metadata: authors: 0: author: Hoque,Mohammad Mainul
1: author: King,Elizabeth M.
2: author: Montenegro,Claudio E.
3: author: Orazem,Peter F.
count: World
docna: 0: docna: Life Expectancy at Birth and Lifetime
            Education and Earnings
docty: Policy Research Working Paper
subtopic: Population & Development,Educational Sciences,Health Care Services Industry,Law and Justice Institutions,Labor Markets
teratopic: Health, Nutrition and Population,Education,Industry,Law and Development,Social Protections and Labor
abstracts: cdata!: Exploiting cross–birth cohort and
            cross-country variation from a pool of 188 household surveys
            from 111 countries, this paper measures how life expectancy
            at birth affects lifetime education and earnings. On
            average, individuals add one year of schooling for every 8.3
            years of increased life expectancy at birth. Lifetime
            earnings increase by 1.7 percent per year of added life
            expectancy at birth. The estimates imply that rising life
            expectancy at birth explains 75 percent of the increase in
            average years of schooling worldwide for birth cohorts
            between 1922 and 1987 and 38 percent of the increase in
            average gross domestic product per capita in the 20th century.
repnme: repnme: Life Expectancy at Birth and Lifetime
            Education and Earnings
display_title: Life Expectancy at Birth and
            Lifetime Education and Earnings
listing_relative_url: /research/2020/09/32449117/life-expectancy-birth-lifetime-education-earnings
topicv3: Education m1327048 470,Labor Supply m1504055 2751,Life Expectancy m1504363 2666,Mortality m1504365 3661,Health m1504661 3480
publishtoextweb_dt: 2020-09-30T00:00:00Z
historic_topic: Health, Nutrition and Population,Education,Industry,Law and Development,Social Protections and Labor
origu: Education ECA (HECED)
no_of_pages: 66
author_keys: 0: author_key: 929428
1: author_key: 93250
2: author_key: 99069
model_extractions: Policy Research Working Paper 9418 Life Expectancy at Birth and Lifetime Education and Earnings _Mohammad Mainul Hoque Elizabeth M. King Claudio E. Montenegro Peter F. Orazem_ Education Global Practice September 2020
Policy Research Working Paper 9418

# **Abstract**

Exploiting cross–birth cohort and cross-country variation from a pool of 188 household surveys from 111 countries, this paper measures how life expectancy at birth affects lifetime education and earnings. On average, individuals add one year of schooling for every 8.3 years of increased life expectancy at birth. Lifetime earnings increase by 1.7 percent per year of added life expectancy at birth. The estimates imply that rising life expectancy at birth explains 75 percent of the increase in average years of schooling worldwide for birth cohorts between 1922 and 1987 and 38 percent of the increase in average gross domestic product per capita in the 20th century.\.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at cmontenegro@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Life Expectancy at Birth and Lifetime Education and Earnings**

Mohammad Mainul Hoque mmahoq@gmail.com Bangladesh Institute of Development Studies Elizabeth M. King bethking1818@gmail.com Brookings Institution Claudio E. Montenegro cmontenegro@worldbank.org Department of Economics, University of Chile; The World Bank, and the German Development Institute.

Peter F. Orazem pfo@iastate.edu Department of Economics, Iowa State University, and IZA

**JEL Classification** : I12, I15, I21, J24, O11, O15

**Key Words** : <mark>Health, Education, Human Capital, Human Development, Life Expectancy, Return to Education</mark>

**________________________**

Corresponding author: Peter F. Orazem, 267 Heady Hall, Department of Economics, Iowa State University, Ames, IA 50011-1070. Email: pfo@iastate.edu Phone: (515) 294-8656/ FAX: (515) 294-0221.
# **Life Expectancy at Birth and Lifetime Education and Earnings**

## **1. Introduction**

Improvements in nutrition, education, transportation, sanitation, and knowledge of diseases have had a dramatic effect on life expectancy worldwide. Vallin and Meslé (2009) have estimated that various improvements in knowledge and technology increased potential life expectancy at birth by 0.33 year per year from 1885 to 1960 and by 0.2 year per year thereafter. A woman born in 2000 is expected to live 33 years longer than a woman born in 1885.<sup>1</sup> This 63% increase in expected length of life has been accompanied by improved health status and enhanced physical abilities which, in turn, should have had profound effects on life-cycle investments in skills, the application of skills to the labor market, and the ability to enjoy life.

Ben-Porath (1967) and Becker (1993) posited that increased length of life would have an unambiguously positive effect on investments in human capital. Both models limited agents to choices of working versus acquiring additional human capital, ignoring the possibility that individuals would consume more leisure rather than spending more time working or learning. Heckman (1976) extended the model to allow both time and financial investments in human capital production and to allow agents to choose labor supply and the consumption of goods and leisure over the life cycle. Still, his model predicts that increased life expectancy at birth would cause individuals to increase also their lifetime human capital production.

> 1 Vallin and Meslé (2009) focused on women’s life expectancy to avoid the effects of war and the higher probability of violent or accidental deaths in their analysis of vital statistics.  Potential life expectancy is based on the highest country life expectancy in each of the years they evaluated between 1750 and 2000.

2
The link between health and human capital investment has been examined intensively in previous research and at different points in the life-cycle. Shocks to fetal or infant health have been shown to lower educational investments or returns. These include maternal malnutrition (Field et al. 2009; Maluccio et al. 2009; Almond and Majumder 2011); low birth-weight (Behrman and Rosenzweig 2004); excessive or insufficient rainfall during the first year of childhood (Maccini and Yang 2009; Shah and Steinberg 2014); exposure to diseases (Almond 2006; Currie and Vogl 2013); and famine during early childhood (Almond and Currie 2011; Gorgens et al. 2012). Exogenous shocks from exposure to environmental pollution or hazards (Chay and Greenstone 2003; Foster et al. 2009; Jayachandran 2009; Almond et al. 2009), and exposure to violence or civil war (Akresh et al. 2011; Camacho 2008; Blattman and Annan 2010; Leon 2012; Yuksel 2014) have also reduced educational attainment and labor market earnings.

While the link between these shocks and education might be due to health or related income shocks, past studies have been able to isolate the effects of health on human capital investments. Exploiting variation in the timing and intensity of hookworm and malaria eradication in the American South and in developing countries, Bleakley (2007, 2010a) and Lucas (2010) demonstrated improved education and earnings outcomes for populations with early, compared to later, exposure to the public health interventions. Miguel and Kremer (2004) found that children who received treatment for intestinal worms in Kenya were absent 25% less frequently than students in the schools that were randomly assigned to receive the treatment 2-3 years later. Follow-up surveys of these students reveal that those students from the treatment schools worked 13% more hours and earned 20%-29% more than those from the control schools (Karlan and Appel 2011). Bhalotra and Venkataramani (2012) found that the availability of 3
antibiotics to combat pneumonia at the time of infancy increased education and earnings when those infants reached adulthood.

A few studies have examined the role of life expectancy on education. Jayachandran and Lleras-Muney (2009) used improvements in maternal health to instrument for presumed endogenous life expectancy of children and found that an additional year of expected life increased schooling by 0.11 year. Oster et al _._ (2013) used information on when individuals learned that they had the fatal Huntington’s disease as the measure of a life cycle shock and found that schooling increased by 0.17 year for every additional year of expected life.

Studies that examine the effect of increased life expectancy on human capital investment have had more mixed results. Acemoglu and Johnson (2006, 2007) found no effect of increasing life expectancy due to improved control of infectious disease on schooling. They argued that because improved length of life effectively increases cohort size, returns to human capital may fall due to rising labor supply outpacing any growth in demand for skills. Bloom, Canning and Fink (2014) found that the Acemoglu and Johnson result reverses, however, when controls for initial health are added. Hazan (2009) argued that this Ben-Porath model requires an increase in lifetime labor supply for the gain in life expectancy to increase investments in human capital. Given that American men born between 1840 and 1970 actually reduced lifetime labor supply, he concluded that life expectancy has either a negligible, or possibly even, a negative effect on investments in education. His subsequent analysis (Hazan 2012) found no correlation between life expectancy at age 5 and educational attainment.<sup>2</sup> However, other analyses of similar

> 2Following Soares (2005), Hazan included “post demographic transition countries,” which are basically a group of countries that exhibited life expectancy at birth above 50 in 1960. He preferred life expectancy at age 5 or 10 instead of that at birth since the later displayed widespread variability due to high infant mortality.

4
country-level data still found a positive correlation between life expectancy and schooling (Cervellati and Sunde 2013; Hansen 2013; Cohen and Leker 2014).

This paper makes several improvements over the previous cross-country studies of life expectancy’s effect on human capital investments. First, the analysis is based on a much larger number of 111 developed and developing countries. The analysis is conducted at each cohort level so that there is a one-to-one correspondence between a cohort’s year of birth in a country and the corresponding life expectancy at birth. Secondly, the schooling attainment of each cohort per country was calculated from household survey data for each country in place of the noisier approximations based on school enrollment data, extrapolated estimates for missing values, and _ex post_ adjustments for mortality that were used in previous studies. Estimates are generated separately for men and women and for urban and rural residents to establish the robustness of the findings. Thirdly, estimates of parental life expectancy at birth are incorporated into the analysis to examine evidence of the intergenerational transmission of human capital from parent to child. Finally, we estimate the impact of life expectancy on both lifetime years of schooling and lifetime earnings. We find that an additional year of life expected at time of birth increases years of schooling by 0.12 year and earnings by about 1%. The implied Wald estimate of returns to schooling are 9.9% for men, 4.3% for women, 10.2% for urban residents and 2.9% for rural residents. These cross-country results are very consistent with the findings based on individual data.

The next section applies Heckman’s (1976) model of life-cycle earnings, learning, and consumption to the question of how increased life expectancy at birth will affect lifetime schooling and earnings. Section 3 explains how we utilize these implications to derive the reduced form specifications for our estimations. Section 4 elaborates on the data sources, and 5
section 5 specifies the empirical model. Section 6 reports the findings and presents some robustness tests, and section 7 presents the individual analysis. Section 8 discusses and interprets the findings and draws some conclusions.

# **2. Theoretical Framework**

Heckman (1976) developed a life cycle model of earnings, learning, and consumption by merging the theory of labor supply with that of human capital production. His model relaxes several assumptions of the Ben-Porath (1967) model that are important to our analysis, including that labor supply decisions are endogenous, that budgets can be used to consume leisure and invest in human capital as well as to purchase market goods and services, and that initial endowments of assets and human capital can alter the entire trajectory of consumption, investment and labor supply. This study uses Heckman’s framework to motivate the analysis of how life expectancy at birth will alter lifetime human capital investments and earnings.

At each instant, the individual is endowed with one unit of time, which s/he allocates among leisure , investment in human capital , and work ( ). Human LL(tt) II(tt) 1 −LL(tt) −II(tt) capital _H(t)_ augments individual time in the production of additional human capital in income generation and in leisure consumption. Human capital is accumulated at the rate (1) HH(tt)̇ = FF[II(tt)HH(tt), DD(tt)] −σσHH(tt)0 (2) HH(0) = HHwhere is the time allocated to human capital production, and is the input of market goods into human capital production in period II(tt) _t. F_ is a concave production function. No human DD(tt) 6
capital is produced unless time is allocated to it, _i.e._ , . Human capital depreciates at the rate of σ in every period.<sup>3</sup> II(tt) > 0 Consumers’ income in period _t_ comes from two sources: interest earnings from assets accumulated and wage earnings conditional on accumulated human capital. The market price for a unit of human capital is _R_ . Labor income in period _t_ can be at most if the individual devotes no time to human capital production or leisure. Income can be allocated to direct RRHH(tt) investment in education goods ( ) and to consumption of durable and nondurable goods ( ) at price _P_ . Given an endowment of initial assets DD(tt) 0 and the human capital endowmentXX(tt) 0, an individual will accumulate wealth AA according to HH AA(tt) , (3) AA(tt)̇ = rrAA(tt) + RRHH(tt)[1 −II(tt) −LL(tt)] −PPDD(tt) −PPXX(tt)0. (4) In equation (3), AA(0) = AA _r_ is the risk-free rate of return on accumulated assets. The individual’s instantaneous utility function takes the form , where utility is concave in its arguments UU[XX(tt), LL(tt)HH(tt)] and . Note that is leisure in natural units of time while is the human capital augmented leisure. The individual seeks to XX(tt) LL(tt)HH(tt) LL(tt) maximize lifetime utility as LL(tt)HH(tt) 0 . (5) TT ∫ee<sup>−ρ</sup> UU[XX(tt), LL(tt)HH(tt)]ddtt<sup>4</sup> The current value Hamiltonian of the above This is maximized subject to the constraints (1)-(4).<sup>4</sup> The current value Hamiltonian of the above problem is

> 3 Although human capital might exhibit a differentiated rate of depreciation at older ages, we adopt Heckman’s assumption that is constant throughout life. This will not change the implication of the model for our setting. 4 We ignore the bequest motive for simplicity. Bequests do not make much sense in a model without hierarchical families. σσ

7
|J(t): e<sup>−ρ</sup>U[X(t), L(t)H(t)] + λ(t){ rA(t) + RH(t)[1 −I(t) −L(t)] −PD(t) −PX(t)} +<br>|
|---|
|<br>μ(t){F[I(t)H(t), D(t)] −σH(t)},|
|<br>(6)<sup>5</sup><br>|
|whereλ(t)is the shadow value of an additional unit of wealth and_μ(t)_is the shadow value of an<br>additional unit of human capital in period_t_. The first-order conditions are<br>|
|J(t)X(ρ): U1(t) = λ(t)e<sup>ρ</sup>P.<br>(7)<br>|
|<br>J(t)L(ρ): U2(t)H(t) = λ(t)e<sup>ρ</sup>RH(t).<br>(8)<br>|
|<br>J(t)I(ρ): μ(t)F1(t)H(t) = λ(t)RH(t).<br>(9)<br>|
|<br>J(t)D(ρ): μ(t)F2(t) = λ(t)P.<br>(10)<br>|
|<br>J(t)A(ρ): λ(t) = −rλ(t).<br>(11)<br>|
|<br>Equation (11) is a first-order differential equation inλ(t). The solution forλ(t)is<br>|
|λ(t) = λ(0)e<sup>−rρ</sup>.<br>(12)|
|<br>The last first-order condition is<br>|
|J(t)H(ρ): μ(t) = σμ(t) −Rλ(t).<br>(13)|
|<br>The terminal condition for human capital is<br>|
|μ(T) = 0,<br>(14)<br>|
|<br> <br>while the assumption of non-satiationλ(T) > 0,together with the “no Ponzi scheme” condition<br>A(T) ≥0,implies that the terminal condition for wealth is<br>|
|<br>λ(T)A(T) = 0.<br>(15)<br>In equation (12),λ(0)is the shadow value or marginal utility of wealth at the beginning<br>of life. It is also period 0’s shadow value of lifetime earnings. Since resources are finite and an|

> 5 For simplification, we assume that there is no income tax in the model. Heckman assumed a tax rate of (1-α) so that a households could keep only a fraction (α) of the income.

8
~~—|l~~ -e

|.

X(t) = X[A(O)e H(t)L(t) = HL[AO)e

|

|.

D(t) = ~~D|— —|~~

)H(t) = 1H ~~[— —].~~
The stock of human capital at any period _t_ , _H(t),_ is the depreciation-weighted accumulated investments in human capital through period _t_ , plus the depreciated initial stock. Human capital stock at period _t_ is specified as follows, 0 . (21) ρρ HH(tt) = ∫eeThus, the lifetime human capital stock is the accumulated human capital over a lifetime<sup>σσ(ττ−ρρ)</sup> FF[II(ττ)HH(ττ), DD(ττ)]ddττ+ HH(0)ee<sup>−σσρρ</sup> _T_ is 0 . (22) TT HH(TT) = ∫ee<sup>σσ(ττ−TT)</sup> FF[II(ττ)HH(ττ), DD(ττ)]ddττ+ HH(0)ee<sup>−σσTT</sup> The value of human capital is equal to the earnings generated from selling this human capital in the market in each period, net of its explicit and implicit production costs. The shadow value of human capital in terms of wealth, _g(t)_ , can be used to convert human capital into wealth. The value of the lifetime accumulated human capital evaluated at the initial period is 0 . (23) TT VV= gg(0)HH(0) + ∫ee<sup>−rrρρ</sup> {gg(tt)FF[II(tt)HH(tt), DD(tt)] −PPDD(tt) −RRIIHH(tt)} ddtt The term within the integral is the lifetime net earnings from human capital investments.

# **2.1 Comparative dynamics from increase in life expectancy at birth T**

This section derives the effect of changes in life expectancy, _T_ , on human capital investment decisions, lifetime human capital production, and lifetime earnings from human capital investments.

capital investments. **_Proposition 1_** : The shadow value of human capital in terms of wealth _, ,_ increases when life <u>g( )</u> expectancy, _T_ , increases.<sup>6</sup> This happens for all as gg(tt) > 0. ∂∂ ρρ As increases, the effective cost of the inputs into human capital production falls, as tt∈[0, TT] ∂∂TT = RRee<sup>(σσ+rr)(ρρ−TT)</sup> expressed in equations (18-19), leading us to proposition 2. gg(tt)

> 6 All proofs to these propositions are presented in Appendix A.

10
**_Proposition 2_ :** As life expectancy at birth, _T_ , increases, purchases of educational investment goods, _D(t)_ , and effective time investment, _I(t)H(t)_ , increase in every period of life _t_ < _T_ . **_Proposition 3_** : As life expectancy at birth, _T_ , increases, the human capital stock _H_ ( _t_ ) accumulated by time _t_ increases in every period as does the total human capital _H_ ( _T_ ) accumulated over the lifetime. This is a direct consequence of the increased use of tt∈[0, TT], _D(t)_ and _I(t)H(t)_ in every period _t_ , as governed by the production function .<sup>7</sup> **_Proposition 4_** : As life expectancy at birth, _T_ , increases, lifetime labor income increases.FF[II(tt)HH(tt), DD(tt)] **_Proposition 5_** : As life expectancy at birth, _T_ , increases, the marginal utility of lifetime wealth at the beginning of life, _,_ decreases. **_Proposition 6:_** As life expectancy at birth, λλ(0) _T_ , increases, the consumption of leisure in human capital-adjusted efficiency units, , increases. However, measured hours of leisure, , may increase or decrease. The proof in Appendix A shows that an increase in life expectancy at HHLL(tt) LL(tt) birth has two opposing effects on the units of leisure.  Effective leisure becomes cheaper due to a fall in the marginal utility of wealth; at the same time, the opportunity cost of hours spent in leisure increases due to rising human capital investments.  It is possible that lifetime leisure will rise or fall as _T_ increases, contrary to the assertion made by Hazan (2009).

# **3. Reduced Form and Econometric Specifications**

The model predicts that in every period of life, increased life expectancy at the start of life will increase accumulated human capital and will raise lifetime earnings. Simultaneously solving the first-order conditions given by equations (7-15) results in reduced-form solutions of

> 7 We are assuming that the per unit value of human capital is not bid downward due to the outward shift of the supply of skilled workers.  As we demonstrate, the estimated impact of increased life expectancy on human capital investment is sufficiently small in magnitude that the positive effects of human capital on income have dominated the downward pressure from increased supply.

11
(r,o,P,R,H — ,T,g(t)) Wt € [0,T] ~~—|1~~ -e | ~~=—~~

~~—|1~~ -e |
where represents the re-optimized sequence of relative shadow values of human capital to assets. That means that changes made to planned sequences of human capital 0 ∀ tt> tt<sup>′</sup> g(tt) investments, labor supply, and lifetime consumption paths from the plans made at time 0 will be uncorrelated with the values of the exogenous variables at time 0, including the life expectancy at birth.<sup>8</sup> This result has important implications for estimating lifetime human capital investments and earnings as a function of life expectancy at birth. Suppose that the planned human capital stock at time _t_ conditional on initial information is and the updated plan after changes in information on life expectancy is ). A survey will reveal information on actual human HH(tt|TT), capital investments , but H(tt|TT<sup>′</sup> 0] = 0 and so the projection of observed on _T_ will yield the effect of life expectancy at birth on planned human capital investments H(tt) EE[H(tt|TT<sup>′</sup> ) −H(tt)|Ω H(tt)at birth. On the other hand, 0 ] ≠0 and so a regression of on will not generate the unbiased effect of life expectancy on planned human capital investments. In EE[(H(tt|TT<sup>′</sup> ) −HH(tt)|Ω , TT<sup>′</sup> H(tt) TT<sup>′</sup> particular, if individual decisions made after birth due to new information on any of the exogenous variables result in changes in life expectancy, the observed human capital outcomes and the observed life expectancy will be jointly determined. A similar argument H(tt)suggests that to derive the effect of life expectancy on lifetime earnings, one should also regress TT<sup>′</sup> observed earnings on life expectancy at birth and not life expectancy at later ages.

> 8 Hazan (2012) proposed that life expectancy at age five instead of life expectancy at birth is more suitable to explain human capital investment decisions due to the selection problem with respect to who survives infancy or early childhood. In practice, by the time a child reaches age five, parents or government or both have made significant investment in the child, which makes life expectancy at age five higher compared to what it was at birth, and thus makes life expectancy at five an endogenous variable.

13
# **4. Data**

We require data with considerable variation in life expectancy at birth and information on lifetime human capital investment and earnings. We exploit the World Bank’s _International Income Distribution Database (I2D2)_ for this purpose. I2D2 is a harmonized collection of household surveys conducted in 111 countries from all regions and income groups. A list of the countries and survey years is presented in Table B1 in Appendix B. Of our 111 countries, 30 are developed countries, 11 in Asia and Pacific, 17 in Central Asia and Eastern Europe, 23 in Latin America, 4 in the Middle East and North Africa, and 26 in Sub-Saharan Africa and South Asia.

From each survey, we include only those individuals who have complete information on both education and wages. Since our interest lies in how life expectancy at birth affects human capital accumulation and earnings, we focus on those individuals who were working at the time of the survey and reported positive incomes. We limit our analysis to the age group 25-60 years. Focusing on individuals over age 25 limits the probability that they would still in school;<sup>9</sup> the upper bound of 60 years avoids selection issues related to retirement and rising mortality with age.

Because life expectancy at birth sets the trajectory for lifetime human capital investment and earnings, we do not aggregate across individuals with different life expectancies, but instead define each cohort in each country as the unit of observation. Our earliest available survey is in 1970 and the latest is in 2012. To fit our age range of 25-60, we include 77 birth cohorts born between 1911 and 1987.

Many of the countries have multiple surveys, giving us repeated observations for many cohorts. However, completed schooling will be the same for the same cohort across surveys.

> 9 Both Barro and Lee (2010) and Cohen and Soto (2007) assume that years of schooling are fixed by age 25.

14
We opted to use the earliest available survey for each country to limit mortality bias in the estimated completed schooling and then the most recent survey to capture the completed schooling for the youngest cohorts in the country. In total, we used 188 surveys from 111 countries to generate 4,670 cohort observations covering almost 4 million individual observations. We further disaggregate the cohorts by gender, and if possible, also by urban and rural residence. For each birth cohort, we computed average years of schooling, average earnings, and incidence of marriage. To compare lifetime earnings across countries, we require a common unit of time. Across the 188 surveys, wages are measured per hour, day, week, month, quarter, or year. These surveys are internally consistent, so a survey dummy variable standardizes both time units and currency units. The survey dummy variable also controls for country-specific effects. In effect, the source of identification in our estimates is the variation in schooling and earnings across cohorts within surveys.

The data on life expectancy at birth by country were compiled from 1950 from the United Nation’s population database.<sup>10</sup> For earlier birth cohorts, GapMinder constructs a measure of life expectancy at birth for almost 200 countries back to 1900. Figure 1 shows the pattern of life expectancy at birth by birth cohort starting in 1910. Worldwide life expectancy has risen from 38 to 72 years over the 90-year period. Over that same period, the global average years of schooling rose from 6.8 to 12.4 years. As shown in Figure 2, these patterns are common across regions and income groups.

As life expectancy increases, the fraction of the birth cohort that enters working age increases. If workers of different ages are not perfect substitutes for one another, members of unusually large working-age cohorts will face depressed earnings (Welch, 1979). We use the

> 10 The UN database is available at http://data.un.org/Default.aspx.

15
number in the cohort relative to the total population as our measure of the relative cohort supply. Figure 3 shows the path of average wages across birth cohorts after netting out the survey fixed effect.<sup>11</sup> Starting with the oldest cohorts, average earnings rise over birth cohorts until the mid1950s when the average earnings begin to decline. The reversal is due to the declining age of the more recent birth cohorts, illustrating that we will need to control for position in the life cycle to remove the effects of age on lifetime earnings. As we demonstrate in the next section, using quadratic terms in age of the cohort or using cohort-specific fixed effects serves to correct for the age effect on earnings.

# **5. Empirical Specification**

The theory suggests that the reduced-form equation for completed schooling and earnings will depend on conditions known at the time of birth, plus changes to those variables conditioned on information orthogonal to the variables known at birth. We specify the equations for completed years of schooling j and log earnings j ) for cohort _j_ , country _c_ , and survey ρρ ρρ year _t_ as SS ln(YY j 0 j j j j j , (26) PP ρρ LL RR ρρ MM ρρ PPLL jj SSjjρρ ρρ SS = γγj + γγ0 LLEE + γγj RRUURR + γγj MMAALLEE + γγj LLEE + aaj + θθ+ ∑ + εε j + PP JJ ρρ LL RR ρρ MM ρρ PPLL pp=1 pp ρρ jj YYjjρρ Ln(YY j ) = aa. + ββ LLEE + ββ RRUURR + ββ MMAALLEE + ββ LLEE ββ ZZ + aa + θθ (27) ρρ ωω The focus on years of schooling is a matter of convenience in that we know that human capital investment will rise in every period _t_ as life expectancy for cohort _j_ , j , rises, but schooling is the most readily observable and consistent measure of human capital investment LLEE

> 11 <mark>The survey fixed effects give us an estimate of average wages net of fixed factors such as the home currency; the time unit used for wages, whether hourly, weekly, monthly or annually; country-specific fixed effects; and inflation. The wages are presented on a logarithmic scale.</mark>

16
across countries and time. As it is also a form of human capital investment that is fixed at a relatively young age, we can assume that for birth cohorts aged 25 and over, years of schooling are fixed for the rest of their lives. In equations (26) and (27), includes cohort-specific effects jj that are known at birth and common across countries, and aa and are survey-specific fixed SSjjρρ YYjjρρ effects that also incorporate country-specific effects that are common across cohorts within a θθ θθ country. j and j are, respectively, the average proportion male and average ρρ ρρ proportion rural in cohort MMAALLEE RRUURR _j_ , country _c_ , and survey year _t_ . We also include an approximation of the life expectancy at birth for each birth cohort, which we assume as the 25-year lagged life expectancy at birth in a country. Our rationale is evidence of intergenerational transmission of human capital (Lindahl et al. 2015) and life expectancy (Björkegren et al. 2019) that may suggest a further gain from improving health conditions on lifetime schooling and earnings.<sup>12</sup> The log earnings equation (27) shares many of the same features as equation (26). Unique elements in **_Z_** include the cohort-specific marriage incidence rate and size of the cohort within a country-survey year. Cohort-specific fixed effects will correct for position in the life jj cycle. aa

> 12 Our findings of intergenerational transfers through parental life expectancy were not overly sensitive to the assumption of the lag length.

17
(S_ olY j+o
countries.<sup>14</sup> In specification IV, we include cohort dummies, where cohort is defined by the year of birth to control for time-varying factors across countries.<sup>15</sup> The coefficient of life expectancy at birth, in equation (26), is always positive and LL statistically significant. The effect ranges from 0.094 to 0.12 year of schooling per additional γγ year of life expectancy. The 30-year increase in world life expectancy between the 1922 and 1987 birth cohorts (Figure 1) by itself would have increased completed schooling by 2.8 to 3.6 years. The coefficient of parental life expectancy in specification IV suggests that there is also an intergenerational channel through which parental health affects children’s education. The effect of parental life expectancy is 22% of the own life expectancy effect, raising the combined effect of rising life expectancy to 3.4 to 4.4 years of schooling.

The effect of life expectancy gain at birth might have different effects on different groups. We investigate this by estimating equation (26) for four subsamples separately: (i) males, (ii) females, (iii) urban residents, and (iv) rural residents (Table 2). Across all specifications, we observe a larger effect of life expectancy gain at birth on women’s schooling than on men’s schooling, but there is little difference in this life expectancy effect between rural and urban residents. For all four groups, the parental life expectancy effect is positive but not statistically significant.

# **6.2 Life expectancy at birth and earnings**

Table 3 reports estimates obtained from regression equations (27) and (28). The first column implies a negative coefficient on life expectancy at birth, but as shown by the inverted

> 14 Cohorts born during the Great Depression or during World War II might experience common shocks to schooling availability. Similarly, there were several United Nations programs and activities to improve health and education across the countries, implying that cohorts born after the 1960s in the low-income countries might have been exposed to similar global campaigns for education.

> 15 Later, in the robustness section, we include cohort dummies defining cohorts by five-year birth range.

19
U-shaped plot of log of wages across birth cohorts in Figure 3, we need to control for the age of the cohort when we observe its wage. The latest birth cohorts will be observed early in their life cycle when their ages are low, even as their life expectancy is high. Once we control for lifecycle effects by including age or cohort-fixed effects and other potential confounders, the coefficient of life expectancy at birth reverses sign: the effect of life expectancy at birth on log of wages turns out to be positive and statistically significant. Increasing life expectancy at birth by one year increases lifetime earnings by roughly 1%. In all the specifications, the log of earnings increases with age at a decreasing rate. Consistent with the findings in the literature, married people and urban residents earn relatively more compared to their unmarried and rural counterparts. A larger cohort size lowers cohort earnings, consistent with the presumption that unusually large cohorts receive depressed earnings.<sup>16</sup> As with schooling, there is an intergenerational gain from parental life expectancy that is statistically significant in all of the specifications with cohort fixed effects. The parental life expectancy effect is 60%-88% as large as the own life expectancy effect.

In Table 4, we report separate estimates by gender and region. The returns to life expectancy are somewhat larger for men than women and for urban than rural residents.

However, for males, the life expectancy coefficient turns out to be statistically significant only in specification III where we control for lifecycle position by birth-year fixed effect. For rural residents, life expectancy at birth is never statistically significant. Parental life expectancy retains a small positive effect on the earnings of their children, but the effect is statistically significant for men, women and rural residents, and only in the specification with the birth-year fixed effect.

> 16 Cohort-specific dummies would absorb this effect if one specific cohort experiences a surge in population across the world.

20
Using the worldwide increase in life expectancy at birth of 30 years between 1922 and 1987, these estimates suggest that rising life expectancy increased lifetime earnings by 27%. The gains are largest for males (36%) and smallest for rural residents (21%).

# **6.3 Returns to schooling**

The Mincerian earnings function (29) generates a measure of the returns to education. This can be compared to the Wald estimator.<sup>17</sup> Estimates from the Mincerian earnings function are reported in Table 5, and these estimates are compared to the corresponding Wald estimators in Table 6.

In Table 5, additional schooling increases lifetime earnings irrespective of the specification used. For the pooled sample, the coefficient of years of schooling shows that one additional year of schooling increases lifetime earnings by 11.8% in the simple specification I; this shrinks to 9.4% once we include a broad set of controls.<sup>18</sup> The estimates on the age terms, percentage male, percentage urban-rural, percentage married, and cohort size exhibit the usual signs. The estimates of the earnings function by gender and urban-rural residence do not show any notable variation across groups. Specification II reveals that one additional year of schooling increases lifetime earnings for males, females, urban, and rural groups by 10.6%, 7.3%, 9.5%, and 8.8%, respectively.

In Table 6, the Wald estimate of the returns to schooling is obtained by dividing the life expectancy coefficients from specification IV in Table 3 by the life expectancy coefficient from specification IV in Table 1. Similarly, for the male, female, urban, and rural groups, we divide the group-specific life expectancy coefficient obtained from specification II in Table 4 by the life

> 17 The Wald estimator for r <u>g l y g y</u> r 18 In all specifications reported in Table 5, we have used survey fixed effects to facilitate comparison across ρρr dd ρρtt ddl rrρρl rr rreerrddlldd gee lrrttll 1 yrreerr geelldd lldd ddl rr rreepprrjjρρeeddjj y eeρρ bbllrrρρh RReettRRrrRR ttoo EEddRRSSaattEEooRR= ρρr dd ρρtt eejjht ddllddgg llrrttll 1 yyrreerr  ggeelldd lldd ddl rr rreepprrjjρρeeddjjyy eeρρ bbllrrρρh countries and time periods.

> ~~.~~

21
expectancy coefficient from specification IV in Table 2. The standard errors for the Wald estimates are obtained from 500 bootstrap replications with the corresponding sample. In the pooled sample, the Wald estimates are 16% lower compared with the Mincerian estimates (7.90% vs. 9.40%). The Wald estimate is slightly higher for urban residents (10.21% vs. 9.50%), but it is lower for males (9.90% vs. 10.60%), females (4.30% vs. 7.30%), and rural residents (2.90% vs. 8.8%). For both Mincerian and Wald estimates, the return to schooling is always higher for males than females, and higher for urban than rural residents.

# **6.4 Robustness checks**

We reexamine our findings using several estimation methods and samples. We reestimated the models (i) without weights, (ii) using a different sample consisting only of data from one survey per country, (iii) using an alternative definition of parents’ life expectancy,<sup>19</sup> (iv) including cohort fixed effect with an alternative definition of cohort,<sup>20</sup> (v) using a sample consisting only of young age groups, and (vi) using a higher-order age variable to wipe out all age effects while estimating the life expectancy effects on earnings.<sup>21</sup> These robustness checks generate similar estimates of life expectancy effects on schooling and earnings. In most cases, the sign and statistical significance of the life expectancy coefficient is positive and statistically significant. The results for all robustness checks are briefly discussed and reported in Appendix C _._

> 19 Previously, parents’ life expectancy was constructed by taking a 25-year lag of life expectancy at birth. The youngest cohort in our survey was born in 1987. In the 1980s, in many regions, especially in Sub-Saharan Africa and South Asia, mother’s age at first child birth was less than 20. For example, in Niger half of the women had given birth by age 18 (Source: http://www.unicef.org/pon95/fami0009.html). An alternative measure of parents’ life expectancy assumed a 15-year lagged value of own life expectancy.

> 20 While constructing the five-year birth cohorts, we collapse all individuals aged 25-59 into different five-year birth cohorts, except for the first and last cohorts. In total, we define 13 cohorts based on 5-year birth groups. Since the number of observations before 1930 is too thin, we group those observations into one cohort. Similarly, all individuals who were born during 1985-87 were collapsed to form the youngest cohort.

> 21 In addition to those attempted for schooling, we try one additional robustness check for lifetime earnings. Following Card (1999) and Murphy and Welch (1990), we add higher-order age terms to sponge out all age effects.

22
The final check of robustness involves adding exogenous variables that vary by country and cohort at the time of birth, and substituting life expectancy at birth by life expectancy measured at older ages. The theory predicts that the effect of life expectancy at birth is exogenous to any random shock realized in a later period in life. To demonstrate the validity of this, we incorporate average temperature and average precipitation that was observed for a birthcohort at the time of birth in its country of origin. Note that similar to life expectancy at birth, average temperature and average precipitation also vary by birth-year and country. The results and discussion on this are presented in Appendix C. In sum, the estimates reveal that the statistically significant positive association of life expectancy at birth with schooling and earnings is not altered even after inclusion of these exogenous variables.

Concerns related to high infant and child mortality rates led some recent papers to argue against using life expectancy at birth. Hazan (2012) suggests that life expectancy beyond early childhood is more appropriate to capture its true effect on human capital investment decisions since parents make schooling decisions for their children at age five or later.<sup>22</sup> However, by the time a child reaches age five or ten, the parents have made substantive investments in the child’s health based on new information on the child’s survival prospects. That makes life expectancy at ages 5 or 10 endogenous responses to observed child survival, making their use inappropriate as explanatory variables for other parental investments in children. However, results using life expectancy at higher ages still have positive and significant effects on years of schooling and lifetime earnings, as reported in Appendix C. These findings contrast with Hazan’s (2012) finding that life expectancy at older ages does not exhibit any statistically significant association with schooling years using a cross-country panel database.

> 22 The argument is based on the observation that cross-country life expectancy at birth exhibits more variation compared to life expectancy at five due to high infant and child mortality.

23
# **7. Individual-level analysis**

We extend the analysis utilizing individual-level observations instead of cohort-level mean observations as reported above. Since the theoretical model suggests that life expectancy at birth is exogenous in determining human capital investment and lifetime earnings, an individuallevel analysis will confirm if country-cohort specific unobservables are contaminating the cohort-mean based empirical results. We estimate three equivalent specifications of equations 26, 28, and 29 using individual-level data: 0 j , (26a) lljjρρ ′ LL ′ RR ′ lljjρρ MM ′ lljjρρ jj2 SSjjρρ′ lljjρρ ′ SS = γγ + γγ0 LLEE + γγj RRUURRA + γγ MMAALLEEA (A + aa) + θθ + εε + lljjρρ LL AA′ lljjρρ lljjρρ RR lljjρρ MM lljjρρ ln(YY ) = aa + ββ LLEE + ββ EE + ββ EE + ββ RRUURR + ββ MMAALLEE ∑ , (28a) JJ pp=1 pp lljjρρ YYjjρρ lljjρρ j 0 ββ ZZ(A + θθ + ωωA (A )<sup>2</sup> (S ho + ρρ AA lljjρρ lljjρρ SS lljjρρ RR lljjρρ ln(YY ) = φφ + φφ EE ) + φφ . EE + φφ ooYYeeaarroo ) + φφ RRUURR (29a) MM lljjρρ YYjjρρ lljjρρ φφWe do not observe individual life expectancy at birth, but a cohort’s life expectancy at MMAALLEE + φφ + θθ = birth, an average measure of individual life expectancies at birth within a cohort ( j ∑NN L i ), is exogenous to an individual’s completed years of schooling or lifetime earnings. LLEE ii=1 ii NN Group means are often used to instrument for endogenous variables in empirical analysis using individual-level data.<sup>23</sup> In the above specifications, j , which states that llj lljj individual _i_ ’s life expectancy at birth in country _c_ deviates from cohort LLEE = LLEE + μμ _j’_ s mean life expectancy by which, by construction, is orthogonal to the mean. Since will be contained in the error lljj lljj term, the following conditions hold: μμ μμ 26, 28, and 29 using individual-level data:

> 23 Royalty (2000) used the state tax rate as an instrument for marginal tax rate in explaining employees’ health insurance eligibility. Similarly, a series of studies following Ruhm (2000) exploited variation in state or county level unemployment rate while explaining individual health behavior during a recession.

24
Cov(e ) = 0, Cov(w ) =0 Cov(9 ) =0
somewhat larger with the overall return to schooling rising to 11.2% per year of schooling compared to 7.9% using the cohort-level data.

In contrast to the cohort-mean level analysis, the findings from individual-level empirical analysis consistently suggest that parents’ life expectancy has a statistically significant positive influence on both human capital investments and lifetime earnings in the pooled sample as well as in the male, urban and rural subsamples. Parents’ life expectancy effects on years in school are 19%-26% of the cohort’s own-life expectancy effect; the effects on earnings are even larger— 38.5% to 54.5% of the own-life expectancy effect. This is strong evidence of an intergenerational transfer effect with respect to increases in life expectancy at birth. Such high transmission across generations is not uncommon considering the recent findings by Lindahl et al. (2015) of intergenerational persistence in human capital and lifetime earnings across several generations.

# **7.1 Instrumental Variables Estimation**

Thus far, we have assumed that life expectancy at birth is exogenous. This assumption may be supportable by evidence that, in Sweden, all of the persistence in mortality across generations is attributable to pre-birth factors (Björkegren et al. 2019), and yet findings for one country is hardly generalizable. We investigate the possible biases due to endogenous life expectancy at birth by using a plausible instrument in equations (24) and (25): the observed mortality rate of children of age five or less. The presumption is that parents use the prevailing under-5 mortality rate as a signal of a newborn’s expected length of life when they make their fertility decisions, and so it should be a reasonable indicator of the exogenous component of the health endowment, 0. The instrumental variables estimates for completed years of schooling are presented in HH panel (a) of Table 8 while those for earnings are presented in panel (c). To facilitate a 26
comparison of these IV estimates with the OLS estimates obtained from the corresponding sample, related OLS estimates on completed years of schooling and earnings are presented in panels (b) and (d), respectively. In all first stages, the under-5 mortality rate shows a statistically significant negative relationship with life expectancy at birth, and the first-stage F-test statistics imply that our estimates are not subject to weak instrument bias. The reported IV estimates suggest that a one-year gain in life expectancy is associated with 0.146 additional year of completed schooling versus 0.113 additional year in the OLS estimates. An additional year of life expectancy raises lifetime earnings by 1.7% using the IV specification versus 1.2% using the OLS specification. The schooling estimates are larger for females than for males, and larger for rural than urban birth cohorts. The gain in lifetime earnings is higher for males than for females, and higher for urban than for rural workers. Applying the historical increase in life expectancy at birth, the implied gain in education and earnings for the 1987 birth cohort over the 1922 birth cohort is 4.4 more years of schooling and 51% higher lifetime earnings.

We report the Wald estimates of the returns to schooling based on the IV estimates at the bottom of Table 6. The overall return to schooling rises to 12% per year of schooling. Returns for males and urban residents increase while returns to females and rural residents become smaller. However, the overall result from all of these estimates is that our Wald estimates correspond to consensus estimates from the very large literature on returns to schooling of about 10% per year, somewhat larger for men and urban residents and somewhat smaller for women and rural residents.

# **7.2 Selection bias due to the labor force participation decision**

Our analysis for lifetime earnings includes those who are in the labor force at the time of the survey. If life expectancy at birth affects an individual’s labor force participation decision, 27
our estimates will be subject to bias and the direction of that bias is uncertain. Note that Hazan (2009) observed that a gain in life expectancy at birth was associated with a decrease in the labor force participation of U.S. males born between 1840 and 1970. We investigate the labor force selection issue with individual-level data in two ways: (i) first, by including a birth-year specific correction measure for selection in each survey, which is constructed as the proportion of individuals in that cohort participating in the labor force in a survey, and (ii) by estimating a twostep Heckman selection correction for an individual’s labor force participation status. To fulfill the exclusion criterion, the selection equation includes household size (number of household members) and the interviewee’s relationship to the household head.<sup>25</sup> These estimates are done separately for the pooled sample and the male, female, urban and rural subsamples.

The results are reported in Tables 9 and 10. After controlling for the proportion of the birth-year cohort in the labor force, neither the pooled sample nor any of the subsamples shows a different pattern in the effect of a gain in life expectancy at birth on lifetime earnings from that obtained without addressing the selection issue, as reported in Table 7. However, estimates obtained following Heckman’s two-step procedure (Table 10) in which we utilize information on individual-level labor force status are positive and statistically significant in the second stage equation, and are larger in magnitude compared to those obtained without any selection correction.<sup>26</sup> The life expectancy effects on lifetime earnings for the pooled sample and the male and rural subsamples are larger in magnitude than our estimates without correcting for selection.

> 25 In the first stage, we estimate a fixed effects logit model of labor force participation decision including age, gender, urban/rural residence, marital status, life expectancy at birth, parents’ life expectancy, household size, relationship to the household head, and survey/country fixed effects. Using the parameters from the logit model, we calculate a linear predicted probability which we then convert into normal densities to construct the inverse Mills ratio used in the second stage for selection correction.

> 26 Replicating the analysis in Table 10 on the same sample but without including the selection correction term produces life expectancy coefficients of 0.13, 0.15, 0.12, 0.12 and 0.10 for the pooled, male, female, rural, and urban samples.

28
For the urban subsample, the selection correction does not alter the life expectancy effects on lifetime earnings. Household size and the individual’s relationship to the household head, our identification variables in the selection equation, always turn out to be statistically significant predictors of individual labor force participation.

The life expectancy coefficient in the selection equation is positive and statistically significant, except in the rural subsample, though it is smaller in magnitude. Since we control for the life cycle stage in the selection specification, this positive association suggests that a gain in life expectancy at birth influences labor force participation marginally at any stage of the lifecycle. In the earnings equation, the negative and statistically significant inverse Mills ratios suggest a negative selection bias: individuals with jobs have lower wages compared with those for whom we do not observe wages. This is not the case for the urban subsample. Controlling for selection, the corresponding coefficient for urban residents is lower by 9%, suggesting that those urban residents with complete wage information are drawn from a relatively higher income group than those urban residents without wage information. One possible explanation for this disparity is that the urban residents without wage information include not only those whose shadow price of labor is higher than the market wage offer but also those residents who work in the informal sector or those who own large businesses. Overall, the effects of a gain in life expectancy at birth on lifetime earnings without correcting for selection bias tend to be conservatively lower using a cohort-based analysis.

# **8. Conclusion**

Using time-series data on birth cohorts from 111 countries, we find that gains in life expectancy at birth increase both investments in human capital and lifetime earnings. An individual completes an additional 0.12 year of schooling for each year added to life expectancy 29
at birth. This estimate is comparable to the estimate of 0.11 year by Jayachandran and LlerasMuney (2009) for Sri Lanka and to 0.17 year by Hansen (2013) using a 70-country study. In our data, global average life expectancy at birth increased 29.7 years between the 1922 and 1987 birth cohorts. Based on our results, this gain implies an increase of 3.6 years in completed schooling. The actual years of schooling in our sample increased by 4.8 years for the same cohorts, so our results suggest that increased life expectancy at birth explains 75% of the increase in average schooling worldwide.

We have also estimated the effect of an additional year of life expectancy at birth on lifetime earnings and find this to be a gain of 1.7%. Global per capita GDP increased by 380% or by 1.3 log points between 1900 and 2000. Our analysis implies that real earnings increase by 1.7% per year of added life expectancy, or by 0.50 log points evaluated at the average gain in life expectancy. Thus, the gain in life expectancy alone explains 38% of the gain in per capita GDP. We illustrate the effect in Figure 4.

In the U.S., estimates by Steckel (2002) suggest that life expectancy at birth rose 31.4 years between 1870 and 1990. This gain in life expectancy alone would have increased years of schooling by 3.8 years according to our results, or by 45% of the 8.3 years increase noted by Lee and Lee (2016) for that period. Steckel (2002) also reported that per capita GDP rose 2.25 log points; based on our results, we can tie 0.53 of this increase to rising life expectancy at birth. Hence, while the gains in human capital and lifetime earnings are largest for poorer countries, they can be substantial even for much richer countries.

30
# **9. References**

Acemoglu, D., and S. Johnson 2007. “Disease and Development: The Effect of Life Expectancy on Economic Growth.” _Journal of Political Economy_ 115(6): 925–985.

Acemoglu, D., and S. Johnson. 2006. “Disease and Development: The Effect of life expectancy on economic growth.” _NBER Working Paper_ , No. 12269.

<mark>Akbulut-Yuksel, M. 2014. “Children of War: The Long-Run Effects of Large-Scale Physical Destruction and Warfare on Children.”</mark> _<mark>Journal of Human Resources</mark>_ <mark>49(3): 634-662.</mark> <mark>Akresh, R., and D. de Walque. 2011. “Armed Conflict and Schooling: Evidence from the 1994 Rwandan Genocide.”</mark> _<mark>World Bank Policy Research Working Paper</mark>_ <mark>No. 4606.</mark> Almond, D. 2006. “Is the 1918 Influenza Pandemic Over? Long-term Effects of _in utero_ Influenza Exposure in the Post-1940 U.S. Population.” _Journal of Political Economy_ 114 (4): 672-712.

Almond, D., L. Edlund L, and M. Palme. 2009. “Chernobyl’s Subclinical Legacy: Prenatal Exposure to Radioactive Fallout and School Outcomes in Sweden.” _Quarterly Journal of Economics_ 124(4): 1729–1772.

Almond, D., and J. Currie. 2011. “Killing Me Softly: The Fetal Origins Hypothesis.” _Journal of Economic Perspectives_ 25(3): 153-72.

Almond, D., and B. Mazumder. 2011. "Health Capital and the Prenatal Environment: The Effect of Ramadan Observance during Pregnancy." _American Economic Journal: Applied Economics_ 3(4): 56-85.

Barro, R., and J-W Lee. 2013. “A New Data Set of Educational Attainment in the World, 1950– 2010.” _Journal of Development Economics_ 104: 184–198.

Becker, G. S. 1993. _Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, 3rd edition_ . Chicago: University of Chicago Press.

Behrman, J. R., and M. R. Rosenzweig. 2004. “Returns to Birthweight.” _Review of Economics and Statistics_ 86(2): 586–601.

Ben-Porath, Y. 1967. “The Production of Human Capital and the Life Cycle of Earnings.” _Journal of Political Economy_ 75(4, Part I): 352–365.

Bhalotra, S., and A. Venkataramani. 2012. “Shadows of the Captain of the Men of Death: Early Life Health Interventions, Human Capital Investments, and Institutions.” _unpublished manuscript,_ University of Bristol.

31
Björkegren, E., M. Lindahl, M. Palme, and E. Simeonova. 2019. “Pre- and Post-Birth Components of Intergenerational Persistence in Health and Longevity: Lessons from a Large Sample of Adoptees.” _Journal of Human Resources,_ <mark>0318-9421R1</mark> .

Blattman, C., and J. Annan. 2010. “The Consequences of Child Soldiering.” _Review of Economics and Statistics_ 92(4): 882-98.

Bleakley, H. 2007. “Disease and Development: Evidence from Hookworm Eradication in the American South.” _Quarterly Journal of Economics_ 122(1): 73–11.

Bleakley, H. 2010. “Malaria Eradication in the Americas: A Retrospective Analysis of Childhood Exposure.” _American Economic Journal: Applied. Economics_ 2(2): 1–45.

Bloom, D., D. Canning, and G. Fink. 2014. “Disease and Development Revisited.” _Journal of Political Economy_ <mark>122(6):1355-1366 .</mark> Card, D. 1999. “The causal effect of education on earnings.” In O. Ashenfelter and D. Card (ed.), _Handbook of Labor Economics,_ Edition 1, volume 3, chapter 30, pages 1801-1863. Elsevier.

Cervellati, M., and U. Sunde. 2013. “Life Expectancy, Schooling, and Lifetime Labor Supply: Theory and Evidence Revisited.” _Econometrica_ 81(5): <mark>2055–2086.</mark> Chay, K.Y., and M. Greenstone. 2003. “The Impact of Air Pollution on Infant Mortality: Evidence from Geographic Variation in Pollution Shocks Induced by a Recession.” _Quarterly Journal of Economics_ 118(3): 1121-1167.

Camacho, A. 2008. “Stress and Birth Weight: Evidence from Terrorist Attacks.” _<mark>American Economic Review</mark>_ <mark>,</mark> _Paper and. Proceedings_ . 98(2): 511-515.

Cohen, D., and M. Soto. 2007. “Growth and Human Capital: Good Data, Good Results.” _Journal of Economic Growth_ 12(1): 51–76.

Cohen, D., and L. Leker. 2014. “Health and Education: Another Look with the Proper Data.” Available at _http://www.parisschoolofeconomics.eu/docs/cohen-daniel/cohen-leker-health-andeducation-2014.pdf_ ( _last accessed on July 17th, 2015_ ).

Currie, J., and T. Vogl. 2013. <mark>“Early-life Health and Adult Circumstance in Developing countries."</mark> _<mark>Annual Review of Economics</mark>_ <mark>5(1):1-36.</mark> Dell, M., B.F. Jones, and B. A. Olken. 2014. “What Do We Learn from the Weather? The New Climate-Economy Literature.” _Journal of Economic Literature_ 52(3): 740-798.

Field, E., O. Robles, and M. Torero. 2009. “Iodine Deficiency and Schooling Attainment in Tanzania.” _American Economic Journal: Applied Economics_ 1(4): 140–169.

32
Finis, W. 1979. “Effect of Cohort Size on Earnings: The Baby Boom Babies’ Financial Bust.” _Journal of Political Economy_ 87(5): S65-S97.

<mark>Foster, A., E. Gutierrez, and N. Kumar. 2009. "Voluntary Compliance, Pollution Levels, and Infant Mortality in Mexico."</mark> _<mark>American Economic Review</mark>_ <mark>99(2): 191-97.</mark> Gørgens, T., X. Meng, and R. Vaithianathan. 2012. _“_ Stunting and Selection Effects of Famine: A Case Study of the Great Chinese Famine.” _Journal of Development Economics_ 97(1): 99–111.

Halla, M., and M. Zweimüller. 2014. “Parental Response to Early Human Capital Shocks: Evidence from the Chernobyl Accident.” _Discussion Paper 7968_ , Institute for the Study of Labor.

Hansen, C. W. 2013. “Life Expectancy and Human Capital: Evidence from the International Epidemiological Transition.” _Journal of Health Economics_ 32(6): 1142–1152.

Hazan. M. 2009. “Longevity and Lifetime Labor Supply: Evidence and Implications.” E _conometrica_ 77(6): 1829–1863.

Hazan. M. 2012. "Life Expectancy and Schooling: New Insights from Cross-Country Data." _Journal of Population Economics_ 25(4): 1237-1248.

<mark>Heckman, James J. 1976</mark> . <mark>“A Life-cycle Model of Earnings, Learning, and Consumption.”</mark> _<mark>Journal of Political Economy</mark>_ <mark>84(4, Part 2):</mark> S9– <mark>S44.</mark> Jayachandran, S., and A. Lleras-Muney. 2009. “Life Expectancy and Human Capital Investments: Evidence from Maternal Mortality Declines.” _Quarterly Journal Economics_ 124(1): 349–398.

Jayachandran, S. 2009. _“_ Air Quality and Early-life Mortality Evidence from Indonesia’s Wildfires.” _Journal of Human Resources_ 44(4): 916–54.

Karlan, D., and J. Appel. 2011. _More than Good Intentions: How a New Economics is Helping to Solve Global Poverty._ New York: Dutton.

Lee, J.-W., and H. Lee. 2016. “Human Capital in the Long Run.” _Journal of Development Economics_ 122: 147-169.

León, G. 2012. <mark>“Civil Conflict and Human Capital Accumulation: Long-term Consequences of Political Violence in Perú.”</mark> _<mark>Journal of Human Resources</mark>_ <mark>47(4): 991-1023.</mark> Lucas, A. M. 2010. “Malaria Eradication and Educational Attainment: Evidence from Paraguay and Sri Lanka.” _American Economic Journal: Applied Economics_ 2(2): 46-71.

Maccini, S., and D. Yang. 2009. “Under the Weather: Health, Schooling, and Economic Consequences of Early-life Rainfall.” _American Economic Review_ 99(3): 1006–1026.

33
Maluccio, J. A., J. Hoddinott, J. R. Behrman, R. Martorell, A.R. Quisumbing, and A. D. Stein. 2009. “The Impact of Improving Nutrition during Early Childhood on Education among Guatemalan Adults.” _Economic Journal_ 119(537): 734–63.

Lindahl, M., M. Palme, S.S. Massih, and A. Sjögren. 2015. “Long-Term Intergenerational Persistence of Human Capital: An Empirical Analysis of Four Generations.” _Journal of Human Resources_ 50(1): 1-33 _._ Miguel, E., and M. Kremer. 2004. “Worms: Identifying Impacts on Education and Health in the Presence of Treatment Externalities.” _Econometrica_ 72(1): 159-217.

Murphy, K. M., and F. Welch. 1990. "Empirical Age-Earnings Profiles." _Journal of Labor Economics_ 8(2): 202-229.

Oster, E., I. Shoulson, and R. Dorsey. 2013. “Limited Life Expectancy, Human Capital and Health Investments.” _American Economic Review_ 103(5): 1977–2002.

Royalty, A.B. 2000. “Tax Preferences for Fringe Benefits and the Health Insurance Offered by Employers.” _Journal of Public Economics_ 75(2): 209-227.

Ruhm, C.J. 2000. “Are Recessions Good for Your Health?” _Quarterly Journal of Economics_ 115 (2): 617–650.

Shah, M., and B. M. Steinberg. 2014 _. “_ Drought of Opportunities: Contemporaneous and Longterm Impacts of Rainfall Shocks on Human Capital. _” Working Paper No. 19140,_ NBER _._ Soares, R. R. 2005. “Mortality Reductions, Educational Attainment, and Fertility Choice.” _American Economic Review_ 95(3): 580–601.

Steckel, R. 2002. “A History of the Standard of Living in the United States” in R. Whales (ed.), _EH.Net Encyclopedia_ Economic History Association.

University of East Anglia Climatic Research Unit (CRU); [Ian Harris]. Climatic Research Unit (CRU) Year-by-Year Variation of Selected Climate Variables by Country (CY), [Internet]. NCAS British Atmospheric Data Centre, 2013, _25_<sup>_th_</sup> _August, 2014_ . Available at http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__DE_56531370-2613-11e3-9fca00163e251233 Vallin, J., and F. Meslé. 2009. “The Segmented Trend Line of Highest Life Expectancies.” _Population and Development Review_ 35(1): 159-187.

34
<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1905 1912 1919 1926 1933 1940 1947 1954 1961 1968 1975 1982<br>Year of Birth<br>Life Expectancy at Birth<br><!-- End of picture text -->

**Figure 1** : Life Expectancy at Birth across Cohorts, World Average

35
<!-- Start of picture text --> 80 15 80 15<br>80 15<br>High Income Countries East Asia and Pacific Europe & Central Asia<br>60 60 60<br>10 10 10<br>40 40 40<br>Life Exp 5 Life Exp 5 5<br>20 School Year 20 School Year 20 Life Exp<br>School Year<br>0 0 0 0 0 0<br>1911 1919 1927 1935 1943 1951 1959 1967 1975 1983 1931 1937 1943 1949 1955 1961 1967 1973 1979 1985 1935 1942 1949 1956 1963 1970 1977 1984<br>80 15 80 15 80 15<br>Latin America and the Caribbean Middle East & North Africa South Asia<br>60 60 60<br>10 10 10<br>40 40 40<br>5 5 5<br>20 Life Exp 20 Life Exp 20 Life Exp<br>School Year School Year<br>0 0 0 0<br>0 0<br>1922 1929 1936 1943 1950 1957 1964 1971 1978 1985 1924 1932 1940 1948 1956 1964 1972 1980<br>1942 1947 1952 1957 1962 1967 1972 1977 1982<br>80 15<br>Sub Saharan Africa<br>60<br>10<br>40<br>5<br>20 Life Exp<br>School Year<br>0 0<br>1933 1937 1941 1945 1949 1953 1957 1961 1965 1969 1973 1977 1981 1985<br>Year of Birth<br>152<br>Years in School<br>Life Expectancy at Birth<br><!-- End of picture text -->

**Figure 2** : Life Expectancy and Schooling by Birth Cohort across Regions
37 <!-- Start of picture text --> 6<br>5.8<br>5.6<br>5.4<br>5.2<br>5<br>4.8<br>4.6<br>4.4<br>4.2<br>4<br>1900 1920 1940 1960 1980<br>Birth-Year Cohort<br>ln(wage)<br><!-- End of picture text --> Note: The birth-year specific wages are the coefficients of Birth-Year dummies in the regression of log (wages) on birth-year dummies and survey dummies, where the survey dummies are taking care of across survey differences in exchange rates, inflation, unit of wages, differences in survey instruments.

**Figure 3** : Log(wage) by 1905-1987 Birth-year Cohorts, World Averages
<!-- Start of picture text -->
1.5 80<br>1.4 75<br>1.3 70<br>1.2 65<br>1.1 60<br>1 55<br>0.9 50<br>1920 1940 1960 1980<br>Year of Birth<br>Life Expectancy at Birth<br>Index of Lifetime Earnings (1922=1)<br><!-- End of picture text -->

Note: We assume 1922 as the base year. We plot the implied present value of lifetime earnings (adjusted for the base year) from the specification II in Table 3 against the Birth Year. The lifetime earnings estimates are assumed for male residing in urban areas. The life expectancy at birth numbers are the maximum life expectancy enjoyed by a cohort across the countries, which is to capture what an average person would expect to enjoy staying on the frontier of health technology at the time of birth. While calculating the net present value of log of lifetime earnings, we try two different discount rates 2% and 5%. The period in the figure ranges from 1922 to 1987 as prior to 1922, the information on urban/rural residence is missing.

**Figure 4** : World Average Life Expectancy at Birth and Implied Lifetime Earnings Index

38
**Table 1** : Life Expectancy at Birth and Schooling

||I|II|III|IV|
|---|---|---|---|---|
|Life expectancy at birth|_0.120***_<br>_[0.015]_|_0.097***_<br>_[0.014]_|_0.094***_<br>_[0.008]_|_0.117***_<br>_[0.026]_|
|% Urban||6.009***<br>[1.687]|6.043***<br>[1.748]|6.737***<br>[1.057]|
|% Male||-1.212|-1.342|-0.858*|
|||[1.047]|[0.922]|[0.516]|
|Parents Life Expectancy|||0.004<br>[0.019]|0.026*<br>[0.014]|
|Birth Year Fixed Effects||||YES|
|Survey Fixed Effects|YES|YES|YES|YES|
|Constant|3.476***|1.574|1.625|-1.748|
||[0.916]|[2.298]|[2.455]|[2.256]|
|N|4670|4185|3861|3861|
|Adjusted R-square|0.987|0.985|0.985|0.987|

Note: Standard errors in brackets. Significance levels: * p<0.05, ** p<0.01, *** p<0.001.

39
**<u>Table 2</u>** <u>: Heterogeneity in Effects of Life Expectancy at Birth on Schooling</u>

|||Ma|les|||Fem|ales||
|---|---|---|---|---|---|---|---|---|
||I|II|III|IV|I|II|III|IV|
|Life Expectancy at Birth|**_0.098***_**<br>**_[0.015]_**|**_0.082***_**<br>**_[0.013]_**|**_0.084***_**<br>**_[0.009]_**|**_0.107***_**<br>**_[0.028]_**|**_0.157***_**<br>**_[0.018]_**|**_0.141***_**<br>**_[0.018]_**|**_0.135***_**<br>**_[0.014]_**|**_0.157***_**<br>**_[0.032]_**|
|% Urban||5.679***|5.766***|5.958***||4.249***|4.351***|4.737***|
|||[1.860]|[1.911]|[1.155]||[0.845]|[0.927]|[0.645]|
|% Male|||||||||
|Parents Life Expectancy|||-0.005|0.02|||0.007|0.019|
||||[0.022]|[0.017]|||[0.018]|[0.015]|
|Birth Year FE||||YES||||YES|
|Constant|4.420***|1.801|1.888|-1.172|1.622|-0.419|-0.52|-3.001|
||[0.905]|[1.713]|[1.949]|[2.288]|[1.148]|[1.491]|[1.755]|[2.623]|
|N|4690|4204|3878|3878|4622|4149|3822|3822|
|Adjusted R-square|0.984|0.98|0.98|0.983|0.983|0.981|0.982|0.982|
|||<br>Ur|<br>ban|||<br>Ru|<br>ral||
|Life Expectancy at Birth|**_0.112***_**<br>**_[0.018]_**|**_0.103***_**<br>**_[0.018]_**|**_0.097***_**<br>**_[0.011]_**|**_0.108***_**<br>**_[0.028]_**|**_0.129***_**<br>**_[0.017]_**|**_0.119***_**<br>**_[0.011]_**|**_0.114***_**<br>**_[0.010]_**|**_0.118***_**<br>**_[0.029]_**|
|% Urban|||||||||
|% Male||-0.25|-0.291|-0.106||-0.56|-0.68|-0.448|
|||[0.807]|[0.707]|[0.441]||[0.548]|[0.576]|[0.467]|
|Parents Life Expectancy|||0.007<br>[0.016]|0.02<br>[0.013]|||0.01<br>[0.019]|0.012<br>[0.018]|
|Birth Year FE||||YES||||YES|
|Constant|4.539***|5.461***|5.492***|3.861*|2.195**|2.567***|2.444**|1.785|
||[1.104]|[1.433]|[1.513]|[2.167]|[0.996]|[0.884]|[1.202]|[2.510]|
|N|4684|4200|3874|3874|4446|3959|3657|3657|
|Adjusted R-square|0.982|0.963|0.963|0.965|0.992|0.987|0.988|0.988|
|Note: Standard errors in|brackets. S|ignificance|levels: * p|<0.05, ** p|<0.01, ***|p<0.001.|||

40
**Table 3** : Effect of Life Expectancy at Birth on Earnings

||**I**|**II**|**III**|**IV**|**V**|**VI**|
|---|---|---|---|---|---|---|
|Life Expectancy at Birth|**_-0.017***_**<br>**_[0.005]_**|**_0.010*_**<br>**_[0.006]_**|**_0.011*_**<br>**_[0.006]_**|**_0.009*_**<br>**_[0.005]_**|**_0.013**_**<br>**_[0.005]_**|**_0.009**_**<br>**_[0.004]_**|
|Age||0.091***<br>[0.015]|0.094***<br>[0.015]|0.077***<br>[0.013]|||
|Age square*(1/100)||-0.10***<br>[0.000]|-0.10***<br>[0.000]|-0.10***<br>[0.000]|||
|% Urban||1.303***<br>[0.421]|1.396***<br>[0.423]|1.112***<br>[0.196]|1.348***<br>[0.323]|1.087***<br>[0.186]|
|% Male||0.409***<br>[0.111]|0.412***<br>[0.116]|0.206*<br>[0.118]|0.154<br>[0.130]|-0.059<br>[0.145]|
|Parents Life Expectancy|||0.004<br>[0.003]|0.003<br>[0.003]|0.008**<br>[0.003]|0.008**<br>[0.003]|
|% Married||||0.471***<br>[0.153]||0.610***<br>[0.124]|
|Cohort Size||||-8.577***<br>[1.534]||-7.733***<br>[1.544]|
|Birth Year FE|||||YES|YES|
|Constant|8.490***|3.738***|3.341***|4.206***|5.278***|5.637***|
||[0.331]|[0.746]|[0.819]|[0.671]|[0.481]|[0.415]|
|N|4670|4185|3861|3861|3861|3861|
|Adjusted R-square|0.996|0.998|0.998|0.998|0.998|0.998|

Note: Standard errors in brackets. Significance levels: * p<0.05, ** p<0.01, *** p<0.001.

41
**Table 4** : Heterogeneity in Effect of Life Expectancy at Birth on Earnings

|||**Males**|||**Females**||
|---|---|---|---|---|---|---|
||I|II|III|I|II|III|
|Age|0.103***<br>[0.018]|0.084***<br>[0.018]||0.069***<br>[0.011]|0.054***<br>[0.007]||
|Age-squared*(1/100)|-0.10***<br>[0.000]|-0.10***<br>[0.000]||-0.10***<br>[0.000]|-0.10***<br>[0.000]||
|% Urban|1.252***|0.947***|0.892***|0.907***|0.933***|0.949***|
||[0.475]|[0.201]|[0.154]|[0.121]|[0.130]|[0.151]|
|% Male|||||||
|Life expectancy at birth|**_0.011_**<br>**_[0.008]_**|**_0.010_**<br>**_[0.007]_**|**_0.012**_**<br>**_[0.006]_**|**_0.009**_**<br>**_[0.004]_**|**_0.007**_**<br>**_[0.003]_**|**_0.009***_**<br>**_[0.003]_**|
|Parents’ life expectancy||0.002|0.008*||0.001|0.004*|
|||[0.004]|[0.004]||[0.003]|[0.002]|
|% Married||0.472**<br>[0.180]|0.578***<br>[0.144]||0.429***<br>[0.125]|0.434***<br>[0.074]|
|Cohort size||-12.837***|-12.065***||-9.243|-10.247|
|||[0.643]|[1.059]||[7.188]|[7.816]|
|Birth-year Fixed Effects|||YES|||YES|
|<br>Constant|3.796***|4.350***|5.671***|4.596***|4.905***|5.708***|
||[0.893]|[0.980]|[0.562]|[0.370]|[0.334]|[0.324]|
|N|4204|3878|3878|4149|3822|3822|
|Adjusted R-square|0.997|0.998|0.998|0.997|0.998|0.998|
|||**Urban**|||**Rural**||
|Age|0.096***<br>[0.012]|0.083***<br>[0.010]||0.069***<br>[0.013]|0.054***<br>[0.015]||
|Age-squared*(1/100)|-0.10***<br>[0.000]|-0.10***<br>[0.000]||-0.10***<br>[0.000]|-0.10***<br>[0.000]||
|% Urban|||||||
|% Male|0.495***|0.377***|0.103|0.193|0.124|0.074|
||[0.091]|[0.074]|[0.091]|[0.140]|[0.133]|[0.147]|
|Life expectancy at birth|**_0.012***_**|**_0.011***_**|**_0.010***_**|**_0.002_**|**_0.003_**|**_0.007_**|
||**_[0.004]_**|**_[0.003]_**|**_[0.003]_**|**_[0.008]_**|**_[0.009]_**|**_[0.007]_**|
|Parents’ life expectancy||0.002|0.005||0.007|0.011**|
|||[0.002]|[0.003]||[0.006]|[0.005]|
|% Married||<br>0.294***|<br>0.501***||<br>0.534**|<br>0.592***|
|||[0.089]|[0.095]||[0.237]|[0.124]|
|Cohort size||-7.169*|-5.264||-7.316***|-6.624***|
|||[4.065]|[3.542]||[1.637]|[2.369]|
|Constant|4.337***|<br>4.653***|<br>6.396***|5.706***|<br>5.380***|<br>5.988***|
||[0.543]|[0.473]|[0.336]|[0.900]|[1.086]|[0.642]|
|N|4200|3874|3874|3959|3657|3657|
|Adjusted R-square|0.998|0.999|0.998|0.996|0.996|0.996|

Note: Standard errors in brackets. Significance levels: * p<0.05, ** p<0.01, *** p<0.001.

42
**Table 5** : Mincerian Earnings Functions and Returns to Schooling

||**Pooled**|**sample**|**Ma**|**les**|**Fem**|**ales**|**Ur**|**ban**|**Ru**|**ral**|
|---|---|---|---|---|---|---|---|---|---|---|
||I|II|I|II|I|II|I|II|I|II|
|Age|0.081***<br>[0.007]|0.072***<br>[0.006]|0.088***<br>[0.008]|0.077***<br>[0.008]|0.069***<br>[0.005]|0.054***<br>[0.005]|0.087***<br>[0.007]|0.081***<br>[0.005]|0.073***<br>[0.009]|0.049***<br>[0.009]|
|Age-squared|-0.10***|-0.10***|-0.10***|-0.10***|-0.10***|-0.10***|-0. 10***|-0.10***|-0.10***|-0.000***|
|*(1/100)|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Years of<br>schooling|**_0.121***_**<br>**_[0.025]_**|**_0.094***_**<br>**_[0.013]_**|**_0.137***_**<br>**_[0.028]_**|**_0.106***_**<br>**_[0.020]_**|**_0.093***_**<br>**_[0.012]_**|**_0.073***_**<br>**_[0.008]_**|**_0.102***_**<br>**_[0.018]_**|**_0.095***_**<br>**_[0.010]_**|**_0.096***_**<br>**_[0.022]_**|**_0.088***_**<br>**_[0.024]_**|
|%Urban||0.528***||0.406**||0.604***|||||
|||[0.172]||[0.171]||[0.093]|||||
|%Male||0.363***<br>[0.116]||||||0.392***<br>[0.099]||0.244**<br>[0.108]<br>1|
|% Married||0.329***||0.308***||0.341***||0.172*||0.406**<br>59|
|||[0.110]||[0.097]||[0.127]||[0.103]||[0.182]<br>|
|Cohort size||-5.78***||-7.35***||-6.91||-4.073*||-4.967***|
|||[1.208]||[1.141]||[6.369]||[2.291]||[1.486]|
|Constant|4.291***|4.243***|4.135***|4.416***|4.665***|4.748***|4.302***|4.352***|4.746***|5.110***|
||[0.337]|[0.307]|[0.373]|[0.335]|[0.211]|[0.126]|[0.343]|[0.230]|[0.348]|[0.386]|
|Survey Fixed<br>Effect|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|N|4670|4185|4690|4204|4622|4149|4687|4201|4446|3959|
|Adjusted R-<br>square|0.998|0.998|0.998|0.998|0.997|0.998|0.998|0.999|0.996|0.996|

Note: Standard errors in brackets. Significance levels: *p<0.05, ** p<0.01, ***p<0.001.

43
**Table 6** : Comparison of Returns to Schooling using Mincerian and Wald Estimators

||**Pooled sample**|**Males**|**Females**|**Urban**|**Rural**|
|---|---|---|---|---|---|
|**_Panel a. Birth cohort data_**<br>Mincerian earnings function|9.40%***|10.60%***|7.30%***|9.50%***|8.80%***|
|[std. error]|[0.013]|[0.020]|[0.008]|[0.010]|[0.024]|
|Wald estimate [std. error]|7.90%***<br>[0.018]|9.90%***<br>[0.024]|4.30%***<br>[0.011]|10.21%***<br>[0.017]|2.90%<br>[0.026]|
|**_Panel b. Individual-level data_**<br>Mincerian earnings function|9.7%***|8.9%***|10.7%***|8.6%***|10.0%***|
|[std. error]|[0.001]|[0.001]|[0.001]|[0.001]|[0.001]|
|Wald estimate<br>[std. error]|11.2%***<br>[0.004]|15.0%***<br>[0.003|7.8%***<br>[0.003]|10.6%***<br>[0.004]|10.1%***<br>[0.004]|
|Wald estimate using IV<br>estimator<br>[std. error]|12.02.%***<br>[0.003]|17.2%***<br>[0.004]|7.4%***<br>[0.004]|13.8%***<br>[0.004]|9.4%***<br>[0.004]|

Note: The Mincerian return to schooling estimates are taken from specification II for each group in Table 5 while the Wald estimates for pooled sample is derived by dividing the coefficients of life expectancy at birth from specification IV in Table 3 by the coefficient of life expectancy at birth in specification IV in Table 1. Similarly, for each gender and Rural-Urban group, the Wald estimates are obtained by dividing the life expectancy coefficients from specification II in Table 4 by life expectancy coefficients from specification IV in Table 2. Standard errors for Wald estimates are obtained by 500 bootstrap replications.

44
**Table 7** : Life Expectancy at Birth Effects on Schooling and Earning, Individual-Level Analysis

||**Pooled**<br>|**Sample**<br>|**M**<br>|**ales**<br>|**Fem**<br>|**ales**<br>|**Ru**<br>|**ral**<br>|**U**<br>|**rban**<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Schooling|Earnings|Schooling|Earnings|Schooling|Earnings|Schooling|Earnings|Schooling|Earnings|
|Urban|-1.621***<br>[0.087]|-0.424***<br>[0.017]|-1.812***<br>[0.087]||-1.253***<br>[0.074]|||-0.483***<br>[0.016]||-0.396***<br>[0.017]|
|Male/Female|0.247***<br>[0.032]|-0.366***<br>[0.010]||-0.358***<br>[0.012]||-0.355***<br>[0.008]|0.0932*<br>[0.041]||0.287***<br>[0.031]||
|_Life expectancy at_<br>_birth_|**_0.115***_**<br>**_[0.008]_**|**_0.013***_**<br>**_[0.001]_**|**_0.103***_**<br>**_[0.008]_**|**_0.015***_**<br>**_[0.002]_**|**_0.150***_**<br>**_[0.011]_**|**_0.012***_**<br>**_[0.002]_**|**_0.120***_**<br>**_[0.009]_**|**_0.012***_**<br>**_[0.002]_**|**_0.0996***_**<br>**_[0.007]_**|**_0.011***_**<br>**_[0.001]_**|
|Parents’ life<br>expectancy|0.0219***<br>[0.006]|0.005***<br>[0.001]|0.0270***<br>[0.007]|0.008***<br>[0.002]|0.008<br>[0.006]|0.002<br>[0.001]|0.0233**<br>[0.007]|0.006***<br>[0.002]|0.0233***<br>[0.006]|0.006***<br>[0.001]|
|Age||0.077***<br>[0.004]||0.087***<br>[0.005]||0.060***<br>[0.005]||0.064***<br>[0.003]||0.081***<br>[0.005]|
|Age square*(1/100)||-0.10***<br>[0.000]||-0.10***<br>[0.000]||-0.10***<br>[0.000]||-0.10***<br>[0.000]||-0.10***<br>[0.000]|
|Marital status||-0.107***<br>[0.008]||-0.245***<br>[0.008]||0.024<br>[0.015]||-0.100***<br>[0.008]||-0.113***<br>[0.009]|
|Cohort size||-2.483***<br>[0.286]||-2.666***<br>[0.357]||-1.270***<br>[0.375]||-1.771***<br>[0.335]||-2.497***<br>[0.279]|
|Constant|3.315***<br>[0.645]|4.925***<br>[0.217]|3.749***<br>[0.640]|4.474***<br>[0.279]|2.112*<br>[0.835]|4.979***<br>[0.245]|1.297<br>[0.796]|5.062***<br>[0.218]|4.242***<br>[0.569]|4.838***<br>[0.225]|
|Birth-Year Fixed<br>Effect|YES||YES||YES||YES||YES||
|Survey Fixed Effect|YES|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|N|2725213|2543741|1589055|1473199|1136158|1070542|872016|832947|1853197|1710794|
|Adjusted R-square|0.444|0.898|0.442|0.916|0.451|0.869|0.589|0.888|0.340|0.904|
|F-statistic|36.730|1371.40|39.200|1040.50|49.770|350.70|39.120|292.50|30.190|468.70|

Note: Standard errors in brackets. Significance levels: *p<0.05, ** p<0.01, *** p<0.001.

45
**<u>Table 8</u>** <u>: Instrumental Variables Estimation using Infant Mortality as an Instrument for Life Expectancy at Birth</u>

||**Pooled**|**sample**|**M**|**ale**|**Fe**|**male**|**Ur**|**ban**|**R**|**ural**|
|---|---|---|---|---|---|---|---|---|---|---|
||Stage 1|Stage 2|Stage 1|Stage 2|Stage 1|Stage 2|Stage 1|Stage 2|Stage 1|Stage 2|
|**_Panel a: Dependent_**|<br>**_Variable in_**|<br>**_Stage 1 is_**|<br>**_life expect_**|<br>**_ancy at birt_**|<br>**_h; in stage_**|<br>**_2, years of c_**|<br>**_ompleted s_**|<br>**_chooling_**|||
|Death rate for|**-0.864*****||**-0.848*****||**-0.889*****||**-0.791*****||**-0.954*****||
|under-5 children|[0.106]||[0.116]||[0.088]||[0.095]||[0.113]||
|Life expectancy at||**0.146*****||**0.138*****||**0.176*****||**0.128*****||**0.152*****|
|birth||[0.028]||[0.028]||[0.036]||[0.032]||[0.029]|
|Parents’ life|**-0.117*****|**0.039*****|**-0.122*****|**0.040****|**-0.110*****|**0.034*****|**-0.079****|**0.042*****|**-0.163*****|**0.038****|
|expectancy|[0.037]|[0.014]|[0.037]|[0.016]|[0.038]|[0.012]|[0.037]|[0.012]|[0.046]|[0.018]|
|Urban|0.006|-1.520***|0.011**|-1.698***|-0.003|-1.186**|||||
||[0.004]|[0.580]|[0.005]|[0.583]|[0.005]|[0.489]|||||
|Gender|<br>0.009|<br>0.278**|||||0.010*|0.316***|0.008|0.14|
||[0.005]|[0.117]|||||[0.006]|[0.091]|[0.007]|[0.214]|
|First Stage F-<br>|67.067||53.721||101.876||69.877||71.851||
|Statistic (Weak<br>Identification Test)|||||||||||
|**_Panel b: Correspon_**|**_ding OLS E_**|**_stimates (Li_**|**_fe expecta_**|**_ncy at birth_**|**_is exogen_**|**_ous for indiv_**|**_idual scho_**|**_oling)_**|||
|Life expectancy at||**0.113*****||**0.103*****||**0.141*****||**0.090*****||**0.124*****|
|birth||[0.02]||[0.02]||[0.04]||[0.02]||[0.03]|
|Parents’ life||**0.037****||**0.036****||**0.032****||**0.042*****||**0.031***|
|exectanc||[001]||[002]||[001]||[001]||[002]|
|py||.||.||.||.||.|
|**_Panel c: Dependent_**|**_Variable in_**|**_Stage 1 is l_**|**_ife expecta_**|**_ncy at birt_**|**_h, in stage_**|**_2, it is log of_**|**_wage_**||||
|Death rate for|**-0.818*****||**-0.807*****||**-0.835*****||**-0.752*****||**-0.900*****||
|under-5 children|[0.087]||[0.092]||[0.081]||[0.072]||[0.105]||
|Life expectancy at||**0.017*****||**0.024*****||**0.013*****||**0.018*****||**0.014****|
|<br>birth||[0.005]||[0.008]||[0.004]||[0.005]||[0.007]|
|Parents’ life|**-0.092****|**0.007*****|**-0.099****|**0.009*****|**-0.082***|**0.003*****|**-0.06**|**0.007*****|**-0.130****|**0.007***|
|expectancy|[0.046]|[0.002]|[0.048]|[0.003]|[0.043]|[0.001]|[0.040]|[0.001]|[0.062]|[0.004]|
|<br>Urban|<br>0.002|<br>-0.361***|<br>0.006|<br>-0.352***|<br>-0.005|<br>-0.352***|||||
||[0.003]|[0.063]|[0.004]|[0.071]|[0.006]|[0.046]|||||
|Individual age|-0.077|0.074***|-0.094|0.085***|-0.056|0.055***|-0.062|0.079***|-0.098|0.061***|
||[0.088]|[0.009]|[0.100]|[0.012]|[0.072]|[0.008]|[0.091]|[0.009]|[0.099]|[0.011]|
|Individual age-|-0.002*|-0.001***|-0.002*|-0.001***|-0.002**|-0.001***|-0.002*|-0.001***|-0.002*|-0.001***|
|<br>squared|[0.001]|[0.000]|[0.001]|[0.000]|[0.001]|[0.000]|[0.001]|[0.000]|[0.001]|[0.000]|
|Gender|0.011|-0.415***|||||0.011*|-0.387***|0.014|-0.476***|
||[0.007]|[0.060]|||||[0.006]|[0.057]|[0.012]|[0.072]|
|Married|<br>0.035|<br>0.123***|0.044|0.258***|0.031|-0.038|<br>0.017|<br>0.121***|<br>0.053|<br>0.124***|
||[0.029]|[0.014]|[0.033]|[0.050]|[0.031]|[0.028]|[0.020]|[0.012]|[0.039]|[0.022]|
|Cohort size|11.325*|-2.645***|10.407**|-2.894***|13.286*|-1.370**|8.9|-2.821***|13.778*|-1.833***|
||[5824]|[0531]|[5076]|[0539]|[7778]|[0621]|[5476]|[0695]|[7399]|[0204]|
|First Stage F-<br>|.<br>95.603|.|.<br>83.083|.|.<br>**114.28**|.|.<br>108.877|.|.<br>73.451|.|
|Statistic (Weak|||||||||||
|Identification Test)|||||||||||
|**_Panel d: Correspon_**|**_ding OLS E_**|**_stimates (Li_**|**_fe expecta_**|**_ncy at birth_**|**_is exogen_**|**_ous for indiv_**|**_idual wag_**|**_e earnings)_**|||
|Life expectancy at||**0.012*****||**0.015*****||**0.011*****||**0.011*****||**0.011****|
|birth||[0.00]||[0.01]||[0.00]||[0.00]||[0.00]|
|Parents’ life||**0.006*****||**0.009*****||**0.003*****||**0.007*****||**0.006***|
|expectancy||[0.00]||[0.00]||[0.00]||[0.00]||[0.00]|

Note: Standard errors reported in brackets are clustered at the country level. Significance levels: * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01. All covariates are at the individual level except life expectancy at birth and infant mortality rate, which are birth-year level means. All of the specifications include country fixed effects and birth year fixed effects. Specifications under panel (b) & (d) are to facilitate comparison with estimates obtained from IV and reported in panels (a) and (c).

46
**Table 9:** Life Expectancy at Birth Effects on Lifetime Earnings with Correction for Selection including Cohort Size in Labor Force

||**Pooledsample**|**Males**|**Females**|**Rural**|**Urban**|
|---|---|---|---|---|---|
|**Life**e**xpectancy at**b**irth**|**_0.013***_**<br>**_[0.003]_**|**_0.015***_**<br>**_[0.004]_**|**_0.013***_**<br>**_[0.003]_**|**_0.013***_**<br>**_[0.004]_**|**_0.010***_**<br>**_[0.003]_**|
|Age|0.077***<br>[0.011]|0.090***<br>[0.015]|0.060***<br>[0.009]|0.062***<br>[0.012]|0.083***<br>[0.012]|
|Age-squared*(1/100)|-0.10***<br>[0.000]|-0.10***<br>[0.000]|-0.10***<br>[0.000]|-0.10***<br>[0.000]|-0.10***<br>[0.000]|
|Rural|-0.367***<br>[0.066]|-0.360***<br>[0.073]|-0.357***<br>[0.050]|||
|Male|0.431***<br>[0.017]|||0.486***<br>[0.016]|0.403***<br>[0.018]|
|Marital status|-0.103***|-0.240***|0.076***|-0.114***|-0.096***|
||[0.021]|[0.046]|[0.029]|[0.034]|[0.019]|
|Cohort size|-2.564|-0.009|-1.548|-3.384|-1.228|
||[3.161]|[4.284]|[2.835]|[4.292]|[3.315]|
|Parents’ life expectancy|0.005***<br>[0.002]|0.008***<br>[0.003]|0.002<br>[0.001]|0.006***<br>[0.003]|0.006***<br>[0.002]|
|Correction term for|0.202|-4.038|0.984|2.349|-1.619|
|selection|[4.499]|[6.217]|[4.005]|[6.324]|[4.074]|
|(Proportion of birth-year<br>cohort in labor force)||||||
|Constant|4.473***|4.467***|4.898***|4.552***|4.398***|
||[0.475]|[0.698]|[0.301]|[0.593]|[0.425]|
|N|2478294|1434525|1043769|822189|1656105|
|Adjusted R-square|0.899|0.918|0.869|0.888|0.906|

Note: Standard errors in brackets. Significance levels: *p<0.05, ** p<0.01, *** p<0.001.

47
**Table 10** :  Life Expectancy at Birth Effects on Lifetime Earnings with Correction for Selection Due to Participation in Labor Force

||**Pooled S**|**ample**|**M**|**ale**|**Fem**|**ale**|**Ru**|**ral**|**Ur**|**ban**|
|---|---|---|---|---|---|---|---|---|---|---|
||Selection<br>Equation|Earnings<br>Equation|Selection<br>Equation|Earnings<br>Equation|Selection<br>Equation|Earnings<br>Equation|Selection<br>Equation|Earnings<br>Equation|Selection<br>Equation|Earnings<br>Equation|
|Household size|-0.039***<br>[0.001]||0.002<br>[0.001]||-0.075***<br>[0.001]||-0.022***<br>[0.001]||-0.056***<br>[0.001]||
|**Respondent is**|0.787***||0.862***||0.538***||0.721***||0.810***||
|**household head**|[0.003]||[0.007]||[0.004]||[0.006]||[0.004]||
|**Life**e**xpectancy at**<br>b**irth**|**_0.003***_**<br>**_[0.000]_**|**_0.014***_**<br>**_[0.004]_**|**_0.002**_**<br>**_[0.001]_**|**_0.018***_**<br>**_[0.007]_**|**_0.007***_**<br>**_[0.000]_**|**_0.012***_**<br>**_[0.003]_**|**_-0.006***_**<br>**_[0.001]_**|**_0.014***_**<br>**_[0.005]_**|**_0.016***_**<br>**_[0.001]_**|**_0.010***_**<br>**_[0.003]_**|
|Age|0.204***<br>[0.001]|0.045***<br>[0.005]|0.203***<br>[0.002]|0.035***<br>[0.009]|0.209***<br>[0.001]|-0.005<br>[0.016]|0.199***<br>[0.002]|0.046***<br>[0.006]|0.209***<br>[0.002]|0.043***<br>[0.006]|
|Age-squared*(1/100)|-0.3***|-0.0***|-0.3***|0|-0.3***|0|-0.3***|-0.0***|-0.3***|-0.0***|
||[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|[0.000]|
|Rural|0.106***<br>[0.003]|-0.378***<br>[0.062]|-0.061***<br>[0.006]|-0.351***<br>[0.076]|0.172***<br>[0.003]|-0.401***<br>[0.037]|||||
|Male|<br>1.691***<br>[0.003]|<br>0.154***<br>[0.036]|||||1.851***<br>[0.006]|0.301***<br>[0.047]|1.630***<br>[0.004]|0.105**<br>[0.041]|
|Marital status, never<br>married|0.070***<br>[0.004]|-0.116***<br>[0.031]|-0.914***<br>[0.007]|-0.026<br>[0.035]|0.558***<br>[0.005]|-0.122**<br>[0.056]|-0.030***<br>[0.007]|-0.108***<br>[0.036]|0.109***<br>[0.005]|-0.121***<br>[0.030]|
|Cohort Size||-2.286***||-2.471***||-0.666||-1.761***||-2.120**|
|(Proportion of birth-<br>year cohort in labor||[0.749]||[0.839]||[0.950]||[0.273]||[0.972]|
|force)|||||||||||
|Parents’ life|0.007***|0.004**|-0.001|0.008***|0.010***|-0.001|0.012***|0.005|-0.002***|0.007***|
|expectancy|[0.000]|[0.002]|[0.001]|[0.003]|[0.000]|[0.001]|[0.001]|[0.003]|[0.000]|[0.002]|
|Inverse Mills Ratio||-0.742***<br>[0.278]||-1.575***<br>[0.566]||-1.077***<br>[0.390]||-0.443**<br>[0.214]||-0.849**<br>[0.325]|
|Constant|-3.420***|5.356***|-0.742***|5.262***|-4.003***|6.872***|-3.137***|5.034***|-3.747***|5.497***|
||[0.038]|[0.297]|[0.084]|[0.511]|[0.045]|[0.569]|[0.058]|[0.483]|[0.050]|[0.266]|
|N|4453600|2478294|2149376|1434525|2304224|1043769|1831164|822189|2622436|1656105|
|Adjusted. R square||0.9||0.919||0.871||0.888||0.907|
|F-statistics||344.508||130.152||319.203||61.128||201.815|

Note: Standard errors in brackets. Significance levels: *p<0.05, ** p<0.01, *** p<0.001. From the selection equation, we calculate the linear predicted probabilities, and then convert these into normal densities to calculate the inverse Mills ratio. The group-specific inverse-mills ratios are used in the earnings specifications, which is estimated only on those who are in the labor force, to correct for the selection.

48
~~—-(-h~~ -e ~~—~~ r)(-e )) = )I.

~~—=R~~ |(e

)| > 0.

~~se = f=~~ ~~~ ol~~ :

|
<!-- Start of picture text -->
|<br>_<br>—}<br>( r —<br>H(T)= f le<br><!-- End of picture text -->

<!-- Start of picture text --> a<br>: 7 +l<br>RI<br>H(t)} de|<br>dt| +<br>-<br>—}a t]<br><!-- End of picture text -->
~~— ——~~ at ~~—~~ H {RI ] + e {F (I(t) H(t), D(t)] ~~—}~~ dt|

~~—~~ H(t)L(t) = HL[AO)e

|

(),D(© ~~)|—}~~ ae]
52