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Alternative Estimates of the Effect of the Increase of Life
Expectancy on Economic Growth
Muhammad Jami Husain
Keele Management SchoolKeele 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 lifeexpectancy 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. ITRODUCTIO
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
1A 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 theinitial 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); Bloomand 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 transitionled 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 todays 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 todays 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 onincome 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.
3However, the results of many micro-level studies (see e.g. Strauss and Thomas, 1998; Suhrcke et al., 2005;
Thomas and Frankenberg, 2002; Rugeret 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 ofhealth 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 AD 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 returnsto scale.
= 1)( ititititit LKHAY (1)
Where 1+ ,Kit denotes capital,Litdenotes the supply of land, and Hitis the effective
units of labour given by Hit= hitit ; 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 iit AA = ). Also, cross-country differences in human capital per person and population are assumed to be constant.
Therefore, iit hh = and iit = .
Economies face depreciation of capital at the rate )1,0( and the savings (investment)
rate of country i is constant and equal to )1,0(is . This implies that the evolution of capital
stock in country i at time twill be ititiit KYsK )1(1 +=+ . Suppose that life expectancy
changes from0it
X to1it
X and remains at this level thereafter. After population and the
capital stock have adjusted, the steady-state capital stock level will be ii
i Ys
K
= .
Substituting into (1) and taking logs we obtain a simple relationship between income per
capita, the savings rate, human capital, technology, and population:
iiii
i
ii shA
Yy log
1
1log
1log
1log
1log
1log
+
+
=
(2)
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 01 . 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 01 . Nevertheless, for many less-developed countries,
where agriculture is still important, 01 > 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
5See Galor and Weil (2000), Hansen and Prescott (2002), and Galor (2005) for models in which at differentstages 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
births), so that: itiit X = (3)
whereXit
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:
itiit XAA = and
itiit Xhh = (4)
Where iA and ih 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:
= iiitiitiitiit YsXXhXAY )(
it
iiii
it
it
X
shA
Y
log))1()((1
1
log1
1log
11log
1log
1)log(
+
+
+
+
=
Denoting itit Xx log is log life expectancy and
it
itit
Yy log , we obtain:
i
iiiiit
x
shAy
))1()((1
1
log1
1log
1log
1log
1log
1
+
+
+
+
=
(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 is small) and when life expectancy creates a substantial externality
on technology (high ) and/or encourages significant increases in human capital (high ).On the other hand, when and are small and 1 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 meansfrom each of his observations before estimating the model.
Adding an error term, the generalized version of the estimating equation becomes:
ittiitit xy +++= (7)
where y is log income per capita, i is a fixed effect capturing potential technology
differences and other time-invariant omitted effects (i.e. iA , ih , i , and iK or is ), t
incorporates time-varying factors common across all countries, andx is log life expectancy
at birth. The coefficient is the parameter of interest equal to ))1()((1
1
+
when
equation (5) applies. Including a full set of country fixed effects, the is, is important, sincethe country characteristics iA , ih , i ,and iK or is 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, it+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:
itti
I
itit uMx +++= ~~ , where IitM 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, it) equal to 0 is assumed, where itis the residual from equation (7).
Similarly in this paper, the first stage relationship using the alternative instruments is
presented in the equation below:
ittiitit uVx +++= ~~' (8)
Where 'itV includes alternative instruments like immunization coverage, occurrences of
conflicts and natural disaster dummies, used separately or in combinations. t~ represents
the time fixed effect controlling for unobserved omitted variables that changes over time
but are constant across entities; i~ captures entity fixed effect controlling for unobserved
omitted variables that differ across countries.
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3. ALTERATIVE ISTRUMETS AD TIME-LIE
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 period1940-1980 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 acrosscountries, 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 andJohnson (2007) analysis. Worldwide cross-country data for the period 1960 onwards
are relatively more accessible. One could then undertake the study with a similarmodeling 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.
6James 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 atwww.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 andpublic 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 developingworld 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 Organizations 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 withfecal 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 lifeexpectancy.
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. IMMUIZATIO 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
Frontires, and Rotary International.14. Since 2000, the GAVIhas 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 fullyimmunizing 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 isBacille 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
14However, 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 Billand Melinda Gates Foundation, the World Bank (WB), NGOs, foundations, vaccine manufacturers, andtechnical agencies such as the US Centers for Disease Control and Prevention (CDC).
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14
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 healthachievement 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 Plmper (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 isweakened 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 andimmediately 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).
Peoples immune systems are drastically weakened due to malnutrition and stress. Children
17The 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).
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15
become especially vulnerable to infections.18 Tooles (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 warsbecome overwhelmed, especially if new strains of infectious disease bloom. For example,
efforts to eradicate Guinea worm, river blindness, and poliosuccessful 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 sleepin 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, Iraqs 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.
18For more please see e.g. Smallman-Raynor and Cliff (2004).
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16
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 channelsthrough 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 aresidual 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 Chinas great famine (Meng and Qian, 2006; Chen and
Zhou, 2007) indicated significant long-run effects on adult morbidity.
3.4. CALORIE PER CAPITA AD 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, includingboth 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 andfinished 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
19The content of this section is largely based on Evenson and Gollin (2003).
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19
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 t-
value 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 lifeexpectancy. 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 DPTand 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 macro-
variables (i.e. population, GDP, and GDP per capita), the immunization variables are used
individually as instruments.
4.2. MAJOR COFLICTS AD LIFE EXPECTACY
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 paneluses 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 forthe countries that faced at least one major conflict during 1950-2005.
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20
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 thecountries 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 AD LIFE EXPECTACY
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 EXPECTACY ISTRUMETED BY CALORIE PER CAPITA AD 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 t-
values indicate that the coefficients are significant at the 1% level even when the standarderrors 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 EXPECTACY ISTRUMETED BY CALORIE PER CAPITA AD
IMMUIZATIO 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.
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Table 1: First Stage Estimates: Life Expectancy and Immunization Coverage
1 2 3 4 5
All Countries WB Low
incomecountries
WB Lower
MiddleIncome
countries
Countries that
began healthtransition after
1940
Countries that
began healthtransition between
1900 and 1940
Co
bet
be
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 1
Dependent Variable: Log Life Expectancy
Log BCG 0.029 0.043 0.039 0.042 0.02
Standard error (Robust) 0.007 0.013 0.013 0.015 0.007
t-value 4.29 3.36 2.97 2.85 2.93
F-statistics 18.40 11.29 8.83 8.14 8.57
Number of observation 776 262 272 351 232
Number of countries 158 53 55 67 44
Dependent Variable: Log Life Expectancy
Log DPT 0.028 0.045 0.04 0.039 0.021
Standard error (Robust) 0.006 0.012 0.014 0.011 0.007
t-value 5.24 3.69 2.82 3.33 3.08
F-statistics 27.44 13.62 7.98 11.10 9.48
Number of observation 934 263 282 367 253
Number of countries 183 53 56 70 46
Dependent Variable: Log Life Expectancy
Log Measles 0.03 0.046 0.04 0.058 0.019
Standard error (Robust) 0.006 0.014 0.015 0.016 0.005 t-value 5.28 3.38 2.74 3.60 4.10
F-statistics 27.83 11.44 7.51 12.97 16.77
Number of observation 906 257 273 361 244
Number of countries 183 53 56 70 46
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
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Table 2: First Stage Estimates: Life Expectancy and Immunization Coverage (Combined Effect)
1 2 3 4 5
All Countries WB Low
incomecountries
WB Lower
MiddleIncome
countries
Countries that
began healthtransition after
1940
Countries that
began healthtransition between
1900 and 1940
Coun
begtra
bef
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 198
Dependent Variable: Log Life Expectancy
Log BCG 0.001 -0.035 0.03 0.013 -0.019 0
t-value 0.13 -0.95 2.35 0.86 -0.57
Log DPT 0.02 0.05 0.02 0.02 0.02
t-value 2.27 2.62 0.94 1.87 0.91
Log Measles 0.02 0.03 0.22 0.04 0.02
t-value 2.08 1.33 1.74 2.28 0.99
Joint test of significance 9.07 4.67 4.12 4.41 4.56
Number of observation 750 251 263 339 224
Number of countries 158 53 55 67 44
Dependent Variable: Log Life Expectancy
Log DPT 0.018 0.037 0.028 0.021 0.011 0
t-value 2.19 2.44 1.69 1.95 0.75
Log Measles 0.02 0.02 0.02 0.04 0.01
t-value 2.50 1.41 1.74 2.76 1.14
Joint test 16.28 7.48 4.46 7.23 7.98
Number of observation 901 254 273 358 243
Number of countries 183 53 56 70 46
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
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Table 3: First Stage Estimates: Life Expectancy and Major Conflicts
1 2 3 4
All Countries WB Low income
countries
WB Lower
Middle Incomecountries
Countries that
began healthtransition after
1940
Countri
healthbetwee
1950-2005 1950-2005 1950-2005 1950-2005 19
Log Life Expectancy (World Development Indic
Conflict Dummy -1.43 -2.196 -0.496 -1.165 -
Standard error (Robust) 0.608 1.075 0.595 0.69
t-value -2.35 -2.04 -0.83 -1.69
F-statistics 5.53 4.17 0.69 2.85
Number of observation 1295 527 488 617
Number of countries 64 28 26 33
Log Life Expectancy (Demographic Yearbook
Conflict Dummy -2.182 -2.886 -0.618 -1.396 -
Standard error (Robust) 0.616 1.054 0.559 0.82
t-value -3.55 -2.74 -1.11 -1.70
F-statistics 12.61 7.50 1.22 2.90
Number of observation 658 262 268 297
Number of countries 63 28 25 32
Log Life Expectancy (5-year interval pan
Conflict Dummy -1.606 -2.157 -0.943 -0.99 -
Standard error (Robust) 0.642 0.766 1.14 0.505
t-value -2.50 -2.82 -0.83 -1.96
F-statistics 6.26 7.94 0.68 3.85
Number of observation 501 222 194 261
Number of countries 64 28 26 33
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
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Table 4: First Stage Estimates: Life Expectancy and atural Disasters
All Countries WB Lowincome
countries
WB LowerMiddle
Income
countries
Countriesthat began
health
transition
after 1940
Countriesthat began
health
transition
between
1900 and
1940
1980-2000 1980-2000 1980-2000 1980-2000 1980-2000
Dependent Variable: Log Life Expectan
Dummy for Flood Occurrence -0.346 -0.456 0.01 -0.163 -0.363
Standard error (Robust) 0.242 0.428 0.433 0.521 0.408
t-value -1.43 -1.07 0.02 -0.31 -0.89 Number of observation 2711 574 721 819 665
Number of countries 189 52 56 69 46
1970-2000 1970-2000 1970-2000 1970-2000 1970-2000
Dependent Variable: Log Life Expectan
Dummy for Drought Occurrence -0.086 -1.139 -0.187 0.364 -1.308
Standard error (Robust) 0.41 0.73 0.417 0.649 0.717
t-value -0.21 -1.56 -0.45 0.56 -1.82
Number of observation 1366 236 390 417 329 Number of countries 108 29 36 42 26
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
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Table 5: First Stage Estimates: Life Expectancy Agricultural Revolution
1 2 3 4 5
All Countries WB Low
incomecountries
WB Lower
MiddleIncome
countries
Countries that
began healthtransition after
1940
Countries that
began healthtransition
between 1900 and
1940
Count
begantran
befor
1960-2000 1960-2000 1960-2000 1960-2000 1960-2000 1960
Dependent Variable: Log Life Expectancy
Log Calorie Per Capita 0.245 0.239 0.25 0.213 0.281 0.
Standard error (Robust) 0.043 0.07 0.062 0.06 0.068 0.
t-value 5.72 3.43 4.04 3.56 4.13 3
F-statistics 32.72 11.77 16.30 12.68 17.03 11
Number of observation 934 273 268 378 285 2Number of countries 129 39 38 54 40 2
Dependent Variable: Log Life Expectancy
Log Yield 0.032 0.03 0.037 0.038 0.063 0.
Standard error (Robust) 0.012 0.027 0.019 0.019 0.021 0.
t-value 2.59 1.09 1.90 2.02 3.05 2
F-statistics 6.72 1.19 3.61 4.09 9.31 7
Number of observation 1154 334 319 458 313 2
Number of countries 181 51 54 68 47 2
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
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Table 6: First Stage Estimates: Instruments Used in Combinations
1 2 3 4 5 6
All
Countries
WB Low
incomecountries
WB Lower
MiddleIncome
countries
Countries that
began healthtransition after
1940
Countries that
began healthtransition
between 1900and 1940
Countrie
began htransitio
before 1
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 1980-20
Dependent Variable: Log Life Expectancy
Log DPT 0.020 0.028 0.035 0.030 0.015 0.008 Standard Error 0.050 0.011 0.013 0.012 0.007 0.005
t-value 4.050 2.490 2.610 2.490 2.080 1.640
Log Calorie per capita 0.167 0.241 0.127 0.243 0.082 -0.026 Standard Error 0.052 0.085 0.660 0.081 0.040 0.030
t-value 3.220 2.840 1.910 3.000 2.030 -0.800
Number of observation 578 161 178 230 189 117
Number of countries 128 39 38 54 40 25
Joint test (F-statistics) for log
DPT & log Calorie/capita
14.82 7.50 7.75 7.65 4.66 1.36
Dependent Variable: Log Life Expectancy
Log Measles 0.025 0.033 0.054 0.057 0.014 0.015 Standard Error 0.005 0.010 0.017 0.014 0.004 0.006
t-value 4.990 3.250 3.190 4.150 3.510 2.370
Log Calorie per capita 0.172 0.253 0.126 0.271 0.074 -0.031 Standard Error 0.053 0.082 0.060 0.081 0.039 0.030
t-value 3.23 3.100 2.030 3.250 1.900 -1.040
Number of observation 555 156 171 225 180 110
Number of countries 128 39 38 54 40 25
Joint test (F-statistics) for log
Measles & log Calorie/capita
16.79 11.76 9.14 12.10 8.59 3.00
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
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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 incombination. 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 F-
statistic of weak identification test; Hansen J-statistics; and p-value associated with the
Hansen J-Statistic.20
5.1. LIFE EXPECTACY AD 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 60countries 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).
20The Hansen J-statistics are reported by partialling out the country-clusters due to the existence of a rank-
deficient 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
WaughLovell (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, theendogenous 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).
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Table 7: 2SLS Estimates: Impact of Life Expectancy on Population
1 2 3 4 5
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
Cou
that
hea
tran
befo
Page 1 of 3 for Table 7Dependent Variable: Log Population
Panel 1: Life expectancy instrumented by BCG Vaccin
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 198
Log of life expectancy 2.467 -0.156 1.995 0.772 2.813 4.86
Standard error (Robust) 1.153 0.440 1.205 0.841 2.209 3.10
t-value 2.140 -0.360 1.660 0.920 1.270 1.56
Number of observation 760 251 267 345 232 67
Number of countries 155 51 54 66 44 14
Dependent Variable: Log Population
Panel 2: Life expectancy instrumented by DPT Vaccin
Log of life expectancy 2.536 0.137 2.586 -0.083 3.351 3.44
Standard error (Robust) 0.761 0.364 1.400 0.443 2.073 5.14
t-value 3.330 0.380 1.830 -0.190 1.620 0.67
Number of observation 919 253 277 362 253 162
Number of countries 180 51 55 69 46 30
Dependent Variable: Log PopulationPanel 3: Life expectancy instrumented by Measles Vacc
Log of life expectancy 1.815 -0.001 1.651 0.221 1.969 0.70
Standard error (Robust) 0.652 0.324 1.213 0.248 1.148 3.62
t-value 2.790 0.000 1.360 0.890 1.720 0.20
Number of observation 890 246 268 335 244 154
Number of countries 180 51 55 69 46 30
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1 2 3 4 5
All Countries WB Low
income
countries
WB Lower
Middle
Income
countries
Countries
that began
health
transition
after 1940
Countries
that began
health
transition
between 1900and 1940
Cou
that
hea
tran
befo
Page 2 of 3 for Table 7Dependent Variable: Log Population
Panel 4: Life expectancy instrumented by Major Conf
1950-2004 1950-2004 1950-2004 1950-2004 1950-2004 195
Log of life expectancy 0.058 -0.242 1.224 0.069 0.530
Standard error (Robust) 0.601 0.702 2.223 1.090 0.581
t-value 0.100 -0.350 0.550 0.060 0.920
Number of observation 1153 443 461 531 452
Number of countries 60 25 26 30 23
Dependent Variable: Log Population
Panel 5: Life expectancy instrumented by Major Conflict Dummy
Log of life expectancy -0.549 0.835 -1.893 0.690 -1.206
Standard error (Robust) 0.984 0.716 3.303 1.372 1.336
t-value -0.560 1.170 -0.570 0.500 -0.900
Number of observation 469 198 194 237 179
Number of countries 60 25 26 30 23
Dependent Variable: Log Population
Panel 6: Life expectancy instrumented by Calorie p
1960-2000 1960-2000 1960-2000 1960-2000 1960-2000 196
Log of life expectancy 0.678 0.218 0.057 0.179 0.558 4.2
Standard error (Robust) 0.446 0.481 0.969 0.624 0.755 1.50
t-value 1.520 0.450 0.060 0.290 0.740 2.80
Number of observation 849 266 247 371 271 195
Number of countries 116 38 35 53 38 23
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1 2 3 4 5
All Countries WB Low
income
countries
WB Lower
Middle
Income
countries
Countries
that began
health
transition
after 1940
Countries
that began
health
transition
between 1900and 1940
Cou
that
hea
tran
befo
Page 3 of 3 for Table 7Dependent Variable: Log Population
Panel 7: Life expectancy instrumented by DPT vaccine coverage
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 198
Log of life expectancy 1.327 0.249 1.652 -0.102 2.750 0.98
Standard error (Robust) 0.671 0.345 1.292 0.351 1.751 3.91
t-value 1.980 0.720 1.280 -0.290 1.570 0.25
Number of observation 521 157 163 226 179 107
Number of countries 116 38 35 53 38 23
Under Identification LM Test* 17.79 8.73 6.494 10.19 6.967 3.93Weak Identification test** 9.36 4.49 5.98 4.72 3.473 1.15
Hansen J-test Statistics *** 5.064 1.91 0.00 0.095 0.625 0.87
p-value, Hansen J-test 0.024 0.17 0.995 0.758 0.43 0.35
Dependent Variable: Log Population
Panel 8: Life expectancy instrumented by Measles vaccine coverag
Log of life expectancy 1.057 0.146 1.243 0.054 1.944 -0.1
Standard error (Robust) 0.591 0.266 1.178 0.238 1.380 2.72
t-value 1.790 0.550 1.050 0.230 1.410 -0.0
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 *** 1.12 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 0.37
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
* Kleinbergen-Paap LM statistic; ** Kleinbergen-Paap Wald F statistic; ***Partialling out the country dummies
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31
5.2. LIFE EXPECTACY AD 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 expectancyon 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 appliedonly 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.
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Table 8: 2SLS Estimates: Impact of Life Expectancy on GDP
1 2 3 4 5
All Countries WB Low
incomecountries
WB Lower
MiddleIncome
countries
Countries
that beganhealth
transition
after 1940
Countries
that beganhealth
transition
between 1900
and 1940
C
thh
tr
b
Page 1 of 3 for Table 8Dependent Variable: Log GDP
Panel 1: Life expectancy instrumented by BCG Vacc
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 19
Log of life expectancy 1.062 2.311 2.590 0.493 3.905 -0
Standard error (Robust) 1.404 1.015 1.749 1.754 3.282 5
t-value 0.760 2.280 1.480 0.280 1.190 -0
Number of observation 662 225 228 325 220 6
Number of countries 135 46 46 62 42 14
Dependent Variable: Log GDP
Panel 2: Life expectancy instrumented by DPT Vacc
Log of life expectancy 2.019 2.436 0.239 1.186 5.360 6
Standard error (Robust) 1.007 1.080 1.747 1.121 3.094 9
t-value 2.010 2.250 0.140 1.060 1.730 0
Number of observation 777 227 232 342 235 14
Number of countries 152 46 46 65 43 2
Dependent Variable: Log GDPPanel 3: Life expectancy instrumented by Measles Va
Log of life expectancy 2.220 2.097 1.250 0.404 6.325 -2
Standard error (Robust) 1.045 0.778 1.945 0.916 2.775 4
t-value 2.120 2.690 0.640 0.440 2.280 -0
Number of observation 758 222 228 337 228 1
Number of countries 152 46 46 65 43 2
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1 2 3 4 5
All Countries WB Low
income
countries
WB Lower
Middle
Income
countries
Countries
that began
health
transition
after 1940
Countries
that began
health
transition
between 1900and 1940
C
th
h
tr
b
Page 2 of 3 for Table 8Dependent Variable: Log GDP
Panel 4: Life expectancy instrumented by Major Con
1950-2004 1950-2004 1950-2004 1950-2004 1950-2004
Log of life expectancy 4.722 3.734 8.774 4.483 3.598
Standard error (Robust) 2.510 1.788 8.770 2.578 4.207
t-value 1.880 2.090 1.000 1.740 0.860
Number of observation 959 388 386 480 375
Number of countries 60 25 26 30 23
Dependent Variable: Log GDP
Panel 5: Life expectancy instrumented by Major Conflict Dumm
Log of life expectancy 8.436 6.159 7.187 7.243 4.426
Standard error (Robust) 4.274 2.852 6.522 5.028 2.707
t-value 1.970 2.160 1.100 1.440 1.630
Number of observation 437 193 179 237 168
Number of countries 60 25 26 30 23
Dependent Variable: Log GDP
Panel 6: Life expectancy instrumented by Calorie
1960-2000 1960-2000 1960-2000 1960-2000 1960-2000 19
Log of life expectancy 4.669 3.328 4.369 4.092 4.957 8
Standard error (Robust) 1.022 1.465 1.647 1.638 1.393 3
t-value 4.570 2.270 2.650 2.500 3.560 2
Number of observation 849 266 247 371 271 1
Number of countries 116 38 35 53 38 2
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1 2 3 4 5
All Countries WB Low
income
countries
WB Lower
Middle
Income
countries
Countries
that began
health
transition
after 1940
Countries
that began
health
transition
between 1900and 1940
C
th
h
tr
b
Page 3 of 3 for Table 8Dependent Variable: Log GDP
Panel 7: Life expectancy instrumented by DPT vaccine coverag
1980-2000 1980-2000 1980-2000 1980-2000 1980-2000 1
Log of life expectancy 3.511 2.774 2.283 1.076 9.249 3
Standard error (Robust) 1.191 1.209 2.010 0.739 3.760 7
t-value 2.950 2.300 1.130 1.460 2.460 0
Number of observation 521 157 163 226 179 1
Number of countries 116 38 35 53 38 2
Under Identification LM Test* 17.79 8.73 6.49 10.19 6.97 3
Weak Identification test** 9.37 4.49 5.98 4.72 3.47 1
Hansen J-test Statistics 5.06 0.497 2.20 4.57 1.16 4
Hansen J-test p-value 0.03 0.48 0.14 0.033 0.28 0
Dependent Variable: Log GDP
Panel 8: Life expectancy instrumented by Measles vaccine covera
Log of life expectancy 3.723 2.533 3.236 0.803 9.385 -2
Standard error (Robust) 1.161 1.021 1.864 0.606 3.438 3
t-value 3.210 2.480 1.740 1.330 2.730 -0
Number of observation 502 151 159 220 172 1
Number of countries 116 38 35 53 38 2Under Identification LM Test* 19.69 8.92 6.13 12.36 7.46 6
Weak Identification test** 11.36 6.097 6.92 7.11 6.53 2
Hansen J-test Statistics *** 1.681 0.347 1.14 3.69 1.15 3
p-value, Hansen J-test 0.195 0.556 0.29 0.06 0.28 0
Note: Regressions with a full set of year and country fixed effects. Robust standard errors, adjusted for clustering by country are
* Kleinbergen-Paap LM statistic; ** Kleinbergen-Paap Wald F statistic; ***Partialling out the country dummies.
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35
5.3. LIFE EXPECTACY AD 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. thecoefficients 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
WBLower
Middle
Incomecountries
Countriesthat began
health
transitionafter 1940
Countriesthat beganhealth
transition
between1900 and1940
Countriesthat beganhealth
transition
before1900
Panel of
59
countries
InitialMiddle
Income
and Poorcountries
Life expectancy
Instrumented by
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 andCalorie 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 theimpact 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 2 3 4 5
All Countries WB Low
incomecountries
WB Lower
MiddleIncome
countries
Countries
that beganhealth
transition
after 1940
Countries
that beganhealth
transition
between 1900
and 1940
Cou
thathea
tran
befo
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 198
Page 1 of 3 for Table 10 Dependent Variable: Log GDP Per CapitaPanel 1: Life expectancy instrumented by BCG Vaccin
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 198
Log of life expectancy -1.105 2.408 1.294 -0.255 1.220 -5.1
Standard error (Robust) 1.953 1.029 1.377 2.127 2.140 5.19
t-value -0.570 2.340 0.940 -0.120 0.570 -0.9
Number of observation 662 225 228 325 220 67
Number of countries 135 46 46 62 42 14
Dependent Variable: Log GDP Per Capita
Panel 2: Life expectancy instrumented by DPT Vaccin
Log of life expectancy -0.332 2.228 -1.618 1.281 2.030 3.92
Standard error (Robust) 1.054 0.969 1.682 1.274 2.193 9.51
t-value -0.310 2.300 -0.960 1.010 0.930 0.41
Number of observation 777 227 232 342 235 145
Number of countries 152 46 46 65 43 27
Dependent Variable: Log GDP Per CapitaPanel 3: Life expectancy instrumented by Measles Vacc
Log of life expectancy 0.703 2.134 -0.041 0.210 4.367 -2.7
Standard error (Robust) 1.054 0.831 1.870 0.909 2.254 2.22
t-value 0.670 2.570 -0.020 0.230 1.940 -1.2
Number of observation 758 222 228 337 228 138
Number of countries 152 46 46 65 43 27
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1 2 3 4 5
All Countries WB Low
income
countries
WB Lower
Middle
Income
countries
Countries
that began
health
transition
after 1940
Countries
that began
health
transition
between 1900and 1940
Cou
that
hea
tran
befo
1980-2004 1980-2004 1980-2004 1980-2004 1980-2004 198
Page 2 of 3 for Table 10 Dependent Variable: Log GDP Per