While definitional and measurement problems pose a challenge, there is no doubt that disability affects a noticeable share of the population, the vast majority of whom live in low- and middle-income countries (LMICs). The still comparatively scarce empirical data and evidence suggests that disability is closely associated with poverty and other indicators of economic deprivation at both the country and—if with slightly greater nuance—at the individual/household level. There is also a growing body of evidence documenting the sizeable additional costs incurred by persons with disabilities (PwDs) as a direct or indirect consequence of their disability, underlining the increased risk of PwDs (and the households they are part of) falling under the absolute poverty line in any given LMIC. Looking ahead, there remains considerable scope for more evidence on the causal nature of the link between disability and poverty, as well as on the (cost-)effectiveness of interventions and policies attempting to improve the well-being of PwDs.
Francisco H. G. Ferreira, Emanuela Galasso, and Mario Negre
“Shared prosperity” is a common phrase in current development policy discourse. Its most widely used operational definition—the growth rate in the average income of the poorest 40% of a country’s population—is a truncated measure of change in social welfare. A related concept, the shared prosperity premium—the difference between the growth rate of the mean for the bottom 40% and the growth rate in the overall mean—is similarly analogous to a measure of change in inequality. This article reviews the relationship between these concepts and the more established ideas of social welfare, poverty, inequality, and mobility. Household survey data can be used to shed light on recent progress in terms of this indicator globally. During 2008–2013, mean incomes for the poorest 40% rose in 60 of the 83 countries for which we have data. In 49 of them, accounting for 65% of the sampled population, it rose faster than overall average incomes, thus narrowing the income gap. In the policy space, there are examples both of “pre-distribution” policies (which promote human capital investment among the poor) and “re-distribution” policies (such as targeted safety nets), which when well-designed have a sound empirical track record of both raising productivity and improving well-being among the poor.
James P. Ziliak
The interaction between poverty and social policy is an issue of longstanding interest in academic and policy circles. There are active debates on how to measure poverty, including where to draw the threshold determining whether a family is deemed to be living in poverty and how to measure resources available. These decisions have profound impacts on our understanding of the anti-poverty effectiveness of social welfare programs. In the context of the United States, focusing solely on cash income transfers shows little progress against poverty over the past 50 years, but substantive gains are obtained if the resource concept is expanded to include in-kind transfers and refundable tax credits. Beyond poverty, the research literature has examined the effects of social welfare policy on a host of outcomes such as labor supply, consumption, health, wealth, fertility, and marriage. Most of this work finds the disincentive effects of welfare programs on work, saving, and family structure to be small, but the income and consumption smoothing benefits to be sizable, and some recent work has found positive long-term effects of transfer programs on the health and education of children. More research is needed, however, on how to measure poverty, especially in the face of deteriorating quality of household surveys, on the long-term consequences of transfer programs, and on alternative designs of the welfare state.
Financial protection is claimed to be an important objective of health policy. Yet there is a lack of clarity about what it is and no consensus on how to measure it. This impedes the design of efficient and equitable health financing. Arguably, the objective of financial protection is to shield nonmedical consumption from the cost of healthcare. The instruments are formal health insurance and public finances, as well as informal and self-insurance mechanisms that do not impair earnings potential. There are four main approaches to the measurement of financial protection: the extent of consumption smoothing over health shocks, the risk premium (willingness to pay in excess of a fair premium) to cover uninsured medical expenses, catastrophic healthcare payments, and impoverishing healthcare payments. The first of these does not restrict attention to medical expenses, which limits its relevance to health financing policy. The second rests on assumptions about risk preferences. No measure treats medical expenses that are financed through informal insurance and self-insurance instruments in an entirely satisfactory way. By ignoring these sources of imperfect insurance, the catastrophic payments measure overstates the impact of out-of-pocket medical expenses on living standards, while the impoverishment measure does not credibly identify poverty caused by them. It is better thought of as a correction to the measurement of poverty.
David E. Bloom, Michael Kuhn, and Klaus Prettner
The strong observable correlation between health and economic growth is crucial for economic development and sustained well-being, but the underlying causality and mechanisms are difficult to conceptualize. Three issues are of central concern. First, assessing and disentangling causality between health and economic growth are empirically challenging. Second, the relation between health and economic growth changes over the process of economic development. In less developed countries, poor health often reduces labor force participation, particularly among women, and deters investments in education such that fertility stays high and the economy remains trapped in a stagnation equilibrium. By contrast, in more developed countries, health investments primarily lead to rising longevity, which may not significantly affect labor force participation and workforce productivity. Third, different dimensions of health (mortality vs. morbidity, children’s and women’s health, and health at older ages) relate to different economic effects. By changing the duration and riskiness of the life course, mortality affects individual investment choices, whereas morbidity relates more directly to work productivity and education. Children’s health affects their education and has long-lasting implications for labor force participation and productivity later in life. Women’s health is associated with substantial intergenerational spillover effects and influences women’s empowerment and fertility decisions. Finally, health at older ages has implications for retirement and care.