Educational Expansion Regimes and Wealth Inequality in Sub‐Saharan Africa

Measuring Gender Equality in Education: Lessons from Trends in 43 Countries

As a continent with 54 independent states Africa’s diversity is often highlighted but frequently forgotten when fertility is discussed. Fifty and more years ago to consider that all African countries and societies had a single fertility pattern (large numbers of children) and single trend (unchanging over time) was a valid characterization. Since the 1960s however that uniformity has disappeared replaced by substantial inter- and intra-country differences in fertility patterns and trends that render previous perceptions of continent-wide homogeneity obsolete. In this chapter we consider two African countries—Ghana and Kenya—whose fertility patterns and trends and their determinants have been well documented (Bongaarts 2008; Garenne 2008; Machiyama 2010; Shapiro and Gebreselassie 2008; Sneeringer 2009). Both countries have benefited from regularWorld Fertility Surveys (WFS) and Demographic and Health Surveys (DHS) that record trends in fertility family planning (FP) and other relevant indicators. The recently introduced Performance Monitoring and Accountability 2020 (PMA2020) surveys monitor progress since 2012 for the FP2020 initiative and occasional Situation Analysis and Service Provision Assessment surveys have also detailed the readiness of the health system in both countries to make quality FP services available. Ghana and Kenya share some common history: both have relatively strong health system legacies from the period of British colonialization; both were among the earliest countries to achieve independence; they were the first two African countries that developed policies to address population growth in the 1960s; and both have received substantial and sustained resources over several decades from many external donors and technical assistance organizations explicitly intended to increase the availability and quality of family planning services. However they are composed of cultures that are both diverse within each country and markedly different in many ways between the two countries. The two countries demonstrate remarkably different pathways in fertility and family planning patterns and trends from the 1970s to the present. We highlight some of the key differences and similarities explain why they have occurred and identify insights that could inform a wider understanding of fertility transitions and the role of family planning in other African countries. (excerpt)

Introducing an employment variable with five levels of educational attainment per capita and employing inequality decomposition, this study addresses three questions. How does labour force vary by education and provinces? How does labour force utilization vary by education and provinces? What are the potential causes of differences? We find that the no‐primary‐education group is more endowed in less‐developed provinces and allocated most unequally among education groups across provinces, despite past universal primary education policies. The senior‐secondary‐education group with the largest labour share is a growing concern due to the lower employment rate and largest interprovincial inequality.

This paper investigates whether community-level wealth inequality predicts HIV serostatus using DHS household survey and HIV biomarker data for men and women ages 15-59 pooled from six sub-Saharan African countries with HIV prevalence rates exceeding 5%. The analysis relates the binary dependent variable HIV-positive serostatus and two weighted aggregate predictors generated from the DHS Wealth Index: the Gini coefficient, and the ratio of the wealth of households in the top 20% wealth quintile to that of those in the bottom 20%. In separate multilevel logistic regression models, wealth inequality is used to predict HIV prevalence within each statistical enumeration area, controlling for known individual-level demographic predictors of HIV serostatus. Potential individual-level sexual behaviour mediating variables are added to assess attenuation, and ordered logit models investigate whether the effect is mediated through extramarital sexual partnerships. Both the cluster-level wealth Gini coefficient and wealth ratio significantly predict positive HIV serostatus: a 1 point increase in the cluster-level Gini coefficient and in the cluster-level wealth ratio is associated with a 2.35 and 1.3 times increased likelihood of being HIV positive, respectively, controlling for individual-level demographic predictors, and associations are stronger in models including only males. Adding sexual behaviour variables attenuates the effects of both inequality measures. Reporting eleven plus lifetime sexual partners increases the odds of being HIV positive over five-fold. The likelihood of having more extramarital partners is significantly higher in clusters with greater wealth inequality measured by the wealth ratio. Disaggregating logit models by sex indicates important risk behaviour differences. Household wealth inequality within DHS clusters predicts HIV serostatus, and the relationship is partially mediated by more extramarital partners. These results emphasize the importance of incorporating higher-level contextual factors, investigating behavioural mediators, and disaggregating by sex in assessing HIV risk in order to uncover potential mechanisms of action and points of preventive intervention.

BackgroundThe burden of under-5 deaths is disproportionately high among poor households relative to economically viable ones in developing countries. Despite this, the factors driving this inequality has not been well explored. This study decomposed the contributions of the factors associated with wealth inequalities in under-5 deaths in low- and middle-income countries (LMICs).MethodsWe analysed data of 856,987 children from 66,495 neighbourhoods across 59 LMICs spanning recent Demographic and Health Surveys (2010-2018). Under-5 mortality was described as deaths among live births within 0 to 59 months of birth and it was treated as a dichotomous variable (dead or alive). The prevalence of under-five deaths was stratified using household wealth status. A Fairlie decomposition analysis was utilized to investigate the relative contribution of the factors associated with household wealth inequality in under-5 deaths at p<0.05. The WHO health equity assessment toolkit Plus was used to assess the differences (D) ratios (R), population attributable risk (PAR), and population attributable fraction (PAF) in household wealth inequalities across the countries.ResultsThe proportion of children from poor households was 45%. The prevalence of under-5 deaths in all samples was 51 per 1000 children, with 60 per 1000 and 44 per 1000 among children from poor and non-poor households (p<0.001). The prevalence of under-5 deaths was higher among children from poor households than those from non-poor households in all countries except in Ethiopia, Tanzania, Zambia, Lesotho, Gambia and Sierra Leone, and in the Maldives. Thirty-four of the 59 countries showed significantly higher under-5 deaths in poor households than in non-poor households (pro-non-poor inequality) and no significant pro-poor inequality. Rural-urban contexts, maternal education, neighborhood socioeconomic status, sex of the child, toilet kinds, birth weight and preceding birth intervals, and sources of drinking water are the most significant drivers of pro-poor inequities in under-5 deaths in these countries.ConclusionsIndividual-level and neighbourhood-level factors were associated with a high prevalence of under-5 deaths among poor households in LMICs. Interventions in countries should focus on reducing the gap between the poor and the rich as well as improve the education and livelihood of disadvantaged people.

Household wealth inequality refers to the unequal distribution of wealth among Households within a society. Factors such as education, employment and inflation significantly contribute to this phenomenon. The general objective of this research study was to determine Economic factors affecting Household wealth inequality. This is a case study of Lusaka Central Constituency randomly selected households. Specific objectives of the study were to; to analyse the effects of employment status (income inequality) on household wealth inequality; to analyse the effects of Household demographics (Family size, Employment, Education, profession, age, gender) on Household wealth inequality and to assess effects of inflation on Household wealth inequality. This research employed a case study among other designs of study. Target population is the entire aggregation of respondents whose need meet the designation set of criteria (Kalpesh, J, 2010). Creswell (2008) refer to population as “a group of individuals objects or items from which samples are taken for measurement.” The population of the study comprised of participants within the study area. This study targeted a population of ninety (90) from within the catchment area. Respondents from within Lusaka; participated in the survey questions and also sample a few participants as key informants based on their experience in the study. This research used both primary and secondary data collection techniques. The Primary data helped in collecting data direct from the respondents using different tools while secondary data involved collecting of data from what has been coined by other scholars in form of books, online publications, journals, previous researches and many other secondary sources of data. The Statistical Package for Social Sciences (SPSS) was used for analyzing the empirical data for this paper. This study will contribute to the existing literature on wealth inequality, providing insights into specific context of Lusaka, Lusaka District and the findings will inform policy interventions aimed at reducing household inequality and promoting inclusive economic growth, The study’s results will have implications for resource allocation, social welfare programs and economic development initiatives in the area.

This paper analyses the effects of borrower-specific credit constraints on macroeconomic outcomes in an agent-based housing market model, calibrated using U.K. household survey data. We apply different Loan-to-Value (LTV) caps for different types of agents: first-time-buyers, second and subsequent buyers, and buy-to-let investors. We then analyse the outcomes on household debt, wealth inequality and consumption volatility. The households’ consumption function, in the model, incorporates a wealth term and income-dependent marginal propensities to consume. These characteristics cause the consumption-to-income ratios to move procyclically with the housing cycle. In line with the empirical literature, LTV caps in the model are overall effective while generating (distributional) side effects. Depending on the specification, we find that borrower-specific LTV caps affect household debt, wealth inequality and consumption volatility differently, mediated by changes in the housing market transaction patterns of the model. Restricting investors’ access to credit leads to substantial reductions in debt, wealth inequality and consumption volatility. Limiting first-time and subsequent buyers produces only weak effects on household debt and consumption volatility, while limiting first-time buyers even increases wealth inequality. Hence, our findings emphasise the importance of applying borrower-specific macroprudential policies and, specifically, support a policy approach of primarily restraining buy-to-let investors’ access to credit.

This paper analyses the effects of borrower-specific credit constraints on macroeconomic outcomes in an agent-based housing market model, calibrated using UK household survey data. We apply different Loan-to-Value (LTV) caps for different types of agents: first-time buyers, second and subsequent buyers, and buy-to-let investors. We then analyse the outcomes on household debt, wealth inequality and consumption volatility. The households’ consumption function, in the model, incorporates a wealth term and income-dependent marginal propensities to consume. These characteristics cause the consumption-to-income ratios to move procyclically with the housing cycle. In line with the empirical literature, LTV caps in the model are overall effective while generating (distributional) side effects. Depending on the specification, we find that borrower-specific LTV caps affect household debt, wealth inequality and consumption volatility differently, mediated by changes in the housing market transaction patterns of the model. Restricting investors’ access to credit leads to substantial reductions in debt, wealth inequality and consumption volatility. Limiting first-time and subsequent buyers produces only weak effects on household debt and consumption volatility, while limiting first-time buyers even increases wealth inequality. Hence, our findings emphasise the importance of applying borrower-specific macroprudential policies and, specifically, support a policy approach of primarily restraining buy-to-let investors’ access to credit.

Background: Social, economic and environmental factors have been identified as causes of child mortality. No known research has tried to disentangle these factors and quantify the contribution of these diverse factors to child mortality across countries using the decomposition analysis of the concentration index. Decomposition analysis is one method that can be used to disentangle the relative contribution of multiple variables to child mortality. A concentration index is a tool to measure wealth-related health inequalities. Methods: This is the first study to decompose the concentration index to quantify the contribution of water and sanitation access to maternal educational inequality and household wealth inequality across all 43 low and middle-income countries (LMICs) with data available between 2009-2013 from the nationally representative Demographic and Health Surveys (DHS). Findings: On average, the largest disparities in child mortality were found to come from mothers who had the least education. Unimproved sanitation contributed to greater child mortality inequalities than other variables, in over two-thirds of the countries. Interpretation: To increase child survival in LMICs included in this analysis, policies and interventions, on average, should focus on improving girls' education and access to water, and, especially, sanitation in the poorest households. Benefits in South Asia and sub-Saharan Africa have the greatest potential to impact population health and health inequalities from these interventions. Funding Statement: This research was funded by Health Sciences and Family Medicine and Public Health in the School of Medicine at the University of California, San Diego. Declaration of Interests: We declare no competing interests. Ethics Approval Statement: The DHS data-collection procedures were approved by the ICF International institutional review board and by the relevant human subject committees in each country. Survey respondents provided oral informed consent.

This research has two main goals: (i) to examine fertility desires (number of children) in sub-Saharan Africa: levels as compared to other major regions and recent trends; and (ii) to assess the extent to which fertility decline in sub-Saharan Africa is contingent on decline in fertility desires (singly and in combination with other reproductive changes).

This paper examines the impact of wealth inequality on education inequality in the sub-Saharan African (SSA) countries, including by decomposing inequality across gender. Specifically, it analyzes the impact of wealth concentration on the distribution of educational attainment using a sample of cross-sectional data from the USAID Demographic Health Survey on 25 countries, and deploying the education inequality Gini, generalized Lorenz curve, and Lorenz concentration curve. Educational attainment and wealth index were estimated to capture education inequality and wealth concentration. The findings confirm that social exclusion has remained chronic in SSA. Apart from Zimbabwe, SSA have not only failed to raise incidence levels of secondary and tertiary education, but also have largely been unable to reduce education inequality. Regardless of oil and gas endowments, investments going to education have remained low in these countries, while gender disparity on educational attainment burdening females has been high in Nigeria, Gambia, Ghana and Guinea. With the exception of Comoros, wealth concentration is the prime cause of education inequality in the remaining countries. The findings call for effective policies for the poor and females to raise their education levels thereby enabling them to earn incomes to break the vicious cycle of education inequality caused by wealth inequality.

BackgroundHousehold food insecurity and under-nutrition remain critically important in developing countries struggling to emerge from the scourge of poverty, where historically, improvements in economic conditions have benefited only certain privileged groups, causing growing inequality in health and healthcare among the population.MethodsUtilizing information from 5,977 children aged 0-59 months included in the 2004 Bangladesh Demographic and Health Survey , this study examined the relationship between household wealth inequality and chronic childhood under-nutrition. A child is defined as being chronically undernourished or whose growth rate is adversely stunted, if his or her z-score of height-for-age is more than two standard deviations below the median of international reference. Household wealth status is measured by an established index based on household ownership of durable assets. This study utilized multivariate logistic regressions to estimate the effect of household wealth status on adverse childhood growth rate.ResultsThe results indicate that children in the poorest 20% of households are more than three time as likely to suffer from adverse growth rate stunting as children from the wealthiest 20% of households (OR=3.6; 95% CI: 3.0, 4.3). The effect of household wealth status remain significantly large when the analysis was adjusted for a child's multiple birth status, age, gender, antenatal care, delivery assistance, birth order, and duration that the child was breastfed; mother's age at childbirth, nutritional status, education; household access to safe drinking water, arsenic in drinking water, access to a hygienic toilet facility, cooking fuel cleanliness, residence, and geographic location (OR=2.4; 95% CI: 1.8, 3.2).ConclusionThis study concludes that household wealth inequality is strongly associated with childhood adverse growth rate stunting. Reducing poverty and making services more available and accessible to the poor are essential to improving overall childhood health and nutritional status in Bangladesh.

Sub-Saharan Africa (SSA) is undergoing rapid transformations in the realm of union formation in tandem with significant educational expansion and rising labor force participation rates. Concurrently, the region remains the least developed and most unequal along multiple dimensions of human and social development. In spite of this unique scenario, never has the social stratification literature examined patterns and implications of educational assortative mating for inequality in SSA. Using 126 Demographic and Health Surveys from 39 SSA countries between 1986 and 2016, this study is the first to document changing patterns of educational assortative mating by marriage cohort, subregion, and household location of residence and relate them to prevailing sociological theories on mating and development. Results show that net of shifts in educational distributions, mating has increased over marriage cohorts in all subregions except for Southern Africa, with increases driven mostly by rural areas. Trends in rural areas align with the status attainment hypothesis, whereas trends in urban areas are consistent with the inverted U-curve framework and the increasing applicability of the general openness hypothesis. The inequality analysis conducted through a combination of variance decomposition and counterfactual approaches reveals that mating accounts for a nonnegligible share (3% to 12%) of the cohort-specific inequality in household wealth, yet changes in mating over time hardly move time trends in wealth inequality, which is in line with findings from high-income societies.

African capacity building was first identified as a major development problem during the transition to independence in the 1950s. After billions of dollars of investment in education over the past three decades, capacity building has been identified as one of Africa's most serious development problem of the 1990s. This paper analyzes African capacity building experiences over the last four decades. It focuses on the treatment of African capacity building by colonial powers, African governments, donors, and international agencies during the transition to independence in the 1950s and the post independence period since 1960. Nigeria and Kenya are used as case studies to highlight some of the key issues and draw some lessons for South Africa. Capacity building in this study focuses on the development and strengthening of education and decision-making skills required for sustainable economic development. Attention is focused on both the supply and demand sides of capacity building, namely education expansion and absorptive capacity of the economy (i.e employment creation). Key findings include the following: Uncompromising political and economic demands for education at independence in the 1960s led to a massive expansion of education at all levels, beginning with universal free primary education in the 1960s, followed by the expansion of secondary and university education in the 1970s. Manpower training programs focused on the urgent Africanization of the civil service. However, after a major expansion of education from 1960 to 1980, problems emerged in the 1980s, including an intolerable level of government expenditure on education, declining education quality and relevance, and inability to absorb school leavers and curb the brain drain. Donors and international agencies have increasingly come under fire for paying insufficient attention to African capacity building while overemphasizing the provision of physical infrastructure which they assumed to be the missing factor. These projects promised quick, visible, and measurable results but required crisis management. Technical assistance has also come under scrutiny. The new African Capacity Building Initiative was established to redress capacity problems in Africa. Six lessons flow from this analysis for South Africa: (1) The hard trade-offs between elimination of illiteracy (i.e primary education expansion) and university education expansion, (2) The burden of financing the expansion of education, (3) The high cost of education per student, (4) Absorptive capacity of the economy, (5) Effects of education expansion on quality and relevance of education , and (6) The selective borrowing of skills from the international community in nation building.

Macroprudential policies, such as caps on loan-to-value (LTV) ratios, have become part of the policy paradigm in emerging markets and advanced countries alike. Given that housing is the most important asset in household portfolios, relaxing or tightening access to mortgages may affect the distribution of household wealth in the country. In a stylised model we show that the final level of wealth inequality depends on the size of the LTV ratio, housing prices, credit cost and the strength of a bequest motive; ultimately with no unequivocal effect of LTV ratios on wealth inequality. These trade-offs are illustrated with estimations of Gini Recentered Influence Function regressions on household survey data from 12 Eurozone countries that participated in the first wave of the Household Finance and Consumption Survey (HFCS). The results show that, among the households with active mortgages, high LTV ratios at the time of acquisition are related to high contributions to wealth inequality today, while house price increases are negatively related to inequality contributions. A proxy for the strength of bequest motives tends to be negatively related with wealth inequality, but credit cost does not show a significant link to the distribution of wealth.