Every 40 seconds a child dies of malaria, resulting in a daily loss of more than 2,000 young lives worldwide. These estimates render malaria the pre-eminent tropical parasitic disease in most of the developing countries. Where malaria prospers the most, human societies have prospered least. Poverty is concentrated in the tropical and subtropical zones, the same geographical boundaries that most closely frame malaria transmission. Malaria might not be the single most important cause of poverty, but the two seem to have a complex relationship. Malaria has a substantial impact on health, which is an important non-income component that one would want to include in a measure of long term development for two reasons:
1) individuals clearly assign very high value to a long and healthy life,
2) due to the large extent to which achievement of this aim varies among countries as well as historically.
Both theory and evidence suggest that we should stop thinking of health as an univariate object. Health’s impact on income likely depends on how health changes (for example what’s the chance of dying when infected with malaria) and at what point in life you get infected (childhood, working age, or old age).
Malaria mortality rate is substantially higher in African countries and its impact on childhood mortality is even worse. According to the WHO World Malaria report in 2014, there were an estimated 584 000 deaths (90% of all malaria deaths occur in Africa), of which an estimated 437 000 were African children who died before their fifth birthday due to malaria.
This post will attempt to analyse the long-term effects of malaria on labour productivity. Firstly, it will focus on the effects of reduced mortality on population growth and income per capita. Secondly, it will assess malaria’s effect on education and its influence on labor productivity on a micro-economic scale. Lastly, it will evaluate the effects of malaria on the macro-economy and how that affects economic growth.
Effects of Reduced Mortality on Economic Growth
A potentially large effect of malaria eradication is to change the size and composition of the population. A standard idea in demographic transition theory is that when mortality falls, there is a delay in the response of fertility, and as a result of this delay there is a spurt of population growth.
Mortality reduction increases population growth
Acemoglu and Johnson (2007) attribute their finding - that mortality reductions in developing countries between 1940 and 1980 led to a decline in income per capita - to exactly this channel. Their findings imply that an increase in life expectancy from 40 to 60 years would double the population size over this forty year period, which is an increase in the annual growth rate of slightly less than 2%. They claim that the negative economic effects of rapid population growth more than compensated for direct economic benefits from better health, and so income per capita fell.
Increased life expectancy reduced income per capita
Ashraf et al. consider a stylized economy in which age-specific mortality and fertility rates have been constant for sufficiently long that the age structure of the population is unchanging. The authors then consider an instantaneous shock to health that raises life expectancy at birth from 40 to 60 years, which raises population size by a factor of 1.36. As a result of this change they assess that at a horizon of 40 years, income per capita would fall by 20% in response to the rise in life expectancy from 40 to 60.
Ashraf et al. find a short run effect that is consistent with the findings of Acemoglu and Johnson discussed above, but they believe it’s primarily through the demographic channel of raising the ratio of dependent children to working age adults.
Fifteen years into the simulation, income per capita is five percent lower than it would have been absent the health improvement. In the long run though, they claim that the demographic effect is undone by endogenously falling fertility (as shown in Figure 1), while better health and higher education raise worker productivity, and so the effect on income reverses. Income per capita returns to its baseline level after 30 years, and in the long run is 15% higher thanks to the health improvement(as shown in Figure 2).
Figure 1: Effect of Adjustment speed on population size (Figure and text adapted from Ashraf, Q. H., Lester, A.,Weil, D. N, p. 177, 2009).
Figure 1 shows the path of population size under the different scenarios. The base case assumed that it would take 50 years for fertility to adjust to its new long‐run rate. Relative to the baseline in which there is no change in life expectancy, the long‐run increases in population are 31%, 52%, and 76%, respectively, as fertility takes 25, 50, and 75 years to adjust. After 25 years, the population is 20%, 24%, and 26% bigger in the three scenarios. After 50 years, however, the differences are apparent, with the population increase being only 27% in the 25‐year adjustment case but 42% in the base case and over 50% in the 75‐year adjustment case.
Figure 2: Effect of Adjustment speed income per capita (Figure and text adapted from Ashraf, Q. H., Lester, A.,Weil, D. N, p. 178, 2009).
Fall in income per capita 15 years after the shock is between 2.5-4%. Income per capita recovers to the baseline level after about 20, 35, and 45 years after the shock, respectively.
The 25‐year adjustment case leads to long‐run income gains of about 18%, and the 75‐year adjustment case raises income by only about 13%. These long‐run effects run entirely through the land‐labour ratio.
Increases in investment in children as result of decreased mortality
Similarly, Soares studied the effect of mortality reduction in developing countries, such as Bolivia, Honduras, and Nicaragua, and he evaluated that gains in life expectancy of roughly 20 years took place during periods of modest or even negative income growth. He assessed that reductions in both child and adult mortality lead parents to increase investment in their children's human capital and also to lower fertility. In the long run though, reduction in fertility goes beyond the amount that would be induced by lower mortality, if parents were aiming to hold the expected number of survivors fixed.
Bloom et al show, in the context of a life cycle model, that increased longevity will raise saving rates at every age, even allowing for endogenous changes in retirement age. In other words, he believes that individuals will interpret the increased longevity as a higher probability of using their savings. This in turn will raise capital accumulation and output. In their empirical work they find that higher life expectancy raise national saving rates, controlling for the age structure of the population.
However, Hazan, and Zoabi argue that the effect of longevity on human capital investment is not clear in the presence of quality-quantity tradeoffs (the effect of family’s size in the investment of human capital of each child) because increased longevity positively affects quantity as well as quality. For instance, they argue that longevity will increase both the number of children and early-life investments and as a result it’s unclear whether the individual investment per child will actually increase.
Overall, the question of whether reduced mortality affects the economy and labor productivity is hotly debated. Some economists argue that reduced mortality will cause a decline in income per capita which can be as high as 20%.
Others attribute this reduction to the demographic channel of raising the ratio of dependent children to working age adults and argue that this is only a temporary shift: as children get to working age, the demographic effect will be undone and that will spark an increase in labour productivity and economic growth.
Lastly, others claim that increased life expectancy raises saving rates allowing for an increase in capital accumulation and output, whereas others consider that to be an unreliable argument due to its complex connection with quality-quantity tradeoffs.
Effect of malaria on Education
Negative effects of Malaria on education
Early-life health conditions and education predetermine one's health capital, which has an effect on one's economic capabilities throughout life.
Malaria mortality and morbidity rates remain significant in most countries in Africa. Children that go to school greatly suffer from exposure to a high malaria environment, as the chance of being infected by malaria is high. Getting infected with malaria usually leads to school absenteeism.
Exposure to Malaria reduces years of schooling
Alan Barreca used an instrumental-variables identification strategy and historical data from the United States to estimate the long-term economic impact of in utero and postnatal exposure to malaria. He found that exposure to an environment with 10 additional malaria deaths per 100,000 reduces years of schooling by approximately 0.4 years, and reduces the probability of attaining at least 12 years of education by 5.1 percentage points.
This study further demonstrates the significance of malaria in school absenteeism by comparing schools in low and high malaria environments. It concludes that cohorts born in high malaria states had 9.71 years of schooling, while cohorts born in low malaria states had approximately 11.15 years of schooling.
Bleakley did a similar study on Brazil, Mexico, Colombia and US and he estimates the effect of childhood malaria infection on adult wages to be substantial: being infected with malaria through childhood leads to a reduction in adult income of approximately 50 percent.
His methodology was based on normalizing the reduced-form differences with the estimated decline in malaria( a process of reducing redundancies of data in a database). in order to assess its effect on the total and earned income. The total income estimates were 62% for Brazil and the earned income estimates for Mexico, Colombia and US were 43%, 39% and 37% respectively .
Effects of Malaria Eradication on Education
Evaluating the positive effects of malaria prevention in schooling can be achieved by investigating cases of malaria eradication and their consequences on education and overall labor productivity.
Malaria eradication programs increase days of schooling
Barofsky (2013) conducted a research on a malaria eradication effort in Uganda, in order to investigate the long term effects on educational attainment and economic status. In his study he employed a difference-in-difference methodology to compare changes in outcomes for the intervention district against changes in the rest of Uganda. The treatment effect produced a gain in schooling of 10% and 5% for males and females respectively and a 53% rise in primary school completion. Barofsky concludes that the gain in schooling increased overall income gains by 3-11% depending on the rate of return to education assumed.
Effects of gain in schooling to future income
Bleakley (2010), however, seems to be more skeptical about translating increased schooling into economic growth. He believes that its difficult to determine whether the educational benefits of being healthy during school are more profitable than the teenager working from a younger age.
More years of schooling is not a sufficient statistic for measuring the impact of early-life health on lifetime income. It is possible that when health improves, lifetime income goes up, but the number of years of schooling declines. All that needs to happen is that being a healthy child raises wages more than the returns to the schooling.
Improvements in health lead to income gains
He believes that changes in the quantity of education (time in school, that is) are not of first-order importance but claims that improvements in adult health provide returns to human capital, which later lead into income gains. He believes that adults who are healthy can work harder (being more productive), leading to an increase in human capital.
In addition, malaria eradication decreases the chance that a parent will stay at home to take care of his sick children, and will instead go to work, which further increases human capital. If this is expected, early-life investments in human capital should increase. He also adds that a decline in mortality means the asset called ‘human capital’ now depreciates more slowly, which increases the benefit of going to school. Even if the days of schooling weren’t increased per student, people can spend more time working because they do not die as quickly, which generates a first-order gain.
On the whole, malaria seems to have an important impact on education mainly through school absenteeism.
Bleakley tries to measure the cost of childhood malaria on future adult income and he comes up with some interesting estimates. Even though these numbers’ accuracy is debatable, they are useful because they indicate that childhood malaria possibly has a negative impact on adult income. Moreover, Barofsky estimates the gain in schooling as a result of a malaria eradication program and he concludes that this leads to an income gain.
Bleakley is more skeptical to such a notion because he believes that increased schooling isn’t a sufficient measure of income gain. However, he claims that improvements in adult health will increase early-life investments in human capital which will potentially lead to income gains.
Effect of malaria on macroeconomy
The global distribution of per-capita gross domestic product (GDP) in 1995 shows an interesting correlation between poverty and malaria. A comparison of income in countries with and without malaria indicates that average GDP (PPP adjusted) in countries with malaria was US$1,526, compared with US$8,268 in countries without intensive malaria, which is more than a fivefold difference.
Even though it might be nearly impossible to determine the beginning of the causality circle between the two, it’s nevertheless evident that countries that have low malaria rates or have undergone a successful and long-lasting malaria eradication program have enjoyed a higher rate of economic growth.
Malaria eradication in southern Europe has been a clear success story. Major control efforts in Greece (the most malaria-infected country in Europe, reaching infection rates as high as 25% in 1945), Italy, and Spain were started in the 1930s and completed in the late 1940s. Comparing growth during the post malaria eradication years of 1950-1955 to growth in the period 1913-1938, all 3 countries experienced a higher economic growth than in the prewar period and a higher growth compared to the western European countries.
Malaria might not have been the major driving force behind such a shift, but it nevertheless played an important role, especially due to the fact that the rapid development of the tourism industry was feasible due to the malaria eradication.
Analysis through growth regressions
This rather simple cross-country analysis indicates that malaria might play an important role in the long-term economy of a country and on its labor productivity, However, it remains a mystery whether that effect can be quantified. Many economists believe that this effect can be ‘captured’ in the residual of growth regressions: to the degree that malaria, controlling for other factors, exerts a significant adverse effect on growth.
Increases in Malaria morbidity associated with negative growth
Desmond et al attempt to determine the impact of macro policy variables on malaria morbidity across countries, and its indirect effects on total factor productivity(the output per worker in a country). The study demonstrates a robust negative baseline growth effect for malaria morbidity per 100,000 population, reducing annual per capita growth by at least 0.25%. For the most affected countries, the effect size is as high as 3.22%.
David Cutler measures the effect of the malaria index as a function of household consumption for treated people in India. He estimates that a 40 percentage point reduction in the malaria morbidity rate(which is medical term for measuring malaria morbidity rates) is associated with a 2 percent increase in per capita household expenditure for treated men and observes no notable difference in females.
The fact that treated men have a significantly higher per capita household consumption increase than women indicates an improvement in the labor market productivity which is mainly composed of men.
Building on previous studies, Bleakley utilized a retrospective/cohort design with two proxies, the occupational income score and the Duncan socioeconomic index, and concluded that persistent childhood malaria infection reduces adult income by 40 to 60 percent (highest for Brazil, lowest for US).
Positive impact of malaria eradication programs on big industries
Lastly, another study ran a gross and net cost-effectiveness analysis of integrated malaria control in four copper mining communities of Zambia during 1930-1950. Effective malaria control measures were sustained, and there was a significant improvement in overall living and working conditions. This was a key incentive in the employment seeking behaviour of potential workers.
The total work force almost doubled from 1933 to 1934, whereas these dramatic increases in the number of employees were in stark contrast to neighbouring Belgian Congo and Southern Rhodesia. The total increase in the national income statistics for the same year revealed that 55% of the revenue was from mining. Thus, effective malaria control was a crucial driving force behind Northern Rhodesian economic development. Without it, the taxable national income would have been 28% lower and the percentage of national income from mining activities would have dropped to 37%. Integrated malaria control costed approximately 0.07% of the total revenues of US$ 7.1 billion by the copper mining companies, a minuscule amount compared to the growth of productivity that it triggered.
Overall, a cross-country analysis indicated that nations with low malaria rates have a higher rate of economic growth. However, this analysis was based on a limited amount of countries and, as a result, it’s difficult to determine how relevant these cases are to other African countries.
Economists who performed a growth regression analysis found that malaria can negatively affect economic growth. The index of malaria prevalence seems to have a negative impact on adult income (40-60% decrease) or per capita household consumption (0.25-3.22%) but these values should mainly be interpreted as rough estimates .
A central problem in assessing the impact of malaria is the identification of a suitable indicator for the prevalence of the disease. The most severely affected countries often lack high-quality data on disease burden of malaria. As such, researchers use historical maps of the geographical distribution of malarial risk to derive an index of malaria prevalence (Cutler, 2010). Therefore, the malaria index is a variable that is dependent on the accuracy of the model that the economist used to ‘map’ its effect, and its accuracy is to a large extend debatable.
Lastly, the study on the mining companies in Zambia might demonstrate some substantially positive results of malaria eradication on growth and productivity but it’s highly uncertain if it would have worked that well for another mining industry in another country or at a different time period.
To conclude, analyzing malaria’s long-term effects on economic growth and labor productivity is a rather challenging task. Malaria can affect labor productivity through various channels. I mainly focused on reduced mortality and education.
There is sufficient evidence to suggest that malaria has a negative effect in economic growth and labor productivity but its magnitude remains unclear. Comparing income growth with improvements in health outcomes through malaria eradication campaigns, makes things a bit more complicated. In the short run, there is relatively weak correlation between changes in income and changes in life expectancy.
My analysis shows that fighting malaria on its own will not pull nations out of poverty. However, there are good reasons to think that malaria eradication programs can have a positive impact on labour productivity. People who are healthier can work harder and learn more in school. Further, where people live longer they will be incentivized to invest more in education. Thus we can expect better health to cause economic growth.
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