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Disease and Development: The Effect of Life Expectancy on Economic Growth Daron , Acemoglu & Johnson. Proponent Section. Does increasing life expectancy accelerate economic growth?. Improving global health is an important social objective, but is it also an economic objective? - PowerPoint PPT Presentation

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Disease and Development: The Effect of Life Expectancy on Economic Growth

Disease and Development:The Effect of Life Expectancy on Economic GrowthDaron, Acemoglu & JohnsonProponent SectionProfessors Acemoglu and Johnson are both at MITProf Acemoglu has written a lot on what makes prosperous nations prosperousfactors ranging from geographic advantages to health conditions, as in this paperProf Johnson has done a lot of work with the IMF and other NGOs on data collection and descriptive statistics1Does increasing life expectancy accelerate economic growth?Improving global health is an important social objective, but is it also an economic objective?

There is a growing consensus among academics and NGOs that improving health can accelerate economic growthEliminating malaria in sub-Saharan Africa could increase GDP/capita growth rate by 2.6%/yr (Gallup & Sachs 2006)However, the only conclusive evidence shows correlation and not causation.

Richer countries could be healthier because of an omitted variable, like education, or reverse causation; see PRESTON curve.2Health interventions appear to have clear microeconomic benefits, butMicro studies have demonstrated improved healths positive effect on individual productivity

Yet no general equilibrium study has shown that these micro effects extend to increasing economic growth rates.Weil (2007) finds positive returns to health, but studies only constant population scenariosYoung (2005) finds positive GDP per capita returns to one specific disease, the HIV/AIDS epidemic, as it thinned the population

Two confounding effects in general equilibrium could be countering the benefits to increasing productivity:Diminishing returns to individual productivity given capital constraintsIncreasing competition in labor market as population increasesWe talked about pos health effects on productivity in the beginning & the nutritional poverty trap.3Acemoglu & Johnson test the macro effect of health on economic growthExperimental setup:Proxy for health: life expectancy at birthExogenous shock to health: the international epidemiological transition occurring in the 1940sInstrumental variable: predicted mortality, based on the interaction of two variables:Intervention dates for specific diseasesBaseline cross-country disease burden for each diseaseData obtained from UN, WHO and League of Nations records for burdens of 15 major diseases, fertility rates and life expectancy across 75 countries

Dates of intervention, e.g. when penicillin was discovered and mass produced, or when DDT was used to eliminate mosquito vectors4Exogenous shock: international epidemiological transitionPre-1940 few places aside from western Europe and the United States made significant public health efforts. Then a 3-part transition:New treatments availableAntibiotics (e.g. penicillin, streptomycin)Vaccines (e.g. yellow fever)Chemicals (e.g. DDT)New organizations spread tech and practicese.g. World Health Organization, United Nations International Childrens Emergency FundGlobalization of international valuesPost-1950 most new developments had already been universally implemented.Pre-1940 slacking due to lack of effective drugs, leading to reliance on exp public works projects like swamp draining, and the lack of incentive for the many large colonial powers5Exogenous shock: log life expectancy at birth over time

Note that the curves for different wealth cohorts converge, particularly after 1940. This demonstrates that the 1940 international epidemiological transition did coincide with an increase in our proxy for general health conditions; our setup could be reasonable6Exogenous shock: log GDP per capita over time

Note the absence of any convergent trend. The shock to health does not appear to have change trends in GDP/capita.7Hypothesis: increased population and limited resources stunts growthIncreasing population depresses capital-to-labor ratio temporarily (with rebound possible as more capital is accumulated)Increasing population depress land-to-labor ratios permanently (land supply is inelastic)8Hypothesis: closed-economy neoclassical growth modelFor economy i at time t, a closed-economy neoclassical growth model (essentially a Cobb-Douglas production function) gives:Yit = (AitHit)KitLit1 - - , for + 1Ait is a fixed function of cross-country baseline differencesKit is the supply of capitalLit is the supply of landHit is the effective supply of labor: Hit = hitNitNit is total population (employed and unemployed)hit is total human capital per person

9Applying the model to health shocksTo capture these effects in a reduced-form manner (writing production Y as a function of life expectancy X), we assume:Ait = AiXit, hit = hiXit, Nit = NiXitXit is life expectancy in country i at time ti , hi , and Ni designates baselines differences across countriesThen substitution gives:Yit = [(AiXit)(hiXit )(NiXit)]KitLit1 - Taking logs:yit = logKit0 + logAi + loghi - (1-)logNi + [(+)-(1-)]xit

10Applying the model to health shocksyit = logKit0 + logAi + loghi - (1-)logNi + [(+)-(1-)l]xitThe increase in log life expectancy xit will raise income per capita yit ifthe positive effects of health on total factor productivity (TFP, measured by [+]) exceed the potential negative effects arising from the increase in population because of fixed land and capital supply, (1-) .Otherwise, increases in log life expectancy will reduce income per capita.11Applying the model to health shocksTo accommodate the elasticity of the capital supply: Suppose that country i has a constant saving rate equal to si (0, 1) and that capital depreciates at the rate i (0, 1)Then the capital stock in country i at time t is given by Kit+1 = siYit +(1- i)Kit, and we find:

12Applying the model to health shocksThen the condition for a positive correlation between x and y is that TFP be greater than the negative effects arising from the increase in population because of fixed land supply, (1--) .Because fixed land supply is only a major concern for agriculture-dominated economies, the term (1--) approaches zero for developed nations and has a significant detrimental effect in developing nations.

13Applying the model to health shocksEstimation equation:

is the parameter of interesti denotes the set of fixed effects that are functions of Ai, hi, Ni, sii is set of time-variant factors common across all countriesZi is a vector of other controls

Defining pi to be the function of beta, alpha and lambda in previous slide that determines the magnitude and sign of the effect of health on productivity14Instrumental variable: predicted mortalityPredicted mortality M at time t in country i after intervention I for disease d:

MdFt is the mortality at the health frontier of the world, assumed to be 0 in this paper.First-stage equation:

Exclusion restriction: M is not correlated with

Is the exclusion restriction reasonable? (Yes; M does not depend on anything in-country, but rather baseline prevalence and intervention date globally.)15Change in log life expectancy over change in predicted mortality

This is the first-stage relationship. Furthermore, it holds even when rich countries are excluded.16Change in log life expectancy over change in predicted mortality

Rich countries excluded!17What effect did health shock have?How did the apparent shock to general health impact economic growth?18Main results: health shock did not change GDP/cap growthThe predicted mortality instrument did have a large & robust effect on changes in life expectancy after 1940 (and not before)The instrumented changes in life expectancy did have an effect on population (1.7-2% increase in population per 1% increase in life expectancy) because the increase in life expectancy was only partly compensated for in fertilityInstrument caused population increases19Main results: health shock increased population

Larger reductions in pred mortality lead to larger population20Main esults: exogenous shock did not change GDP/cap growthThe predicted mortality instrument did have a large & robust effect on changes in life expectancy after 1940 (and not before)The instrumented changes in life expectancy did have an effect on population (1.7-2% increase in population per 1% increase in life expectancy) because the increase in life expectancy was only partly compensated for in fertilityThere was no statistically significant effect on GDP, but two standard error range was so large this result is inconclusive.21Final result: health shock had negligible effect on total GDP

Note this is GDP overall; GDP per capita actually would trend downward. This casts serious doubt on the proposal that health has a first-order impact on economic growth.22