Alternative Estimates of the Effect of the Increase of Life Expectancy on Economic Growth Muhammad Jami Husain Keele Management School Keele University

Abstract Until recently the literature has found evidence of a positive, significant, and sizable influence of life expectancy on economic growth. This view has been challenged by Acemoglu and Johnson (2007). They find no evidence that the large exogenous increase in life expectancy led to a significant increase in per capita economic growth. This paper takes up the modelling and estimation framework presented in Acemoglu and Johnson (2007), and presents alternative estimates on the impact of life expectancy on population, GDP, and GDP per capita by using alternative instruments, timeline, and country groups. The findings suggest that the results differ significantly from that provided in Acemoglu and Johnson (2007); that the generalization of the pessimistic outcomes with regards to the impact of life expectancy on income per capita requires caution; and the increase of life expectancy may have had a positive impact on income per capita growth.

JEL:

I10, O40, J11

Keywords: Life Expectancy; Income; Economic Growth; Immunization; Instruments.

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Alternative Estimates of the Effect of the Increase of Life Expectancy on Economic Growth

1. I#TRODUCTIO#

Improvements of health and longevity are not simply viewed as a mere end- or by-product of economic development but argued as one of the key determinants of economic growth, and therefore provide means to achieve economic development and poverty reduction. Hence, better health does not have to wait for an improved economy; rather, measures to reduce the burden of disease, to give children healthy childhoods, to increase life expectancy etc. will in themselves contribute to creating richer economies (see e.g. Suhrcke et al, 2005). Until recently the literature has found evidence of a positive, significant, and sizable influence of life expectancy (or some related health indicator) on economic growth.1 This view has been challenged in a recent paper by Acemoglu and Johnson (2007). In the face of critics’ scepticism that the cross-country regression studies may have established merely a strong correlation between measures of health and both the level of economic development and recent economic growth without being able to establish a causal effect of health and disease environments on economic growth, they provided an empirical analysis based on the international epidemiological transition, apparently led by the wave of international health innovations and improvements that began in the 1940s, and found that there was no evidence that the large exogenous increase in life expectancy led to a significant increase in per capita economic growth. 2 An instrument was constructed, referred to as predicted mortality, based on the pre-intervention distribution of mortality from 14 major diseases around the world and dates of global interventions. This instrument appeared to have a large and robust effect on changes in life expectancy starting in 1940. Then, it was shown that instrumented changes in life expectancy had a large effect on population, a 1% increase in life expectancy leading to an increase in population of about 1.7-2%, but a much smaller effect on total GDP both initially and over a 40-year horizon. Accordingly, the impact of an increase in life expectancy on per capita income was found 1

A common empirical approach toward examining the impact of health on economic growth has been to focus on data for a cross-section of countries and to regress the rate of growth of income per capita on the initial level of health (typically measured by life expectancy, or survival rate), with controls for the initial level of income and for other factors believed to influence steady-state income levels. These factors might include, for example, policy variables such as openness to trade; measures of institutional quality, educational attainment, and rate of population growth; and geographic characteristics. The application of growth regression approach can be seen, inter alia, in Barro (1996); Barro and Lee (1994); Barro and Sala-I-Martin (1995); Bhargava et al., (2001); Bloom, Canning, and Malaney (2000); Bloom and Malaney (1998); Bloom and Sachs (1998); Bloom and Williamson (1998); Caselli, Esquivel, and Lefort (1996); Gallup and Sachs (2000); Hamoudi and Sachs (1999). The estimated effects of an increase in the life expectancy (expressed in log terms) economic growth are the followings: 4.2% in Barro, 1996; 7.3% in Barro and Lee (1994); 5.8% in Barro and Sala-I-Martin (1995); 6.3% Bloom, Canning, and Malaney (2000); 2.7% in Bloom and Malaney (1998); 3.7% in Bloom and Sachs (1998); 4% in Bloom and Williamson (1998); 0.1% in Caselli, Esquivel, and Lefort (1996); 3% in Gallup and Sachs (2000); and 7.2% in Hamoudi and Sachs (1999). 2 The wave of international health innovations and the concomitant international epidemiological transition led by the dramatic decrease in deaths from the infectious and parasitic diseases provide us with an empirical strategy to isolate potentially-exogenous changes in health conditions.

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to be insignificant or negative. The interpretation of the insignificant or negative impact of life expectancy on GDP per capita is that, in the face of population growth, the other factors in the production function, capital and land in particular, did not adjust. As population increased, the capital-labour ratio decreased, which eventually led to a decrease in the per capita income.3 The political economy consequences of such findings bear critical implications in terms of direct investments in health sectors. Such a finding is rather at odds with the advocates of investing in health. Generalization of the pessimistic findings of Acemoglu and Johnson (2007) should be subject to more scrutiny – and this paper contributes towards this. In particular, there is a need for considerable caution in interpreting the results obtained from their framework of analysis for two reasons. First, the nature of the international epidemiological transition that occurred around the 1940s and 1950s was unique and may not be applicable to today’s world. The changes in life expectancy that occurred mainly due to the infectious diseases during that time may have different implications than the changes that are being observed in recent times. For example, improvement in life expectancy in the recent decades may not increase the population to the extent that it did during the epidemiological transitions. Second, the nature of diseases has changed in today’s world. The diseases that affect the productive ages are probably becoming more important (e.g. HIV/AIDS, heart diseases, cancer.) in recent times instead of the diseases that are more fatal for children. Further study of the effects of the HIV/AIDS epidemic on economic outcomes as well as more detailed analysis of different measures of health on human capital investments and economic outcomes are major areas to be explored. Again, Acemoglu and Johnson (2007) were unable to include Africa in their baseline analysis owing to lack of data. Africa may be an important source of variation in the data and its inclusion in the sample might have led them to different conclusions.4 This paper presents further analysis and estimates of the impact of life expectancy on income by using alternative instruments, timelines, and country groups. The objective is to investigate whether the findings of Acemoglu and Johnson (2007) prevail if different instruments, time-lines, or country groups are used. Section 2 provides a description of alternative sources of exogenous variations in life expectancy which can be used as potential instruments. Section 3 presents the first stage estimates to highlight instrument relevance and strengths. Section 4 presents the two-stage least square (2SLS) estimates of the impact of life expectancy on the three major macro-variables, i.e. population, GDP, and GDP per capita. What is shown below is that the results differ significantly from those provided in Acemoglu and Johnson (2007); that the pessimistic outcome with regards to the impact of life expectancy on income per capita requires caution; and that the positive impact of life expectancy on income per capita is more apparent. 3

However, the results of many micro-level studies (see e.g. Strauss and Thomas, 1998; Suhrcke et al., 2005; Thomas and Frankenberg, 2002; Ruger et al., 2006; Behrman and Rosenzweig, 2001; Miguel and Kremer, 2004; Schultz, 2002) and cohort-level studies (e.g. Bleakley, 2006a; 2007) obtained large positive effects of health on productivity. In that case, these latter effects, assuming that they exist and of partial equilibrium nature, were fully offset by the crowding-out effect of population growth (Bleakley, 2006). 4 Cervellati and Sunde (2009) suggest differing causal effects of life expectancy on income per capita due to different phases of development. The negative impact of the increases in life expectancy on income is observed for countries that did not go through the demographic transition, whereas in post-transitional countries gains in life expectancy increase per capita income. By pooling the countries in their sample, they argue, Acemoglu and Johnson (2007) cannot capture this nonlinear dynamic. Also, Bloom, Canning and Fink (2009) highlight that the healthiest nations in 1940 are those that benefited least from the health interventions and also the ones that grew the most, giving a negative relationship between health interventions and growth.

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2. THEORETICAL A#D ESTIMATIO# FRAMEWORK

The implication of the increase in the length of human life is modeled in a closed-economy Solow type neoclassical growth model. The health variable is represented in terms of life expectancy only. The aggregate production function of the economy i has constant returns to scale. Yit = ( Ait H it ) α K itβ L1it−α − β

(1)

Where α + β ≤ 1 , Kit denotes capital, Lit denotes the supply of land, and Hit is the effective units of labour given by Hit= hit it ; where it is the total population (henceforth, representing those who are employed), while hit is the human capital per person. All labour and land are inelastically supplied. The production function does not lose its generality if the supply of land is normalized to unity for all countries (i.e. Lit = Li = 1). With regards to the technological progress, the model maintains constant technology differences across countries, but ignores differentiated technological progress (i.e Ait = Ai ). Also, crosscountry differences in human capital per person and population are assumed to be constant. Therefore, hit = hi and  it =  i . Economies face depreciation of capital at the rate δ ∈ (0,1) and the savings (investment) rate of country i is constant and equal to si ∈ (0,1) . This implies that the evolution of capital stock in country i at time t will be K it +1 = si Yit + (1 − δ ) K it . Suppose that life expectancy changes from X it0 to X it1 and remains at this level thereafter. After population and the capital stock have adjusted, the steady-state capital stock level will be K i =

si

δ

Yi .

Substituting into (1) and taking logs we obtain a simple relationship between income per capita, the savings rate, human capital, technology, and population:

Y yi ≡ log i  i

 1−α − β α α β β  = log Ai + log hi + log si − log δ − log  i (2) 1− β 1− β 1− β 1− β  1− β

Equation (2) shows that income per capita is affected positively by technology Ai, human capital, hi, and the investment rate si. Population, i, has a negative effect on income per capita. The term that captures the impact of population would drop out from the equation if 1 − α − β ≅ 0 . Acemoglu and Johnson (2007) suggests that for industrialized countries land plays a small role in production due to only a small fraction of output being produced in agriculture, so that 1 − α − β ≅ 0 . Nevertheless, for many less-developed countries, where agriculture is still important, 1 − α − β > 0 and the direct effect of an increase in population may be to reduce income per capita even in the steady state (i.e., even once the capital stock has adjusted to the increase in population).5

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See Galor and Weil (2000), Hansen and Prescott (2002), and Galor (2005) for models in which at different stages of development the relationship between population and income may change because of a change in the composition of output or technology. In these models, during an early Malthusian phase, land plays an important role as a factor of production and there are strong diminishing returns to capital. Later in the development process, the role of land diminishes, allowing per capita income growth. Hansen and Prescott (2002), for example, assume a Cobb-Douglas production function during the Malthusian phase with a share of land equal to 0.3.

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The impact of increased life expectancy is assumed to be working initially through increased population (both directly and also potentially indirectly by increasing total (3) births), so that:  it =  i X itλ where Xit is life expectancy in country i at time t. Another important channel of influence includes the impact that better health and longer life spans has in terms of increased productivity. This increase in productivity may originate through a variety of channels, including more rapid human capital accumulation (e.g Kalemli-Ozcan, Ryder, and Weil, 2000; Kalemni-Ozcan, 2002; Soares, 2005) or direct positive effects on (total factor) productivity (e.g. Bloom and Sachs, 1998). Acemoglu and Johnson (2007) have used the following isoelastic function in order to capture the beneficial effects of these variables on productivity: Ait = Ai X itγ and hit = hi X itη

(4)

Where Ai and hi are some baseline differences across countries. Using the steady-state value of capital stock together with (1), (3) and (4), we derive the long-run relationship between log life expectancy and log per capita income below: Yit = ( Ai X itγ hi X itη  i X itλ ) α s iβ Yi β δ − β ⇒ log( +

Yit β β 1−α − β α α )= log Ai + log hi + si − log δ − log  i  it 1− β 1− β 1− β 1− β 1− β

1 (α (λ + η ) − (1 − α − β )λ ) log X it 1− β

Y Denoting xit ≡ log X it is log life expectancy and yit ≡ log it   it y it = +

α 1− β

log Ai +

α 1− β

log hi +

β 1− β

log s i −

β 1− β

log δ −

  , we obtain: 

1−α − β log  i 1− β

1 (α (γ + η ) − (1 − α − β )λ ) x i 1− β

(5) The last term in equation (5) shows that an increase in life expectancy will lead to a significant increase in long-run income per capita when there are limited diminishing returns (i.e., 1 − α − β is small) and when life expectancy creates a substantial externality on technology (high γ) and/or encourages significant increases in human capital (high η). On the other hand, when γ and η are small and 1 − α − β is large, an increase in life expectancy would reduce income per capita even in the steady state. Given the modeling framework, the empirical strategy followed by Acemoglu and Johnson (2007) basically is to estimate equations similar to (5), and compare the estimates to the parameters in these equations. More specifically, a fixed effects panel regression method is used to capture the impact of life expectancy on the following four variables: population, number of births, GDP, and GDP per capita. The fixed effects model examines country differences in intercepts, assuming the same slopes and constant variance across groups. Fixed effects models use least squares dummy variable (LSDV), within effect, and between effect estimation methods. Thus, ordinary least squares (OLS) regressions with dummies, in fact, are fixed effects models. Such models assist in controlling for unobserved

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heterogeneity, when this heterogeneity is constant over time. This paper uses panel data where the unit of observations are the countries and time (decennial data since 1930 through 2000), and thus may have group (i.e. country specific) effects, time effects, or both. This constant heterogeneity is the fixed effect for the individual countries. This constant can be removed from the data, for example by subtracting each individual's means from each of his observations before estimating the model. Adding an error term, the generalized version of the estimating equation becomes:

yit = πxit + ς i + µt + ε it

(7)

where y is log income per capita, ζi is a fixed effect capturing potential technology differences and other time-invariant omitted effects (i.e. Ai , hi ,  i , and K i or s i ), µt incorporates time-varying factors common across all countries, and x is log life expectancy at birth. The coefficient π is the parameter of interest equal to 1 (α (λ + η ) − (1 − α − β )λ ) when 1− β

equation (5) applies. Including a full set of country fixed effects, the ζi’s, is important, since the country characteristics Ai , hi ,  i ,and K i or s i would be correlated with life expectancy (or health). Also, many country-specific factors will simultaneously affect health and economic outcomes. Fixed effects at least remove the time-invariant components of these factors. Prior to investigating the effect of life expectancy on income per capita, its effects on population, total births, and total income are reported. The equations for these outcome variables are identical to (7), with the only difference being the dependent variable. Equation (7), however, while estimated as it is, may be beset with the potential omitted variable bias and reverse causality problems. In that case the causal effects of life expectancy on income per capita or population are misleading. In particular, in equation (7), typically the population covariance term Cov(xit, εit+k) is not equal to 0, because even conditional on fixed effects, health could be endogenous. In Acemoglu and Johnson (2007), the endogeneity problem has been addressed by exploiting the potentially-exogenous source of variation in life expectancy attributable to global health innovations and interventions. Specifically, the first-stage relationship is: xit = ψM itI + ς~i + µ~t + u it , where M itI is the instrument, termed as predicted mortality, derived from the worldwide variations in the death rates from different diseases, and due to disease specific interventions at different points in time. The key exclusion restriction of Cov(xit, εit) equal to 0 is assumed, where εit is the residual from equation (7). Similarly in this paper, the first stage relationship using the alternative instruments is presented in the equation below:

xit = φVit' + ς~i + µ~t + u it

(8)

Where Vit' includes alternative instruments like immunization coverage, occurrences of conflicts and natural disaster dummies, used separately or in combinations. µ~ represents t

the time fixed effect controlling for unobserved omitted variables that changes over time but are constant across entities; ς~i captures entity fixed effect controlling for unobserved omitted variables that differ across countries.

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3. ALTER#ATIVE I#STRUME#TS A#D TIME-LI#E

Acemoglu and Johnson (2007) use a predicted mortality instrument based on the death rates from different diseases and the global health intervention dates that contributed to rapid declines in mortality from different diseases. The analysis mainly refers to the period 1940-1980 for which the predicted mortality instrument is deemed to be suitable. However, the construction of the instrument raises several issues: 1. The assigned global intervention dummies assume uniform health interventions across countries, which is clearly subject to doubt. The predicted mortality instrument assumes zero values after the global intervention dates (i.e. from 1960 onwards). This may render usage of this instrument artificial, or at least unique and applicable to analysis pertaining to that timeline only. It is then worth investigating the notion that instrumented life expectancy led to a much higher population increase and led to insignificant or negative impact on per capita GDP with alternative instruments and/or timelines. 2. The notion that epidemiological transition occurred only during the post World War II era requires caution. Riley (2001, 2007), using a vast bibliography6 that enabled him to track the historical life expectancy situation for the majority of the countries around the world, presents the approximate date (year) for each country from when the respective countries registered sustained gains in life expectancy (termed ‘health transition’). Out of more than 150 countries studied, 31 countries started their health transitions before 19007, 49 countries between 1900 and 19408, and 70 countries after 1940s9. 3. Due to scarcity of data African countries were excluded from the Acemoglu and Johnson (2007) analysis. Worldwide cross-country data for the period 1960 onwards are relatively more accessible. One could then undertake the study with a similar modeling approach to quantify the impact of life expectancy on economic growth for the period 1960- to date, by which time the major health innovations to reduce infectious diseases had occurred. In that case, the task is to find suitable instruments to explain life expectancy during that period and its potential impact on growth through the channels of human capital accumulation, total factor productivity and population growth.

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James C. Riley, “Bibliography of Works Providing Estimates of Life Expectancy at Birth and of the Beginning Period of Health Transitions in Countries with a Population in 2000 of at least 400,000” at www.lifetable.de/RileyBib.htm (Last browsed on September 09, 2010) . 7 Argentina, Australia, Austria, Belgium, Canada, Costa Rica, Cyprus, Czech Republic, Denmark, England and Wales, Finland, France, Germany, Hungary, Iceland, Ireland, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Russia, Slovak Republic, Spain, Sweden, Switzerland, Scotland, United States. 8 Albania, Algeria, Bangladesh, Brazil, Bulgaria, Chile, China, Colombia, Cuba, Egypt, Estonia, Fiji, Ghana, Greece, Guatemala, Guyana, India, Indonesia, Jamaica, Jordan, Kenya, Latvia, Lithuania, Malaysia, Mauritius, North Korea, Pakistan, Panama, Paraguay, Philippines, Portugal, Puerto Rico, Romania, Singapore, South Africa, South Korea, Sri Lanka, Suriname, Syria, Taiwan, Trinidad and Tobago, Tunisia, Turkey, Uganda, Ukraine, Uruguay, Venezuela, Vietnam, Yugoslavia. 9 Eritrea, Ethiopia, Gabon, Gambia, Guinea, Guinea Bissau, Haiti, Honduras, Iran, Iraq, Kuwait, Laos, Lebanon, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mongolia, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Oman, Papua New Guinea, Peru, Qatar, Rwanda, Saudi Arabia, Senegal, Sierra Leon, Solomon Islands, Somalia, Sudan, Swaziland, Tanzania, Thailand, Togo, United Arab Emirates, Yemen, Zambia, Zimbabwe

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The conjecture that the advent of antibiotics and vaccines in the 1940s or early 1950s (and their subsequent worldwide application) allowed abruptly substantial gains in life expectancy in general, and for low-income countries in particular, has been subject to scrutiny. Widespread and effective dissemination of antibiotics and vaccines during the late 1940s until 1960 is doubtful, because this would have required developed health care and public health systems. These include physical infrastructure such as hospitals, health centres, clinics and dispensaries as well as human capital in the forms of physicians and nurses trained in Western medicine. Many of the countries that began sustained health transitions in the 1940s and 1950s lacked such arrangements. According to Riley (2007) – “if antibiotics and vaccines were in fact instrumental, one must imagine that they were distributed outside the system, or mostly outside, such systems. In addition, ordinary people must have learned quickly about these remedies, how to obtain them, and when and how to use them, and they must also have been able to afford the new medications and vaccines. Put that away, the proposition looks more doubtful” (Riley, 2007, p.47). The arrival of antibiotics (e.g. Penicillin and other antibiotics) in Latin America, Asia, and Africa began in the late 1940s and early 1950s (Riley, 2007). Venezuela, for instance, being a country with close contacts to the United States, started receiving antibiotics in 1950 (Rivora, 1998; cited in Riley, 2007). The question remains - did antibiotics actually reach large proportions of the populations of Africa, Asia, and Latin America by 1950, or even by 1960? Riley (2001, 2007) asserts that in most countries that began health transitions between the 1890s and the 1920s or 1930s, mortality from diseases treatable with antibiotics, such as tuberculosis and typhoid fever, was already much reduced by the time the antibiotics became available. 10 That was true also for the countries that began health transitions before the 1890s.11 Thus the effects posited for antibiotics apply chiefly to countries where bacterial infections remained leading cause of death, which would include all the countries that initiated health transitions in the 1940s or later.12 Antibiotics, and to much lesser degree sulfa drugs especially effective in treating wounds and sores, are said to have allowed those countries to make rapid progress in controlling infectious diseases. In case of vaccines, widespread administering is slower. 13 Two of the most established vaccines namely smallpox and typhoid were first introduced in 1796 and 1896 respectively. However decades passed before usage was general in any large region in Africa, Asia, or Latin America. The same delay applied to other vaccines. The date when a vaccine was introduced in the West has little usefulness in determining when that vaccine was first used, 10

The list of countries in this category includes Hungary, Czech Republic, Slovak Republic, Spain, Argentina, Cyprus, Russia, Costa Rica, Estonia, Latvia, Lithuania, Ukraine, Cuba, Puerto Rico, Greece, Singapore, Yugoslavia, Korea, Panama, Romania, Malaysia, Philippines, Bangladesh, Bulgaria, Chile, China, Fiji, Ghana, Guyana, India, Indonesia, Jamaica, Mauritius, Pakistan, Portugal, Sri Lanka, Suriname, Taiwan, Trinidad and Tobago, Tunisia, Uruguay, Albania, Brazil, Paraguay, South Africa, Turkey, Venezuela, Vietnam, Algeria, Colombia, Egypt, Guatemala, Jordan, Kenya, Syria, Uganda (Riley, 2007). 11 The list of countries in this category includes Denmark, France, Sweden, England, Norway, Canada, Belgium, Ireland, Australia, Netherlands, New Zealand. Mexico, Finland, Germany, Iceland, Switzerland, Scotland, Italy, Luxembourg, Japan, Poland, United States, Austria (Riley, 2007). 12 The list of countries in this category includes Bahrain, Lesotho, Papua New Guinea, Sierra Leon, Solomon Islands, Afghanistan, Angola, Benin, Botswana, Burkina Faso, Cambodia, Cameroon, Central African Republic, Chad, Comoros, Congo Republic, Cote d'Ivoire, Djibouti, Equatorial Guinea, Eretria, Ethiopia, Gabon, Gambia, Guinea, Guinea Bissau, Haiti, Liberia, Madagascar, Malawi, Mali, Mauritania, Mongolia, Nepal, Niger, Nigeria, Saudi Arabia, Somalia, Swaziland, Tanzania, Togo, Yemen, Zambia, Mozambique, Oman (Riley, 2007). 13 The terms ‘vaccination’ and ‘immunization’ are used synonymously and interchangeably.

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or first widely used, in the developing world, where, in any case, the medical infrastructure was rarely developed enough to support mass immunizations. Vaccines with general or limited usefulness against smallpox, typhoid fever, yellow fever, tuberculosis, tetanus, plague, and whooping cough were introduced in the West before World War II, but only smallpox vaccination seems to have been used widely in some parts of the developing world before 1940s. After the war vaccines for polio (introduced in 1954), measles (1963), mumps (1967), and rubella (1969) might have had substantial and immediate effects, but it was not until the 1970s or even the 1980s before many of them were used widely. In the absence of a detailed reconstruction of the timing and scale of vaccine use, it is difficult to determine when they actually had a general effect. One exception was smallpox, which was introduced in the 1970s and 1980s as part of the World Health Organization’s Expanded Program on Immunization (see, e.g Plotkin and Mortimer, 1994; Riley, 2007). In sum, although antibiotics and vaccinations may have helped initiate declines in mortality rates in many countries in the 1940s and 1950s; it seems likelier that their effect was felt somewhat later and more weakly. Nevertheless, there were significant survival gains across at least three decades, from the 1950s through the 1970s. While some of the improvements in health are the result of overall social and economic gains, the major achievements were obtained by the specific efforts to address major causes of disease and disability, such as providing better and more accessible health services, introducing new medicines and other health technologies, and fostering healthier behaviours through extensive campaigns and education. Nearly all of the low-income countries that began health transitions between the 1890s and the 1920s or 1930s, before the availability of antibiotics or sulfa drugs, continued to add to those earlier gains in the period 1970-2000 as well. But many countries that began transitions in the 1940s or later saw their gains falter in the 1980s in the face of HIV/AIDS, tuberculosis (both drug resistant and non-resistant strains), resurgent malaria, unresolved problems with fecal disease, and other communicable diseases. The steps taken toward improved survival among countries that initiated health transitions between the 1890s and the 1920s or 1930s seem to have laid a better foundation for long-run gains than did the antibiotics and vaccines deployed in the late 1940s and thereafter, or whatever other factors might be posited as having mattered (Riley, 2007). Whatever the case, in the post epidemiological transition period, one important element of the world-wide large scale health intervention was the programme of immunization. Examples of effective public health programmes, not necessarily hinging upon the national income level, exist to facilitate understanding the determinants of the changes in population health (see for example, Levine et al., 2004; Chandra, 2006). In this light, the cross-country vaccine adoption and implementation rates by diseases may be a more relevant instrument in explaining exogenous variations in life expectancy. Another source of exogenous variation in life expectancy may be caused by epidemics, wars, famines and other disastrous events. In this light, the cross-country occurrences of major conflicts may potentially have contributed to the health outcomes. Therefore, for the period 1960-2005 the alternative instruments to explain life expectancy may be divided into two categories: public health interventions measured in terms of (or proxied by) percentage of population covered under several types of vaccines (e.g. DPT, BCG, Measles.); and natural disasters (e.g. occurrence of major conflicts, drought, and floods). Post-war remarkable achievements in the world also include the agricultural (‘green’) revolution, perceived to be the outcome of the extensive research and development of high yielding and highly resistant varieties of seeds, and leading to the increased availability of

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food and nutrition across the world in general. This paper also highlights the link between increased energy intake and life expectancy; and investigates how life expectancy instrumented by calories per capita affects economic outcomes. The influences of these factors will be discussed in detail below. 3.1. IMMU#IZATIO# PROGRAMMES

Immunization is a proven tool for controlling and eliminating life-threatening infectious diseases and is estimated to avert over 2.5 million deaths each year. It has clearly defined target groups; it can be delivered effectively through outreach activities; and vaccination does not require any major lifestyle change (WHO, 2003). The Expanded Programme on Immunization (EPI) launched by the World Health Organization (WHO) in 1974 increased immunisation from five percent of all children to 80 percent in a span of thirty years (Tangermann, 2007). This was mainly possible due to coordinated efforts from a coalition of partners: governments, the United Nations Development Programme, UNICEF, development agencies, the World Bank, the Rockefeller Foundation, Medecins sans Frontières, and Rotary International.14. Since 2000, the GAVI has been very successful at re-focusing immunization activities globally. 15 For people in developing countries, successful immunisation programmes save thousands of lives, and organisations including UNICEF and the WHO are committed to making vaccines against measles, polio and other serious diseases available to as many children as possible. Immunization is one of the most cost-effective public health interventions, with demonstrated strategies that make it accessible to even the most hard-to-reach and vulnerable populations. It has eradicated smallpox, lowered the global incidence of polio by 99% since 1988 and achieved dramatic reductions in diseases such as measles, diphtheria, whooping cough (pertussis), tetanus and hepatitis B. The cost of fully immunizing a child with the six traditional EPI vaccines through routine health services were estimated to be approximately 15 USD per child in the 1980s and approximately 17 USD per child in the 1990s. Thus, with an annual birth cohort of approximately 91.4 million in low-income countries, estimates of total immunization costs in 1998 were 1.123 billion USD (GAVI, 2001). Yet in almost 50 nations, 60 percent of the children are not immunised. The result is three million children dying every year from diseases that are entirely preventable, 30 million infants having no access to basic immunisation each year, and a child in the developing world ten times more likely to die of a vaccine-preventable death than a child in an industrialised nation. National income levels may have played a minor role in this regard. The most widely used vaccination in the world is Bacille Calmette Guerin (BCG), which is made of a live, weakened strain of mycobacterium bovis (a cousin of mycobacterium tuberculosis, the TB bacteria). BCG is effective in reducing the likelihood and severity of TB in infants and young children. Developed in the 1930s it still remains the only vaccination available against tuberculosis. This vaccine is particularly important in areas of 14

However, the programs could not have been successful without the involvement of political, religious and community leaders, all of whose contribution amounted to what has been described as the greatest social mobilisation effort in peace-time. (http://www.immunisation.nhs.uk/About_Immunisation/Around_the_world /The_Expanded_Program_on_Immunization_EPI; browsed in December, 2008) 15 GAVI partners include governments in industrialized and developing countries, UNICEF, WHO, the Bill and Melinda Gates Foundation, the World Bank (WB), NGOs, foundations, vaccine manufacturers, and technical agencies such as the US Centers for Disease Control and Prevention (CDC).

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the world where TB is highly prevalent, and the chances of an infant or young child becoming exposed to an infectious case are high. In the United States or Great Britain, BCG is not widely used because TB is not prevalent and the chances are small that infants and young children will become exposed. Figure 1: Cross country changes in BCG immunization coverage and life expectancy

20

Change during 1980-2000

15

BTN EGY SLVOMN ECU PER PHL

CHL

0

5

BRA SGP LKA PNG WSM COL MYS MUS MNG BRN CRI VENTHA FRA PAN ARG URY TON GRC ALB POL FINSWE PRY FJI CUB SLB HUN BLZ GUY BGR

NPL BOL SAUNIC SYR GTM PAK MEX SDN MDG

YEM ARE

IRN MDV

HNDMMR QAT DOM

SOM JAM

-5

PRK COG

ZAR TZA

CAF

-10

Change in life expectancy

10

LBY GMB IDN

-15

LSO

-20

BWA -20

0

20

40

60

80

100

Change in BCG vaccine coverage (%)

Figure 1 plots changes in the BCG vaccine coverage (%) during the period 1980-2000 against the changes in life expectancies (years) during the same period. The fitted line suggests a positive association with countries such as Congo, Tanzania, Lesotho, North Korea, Botswana, and the Central African Republic lying in the lower panel because of the prevalence of other prominent diseases and determinants of deaths. Another important composite vaccine is the DPT vaccine, which protects against the diseases - diphtheria, pertussis, and tetanus. Five doses are commonly given to children between the ages of two months to five years old. Diphtheria vaccines are based on diphtheria toxoid, a modified bacterial toxin that induces a protective antitoxin. Diphtheria toxoid combined with tetanus and pertussis vaccines (DPT), has been part of the WHO Expanded programme on Immunization (EPI) since its inception in 1974. During the period 1980–2000, the total number of reported diphtheria cases was reduced by more than 90 percent. Following the primary immunization series, the average duration of protection is about 10 years. The DPT provides lifelong immunity, in most cases to diphtheria and pertussis, but do not provide lifelong immunity to tetanus. Tetanus vaccinations need to be repeated every 8-10 years in order to remain effective. Diphtheria is still a significant child health problem in countries with poor EPI coverage. Where EPI coverage is high and natural boosting low, as in most industrialized countries, a large proportion of the adult population is gradually rendered susceptible to diphtheria as a result of waning immunity.

11

Figure 2: Cross country changes in DPT immunization coverage and life expectancy

20

Changes during 1980-2000

15

BTN SLV

10 5 0 -5

PRK COG

MWI TZACAF

-10

Change in life expectancy

EGY

OMN YEM NPL BOL IRN NIC ECU SAU ARE MDV SYR GTM PER IND PHL JOR HND PAK CHL MMR MEX BRA SDN TUR SGP QAT BHR WSM LKA COL NZL MNG PNG KWT MYS MUS AUT ISR BRN THA ISLCRI PRT JPN VEN VCT FRA AUS DOMURY LBN BEL PAN ARG FIN SWE GBR IRLTON GHA POL GRC FJI PRY CYP ALB USA BRB CUB DNK SUR SLB NLD HUN BLZ TTO BHS GUY JAM BGR HTI LBY

GMB

-15

LSO

-20

BWA -20

0

20

40

60

80

100

Change in DPT vaccine coverage (%)

As in the case of the BCG vaccine, a similar positive relationship between life expectancy and DPT vaccine coverage is seen in Figure 2. The correlation statistic between change in life expectancy and change in DPT coverage is about 0.4. Measles is an extremely contagious viral disease that, before the widespread use of measles vaccine, affected almost every child in the world. High-risk groups for measles complications include infants and persons suffering from chronic diseases and impaired immunity, or from severe malnutrition, including vitamin A deficiency. Measles vaccine has been available since the 1960s and currently reaches about 70% of the world’s children through national childhood immunization programmes. In most industrialized countries, measles is now well controlled or even eliminated. A comprehensive immunization strategy, including strengthening of routine immunization services, periodic supplementary immunization activities (SIAs) and intensified surveillance, has also proved successful in many developing countries. However, the high infectivity of the measles virus means that a small percentage of susceptible individuals are sufficient to maintain viral circulation in populations of a few hundred thousand. In many countries, including several in Africa and Asia, national childhood immunization programme coverage remains low. These countries carry a disproportionate burden of global measles deaths, which in 2002 were estimated to number about 610,000, mostly among infants and young children. Many more individuals suffer from measles complications such as severe malnutrition (including aggravated vitamin A deficiency), deafness, blindness or damage to the central nervous system. In countries that have achieved and maintained relatively high levels of measles vaccination coverage, there has been a gradual increase in the average age of affected individuals, with more cases occurring in older children, adolescents and young adults. The live, attenuated measles vaccines that are now internationally available are safe, effective and relatively inexpensive and may be used interchangeably in immunization programmes.

12

The recommended age for measles vaccination depends on the local measles epidemiology as well as on pragmatic considerations. In most developing countries, high attack rates and serious disease among infants necessitate early vaccination, usually at nine months of age. Figure 3 plots cross-country changes in life expectancy during 1980-2000 against changes in measles immunization coverage. A steeper positive relationship, compared to Figure 1 and Figure 2, is evident. Almost the same group of countries (i.e. Congo, Tanzania, Lesotho, Botswana, Central Africa, Malawi, and North Korea) appears in the lower panel indicating poor performance on both counts. Figure 3: Cross country changes in Measles immunization coverage and life expectancy

20

Change during 1980-2000

15

BTN

CHL 5

NZL

ISR

SWE POL ALB USA HUN NLD

-5

0

BGR

SLV EGY

OMN YEM IRN BOL NIC ECU SAU KOR ARE GTM SYR PER JOR PAK HND MEX BRA SGP TUR QAT BHR COL KWT MNG DEU CRI BRN JPN PRT VEN URY DOM LBN PANARG GBR GHA PRY CYP FJI BRB CUB SOM BLZ ETH LBY

PRK

MWI ZAR TZA CAF

COG

-10

Change in life expectancy

10

GMB

-15

LSO

-20

BWA -20

0

20

40

60

80

100

Change in Measles vaccine coverage (%)

3.2. I#TER-STATE A#D I#TRA-STATE CO#FLICTS

After the Second World War numerous interstate and intrastate conflicts around the world occurred, which besides causing direct casualties and deaths during combat, also resulted in widespread death and disability among the civilian populations. 16 Such conflict also resulted in conditions that contributed to the spread of disease and the retardation of health care systems (Iqbal, 2006). Davis and Kuritsky (2002) report that severe military conflict in sub-Saharan Africa cut life expectancy by more than two years and raised infant mortality 16

The World Health Organization’s World Report on Violence and Health reveals that 1.6 million people die each year because of violence, including collective violence such as conflicts within or between states. A large number of the people who lose their lives because of militarized conflict are non-combatants. Furthermore, the 25 largest instances of conflict in the twentieth century led to the deaths of approximately 191 million people, and 60% of those deaths occurred among people who were not engaged in fighting (WHO, 2002a). Russia lost 10.1% of its population during the Second World War,; 10 percent of the Korean population died in the Korean War; and 13% of the Vietnamese population died in the Vietnam War (Garfield and Neugut, 1997).

13

by 12 per thousand. Ghobarah, Huth, and Russett (2003) conduct a cross-national analysis on the impact of conflict on public health and highlight the significant burden of death and disability. Iqbal (2006) assesses this relationship between conflicts and population health by analyzing cross-country data on summary measures of public health between 1999 and 2001 and asserts that interstate and intrastate conflict negatively influences the health achievement of states and, therefore, the human security of their populations. Moreover his analysis suggests that the negative effect of war on health is particularly intense in the short term following the onset of a conflict. Neumayer and Plümper (2006) emphasise the gender dimension of health impacts by providing the first rigorous analysis of the impact of armed conflict on female relative to male life expectancy. On average, they find that over the entire conflict period of interstate and civil wars women are more adversely affected than men. Glei et al. (2005) show how the national mortality and life expectancy estimates can be significantly underestimated for countries that experience substantial war losses in a given time period. They conducted a case study of Italy and their results indicate that estimates currently available from the Human Mortality Database (HMD) greatly underestimate period mortality during wartime among all Italian males, and may even underestimate mortality among civilian males. Their work also highlights how failing to account for war mortality presents problems in making inter-country mortality comparisons. Ghobarah et al. (2003, 2004) discusses different channels through which public health is affected by major conflicts in general, and civil wars in particular. These conflicts affect the public health of a population at several levels. Firstly, the most observable direct effect of conflict is the number of people killed and wounded. Conflicts render a large part of the population exposed to hazardous conditions caused by events such as refugee flows and movements of soldiers, giving rise to epidemics as the mobile groups act as vectors for disease. The ability of war-torn societies to deal with new threats to public health is weakened by conflict and, consequently, the negative effect on health outcomes continues to grow. During periods of conflict, resources get diverted toward military purposes, and public health spending goes down, even as the public health needs of the population escalate. This diversion of resources away from public health is often accompanied by a decline in infrastructure that further impedes the ability of a society to handle public health issues. Damage to the general infrastructure combines with damage to, or possible destruction of, the health care infrastructure to make a society incapable of facing its increasing public health challenges. Public health is affected not only by direct damage to hospitals and other medical facilities, but also by damage to elements of the larger infrastructure such as transportation, the water supply, and power grids. In addition to infrastructural damage, food shortages and possibly famines often ensue during and immediately after wars (Iqbal, 2006; Ghobarah et al., 2004). Additionally, major conflicts often induce out-migration of highly trained medical professionals, and this loss of human capital may not be reversed by their return or replacement until long after the wars end. Armed conflicts often inflict displacement of population, either internally or as refugees; and induce them to stay in crowded makeshift camps for years.17 Unhealthy food, polluted water, improper sanitation, and dismal housing turn these sites into new vectors for infectious disease (e.g. measles, acute respiratory disease, and acute diarrheal disease). People’s immune systems are drastically weakened due to malnutrition and stress. Children 17

The Rwanda civil war generated 1.4 million internally displaced persons and another 1.5 million refugees into neighboring Zaire, Tanzania, and Burundi (Ghobarah et al., 2004).

14

become especially vulnerable to infections.18 Toole’s (2000) analysis of civil wars asserts that the crude mortality rates among newly arrived refugees had been five to twelve times above the normal rate. The spread of diseases from the refugee camps to the larger segment of the population puts non-displaced populations at greater risk. Prevention and treatment programmes already weakened by the destruction of health care infrastructure during wars become overwhelmed, especially if new strains of infectious disease bloom. For example, efforts to eradicate Guinea worm, river blindness, and polio—successful in most countries-have been severely disrupted in states experiencing the most intense civil wars. Drug resistant strains of tuberculosis develop and in turn weaken resistance to other diseases. Conflicts reduce the pool of available resources for expenditures on the health care system, and deplete the human and fixed capital of the health care system. For example, heavy fighting in urban areas is likely to damage or destroy clinics, hospitals, laboratories, and health care centres, as well as water treatment and electrical systems. Rebuilding this infrastructure is unlikely to be completed quickly in the post-war period (Ghobarah et al., 2004). The Liberian civil war in 2003 may be a classic example of the way population health is affected by war conditions. Cholera, among other diseases, spread at alarming rates as internally displaced people with the disease headed towards the refugee camps outside the city. The conflict situation made it impossible for either Liberian authorities or international agencies to carry out the extensive process of water chlorination that would halt further spread of cholera (Iqbal, 2006). Furthermore, afflicted people are unable to access medical facilities because of the security situation. In September 2003, the World Health Organization reported that only 32% of the Liberian population had access to clean water, no more than 30% of the population had access to latrines, and there had been no regular garbage collection in Monrovia since 1996. The SKD Stadium, the largest camp for internally displaced people in Monrovia, housed about 45,000 people who ‘‘cook and sleep in any sheltered spot they can find, in hallways and in tiny slots under the stadium seats”, and there were six nurses in the health centre for 400 daily patients (WHO 2003). In the Sudan, two decades of conflict have exposed the population to diseases (such as yellow fever), malnutrition, displacement of large groups, poverty, and famine. The Iraqi population experienced near destruction of their health care system, previously one of the best in the Middle East, during the first Gulf War. Public health in Iraq continued on a path of steady decline during a decade of sanctions and internal repression, before the general and health infrastructures were subjected to a second war. In 1993, Iraq’s water supply was estimated at 50% of pre-war levels (Hoskins 1997), and war-related post-war civilian deaths numbered about 100,000 (Garfield and Neugut 1997). The Conflict Variable (Dummy)

To assess the presence of conflict, this paper uses data from the Peace Research Institute of Oslo (PRIO) Dataset on Armed Conflict (Gleditsch et al., 2002). These data measure conflict according to both its intensity and its type including domestic and international conflict. In the PRIO dataset the conflicts are categorised in terms of two levels of intensity: “Minor: between 25 and 999 battle-related deaths in a given year” and “War: at least 1,000 battle-related deaths in a given year”. This paper creates dummies for the conflict variable only using the second category. This gives 467 occurrences of conflicts in 59 countries during 1950-2007 periods.

18

For more please see e.g. Smallman-Raynor and Cliff (2004).

15

3.3. #ATURAL DISASTER

Another type of exogenous variation in life expectancy is provided by the occurrences of natural disasters (e.g. drought, floods, cyclones), which often led to famine and severe epidemics, and nutritional shocks to a large segment of the population. The channels through which these disastrous events impart a negative impact on life expectancy intuitively resemble those depicted earlier for the major conflicts, which include the direct effects in terms of deaths, malnourishment, reduced immune systems, crop destruction, loss of productive land and water recourses, and increased morbidity; and indirect effects of resource constraints, infrastructure destruction, stresses and social tensions. However, the potential long run impacts are also highlighted in many studies. For instance, van den Berg, Lindeboom and Portait (2007) use historical data for the period of 1845-48, which includes the Dutch potato famine, and investigate whether exposure to nutritional shocks early in life affects later-life mortality. During this period, all potato and grain crops in Europe failed due to Potato Blight and bad weather conditions. They found that men who were exposed to severe famine at least four months before birth and directly thereafter had a residual life expectancy at age 50 that was significantly lower (a few years) than otherwise, but that the mortality rate at earlier ages was not affected. Studies based on the Dutch “hunger winter” under German occupation at the end of World War II (Ravelli et al., 1998; Roseboom et al., 2001) and on China’s great famine (Meng and Qian, 2006; Chen and Zhou, 2007) indicated significant long-run effects on adult morbidity. 3.4. CALORIE PER CAPITA A#D YIELD19

The world since the second half of the twentieth century has witnessed decades of high productivity growth in crop agriculture led mainly by crop genetic improvement, including both the diffusion of existing high yielding varieties and the development of new varieties. In the face of unprecedented population increases and limited natural resources per capita, food production in most developing countries has increased over the decades, which enabled them to avert large scale hunger and malnutrition leading to premature death. The process was facilitated by the establishment of the extensive research infrastructure – the system of international agricultural research centres (IARCs) that were organized under the rubric of the Consultative Group for International Agricultural Research (CGIAR). IARCs, in collaboration of the national agricultural research systems (NARS) around the world, developed and maintained genetic resource collections (gene banks) and fostered free exchange of genetic resources between NARS and IARC programmes. Most IARC programmes developed strong breeding programmes where advanced breeding lines and finished varieties were developed. These materials were made available to NARS through international testing and exchange programmes. The initial apparent success in crop productivity was observed in wheat and rice, with shorter, early-maturing, and less photoperiod sensitive plant types. These high yielding and highly resistant new plant types led to the incipient popular concept of ‘Green Revolution’. The achievements of the international and national collaborative research initiatives may be measured in terms of the releases of new crop varieties, adoption of modern varieties, and subsequent crop yield increases. There was a steady increase of varietal releases in almost all of the crops through the 1960s into the late 1980s and 1990s. For instance, average 19

The content of this section is largely based on Evenson and Gollin (2003).

16

annual wheat releases during 1965-70 were 40.8 varieties per year, which increased to 81.2 per year during the period 1986-90. In case of rice the annual releases tripled from 19651970 to 1986-1990. There were even more pronounced increments of varietal releases for crops like maize, sorghum, millet, barley, and lentils. Similarly, there was continued adoption of modern varieties across the developing world and for all crops. For all developing countries the modern variety adoption, aggregated over all crops, reached 9 percent by 1970 and increased to 29 percent in 1980. During the subsequent two decades the rate of adoption accelerated more, reaching 46 percent in 1990, and 63 percent by 1998. Furthermore, first-generation modern varieties are increasingly been replaced by the second and third-generation modern varieties in many areas and in many crops (Evenson and Gollin, 2003). The outcome of such advancements in agricultural research and their field-level implementations is potentially manifested in the increases of crop yields and the consequent increase in the intake of calories per capita. The FAO data indicates that for all developing countries yield increased from 1980 to 2000 by 69%, 42%, 40%, 38% and 13% for wheat, rice, maize, potato, and cassava, respectively. The increments were more during the 1980-2000 periods than the first two decades of the green revolution (i.e. 1960-1980). These increases in yields were more than the population increase and translated into increased per capita energy intake (i.e. calories per capita measured in kcal/capita/day). Using a multi-market multi-country partial equilibrium model, named ‘the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT)’ developed at the International Food Policy Research Institute (IFPRI), Evenson and Gollin (2003) asserts that in the absence of major agricultural innovations (i.e. high yielding varieties of seeds for different crops) food consumption per capita would have declined significantly for many groups. For all developing countries, the average reduction in calorific availability per capita would have been 4.5-5%, and up to 7% in the poorest regions. Furthermore, approximately 2-2.3% more children (13-15 million) – predominantly located in south Asia – would have been malnourished than otherwise, and infant mortality would have been higher (Evenson and Gollin, 2003). Fogel’s (1991, 1994, 1997) analysis of the distribution of body stature (height and weight) across the population in Great Britain and France and their association with food supply and calorie intake suggests that the latter contributed to the secular decline in morbidity and mortality, and to enhancing long-term productivity. The modern agricultural innovations, therefore, may have played a major role in the relationship between diet and health. Increases in per capita food intake, which was brought about by modern agricultural research and innovations may have contributed to reducing mortality and increasing morbidity for the people of productive ages. This paper assumes the increase in yield and per capita food intake to be mainly the outcome of the collaborative international and national crop research initiatives; and therefore they are potentially exogenous. These variables are then used, firstly as instruments to explain life expectancy, and subsequently ‘yield’ as an additional control in the regression estimation in order to examine a land-augmenting growth specification. Figure 4 plots cross-country changes in energy intake (i.e. calorie per capita) and changes in life expectancy during the period 1960-2000, suggesting a positive relationship between the two, with the correlation coefficient of 0.48.

17

Figure 4: Cross country changes in calories per capita and life expectancy

Change during 1960-2000 .6

CHN

.5

GMB

Change in log of life expectancy 0 .1 .2 .3 .4

SEN

SAU IDN

NPL VNM JOR BOL

MAR SYR PAK GAB KOR MLI SDN SLV ECU MDG DJI TUR MNGCHL MRT LAOMYS KHM PHL LKA GNB MEX BEN DOM KWT BRA COL CRI SLE GHA THA TCD PANMOZ BRN AGO VEN MUS PRT JPN HTICMR CUB BFA BLZ BRB ALBNERLBN ITA SUR ESP AUT MLT CYP PRK ARG GRC NGA FIN SWZ FRA DEU AUS NAM CHE CAN TTOGUY ISR NZL USA JAM URY PRY IRLBHS COG POL SWE ROM ISL GBR NOR TZA MWINLD CIV LBR DNK HUN KEN BGR UGA COM

ZAR

BGD GIN NIC

BDI

GTMHND PER IND

TUN EGY

MMR

DZA MDV IRN CPV

-.1

ZAF RWA ZMB

LSO

-.2

BWA

ZWE

-.4

-.2

0 .2 Change in log of calorie per capita

.4

.6

4. FIRST STAGE ESTIMATES: IMPACT OF I#STRUME#TS O# LIFE EXPECTA#CY

The first stage relationship is described as in equation (8). The estimates are reported in Table 1 to Table 6. The regressions are run on different sample of countries. These are: (i) “All Countries” – for which data on all the variables are available, (ii) “WB low income countries” – referring to the World Bank (WB) country classification, (iii) “WB Lower Middle Income countries”, (iv) “Countries that began health transition after 1940” – according to the classification used by Riley (2007), (v) “Countries that began health transition between 1900 and 1940”, (vi) “Countries that began health transition before 1900”, (vii) Panel of 59 countries – the core list of countries used by Acemoglu and Johnson (2007), and (viii) Initial Middle Income and Poor countries – according to the Acemoglu and Johnson (2007) classification. While the first category includes all the countries, the second, third and the fourth category mostly includes the countries lying in the lower income ranges. The Panel of 59 countries include 11 rich countries, 22 middle income countries, and 16 initially poor countries, as classified in Acemoglu and Johnson (2007). 4.1. IMMU#IZATIO# PROGRAMMES A#D LIFE EXPECTA#CY

Table 1 provides the first stage estimates on the impact of immunization coverage on life expectancy. The three panels in the table report the main results for three of the vaccine groups: BCG, DPT, and Measles vaccines covering the period 1980-2004. The coefficients are mostly significant at less than the 5% level of significance. The corresponding Fstatistics for individual coefficients mostly complies with the rule of thumb to be qualified as a strong instrument, which is that they should be more than 10 (see, e.g. Stock and Watson, 2006). 18

The elasticity values for the BCG vaccine coverage range between 0.018 and 0.043 with an average of about 0.030. When the regression is run for the group of countries that began health transition before 1900, the coefficient is very low (0.009) and imprecise with the tvalue of only 1.21. This group includes countries are the economically developed nations mostly from the West, where the BCG vaccination coverage matters less to influence life expectancy. The elasticity values are higher for the low income groups (e.g. 0.043, 0.039, and 0.042 in columns 1, 2, and 3). In all cases the F-statistics for the log of the BCG coverage coefficient are more than 8 (except in column 6) suggesting this variable to be a potential instrument for explaining life expectancy. Relatively higher elasticity values are observed when the log of DPT coverage is used. The elasticity values range from 0.021 to 0.045 with an average value of 0.031. The coefficient is again low and imprecisely estimated from the countries that began the transition of life expectancy before the twentieth century. The coefficients in general are more significant and the corresponding F-statistics qualifies the variable to be a potential instrument. In the bottom panel, similar and even higher elasticity values are observed when the log of measles coverage is used to explain life expectancy. This variable appears to be the strongest instrument of the three used. The relatively higher elasticity values for the measles coverage is probably due to the fact that this vaccine is usually administered to most children over 9 months and that most of the children may have already been covered with DPT and BCG vaccines. Therefore, this variable captures a wider impact of immunization. This is further clarified with the estimates presented in Table 2. The first panel uses all the vaccine coverage as explanatory variables in the regression specification. The resulting coefficients become statistically insignificant for most samples, despite the coefficients jointly being significant in terms of the model fit (F-Statistics). Also, the elasticity values appear to be unpredictable. This indicates the collinear relationship between the variables. The bottom panel uses only DPT and measles as explanatory variables, which then provide better estimates, although in many cases the elasticity values are statistically insignificant. Therefore, in the two-stage least squares estimates used to estimate the impact of life expectancy on the major macrovariables (i.e. population, GDP, and GDP per capita), the immunization variables are used individually as instruments. 4.2. MAJOR CO#FLICTS A#D LIFE EXPECTA#CY

Table 3 presents the first stage relationship between life expectancy and the occurrence of major conflicts between 1950 and 2005. There are three panels in the table: the first panel uses yearly cross-country data on the life expectancy from the World Development Indicators by the World Bank; the second (middle) panel uses data on the life expectancy as found in the Demographic Year Book (2005). The reason for this was primarily due to the fact that data frequency and data points differ between these two sources. The bottom panel uses 5-yearly interval data on life expectancy from the World Development Indicators (2007) and the conflict dummy. The conflict dummy in this later case is different from the one used in the yearly data – in this case a country would carry a value of 1 if it faced at least one conflict in the preceding 4 years. For instance, the conflict occurrence dummy for country ‘X’ will have a value of 1 in time ‘t’ if it suffers from at least one conflict during t-4 to t. In doing so it assumes a lagged effect of wars and conflicts along with the contemporary impacts. In each country group, the regression is applied only for the countries that faced at least one major conflict during 1950-2005.

19

A negative relationship between conflicts and life expectancy is evident from Table 3. The mean coefficients in the three panels are -1.28, -1.69, and -1.37 respectively, which indicates that occurrence of conflicts are associated with the reduction of life expectancy (in units of year). The coefficients for five of the country groups are insignificant at the 5% level, but the coefficients are statistically significant when the regression is run with all the countries and with WB low income countries. Using the life expectancy data from the Demographic Year Book provided more statistically significant coefficients for these two groups. The associated F-statistics indicate that occurrence of major conflict may not be a strong instrument, since the values lie below 10 (see, Stock and Watson, 2006). Nevertheless, this variable is used in the 2SLS estimates as a robustness check. 4.3. #ATURAL DISASTERS A#D LIFE EXPECTA#CY

Table 4 reports the impact of natural disasters on life expectancy for different samples of countries. After controlling for country and time fixed effects none of the coefficients is statistically significant, suggesting no impact of floods and drought occurrence on the outcome of national level life expectancy. Therefore, natural disaster variables are dropped off the instrument list in the 2SLS estimates. 4.4. LIFE EXPECTA#CY I#STRUME#TED BY CALORIE PER CAPITA A#D YIELD

Table 5 shows the first stage calorie per capita - life expectancy and yield – life expectancy relationship. Controlling for the fixed country and time effects, the estimates show a very significant and robust relationship. A 1 percent increase in calories per capita increases life expectancy in the range of 0.16 – 0.28 percent, with a mean elasticity value of 0.24. The tvalues indicate that the coefficients are significant at the 1% level even when the standard errors are robust, and that corresponding F-statistics are large enough to safely consider this variable as an instrument. The bottom panel in Table 5 shows the impact of increases in yield on life expectancy. The yield variable here is the average of the yields (kg/hectare) of the ten major crops weighted by the area harvested for the corresponding crops. The elasticity values range from 0.03 to 0.07 and half of them could not be estimated precisely as evident from the robust standard errors and the t-values. Therefore, the subsequent instrumental variable estimates capturing the impact of life expectancy increases on population, GDP, and per capita income use calories per capita as the instrument, along with the immunization and conflict variables. 4.5. LIFE EXPECTA#CY I#STRUME#TED BY CALORIE PER CAPITA A#D IMMU#IZATIO# COVERAGE

Table 6 reports the first stage relationship while more than one instrument is used to explain life expectancy. The upper panel uses DPT coverage and calorie per capita as instruments and the lower panel uses measles and calorie per capita as instruments. The associated t-values, joint tests of significance (F-statistics) indicate that these instruments may perform very well for many of the country groups even when used in combinations.

20

Table 1: First Stage Estimates: Life Expectancy and Immunization Coverage 1

2

3

4

5

6

7

8

All Countries

WB Low income countries

WB Lower Middle Income countries

Countries that began health transition after 1940

Countries that began health transition between 1900 and 1940

Countries that began health transition before 1900

Panel of 59 countries

Initial Middle Income and Poor countries

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

0.009 0.007 1.21 1.46 67 14

0.018 0.004 4.21 17.74 253 45

0.02 0.003 6.27 39.37 200 35

0.04 0.014 2.82 7.98 282 56

Dependent Variable: Log Life Expectancy 0.039 0.021 0.011 0.007 3.33 3.08 11.10 9.48 367 253 70 46

0.008 0.005 1.42 2.02 162 30

0.024 0.004 5.93 35.21 334 59

0.021 0.004 5.75 33.06 221 28

0.04 0.015 2.74 7.51 273 56

Dependent Variable: Log Life Expectancy 0.058 0.019 0.016 0.005 3.60 4.10 12.97 16.77 361 244 70 46

0.013 0.006 2.03 4.13 154 30

0.017 0.004 4.21 17.72 320 59

0.013 0.004 3.67 13.46 210 38

Dependent Variable: Log Life Expectancy Log BCG Standard error (Robust) t-value F-statistics Number of observation Number of countries Log DPT Standard error (Robust) t-value F-statistics Number of observation Number of countries Log Measles Standard error (Robust) t-value F-statistics Number of observation Number of countries

0.029 0.007 4.29 18.40 776 158 0.028 0.006 5.24 27.44 934 183 0.03 0.006 5.28 27.83 906 183

0.043 0.013 3.36 11.29 262 53 0.045 0.012 3.69 13.62 263 53 0.046 0.014 3.38 11.44 257 53

0.039 0.013 2.97 8.83 272 55

0.042 0.015 2.85 8.14 351 67

0.02 0.007 2.93 8.57 232 44

Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported.

21

Table 2: First Stage Estimates: Life Expectancy and Immunization Coverage (Combined Effect) 1

2

3

4

5

6

7

8

All Countries

WB Low income countries

WB Lower Middle Income countries

Countries that began health transition after 1940

Countries that began health transition between 1900 and 1940

Countries that began health transition before 1900

Panel of 59 countries

Initial Middle Income and Poor countries

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

Dependent Variable: Log Life Expectancy Log BCG t-value

0.001 0.13

-0.035 -0.95

0.03 2.35

0.013 0.86

-0.019 -0.57

0.006 1.69

0.004 0.33

0.007 0.92

Log DPT t-value

0.02 2.27

0.05 2.62

0.02 0.94

0.02 1.87

0.02 0.91

0.01 1.25

0.02 2.48

0.02 2.40

Log Measles t-value

0.02 2.08

0.03 1.33

0.22 1.74

0.04 2.28

0.02 0.99

0.03 2.59

0.00 0.05

0.00 -0.57

Joint test of significance Number of observation

9.07 750

4.67 251

4.12 263

4.41 339

4.56 224

4.46 64

8.19 242

14.78 189

Number of countries

158

53

55

67

44

14

45

35

Dependent Variable: Log Life Expectancy Log DPT t-value

0.018 2.19

0.037 2.44

0.028 1.69

0.021 1.95

0.011 0.75

0.001 0.11

0.018 2.18

0.018 2.71

Log Measles t-value

0.02 2.50

0.02 1.41

0.02 1.74

0.04 2.76

0.01 1.14

0.01 2.27

0.01 0.95

0.00 0.37

Joint test

16.28

7.48

4.46

7.23

7.98

2.67

10.96

9.30

Number of observation

901

254

273

358

243

153

318

209

Number of countries

183

53

56

70

46

30

59

38

Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported.

22

Table 3: First Stage Estimates: Life Expectancy and Major Conflicts 1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Panel of 59 countries

7 Initial Middle Income and Poor countries

1950-2005

1950-2005

1950-2005

1950-2005

1950-2005

1950-2005

1950-2005

-0.871 0.665 -1.31 1.71 503 25

-0.853 0.437 -1.95 3.80 348 17

-1.269 0.491 -2.58 6.68 266 25

-0.589 0.553 -1.07 1.14 186 17

-0.963 0.8 -1.21 1.45 200 25

-1.197 1.3 -0.92 0.85 137 17

Log Life Expectancy (World Development Indicator data) Conflict Dummy Standard error (Robust) t-value F-statistics Number of observation Number of countries

-1.43 0.608 -2.35 5.53 1295 64

-2.196 1.075 -2.04 4.17 527 28

-0.496 0.595 -0.83 0.69 488 26

-1.165 0.69 -1.69 2.85 617 33

-1.916 1.077 -1.78 3.16 479 23

Log Life Expectancy (Demographic Yearbook Data) Conflict Dummy Standard error (Robust) t-value F-statistics Number of observation Number of countries

-2.182 0.616 -3.55 12.61 658 63

-2.886 1.054 -2.74 7.50 262 28

-0.618 0.559 -1.11 1.22 268 25

-1.396 0.82 -1.70 2.90 297 32

-2.879 1.096 -2.63 6.90 251 23

Log Life Expectancy (5-year interval panel) Conflict Dummy Standard error (Robust) t-value F-statistics Number of observation Number of countries

-1.606 0.642 -2.50 6.26 501 64

-2.157 0.766 -2.82 7.94 222 28

-0.943 1.14 -0.83 0.68 194 26

-0.99 0.505 -1.96 3.85 261 33

-1.721 1.242 -1.39 1.92 179 23

Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported.

23

Table 4: First Stage Estimates: Life Expectancy and #atural Disasters All Countries

WB Low income countries

WB Lower Middle Income countries

Countries that began health transition after 1940

Countries that began health transition between 1900 and 1940

Countries that began health transition before 1900

Panel of 59 countries

Initial Middle Income and Poor countries

1980-2000

1980-2000

1980-2000

1980-2000

1980-2000

1980-2000

1980-2000

1980-2000

Dependent Variable: Log Life Expectancy Dummy for Flood Occurrence Standard error (Robust) t-value Number of observation Number of countries

-0.346 0.242 -1.43 2711 189

-0.456 0.428 -1.07 574 52

0.01 0.433 0.02 721 56

-0.163 0.521 -0.31 819 69

-0.363 0.408 -0.89 665 46

-0.346 0.302 -1.15 672 30

0.124 0.158 0.79 964 59

-0.026 0.154 -0.17 594 38

1970-2000

1970-2000

1970-2000

1970-2000

1970-2000

1970-2000

1970-2000

1970-2000

0.062 0.238 0.26 450 32

0.063 0.304 0.21 297 21

Dependent Variable: Log Life Expectancy Dummy for Drought Occurrence Standard error (Robust) t-value Number of observation Number of countries

-0.086 0.41 -0.21 1366 108

-1.139 0.73 -1.56 236 29

-0.187 0.417 -0.45 390 36

0.364 0.649 0.56 417 42

-1.308 0.717 -1.82 329 26

0.236 0.325 0.73 386 19

Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported.

24

Table 5: First Stage Estimates: Life Expectancy Agricultural Revolution 1

2

3

4

5

6

7

8

All Countries

WB Low income countries

WB Lower Middle Income countries

Countries that began health transition after 1940

Countries that began health transition between 1900 and 1940

Countries that began health transition before 1900

Panel of 59 countries

Initial Middle Income and Poor countries

1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

Dependent Variable: Log Life Expectancy

Log Calorie Per Capita

0.245

0.239

0.25

0.213

0.281

0.161

0.298

0.22

Standard error (Robust) t-value F-statistics Number of observation Number of countries

0.043 5.72 32.72 934 129

0.07 3.43 11.77 273 39

0.062 4.04 16.30 268 38

0.06 3.56 12.68 378 54

0.068 4.13 17.03 285 40

0.069 3.34 11.16 210 25

0.656 4.55 20.67 421 56

0.098 2.25 5.07 272 37

Dependent Variable: Log Life Expectancy

Log Yield

0.032

0.03

0.037

0.038

0.063

0.031

0.043

0.069

Standard error (Robust) t-value F-statistics Number of observation Number of countries

0.012 2.59 6.72 1154 181

0.027 1.09 1.19 334 51

0.019 1.90 3.61 319 54

0.019 2.02 4.09 458 68

0.021 3.05 9.31 313 47

0.011 2.77 7.69 215 28

0.032 1.36 1.85 433 58

0.042 1.67 2.78 278 38

Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported.

25

Table 6: First Stage Estimates: Instruments Used in Combinations 1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Countries that began health transition before 1900

7 Panel of 59 countries

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

8 Initial Middle Income and Poor countries 1980-2004

Dependent Variable: Log Life Expectancy

Log DPT

0.020

0.028

0.035

0.030

0.015

0.008

0.021

0.018

Standard Error t-value

0.050 4.050

0.011 2.490

0.013 2.610

0.012 2.490

0.007 2.080

0.005 1.640

0.004 4.950

0.003 5.100

Log Calorie per capita

0.167

0.241

0.127

0.243

0.082

-0.026

0.059

0.033

Standard Error t-value Number of observation Number of countries Joint test (F-statistics) for log DPT & log Calorie/capita

0.052 3.220 578 128 14.82

0.085 2.840 161 39 7.50

0.660 1.910 178 38 7.75

0.081 3.000 230 54 7.65

0.040 2.030 189 40 4.66

0.030 -0.800 117 25 1.36

0.040 1.400 262 56 16.85

0.034 0.950 178 37 16.61

Dependent Variable: Log Life Expectancy

Log Measles

0.025

0.033

0.054

0.057

0.014

0.015

0.015

0.011

Standard Error t-value

0.005 4.990

0.010 3.250

0.017 3.190

0.014 4.150

0.004 3.510

0.006 2.370

0.004 4.010

0.003 3.390

Log Calorie per capita

0.172

0.253

0.126

0.271

0.074

-0.031

0.066

0.034

Standard Error t-value Number of observation Number of countries Joint test (F-statistics) for log Measles & log Calorie/capita

0.053 3.23 555 128 16.79

0.082 3.100 156 39 11.76

0.060 2.030 171 38 9.14

0.081 3.250 225 54 12.10

0.039 1.900 180 40 8.59

0.030 -1.040 110 25 3.00

0.049 1.350 249 56 9.98

0.044 0.770 167 37 7.45

Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported.

26

5. MAI# RESULTS: 2SLS ESTIMATES

This section presents the two stage least square estimates of the impact of life expectancy on the three major macro variables: Population, GDP, and per capita GDP. The life expectancy variable is instrumented using different instruments separately and in combination. All the resulting elasticities are reported to assess the potential impact of life expectancy on the variable of interest. Table 7, Table 8, and Table 9 report the results, where each table consists of eight panels and therefore continues in multiple pages to accommodate large volume of results. When more than one instrument is used (i.e. panel 7 and panel 8 in Table 7, Table 8, and Table 9) four additional statistics are reported: Kleinbergen-Paap LM Statistics of under-identification test; Kleinbergen-Paap Wald Fstatistic of weak identification test; Hansen J-statistics; and p-value associated with the Hansen J-Statistic.20 5.1. LIFE EXPECTA#CY A#D POPULATIO#

The instrumented life expectancy by the predicted mortality instrument in Acemoglu and Johnson (2007) suggests that there is a large, and relatively precise and robust effect of life expectancy on population. However, we do not observe similar result using alternative instruments, timelines, and country classifications reported in Table 7. Large elasticity values are observed in column 1 where the regression is run on all the countries for which data are available. One percent increase in life expectancy leads to the increase of population in the range of 1.06 to 2.53 while using immunization coverage and calorie per capita, individually and in combinations, as instruments. But this is not the case when we use major conflict as the instrument for life expectancy. The impact of life expectancy on population cannot be measured precisely. The list of countries in this case includes only 60 countries that faced at least one major conflict. Although in column 3 (WB Lower Middle Income countries), column 5 (countries that began health transition between 1900 and 1940), column 6 (countries that began health transition before 1900, column 7 (panel of 59 countries), and column 8 (initial middle and poor countries) the elasticity values are high while using immunization and calorie per capita as instruments, most of them are statistically insignificant. Strikingly, for the WB low income countries (column 2) and the countries that health began health transition began after 1940, no discernible impact of life expectancy on population is observed whatever instrument is used. To summarize, elastic population response due to increase in life expectancy is observed when immunization programme or calorie intake is used as instruments, with most of them imprecisely measured; the population response is absent while running the regression for low income countries; and using the conflict dummy as instrument tends to produce negative impact of life expectancy on population, but all the coefficients are statistically insignificant. Therefore, the elastic response of population to the increase of life expectancy is not obvious, as found in Acemoglu and Johnson (2007). 20

The Hansen J-statistics are reported by partialling out the country-clusters due to the existence of a rankdeficient estimate of the covariance-matrix of orthogonality conditions. This is common when the ‘cluster’ option is used, as well as, in the presence of singleton dummy variables. However, according to the Frisch– Waugh–Lovell (FWL) theorem (Frisch and Waugh, 1933; Lovell, 1963) the coefficients estimated for a regression in which some exogenous regressors are partialled out from the dependent variable, the endogenous regressors, the other exogenous regressors, and the excluded instruments will be the same as the coefficients estimated for the original model (Baum et al., 2007).

27

Table 7: 2SLS Estimates: Impact of Life Expectancy on Population 1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Countries that began health transition before 1900

7 Panel of 59 countries

8 Initial Middle Income and Poor countries

Page 1 of 3 for Table 7 Dependent Variable: Log Population Panel 1: Life expectancy instrumented by BCG Vaccine Coverage 1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

Log of life expectancy

2.467

-0.156

1.995

0.772

2.813

4.860

1.670

1.769

Standard error (Robust) t-value Number of observation Number of countries

1.153 2.140 760 155

0.440 -0.360 251 51

1.205 1.660 267 54

0.841 0.920 345 66

2.209 1.270 232 44

3.106 1.560 67 14

1.036 1.610 253 45

1.077 1.640 200 35

Dependent Variable: Log Population Panel 2: Life expectancy instrumented by DPT Vaccine Coverage

Log of life expectancy

2.536

0.137

2.586

-0.083

3.351

3.445

2.405

1.934

Standard error (Robust) t-value Number of observation Number of countries

0.761 3.330 919 180

0.364 0.380 253 51

1.400 1.830 277 55

0.443 -0.190 362 69

2.073 1.620 253 46

5.140 0.670 162 30

0.812 2.960 334 59

1.012 1.910 221 38

Dependent Variable: Log Population Panel 3: Life expectancy instrumented by Measles Vaccine Coverage

Log of life expectancy

1.815

-0.001

1.651

0.221

1.969

0.707

1.890

1.241

Standard error (Robust) t-value Number of observation Number of countries

0.652 2.790 890 180

0.324 0.000 246 51

1.213 1.360 268 55

0.248 0.890 335 69

1.148 1.720 244 46

3.620 0.200 154 30

0.870 2.160 320 59

1.035 1.200 210 38

28

1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Countries that began health transition before 1900

7 Panel of 59 countries

8 Initial Middle Income and Poor countries

1950-2004

1950-2004

Page 2 of 3 for Table 7 Dependent Variable: Log Population Panel 4: Life expectancy instrumented by Major Conflict Dummy 1950-2004

1950-2004

1950-2004

1950-2004

1950-2004

1950-2004

Log of life expectancy

0.058

-0.242

1.224

0.069

0.530

-1.109

-1.102

Standard error (Robust) t-value Number of observation Number of countries

0.601 0.100 1153 60

0.702 -0.350 443 25

2.223 0.550 461 26

1.090 0.060 531 30

0.581 0.920 452 23

1.439 -0.770 474 25

1.557 -0.710 326 17

Dependent Variable: Log Population Panel 5: Life expectancy instrumented by Major Conflict Dummy and using 5-yearly data

Log of life expectancy

-0.549

0.835

-1.893

0.690

-1.206

-2.670

-2.032

Standard error (Robust) t-value Number of observation Number of countries

0.984 -0.560 469 60

0.716 1.170 198 25

3.303 -0.570 194 26

1.372 0.500 237 30

1.336 -0.900 179 23

2.914 -0.920 200 25

3.268 -0.620 137 17

1960-2000

1960-2000

1960-2000

Dependent Variable: Log Population Panel 6: Life expectancy instrumented by Calorie per capita 1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

Log of life expectancy

0.678

0.218

0.057

0.179

0.558

4.212

1.571

0.660

Standard error (Robust) t-value Number of observation Number of countries

0.446 1.520 849 116

0.481 0.450 266 38

0.969 0.060 247 35

0.624 0.290 371 53

0.755 0.740 271 38

1.502 2.800 195 23

0.560 2.810 421 56

0.938 0.700 272 37

29

1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Countries that began health transition before 1900

7 Panel of 59 countries

8 Initial Middle Income and Poor countries

Page 3 of 3 for Table 7 Dependent Variable: Log Population Panel 7: Life expectancy instrumented by DPT vaccine coverage and Calorie per capita 1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

-0.102

2.750

0.985

1.693

1.532

Log of life expectancy

1.327

0.249

1.652

Standard error (Robust) t-value Number of observation Number of countries Under Identification LM Test* Weak Identification test** Hansen J-test Statistics *** p-value, Hansen J-test

0.671 1.980 521 116 17.79 9.36 5.064 0.024

0.345 0.720 157 38 8.73 4.49 1.91 0.17

1.292 1.280 163 35 6.494 5.98 0.00 0.995

0.351 1.751 3.913 0.707 -0.290 1.570 0.250 2.390 226 179 107 262 53 38 23 56 10.19 6.967 3.93 13.88 4.72 3.473 1.155 13.21 0.095 0.625 0.878 2.84 0.758 0.43 0.35 0.092 Dependent Variable: Log Population Panel 8: Life expectancy instrumented by Measles vaccine coverage and Calorie per capita

0.948 1.620 178 37 8.809 13.12 2.55 0.11

Log of life expectancy

1.057

0.146

1.243

1.275

0.899

0.748 1.700 249 56 9.82 7.72 1.60 0.21

0.909 0.990 167 37 5.11 5.77 1.35 0.25

0.054

1.944

-0.100

Standard error (Robust) 0.591 0.266 1.178 0.238 1.380 2.725 t-value 1.790 0.550 1.050 0.230 1.410 -0.040 Number of observation 502 151 159 220 172 101 Number of countries 116 38 35 53 38 23 Under Identification LM Test* 19.69 8.92 6.13 12.36 7.46 6.153 Weak Identification test** 11.36 6.097 6.92 7.11 6.53 2.37 Hansen J-test Statistics *** 1.12 6.465 0.06 0.024 0.002 0.80 p-value, Hansen J-test 0.29 0.011 0.81 0.87 0.967 0.372 Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported. * Kleinbergen-Paap LM statistic; ** Kleinbergen-Paap Wald F statistic; ***Partialling out the country dummies

30

5.2. LIFE EXPECTA#CY A#D GDP

The 2SLS estimates for the impact of life expectancy on GDP are remarkably different from those that we observe in Acemoglu and Johnson (2007). The general finding in Acemoglu and Johnson (2007) suggests an inelastic or negative impact of life expectancy on GDP, or at least a smaller impact of life expectancy on GDP than on population. Furthermore most of the coefficients are statistically insignificant. This scenario is reversed if alternative instruments are used. Table 8 presents the impact of life expectancy on GDP using different instruments, different country groups, and varying timelines. In most of the cases the GDP response is highly elastic and in many cases the coefficients are precisely estimated with small robust standard errors. The exceptions are observed only when the regressions are run on country groups comprised of mostly developed nations. For example, in column 1 of Table 8, when the regression is run using all country data the elasticity values are 1.06, 2.02, and 2.22 when immunization coverage is used as an instrument; 4.72, 8.44, and 4.67 when major conflict is used as an instrument (and applied only to countries affected by major conflict); 4.67 with calories per capita as an instrument for life expectancy; 3.51 and 3.72 when life expectancy is instrumented by both calorie per capita and immunization coverage. All of the coefficients are significant except the first one. The estimated impact of life expectancy on GDP is also robust and significant for the WB low-income countries. Column wise, the highest elasticity values are observed for countries that began health transitions between 1900 and 1940. These are the countries, according to Riley (2007) that continued to perform better during the second half of the twentieth century into the 1980s and 1990s. The countries that began health transition after the 1940s lost some of the life expectancy gains of the 1980s and 1990s; and lower elasticity values are observed for these countries when immunization coverage is used as an instrument.

31

Table 8: 2SLS Estimates: Impact of Life Expectancy on GDP 1 All Countries

2 WB Low income countries

Page 1 of 3 for Table 8

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Countries that began health transition before 1900

7 Panel of 59 countries

8 Initial Middle Income and Poor countries

Dependent Variable: Log GDP Panel 1: Life expectancy instrumented by BCG Vaccine Coverage 1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

1980-2004

Log of life expectancy

1.062

2.311

2.590

0.493

3.905

-0.299

2.130

1.256

Standard error (Robust) t-value Number of observation Number of countries

1.404 0.760 662 135

1.015 2.280 225 46

1.749 1.480 228 46

1.754 0.280 325 62

3.282 1.190 220 42

5.773 -0.050 67 14

2.258 0.940 253 45

2.078 0.600 200 35

Dependent Variable: Log GDP Panel 2: Life expectancy instrumented by DPT Vaccine Coverage

Log of life expectancy

2.019

2.436

0.239

1.186

5.360

6.503

0.330

0.381

Standard error (Robust) t-value Number of observation Number of countries

1.007 2.010 777 152

1.080 2.250 227 46

1.747 0.140 232 46

1.121 1.060 342 65

3.094 1.730 235 43

9.486 0.690 145 27

1.732 0.190 334 59

2.099 0.180 221 38

Dependent Variable: Log GDP Panel 3: Life expectancy instrumented by Measles Vaccine Coverage

Log of life expectancy

2.220

2.097

1.250

0.404

6.325

-2.575

3.356

5.045

Standard error (Robust) t-value Number of observation Number of countries

1.045 2.120 758 152

0.778 2.690 222 46

1.945 0.640 228 46

0.916 0.440 337 65

2.775 2.280 228 43

4.340 -0.590 138 27

2.415 1.390 320 59

3.510 1.440 210 38

32

1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Countries that began health transition before 1900

7 Panel of 59 countries

8 Initial Middle Income and Poor countries

Page 2 of 3 for Table 8 Dependent Variable: Log GDP Panel 4: Life expectancy instrumented by Major Conflict Dummy 1950-2004

1950-2004

1950-2004

1950-2004

1950-2004

1950-2004

1950-2004

Log of life expectancy

4.722

3.734

8.774

4.483

3.598

1.278

6.698

Standard error (Robust) t-value Number of observation Number of countries

2.510 1.880 959 60

1.788 2.090 388 25

8.770 1.000 386 26

2.578 1.740 480 30

4.207 0.860 375 23

4.048 0.320 434 25

4.808 1.390 298 17

Dependent Variable: Log GDP Panel 5: Life expectancy instrumented by Major Conflict Dummy and using 5-yearly data

Log of life expectancy

8.436

6.159

7.187

7.243

4.426

5.500

9.165

Standard error (Robust) t-value Number of observation Number of countries

4.274 1.970 437 60

2.852 2.160 193 25

6.522 1.100 179 26

5.028 1.440 237 30

2.707 1.630 168 23

5.810 0.950 200 25

13.160 0.700 137 17

Dependent Variable: Log GDP Panel 6: Life expectancy instrumented by Calorie per capita 1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

Log of life expectancy

4.669

3.328

4.369

4.092

4.957

8.919

3.684

5.015

Standard error (Robust) t-value Number of observation Number of countries

1.022 4.570 849 116

1.465 2.270 266 38

1.647 2.650 247 35

1.638 2.500 371 53

1.393 3.560 271 38

3.756 2.370 195 23

0.975 3.780 421 56

2.484 2.020 272 37

33

1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

5 Countries that began health transition between 1900 and 1940

6 Countries that began health transition before 1900

7 Panel of 59 countries

8 Initial Middle Income and Poor countries

Page 3 of 3 for Table 8 Dependent Variable: Log GDP Panel 7: Life expectancy instrumented by DPT vaccine coverage and Calorie per capita

Log of life expectancy

1980-2000 3.511

1980-2000 2.774

1980-2000 2.283

Standard error (Robust) t-value Number of observation Number of countries Under Identification LM Test* Weak Identification test** Hansen J-test Statistics Hansen J-test p-value

1.191 2.950 521 116 17.79 9.37 5.06 0.03

1.209 2.300 157 38 8.73 4.49 0.497 0.48

2.010 1.130 163 35 6.49 5.98 2.20 0.14

Log of life expectancy

3.723

2.533

3.236

1980-2000 1.076

1980-2000 9.249

1980-2000 3.663

1980-2000 1.728

0.739 3.760 7.129 1.556 1.460 2.460 0.510 1.110 226 179 107 262 53 38 23 56 10.19 6.97 3.93 13.88 4.72 3.47 1.16 13.214 4.57 1.16 4.84 5.76 0.033 0.28 0.028 0.016 Dependent Variable: Log GDP Panel 8: Life expectancy instrumented by Measles vaccine coverage and Calorie per capita

0.803

9.385

-2.127

Standard error (Robust) 1.161 1.021 1.864 0.606 3.438 3.355 t-value 3.210 2.480 1.740 1.330 2.730 -0.630 Number of observation 502 151 159 220 172 101 Number of countries 116 38 35 53 38 23 Under Identification LM Test* 19.69 8.92 6.13 12.36 7.46 6.15 Weak Identification test** 11.36 6.097 6.92 7.11 6.53 2.37 Hansen J-test Statistics *** 1.681 0.347 1.14 3.69 1.15 3.52 p-value, Hansen J-test 0.195 0.556 0.29 0.06 0.28 0.06 Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported.

1980-2000 1.690 2.044 0.830 178 37 8.81 13.11 4.36 0.037

5.921

7.585

2.360 2.510 249 56 9.82 7.72 0.81 0.37

4.143 1.830 167 37 5.11 5.77 0.70 0.40

* Kleinbergen-Paap LM statistic; ** Kleinbergen-Paap Wald F statistic; ***Partialling out the country dummies.

34

5.3. LIFE EXPECTA#CY A#D GDP PER CAPITA

The estimated impact of life expectancy on GDP per capita is summarized in Table 9 below, and presented in detail in Table 10. The figures are the elasticity values, i.e. the coefficients of log life expectancy, after controlling for country and time fixed effects, using instrumental variables, and different country groups. The statistical significance of the coefficients (as indicated by asterisk) is based on robust standard errors. Table 9: Impact of Life Expectancy on GDP per Capita: Summary of 2SLS estimates

All Countries

WB Low income countries

Life expectancy Instrumented by

WB Lower Middle Income countries

Countries that began health transition after 1940

Countries that began health transition between 1900 and 1940

Countries that began health transition before 1900

Panel of 59 countries

Initial Middle Income and Poor countries

Impact of Life Expectancy on GDP per Capita (the elasticity values)

BCG coverage

-1.11

2.41*

1.29

-0.26

1.22

-5.16

0.46

-0.51

DPT coverage

-0.33

2.23 *

-1.62

1.28

2.03

3.93

-2.08

-1.55

Measles coverage

0.70

2.13*

-0.04

0.21

4.37*

-2.80

1.47

3.80

Conflict Dummy (1)

4.59*

3.80*

7.30

4.24

3.22

2.30

7.51

Conflict Dummy (2)

8.93*

5.28*

8.24

6.56

5.28

8.17

11.20

Calorie per capita

3.99*

3.11*

4.31*

3.91*

4.40*

4.71

2.11*

4.36*

DPT coverage and Calorie per capita

2.18*

2.53*

0.63

1.18

6.50*

2.68

0.04

0.16

Measles coverage & calorie per capita

2.67*

2.39*

1.99

0.75

7.44*

-2.03

4.65*

6.69

Note: (*) beside the values indicates that the coefficient of log life expectancy is significant at 5% level.

The estimated coefficients closely follow the pattern of the effects of life expectancy on population and GDP. We find a positive impact on GDP per capita in cases where the impact on GDP is larger (more positive) than on population. A total of 62 coefficients are reported in Table 9, out of which 44 take a value of more than one, seven coefficients lie between 0 and 1, and 11 coefficients are negative indicating that an increase in life expectancy reduces GDP per capita. However, all the inelastic and negative coefficients are statistically insignificant, whereas 22 out of 44 coefficients with an elasticity value larger than one are statistically significant at the 5% significance level. All the coefficients for the WB low income countries are significant and show that a 1% increase in life expectancy increases GDP per capita by 2.13 to 5.28%. While the pattern of impact varies across country groups and instruments, the reported coefficients suggest a large positive impact of life expectancy on GDP per capita in a large number of specifications, especially for the group of countries at the lower range of income.

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Table 10: 2SLS Estimates: Impact of Life Expectancy on GDP Per Capita 1 All Countries

2 WB Low income countries

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

1980-2004

1980-2004

1980-2004

1980-2004

Page 1 of 3 for Table 10 1980-2004

1980-2004

5 Countries that began health transition between 1900 and 1940 1980-2004

6 Countries that began health transition before 1900

7 Panel of 59 countries

8 Initial Middle Income and Poor countries

1980-2004

1980-2004

1980-2004

Dependent Variable: Log GDP Per Capita Panel 1: Life expectancy instrumented by BCG Vaccine Coverage 1980-2004 1980-2004 1980-2004 1980-2004 1980-2004

1980-2004

Log of life expectancy

-1.105

2.408

1.294

-0.255

1.220

-5.159

0.460

-0.514

Standard error (Robust) t-value Number of observation Number of countries

1.953 -0.570 662 135

1.029 2.340 225 46

1.377 0.940 228 46

2.127 -0.120 325 62

2.140 0.570 220 42

5.193 -0.990 67 14

2.279 0.200 253 45

2.032 -0.250 200 35

Dependent Variable: Log GDP Per Capita Panel 2: Life expectancy instrumented by DPT Vaccine Coverage

Log of life expectancy

-0.332

2.228

-1.618

1.281

2.030

3.925

-2.075

-1.550

Standard error (Robust) t-value Number of observation Number of countries

1.054 -0.310 777 152

0.969 2.300 227 46

1.682 -0.960 232 46

1.274 1.010 342 65

2.193 0.930 235 43

9.511 0.410 145 27

1.821 -1.140 334 59

2.027 -0.770 221 38

Dependent Variable: Log GDP Per Capita Panel 3: Life expectancy instrumented by Measles Vaccine Coverage

Log of life expectancy

0.703

2.134

-0.041

0.210

4.367

-2.798

1.466

3.803

Standard error (Robust) t-value Number of observation Number of countries

1.054 0.670 758 152

0.831 2.570 222 46

1.870 -0.020 228 46

0.909 0.230 337 65

2.254 1.940 228 43

2.224 -1.260 138 27

2.976 0.590 320 59

3.445 1.100 210 38

36

1 All Countries

2 WB Low income countries

1980-2004

1980-2004

1950-2004

1950-2004

Page 2 of 3 for Table 10

3 WB Lower Middle Income countries

5 6 7 Countries Panel of 59 Countries countries that began that began health health transition transition between 1900 before 1900 and 1940 1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 Dependent Variable: Log GDP Per Capita Panel 4: Life expectancy instrumented by Major Conflict Dummy 1950-2004

4 Countries that began health transition after 1940

1950-2004

1950-2004

1950-2004

8 Initial Middle Income and Poor countries 1980-2004

1950-2004

1950-2004

Log of life expectancy

4.593

3.801

7.302

4.237

3.216

2.299

7.514

Standard error (Robust) t-value Number of observation Number of countries

2.390 1.920 959 60

1.736 2.190 388 25

8.679 0.840 386 26

2.416 1.750 480 30

4.260 0.750 375 23

4.533 0.510 434 25

5.790 1.300 298 17

Dependent Variable: Log GDP Per Capita Panel 5: Life expectancy instrumented by Major Conflict Dummy and using 5-yearly data

Log of life expectancy

8.927

5.279

8.235

6.555

5.283

8.170

11.198

Standard error (Robust) t-value Number of observation Number of countries

4.531 1.970 437 60

2.514 2.100 193 25

6.873 1.200 179 26

4.631 1.430 237 30

3.208 1.650 168 23

8.275 0.990 200 25

16.046 0.700 137 17

1960-2000

1960-2000

1960-2000

Dependent Variable: Log GDP Per Capita Panel 6: Life expectancy instrumented by Calorie per capita 1960-2000

1960-2000

1960-2000

1960-2000

1960-2000

Log of life expectancy

3.991

3.111

4.308

3.912

4.399

4.707

2.113

4.356

Standard error (Robust) t-value Number of observation Number of countries

0.981 4.070 849 116

1.432 2.170 266 38

1.765 2.440 247 35

1.696 2.310 371 53

1.322 3.330 271 38

4.315 1.090 195 23

1.069 1.980 421 56

2.640 1.650 272 37

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1 All Countries

1980-2004 Page 3 of 3 for Table 10 1980-2000

2 WB Low income countries

5 6 7 Countries Panel of 59 Countries countries that began that began health health transition transition between 1900 before 1900 and 1940 1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 Dependent Variable: Log GDP Per Capita Panel 7: Life expectancy instrumented by DPT vaccine coverage and Calorie per capita 1980-2000

3 WB Lower Middle Income countries

4 Countries that began health transition after 1940

8 Initial Middle Income and Poor countries 1980-2004

1980-2000

1980-2000

1980-2000

1980-2000

1980-2000

1980-2000

1.177

6.499

2.677

0.036

0.157

Log of life expectancy

2.184

2.525

0.629

Standard error (Robust) t-value Number of observation Number of countries Under Identification LM Test* Weak Identification test** Hansen J-test Statistics *** p-value, Hansen J-test

0.962 2.270 521 116 17.79 9.37 11.54 0.00

1.017 2.480 157 38 8.73 4.49 0.05 0.83

1.334 0.470 163 35 6.49 5.98 3.58 0.06

0.850 2.687 7.733 1.478 1.380 2.420 0.350 0.020 226 179 107 262 53 38 23 56 10.19 6.97 3.93 13.88 4.72 3.47 1.16 13.21 3.41 2.75 4.66 6.79 0.07 0.097 0.031 0.01 Dependent Variable: Log GDP Per Capita Panel 8: Life expectancy instrumented by Measles vaccine coverage and Calorie per capita

1.887 0.080 178 37 8.81 13.12 5.17 0.023

Log of life expectancy

2.667

2.387

1.991

4.646

6.687

2.251 2.060 249 56 9.82 7.72 1.42 0.23

4.047 1.650 167 37 5.11 5.77 1.15 0.28

0.748

7.440

-2.027

Standard error (Robust) 0.913 0.934 1.228 0.660 2.757 2.151 t-value 2.920 2.550 1.620 1.130 2.700 -0.940 Number of observation 502 151 159 220 172 101 Number of countries 116 38 35 53 38 23 Under Identification LM Test* 19.69 8.92 6.13 12.36 7.46 6.15 Weak Identification test** 11.36 6.1 6.922 7.11 6.53 2.37 Hansen J-test Statistics *** 2.90 0.02 1.38 2.84 1.32 3.71 p-value, Hansen J-test 0.09 0.90 0.24 0.09 0.25 0.05 Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are reported. * Kleinbergen-Paap LM statistic; ** Kleinbergen-Paap Wald F statistic; ***Partialling out the country dummies

38

6. CO#CLUSIO#

The objective of this paper has been to investigate the pessimistic finding of Acemoglu and Johnson (2007) with respect to the impact of life expectancy on income per capita. The endogenous relationship between life expectancy and income required finding appropriate instruments to work with the exogenous variations in life expectancy. Acemoglu and Johnson (2007) used the predicted mortality instrument based on the international epidemiological transition that occurred during 1940s through 1960s. This paper highlights various potential alternative exogenous determinants of life expectancy and subsequently uses those as instruments to track the impact of life expectancy on income per capita. The estimates on the impact of life expectancy on population and GDP provide insights into the pattern of the corollary impact on GDP per capita. This paper finds positive income effects generated by rising life expectancy. Using the predicted mortality instrument Acemoglu and Johnson (2007) find a robust and large impact of life expectancy on population; a relatively smaller and non-robust impact on GDP; and an insignificant or negative impact on GDP per capita. Conversely, using alternative instruments, similar and different country groups, and varying timelines, this paper demonstrates remarkably different results. The impact of life expectancy on population in general is relatively weaker or insignificant in many cases; the impact on GDP is much larger and significant in quite a few cases, with a correspondingly large positive impact on per capita GDP. This is particularly evident for the lower income countries. What came out of this exercise is that the pessimistic conclusion about the role of life expectancy in raising per capita GDP, as shown by Acemoglu and Johnson (2007) is not robust, and requires caution.

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