Millennium Development Goal 1 focuses on the eradication of poverty and hunger by 2015. While progress towards achieving this goal is promising in many developing countries, it is estimated that 920 million people would still be living under the adjusted poverty threshold of US$1.25 per day. This study employed data from the Malawi 2010 Demographic and Health Survey to examine the relative ranking of women (
Millennium Development Goal (MDG) 1 calls for global efforts to eradicate poverty and hunger by 2015. Specifically, MDG 1 targets three areas: (a) halve the proportion of people whose income is less than US$1 a day between 1990 and 2015, (b) achieve full and productive employment and decent work for all, including women and young people, and (c) halve the proportion of people who suffer from hunger between 1990 and 2015. Although addressing this goal has been a challenge due to the economic setbacks of the 2008-09 economic downturn, the world is generally on track to meet this goal. For example, overall poverty rates fell from 46% in 1990 to 27% in 2005 in developing regions, and progress in many developing regions is promising. Nevertheless, it is estimated that in 2015, about 920 million people would still be living under the adjusted international poverty threshold of US$1.25 a day [
The need to eradicate poverty in the developing world cannot be overemphasized since, in broad terms, the level of poverty is a summary measure of individuals’ ability to access resources which may improve different aspects of their lives. Along these lines, majority of countries in the world collect data periodically through household surveys and other sources in order to assess the general living conditions of people. A number of developing countries such as Malawi are undertaking efforts to reduce poverty through sustained economic growth and infrastructure development through growth development strategies. Generally, these strategies aim at transforming countries from being predominantly importing and consuming economies to predominantly manufacturing and exporting economies [
With an estimated population of 15.3 million people in 2012, Malawi is a largely rural (85%), low income country located in south east Africa with a gross domestic product (GDP) per capita of US$370 and a GDP growth of 4.5% per annum. Life expectancy at birth is estimated at 54 years whereas the poverty ratio is 67.3% [
Despite the challenges associated with macroeconomic performance and service provision to the populace, the Malawi Government with support from the international donors has engaged in a number of development strategies in order to accelerate progress towards achieving the MDGs by 2015. In this context, this study examined the relative ranking of women interviewed in a nationally representative household survey across the wealth index scale. Specifically, the objective of the study was to identify the characteristics of women which influence their likelihood of belonging to “poor” or “rich” households. Adopting this strategy can assist policymakers in assessing the layers or profile of women and their households from which efforts to eradicate poverty can be targeted at.
The rest of the paper is organized as follows. Section
Data for this study come from the 2010 Malawi Demographic and Health Survey (DHS). The MEASURE DHS program collected, analysed, and disseminated representative data on population, health, HIV, and nutrition through more than 200 surveys in over 75 countries throughout Africa, Asia, the Middle East, Latin America, and the Caribbean. The MEASURE DHS program is funded by the U.S. Agency for International Development including contributions from other donors as well as funds from participating countries. The program is implemented by ICF Macro, an ICF International Company. DHS typically have large sample sizes of up to 33,000 households. These surveys provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition.
The core questionnaire for DHS emphasizes basic indicators and flexibility. It allows for the addition of special modules in order to ensure that questionnaires can be tailored to meet host country and donor data needs. The standard DHS consists of a household questionnaire and a women’s questionnaire. A nationally representative sample of women aged 15–49 years is interviewed. The household schedule collects a list of household members’ information about age, sex, relationship to the head of the household, education, and parental survivorship and residence. In addition, information on household characteristics includes the source of drinking water, toilet facilities, cooking fuel, assets, and use of bed nets. Information is also collected on nutritional status and anemia with more recent DHS collecting data on HIV testing. Among other things, detailed information on reproductive health is collected including information on the height and weight of women aged 15–49 years and young children to assess nutritional status. For the same individuals, the level of haemoglobin in the blood is measured to assess the level of anemia [
A total of 27,307 households were sampled in the 2010 Malawi DHS of which 25,311 were occupied and a final sample of 24,825 was interviewed, a response rate of 98%. From these households, 23,748 women were eligible for interviews out of which 23,020 ever and currently married women were finally interviewed representing a response rate of 96.9%. This is the sample on which this study is based [
The following are the core and summary variables considered in the analysis of the relationship between the profile of women and the ranking of wealth status.
The wealth index is the dependent variable and serves as a proxy for measuring the long-term standard of living. It is constructed using information on the household’s ownership of consumer goods; dwelling characteristics; type of drinking water source; toilet facilities; and other characteristics that are related to a household’s socioeconomic status. To derive the index, each of these assets was assigned a weight (factor score) generated through principal component analysis, and the resulting asset scores were standardized in relation to a standard normal distribution with a mean of zero and, standard deviation of one [
Measured as a continuous variable for women aged from 15 to 49 years, it is used based on the conventional wisdom on the influence of age on a number of socioeconomic outcomes such as health and other mortality outcomes. For example, older women are more likely to have completed schooling and able to engage in income-generating activities either through work or business activities.
The literature states that married women are more likely to have good health or socioeconomic status due to the impact of pooled income (from their husbands) on their lives [
A number of studies [
Malawi is administratively divided into three regions: northern, central, and southern. These regions have distinct characteristics that may influence ranking of wealth status. For example, based on the 2008 population and housing census, the southern region was the most populous with 45% of the total population of 13 million people whereas the central and northern regions accounted for 42% and 13% of the population, respectively [
The relationship between religious affiliation and human behaviour or outcomes related to fertility, marriage, and educational attainment, among others, has been extensively studied [
We group religion into three categories: “mainstream Christianity” (Catholic, Presbyterian, Anglican, and Seventh Day Adventist/Baptist) “other Christian,” and “Islam.” The Malawi 2010 DHS data do not specify the “Other Christian” group though these are likely to include majority of Charismatic and Pentecostal churches. Those professing “no religion” or “other” unspecified group accounted for 0.84% (
Women are classified based on formal schooling: those with some formal schooling and those without. This variable serves as a proxy for the ability to improve an individual’s wealth status.
The study uses an ordered probit model to assess the relationship between the socio-demographic factors and assessment of wealth status. The probit model is based on a formulation of a latent variable
The
In the probit regression models, the signs of the coefficients show the tendency of the variation in the probability of belonging to the highest response (or wealth group) due to an increase in the corresponding explanatory variable. A negative coefficient means that an increase in the independent variable has the effect of increasing the probability of being in a higher group of the dependent variable (see [
Table
Descriptive statistics of the study population, Malawi DHS 2010.
Characteristics | Number (percentage) |
---|---|
Wealth Index | |
Poorest | 4,539 (19.7) |
Poorer | 4,506 (19.6) |
Middle | 4,721 (20.5) |
Richer | 4,669 (20.4) |
Richest | 4,555 (19.8) |
Mean age* | 28.1 (9.3) |
Current marital status** | |
Never married | 4,526 (19.7) |
Married/cohabiting | 15,445 (67.1) |
Widowed/divorced/separated | 3,049 (13.3) |
Sex of household head | |
Male | 16,770 (72.9) |
Female | 6,250 (27.2) |
Region of Residence | |
Northern | 4,189 (18.2) |
Central | 7,862 (34.2) |
Southern | 10,969 (47.7) |
Religion | |
Mainstream Christianity | 10,725 (47.0) |
Other Christianity | 9,559 (41.9) |
Islam | 2,530 (11.1) |
Formal schooling | |
None | 3,390 (14.7) |
Some schooling | 19,630 (85.3) |
| |
Number of women | 23,020 (100.0) |
Table
Ordered probit regression analysis of wealth status ranking on selected individual characteristics, Malawi DHS 2010.
Variable | Coefficient |
|
---|---|---|
Age | 0.02 | 17.44* |
Current marital status | ||
Never married (r) | — | — |
Married/cohabiting | −0.43 | −19.51* |
Widowed/divorced/separated | −0.58 | −19.32* |
Sex of household head | ||
Male (r) | — | — |
Female | −0.31 | −16.50* |
Region of residence | ||
Northern (r) | — | — |
Central | −0.47 | −22.68* |
Southern | −0.23 | −11.66* |
Religion | ||
Mainstream Christianity (r) | — | — |
Other Christianity | −0.32 | −20.77* |
Islam | −0.17 | −7.23* |
Formal schooling | ||
None (r) | — | — |
Some schooling | 0.59 | 27.02* |
LR |
2,718.46 | — |
Prob |
0.000 | — |
Log likelihood | −35353.10 | — |
| ||
Number of observations | 22,814 | — |
Residence in the southern and central regions was associated with lower categories of wealth status relative to residence in the northern region. With respect to Christianity, women belonging to “other Christian” denominations and Islam were more likely to belong to lower categories of wealth status than those belonging to the mainstream Christian groups. The results also show that individuals who had some formal schooling were more likely to belong to the higher categories of wealth status than women who had no formal education.
It has been documented elsewhere [
This study found that individual factors such as age and schooling have a significant and positive influence on the wealth status ranking of individuals. As expected, increased age and having some formal schooling place individuals at an advantage since they were more likely to be placed in higher categories of wealth status. However, if older women have no access to schooling, the effect of age on wealth status ranking may be attenuated. As the World Bank states, Malawi’s best opportunity to improve education was at the time of this study (in 2012) since a combination of factors had set the stage for a better chance to improve education in the country [
The finding that married/cohabiting as well as the widowed/divorced/separated women were less likely for their households to be categorized in the higher wealth status group than the never married women challenges the tenets of the role of pooled income in influencing wealth status [
Female-headed household were found to be disadvantaged on the wealth status scale than male-headed households. Appeals aimed at focusing on policies or interventions targeting women who head households have been the focus of debate since the early 1990s. To a large extent, targeting female headship to reduce poverty in developing countries has been considered worthwhile in theory and can also work in practice. Nevertheless, the gut of contention has been on the design and implementation of interventions that are targeted to poor female-headed families. One area which has been consistent in the literature is the fact that female-headed households require interventions that are directed specifically to them such as income-generating activities and child-care support, as well as affirmative policies to prevent discrimination in access to markets and resources, aggressive health and education campaigns (e.g., services for pregnant teenagers), and the establishment of effective social support networks through formal or informal organizations [
The findings again show that residence in the central and southern regions is associated with more women being categorized in the lower wealth status group than their counterparts in the northern region. Although the southern region is highly industrialized than the central and northern regions, these regional differences may be an artefact of other unmeasured outcomes in this study. For example, and as stated earlier, adult HIV/AIDS prevalence is lower in the northern region than the other regions. Differences in HIV/AIDS prevalence may put pressure on the resources of households affected by death or illness related to HIV/AIDS (e.g., [
Related to the influence of region of residence on wealth status ranking is the influence of religion which has shown that women belonging to other Christian groups and Islam were more likely to be ranked low on the wealth status scale. While the nature of the effects of religion on wealth status ranking may be difficult to disentangle in this study due to lack of information on the religiosity of the women and the fact that religion and schooling are all independent variables; prior studies [
The study reported here has some limitations. First, the religion variable may not permit us to assess the direct effects of religious theology, attitudes, practices, and norms related to variations in wealth status. This is critical since the religious groups considered here may have different teachings related to gender roles, female status, and education, among others [
In the present study, the Malawi 2010 DHS data were used to assess the effect of selected individual characteristics of women on their wealth status ranking. The study revealed that increased age and having some formal schooling significantly influenced the likelihood of women being ranked on the higher categories of wealth status. This confirms the well-founded and currently strong recommendation by the international donor community of investing in education in Malawi by taking advantage of the Malawi National Education Sector Plan in order to accelerate efforts aimed at reducing poverty and hunger. If the overarching goal of reducing poverty and hunger is to be achieved by 2015, there is a need for policies or interventions to address the different layers of socioeconomic vulnerability that have the potential to accelerate the achievement of MDG 1. These layers include (1) the institution of marriage—which is an orientation of social and economic behaviours and a trigger for fertility outcomes, (2) the economic plight of female-headed households, (3) the socioeconomic and infrastructure variations at the regional level, (4) religious groups as agents of socialization and change, and (5) the interface between quantity and quality of education. Creating an enabling environment for continued policy debates on ways to respond to these layers—and other layers not considered here—should be a priority for the Malawi Government and the development partners. We hope that this paper has rekindled the socioeconomic challenges and presented a necessary step in recognizing the angles from which poverty can be attacked as we move towards the MDG deadline of 2015.
The author would like to thank the U.S. Agency for International Development, ICF Macro, and the National Statistical Office (Malawi) and other partners for supporting the collection and processing of the Malawi 2010 DHS data as well as making the data available for public use.