Impacts and Determinants of Health Levels in Low-Income Countries

Abstract

Improved health in low-income countries could considerably improve wellbeing and possibly promote economic growth. The last decade has seen a surge in field experiments designed to understand the barriers that households and governments face in investing in health and how these barriers can be overcome, and to assess the impacts of subsequent health gains. This chapter first discusses the methodological pitfalls that field experiments in the health sector are particularly susceptible to, then reviews the evidence that rigorous field experiments have generated so far. While the link from in utero and child health to later outcomes has increasingly been established, few experiments have estimated the impacts of health on contemporaneous productivity among adults, and few experiments have explored the potential for infrastructural programs to impact health outcomes. Many more studies have examined the determinants of individual health behavior, on the side of consumers as well as among providers of health products and services.

How effective and fair is user fee removal? Evidence from Zambia using a pooled synthetic control

“…Our results echo other recent robust empirical studies of the effects of abolishing user fees on the demand for curative care in other low‐income settings (King et al, ; Mohanan et al, ), although one study from Ghana found a slightly more encouraging increase of the demand by 3.7 percentage points (Powell‐Jackson et al, ). These results are at odds with the recent experimental literature on the price effects of the demand for preventive health products and services (Dupas & Miguel, ). There are three main explanations for the lack of effectiveness of the policy change on the demand for curative health care services.…”

Demand for self‐tests: Evidence from a Becker–DeGroot–Marschak mechanism field experiment

“…Over 70% of respondents are willing to pay at least 90 Ksh (US$0.86) and approximately 95% are willing to pay at least 50 Ksh (US$0.48). Demand in our study appears to be greater than that in previous self-testing economic literature and greater than that suggested by the stylized fact of low demand for preventive health inputs in low-and middle-income countries (e.g., see Dupas, 2011, andDupas &Miguel, 2017, for reviews). 39,40 One notable feature of our study is that we survey a convenience-based sample of individuals who have selected to attend a health facility/pharmacy in a large urban area; a subpopulation that is likely wealthier and better educated than Kenya as a whole.…”

“…Having heard of HIVSTs is weakly associated with WTP and is not statistically significant. Although stated preference and BDM WTP are correlated, it may be somewhat surprising that the correlation is not stronger, particularly given the findings in Dupas and Miguel (). One explanation for the seemingly modest correlation in our study is that the stated preference measure in our study is a coarse measure of WTP—a stated intention of likely/unlikely to purchase the test—rather than a detailed measure of WTP (e.g., stated WTP expressed in Ksh).…”

“…Preventive and diagnostic health inputs often are life‐saving and low cost, yet demand for these inputs is low (WHO, ). Recent work on this puzzle highlights the role of a variety of frictions (e.g., lack of information, time‐inconsistent preferences, distance, and health provider absence) associated with accessing health inputs (e.g., see Dupas, , and Dupas & Miguel, , for reviews). By allowing an individual to take a diagnostic test at a convenient time, private location, and independent of a health provider, self‐tests offer one approach to reducing these frictions and increasing demand.…”

Menstrual Health, Worker Productivity and Well-being among Female Bangladeshi Garment Workers

“…73 percent of workers from the control group who reported to not have used pads at baseline reported to use them at endline. This increase must be due to some combination of time trends in pad adoption, spill-overs from treated groups, the effects of going through a detailed 30-minutes long baseline survey on MHM practice on subsequent pad adoption, and some form of desirability bias in reporting to use pads that is triggered by a repeated survey on MHM practice, after having already gone through the baseline survey (see Zwane et al (2011); Dupas and Miguel (2017) on discussions of the latter two effects).…”