Feeding the African Child: Socioeconomic Variables and Child Nutritional Status in the Nigeria’s Geopolitical Zones

Emmanuel O. Nwosu (Phd)Department of Economics, University of Nigeria Nsukka, Enugu State, Nigeria

Email: emmanuel.nwosu@unn.edu.ngPhone: +2347062977126

Anthony Orji (Phd) Department of Economics, University of Nigeria Nsukka, Enugu State, Nigeria

Email: anthony.orji@unn.edu.ngPhone: +2348038559299

AbstractThis study analyses variations in child health/nutritional status with socio-economic status and other factors in Nigeria’s geopolitical zones using data from the Nigeria demographic and health surveys (NDHS). The study applied multivariate probit regression analysis and descriptive approaches on measures of child nutritional status such as height-for-age and height-for-age z-scores. The results indicate that child nutritional status varies significantly with socioeconomic variables-education level of mother and household income. Specifically, the study shows that higher household income is significantly associated with 1.47 percent lower probability of stunting and 1.14 percent lower probability of underweight. The results also show that children born to women with primary education are 8.16 percent less likely to be stunted relative to children born to women with no education, while children born to women with secondary education and higher education respectively have 13.9 percent and 22.1 percent lower probability of being stunted relative to children born to women with no education. These vary across geopolitical zones. The study recommends that zonal-specific policy interventions are needed to improve child health in Nigeria.

Keywords: Feeding, Nutrition, Stunting, Underweight, Child health, Geopolitical zones, Nigeria

JEL Classification: H51; I15; I31; J13

1

1. IntroductionThis study analyses variations in child nutritional status with social economic status and

other factors in Nigeria’s geopolitical zones in a multivariate context. The Nigerian health indicators and variations across the six geopolitical zones have deteriorated significantly over the past years to the extent that Nigeria has continued to rank high in poor population health. The country is remarkably diverse in social and economic development especially across the six geopolitical zones. The coverage of the national health system is limited and health education and enlightenment is weak, partly due to high level illiteracy (NHR, 2006). Available statistics also indicate that the rates of malnutrition are high with 43% of children under-five being stunted (chronically malnourished) and 27% being underweight. At birth, 17% of children are underweight (WHO, 2006).

The maternal and child health situation is one of the indicators of a society’s level of development, as well as an indicator of the performance of the health care delivery system. According to Nigeria Health Review (2006) Report, Nigeria is one of the countries lagging behind in all the Millennium Development Goals (MDGs), which 191 countries, including Nigeria, signed in 2001. In 2000, the World Health Organization (WHO) ranked Nigeria’s overall health system performance 187th among its 191 member states. The patterns of health status in Nigeria mirror many other Sub-Saharan African nations but are worse than would be expected given Nigeria’s GDP per capita. For example after 2003 Demographic and Health Survey (DHS), infant mortality rate (IMR) was estimated at 115 out of 1000, under-5 mortality rate was estimated at 205 out of 1000, and the maternal mortality ratio (MMR) was estimated at 948 out of 100,000 (NHR, 2006; Gustafsson-Wright, and Gaag, 2008).

Despite astounding numbers that show poor child nutritional status in Nigeria, very little is known about its variations with socioeconomic and other factors especially in Nigeria’s geopolitical zones. The Health literature in Nigeria known to us have been devoted to other issues such as the distributive effect of the demand for healthcare (Onwujekwe and Uzochukwu, 2005 and Amaghionyeodiwe, 2008), healthcare financing (Ichoku and Fonta, 2006, 2009), inequality in healthcare provision (Ibiwoye and Adeleke, 2008), macroeconomic analysis of population health (Anyanwu and Erhijakpor, 2009 and Omotor, 2009) and determinants of healthcare utilization (Nwosu, et al. 2012). Furthermore, our study uses a nationally-representative demographic and health surveys (DHS) data for Nigeria which few of the existing micro-level studies in the country could not explore in their analyses. These studies, to the best of our knowledge, relied on individually collected datasets within a small area thus making the validity of the recommendations of those studies for national health policy formulations vulnerable to similar criticisms as cross country studies. However, in order to gain a better understanding of the determinants of health there is need to use more reliable data to measure

2

health inequality as well as health outcomes. In view of this, demographic and health surveys which have been conducted since 1985 for over 60 low- income countries, are a superior and credible data source. These data use same methodology to estimate health and other socio-economic indicators and they are comparable across countries. The data are available both for rural and urban areas separately and for the national level at large. Therefore, empirical studies or analysis on health determination or other issues on health are expected to generate more reliable and robust results if they are based on the DHS data (Wang, 2002). Our study is different in the sense that it analyses child nutritional status in a zonal specific context using a superior dataset.

The study therefore covers all the six geopolitical zones in Nigeria (urban and rural) namely, the South-South, South-East, South-West, North-Central, North-East and North-West. The major focus of the study is child nutritional status measured by height-for-age and weight-for-age z-scores. The focus on child health is important for two reasons. First, child health is worsening in Nigeria with high malnutrition, infant and under5 mortality rates with little efforts policy wise to address these issues. Second, child health is one of the main goals of Millennium Development Goals (we has now been replaced by the sustainable development goals) and as we approach the deadline of 2030, more policy prescriptions will be important on how to move towards the SDG health targets for Nigeria. The analysis covers two Demographic and Health Surveys, 2003 and 2008 to permit dynamic analysis. The study is basically a cross sectional and pooled cross sectional analysis. Most of the analyses are highly descriptive in some sense and involve multivariate probit regression analysis. Our results indicate that child nutritional status varies significantly with socioeconomic variables-education level of mother and household income. We also found that correlates of child nutritional status varies significantly across the geopolitical zones. Finally, our results show that between 2003 and 2008 most of the geopolitical zones witnessed increase in malnutrition.

2.0. Methodology and Data 2.1 Conceptual Framework Nutritional Status

Malnutrition is one of the problems ravaging many developing countries especially the poorest and the most vulnerable segments of the society. In studying malnutrition, one approach is to assess nutritional status on the basis of anthropometric indicators.Anthropometric indicators are useful both at an individual and at a population level. At an individual level, they can be used to assess nutritional well-being or compromised health. On the other hand, anthropometric indicators can be used to assess the nutrition status within a socioeconomic group, community, region, or the entire country and to study both the consequences and determinants of malnutrition at the population level (O’Donnell, et.al, 2008). In terms of measurement, height-for-age, weight-for-age and weight-for-height, are three of the most commonly used anthropometric indicators for infants and children. According to WHO

3

(1995), the indicators can be constructed by comparing indicators based on height, age, weight and gender with reference data for “healthy” children.

Height-for-ageAccording to O’Donnell, et.al, (2008), Height-for-age (H/A) shows cumulative linear growth. A condition of Height-for-age deficits reflect chronic or past inadequacies of nutrition and/or chronic or frequent illness. However, it cannot measure changes in malnutrition in the short term. Furthermore, “shortness” refers to low Height-for-age relative to a child of the same age and sex in the reference population, while “stunting” has to do with extreme cases of low Height-for-age, in which shortness is interpreted as pathological.

Weight-for-ageThe concept of weight-for-age (W/A) is used to represent body mass relative to age. Thus, in a particular reference population, low weight-for-age relative to a child of the same age and sex is referred to as “lightness”. On the other hand, pathological or sever deficits in weight-for-age is known as “underweight”. Weight-for-age is also used for assessing changes in the magnitude of malnutrition and for monitoring growth over time. However, According to O’Donnell, et.al, (2008), weight-for-age confounds the effects of short- and long-term nutrition and health problems.

StandardizationThe health literature has shown that the preferred and most common way of expressing anthropometric indices is in the form of z-scores. For the population or subpopulations, one important advantage of z-scores is that they can be useful in estimating summary statistics such as mean and standard deviation. In order to define abnormal anthropometry, the most commonly used cutoff while using the z-score measure, is a value of -2. That is, two standard deviations below the reference median, irrespective of the indicator used. For example, a child is considered “stunted” whose height-for-age z-score is less than –2. According to O’Donnell, et al (2008), this provides the basis for estimating prevalence of malnutrition in populations or subpopulation.

The WHO has also proposed a classification scheme for population-level malnutrition. These are shown in the following table.Table 1: WHO Classification Scheme for Degree of Population Malnutrition

Prevalence of malnutrition (% of children <60 months, below –2 z-scores)

Degree of malnutrition W/A and H/A W/H Low <10 <5 Medium 10–19 5–9 High 20–29 10–14

4

Very high ≥30 ≥15

Source: WHO 1995 in O’Donnell, et.al, 2008

2.2. Model SpecificationLet the health outcome H be dichotomous. That is, a child may have poor nutritional

status or not. Hence we specify a multivariate probit model of child nutritional status. In the probit model, the binary dependent variable yi is replaced by a latent continuous dependent

variable yi¿

such that y i¿≥0 then yi=1 and y i

¿≤0 then yi=0. In other words, in the first case the event occurs, while in the latter not. Following Salardi (2007) we assume the following regression model in matrix form:y i

¿=x i β+μi with i=1, . .. ,n .. .. .. . .. .. . .. .. . .. .. . .. .. . .. .. . .2 .1

where μi≈Ν (0 , σ 2) and y i¿≈Ν (xi β ,σ2)

Xi is a vector of socioeconomic and other factors. Then, the probability that the event occurs is:

prob [ y i=1 ]=prob [ y i¿≥0 ]= prob [ μ i

σ≤

xi' βσ ] . .. . .. .. . .. .. . .. .2 . 2

Equation (3.6) shows the probability that the cumulated probabilities from −∞ to the point

delineated by xi

' βσ . We can rewrite equation (6) as follows

prob( y i=1 )=Φ (xi' β ) .. . .. .. .. . .. .. . .. .. . .. .. . .. .. . .. .. . .. .. .. . .2 . 3

where Φ (. )is the cumulative distribution function for a standard normal random variable.In order to interpret the regressor’s impact on the probability of an event occurring, we need to compute marginal effects if the regressor is a continuous variable or impact effects if the regressor is a binary variable. Instead of using the matrix expression of the index, we use the following simple expression:x i

¿ β=α +βΧ i+δD i .. .. . .. .. . .. .. . .. .. . .. .. .. . .. .. . .. .. . .. .. . .. .. . .. .. . .. .. .. . .. .. . .. . 2. 4 where the index contains a constant term, a continuous regressor Χ i and a dummy variableDi . We can express the model as follows:

The marginal effect is therefore given by the expression:∂ P∂ Χ i

=φ(α +βΧ i+δDi .) β . .. . .. .. . .. .. . .. .. .. . .. .. . .. .. . .. .. . .. .. . .. .. . .. .. .. . .. .. 2 .7

The impact effect is given by:Δ=Φ (α+βΧ i+δ )−Φ (α+ βΧ i) .. . .. .. . .. .. .. . .. .. . .. .. . .. .. . .. .. . .. .. . .. .. . 2. 8

2.3. The Data

5

prob [ y i=1 ]=Pi=Φ (α+βΧ i+δDi ). . .. .. . .. .. . .. .. . .. .. . .. .. . .. .. .. . .. .. . .2 .6

Nigeria Demographic and Health Surveys (NDHS) for 2003 and 2008 provided the secondary data source used in the study. The data were collected to provide estimates of health, social and population indicators for Nigeria. The data covered rural and urban areas, and the six geo-political zones as well as the whole country. For the 2003 and 2008 NDHS, representative probability samples of 7,864 and 36,000 households were selected respectively. Using a stratified two-stage cluster design consisting of 365 clusters for 2003 and 888 clusters for 2008, the sample was selected enumeration areas developed from 1991 and 2006 population census frame respectively. In the second stage, a complete listing of households was carried out in each selected cluster. Thus, in 2003 and 2008, an average of 21 and 41 households was respectively selected in every cluster by equal probability systematic sampling. Essentially, all men between the ages of 15-59 and women between the ages of 15-49, who were residents of the households were interviewed. The instrument used for data collection was questionnaire. The anthropometric indicators of child health used in this study were constructed from the data. These include; height-for-age (which measures the nutritional status) and weight-for-age z-scores, calculated for children less than 10 years. We used the WHO (2006) ZANTHRO STATA programme for the estimation.

3.0. Results and Discussions3.1. Distribution of Prevalence of MalnutritionThe prevalence of malnutrition and its distribution across different socioeconomic groups in the six geopolitical zones are shown in Table 6 (in appendix). Across all groups, we are able to rank the level or degree of malnutrition following WHO classification of population. This is shown in Table 1.Across different zones, the result in Table 6 shows that there is evidence of some degree of malnutrition. This also varies by socioeconomic status. Again the results show that, among the population there is a strong evidence of high to very high degree of malnutrition.For example, in 2003, 26.8 percent and 43.3 percent of children in the North East and North West region were respectively exposed extensively to malnutrition; while 47 percent and 61 percent of children under 10 years were moderately malnourished in the same regions respectively. On the other hand, 15.6 percent of children under 10 years in the North Central were severely malnourished while 34.3 percent of children under the same category in the same zone were moderately malnourished.

Essentially, in both 2003 and 2008, there were high cases of both moderate and severe malnutrition in all the zones but it was worst in the Northern regions than in the South. However, the results show that in the North and South the degree of malnutrition of children began to worsen in 2008. Furthermore, the table shows significant variation in the degree of malnutrition across education levels and across the wealth quintiles in both 2003 and 2008. Overall there was deterioration in the population malnourishment over time, as shown by the table.

3.2. Regression Results of Correlates of Nutritional Status

6

Tables 2 and 3 (in appendix) show the probit model estimates of the correlates of child

nutritional status. Table 2 reports the overall results of correlates of stunting as well as by

geopolitical zones as shown in columns 1 to 7. Table 3, on other hand, reports the probit

estimates of determinants of underweight. Since the probit model is better interpreted using the

marginal effects, tables 4 and 5 report the corresponding marginal effects for correlates of

stunting and underweight respectively. Overall the marginal effects of the correlates of stunting

in table 4 show that having access to health infrastructure (for example, giving a child vitamin A

at least two months after delivery) is significantly associated with lower probability that the child

will be stunted. Specifically, giving a child vitamin A at least two months after delivery

significantly reduces the likelihood of stunting by about 4.86 percent. But looking at geopolitical

zone specifics, giving a child vitamin A reduces the probability of stunting by 6.64 percent in

North Central and 6.03 percent in the North East, while this is not significantly correlated with

prevalence of stunting in the other geopolitical zones. The results in table 4 also show that the

use of sanitary toilet facilities lead to significant reduction in the prevalence of stunting by 6.03

percent in the North Central while it increases it by 4.85 percent the North East Zone. This is not

significant in other zones. However, table 5 shows that the use of safe toilets significantly

reduces the probability of a child being underweight by 4.08% in North Central. This is not

significant in other zones. As shown in table 4 and 5, the sex of child significantly correlated

with lower probability that the child will be stunted or underweight. Specifically, being a female

child is significantly associated with 6.97 per cent and 5.24 per cent lower probability of being

stunted and underweight respectively. This finding is similar across all the geopolitical zones

except in the South East (SE). Specifically, North Central has the highest probability of 9.81

percent followed by South South and North West which had 8.34 per cent and 7.47 per cent

respectively. For South West and North East the probabilities are 6.52 per cent and 3.99 per cent

respectively. So being a female child is significantly associated with 9.81 per cent and 3.99 per

cent lower probability of being stunted and underweight respectively. This is highest in North

Central and lowest in the North East.

The results in tables 4 and 5 further show that the probability of prevalence of stunting

and underweight significantly increases with child’s age, and the significance of the square of

7

age shows nonlinearity which implies that after certain age, the prevalence of malnutrition

decreases significantly in all the Zones. Overall, the result shows that the probability of

prevalence of stunting and underweight increases by 2.2 per cent and 1.4 per cent respectively

across the Zones, while the age square indicates that after a certain age the overall probability of

being stunted and underweight decreases by 3.45 per cent and 2.34 per cent respectively across

the Zones. This could be explained by the fact that when a child is growing up, he/she may not

have independent choices on the choice of diet, and this can have some impact on his height and

weight. However, after a certain age, when the child begins to be selective on the type of diet

he/she consumes, there may be changes in the probability of being stunted or underweight to a

large extent.

The findings also show that malnutrition varies with household demographic

characteristics. Overall, children born to female-headed households are about 3.99 per cent less

likely to be stunted and 3.91 per cent less likely to be born underweight. This is consistent across

almost all the geographical zone though only statistically significant in the North West (NW).

This finding may be attributed to the fact that female household heads are better able to manage

child health than male household heads. The results also show that the likelihood of malnutrition

is significantly negatively associated with increasing age of the head of household. This means

that as the household head grows old, he or she acquires more experience on better approaches to

health issues which may lead to improvement in child health.

Across the six geopolitical zones education of mother, both secondary and higher levels

of education, is statistically negatively correlated with the probability of being stunted and the

degree of correlation varies across geopolitical zones with higher negative association in the SW,

SE and NW. Similar results were seen in table 3 with respect to prevalence of underweight

among children less than 10 years. Overall, the results in table 4 and 5 show that children born to

women with primary education are 8.16 per cent and 12.8 per cent less likely to be stunted and

underweight respectively, relative to children born to women with no education. In terms of

geopolitical zones, the effect of primary education is only statistically significant in the South

East (for stunting) while is significant in North Central (NC), North East (NE) and North West

8

(NW) (for underweight). Specifically, women with primary education in the South East are 9.93

per cent less likely to have stunted children relative to women with no education.

On the other hand, women with primary education in the NC, NE and NW are

respectively 11.0 per cent, 7.74 per cent and 5.83 per cent significantly less likely to have

underweight children relative to women with no education. From the results, it is also noticeable

that each additional level of completed education of mother is associated with lower probability

that the child will be stunted. Overall, children born to women with secondary education have

13.9 per cent and 17.7 per cent lower probability of being stunted and underweight respectively.

On the other hand, children born to women with higher education have about 22.1 per cent and

20.0 per cent lower probability of being stunted and underweight respectively.

These results are in agreement with the theoretical postulates in health economics

literature that women who are more educated know more about health issues and how to use

good habits to promote good health efficiently (Grossman, 1972). It also follows from theory that

healthier children are more likely to be borne by mothers with higher levels of education since

they understand the dynamics of health better than less educated ones and are also able to bring

up their children with better knowledge of health issues. Again, more educated mothers are less

likely to seek for medical counsel from nonqualified healers and traditional practitioners since

they are more likely to use better diet system for the family. This better knowledge of health

among educated mothers is expected to drive health behavior among different households and

thus, reduce health inequality.

The findings show that living in rural area increases the likelihood of malnutrition but the

effect is significant only in NE and NW. Overall, living in the rural areas significantly increases

the probability of having stunted and underweight children by 4.15 per cent and 3.04 per cent

respectively. Specifically, children born in rural areas in the NE and NW are respectively 6.57

per cent and 7.92 more likely to be stunted relative to those in urban areas. In the same vein,

children born in rural areas in NW are significantly more likely to be underweight by 7.85 per

cent relative to those born in urban areas.

One of the socioeconomic variables used in this study is household wealth index which is

used as proxy for household income. The results in tables 4 and 5 indicate that the higher the

9

household income the lower the probability that a child born to the household will be

malnourished. Overall, higher household income is significantly associated with about 1.47

percent lower probability of stunting and 1.14 percent lower probability of underweight.

Specifically, higher household income is significantly associated with 3.38 percent, 2.32 percent

and 3.78 percent lower probability that a child will be stunted in the NC, NE and SE geopolitical

zones respectively, whereas asset index is not a significant correlate of malnutrition in the SW

and NW geopolitical zones. Again, the results show that higher household income is

significantly associated with 3.82 percent and 2.64 percent lower probability of underweight in

NE and SS geopolitical region respectively. This result supports the finding of Alderman et al

(2001) who showed that income increases at the household and at the national level imply

similar rates of reduction in malnutrition at the same rate of increase in income. Thus, increases

in income are clearly important for reducing child malnutrition.

Year dummy was added in the regression in order to ascertain if malnutrition has changed

over time between 2003 and 2008. Overall, the results indicate that child malnutrition

significantly increased over time in the NC, NE and SW but declined significantly in the NW

and SS. These results suggest that Nigeria significantly departed from the MDG targets in terms

of child nutritional health in most of the six geopolitical zones. Only patchy progress was made

towards realizing the goals related to this.

4.0. Policy Recommendations and ConclusionThese findings therefore call for some important policy interventions that would help to

improve child health in Nigeria. Looking at North-east and North-west zones where most of the

households in the lowest quintile groups are found, one implication of this study is that

improvement of the welfare level of individuals and households in those areas would require

specific interventions. Since education of women is essential for the adoption and utilization of

health care facilities and health information, there is need for government to provide targeted

interventions aimed at providing basic and affordable education for women. Therefore, to reduce

inequality in health and improve health outcomes across the various groups, there is need to give

10

greater opportunities to women to be formally educated like their male counterparts. However,

more emphasis should be placed on at least secondary education than lower levels of education.

Hence, women education matters very significantly in the realization of Sustainable

Development Goals related to child health, which has now replaced the MDGs.

Furthermore, the government should evolve policies that would improve welfare status

and income conditions of the population especially the poorest group. This can be done by

creating more activities that generate income in the public and private sectors. When basic

infrastructure such as electricity, roads, and the like are provided, they can help small scale

enterprises to run at minimal or sustainable costs. Also supporting the private sector with soft

loans can help their business and also increase their income so that they can create more jobs and

income at the household level. This increase in income will be effective in improving child

nutritional status. Consequently, to ensure that income plays dual role of reducing health

inequality and improving mean health, there is need for inclusive growth to be pursued in all

sectors of the economy. Over the years, growth has not been very inclusive not only in Nigeria

but also in many other developing countries especially in the Sub-Saharan Africa.

Again, improvement of basic infrastructure as well as educating people on the need for

utilization of basic health facilities would require some specific interventions by government at

all levels. Provision of good sewage systems, drinking water and other basic amenities by the

government would also be very effective in improving child health in Nigeria. The fragility of

the male child implies that special attention should be given to him from the womb up to at least

the age of 10. The male child is very valuable in many African societies and beyond but

unfortunately is prone to early childhood death or malnutrition compared to the female child.

Reduction in gender disparity in education enrolment also implies reducing the disproportionate

representation of one group in the population from the early childhood. Therefore, the health

sector and health workers should give adequate medical attention to the mother before and after

delivery. Women should be encouraged to complete all the necessary immunization of the child

at the early childhood to make him resistant to frequent outbreak of diseases especially in the

Northern zones. This is not to say that the female child should be ignored. We recommend

11

strongly that the gender disparity in health at the early childhood should be reduced to very low

levels.

References

Alderman, H, Appleton, S, Haddad, L, Song, L. and Yohannes, Y. (2001) “Reducing Child Malnutrition: How Far Does Income Growth Take Us?” Centre for Research in Economic Development and International Trade (CREDIT) Research Paper No 01/05

Amaghionyeodiwe, L. A. (2008). Determinants of the choice of health care provider in Nigeria. Health Care Management Science , 11, 215–227.

Anyanwu, J. C. (2009). Health expenditure and health outcomes in Africa. African Development Review , 21, 400-434.

Nigerian Health Review (NHR),(2006). Nigerian Health Review. Abuja, Nigeria: Federal Ministry of Health.

Nigerian Health Review (NHR), (2008). Nigeria Health System Assessment 2008. Abuja, Nigeria: Federal Ministry of Health.

Fonta, W., M., and Ichoku, H., E. ( 2009). Estimating the willingness to pay for community healthcare insurance scheme in rural nigeria. Pep-Network Working Paper .

Fonta, W., M., and Ichoku, H., E. . (2006). The distributional analysis of healthcare financing in Nigeria: A case study of enugu state. Pep-Network Working Paper .

Grossman, M. (1972b). On the Concept of Health Capital and the Demand for Health. Journal of Political Economy , 80: 223–55.

Gustafsson-Wright, E. and Jacques van der Gaag. (2008) “An Analysis of Nigeria’s Health Sector by State: Recommendations for the Expansion of the Hygeia Community Health Plan”. Amsterdam Institute for International Development (AIID) and Brookings Institution. August 2008.

Nwosu, E.O., Urama, N.E., and Uruakpa, C. (2012). Determinants of Antenatal Care Services Utilisation in Nigeria. Developing Country Studies, 2:41-52.

O’Donnell, Owen; Eddy van Doorslaer; Adam Wagstaff and Magnus Lindelow ( 2008) “Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and their Implementation. World Bank Institute.

12

Omotor, D. G. ( 2009). Determinants of federal government health expenditures in nigeria. International Journal of Economic Perspectives , 3, 5-18.

Onwujekwe, O., and Uzochukwu, B. (2005). Socio-economic and geographic differentials in costs and payment strategies for primary healthcare services in Southeast Nigeria. Health Policy 71, 383–397.

Salardi, P. (2007), The Estimation of the Health Functioning Production Function for Brazil. A paper prepared for the 2007 International Conference of the Capability Approach "Ideas Changing History", September 17-20, hosted by the New School University, New York City

Wang, L. (2002). Determinants of Child Mortality in Low-Income Countries: Empirical Findings from Demographic and Health Surveys. The Word Bank , April 19.

World Health Organisation (WHO) (2006). WHO | Draft outline: World health report 2006. . Retrieved July 15, 2012, from http://www.who.int/hrh/whr06_consultation/en/print.htm

World Health Organisation (WHO) (1995) “Physical Status: The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee”. World Health Organ Tech Rep Ser, 854: 1–452.

13

Results AppendixTable 2: Probit Model of Stunting by Geopolitical Zones

(1) (2) (3) (4) (5) (6) (7)Overall NC NE NW SE SW SS

Vitamin A -0.124*** -0.167*** -0.152** 0.0201 -0.0831 0.0280 -0.0654(0.000) (0.003) (0.026) (0.802) (0.367) (0.691) (0.300)

occupation -0.0100** -0.035*** 0.00433 -0.00755 0.0204 0.00191 -0.00466(0.027) (0.002) (0.629) (0.315) (0.263) (0.903) (0.739)

bednet_type -0.0351 -0.0782 -0.148** 0.00599 -0.261** 0.0682 0.0545(0.279) (0.310) (0.029) (0.918) (0.041) (0.459) (0.628)

safe water -0.0281 -0.105 0.0923* -0.0728 -0.0281 -0.0855 0.0545(0.265) (0.118) (0.098) (0.119) (0.744) (0.278) (0.411)

safe toilet 0.0181 -0.152** 0.122** -0.0307 -0.0211 0.0320 -0.0586(0.460) (0.011) (0.023) (0.475) (0.825) (0.708) (0.429)

electricity 0.000651 0.0747 -0.00634 0.0305 -0.00572 0.00664 -0.0299(0.982) (0.343) (0.924) (0.569) (0.956) (0.932) (0.744)

sex of child -0.176*** -0.246*** -0.100** -0.189*** -0.0810 -0.174*** -0.228***

(0.000) (0.000) (0.030) (0.000) (0.327) (0.009) (0.000)age months 0.0558*** 0.0468*** 0.0850*** 0.0743*** 0.0418*** 0.0393*** 0.0242***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001)Agesq -0.0873*** -0.067*** -0.128*** -0.115*** -0.076*** -0.073*** -0.038***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.004)gender of head

-0.101*** -0.0837 -0.123 -0.255*** 0.178* -0.0222 0.00614

(0.010) (0.321) (0.253) (0.006) (0.095) (0.796) (0.948)age of head -0.0029*** -0.00298 0.000847 -0.0055*** -0.0028 0.00525* -0.0044*

(0.001) (0.130) (0.676) (0.001) (0.363) (0.056) (0.084)Education Levelprimary -0.209*** -0.00716 -0.0896 -0.0980 -0.315** -0.0854 -0.0447

(0.000) (0.904) (0.148) (0.127) (0.018) (0.515) (0.641)secondary -0.358*** -0.158** -0.154* -0.256*** -0.315** -0.270** -0.145

(0.000) (0.020) (0.063) (0.004) (0.025) (0.040) (0.140)higher -0.611*** -0.346*** -0.230 -0.563*** -0.470** -0.599*** -0.354**

(0.000) (0.004) (0.193) (0.001) (0.020) (0.001) (0.010)rural 0.105*** -0.0359 0.165*** 0.201*** 0.0779 -0.00177 0.0870

(0.000) (0.612) (0.010) (0.002) (0.392) (0.984) (0.262)asset index2 -0.0371** -0.0847** -0.0583* -0.00463 -0.115* 0.00996 -0.0534

(0.017) (0.039) (0.093) (0.826) (0.052) (0.802) (0.228)time dummy 0.0137* 0.0830*** 0.0501*** -0.0475*** -0.0132 0.0720** 0.0171

(0.097) (0.000) (0.003) (0.003) (0.708) (0.012) (0.433)Constant -27.62* -166.3*** -101.6*** 95.27*** 25.86 -145.0** -34.39

(0.095) (0.000) (0.003) (0.003) (0.715) (0.012) (0.432)Observations 16571 3168 3725 4379 1316 1827 2156Pseudo R2 0.061 0.060 0.086 0.068 0.041 0.033 0.028chi2 991.7 230.7 346.4 343.8 54.19 61.98 61.02

p-values in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

14

Table 3: Probit Model of Underweight by Geopolitical Zones(1) (2) (3) (4) (5) (6) (7)

Overall NC NE NW SE SW SSVitamin A -0.140*** 0.0557 -0.108 -0.0111 -0.242** 0.104 -0.0624

(0.000) (0.372) (0.138) (0.891) (0.027) (0.222) (0.410)occupation -0.00593 -0.0207 -0.00708 0.0147** -0.0110 -0.0360* -0.0115

(0.223) (0.118) (0.443) (0.050) (0.614) (0.060) (0.497)bednet_type -0.00752 -0.177** -0.00642 0.0637 -0.196 -0.139 0.00984

(0.825) (0.049) (0.927) (0.271) (0.186) (0.216) (0.943)safe water 0.0317 0.105 -0.0793 0.0968** 0.195* -0.319*** -0.0647

(0.244) (0.166) (0.167) (0.036) (0.052) (0.001) (0.425)safe toilet 0.0610** -0.142** 0.0420 -0.0198 -0.0908 -0.0683 0.100

(0.019) (0.029) (0.445) (0.644) (0.422) (0.507) (0.264)electricity -0.100*** -0.110 0.0102 -0.159*** -0.286** 0.0156 -0.0352

(0.002) (0.182) (0.883) (0.003) (0.024) (0.868) (0.763)sex of child -0.162*** -0.136** -0.220*** -0.205*** -0.0611 -0.159** -0.134*

(0.000) (0.010) (0.000) (0.000) (0.515) (0.048) (0.062)age months 0.0442*** 0.0164*** 0.0881*** 0.0590*** 0.000946 0.0148 0.0175**

(0.000) (0.007) (0.000) (0.000) (0.929) (0.106) (0.033)agesq -0.0723*** -0.0241** -0.137*** -0.0983*** -0.0137 -0.0242 -0.0251*

(0.000) (0.034) (0.000) (0.000) (0.513) (0.153) (0.096)gender of head

-0.121*** -0.123 -0.114 0.103 -0.0209 -0.0557 -0.0843

(0.005) (0.210) (0.326) (0.274) (0.864) (0.584) (0.434)age of head -0.0045*** -0.0058*** -0.0058*** -0.00231 -0.00635* 0.00129 -0.00490

(0.000) (0.008) (0.006) (0.168) (0.087) (0.696) (0.116)Education Levelprimary -0.430*** -0.402*** -0.215*** -0.152** -0.0857 -0.0399 -0.132

(0.000) (0.000) (0.001) (0.019) (0.562) (0.786) (0.235)secondary -0.615*** -0.566*** -0.332*** -0.196** -0.142 -0.311** -0.213*

(0.000) (0.000) (0.000) (0.029) (0.360) (0.037) (0.061)higher -0.848*** -0.752*** -0.531*** -0.674*** -0.144 -0.451* -0.477***

(0.000) (0.000) (0.006) (0.000) (0.538) (0.051) (0.005)rural 0.0940*** 0.0115 -0.000534 0.201*** -0.00819 0.142 0.0375

(0.004) (0.887) (0.993) (0.002) (0.940) (0.194) (0.698)asset index2 -0.0352* -0.0423 -0.103** -0.00581 -0.0231 0.0630 -0.121**

(0.061) (0.281) (0.012) (0.805) (0.806) (0.177) (0.027)time dummy 0.00397 0.0382* 0.0643*** -0.0132 0.0281 -0.0500* -0.0399*

(0.655) (0.056) (0.000) (0.396) (0.384) (0.092) (0.091)Constant -8.285 -76.70* -129.5*** 25.69 -56.74 99.69* 79.68*

(0.642) (0.056) (0.000) (0.411) (0.381) (0.095) (0.093)Observations 16571 3168 3725 4379 1316 1827 2156Pseudo R2 0.082 0.059 0.096 0.050 0.039 0.045 0.030chi2 1137.3 160.6 374.1 263.5 34.81 53.56 46.41

p-values in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01

15

Table 4: Marginal Effects of Stunting by Geopolitical Zones(1) (2) (3) (4) (5) (6) (7)

Overall NC NE NW SE SW SSVitamin A -0.0486*** -0.0664*** -0.0603** 0.00792 -0.0271 0.0105 -0.0239

(0.000) (0.003) (0.025) (0.801) (0.364) (0.691) (0.300)occupation -0.00396** -0.0140*** 0.00173 -0.00298 0.00668 0.000716 -0.00171

(0.027) (0.002) (0.629) (0.315) (0.263) (0.903) (0.739)bednet_type -0.0139 -0.0311 -0.0586** 0.00236 -0.0799** 0.0258 0.0201

(0.278) (0.308) (0.028) (0.918) (0.028) (0.462) (0.631)safe water -0.0111 -0.0416 0.0368* -0.0287 -0.00921 -0.0320 0.0200

(0.265) (0.117) (0.098) (0.119) (0.744) (0.276) (0.411)safe toilet 0.00716 -0.0603** 0.0485** -0.0121 -0.00691 0.0120 -0.0214

(0.460) (0.011) (0.023) (0.475) (0.825) (0.708) (0.428)electricity 0.000257 0.0298 -0.00253 0.0120 -0.00188 0.00249 -0.0110

(0.982) (0.343) (0.924) (0.569) (0.956) (0.932) (0.745)sex of child -0.0697*** -0.0981*** -0.0399** -0.0747*** -0.0266 -0.0652*** -0.0834***

(0.000) (0.000) (0.030) (0.000) (0.328) (0.009) (0.000)age in months

0.0220*** 0.0187*** 0.0339*** 0.0293*** 0.0137*** 0.0148*** 0.00888***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001)agesq -0.0345*** -0.0268*** -0.0510*** -0.0455*** -0.0250*** -0.0275*** -0.0139***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.004)head-female -0.0399*** -0.0333 -0.0489 -0.101*** 0.0582* -0.00834 0.00225

(0.010) (0.321) (0.253) (0.006) (0.095) (0.796) (0.948)age of head -0.0011*** -0.00119 0.000337 -0.0022*** -

0.0009040.00197* -0.00160*

(0.001) (0.130) (0.676) (0.001) (0.363) (0.056) (0.084)primary -0.0816*** -0.00285 -0.0356 -0.0388 -0.0993** -0.0319 -0.0163

(0.000) (0.904) (0.147) (0.128) (0.013) (0.513) (0.640)secondary -0.139*** -0.0625** -0.0609* -0.102*** -0.103** -0.101** -0.0530

(0.000) (0.019) (0.060) (0.004) (0.024) (0.039) (0.139)higher -0.221*** -0.135*** -0.0905 -0.220*** -0.135*** -0.198*** -0.121***

(0.000) (0.003) (0.183) (0.001) (0.006) (0.000) (0.005)rural 0.0415*** -0.0143 0.0657*** 0.0792*** 0.0255 -0.000664 0.0319

(0.000) (0.612) (0.010) (0.002) (0.392) (0.984) (0.262)asset index2 -0.0147** -0.0338** -0.0232* -0.00183 -0.0378* 0.00374 -0.0196

(0.017) (0.039) (0.093) (0.826) (0.052) (0.802) (0.228)time dummy 0.00541* 0.0331*** 0.0200*** -0.0187*** -0.00434 0.0271** 0.00626

(0.097) (0.000) (0.003) (0.003) (0.708) (0.011) (0.433)Observations 16571 3168 3725 4379 1316 1827 2156Pseudo R2 0.061 0.060 0.086 0.068 0.041 0.033 0.028chi2 991.7 230.7 346.4 343.8 54.19 61.98 61.02

Marginal effects; p-values in parentheses (d) for discrete change of dummy variable from 0 to 1* p < 0.10, ** p < 0.05, *** p < 0.01

Table 5: Marginal Effects of Underweight by Geopolitical Zones(1) (2) (3) (4) (5) (6) (7)

Overall NC NE NW SE SW SS

16

Vitamin A -0.0443*** 0.0165 -0.0394 -0.00434 -0.0462** 0.0239 -0.0136(0.000) (0.377) (0.131) (0.891) (0.022) (0.228) (0.410)

occupation -0.00192 -0.00606 -0.00263 0.00573** -0.00216 -0.00813* -0.00250(0.223) (0.117) (0.443) (0.050) (0.614) (0.061) (0.497)

bednet_type -0.00242 -0.0489** -0.00238 0.0250 -0.0351 -0.0298 0.00215(0.825) (0.036) (0.927) (0.272) (0.146) (0.192) (0.943)

safe water 0.0103 0.0314 -0.0293 0.0378** 0.0381** -0.0699*** -0.0141(0.246) (0.175) (0.164) (0.037) (0.050) (0.001) (0.425)

safe toilet 0.0198** -0.0408** 0.0157 -0.00772 -0.0177 -0.0153 0.0219(0.019) (0.026) (0.446) (0.644) (0.419) (0.502) (0.267)

electricity -0.0323*** -0.0317 0.00378 -0.0618*** -0.0586** 0.00352 -0.00772(0.002) (0.173) (0.883) (0.002) (0.029) (0.868) (0.766)

sex of child -0.0524*** -0.0399** -0.0819*** -0.0801*** -0.0120 -0.0360** -0.0291*

(0.000) (0.010) (0.000) (0.000) (0.515) (0.049) (0.062)age months 0.0143*** 0.00481*** 0.0327*** 0.0230*** 0.00019 0.0036 0.0038**

(0.000) (0.007) (0.000) (0.000) (0.929) (0.106) (0.032)agesq -0.0234*** -0.00703** -0.0508*** -0.0383*** -0.00268 -0.00548 -0.00547*

(0.000) (0.034) (0.000) (0.000) (0.512) (0.154) (0.096)head is female

-0.0391*** -0.0359 -0.0424 0.0402 -0.0041 -0.0126 -0.0183

(0.005) (0.210) (0.326) (0.275) (0.863) (0.584) (0.434)age of head -0.0015*** -0.0017*** -0.0022*** -0.00090 -0.0013* 0.00029 -0.00106

(0.000) (0.008) (0.006) (0.168) (0.087) (0.696) (0.116)primary -0.128*** -0.110*** -0.0774*** -0.0583** -0.0165 -0.00896 -0.0278

(0.000) (0.000) (0.001) (0.017) (0.554) (0.784) (0.221)secondary -0.177*** -0.144*** -0.116*** -0.0747** -0.0279 -0.0701** -0.0458*

(0.000) (0.000) (0.000) (0.025) (0.357) (0.035) (0.058)higher -0.200*** -0.161*** -0.172*** -0.229*** -0.0265 -0.0822** -0.0842***

(0.000) (0.000) (0.001) (0.000) (0.508) (0.012) (0.000)rural 0.0304*** 0.00336 -0.000199 0.0785*** -0.00161 0.0321 0.00815

(0.004) (0.887) (0.993) (0.002) (0.940) (0.193) (0.698)asset index2 -0.0114* -0.0124 -0.0382** -0.00226 -0.00453 0.0143 -0.0264**

(0.061) (0.280) (0.012) (0.805) (0.806) (0.178) (0.026)time dummy 0.00128 0.0112* 0.0239*** -0.00515 0.00551 -0.0113* -0.00868*

(0.654) (0.055) (0.000) (0.396) (0.388) (0.095) (0.088)Observations 16571 3168 3725 4379 1316 1827 2156Pseudo R2 0.082 0.059 0.096 0.050 0.039 0.045 0.030chi2 1137.3 160.6 374.1 263.5 34.81 53.56 46.41aic 17984.5 3247.0 4477.7 5698.9 993.6 1538.9 1750.2bic 18123.3 3356.0 4589.7 5813.8 1086.9 1638.1 1852.4ll -8974.2 -1605.5 -2220.9 -2831.4 -478.8 -751.5 -857.1

Marginal effects; p-values in parentheses (d) for discrete change of dummy variable from 0 to 1* p < 0.10, ** p < 0.05, *** p < 0.01

17

Table 6 Prevalence of Malnutrition (Stunting and Underweight) by Zones and YearGroups Year and Heath Indicator

2003 2008Mean SD % below

-2SD% below -3SD

Mean SD % below -2SD

% below -3SD

ZonesNorth Central

-1.238 2.3158 34.33 15.61 -2.2156 3.7319 48.58 32.7

North East -1.739 2.3937 47.01 26.77 -2.0499 3.2829 50.76 35.07North West -2.4814 3.2529 61.16 43.32 -2.4019 3.8233 56.83 40.7South East -1.0741 2.771 26.98 16.55 -1.0518 3.6867 29.02 16.63South West -0.8694 2.7342 26.76 11.34 -1.8085 3.8572 38.18 23.83South South -1.0045 2.7447 31.03 12.9 -1.3051 3.1289 35.17 18.74Weath QuintilePoorest -2.081 2.777 53.7 34.5 -2.372 3.639 55.5 40.5Poorer -2.021 2.67 51.6 33 -2.265 3.599 53.2 36Middle -1.86 2.563 47.9 28.9 -1.921 3.471 46.4 29.6Richer -1.304 3.169 35.2 19.1 -1.638 3.581 38.7 23.5Richest -0.839 2.759 26 11 -1.208 3.758 30.1 18.2Education level of MotherNo Education

-2.175 2.596 54.5 35.8 -2.325 3.747 54.6 38.6

Primary -1.507 2.699 41.1 22.1 -1.896 3.269 45.1 28.3Secondary -0.887 3.242 28.3 12.5 -1.489 3.605 36 21.8Higher -0.61 2.329 14.4 5.3 -1.969 3.627 46.7 31.2Locality/SectorUrban -1.293 2.826 35.3 18.8 -1.532 3.64 37.9 23.5Rural -1.856 2.815 48.3 30 -2.136 3.609 5 34.2Overall -1.647 2.832 43.5 25.8 -1.969 3.627 46.7 31.2

Table 7: Definitions of Variables of the Models we estimatedVariable Definition and motivation

Vitamin A Takes the value 1 if the child was given vitamin A at least two months after delivery and 0 otherwise

18

occupation occupation category of the respondent which takes the value 1 if the respondent is in wage employment and 0 otherwise.

bednet_type bet net type takes the value 1 if the net is insecticide treated net and 0 otherwise

safe water safe water takes the value 1 if the source of drinking water is from tap, borehole and pipe borne water, and 0 otherwise

safe toilet safe toilet takes the value 1 if the toilet facilities are sanitary type (such as flushing to sewage, covered pit, etc) and 0 otherwise

electricity This takes the value 1 if the household is connected to public electricity and 0 otherwise.

sex of child this takes the value 1 if the child is female and 0 otherwise

age months age of the child measured in months since delivered

agesq square of age of the child

gender of head gender of the head takes the value 1 if the head is female and 0 otherwise

age of head This is age of the household head measured in years

Education Level PrimarySecondaryhigher, while the base category are heads without education

Locality/Sector Rural

Urban

asset index2 This is the asset index score which is a measure of the household wealth

time dummy This takes the value 1 if the year of the survey is 2008 and 0 otherwise

Zones a categorical variable that indicates the six geopolitical zones in Nigeria namely; North East, North West, North Central, South East, South West and South South

Weath Quintile The wealth index categorised by five quintile namely q1, q2, q3, q4 and q5 where q5 is the richest group and q1 is the poorest group.

Source: Authors’ compilation

19