Demographic behavior and poverty: Micro-level evidence from Southern Sudan

Pergamon

World Development, Vol. 22, No. 7, pp. 1031-1044,1994 Copyright 0 1994 Elsevier Science Ltd

Printed in Great Britain. All rights reserved 0305-750x/94 $7.00 + 0.00

0305750X(94)EOO22-P

Demographic Behavior and Poverty: Micro-level

Evidence from Southern Sudan

BARNEY COHEN National Research Council, Washington, DC

and

WILLIAM J. HOUSE* UNFPA Country Support Team, Suva, Fiji

Summary. - The largely subsistence economy of Southern Sudan remains one of the least developed in sub-Saharan Africa and very little has been documented about the nature of its economy and its demographic situation. Using household-level data drawn from a 5% sample survey in urban Juba, this paper explores some of the complex interrelationships between indicators of poverty and demographic behavior. Given the prevailing noncontracepting regime in Juba, the finding that behavioral factors are not so important in explaining fertility is perhaps not surprising. Success is greater in identifying the determinants of infant mortality, and measures to reduce infant mortality must be the priority goal of a comprehensive population and development policy that is still awaiting formulation. Our results may have a wider application for many other parts of the troubled continent where living standards have regressed in recent years, general fertility decline has yet to appear, and decreases in infant mortality may have tapered off.

1. INTRODUCTION

While many observers consider the alleviation of poverty and inequality a high priority in the development process, there is considerably more debate on whether population growth contributes to, hinders, or has little net effect on this laudable goal. For sub- Saharan Africa, Malthusian commentators argue that socioeconomic development is hindered by rapid population growth, pointing to macro-level data portray- ing the region as one of the world’s poorest while, at the same time, having the world’s highest rate of population growth. Other authors have interpreted the correlation as circumstantial evidence that poverty induces population growth. They argue that programs aimed at lowering the rate of population growth will not eliminate, or even reduce poverty. Indeed, if high fertility is a logical response to poverty, invoking anti- natalist policies will prevent some proportion of the impoverished from rising out of poverty, unless accompanying efforts are also made to ensure a more equitable distribution of income. Others view the correlation between the rate of population growth and poverty as spurious - pointing to some sub-Saharan African countries’ ability to prosper in the face of high rates of population growth (e.g., Botswana and

Lesotho). Finally, several authors maintain that demographic growth induces economic growth.

Set against a background of widespread underdevelopment, poverty and deprivation in prewar Southern Sudan, the focus of this paper is on the nature and size of socioeconomic differentials at the household level and their influence and effect on inducing changes in fertility and mortality behavior. The data were collected in urban Juba in 1983, before the outbreak of civil war, and represent the peacetime situation in the regional capital. Using household- level data drawn from a 5% sample in Juba, this paper’s initial goal is to investigate the nature of income distribution and poverty and to uncover groups of individuals or households who share some observable characteristics, constitute distinct cate- gories, and can be classified as poor according to some

*The authors wish to acknowledge the financial and tech- nical support of the United Nations Population Fund (UNPRA) and the International Labour Organization during the implementation of the project: “Population and Human Resources Development in Southern Sudan,” from which this paper derives. The authors are solely responsible for what follows. Final revision accepted: February 2, 1994.

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1032 WORLD DEVELOPMENT

acceptable definition which incorporates elements from a vector of satisfactions and deprivations. The rest of the paper is exploratory and examines the inter- relationship between indicators of economic status, fertility, and mortality behavior at the household level. Economic indicators include income as well as household asset holdings, water sources, nutritional status, subjective feelings of deprivation and aspects of well- being. Our demographic focus is on trying to explain household-level differentials in behavior with respect to fertility, knowledge and use of family planning, and child survival, all of which are targets for planners and policy makers all over the developing world.

While many African countries have adopted anti- natalist population policies in recent years and con- centrated much of their effort on family planning interventions, very little research has been undertaken to help policy makers understand the nature and causes of differential demographic behavior at the household level. We consider this to be a prerequisite for a successful population policy. Hence, these issues become the focus of the analytical section of our paper.

Given the prevailing natural, noncontracepting fertility regime and the unsatisfactory health environ- ment in urban Juba, where the bulk of the population is exposed to various tropical diseases and infant mortality is high, the finding that behavioral factors are not so important in explaining fertility is perhaps not surprising. Success is greater in identifying the determinants of infant mortality, which in turn may have a biological relationship with fertility. Measures to reduce infant mortality must be the priority goal of a population policy that is still awaiting formulation. More generally, policy interventions are needed to promote social and economic development which will inevitably create real income differentials between households, which may, in turn, lead to further changes in fertility and mortality behavior.

2. COUNTRY SETTING

The Christian and Animist population of Southern Sudan is largely composed of black Africans, estimated to number 5.3 million at the time of our research in 1983; a population of 15.3 million, pre- dominantly Arabic and Moslem, inhabited Northern Sudan (Democratic Republic of Sudan, 1984a). The economy of the southern region is one of the least developed in sub-Saharan Africa, and its meager social and economic infrastructure was further depleted by the 17 years of civil war between the North and the South which formally ended in 1972. In 1984, the war was reactivated; this has closed down virtually all development activity and increased the inaccessibility of the region. The majority of the population is usually engaged in subsistence agriculture

during peacetime, although some limited cash income is generated from the sale of surplus crops. Nomadic pastoralism is also widely practiced.

A recent survey of the limited data and literature sources on the socioeconomic and demographic situation in Southern Sudan painted a rather depressing picture (House, 1989b). The vast majority of citizens in both rural and urban areas do not have access to the barest minimum of basic goods and essential services. The underlying cause is attributed to the very underdeveloped state of the economy, the infrastructure and the stock of human resources. The overwhelming dependence on subsistence agriculture means that the low level of farmers’ cash incomes allows little scope for the purchase of privately consumed goods and services. Nor are government revenues sufficient to provide many of the essential public goods and services such as health, education, transport or social services.

The protracted civil war continues with no end in sight and it has resulted in the widespread destruction of the already inadequate social infrastructure and large-scale migrations out of the region. When peace eventually returns the period of reconstruction will be long and difficult and will require enormous resource inputs which will need to be largely contributed by the international community.

3. CONCEPTUALIZING POVERTY

Absolute poverty is often seen as a condition of failure to meet the barest essentials of physical existence, which means being unable either to produce sufficient food or to have remunerative work in order to purchase enough for oneself and one’s family - in other words, it is almost entirely a measure of inadequate nutrition (Culter, 1984). Poverty needs to be defined, however, to include more than a set of minimum nutritional requirements to include measures in certain “basic needs.” There can be no doubt that every family ought to have, inter din, adequate food and a balanced diet (obviously a determinant of nutrition), water and sanitation, clothing and housing, and access to public transport, good health, education, productive employment, and income (International Labour Office, 1976).

In Southern Sudan, perhaps security against star- vation is the most important and least controversial in the vector of poverty, and this will be dependent on the household’s access to urban farm land and jobs, and its own composition, as measured by the relative number of able-bodied, potential labor-force members. Other family assets, including animals, children, housing, and human capital (education and training), which determine earnings opportunities in nonfarm occupations, will also contribute to the nature of the households’ security. In such a traditional society the network of social obligations, which often necessitate

DEMOGRAPHIC BEHAVIOR AND POVERTY 1033

reciprocal transfers of resources, adds to a family’s security. As regards a particular household, its attitude toward risk-taking, and ability and willingness to introduce entrepreneurial innovations will help to determine its place in the society’s distribution of income.

Intimately related to each other, and to security in general, are the levels of food consumption, nutrition, and health, the principal components in the poverty vector. They depend on the output resulting from the household’s own direct production efforts, as well as relative prices (exchange entitlements), including remittances from absent household members. Health and nutrition are dependent on consumption and income levels, as well as work loads and access to socially provided medical services and good water supplies.

4. MEASURING POVERTY: THE EVIDENCE

(a) Income andpoverty

The authors conducted a random sample survey of over 1,000 households in Juba, or 5% of the town, during June-August 1983 before the outbreak of civil war. The data are, therefore, representative of peacetime conditions in the urban setting. The questionnaire consisted of three parts; a listing of members and their socioeconomic and demographic characteristics, an interview with the household head who provided information about past migration, current employment, earnings, household assets, and subjective feelings of well-being and deprivation, and an interview with the main female in the household who provided information on her fertility and mortality history, and her knowledge and practice of contraception.’

While there are many subsistence activities carried on in urban Juba, for example, food production, water carrying, own house building etc., for the most part the local economy is essentially monetarized. For this reason an attempt was made in the survey to obtain reliable estimates of respondent households’ cash incomes.2 Mean household income in Juba is estimated to be S&2.59 (US$57.6) per monm3 The median income is Sf127 (US$28.2) per month with the poorest 10% of households receiving less than Sf43 (US$9.6) and the richest 10% receiving more than S&460 (US$102.2) per month. Deflating total household income by the number of household members we find mean monthly per capita income to be S&6.5 (US$l4.4) and the median per capita income to be Sf32 (US$7.1). The poorest 20% of households receive less than S&l6 (US$3.6) per capita while the richest 20% receive more than S&70 (US$l5.6) per capita per month. The 1983 GNP per capita for the whole of Sudan is estimated to be US$33.3 per month (UNFPA, 1987), an income level which is exceeded

by only 40% of the households in urban Juba at official conversion rates, and by only 6% of households at free market conversion rates. Given that cash incomes, as well as total incomes, are much greater in Juba than the rural areas, these data are indicative of the extent of relative and absolute deprivation in Southern Sudan compared with the rest of Sudan.

The decile distribution of household income and of per capita income in urban Juba shows that the poorest 20% of households receive only 3% of total income while the richest 20% are in receipt of 63%, and the top 5% of households receive 38% of total income. On this evidence income inequality in urban Juba is extreme.4

(b) Incidence of absolute poverty

How poor, in some absolute sense, are households in Juba with low real incomes? We constructed a simple measure of absolute poverty, based on the cost of acquiring a generally recommended daily calorie intake that assumed that each household adult equivalent required a daily intake of 2,250 calories and that 70% of this derived from the consumption of sorghum and 30% from beans. The minimum food cost of this extremely simple diet was Sf8.58 (US$l.9) per month per adult equivalent using the controlled retail prices at the time.s Since no precise estimates are available on food expenditure we assume that if low-income households spend somewhere between 40 and 60% of their incomes on food then the poverty line adult equivalent total monthly income lies in the range of S&14.30-S&21.45 (US$3.2-US$4.8).” Of course, if the cost of a more diverse and realistic diet were included, which incorporated small quantities of sugar, tea, meat, fish and vegetables, all of which are more expensive than sorghum and beans, then the poverty-level income would be higher. To the extent that low-income consumers spend more than 60% of household income on food, then we are overestimat- ing the poverty level of total income.

Using these criteria we find between 12 and 25% of persons in Juba fall below our absolute poverty line. Six percent of households do not receive enough income to meet the minimum food requirements con- tained in our absolute poverty line and may be considered as destitute.

5. EXPANDING THE DEFINITION OF POVERTY

(a) Subjective indicators of welfare

During the Juba household survey the principal respondents were asked whether they felt that their households received “enough” of various goods and services to satisfy their minimum basic needs. These


included food, housing, clothing, water, medicine, cooking fuel and access to arable land. Similarly, the senior wife in the household was also asked whether she thought her household members consumed “enough” of certain basic foodstuffs. As we might expect, a household’s ability to acquire enough basic goods and services to satisfy its needs correlates very well with its decile of income per adult equivalent. All of the indicators rise with income decile with the most acute deprivation felt with respect to medicine and cooking fuel.

Food is the most basic need of all and fewer than 20% of heads in the poorest three income deciles were able to concede that their families eat enough food. Indeed, only in the case of the top quintile do over half the household heads acknowledge that they receive enough food. The fact that over 60% of Juba households claim not to be satisfied with their intake of food is indicative of the precarious position of many households in a situation where basic staples are expensive due to the poor transport and marketing conditions in the region.’ The inadequacy of water sources is very much felt across all income deciles, and even one- third of the richest 10% are dissatisfied with their access to clean water. The pattern of female satisfac- tion with the adequacy of food supplies is similar and rises with income decile. The most clearly felt deprivation lies in the poorest 30% of households.

(b) Asset holdings

We also explored the extent to which the population of Juba is in possession of certain basic household utensils and developed indices of asset holdings. As we might predict our indices of household ownership of luxury items and basic consumer durables both rise with decile of household income per adult, with the greatest deprivation felt by the poorest 20-30%. The proportion of households owning individual assets rises along with income decile while relative deprivation is greatest for those in the lowest income deciles. For example, only about one-third of the bottom decile own a radio or a clock or watch, and almost 20% are without shoes. Roughly 9-18% of these households do not own such household furniture items as a chair, table, bed and mattress. The sad state of sanitation facilities in Juba is revealed by the 75% of households in the town without access to any form of toilet. This proportion rises to around 90% for the poorest 30% of households, but even a large number of those in the richest decile have no toilet.

(c) Anthropometric status of young children

A variety of consumption goods, services and assets at the household level operate on child health,

nutritional status and mortality through the proximate determinants. Major ways in which household income effects influence on child health include the quantity and quality of food intakes, water supplies, clothing and housing, fuel supplies for proper cooking and refrigeration of food, hygienic care via the purchase of soap, insecticides, vitamins and iron supplements, and preventive services such as antenatal care and immu- nizations (Mosley and Chen, 1984). A very funda- mental consequence of a household’s poverty status therefore should be the poor nutritional status of its children. Studies have shown that anthropometric indicators are significant predictors of mortality risks, where the risk is many times greater for the malnourished. Malnutrition indicates that dietary deficiencies and/or frequent infections have been characteristics in the life of the child (Martorell and Ho, 1984).

In the Juba household survey all children under the age of six years had their weight and height measure- ments recorded and the mother was asked for the number of times each live child had been vaccinated against various diseases. Each under-six’s height and weight was standardized for age and sex against the Harvard standards.* Overall, 10% of the children fall below 80% of standard weight-for-height, and 16% lie below 90% of the standard height-for-age. These are the children for whom malnutrition is highly sus- pected. Furthermore, 0.8% of children fall below 60% of the standard weight-for-height while 3.0% fall below 80% of the standard height-for-age and are considered to be severely malnourished.

We related the proportion of children falling below these standards to the households’ decile of income per adult. There seems to be a definite tendency for the proportion of children stunted and wasted to decline as household income per adult rises. Evidently, poverty and relative deprivation takes its predicted toll on the nutritional status of children in these households. Furthermore, by the time a child has reached the age of one year, ideally it should have received a total of eight vaccines, three for DPT, three for polio, one for measles and one for BCG. Our overall sample mean is only 2.1 vaccines and we find 50% of the under-six year olds in Juba have never received any vaccines. Children from the poorest households are least likely to have been vaccinated with 60% of them having received no vaccinations. This proportion falls to 40% in the top income quintile.

6. FERTILITY AND MORTALITY BEHAVIOR AND HOUSEHOLD POVERTY STATUS

We turn now to the analytical part of our paper and explore the response of fertility and mortality to previ- ously identified socioeconomic differentiation at the micro-level, specifically the manner in which the income and wealth status of households help to


explain observed patterns of their demographic behavior. It should be recognized, however, that demographic outcomes at the household level can be both the cause and the effect of poverty. For example, a large family size may be the reason for a household’s poverty status or realized high fertility may be the response to poverty. Or other factors may be primarily responsible for both. An additional complication arises because some demographic factors may themselves be considered components of a more comprehensive poverty index. High infant mortality, for example, could be considered as one aspect of poverty. It can also have an important influence on the shortening of birth intervals, increasing fecundity and inducing higher fertility (Rodgers, 1984; Rodgers et al., 1986).

Rodgers calls for a multidimensional view of poverty when these relationships are investigated (Rodgers, 1984). For example, where poverty entails poor nutritional status, low education levels, restricted access to land and urban employment opportunities, then relatively low fertility is probable. In a different context, better access to land and employment opportunities results in higher incomes and, consequently in higher fertility.

The approach adopted here has been to construct an index of household income status and well-being from various components such as asset holdings, adequacy of food intakes, water and sanitation. Since household income per adult equivalent could only be calculated on the basis of income derived during the month prior to the survey, it was likely to contain many transitory elements. The intention, therefore, was to produce an alternative indicator of household well-being that would better reflect the household’s more permanent situation and serve as a proxy for lifetime access to health/other services. Five indicators were chosen.

(a) Income per adult equivalent. Households in the bottom three deciles of income per adult equivalent with less than S&27 per month were considered to be poor and assigned a score of one on this measure. Those above this income measure scored zero. (b) Households asset holdings. Our index of asset holdings ranged from 0 to 15. The 28% of households with a score of less than eight were assigned a poverty level score of one while the remainder were given a score of zero. (c) Meals per day. The 20% of households where female respondents said they regularly served only one full meal per day scored one and those with more than one daily meal scored zero. (d) Water supplies. The 38% of households obtaining their domestic water supplies themselves directly from the River Nile or surrounding streams, in an untreated condition, scored one while the remainder, who obtained cleaner water

from various piped sources or from a borewell, scored zero. (e) Sanitation facilities. The 75% of households that did not have access to any form of latrine were scored one. The remainder were given a score of zero. These dichotomous scores on our five basic

indices of well-being were then added together such that the resulting poverty index ranged from zero and five and is denoted as POVERTY. The 18% of households with a score of five could be considered to be very poor, the 15% with a score of zero or one are relatively well off by the prevailing standards in Juba, while the remaining 67% could be thought of as falling in Juba’s middle class, but still with quite wide disparities in living standards within this group. Multivariate analysis was then used to summarize the associations between these indicators of poverty, which may be ameniable to policy intervention, and fertility and mortality behavior.

7. EMPIRICAL RESULTS

(a) Fertility: children ever born

The neoclassical theory of household decision making considers households to be rational in attempting to maximize their welfare or well-being subject to a number of constraints. Children are viewed in a similar manner as other “commodities” and the demand for children is dependent on the relative preferences for children and their relative costs compared with other goods. Cross-section variation in fertility is attributed primarily to differences in the relative value of human time, particularly to the opportu- nity costs of women’s time that is thought to constitute a substantial share of the total costs of child rearing (Schultz, 1981).

In poor, less-developed countries, however, this demand-oriented approach is viewed as being too sim- plistic for a number of reasons (Anker and Knowles, 1982). The economic contribution of children in farm work and child-minding activities is largely ignored in the theory, yet it can be considerable and, together with an obligation to care for parents in old-age, provides an important incentive for high fertility. The neoclassical approach also ignores factors such as poor health, high rates of infant mortality and cultural constraints which affect the supply of children and which may result in desired fertility being greater than that which is capable of being attained.

In the following analysis of interhousehold fertility differentials in urban Juba, we consider a large number of the determinants that the theory of household decision making suggests are important. More education (PRIMARY, SECONDARY+) for the mother is usually considered in the extensive literature to proxy


for the increased value of her time in wider labor market opportunities and to influence her tastes and preferences by enhancing her desire for material goods and leisure, time uses which compete with the care of children. Education may also improve the quality of infant and child care, reducing the need for high fertility to ensure a minimum surviving family size. Labor market activities performed outside the home are introduced into our analysis (AWAYWK) as an additional proxy for the cost of child-bearing. Cultural differences in the demand for children are represented by dummy variables denoting ethnic tribes (NORTH- ERN, etc.) knowledge of family planning methods to reduce fertility (KNOWCONTRA), and various indicators of household well-being to proxy for the wealth effect (POVERTY, etc.). Finally, it is essential to control for life-cycle variation in fecundity by including the age of the female respondent and its square (AGE, AGE*).

Before presenting the empirical results, it is important to note that a number of econometric problems plague the modeling of a fertility function. First, using the number of children a woman has ever borne vio- lates two basic underlying assumptions of ordinary least squares (OLSQ) models: (1) the number of children a woman bears is a discrete rather than a continu- ous variable; and (2) the number of children ever born can never be less than zero. Left-censoring at zero implies that ordinary least squares estimates will be inefficient and biased downward in proportion to the proportion of zero observations (Maddala, 1983, pp. 15-16). Consequently, three estimation strategies are used to model fertility in this paper. The first strategy, ordinary least squares, is included because OLSQ coefficients are conceptually the simplest to interpret. However, additional estimation procedures are also, considered (Ainsworth, 1989). The second strategy takes account of the left-censoring at zero, by estimating a maximum likelihood Tobit model. The assumption is that there is a latent demand for children (y) which can be either positive or negative while the observed number of children ever born (y*) can only take non-negative values. Hence:

v*=X,‘B+E,ifXi’B+Ei>O _ I (1)

y,* = 0 otherwise (2)

where X,‘, is a (1 x k) vector of explanatory variables, B is a vector of (k x 1) parameters to be estimated, and E, are independent and identically distributed normal residuals with a mean of zero and a constant variance. An important characteristic of this model is that we actually know the values of the explanatory variables, (X1’), for the women who do not have any children.

The third estimation strategy assumes that the underlying distribution of the response variable b) under consideration is Poisson. The Poisson probabil-

ity distribution with parameter p is given by the for- mula:

P(y = a; p) = (p” exp-“)/a! (3)

where the random variable p is believed to vary with a set of observable characteristics, (X,). Theoretically, a Poisson random variable can only take non-negative integer values. The parameter of the Poisson distribution (k,) is assumed to be log-linearly dependent on the explanatory variables in order to ensure that all the p, are positive. Thus the relation is specified as:

In (pL,) = X,‘P. (4)

The disadvantages of the Poisson model are: (i) it imposes the restriction that the mean equals the variance, and violation of this assumption can lead to spu- riously small standard errors (Cameron and Trivedi, 1986, cited in Ainsworth, 1989); and (ii) it assumes that the rate of birth arrivals is a fixed constant over time, while, in reality, there must be at least a nine- month interval between independent births.

The results of the fertility analysis are presented in Table I. Two different specifications are presented for each of the three estimation strategies. The first includes the controls discussed above plus our poverty index. In the second specification the various components of the poverty index are introduced separately. In each case the dependent variable is the fertility equation in the number of children ever born. All three specifications produce broadly similar results. Consistently the most important variables are the controls for age and its square, which prove to be highly significant across all equations, doubtless reflecting the cumulative nature of the dependent variables together with differences in fecundity over age. In addition, dummy variables representing female education prove to be consistent with a priori expectations although fertility does not appear particularly respon- sive to small amounts of education, and the coefficients are not always significant. Cultural differences, as reflected in the tribal dummy variables, generally prove to be insignificant. Nor do female migrants to Juba exhibit any difference in fertility (MIGRANT).

The relationship between women’s work and their fertility has now been well established with emphasis given to the competing demands of child care and market work for the mother’s time (Lloyd, 1991). Yet, the negative effects on fertility of mother’s work found in developed countries might only be expected in the case of wage work involving regular absence from the family home which would be incompatible with child-bearing and child-minding. Siblings, grandparents and other relatives in the home, however, often act as substitutes in child care and so reduce this possible conflict. This potential retarding effect of paid work outside the household on fertility


Table 1. Fertility analysis, Juba, 1983 (dependent variable: children ever born)*

Ordinary least squares (1) (2)

Method of estimation

Tobit (3) (4)

Poisson (5) (6)

Constant AGE AGE2 x 100 (NONE) PRIMARY SECONDARY+ (EASTEQ) NORTHERN WESTEQ OTHERSUD FOREIGN AWAYWK MIGRANT KNOWCONTRA POVERTY LN (INCOME) ASSETS FOOD WATER TOILET Sigma Adjusted R* F-Statistic -2 x Log Likelihood Sample Size

-4.77 -3.84 -6.02 -5.06 0.45 (O.OS)T 0.45 (0.05)7 0.52 (0.05)t 0.51 (o.os)t

-0.45 (0.06)t -0.44 (0.06)t -0.51 (0.06)t -0.51 (0.07)t

-0.34 (0.28) -0.44 (0.27) -0.41 (0.30) -0.50 (0.29)$ -0.52 (0.34) -0.63 (0.34)$ -0.44 (0.36) -0.55 (0.37)

-0.68 (0.54) -0.43 (0.56) -0.76 (0.58) -0.52 (0.60) -0.31 (0.29) -0.22 (0.28) -0.41 (0.31) -0.31 (0.31)

0.28 (0.35) 0.28 (0.36) 0.34 (0.38) 0.33 (0.38) -0.78 (0.60) -0.17 (0.62) -1.08 (0.66) -0.42 (0.68) -0.27 (0.30) -0.14 (0.30) -0.22 (0.32) -0.09 (0.32) -0.24 (0.29) -0.30 (0.29) -0.31 (0.31) -0.37 (0.31) -0.02 (0.22) -0.11 (0.22) 0.05 (0.22) 0.09 (0.24) -0.00 (0.09) - -0.01 (0.10) -

- -0.64 (0.14)t - 0.13 (0.05)t - 0.19 (0.16j - -0.18 (0.23) - 0.33 (0.31) - -

0.26 0.29 19.60t 16.82.t

- - 639 623

- -0.65 (0.15)t - 0.13 (0.0518 - 0.21 iO.l8j_ - 0.17 (0.24) - 0.37 (0.33)

2.69 (.08) 2.69 (0.08) - -

203.91t 225.15t 639 623

-1.08 a.89 0.13 (o.ol)t 0.12 (0.01)1

-0.13 (O.Olyt -0.13 (0.00)1

-0.09 (0.05) -0.10 (0.05)$ -0.16 (0.07)# -0.17 (0.07)$

-0.18 (0.11) -0.14 (0.12) -0.07 (0.05) -0.04 (0.06)

0.08 (0.07) 0.08 (0.07) -0.20 (0.13) -0.06 (0.13) -0.03 (0.06) -0.02 (0.06) -0.05 (0.05) -0.07 (0.05)

0.01 (0.04) -0.02 (0.04) 0.00 (0.02) -

- -0.13 (0.02)t - 0.03 (O.Ol)f - 0.04 (0.03) - -0.05 (0.04) - 0.08 (0.06) - -

- - - -

393.20t 421.96t 639 623

*Standard errors in parentheses. tP< 0.01. $P< 0.1. gP< 0.05.

is not confirmed in Table 1 since AWAYWK is negative but never significant. This result suggests that the use of extended family members and inexpensive paid domestic help offsets any possible incompatibility between outside wage work and fertility.

Our poverty index, POVERTY, is not significant in any of the three specifications that it appears. In the other three specifications the components of our poverty index are entered separately. The results indi- cate that the number of children a woman bears is neg- atively correlated to income but positively correlated with assets. The finding of a positive correlation of fertility with assets is consistent with an economic theory of fertility. Perhaps the positive correlation between children and assets could have been predicted: Because our list of assets is so minimal, any increase in household size generates additional demand for such basic assets as an oildrum to hold water, or a metal stove on which to prepare meals. On the other hand, the finding of a negative correlation between fertility and income is surprising. One explanation is that higher income provides access to a wider array of modem goods and services that, in turn,

reduces the demand for children. Overall, these results suggest that, in such a high fertility and high child mortality regime as Southern Sudan, where pronatalist sentiments are widespread, there is little socioeconomic differentiation with regard to fertility. The small number of more educated women have fewer children but the levels of educational attainment are so low that the net effect on the total fertility rate is small.

Evidently, planners in Southern Sudan and other similarly underdeveloped parts of sub-Saharan Africa can anticipate positive population growth for some time to come, necessitating the formulation of population-accommodating policies. Currently, even those households at the peak of the socioeconomic scale find little incentive to depart from a natural fertility regime.

(b) Fertility andfamily planning

Departures from a natural fertility regime will occur through behavioral changes, particularly through the adoption of efficient family planning


methods. Family planning indices were developed from questions about whether the female respondent had ever heard of any method of family planning to prevent childbirth and whether any method had actually ever been used. Only 46% of women admitted to knowing of any contraceptive method and only 22% had ever used any method.9 The pill was the most widely known “modem” contraceptive with almost 30% having heard of this method. This was followed by IUD (7%), sterilization (5%), injection (4%) and condoms (2%). The most common “inefficient” methods that were mentioned were: abstinence (21%), breastfeeding (14%), rhythm (5%) and local medicine (2%). Only 22% admitted to ever having used any method and only 8% of the whole sample had ever used an efficient method. Only 12% of all respondent women claimed to be currently practicing contraception and only one-third of these were using an efficient method. Of the 19% of all women who claimed not to want to bear any more children only one-in-six of these were currently using any method of contraception. The remainder provide some indication of the unmet need for family planning education and services in urban Juba.

Few previous studies have examined the interrelationships between socioeconomic and demographic variables and the use of family planning methods in sub-Saharan Africa. Cochrane and Farid (1989) found that use of contraception is generally positively related to the level of education, with a general tendency for the proportion using efficient methods to be higher among the more educated. Using data from a household survey conducted in Kinshasa, Zaire in 1990, Shapiro and Tambashe (1992) found that all levels of schooling raise the probability of contraception. Finally, Ainsworth and Nyamete (1992) summa- rizing the evidence from 10 recent Demographic and Health Surveys (DHS) in sub-Saharan Africa, con- clude that women’s education raises the probability of current and ever use of a modem method of contraception.

Rodgers et al. (1986) suggest that the lack of knowledge about and use of family planning, in order to control fertility and family size, may be considered as an important component of poverty. On the other hand, the lack of access to family planning may be considered as one of the determinants of poverty if it results in unwanted births, high dependency and stress on family resources. In Table 2 the dependent variables are dummy variables set equal to one if the respondent admits to knowing of any method to control fertility, modem or traditional, and zero otherwise (columns 1 and 2); equal to one if the respondent has ever used any kind of method of family planning, modem or traditional, and zero otherwise (columns 3 and 4); and equal to one if the respondent ever used a modem method, and zero otherwise (column 5). These are then related to a set of socioeconomic and

demographic indicators in Table 2 which include the mother’s age (AGE, AGE*) and education (PRIMARY, SECONDARY+), tribal affiliation (EASTEQ, NORTHERN, WESTEQ, OTHERSUD, FOREIGN), labor force participation (AWAYWK), migrant status (MIGRANT), and various indicators of poverty and well-being (POVERTY, etc.). Logistic regression is employed in all specifications, hence for each outcome the coefficients represents the change in the log-odds of that outcome from a one unit change in each independent variable.

We expect younger women in urban Juba to be more likely to have knowledge about and to use contraceptive methods than older women, since the for- mer are perhaps able to conceive of wider, nontradi- tional lifetime roles for themselves. The separate effect of age on family planning knowledge and use is confirmed in three of the six specifications presented in Table 2. In each case, older women have a lower probability of knowing about or ever using any method of contraception, even after controlling for level of education and work status. The table also points to significant ethnic differences for both knowledge and use of contraception, perhaps reflecting religious or cultural differences towards contraception and ideal family size. Finally, as expected, the knowledge and use of contraceptive methods, especially the use of modem, efficient methods, all rise along with work outside of the home and with secondary school attainment. The finding that, independently or the level of education, knowledge and use of contraception rise for women working outside the home is particularly interesting, and suggests that such women have access to more open networks and that perhaps their greater status, along with having more opportunities and income to seek family planning services.

Although the level of knowledge and use of contraception is very low in our sample, our poverty index is positive and significant at the 10% level (column 1) for contraceptive knowledge and is positive and significant at the 1% level for ever use of modem contraception (column 5). Women in better-off households with a score of zero on the POVERTY index have a 10% greater chance of having ever used a modem contraceptive method than women in very poor households with a score of five on the POVERTY index.‘O Since the absolute level of modem contraceptive use is so low, however, the net effect on the total fertility rate is likely to be minimal.

(c) Mortality: rates of child survival

The mortality indicator used in this study, developed by Trussell and Preston (1982), builds on a well- known Brass technique for estimating child mortality. The method was designed for applications such as


Table 2. Knowledge and use of contraception analysis, Juba, 1983: logistic regressions*

constant AGE AGE* x 100 (NONE) PRIMARY SECONDARY+ (EAS’IEQ) NORTHERN WESTEQ O’IHERSUD FOREIGN AWAYWK MIGRANT POVERTY LN (INCOME) ASSETS FOOD WATER TOILET -2xLog Likelihood Sample Size

Dependent variables

Knowledge Use: Traditional and/or Modem (1) (2) (3) (4)

-2.44 -1.35 -0.354 -2.42 0.07 (O&t)? 0.07 (0.04) 0.11 (0.05)$ 0.12 (0.06)t

XI.09 (0.05)t -0.10 (0.06) a.12 (0.06)t -0.14 (0.08)t

0.32 (0.23) 0.35 (0.23) -0.10 (0.28) -0.13 (0.29) 1.15 (0.28)s 1.24 (0.30)$ 0.73 (0.30)$ 0.78 (0.33)$

1.59 (0.50)$ 1.83 (0.54)g 0.32 (0.47) 0.34 (0.50) 0.52 (0.23)$ 0.62 (0.24)$ 1.44 (0.3O)(j 1.68 (0.32)1 0.04 (0.49j. 0.22 (0.53j- 0.71 (0.25)s 0.66 (0.26)§

a.21 (0.24) -O.26 (0.25) -0.13 (0.07)7

- -0.28 (0.12) - 0.05 (0.04)

-0.13 (0.14) - -0.04 (0.20)

0.53 (0.26)

a.03 (0.29) 0.01 (0.30) 0.55 (0.31) 0.65 (0.33)$ 0.74 (0.50) 0.88 (0.55) 0.31 (0.27) 0.23 (0.28)

a.30 (0.27) -0.36 (0.28) a05 (0.09)

- a.29 (0.15)# - a.02 (0.17)

0.04 (0.05) - -0.56 (0.24)$ - 1 .O6 (0.29)§

103.755 115.431 28.050 ti5.075 87.515 89.925

639 623 639 623 639 639

Use: Modem only (5) (6)

-5.64 a03 (0.07)

0.07 O(O.09)

0.06 0(.53) 1.21 (0.49)$

0.77 0.57) 0.13 (0.47)

-2.27 (1.07)$ 1.06 (0.70) 1.22 (0.40)$

-0.07 (0.43) -0.66 (0.17)s

-7.64 a.02 (0.08)

0.07 (0.09)

0.04 (0.57) 1.21 (0.53)$

0.40 (0.65) 0.22 (0.49)

-2.40(1.10)$ 0.73 (0.82) 1.34 (0.43)s 0.02 (0.48)

0.24g.24) 0.13 (0.10) 0.67 (0.32)$

a.01 (0.41) 0.78 (0.45)‘r

*Standard errors in parentheses. tP < 0.10. $P < 0.05. §P < 0.01.

ours where detailed birth histories are unavailable. The dependent variable, a mortality index, M, is constructed for each woman by dividing the proportion of her children who died by the proportion expected to die, standardized by the duration of exposure to risk. Thus, the mortality index for the i-th woman, M,, is defined as:

Iv, = Di (a)lD* (a) (5)

where Di is the proportion of children who died for the i-th woman in exposure group a; and D* is the expected proportion of children who died for a woman in exposure group a if her experience conformed to the level and pattern predicted by the standard. The expected proportion dead is determined for each age group from the experiences of all women in that age group by applying the Tmssell variant of the standard Brass procedure (United Nations, 1983). A Coale- Demeny model life-table - pattern North, level 11, equivalent to a life expectancy at birth of 45.0 for females and 41.8 for males - was used to calculate the expected proportion of children who died (Coale

and Demeny, 1983). Each woman was weighted by the number of children she bore.

The infant mortality rate is an important demographic indicator as well as a component of overall poverty in the household. Based on the last child born since 1971, the infant mortality rate estimated from the survey is 111 per 1000, compared with the reported 118 per thousand for the whole of Sudan (UNFPA, 1987). An average of 20% of children had died, with, as expected, the proportion of boys slightly higher than girls. High morbidity and mortality in the town is primarily the result of a high incidence of diar- rhea1 disease, respiratory infections, malnutrition, malaria, and measles (Woodruff et al., 1983a, 1983b).

Inequality in mortality experience arises from a number of factors and correlates strongly with health, education and income. There is a large body of evidence that mortality differentials within societies are considerable. World Fertility Survey (WFS) and Demographic and Health Survey (DHS) data show that despite the fact that mortality has declined in most developing countries, socioeconomic differentials have not narrowed between the 1970s and 1980s and,


in some cases, they have widened (Cleland, Bicego and Fegan, 1992). A recent comparative study of the socioeconomic determinants of child mortality in six developing countries by the United Nations stressed the importance of maternal education, degree of urbanization, quality of housing, type of lavatory facilities, method of refuse disposal, and ethnic factors. The study also acknowledged the independent role of quality of water and paternal occupation (Chahnazarian, 1991).

Columns (l)-(6) in Table 3 test some of these hypotheses for urban Juba, where the dependent variable is the ratio of proportion died/expected proportion died-columns (1) and (2), disaggregated for boys - columns (3) and (4) and girls - columns (5) and (6). The regression model is able to explain 8-12% of the variation in the dependent variable. These results are very revealing, given that there is a large exogenous component to mortality in every society.

As expected, mother’s education (PRIMARY, SECONDARY+) has a positive effect on child survival even after controlling for a host of other variables including levels of income, source of water supply, and toilet facilities. Education must affect other variables, not specifically measured in the analysis, e.g., personal hygiene, dietary balance and food preparation.

Our results also suggest that there are strong ethnic differences in child survivalship. Foreign women (FOREIGN) in Juba, mainly refugees from Uganda, suffer from significantly lower rates of child survival for boys, perhaps the result of political turmoil and war in that country. Ugandan parents are also more prone to believe that their children suffer from “false teeth,” also called “plastic teeth” or “Lugbara teeth”(after a tribe in northern Uganda). This “condition” derives from parents’ belief that a child’s canine teeth cause diarrhea or vomiting. Consequently, parents frequently employ local practitioners to crudely extract these teeth, resulting in infection, weight loss and, potentially, the death of the child.

Because the survey was conducted in one of the poorest and least developed regions of the whole, plagued by a myriad of public health hazards, it might be predicted that longer periods of breastfeeding (BREAST) would raise the life chances of infants. But, if the length of time a child is breastfed serves as an indicator of the availability of food supplementa- tion in the household, mortality rates may be higher for children with longer periods of breastfeeding. In columns (3) and (4) we see that longer periods of breastfeeding reduces boys’ but not girls’ mortality, perhaps an indication that boys are favored over girls in the treatment they receive. Furthermore, where a doctor was consulted during the last prenatal period (PRENATAL), as a proxy for usual behavior, there is some evidence that children’s chances of survival improve.

We must proceed carefully when interpreting some of the remaining variables because we are uncer- tain to what extent they reflect permanent differences in risk. For example, there is some suggestion that work outside the home (AWAYWK) raises boys’ mortality, but without complete work histories we cannot tell which women who are currently not working did so at earlier points in their lives.

Many of the determinants of a household’s overall status signify a household’s capacity to buy greater inputs of food and health resulting in higher child survival. Our index of poverty (POVERTY) is highly significant for both sexes. Better-off households, with a score of zero on the index, achieve a 16-20% higher rate of child survival than the very poor. When the overall index is replaced with its various components, more frequent meals (FOOD), access to clean water (WATER), and adequate sanitation facilities (TOI- LET) all improve the survival chances of children, significantly. Interestingly, higher cash incomes (LN(INCOME)) have a negative but insignificant effect on the proportion of boys that die (after controlling for access to water, sanitation, food adequacy, and other asset holdings, etc.), but have a positive and significant effect on the proportion of girls that die. This suggests the importance of other health inputs that are correlated with income but are not directly measured in our analysis, for example, access to health care. Furthermore, the disparity in the coefficients by sex suggests that these unmeasured inputs must either have differential effects by sex, or, be provided in different quantities by sex.

8. CONCLUSIONS

The initial part of this paper examined household income distribution in urban Juba. Twenty percent of households in Juba are in absolute poverty. Persons in these households do not earn enough to purchase sufficient food for even the simplest diet of sorghum and beans. Low household income per adult equivalent was also strongly correlated to low asset holdings, and subjective feelings of deprivation.

The generally pronatalist, high fertility and high mortality, noncontracepting regime prevalent in Southern Sudan is characteristic of many other parts of tropical Africa with a shared cultural heritage.

High fertility (and a considerable number of surviving children) is associated with joy, the right life, divine approval, and approbation by both living and dead ances- tors. Conversely, low fertility is only too easily interpreted as evidence of sin and disapproval . African parents almost certainly receive larger and more certain rewards from reproduction than do parents in any other society, and these upward wealth flows are guaranteed by interwoven social and religious sanctions (Caldwell and Caldwell, 1987, pp. 416417).


Table 3. Mortality analysis, Juba, 1983: ordinary least squares regressions*

Dependent variable: M, ratio of observed to expected deaths

Both Sexes Males Females

(1) (2) (3) (4) (5) (6)

Constant AGE AGE* x 100 (NONE) PRIMARY SECONDARY+ (EASTEQ) NORTHERN WESTEQ OTHERSUD FOREIGN (BREAST 12) BREAST18 BREAST48 PRENATAL AWAYWK MIGRANT POVERTY LN (INCOME) ASSETS FOOD WATER TOILET Adjusted R* F- Statistic Weighted Sample Size

4.19 4.35 4.03 4.28 4.30 4.32

-0.13 (0.02)? a.12 (0.02)t -0.12 (0.03)? -0.11 (0.03)? a.14 (0.03)? -0.13 (0.03)T

0.15 (0.03)? 0.14 (0.03)t 0.14 (0.04)? 0.13 (0.04)? 0.17 (0.05)? 0.15 (0.04)t

-0.13 (0.05)$ -0.15 (0.05)l. 0.05 (0.09) 0.02 (0.09) a.36 (O.lO)t -0.35 (O.lO)$

-0.39 (0.07)? -0.33 (0.07)1_ a.35 (0.12)? -0.30 (0.13)? a.43 (0.13)$ -0.35 (0.13)$

-0.09 (0.11) 0.04 (0.12) 0.15 (0.19) 0.32 (0.20) -0.38 (0.20)§ a.32 (0.21)

a.06 (0.06) -0.09 (0.06) 0.00 (0.10) X).04 (0.10) -0.17 (0.10)s -0.20(0.10)4

0.21 (0.06)t 0.29 (0.07)t 0.31 (0.1 l)? 0.36 (0.1 l)t 0.09 (0.12) 0.18 (0.12)

0.48 (0.13)t 0.39 (0.13)? 0.82 (0.23)t 0.66 (0.23)t -0.16 (0.22) 0.09 (0.23)

-0.13 (0.05yt XI.23 (0.06)t Xl.1 1 (0.05)$

0.14 (0.06)$ a.06 (0.06)

0.18 (0.02)1

0.12 24.56t

-0.12 (o.os)t -0.21 (0.06)t -0.10 (0.05)$

0.06 (0.06) -0.07 (0.06)

0.00 (0.03) -0.02 (O.Ol)$ -0.24 (0.03)t -0.18 (0.05)t a.22 (0.06)t

0.13 21.38+

-0.28 (0.08)t -0.37 (0.10)-F a.18 (0.08)$

0.25 (O.lO)t a.03 i,o.loj’

0.20 (0.03)1

0.08 9.10t

-0.28 (0.08)t a.32 (O.lO)t XI.22 (0.08)t

0.17 (0.11) a.07 (0.10)

-

a.07 (0.05) 0.01 (0.02)

-0.26 (0.06)$ -0.25 (0.08)t -0.22 (0.1 l)$

0.09 7.48t

0.03 (0.08) -0.07 (0.11) -0.01 (0.09)

0.02 (0.11) -0.07 (0.10)

0.16 (0.03)1_

- - -

0.09 9.33t

0.03 (0.08) Xl.10 (0.11)

0.05 (0.09) a.08 (0.11) a.09 (0.10)

0.11 (O& -0.06 (0.02)t -0.23 (0.05)t -0.09 (0.08) -0.20 (O.lO)§

0.11 8.99$

2408 2379 1252 1239 1156 1140

*Standard wars in parentheses. tP<O.Ol. $P < 0.05. $P<O.lO.

In this context it would be surprising if behavioral factors proved to be very important in explaining fertility. Indeed, this seems to be confirmed in our analysis. Women with education in better-off households are more likely to know about and to have practiced family planning, but very few have ever used modem methods of contraception, and the net effect on children ever born is less than one child per woman. The better off, according to our overall indices of poverty, have more surviving children, despite lower fertility, because they experience higher rates of child survival. This association emerges clearly in the bivariate analysis and is confirmed in the regression exercises.

No broadly based population and development policies have ever been formulated in Southern Sudan, although policies to reduce the very high rate of infant mortality must clearly be a high priority. At the same time, greater access to labor market opportunities for the poor crucially depends on improvements in the system of education and vocational training, and

on measures which give the poor better access to scarce capital with which to enter the profitable informal sector. Expanded educational opportunities for females should also be an integral part of this policy.

More generally, the pronatalist aspirations of the population can only be expected to recede over the long run when the status of women in the region is raised to a level where they can identify the incompatibility between their own high fertility and both the share of economic responsibility they bear for raising children and the ever greater costs of feeding and edu- cating them. In addition, much will depend on the time it takes for Southern Sudan to eventually return to a peaceful existence and to be able to experience some broad-based economic development and increased real income. The size of the required investment in human resources, social infrastructure and farming techniques and practices to achieve these goals remains immense.


NOTES

1. Further details on the sampling procedure can be found in House (1989a). “A household” was defined on a de facto basis to consist of one or more persons, irrespective of relationship, living together in the same housing unit and usually eating at least one meal together per day. Persons absent from the household but expected to return within one month from the day of their departure were considered to be part of the household.

2. It is acknowledged, however, that such income data can be subject to reporting error as respondents are very sen- sitive about revealing their true incomes to strangers whom they fear may report such information to the tax authorities. Enumerators did their best to allay such fears, but readers are warned that we have no independent check on how well they succeeded.

3. Throughout the paper, we have used the prevailing free market rate at the time of the survey. In 1983, the official rate of exchange was X1.28 = US$l and the free market rate was approximately X4.5 = US$I.

4. It should be mentioned that expatriates working for the various development agencies in Juba were not included in our sample of households. Only 3% of household heads in the random sample of 1,024 households were foreign nation- als mainly from Uganda.

5. Children are weighted as 0.5 of an adult.

6. The 1978-80 Household Income and Expenditure Survey revealed that the poorest 50% of households in urban Khartoum spent 65-66% of their per capita expenditure on food (Democratic Republic of Sudan, 1984b; Deaton and Case, 1985). Households in urban Khartoum with less than

X500 were also reported to spend 60% of their total outlays on food and drinks (A. Abu-Shaikha, 1982). In this case, our assumptions for urban Juba consumption patterns are not likely to be very different from the real situation.

7. No doubt there is a seasonal element to the availability of food grains in Juba. Since the survey was undertaken between June and August 1983 it would have spanned part of the “hungry” period between the last harvest of the past season and the onset of the initial harvest of the new season. Nevertheless, it would also have covered some part of this initial harvest of grains when prices were easing.

8. There is now general agreement that the Harvard standards are applicable measures of growth across various racial and ethnic groups (The Lance?, 1984). What remains unclear, however, is exactly where the dividing lines should be drawn to distinguish various levels of growth faltering. The usual practice is to express a child’s weight or height as a percent- age of an expected value, measured as the deviation below the 50th-percentile of the Harvard standard. Children falling below 80% of the Harvard standard weight-for-height are usually considered to be malnourished, those falling below 90% of standard height-for-age, which is probably the best long-term nutritional and health status indicator, are believed to be stunted.

9. It is interesting to note the Sudan World Fertility Survey estimated that about half the women in Northern Sudan had ever heard of a contraceptive method, which lies close to the 46% in Juba. The urban rate for the North, however, is much higher at 75% (Democratic Republic of Sudan, 1982).

10. Calculated at the means of the other variables.

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APPENDIX

Table Al Variable dejinitions in regressions of poverty and demographic association

Variable Definition Mean

Dependent variables: KIDS Number of live children ever born KNOWCONTRA DV: One if female respondent ever heard of a method of contraception USECONTRA DV: One if female respondent has ever used any method of contraception USEMODERN DV: One if female respondent has ever used a modem method of contraception M Mortality Index: Proportion of children died divided by expected children died

M, Mortality Index -Boys only

M/ Mortality Index -Girls only

Independent variables: AGE Age of the female respondent in years PRIMARY DV: One if female attended primary school SECONDARY+ DV: One if female attended secondary school NORTHERN DV: One if ethnic group of female respondent is from Northern Sudan WESTEQ DV: One if ethnic group of female respondent is from Western Equatoria EASTEQ DV: One if ethnic group of female respondent is from Eastern Equatoria OTHERSUD DV: One if ethnic group of female respondent is other Sudanese FOREIGN DV: One if ethnic group of female respondent is foreign AWAYWK DV: One if female respondentworks outside the home MIGRANT DV: One if respondent was not born in Juba KNOWCONTRA DV: One if female respondent ever heard of a method of contraception POVERTY An index of well being based on five indicators LN (INCOME) Natural logarithm of household income per adult equivalent ASSETS An index of household assets holdings FOOD Average number of meals served per day WATER DV: One if household has access to clean water supply TOILET DV: One if household has access to latrine PRENATAL DV: One if mother saw a doctor in prenatal stage of pregnancy BREAST12 DV: One if female usually breastfeeds her children O-l 2 months BREAST18 DV: One if female usually breastfeeds her children 13-18 months BREAST48 DV: One if female usually breastfeeds her children 18+ months

4.2 3.0 0.5 0.5 0.2 0.4 0.1 0.3 0.9 1.3 0.9 1.5 0.9 1.4

32.0 10.9 0.2 0.4 0.2 0.4 0.1 0.2 0.2 0.4 0.6 0.5 0.1 0.3 0.0 0.2 0.2 0.4 0.9 0.4 0.5 0.5 3.0 1.3 3.5 0.8 9.7 2.4 2.1 0.7 0.4 0.5 0.2 0.4 0.7 0.4 0.5 0.5 0.2 0.4 0.2 0.4

S.d.

DV = (LO) Dummy Variable.

Demographic behavior and poverty: Micro-level evidence from Southern Sudan

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