and trends in morbidity and mortality, as 

well as in the size and composition of households (Caldwell 1982; Omran 1971, 1998). Some of 

these changes have implications for household welfare, including the health of children (see 

Allendorf 2013; Caldwell 1982; Hatton and Martin 2009). 

The concept of nucleation is at the core of the paper. This term refers to the changing 

structure and composition of households, from highly extended with an associated 
2 

socioeconomic system of production and reproduction, social behavior and values, toward 

nuclear families, with a husband and wife and their children as the core (Goody 1972; Laslett 

1972; Oppong 1981; Nukunya 2003). This process of nucleation, which includes both physical 

and emotional changes, has been associated with some aspects of demographic change, including 

fertility decline (Caldwell 1982). 

Axinn and Ghimire (2013), expanding on the original theory of Caldwell, have defined 

emotional nucleation as the bond between couples that contributes to creating an environment for 

discussion and adoption of strategies to improve family welfare—that is, “the process through 

which individuals’ emotional bonds to their spouse grow stronger relative to their emotional ties 

to parents, siblings, and other relatives” (Axinn and Ghimire 2013:3). Physical nucleation has 

been defined as a household arrangement which involves a couple and their biological underage 

children. The process of physical nucleation is the transition from extended-family households to 

single-family (two-generation) households made up of a couple and their children (Macht 2008; 

du Toit et al. 2013). 

Social and economic considerations may determine household formation (Laslett 1972; 

Titchit and Robette 2008; Nukunya 2003). Living arrangements could be dictated by social 

norms, including responsibilities toward different relations (e.g., parents of a couple) as well as 

choices made through marriage, for example. Also, family or non-family members may live 

together for economic reasons, to contribute to and maximize household resources (Freeman 

2005). A couple’s parents, siblings, cousins and other family members could provide both 

tangible and intangible resources that improve the household’s welfare, for instance contributing 

to childcare while the couple engages in other productive activities, particularly where the 

woman is engaged in non-familial economic activities (Griggs et al. 2010). 

According to Caldwell (1982), the physical and emotional changes lead to 

rearrangements in household composition as well as in availability and use of resources. In the 

extended household structure, the flow of resources could be upward (to parents), downward (to 

own children, nephews and nieces) and/or lateral (to siblings). Caldwell postulated that in an 

extended family the net flow of resources is from children to parents. In a nucleated family the 

flow of resources is mainly from parents to children and, therefore, the net flow of resources in 

the household is directed to children’s welfare. 
3 

Furthermore, as couples live away from their parents, the probability of adopting new 

social values increases. With respect to fertility intentions and actual fertility, for instance, such 

couples could be predicted to have smaller family size compared with their parents and to give 

more attention to their children than they received from their own parents when they were young 

(Caldwell, 1982). This rendition of the wealth-flow hypothesis has been criticized, however. For 

instance, Wills (1982), argued that the direction of flow of resources has always been downwards 

(from parents to children) and not vice-versa. 

Available evidence on the links between household living arrangements and health 

outcomes for children is inconclusive. Griggs et al. (2010) demonstrated that in households 

where grandparents were involved in childcare, children fared better than in households where 

the involvement of grandparents was low or non-existent. Schnitzer and Ewigman (2008) found 

that children in households with unrelated adults, stepparents, or foster parents were more likely 

to experience unintentional injuries and maltreatment compared with children in households with 

biologically related members. In the United States Casper and Bryson (1998) observed negative 

health and emotional outcomes for children who were raised by their grandparents. Other studies 

in low-income countries generally have observed a link between large household size and 

negative outcomes for child health (Hampshire, Panter-Brick, and Kilpatrick 2008; Hatton and 

Martin 2009). 

Dependency and the trend toward nucleation are inherent in the quantity-quality trade-off 

hypothesis on the link between household size and child-health outcomes (Becker and Lewis 

1973; Becker and Tomes 1976; Klemp and Weisdorf 2011). The negative relationship between 

levels of dependency, as measured by the size of household, and child health outcomes is 

premised on the view that high dependency depletes resources, both tangible and intangible, to 

the disadvantage of children. Households with many children are characterized by competition 

for care and resources. However, the effect of high child dependency can be compensated for by 

the number of adults in the household who are capable of providing care and resources for child 

welfare. 

The reverse is possible—that positive child health outcomes are associated with a low 

level of dependency, which is characteristic of nucleated households. The effect of household 

dependency, however, may not be uniform across different living arrangements of children, 
4 

particularly in relation to age and gender. Where resources are limited, higher-order (older) 

children may be at more risk of poor health outcomes (Charmarbagwala et al. undated). 

Similarly, especially in households where priority is given to male children, female children may 

experience worse outcomes, a circumstance Croll (2000) has characterized as “endangered 

daughters.” 
5 

BACKGROUND 

Researchers have long been interested in household composition, and various typologies 

have emerged to categorize living arrangements and their implications. Laslett (1972), for 

instance, provided a classification of household living arrangements with the basic nuclear 

family as core, and various combinations of nuclear and extended households. Extended family 

household structures include those with a nuclear family and either parent(s) of couple, couple 

with children, nieces or nephews, unrelated members, couple with siblings of either male or 

female, children with or without related members, and others, which consist of various 

combinations of relationships with or without unrelated members by blood or marriage (Laslett 

1972; Goody 1972; Amoateng 2007). 

 In this paper a modified version of the typology by Laslett (1972) has been adopted that 

defines nucleation as the movement toward living arrangements involving a couple and their 

children only, while non-nucleation refers to the other types of household arrangements, with 

various arrangements of related and non-related members. 

Traditionally in Ghana, marriage did not necessarily lead to co-residence, and various 

living arrangements have existed and continue to exist among some ethnic groups. For instance, 

a duo-local system has characterized living arrangements in the Ga/Adangme areas of Ghana 

(Manuh 1997; Assimeng 1981; Pellow and Chazan 1982). Among the Ga/Adangme, married 

men and women could live in separate men’s and women’s households, rather than living 

together. Wives visited husbands at night in the men’s houses, and children, irrespective of 

gender, spent their time in women’s houses. 

Among the matrilineal Akan, who account for about 50 percent of Ghana’s population 

(GSS 2012), husbands and wives may live separately with members of their respective matriclan, 

or live together with the husband’s maternal family (avunculocal). In rare cases the couple may 

co-reside with the woman’s maternal family (Assimen 1981; Oppong 1981; Awusabo-Asare 

1990). 

In the northern half of the country, which consists mainly of the patrilineal MoleDagbani, young couples may live with the husband’s parents. Under this arrangement, mothersin-law are important in the household in a wide range of areas, including the care and upbringing 
6 

of children (Goody 1972; Rasheed 2013). Among the Mole-Dagbani, there is the belief that 

young parents are not necessarily the best people to bring up children (Oppong 1981). In some 

areas of the country, such as among the Anlo Ewe and the Guan, couples may live with the 

husband’s parents or live on their own (Nukunya 1969, 2003; Assimeng 1981; Pellow and 

Chazan 1982; Manuh 1997). 

While there are various living arrangements, with varying types of household 

composition, changes are occurring in household living arrangements as Ghana experiences 

increasing urbanization, education and social and spatial mobility, leading to a process of 

nucleation (Twumasi-Ankrah 1995). In this study we first examine the extent to which there 

have been changes in household composition toward nucleation over the last four GDHS (1993-

2008). Secondly, using data from the 2008 GDHS we examine the effect of household 

composition on child health outcomes, with an expectation that children in nucleated households 

will have better health outcomes than children in non-nucleated households. 

The transition from traditional extended families to nucleated household living 

arrangements is expected to lead to redirection of resources toward increased investment in 

good-quality education and child health (Caldwell 1982). Increasing nucleation should lead to a 

decline in the level of dependency, which would manifest itself in positive child health 

outcomes, as measured by levels of stunting, wasting and underweight. Alternatively, given the 

existing sociocultural setting, the extended family structure (non-nucleated households) could 

still be protective of child health by making available more physical and human resources for 

childcare. 

Results from the last three rounds1

 of GDHS do not show significantly different patterns 

in child health outcomes, as measured by stunting, wasting and underweight. While the 

proportion of underweight children declined steadily, from 20 percent in 1998 to 14 percent in 

2008, the proportions of children suffering from wasting fluctuated—decreasing by 2 percent 

between 1998 and 2003 and then increasing by 1 percent between 2003 and 2008. For stunting, 

the proportions have varied narrowly between 35 percent (2003) and 28 percent (2008) (Figure 

 

1

 The two earlier rounds of the GDHS (1988 and 1993) are excluded because anthropometric indicators were 

captured for children under age 36 months. In contrast, for the last three rounds (1998, 2003 and 2008) 

anthropometric indicators were collected for children under age 5 years. 
7 

1). These rates have persisted in spite of various interventions, such as the healthier-happier 

home intervention by the Ghana Health Service to improve child health outcomes. 

Figure 1. Trends of malnutrition in Ghana

Infant mortality declined from 77 deaths per 1,000 live births in 1983-1987 to 50 deaths

per 1,000 in 2004-2008, while under-five mortality declined from 155 to 80 deaths per 1,000 

children (ICF Macro 2010). Childhood mortality, although improved, has not translated into 

positive child health outcomes, as measured by nutritional status. This could partly explain the 

inability of Ghana to achieve the targeted 40 deaths per 1,000 of under-five mortality (target 4A 

of MDG 4) compared with the current rate of 80 per 1,000. 

The focus of this paper, therefore, is to explain some of the driving forces underlying 

changes in child health beyond the socioeconomic factors such as education, residence, religion 

and ethnicity that other studies have used as explanatory variables (Gyimah 2006, 2007; ICF 

Macro 2010). For this study two hypotheses are tested: (1) household composition has changed 

31

35

28

10

8 

9 

20

18

14

2 

4 

5 

0

5

10

15

20

25

30

35

40

1998 2003 2008

Stunting Wasting Underweight Overweight
8 

in Ghana over time; and (2) among households with high dependency, those that are nucleated 

have children with better health outcomes than those that are non-nucleated. 

Conceptual Framework 

This study adapts the UNICEF (1990) model for studying malnutrition to investigate 

three dimensions of child health outcomes—height-for-age (stunting), weight-for-height 

(underweight) and weight-for-age (wasting). According to the model, nutritional status is the 

outcome of the interplay among basic, underlying and immediate factors. The basic factors 

consist of structural characteristics—region and urban-rural residence and background 

(remote)—and household characteristics—nucleation and dependency, wealth index, sources of 

drinking water and toilet facilities—as well as maternal characteristics—education, marital status 

and age. These in turn indirectly influence the underlying factors of child-related variables, and 

directly influence the immediate factors that lead to nutritional status. 

In the modified model, maternal characteristics replace parental characteristics in the 

original model, while a clear separation has been provided for household and maternal 

characteristics. The argument is that the role of the mother is critical, and that role can also be 

enhanced by marriage. Availability of good drinking water and toilet facilities in the home are 

considered indicators of household protective factors for child health outcomes. The interaction 

among these factors is measured using the three health outcomes mentioned above, stunting, 

underweight and wasting (Figure 2). 

 
9 

Figure 2. Conceptual framework on malnutrition 

Source: Adapted from UNICEF (1990) model for studying malnutrition 

Stunting (height-for-age), 

underweight (weight-for-height), 

wasting (weight-for-age) 

Child’s initial health status 

and characteristics 

(size at birth, age sex) 

Disease 

(recent diarrhea) 

Feeding practices 

(ever breastfed) 

Structural factors 

(region, urban-rural) 

Household characteristics 

(Household toilet, household 

water facilities, wealth status) 

Dependency Nucleation 

Maternal characteristics 

(education, marital status, 

age of mother) Care 

Manifestation 

Immediate 

causes 

Basic causes Underlying causes 
10 

DATA, MEASUREMENT OF KEY VARIABLES AND ESTIMATION APPROACH 

Data 

This paper is based on data from the 2008 Ghana Demographic and Health Survey 

(GDHS). In addition, data from the 1993, 1998, 2003 and 2008 GDHS provide background and 

context for trends in household composition. The GDHS is a nationally representative survey of 

health, demographic and other related issues of importance to development, conducted among 

women age 15-49 and men age 15-59. Since 1988, five GDHS surveys have been conducted at 

five-year intervals. The surveys have been conducted by the Ghana Statistical Service (GSS) 

and, occasionally, in collaboration with the Ghana Health Service and/or Noguchi Memorial 

Institute of Medical Research (NMIMR), with technical support from ICF Macro. 

The household member and children's recode files (datasets) are used for the analysis. 

The household member dataset captures background information on all members in the 

household, while the children's file contains information on children under age 5 of interviewed 

women. These two files are used to generate the two main explanatory variables, that is, type of 

household composition based on children’s living arrangement (nucleation) and dependency, 

based on the number of children under age 16 in the household. The household member dataset 

has information on child health outcomes but lacks other child-related variables, such as size at 

birth and disease (diarrhea) status. To capture the data needed for child health outcomes and 

characteristics, the household and children’s datasets were merged. The data are used to test the 

hypothesis that households with high dependency that are nucleated have more positive health 

outcomes for children under age 5 than households with high dependency that are non-nucleated. 

Sample and unit of analysis 

The sample for the study of family types (nucleation) consists of children under age 5 

who were the usual residents of the sampled household at the time of the interview in 1993, 

1998, 2003, and 2008. 

The sample for investigating the links among dependency, nucleation and child health 

outcomes is derived from the 2008 GDHS and based on the 50 percent of respondents in the 
11 

household member data file who were selected for further interview. Therefore, the unweighted 

sample of children with detailed information on issues including child health outcomes and its 

correlates are the 3,113 in the household member dataset and the 2,992 in the children's dataset. 

The corresponding weighted sample of 2,888 was used to describe the patterns of nucleation (see 

Figure 3). Following data cleaning on issues such as children with anthropometric values outside 

the acceptable range, non-response/not applicable and ‘do not know’, and children whose parents 

do not include the head of household, the weighted samples obtained were 2,402 for descriptive 

analysis on child health outcomes and 2,026 for the regression analysis (Figure 3). 

Figure 3. Sample Selection Procedure (2008 GDHS) 

All children under age 5 

(Weighted n = 5,832) 

Children under age 5 belonging to 

households that were selected for individual 

interviews. Sample size for descriptive 

analysis without health data 

(Weighted n = 2,888) 

Sample size for descriptive analysis 

with child’s health data 

(Weighted n = 2,402) 

50 percent of households not randomly 

selected for individual interviews 

Children with anthropometric indicators 

outside range and non-response and do 

not know (for each of the covariates) 

Children with missing values on any 

covariate or whose parents do not include 

the head of household 

Sample size for regression analysis 

(Weighted n = 2,026) 
12 

Measurement of Key Variables 

This sub-section describes the measurement of key variables used in this paper, namely 

nucleation, dependency and child health outcomes. Measurements of the control variables are 

presented in Appendix Table A1. 

Household composition types based on children's living arrangements 

Based on the conceptualization of nucleation as children's living arrangements with 

parents, grandparents and others (both related and unrelated), a typology of household 

composition was developed for the study, based on Laslett (1972) (Table 1). 

Table 1. Typology of household composition based on children's living arrangements 

Types of household Composition 

Core nuclear Both parents + child(ren) under age 5 

Semi-nuclear Single parent + child(ren) under age 5 

Extended I Both parents + child(ren) under age 5 + at least one grandparent 

Extended II Single parents + child(ren) under age 5 + at least one grandparent

Other extended Couple/single parent/non-relation +child(ren) under age 5, at least one grandparent + 

siblings of head with or without other non-related members 

For this study, living arrangements of children of the head of household who are under 

age 5 are categorized into five family types: core nuclear, semi-nuclear, extended I, extended II 

and other extended. (1) Core nuclear is a household where a child or children under age 5 live 

with both parents only; (2) Semi-nuclear is a household where a child or children under age 5 

live with either the father or mother only; (3) Extended I is a household with a child or children 

under age 5 who live with both parents and at least one grandparent (parent/parent-in-law of the 

couple); (4) Extended II is a household with a child or children under age 5 who live with either 

a father or mother and at least one third/fourth generation member—e.g., grandparent(s), 

granduncle/aunt etc.; (5) Other extended is a household in which a child or children live with at 

least one parent and/or another adult other than parents or grandparents. 

The last category can be heterogeneous and may be one of the following: (a) Child or 

children under age 5 who live with both parents and any another member who is not a third-
13 

generation member; (b) Child or children under age 5 who live with a single parent, a thirdgeneration member and any other member who may be related or not related to the head; (c) 

Child or children under age 5 who live with a third-generation member and another member; (d) 

Child or children under age 5 who live with a single parent and another member who is not a 

third-generation member; (e) Child or children under age 5 who live with another member who 

is neither a parent nor a third-generation member; (f) Child or children under age 5 who are not 

living with any of their biological parents but live with any other adult and a third-generation 

member. 

Index of nucleation 

The conceptualization of children’s living arrangements is used to develop an index for 

nucleation, which ranges between 0 and 1. The index is calculated as the number of core 

members (parents and children) (categories 1 and 2 in Table 1) in a household, divided by 

household size. Thus, the index will range from 0 to 1; the closer the value is to 1 the higher the 

level of nucleation, while the closer it is to 0 the higher the level of non-nucleation. The derived 

nucleation index is a continuous variable, as opposed to the nominal categorical variable of 

household composition (Table 1), and is used to calculate the effect of nucleation separately and 

its joint effect with dependency on child health outcomes. 

Comparing the typology of households in Table 1 and the nucleation index, we observe 

that categories 1 and 2 households in Table 1 (core nuclear and semi-nuclear) will each have a 

value of 12

. For the 3rd to 5th categories (extended I, extended II and other extended) the value 

will vary depending on the number of other members in the household. For instance, in a 

household with two grandparents and both parents and a child, the index will be a closer one 

compared to a household with both parents and all four grandparents. The above suggests that 

the index places a premium on the depth of extended family. 

 

2

 This assumes that care and wealth distribution in core nuclear and semi-nuclear households are the same. Given 

that this is not entirely true, the authors are mindful of it and in the future will explore the use of weights to 

distinguish between core nuclear and semi-nuclear households. 
14 

Index of dependency 

Dependency in this paper is measured as the total number3 of children under age 16 in a 

household. The concentration is on child dependency, hence this paper excludes other forms of 

dependency, such as the elderly and household members with chronic ailments, from the 

measure of dependency. 4 The focus is on quantity only, since the variable of interest is 

household dependency, which in the case of non-nuclear households might include other 

children under age 16. For the categorical variables, levels of dependency were classified into 1-

3 children under age 16 in the household (low dependency); 4-6 children (medium dependency); 

and 7+ children (high dependency). 

Nucleation and dependency 

The joint effect of dependency and nucleation is captured as an interactive term by 

multiplying values of dependency with the nucleation index. In this index, dependency is 

considered as the main determinant of child health outcome and the index for nucleation as the 

moderating explanatory variable. 

Child health outcomes 

Child health outcomes are measured using anthropometric indicators of height-for-age, 

weight-for-height and weight-for-age. The indicators are captured as z-scores with values in the 

range of ±6 and thresholds for classifying nutritional status (WHO 2006). Children with z-scores 

less than -2 for height-for-age, weight-for-height and weight-for-age are classified, respectively, 

as stunted, wasted or underweight. In this paper the classifications are used for the descriptive 

analysis and the raw z-scores for the inferential analysis (linear least squares regression). 

 

3

 In contrast to other papers that use dependency ratio (number of children/household size), this paper uses number 

of children because household size is used in the computation of nucleation. Calculating a ratio using household size 

for both nucleation and dependency will amount to duplication in the interaction term. 

4

 In fertility studies the concept of dependency is defined to cover quantity and tempo, which is spacing (e.g., 

Schnaiberg 1973). This definition of dependency is not used in this paper since it would exclude children who are in 

non-nuclear households and are not children of the interviewed woman and therefore would distort the index. 
15 

Estimation Technique 

The first objective — to examine changes in living arrangements of children under age 5 

— is studied using chi-square analysis. The research (alternative) hypothesis is that household 

composition types based on children's living arrangements have changed over time. 

The second objective — to investigate the links between dependency, nucleation and 

their independent or joint effect on child health outcomes — is analyzed using a linear least 

squares regression model. Linear least squares regression is employed because the dependent 

variables (anthropometric indicators) are continuous. Using the raw z-scores for the regression 

analysis allows for assessment of changes across each of the observations rather than observing 

changes across categories. The use of the raw z-scores presents a counter argument in the case of 

the weight-related anthropometric indicators in that higher values are not necessarily desirable as 

they could also lead to overweight. However, in Ghana the proportion of children overweight in 

the 2008 GDHS is quite low (Figure 1). 

Two variants of linear least squares regression models have been employed. The first is a 

hierarchical model in which the predictors of child anthropometric outcomes are plugged into the 

model as: (a) nucleation alone; (b) dependency alone; (c) nucleation and dependency; (d) 

nucleation, dependency and wealth status; and (e) nucleation, dependency, wealth status and 

control variables. The second variant estimates a linear least squares model with the same 

variables as in (d) above, but in this case with an interactive term between dependency and 

nucleation to capture the joint effect on child health outcomes. 
16 

RESULTS 

Children’s Living Arrangements 

This section first provides results from the analysis of the last four GDHS for trends in 

nucleation. It then shows the relationship between nucleation, dependency and their joint effect 

on child health outcomes based on data from the 2008 GDHS. Figure 4 presents trends in 

household dependency from 1993 to 2008. As a reminder, this paper classifies household 

dependency into three categories: low dependency (1–3 children under age 16), medium 

dependency (4–6 children) and high dependency (7+ children). While the proportion of 

households with low dependency increased over the 15-year period 1993 to 2008 (from 58 

percent to 61 percent), the proportion of high-dependency households decreased over the same 

period (9.1 percent to 6.6 percent). The results suggest that households in Ghana are moving 

toward having fewer child dependents. 

Figure 4. Trends of dependency in Ghana 

58 59 58

61

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35 34

32

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6 8 7

0

10

20

30

40

50

60

70

1993 (n=3,748) 1998 (n=3,119) 2003 (n=3,660) 2008 (n=5,832)

Percentage

Low Medium High
17 

The proportion of under-five children who lived in core nuclear households (both parents 

only) increased, from 41 percent in 1993 to 51 percent in 2008 (Figure 5). The proportion of 

children in single-parent (semi-nuclear) households (presence of either mother or father) 

declined, from 21 percent in 1993 to 12 percent in 2003 and 13 percent 2008. The presence of a 

third generation (grandparents) in households of children living with both parents (extended I) 

also fell, from 6 percent in 1993 to 2 percent in 2008. In the case of children living with a single 

parent and a third-generation member in the household (extended II), the proportions ranged 

between 6 percent and 10 percent over the period. The rest (between 23 percent and 30 percent) 

lived in various other forms of extended households. The 2008 GDHS shows that about half of 

children in Ghana lived with both parents, while only about one in every four children lived in 

“other extended” household (Figure 5). From the results, it would appear that there has been a 

clear trend toward nucleation in household living arrangements over the past 15 years in Ghana. 

Figure 5. Living arrangements among children in Ghana: 1993, 1998, 2003, and 2008 

41

43 44

51

21 21

12 13

6

2

5

1

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6

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25

30

28

0

10

20

30

40

50

60

1993 (n=3,748) 1998 (n=3,119) 2003 (n=3,660) 2008 (n=5,832)

Percent

Pearson Chi-Square 523.78 (0.00)

Core Nuclear Semi-Nuclear Extended I Extended II Other Extended
18 

Table 2 shows patterns of household living arrangements by region, residence and 

ethnicity. As suggested earlier, the various ethnic groups in Ghana appear to have varying 

household living arrangements. The lowest proportion of children living with both parents alone 

(core nuclear), 42 percent, is observed among the Ga/Adangme, a group which traditionally did 

not practice conjugal living arrangements, while the highest proportions are among the “other” 

category, at 66 percent, and the Mole-Dagbani (including other ethnic groups in the northern part 

of the country), at 61 percent. 

Table 2. Living arrangements among children under age 5 by region, residence and ethnicity, 

Ghana 2008 (Unweighted Sample Size = 2,970) 

Correlates 

Core 

nuclear 

(%) 

Seminuclear 

(%) 

Extended I 

(%) 

Extended II 

(%) 

Other 

extended 

(%) 

Weighted total 

(%) N 

Region 

Western 49.4 13.6 1.2 6.3 29.5 100.0 270 

Central 44.2 22.0 1.4 8.1 24.3 100.0 285 

Greater Accra 55.7 17.0 2.7 7.5 17.2 100.0 338 

Volta 44.0 9.5 0.5 11.8 34.3 100.0 242 

Eastern 48.5 13.6 0.9 14.9 22.0 100.0 244 

Ashanti 43.9 17.8 0.7 9.3 28.3 100.0 535 

Brong-Ahafo 52.8 20.0 0.6 8.5 18.1 100.0 267 

Northern 66.6 4.2 2.5 3.3 23.4 100.0 482 

Upper East 61.5 9.3 2.2 2.7 24.3 100.0 147 

Upper West 41.5 8.6 1.5 4.4 44.1 100.0 80 

Chi-square 211.6 (0.00)

Residence 

Rural 53.0 12.3 1.4 7.3 26.0 100.0 1,801 

Urban 49.4 16.6 1.4 8.7 23.8 100.0 1,089 

Chi-square 13.7 (0.17) 

Ethnicity 

Akan 46.2 19.3 1.2 10.0 23.3 100.0 1,307 

Ga/Adangme 41.9 24.0 2.6 9.2 22.2 100.0 139 

Ewe 47.8 9.1 0.6 10.4 32.1 100.0 356 

Guan 52.1 17.3 1.3 8.6 20.8 100.0 83 

Mole+Others 60.7 7.0 2.1 3.4 26.8 100.0 893 

Other 66.4 6.8 0.5 6.9 19.4 100.0 112 

Chi-square 165.2 (0.00) 

Total 51.6 13.9 1.4 7.8 25.2 100.0 2,888 
19 

By region of the country, the Northern region has the highest proportion (67 percent) of 

children living with both parents only (core nuclear), followed by the Upper East region (62 

percent) and the Greater Accra region (56 percent). The lowest proportion is between 42 and 44 

percent in the Central, Volta, Ashanti and Upper West regions. In the Upper West region, the 

only region with a higher proportion of non-nucleated households than nucleated households, 44 

percent of children live in other extended households, while the proportion in core nuclear 

households is 42 percent. The chi-square results show that the variation in living arrangements of 

children by region is statistically significant at 1 percent. 

This section examines the extent to which child health outcomes vary by level of 

dependency, nucleation and interaction between the two variables. Table 3 shows patterns of 

malnutrition (stunting, underweight and wasting) by type of household and characteristics of 

children and mothers. The discussion focuses on correlates of child nutritional status that were 

not considered in the 2008 GDHS. 

The results show a significant association between household type and child health 

outcomes, with underweight at 1 percent. Among children in Extended I households (a single 

parent, child(ren) and at least one grandparent) a higher proportion (20 percent) are underweight 

compared with other household types. Household dependency shows a significant association 

with underweight. Households with high dependency (seven or more children under age 16) 

show 36 percent stunting and 21 percent underweight. Among children who had recent diarrhea, 

12 percent are wasted and 19 percent are underweight. The results also show a significant 

association between mother’s marital status and child wasting, with the highest level of wasting 

(15 percent) among children of never-married women. 

There are also significant associations between all three child health outcomes and the 

type of household toilet facility, as well as type of water used in household, with the exception of 

wasting in the context of type of toilet facility. About twice the proportion of children (14.8 

percent) in households without flush toilet facilities are underweight, compared with 7.5 percent 

in households with flush toilet facilities. These two factors, types of water and toilet facility used 

in the household, have been found to provide basic environmental conditions for the health of 

children (Gordon et al. 2003). 
20 

Table 3. Levels of malnutrition among children under age 5 by sociodemographic characteristics, 

Ghana 2008 [unweighted sample size = 2,460] 

Sociodemographic characteristics Stunting (%) Wasting (%) Underweight (%) Weighted total 

Household types 

Core nuclear 26.9 9.3 14.4 1,237 

Semi-nuclear 29.2 7.6 12.3 333 

Extended I 30.6 3.0 19.9 33 

Extended II 29.4 7.0 12.3 179 

Other extended 28.6 10.5 15.0 619 

Chi-square 1.42 (0.89) 7.86 (0.20) 3.48 (0.64) 

Household dependency categories 

Low (1–3, inclusive) 26.4 8.5 13.0 1,449 

Medium (4–6, inclusive) 29.1 9.4 14.8 787 

High (7+) 35.7 12.9 21.2 166 

Chi-square 7.41 (0.19) 3.66 (0.36) 8.78 (0.85) 

Child recently had diarrhea* 

No 27.4 8.2 12.9 1,895 

Yes, last two weeks 29.8 12.4 19.0 504 

Chi-square 1.15 (0.36) 8.46 (0.01) 12.65 (0.00) 

Child ever breastfed* 

No 18.9 0.8 0.0 32 

Yes 27.8 9.2 14.2 2,342 

Chi-square 1.27 (0.30) 2.79 (0.00) 5.40 (0.06) 

Child's age (months) 

< 6 4.6 16.9 8.4 213 

6 - 8 9.9 28.3 15.7 130 

9 - 11 17.9 18.3 15.3 149 

12 - 17 21.8 14.6 15.8 283 

18 - 23 40.1 9.7 20.1 228 

24 - 35 34.1 3.9 13.9 463 

36 - 47 34.4 4.3 15.8 457 

48 - 59 31.9 3.7 10.9 480 

Chi-square 133.5 (0.00) 146.9 (0.00) 19.00 (0.04) 

Child's sex 

Female 26.1 8.0 12.1 1,189 

Male 29.7 10.1 16.2 1,213 

Chi-square 4.06 (0.10) 3.27 (0.16) 8.77 (0.01) 

Region 

Western 27.5 5.7 10.2 223 

Central 36.1 14.4 19.2 217 

Greater Accra 15.2 6.2 6.5 270 

Volta 26.2 5.5 12.6 216 

Eastern 36.9 6.6 8.5 195 

Ashanti 27.4 9.2 13.1 471 

Brong-Ahafo 22.7 5.5 13.4 250 

Northern 30.8 14.3 20.9 384 

(Continued...)
21 

Table 3. – Continued

Sociodemographic characteristics Stunting (%) Wasting (%) Underweight (%) Weighted total 

Upper East 36.4 11.0 26.8 114 

Upper West 24.6 13.9 13.8 64 

Chi-square 47.62 (0.00) 37.54 (0.00) 57.03 (0.00) 

Household wealth index category 

Lowest 33.9 10.5 19.5 628 

Second 34.2 10.0 17.0 550 

Average 28.2 10.7 13.1 427 

Fourth 21.3 6.7 8.3 464 

Highest 15.1 6.3 8.9 334 

Chi-square 60.3 (0.00) 10.11 (0.13) 40.36 (0.00) 

Mother's education 

No education 29.9 11.6 17.3 810 

Primary 32.1 8.1 14.7 572 

Middle/JSS 25.3 8.3 12.5 811 

Secondary+ 18.7 5.0 7.1 209 

Chi-square 18.5 (0.01) 12.20 (0.03) 17.42 (0.01) 

Child's size at birth* 

Very large 23.1 8.1 9.3 528 

Larger than average 26.3 6.5 11.7 782 

Average 29.6 11.4 15.2 747 

Below average 34.4 10.8 21.4 229 

Very small 39.9 12.6 33.2 95 

Chi-square 20.1 (0.01) 14.44 (0.03) 54.52 (0.00) 

Mother's marital status 

Never married 32.0 14.6 18.5 119 

Currently married 27.1 9.2 14.2 2,158 

Formerly married 38.2 2.6 9.1 126 

Chi-square 8.52 (0.11) 10.88 (0.03) 4.67 (0.26) 

Type of household residence 

Rural 32.2 9.6 16.3 1,504 

Urban 20.7 8.2 10.6 899 

Chi-square 37.9 (0.00) 1.40 (0.29) 15.31 (0.00) 

Household type of toilet facility* 

Flush 17.6 5.5 7.5 201 

Other than flush 28.7 9.5 14.8 2,190 

Chi-square 11.7 (0.01) 3.63 (0.14) 8.38 (0.01) 

Household source of drinking water* 

Pipe or bottle 24.2 7.2 12.3 863 

Other than pipe or bottle 29.9 10.2 15.3 1,531 

Chi-square 9.23 (0.03) 6.17 (0.03) 4.35 (0.10) 

Total 27.9 9.1 14.2 2,402 

*

Sample size less than 2,402 (highest difference is 30)
22 

Regression Results 

This section presents results from the hierarchical model and the least squares estimation 

with an interactive term of dependency and nucleation. The measurement and descriptive 

statistics for all the variables used for the regression analysis are presented as Appendix Tables 

A1 and A2, respectively, for cross reference in interpreting the coefficients. The rationale for 

estimating hierarchical and a least squares model with an interactive term of dependency and 

nucleation is to provide a systematic assessment of the independence or the joint effect of 

dependency and nucleation on child health outcomes. 

Table 4a presents the results of the effect of household dependency alone on child health 

outcomes, while Table 4b shows the effects of nucleation alone on child health outcomes. The 

results from the simple linear regression (Table 4a) show that dependency explains variation in 

weight-for-height but not variation in height-for-age and weight-for-age. Table 4b, which deals 

with the effect of nucleation on child health outcomes, shows that children’s living arrangements 

fail to explain variation on all three indicators of child health. The post-estimation tests show that 

the predictive power (R2

) of the model (Table 4b) is nil, and also there is model misspecification 

(Ramsey Specification Test)5 in the case of the weight-for-height and weight-for-age models. 

 

 

5

 Ramsey (1969) proposed a test of linear relationship against a non-linear specification of a model. The 

approach provides an avenue to investigate the possibility of wrongly specifying the functional form of 

the relationship between the dependent and explanatory variables. The null hypothesis supporting the test 

posits that the model has ‘no omitted variables’. A rejection of the null hypotheses (p-value > 0.05) 

suggests that the model has been correctly specified. 
23 

Table 4a. Simple linear regression results of the relationship between child health outcomes and 

dependency, Ghana 2008 - Model 1 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Dependency -0.04 (-1.42) -0.04+ (-1.82) -0.02 (-0.86) 

Constant -0.92** (-9.13) -0.66** (-8.64) -0.21** (-2.97) 

N 2,026 2,026 2,026 

R2 0.002 0.004 0.000 

F-statistics 2.01 (0.16) 3.31 (0.07) 0.74 (0.39) 

Ramsey specification test 0.67 (0.57) 1.15 (0.33) 2.89 (0.03) 

+ p<.10, * p<.05, ** p<.01 

Table 4b. Simple linear regression results of the relationship between child health outcomes and 

nucleation, Ghana 2008 - Model 1 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Nucleation 0.03 (0.09) 0.01 (0.06) 0.03 (0.09) 

Constant -1.08** (-3.46) -0.80** (-3.62) -0.29 (-1.00) 

N 2,026 2,026 2,026 

R2 0.000 0.000 0.000 

F-statistics 0.01 (0.93) 0.00 (0.95) 0.01 (0.93) 

Ramsey specification test 0.65 (0.58) 4.26 (0.01) 6.77 (0.00) 

+ p<.10, * p<.05, ** p<.01 

Table 5a shows results from multiple linear regressions with dependency and nucleation 

as the explanatory variables. The results indicate that households with a large number of child 

dependents, on average, have poor child health outcomes, with the coefficient of nucleation still 

insignificant, as shown in Table 4b. In Table 5a, however, the coefficients for nucleation for the 

three health outcomes are different from those in Table 4b. One possible reason is that in Table 

4b the coefficient of nucleation could be biased as a result of omitting dependency from a child 

health model, pointing to the power of the relationship between nucleation and dependency in 

explaining child health outcomes. The correlation matrix in Appendix Table A3 shows that the 

inverse relationship between nucleation and dependency of -0.26 is statistically significant at 1 

percent. 
24 

Table 5a. Linear regression results of the relationship between child health outcomes on 

nucleation and dependency, Ghana 2008 - Model 2 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Nucleation -0.13 (-0.40) -0.14 (-0.60) -0.04 (-0.11) 

Dependency -0.04 (-1.44) -0.04+ (-1.86) -0.02 (-0.87) 

Constant -0.79* (-2.22) -0.52* (-2.04) -0.18 (-0.56) 

N 2,026 2,026 2,026 

R2 0.002 0.004 0.000 

F-statistics 1.04 (0.35) 1.73 (0.18) 0.38 (0.68) 

Ramsey specification test 0.33 (0.81) 0.24 (0.87) 1.87 (0.13) 

+ p<.10, * p<.05, ** p<.01 

Table 5b. Linear regression results of the relationship between child health outcomes on 

nucleation and dependency, Ghana 2008 – Model with interaction 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Nucleation -1.37+ (-1.75) -1.49** (-2.64) -0.91 (-1.49) 

Dependency -0.35* (-2.05) -0.38** (-2.83) -0.23+ (-1.77) 

Nucleation * Dependency 0.34+ (1.89) 0.37** (2.60) 0.24+ (1.66) 

Constant 0.35 (0.46) 0.72 (1.34) 0.63 (1.09) 

N 2,026 2,026 2,026 

R2 0.004 0.009 0.002 

F-statistics 1.64 (0.18) 3.17 (0.02) 1.17 (0.32) 

Ramsey specification test 6.50 (0.00) 2.27 (0.08) 0.49 (0.69) 

+ p<.10, * p<.05, ** p<.01 

Based on our hypothesized relationship of a joint effect between nucleation and 

dependency, Table 5b shows the effect of the interaction term. In this model there is a significant 

relationship between dependency and the interaction term (dependency and nucleation) in all 

three child health outcomes. Nucleation appears to be significant for height-for-age and weightfor-age in this model, but not in the model shown in Table 4b. The net effect of nucleation on 

height-for-age and weight-for-age takes into account the coefficients of nucleation and the 

interaction term as well as a statistic of dependency. 

Using the average value of dependency (Appendix Table A2), the coefficient for 

nucleation is negative [-1.49 + (0.37 * 3.41) = -0.23] for weight-for-height. However, in 

households with five or more dependents, the coefficient of nucleation changes to positive. With 
25 

dependency, there is a negative effect (statistically significant) on height-for-age and weight-forheight if the household is nucleated [-0.35 + (0.34 * 1) = -0.01]. 

This observation points to the relevance of both dependency and nucleation in child 

health outcome models, which does not seem to have been given much attention in the literature. 

The nature of the relationship is either additive (independent) or joint in the estimation of the 

determinants of child health outcomes. The post-estimation results (last three rows) of Tables 5a 

and 5b have low predictive power (R2

) and joint effect (F-Statistics) of the explanatory variables 

and in five of the six cases fail to explain changes in child health outcomes. Also, model 

misspecification remains an issue (Ramsey Specification Test), hence the need for respecification and inclusion of other variables, as found in the determinants of child health 

outcomes (see Figure 2). 

Household wealth status was included as an additional variable to nucleation and 

dependency in Tables 6a and 6b. In Table 6a nucleation and dependency are treated as 

independent variables, while in Table 6b an interaction between nucleation and dependency is 

introduced. In Table 6a, column 4, children in the middle, fourth and highest household wealth 

quintiles have better health outcomes compared with the lowest and second household wealth 

quintiles. With the inclusion of household wealth status in Table 6a, both nucleation and 

dependency remain insignificant in explaining changes in child health outcomes. However, with 

the inclusion of an interaction between nucleation and dependency, as in Table 6b, dependency 

and the interaction term become significant for two of the three outcomes of child health, with 

nucleation being significant for only weight-for-height. 

Furthermore, the effect of dependency in Table 6b is positive for all three child health 

outcomes, compared with the ‘no effect’ results in Table 5b. The results suggest either an 

independent or joint relationship between nucleation, dependency and wealth status in the 

modeling of the determinants of child health outcomes. The post-estimation results from Tables 

6a and 6b (last three rows) suggest that the explanatory variables have a better predictive power 

(R2

) than in the earlier models (Tables 4 and 5), and jointly the predictors explain variations in 

child outcomes (F-statistics). Also, model specification, as verified by Ramsey’s specification 

test, supports the null hypothesis of ‘no omitted variables’ for all the models. 
26 

Table 6a. Linear regression results of the relationship between child health outcomes on 

nucleation, dependency and wealth, Ghana 2008 - Model 3 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Nucleation -0.02 (-0.08) -0.05 (-0.21) 0.01 (0.03) 

Dependency -0.00 (-0.14) -0.01 (-0.24) 0.00 (0.14) 

Wealth (lowest) # 0.00 0.00 0.00 

Wealth (second) 0.01 (0.12) 0.08 (0.88) 0.10 (0.99) 

Wealth (middle) 0.19 (1.39) 0.23** (2.64) 0.15 (1.35) 

Wealth (fourth) 0.40** (3.01) 0.43** (4.20) 0.28* (2.45) 

Wealth (highest) 0.74** (5.11) 0.62** (6.28) 0.27* (2.45) 

Constant -1.24** (-3.44) -0.96** (-3.82) -0.42 (-1.33) 

N 2,026 2,026 2,026 

R2

 0.027 0.036 0.006 

F-statistics 5.55 (0.00) 8.40 (0.00) 1.75 (0.11) 

Ramsey specification test 0.64 (0.59) 1.37 (0.25) 0.06 (0.98) 

# identifies the reference group; + p<.10, * p<.05, ** p<.01

Table 6b. Linear regression results of the relationship between child health outcomes on 

nucleation, dependency and wealth, Ghana 2008 (with an interaction term of dependency and 

nucleation) 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Nucleation -1.16 (-1.47) -1.34* (-2.36) -0.87 (-1.42) 

Dependency -0.29+ (-1.64) -0.33* (-2.35) -0.22 (-1.60) 

Nucleation * Dependency 0.31+ (1.70) 0.36* (2.41) 0.24+ (1.66) 

Wealth (lowest) # 0.00 0.00 0.00 

Wealth (second) 0.02 (0.14) 0.08 (0.91) 0.10 (1.00) 

Wealth (middle) 0.19 (1.45) 0.23** (2.74) 0.15 (1.39) 

Wealth (fourth) 0.40** (3.06) 0.43** (4.27) 0.28* (2.49) 

Wealth (highest) 0.74** (5.05) 0.62** (6.21) 0.26* (2.42) 

Constant -0.19 (-0.25) 0.23 (0.42) 0.38 (0.66) 

N 2,026 2,026 2,026 

R2

 0.029 0.040 0.008 

F-statistics 5.06 (0.00) 8.00 (0.00) 2.03 (0.05) 

Ramsey specification test 1.54 (0.20) 0.91 (0.44) 0.38 (0.77) 

# identifies the reference group; + p<.10, * p<.05, ** p<.01
27 

Tables 7a and 7b present results of the full model consisting of dependency, nucleation, 

wealth status and other correlates of child health outcomes, following the conceptual framework. 

Table 7a differs from Table 7b with the introduction of the interaction term, as in Tables 6a and 

6b. Consistent with Tables 5a and 6a, nucleation and dependency remain insignificant. 

Household wealth status is significant for middle, fourth and highest wealth status categories in 

the case of height-for-age, and is significant only for the fourth category in the case of weightfor-height. 

Results in Table 7b, with the joint effect of dependency and nucleation as an interaction 

term, indicate that in nucleated households with high dependency children have better health 

outcomes than in non-nucleated households with high dependency. The effect of dependency on 

child health outcomes could have two possible outcomes based on whether or not the household 

is nucleated. If the household is nucleated, the coefficient of dependency in the height-for-age 

model is 0.02 [-0.39 + (0.41 * 1)]. Also in the case of weight-for-height, a positive effect is 

observed, reinforcing the argument that households that are nucleated and have high dependency 

tend to have better child health outcomes. If the household is non-nucleated, the effect of high 

dependency on child health outcomes is negative compared with children in nucleated 

households with high dependency. 

The effect of household wealth status on child health outcomes remains robust in the full 

model, with the exception of weight-for-age. Based on the UNICEF framework adapted for this 

paper, childhood disease is an important variable that has a direct effect on weight-for-height and 

weight-for-age. In the results, a recent bout of diarrhea shows an association with weight-forheight (wasting) and weight-for-age (underweight), but not height-for-age (stunting), in both 

Table 7a and 7b. Because stunting is a measure of chronic malnourishment, it should not be 

affected by an episode of diarrhea within the two weeks preceding the survey, whereas wasting is 

a measure of acute malnourishment, and underweight reflects both acute and chronic 

undernutrition. 
28 

Table 7a. Linear regression results of the relationship between child health outcomes on 

nucleation, dependency and wealth and other covariates, Ghana 2008 - Model 4 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Nucleation index 0.05 (0.16) -0.06 (-0.27) -0.09 (-0.31) 

Dependency -0.02 (-0.74) -0.01 (-0.30) 0.01 (0.55) 

Wealth (lowest) # 0.00 0.00 0.00 

Wealth (second) 0.06 (0.45) -0.04 (-0.39) -0.10 (-0.94) 

Wealth (middle) 0.27+ (1.75) 0.10 (1.05) -0.09 (-0.77) 

Wealth (fourth) 0.35* (2.04) 0.24+ (1.84) 0.07 (0.51) 

Wealth (highest) 0.59** (2.70) 0.22 (1.36) -0.18 (-1.02) 

Female child # 0.00 0.00 0.00 

Male child -0.15+ (-1.93) -0.12* (-2.02) -0.08 (-1.22) 

Child’s age -0.03** (-15.09) -0.01** (-7.18) 0.01** (5.62) 

Child did not recently have 

diarrhea # 0.00 0.00 0.00 

Child recently had diarrhea -0.11 (-1.21) -0.15* (-2.21) -0.14+ (-1.82) 

Child never breastfed # 0.00 0.00 0.00 

Child ever breastfed -0.26* (-2.47) -0.20* (-2.51) -0.09 (-1.04) 

Child’s size at birth (very 

large) # 0.00 0.00 0.00 

Child’s size at birth (above 

average) -0.29* (-2.43) -0.45** (-5.01) -0.43** (-4.49) 

Child’s size at birth 

(average) -0.44** (-2.96) -0.48** (-3.78) -0.33* (-2.30) 

Child’s size at birth (below 

average) -0.63** (-3.73) -0.85** (-5.48) -0.72** (-3.95) 

Child’s Size at Birth (Very 

Small) -0.72* (-2.00) -0.62** (-3.19) -0.29 (-1.38) 

Never married # 0.00 0.00 0.00 

Currently married 0.12 (0.49) 0.14 (0.65) 0.14 (0.50) 

Formerly married -0.41 (-1.31) 0.14 (0.56) 0.58+ (1.75) 

Mother’s age 0.02** (3.03) 0.01+ (1.81) -0.00 (-0.43) 

Mother’s education (none) # 0.00 0.00 0.00 

Mother’s education (primary) -0.00 (-0.02) -0.01 (-0.06) -0.02 (-0.20) 

Mother’s education 

(middle/JHS) 0.09 (0.66) -0.01 (-0.12) -0.09 (-0.88) 

Mother’s education 

(secondary +) 0.11 (0.55) 0.28+ (1.90) 0.29 (1.58) 

Household does not have 

flush toilet # 0.00 0.00 0.00 

Household has flush toilet 0.11 (0.61) 0.25* (1.99) 0.29* (1.97) 

(Continued...)
29 

Table 7a. – Continued

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Household water source: 

other than pipe or bottle # 0.00 0.00 0.00 

Household water source: 

pipe or bottle -0.05 (-0.43) -0.05 (-0.60) -0.03 (-0.26) 

Western region # 0.00 0.00 0.00 

Central region -0.08 (-0.34) -0.13 (-0.68) -0.13 (-0.56) 

Greater Accra region 0.47* (2.20) 0.11 (0.72) -0.21 (-1.35) 

Volta region 0.20 (0.76) 0.02 (0.11) -0.13 (-0.80) 

Eastern region -0.09 (-0.32) 0.13 (0.78) 0.26 (1.41) 

Ashanti region 0.22 (1.08) -0.03 (-0.25) -0.21+ (-1.68) 

Brong-Ahafo region 0.35 (1.65) 0.02 (0.18) -0.25* (-2.02) 

Northern region 0.25 (1.11) -0.32* (-2.03) -0.65** (-5.03) 

Upper East region 0.07 (0.26) -0.31 (-1.54) -0.50** (-2.99) 

Upper West region 0.37 (1.61) -0.19 (-1.26) -0.57** (-3.68) 

Rural residence # 0.00 0.00 0.00 

Urban residence -0.05 (-0.35) -0.02 (-0.22) -0.02 (-0.16) 

Constant -0.19 (-0.34) 0.13 (0.31) 0.25 (0.50) 

N 2,026 2,026 2,026 

R2

 0.165 0.116 0.090 

F-statistics 10.51 (0.00) 5.92 (0.00) 5.78 (0.00) 

# identifies the reference group; + p<.10, * p<.05, ** p<.01

Size of a child at birth and age show consistently significant results across all three 

indicators of child health outcomes. Child’s size at birth (a proxy for initial health status) shows 

that children who are very small at birth tend to have poor health outcomes. Also, older children 

(those 1 to 4 years) tend to have poorer health outcomes than younger children (under age 1). 

This phenomenon of poor health outcomes among children age 1 to 4 was first described in the 

old cocoa-growing areas of the then Gold Coast, and was named Kwashiorkor (Williams 1935). 

In terms of maternal factors, older mothers have children with better height-for-age and weightfor-age z-scores than younger mothers. The expected positive effect of maternal education on 

child health outcomes is observed only in the case of mothers with at least secondary education. 

This is evident with weight-for-height and weight-for-age in the models. 

In terms of sanitation, children in households with flush toilets have better weight-forheight and weight-for-age z-scores than children in households with other types of toilet 

facilities. The results on regional fixed effects are largely insignificant, with the exception of the 
30 

coefficients for the three northern regions in the cases of weight-for-height and weight-for-age. 

Children in the three northern regions have poorer child health outcomes in the context of 

weight-for-height and weight-for-age compared with children in the Western Region. These are 

three regions with consistently high poverty indicators over the past two decades (GSS 2000 and 

2007). 

Table 7b. Linear regression results of the relationship between child health outcomes on 

nucleation, dependency and wealth and other covariates, Ghana, 2008 - Model 5 (interaction) 

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Nucleation index -1.40+ (-1.89) -1.32* (-2.38) -0.64 (-1.03) 

Dependency -0.39* (-2.43) -0.32* (-2.40) -0.12 (-0.93) 

Nucleation * Dependency 0.41* (2.36) 0.35* (2.41) 0.15 (1.03) 

Wealth (lowest) # 0.00 0.00 0.00 

Wealth (second) 0.07 (0.54) -0.03 (-0.29) -0.09 (-0.89) 

Wealth (middle) 0.28+ (1.84) 0.11 (1.16) -0.08 (-0.73) 

Wealth (fourth) 0.36* (2.08) 0.25+ (1.90) 0.07 (0.53) 

Wealth (highest) 0.59** (2.68) 0.22 (1.35) -0.18 (-1.03) 

Female child # 0.00 0.00 0.00 

Male child -0.15+ (-1.88) -0.11+ (-1.97) -0.08 (-1.20) 

Child’s age -0.03** (-15.12) -0.01** (-7.24) 0.01** (5.64) 

Child did not recently have 

diarrhea # 0.00 0.00 0.00 

Child recently had diarrhea -0.10 (-1.12) -0.15* (-2.12) -0.14+ (-1.78) 

Child’s size at birth (very 

large) # 0.00 0.00 0.00 

Child’s size at birth (above 

average) -0.26* (-2.51) -0.21* (-2.57) -0.09 (-1.06) 

Child’s size at birth 

(average) -0.30* (-2.52) -0.46** (-5.16) -0.43** (-4.56) 

Child’s size at birth (below 

average) -0.44** (-2.99) -0.49** (-3.81) -0.33* (-2.32) 

Child’s Size at Birth (Very 

Small) -0.65** (-3.89) -0.86** (-5.70) -0.73** (-3.99) 

Child never breastfed # 0.00 0.00 0.00 

Child ever breastfed -0.75* (-2.03) -0.65** (-3.13) -0.31 (-1.42) 

Never married # 0.00 0.00 0.00 

Currently married 0.10 (0.42) 0.13 (0.59) 0.14 (0.49) 

Formerly married -0.42 (-1.31) 0.13 (0.50) 0.58+ (1.73) 

Mother’s age 0.02** (2.68) 0.01 (1.43) -0.00 (-0.57) 

(Continued...)
31 

Table 7b. – Continued

Explanatory variable 

Height-for-age Weight-for-height Weight-for-age 

Coeff. t.stats. Coeff. t.stats. Coeff. t.stats. 

Mother’s education (none) # 0.00 0.00 0.00 

Mother’s education (primary) 0.00 (0.02) -0.00 (-0.01) -0.02 (-0.18) 

Mother’s education 

(middle/JHS) 0.09 (0.69) -0.01 (-0.09) -0.09 (-0.87) 

Mother’s education 

(secondary +) 0.09 (0.47) 0.27+ (1.82) 0.28 (1.55) 

Household does not have 

flush toilet # 0.00 0.00 0.00 

Household has flush toilet 0.11 (0.61) 0.25* (1.98) 0.29* (1.97) 

Household water source: 

other than pipe or bottle # 0.00 0.00 0.00 

Household water source: 

pipe or bottle -0.05 (-0.46) -0.06 (-0.63) -0.03 (-0.27) 

Western region # 0.00 0.00 0.00 

Central region -0.06 (-0.27) -0.11 (-0.62) -0.13 (-0.53) 

Greater Accra region 0.50* (2.33) 0.13 (0.87) -0.20 (-1.28) 

Volta region 0.22 (0.81) 0.03 (0.17) -0.13 (-0.78) 

Eastern region -0.08 (-0.28) 0.14 (0.83) 0.26 (1.43) 

Ashanti region 0.24 (1.17) -0.02 (-0.14) -0.21 (-1.65) 

Brong-Ahafo region 0.37+ (1.74) 0.04 (0.30) -0.24* (-1.98) 

Northern region 0.29 (1.27) -0.29+ (-1.89) -0.64** (-4.97) 

Upper East region 0.10 (0.37) -0.28 (-1.44) -0.49** (-2.93) 

Upper West region 0.42+ (1.84) -0.15 (-0.96) -0.55** (-3.51) 

Rural residence # 0.00 0.00 0.00 

Urban residence -0.03 (-0.24) -0.01 (-0.10) -0.02 (-0.11) 

Constant 1.22 (1.36) 1.35* (1.98) 0.78 (1.05) 

N 2,026 2,026 2,026 

R2

 0.168 0.120 0.091 

F-statistics 10.23 (0.00) 5.87 (0.00) 5.62 (0.00) 

# identifies the reference group; + p<.10, * p<.05, ** p<.01
32 

DISCUSSION AND CONCLUSION 

Understanding the factors and channels that influence child health outcomes has become 

important in view of the short-term and long-term implications of good health, both for 

individuals and for national development (Black et al. 2013). While household characteristics 

have been featured in models such as the quality-quantity debate on child health outcomes 

(Becker and Lewis 1973; Becker and Tomes 1976; Klemp and Weisdorf 2012), little attention 

has been given to the interaction of the nature of dependency and living arrangements of children 

(UNICEF 1990). 

Variations in household characteristics could influence child health outcomes in one or a 

combination of the following ways: (1) number of children and adults; (2) children’s living 

arrangements—that is, the presence of parents or related and unrelated members; in the 

household (3) health and vulnerability status of household members; and (4) occupational status 

of older members in the household. These characteristics could influence the distribution of 

resources and care in the household in various ways, which in turn would affect child health 

outcomes. This paper has focused on the links through which the number of children under age 

16 in a household (dependency) and household living arrangements (nucleation) influence child 

health, as measured by height-for-age, weight-for-height and weight-for-age among children 

under age 5. 

The paper began with an examination of changes in household composition over time, 

from two perspectives: the number of children under age 16 in the household (dependency), and 

the living arrangements of children under age 5 (nucleation status of the household). Results 

show that living arrangements of children under age 5 have changed over the study period. The 

proportion of children living with both parents without other adults present increased 

substantially, from 35 percent in 1993 to 50 percent in 2008, suggesting a movement away from 

extended families and toward nuclear households, although the trend is not linear. 

To understand the possible reasons for this observed change toward nucleation, the study 

compared household composition status with structural factors, such as region, rural-urban and 

ethnicity. In Ghana these structural factors, especially ethnicity, show a long history of 

differences in living arrangement among couples and children. Across all three structural factors, 

there is evidence of association with household composition. Among the possible reasons for the 
33 

changes in household living arrangements of children are migration, urbanization, increasing 

education and the effects of modernization generally. For instance, the proportion of the 

population in urban areas in the country has more than doubled in the past 50 years, from 23 

percent in 1960 to 51 percent in 2010 (GSS 2013). As explored in this paper, these changes have 

implications for children’s health outcomes, as well as many other consequences. 

The observation that households with high dependency and that are nucleated have 

children with better health outcomes than those with high dependency but that are non-nucleated 

shows the implications of dependency, as measured by the number of children under age 16 in a 

household, for the health outcomes of children under age 5. The results also indicate that the 

significance of the effect of dependency by itself is not consistent across all three child health 

outcomes, and that nucleation on its own also fails to explain variations in the three indicators of 

child health. 

What emerge are the joint effects of dependency and nucleation on child health 

outcomes, with both being statistically significant but dependency exhibiting zero effect and 

nucleation a negative effect. The inclusion of household wealth status and the combined effect of 

nucleation and dependency as well as the effect of dependency in the context of nucleation are 

positive in households with at least four dependents. The results for dependency and nucleation 

remain unchanged with the inclusion of all other possible correlates of child health outcomes. 

The first set of results supports the evidence from the literature (Madise, Matthews, and 

Margets 1999; Sahn 1990) that the effect of the number of young and older children in a 

household on child health outcomes is mixed. One of the possible reasons for the mixed results is 

the inconsistency in age cut-offs used for the studies. The choice of age cut-off has implications 

on the competition for resources, economies of scale for large number of children and the 

distribution of resources and care. The first conclusion from this paper is that the effect of 

dependency on child health outcomes should be conceived as dependent on the characteristics of 

household members, such as the living arrangements of under-five children. One possibility is 

that in households with high dependency and households that are nucleated, older children may 

provide support for care of younger siblings. In non-nucleated household with high dependency, 

the tendency is for young children to compete for attention with older members. 
34 

The conclusion that nucleation, on its own, fails to explain variations in all three 

indicators of child health could be explained by the fact that, for nucleation to have an effect on 

child health outcomes, one would need to demonstrate the nature of resource distribution 

(household wealth status) and competition among members in the household. 

The results from the joint effect of nucleation and dependency provide an avenue for 

exploring the relationship of the nature of living arrangements for children and household 

composition. However, the evidence of ‘no’ and ‘negative’ effect for dependency and nucleation, 

respectively, and the positive effects of nucleation and dependency when wealth status is 

included, imply that the joint effects of these two variables can be realized when the wealth 

status of the household is taken into consideration. The effect of the number of children in a 

household on child anthropometric outcomes is mediated by household nucleation and 

conditioned on the wealth status of the household. Thus, the quantity-quality trade-off debates on 

child health outcomes should be extended to incorporate discussions on the nucleation status of 

the household. This is because the nucleation status provides avenues for focused distribution of 

resources. From a policy perspective, programs for social protection and poverty reduction 

should consider focusing on household composition, such as nucleation status and number of 

dependents, as opposed to simply considering household size. 
35 

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40 

APPENDIX 

Appendix Table A1. Measurement of variables 

Variables Measurement/Units 

Height-for-age z-scores (Child’s height - median of ref. group)/std. dev. of the ref. group 

Weight-for-height (Child’s weight - median of ref. group)/ std. dev. of the ref. group (same height) 

Weight-for-age (Child’s weight - median of ref. group)/ std. dev. of the ref. group 

Nucleation Core members (children and parents)/household size 

Dependency Number of children in a household less than 16 years 

Nucleation * Dependency Nucleation X Dependency 

Sex of child Male child = 1; and Female child = 0 

Age of child Completed age of child captured in months 

Child ever breastfed Ever breastfed = 1; and Not ever breastfed = 0 

Child's size at birth Very large = 1, Above Ave. = 2; Ave. = 3; Below Ave. = 4; and Very Small = 5 

Wealth index Asset index 

Prevalence of diarrhea (child) Child had diarrhea in the last two weeks = 1; and Did not have diarrhea = 0 

Mother's age Mothers age captured as completed years 

Mother’s educational level No education = 0; Primary = 1; Middle/JSS = 2; and Secondary+ = 4) 

Mother’s marital status Never married = 1; Currently married = 2; and Formerly married = 3 

Household type of toilet Flush toilet = 1; and Other = 0 

Household source of water Pipe/bottled water = 1; and Other sources = 0 

Region 10 administrative regions in Ghana 

Residence Rural=0; and Urban=1 

 
41 

Appendix Table A2. Summary statistics of variables used for inferential statistics 

Variables Observations Mean Std. dev. Minimum Maximum 

Height-for-age 2,026 -1.07 1.63 -5.88 5.92 

Weight-for-height 2,026 -0.83 1.20 -5.29 4.58 

Weight-for-age 2,026 -0.31 1.36 -4.93 4.70 

Nucleation index 2,026 0.92 0.13 0.25 1.00 

Dependency 2,026 3.41 1.91 1.00 13.00 

Nucleation * Dependency 2,026 3.08 1.66 0.50 11.29 

Wealth categories 2,026 2.53 1.43 1.00 5.00 

Dummy for male child 2,026 0.51 0.50 0.00 1.00 

Child's age (months) 2,026 29.06 17.05 0.00 59.00 

Child recently had diarrhea 2,026 0.22 0.41 0.00 1.00 

Child's size at birth 2,026 2.41 1.07 1.00 5.00 

Child ever breastfed 2,026 0.99 0.11 0.00 1.00 

Mother's marital status 2,026 1.02 0.22 0.00 2.00 

Mother's age 2,026 30.82 6.77 15.00 49.00 

Mother's educational level 2,026 1.03 1.00 0.00 3.00 

Household has a flush toilet 2,026 0.07 0.26 0.00 1.00 

Household uses piped or bottled drinking water 2,026 0.30 0.46 0.00 1.00 

Regional dummies 2,026 5.80 2.74 1.00 10.00 

Urban residence dummy 2,026 0.34 0.47 0.00 1.00 

Appendix Table A3. Pearson correlation matrix for main variables of interest 

Variables 

Height-forage Weight-forheight Weight-forage Nucleation Dependency Wealth 

index 

Height-for-age 1.00 0.61 -0.20 0.00 -0.07 0.16 

Significance level 0.00 0.00 -0.91 0.00 0.00 

Weight-for-height 1.00 0.65 0.03 -0.09 0.20 

Significance level 0.00 -0.19 0.00 0.00 

Weight-for-age 1.00 0.04 -0.04 0.09 

Significance level -0.11 -0.08 0.00 

Nucleation 1.00 -0.26 0.05 

Significance level 0.00 -0.02 

Dependency 1.00 -0.29 

Significance level 0.00 

Wealth index 1.00 

Significance level