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.
1
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.
2
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.
3
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
5
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.
4
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
5
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.
6
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.
6
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
7
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.
8
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
9
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).
10
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.
35
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
37
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.
39
REFERE#CES
Acemoglu, D., and Johnson, S. (2007). Disease and Development: The Effect of Life Expectancy
on Economic Growth. Journal of Political Economy, December, Vol. 115, No. 6: pp. 925-985.
Barro, R. (1996). Health and Economic Growth. Paper Prepared for the Pan American Health
Organization, under Contract CSA-116-96 of August 02 1996, PAHO, Washington DC.
Barro, R., and Lee J. (1994). Sources of Economic Growth. Carnegie-Rochester Conference Series
on Public Policy, 40, 1–46.
Barro, R., and Sala-I-Martin, X. (1995). Economic Growth. New York, McGraw-Hill.
Baum C. F., Schaffer, M. E., and Steven S. (2007). Enhanced Routines for Instrumental variables/
GMM Estimation and Testing. Boston College Economics Working Paper no 667.
Behrman, J. R., and Rosenzweig, M. R. (2001). The Returns to Increasing Body Weight. PIER
Working Paper 01-052, Penn Institute for Economic Research, University of Pennsylvania.
Bhargava, A., Jamison, D., Lau, L., and Murray, C. (2001). Modeling the effects of health on
economic growth. Journal of Health Economics, 20(3), 423–440.
Bleakley, H. (2006). Disease and Development: Comments on Acemoglu and Johnson (2006).
Remarks Delivered at the NBER Summer Institute on Economic Fluctuations and Growth, July
16, 2006.
Bleakley, H. (2006a). Malaria in the Americas: A Retrospective Analysis of Childhood Exposure.
Documento CEDE 2006-35, Universidad de los Andes, Bogota, September.
Bleakley, H. (2007). Disease and Development: Evidence from Hookworm Eradication in the
American South. The Quarterly Journal of Economics. February 122 (1), 73-117.
Bloom, D. E., and Malaney, P. (1998). Macroeconomic Consequences of the Russian Mortality
crisis. World Development, 26, 2073–2085.
Bloom, D. E., and Sachs, J. (1998). Geography, Demography, and Economic Growth in Africa.
Brookings Papers on Economic Activity, 2, 207–273.
Bloom, D. E., and Williamson, J. G. (1998). Demographic Transitions and Economic Miracles in
Emerging Asia. World Bank Economic Review, 12(3), 419–455.
Bloom, D. E., Canning D., and Fink, G. (2009). Disease and Development Revisited. NBER
Working Paper No. 15137.
Bloom, D. E., Canning, D., and Malaney, P. N. (2000). Demographic Change and Economic
Growth in Asia. Population and Development Review, 26(supp.), 257–290.
Caselli, F., Esquivel, G., and Lefort, F. (1996). Reopening the Convergence Debate: A New Look
at Cross Country Growth Empirics. Journal of Economic Growth, 1, 363–389.
Cervelatti, M., and Sunde, W. (2009). Life Expectancy and Economic Growth: The Role of
Demographic Transition, IZA Discussion Paper No. 4016.
Chandra, A. (2006). Health and Wellbeing in Udaipur and South Africa, by Anne Case and Angus
Deaton. Comment, Version: May 14, 2006.
http://www.nber.org/books_in_progress/boulders05/chandra8-18-06comment.pdf
Chen, Y., and Zhou, L. (2007). The long-term Health and Economic Consequences of the famine in
China. Journal of Health Economics 26: 659-681.
Evenson, R. E. (2001). IARC ‘Germplasm’ Effects on NARS Breeding Programs. In: Evenson, R.,
and Golin, D. (eds.). Crop Variety Improvement and its Effect on Productivity: The Impact of
International Agricultural Research. Chapter 21, Wallingford, UK: CAB International.
40
Evenson, R. E., and Gollin, D. (eds.) (2003). Crop Variety Improvement and Its Effect on
Productivity: The Impact of International Agricultural Research. Wallingford, UK: CAB
International, 2003.
Fogel, R. W. (1991). New sources and New Techniques for the Study of Secular Trends in
Nutritional Status, Health, Mortality and the Process of Aging. National Bureau of Economic
Research, Working Paper Series as Historical Factors and Long Run Growth No. 26, 1991.
Fogel, R. W. (1994). Economic Growth, Population Theory, and Physiology: The Bearing of LongTerm Processes on the Making of Economic Policy. The American Economic Review, vol. 84,
no. 3, pp. 369–95.
Fogel, R. W. (1997). New Findings on Secular Trends in Nutrition and Mortality: Some
Implications for Population Theory. In: Rosenzweig, M. R., and Stark, O. (eds.). Handbook of
population and family economics. Vol 1A. Amsterdam, Elsevier, 433–481.
Frisch, R., and Waugh, F. V. (1933). Partial time regressions as compared with individual trends.
Econometrica 1(4): 387–401.
Gallup, J., and Sachs, J. (2000). The Economic Burden of Malaria. Working Paper No. 52, Center
for International Development, Harvard University, Cambridge.
Galor, O. (2005). From Stagnation to Growth: Unified Growth Theory. In: Aghion, P., and Durlauf,
S. (eds.). Handbook of Economic Growth. Chapter 4, 71-294, Amsterdam: Elsevier, NorthHolland.
Galor, O., and Weil, D., (2000). Population, Technology, and Growth: From Malthusian Stagnation
to the Demographic Transition and Beyond. American Economic Review, 90, 806-829.
Garfield, R., and Neugut. A. I. (1997). The Human Consequences of War. In: Levy, B. S., and
Sidel, V. W (eds.). War And Public Health. New York and Oxford: Oxford University Press.
GAVI (2001). Immunize Every Child: Gavi Strategy for Sustainable Immunization Services.
http://www.unicef.org/immunization/files/immunize_every_child.pdf
Ghobarah, H., Huth, P., and Russett, B. (2003). Civil Wars Kill and Maim People Long after the
Shooting Stops. American Political Science Review, 97:189–202.
Ghobarah, H., Huth, P., and Russett, B. (2004). Comparative Public Health: The Political Economy
of Comparative Human Misery and Well-Being. International Studies Quarterly, 48:73–94.
Gleditsch, N. P., Wallensteen, P., Eriksson, M., Sollenberg, M., and Strand, H. (2002). Armed
Conflict 1946-2001: A New Dataset. Journal of Peace Research, 39(5): 615-637.
Glei, D. A., Bruzzone, S., and Caselli, G. (2005). Effects of war losses on mortality estimates for
Italy: a first attempt, Demographic Research, Volume 13, Article 15, Pages 363-388.
Hamoudi, A., and Sachs, J. (1999). Economic Consequences of Health Status: A Review of the
Evidence. Working Paper No. 30. Harvard Center for International Development, Cambridge.
Hansen, G. D. and Prescott, E. C. (2002). Malthus to Solow. American Economic Review, Vol. 92,
No. 4.
Hoskins, E. (1997). Public Health and The Persian Gulf War. In: Levy, B. S., and Sidel, V. W
(eds.). War And Public Health. New York and Oxford: Oxford University Press.
Iqbal, Z. (2006). Health and Human Security: The Public Health Impact of Violent Conflict,
International Studies Quarterly, 50, 631–649
Kalemli-Ozcan, S. (2002). Does the Mortality Decline Promote Economic Growth? Journal of
Economic Growth, 7 (December): 411-439.
Kalemli-Ozcan, S., Ryder, H. E., and Weil, D. (2000). Mortality Decline, Human Capital
Investment and Economic Growth. Journal of Development Economics. 62 (June): 1-23.
41
Levine, R., Kinder, M., and What Works Working Group (2004). Millions Saved: Proven
Successes in Global Health. Center for Global Development, Washington, D.C.
Lovell, M. (1963). Seasonal Adjustment of Economic Time Series. Journal of the American
Statistical Association, 58: 993–1010.
Meng, X., and Qian, N. (2006). The Long Run Health and Economic Consequences of Famine on
Survivors: Evidence from China’s Great Famine. IZA Discussion Papers 2471, Institute for the
Study of Labor (IZA).
Miguel, E., and Kremer, M. (2004.) Worms: Identifying Impacts on Education and Health in the
Presence of Treatment Externalities. Econometrica, Econometric Society, vol. 72(1), pages 159217, 01.
#eumayer, E., and Plümper, T. (2006). The Unequal Burden of War: The Effect of Armed Conflict
on the Gender Gap in Life Expectancy. International Organization 60(3): 723–754.
Plotkin, S.A., and Mortimer, E. A. Jr. (eds.). (1994). Vaccines. 2nd ed. Philadelphia: W.B. Saundars.
Ravelli, A. C. J. et al. (1998). Glucose tolerance in adults after prenatal exposure to famine. The
Lancet Jan 17, 1998; Vol. 351, Iss. 9097, p.173-177
Riley J. C. (2001). Rising Life Expectancy: A Global History. New York: Cambridge University
Press.
Riley J. C. (2007). Low Income, Social Growth, and Good Health: A History of Twelve Countries.
California / Milbank Books on Health and the Public.
Rivora, J. M. A. (1998). Situacion de salud en Venezuela segun las estadisticas de mortalidad 194019995, Gaceta medica de Caracas 106 (1998): 169-96.
Roseboom, T. J., van der Meulen, J. H., Osmond, C., Barker, D. J., and Bleker, O. P. (2001).
Effects of Prenatal Exposure to the Dutch Famine on Adult Disease in Later Life: An Overview.
Twin Research, 4(5):293-298.
Ruger, J. P, Bloom, D. E, Jamison, D. T., and Canning, D. (2006). Health and the Economy. In:
Merson, M. H., Black, R. E., and Mills, A. J. (eds.). International Public Health: Diseases,
Programs, Systems, and Policies. Second Edition, Boston, MA: Jones and Bartlett, Inc., pp.601647.
Schultz, T. P. (2002). Wage Gains Associated with Height as a Form of Health Human Capital.
Discussion Paper No 841, Yale University, Economic Growth Center, Yale Economic Growth
Center. http://www.econ.yale.edu/growth_pdf/cdp841.pdf
Smallman-Raynor, M. R., and Cliff, A. D. (2004). War Epidemics: An Historical Geography of
Infectious Diseases in Military Conflict and Civil Strife, 1850-2000. Oxford University Press.
Soares, R. R. (2005). Mortality Reductions, Educational Attainment and Fertility Choice. The
American Economic Review, 95 (July): 780-795.
Stock, J. H., and Watson, M. W. (2006). Introduction to Econometrics. Second edition, Pearson
Education ,Inc. Addison Wesley.
Strauss, J., and Thomas, D. (1998). Health, Nutrition, and Economic Development. Journal of
Economic Literature, 36, 766–817.
Suhrcke M., McKee, M., Arce, R. S., Tsolova, S., and Mortensen, J. (2005). The Contribution of
Health to the Economy in the European Union. Health & Consumer Protection DirectorateGeneral, European Commission, Belgium.
Tangermann, R. H., Nohynek, H., and Eggers, R. (2007). Global Control of Infectious Diseases by
Vaccination Programs. In: Schroten, H., and Wirth, S. (eds.). Pediatric Infectious Diseases
Revisited. Birkhäuser Verlag Basel/Switzerland.
42
Thomas, D., and Frankenberg E. (2002). Health, Nutrition, and Economic Prosperity: A
Microeconomic Perspective. Bulletin of the World Health Organization, 80, pp. 106-113.
Toole, M. J. (2000). Displaced Persons and War. In: Levy, B.S., and Sidel, V.W. (Eds.). War and
public health. Updated edition, pp. 197-212. Washington, DC: American Public Health
Association.
United Nations (1985). Demographic Yearbook. United Nations Statistics Division. Department of
Economic and Social Affairs.
United Nations (2005). Demographic Yearbook. United Nations Statistics Division. Department of
Economic and Social Affairs.
Van den Berg, G. J., Lindeboom, M., and Portrait, F. (2007). Long-run Effects on Longevity of a
Nutritional Shock in Early Life: The Dutch Potato Famine 1846-1847. Netspar Working paper,
August.
WDI (2006). World Development Indicator. World Bank.
WDI (2007). World Development Indicator. World Bank.
WHO (2003). Liberia Health Update: 5 September 2003. Geneva: World Health Organization.
WHO (2005). WHO Vaccine-Preventable Diseases: Monitoring System Summary. World Health
Organization . WHO IVB/2005, Geneva.
43