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EC336 Economic Development in a Global Perspective. Abhishek chakravarty 2013-14 Lecture 9. Lecture Outline. We will finish the discussion of Todaro and Smith, ch. 8. We will also look at two papers that examine the two-way relationship between income and human capital investments.
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EC336Economic Development in a Global Perspective Abhishek chakravarty 2013-14 Lecture 9
Lecture Outline • We will finish the discussion of Todaro and Smith, ch. 8. • We will also look at two papers that examine the two-way relationship between income and human capital investments.
Health Measurement and Distribution • Standard practice involves measuring health with under-5 child mortality rates and life expectancy. While these measures have problems, there are not too many feasible alternatives. In both indices, there have been improvements over time in most developing regions. • Life expectancy is not the best health measure, as an extra year of life can be spent in good health in one country and poor health in another. Under-5 mortality does not capture health problems beyond early childhood. • The WHO proposed the use of a measure called disability-adjusted life year (DALY), which is not yet widely accepted due to accuracy problems and issues in cross-country comparisons.
Health Measurement and Distribution • Average measures of health can mask significant levels of inequality between different sections of society. • Analysing the data by wealth quintiles shows that across most developing countries it is the children of the poor who are likeliest to die before age 5 rather than the children of the rich. Similarly the proportion of children under age 5 who are underweight is much higher for poorer than richer quintiles. • There is also significant inequality in health provision between rural and urban areas, with higher concentration where the wealthier population resides. Health worker absenteeism in primary health centres, on which the poor depend, was measured at 43% in India (14 states), 42% in Indonesia, and 35% in Bangladesh in a World Bank study.
Disease Burden: HIV/AIDS, Malaria, and Worms • HIV/AIDS: Much progress has been made in reducing the number of new HIV infections every year in the past few years, and by 2009 about 5 million people in the developing world were receiving life-saving retroviral treatments. But much still remains to be done. • More than 95% of HIV cases and AIDS deaths occur in the developing world. The bulk of the disease burden is in Sub-Saharan Africa, where the disease has become the leading cause of death for adults in their economically active years. • In 2009 approximately 33 million people worldwide were infected, and about 22 million of these people were in Sub-Saharan Africa.
Disease Burden: HIV/AIDS, Malaria, and Worms • Malaria: The disease directly causes about 1 million deaths every year, most of which again are among African children. Severe cases cause about 15% of surviving children with neurological problems and learning disabilities. There is much evidence that malaria lowers productivity and income growth. • Progress has been made towards developing a vaccine, but the financial incentive to develop it further is low due to the affected population largely being poor. The two market failures identified by Harvard economist Michael Kremer are: • Countries are waiting for other nations to incur the research costs, and then benefit from the spillovers to their own citizens from reduced disease incidence. • Pharmaceutical companies fear the pressure to reduce the price of a vaccine and not recoup the large costs of R&D.
Disease Burden: HIV/AIDS, Malaria, and Worms • Parasitic Worms: The incidence of disease from parasitic worms is vast in the developing world, with about 2 billion people affected and 300 million of those severely affected. • One of the worst of these diseases is schistosomiasis, which claims about 200,000 lives each year and retards the growth and school performance of children. It also severely affects the productivity and work performance of adults, and can lead to kidney and liver damage, as well as bladder cancer. • Other examples are African sleeping sickness, hookworm, and trachoma. All such diseases are easily curable at low cost, but tragically claim many lives because basic healthcare provision and medicines are lacking. These deaths are all therefore preventable, leading these parasitic illnesses to be called neglected tropical diseases.
Health and Productivity • There is a lot of robust statistical evidence that better health improves productivity and income, and not just the reverse causality of higher income leading to higher health investments. • Adult height is the most commonly used indicator of the health of a population, based on the research of Robert Fogel that showed stature in developed countries has increased significantly over the past two centuries. Heights have also increased in developing countries over time as health conditions have improved. • John Strauss and Duncan Thomas have found that adult height strongly predicts higher wages in Brazil, even after controlling for education and experience. This is conclusive evidence in favour of the health-to-income causality, as current income could not increase adult height once an individual is fully matured.
Bleakley (2007) – Malaria and Income • Bleakley (2007) exploits anti-malaria measures implemented by governments in the Americas in the twentieth century to estimate the impacts of reduced exposure to malaria in early childhood on adult earnings. • The first measures were implemented by the US in the southern states in the 1920s, using new medical knowledge on transmission learned from European physicians. • The second set of measures were part of the worldwide malaria eradication campaign driven by the discovery of DDT pesticide, as implemented in Mexico, Brazil, and Colombia starting in the 1950s.
Bleakley (2007) – Malaria and Income • The impact on adult earnings of exposure to these malaria eradication measures can confidently be attributed to the measures themselves rather than other unobserved processes or differing trends between treated and untreated regions for various reasons. • Firstly, the eradication campaigns were brought about by innovations in technology and spending external to the studied regions, removing concerns of policy endogeneity to local circumstances. • Secondly, the drop in malaria incidence as a result of the campaigns is sudden and large in the time-span of less than a decade, making it unlikely that any other concurrent health or regional economic shock could be large enough to be responsible for any observed effects on income of individuals born at this time. Also 60 to 150 years of birth cohort data is used to account for long term effects on income.
Bleakley (2007) – Malaria and Income • Finally the results show a sharp decline in malaria incidence in previously malarious regions at the time of the eradication campaigns, and a large increase in earnings for individuals born after the campaigns in previously malarious regions, compared to those born in the same areas who were already adults at the time of the campaigns. • Given that the decline in malaria incidence was much greater in areas that had relatively higher pre-treatment malaria prevalence rates than regions with lower malaria prevalence, the rise in adult earnings for individuals born at the same time as the campaigns in these previously malarious regions is unlikely to have been driven by other factors.
Bleakley (2007) – Malaria and Income • The research design uses variation in malaria prevalence across the four countries in question, combined with variation in year of birth of individuals to identify the impact of reduced exposure to malaria during early childhood as a result of the campaigns on adult income. • Pre-treatment malarious regions are identified using country level surveys, as well as malaria ecology data matched to regions with GIS methods. Individual data on place of birth, year of birth, and standardised adult earnings measured by occupational income score and Duncan score are drawn from IPUMS data extracted from national censuses. • The baseline econometric model used relies on pre-treatment malaria incidence in area of birth, combined with the year of birth of individuals to identify the impact of exposure to the campaigns on cohort-specific earnings.
Bleakley (2007) – Malaria and Income • The specification is: • Yjktis the standardised income measure for a cohort of individuals with state of birth j, census year t, and year of birth k. Mj is the pre-treatment level of malaria incidence in state j. δk is a cohort-specific intercept, and Xj is a vector of state-of-birth controls that is allowed to have cohort-specific impacts measured in coefficients . Finally, vjkt is a random error term. • Figures 4 and 5 from the paper below describe the pattern of results for the coefficient of interest βk.
Bleakley (2007) – Malaria and Income The dashed lines show potential exposure during childhood to the campaigns based on year of birth for all the countries. In Figure 4 depicting the US, for those born before 1900, having higher malaria prevalence in the region of birth predicts lower average income. However for those born after 1920, this negative relationship between malaria and income is no longer present as the coefficient is zero on average. For intermediate cohorts with partial exposure, the coefficient approaches zero gradually between 1900 and 1920 as exposure levels increase.
Bleakley (2007) – Malaria and Income The same pattern of results is seen for Brazil, Colombia, and Mexico. The results are robust in all cases to the addition of other controls.
Jensen (2010) – Economic Opportunities • The paper examines the impact of increased opportunities for female employment in India on the health and employment of girl children. • This is done by offering recruitment services for BPO jobs to girls randomly across villages, and comparing enrolments and BMI of girls in “treated” to “untreated” villages. • The randomisation of the access to the recruitment training removes concerns that the change in health and education outcomes is driven by omitted variables or differing underlying trends in development between villages. Also the industry was new at the time, ruling out pre-existing awareness and other possible mechanisms that could drive the result.
Jensen (2010) – Economic Opportunities • There are various reasons why increased economic opportunities for women would improve human capital investments in girl children: • Higher bargaining power for mothers with employment could lead to higher investment in daughters. • With mothers outside the home working, daughters become more important to help with child care and household responsibilities. • If investments in girl children are a luxury good, then the higher income from female employment will increase these investments. • The opportunity cost of time with higher employment is greater, which could lower fertility and increase investment per head among all children due to less sibling competition.
Jensen (2010) – Economic Opportunities • The experimental design however is such that it increases future returns (earnings) to human capital for young unmarried girls rather than older married mothers. Hence the causal relationship being captured is that of higher future returns to human capital investment rather than maternal employment. • The study focuses on 160 villages, of which 80 villages were randomly chosen for treatment. The intervention consisted of three annual information sessions on applying to BPO industry jobs, and three years of advisory support after the final session. The sessions were open to all women, but the jobs required a secondary schooling degree, and some comfort with the English language and computers. The age-group of interest is children aged 5-15 years at the time of the intervention.
Jensen (2010) – Economic Opportunities • The basic econometric model used is: Yi=β0+β1Treatmenti+ 𝛾𝑖 𝑍𝑖+ εi • Yi is the outcome variable of interest for children born in village i, Treatmenti is an indicator of whether village i was treated, and 𝑍𝑖 is a vector of child-level controls that also determine human capital investment (log of family expenditure per capita, parental education etc.). Results are also presented from estimating the equation in first differences across years. • The data used for the regressions comes from three years of panel data collection via household surveys, including one baseline survey before the interventions were begun.
Jensen (2010) – Economic Opportunities • In summary the results show that increased economic opportunities for female employment increase female child enrolment and BMI, without increasing the same indices for male children. • The mechanism appears to be increased future returns to current human capital investments. Alternative mechanisms such as increased maternal bargaining power and transfers from relative or friends do not seem to be driving the result.
