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\begin_body

\begin_layout Title
Declining health and longevity in America: A comment on Case and Deaton's
 “Morbidity and Mortality in the 21st century”
\end_layout

\begin_layout Author
Adriana Lleras-Muney
\end_layout

\begin_layout Standard
Life expectancy in the US and most developed countries has been increasing
 for the last 150 years rather steadily.
 But life expectancy at birth in the US declined in 2015 for the first time
 since 1994.
 Although small declines have been observed before, Case and Deaton (2015,
 2017) document a disturbing set of facts.
 Mortality rates among middle-aged white non-Hispanics have been rising
 since 2000, in sharp contrast to what is occurring to the mortality rates
 of other populations, such as Europeans of the same age.
 Death rates from suicide, drug and alcohol consumption are rising.
 Death rates from cardiovascular disease are no longer decreasing.
 Moreover, pain, disability and other measures of physical and mental health
 have been worsening.
 These increases in mortality and poor health are concentrated among whites
 without college degrees—in fact mortality among those with college continues
 to fall.
 
\end_layout

\begin_layout Standard
Case and Deaton (2017) show some new remarkable patterns that suggest life
 expectancy will continue to fall.
 When plotted by birth cohort, one finds that among white non-Hispanics
 without a college degree, the age-profile of mortality is getting steeper
 for each successive cohort: mortality at a given age is higher and it rises
 faster with age for more recent cohorts.
 The same is true for measures of disability or disease: health is deteriorating
 faster with age for younger cohorts.
 Case and Deaton further hypothesize that that the decline in health and
 longevity could be caused by worsening labor market conditions for cohorts
 entering the labor market in 1970 or later.
 
\end_layout

\begin_layout Standard
To better understand the findings of this paper, I investigate possible
 underlying causes of these patterns using the model by Lleras-Muney and
 Moreau (2017).
 This is a simple model of evolution of health and death from birth onwards.
 In its simplest form the behavior of mortality is determined by five parameters.
 I estimate this model for the 1940 cohort using cohort life tables from
 the Social Security Administration and show that it can reproduce lifetime
 mortality rates and life expectancy well.
 I then investigate whether changes in the baseline parameters can generate
 patterns in mortality and morbidity similar to those documented by Case
 and Deaton.
 
\end_layout

\begin_layout Standard
Just like Case and Deaton I conclude that there are (at least) two forces
 that could account for their findings.
 First lifetime health resources (either their level or the rate of increase)
 could be falling across successive cohorts.
 Second the rate of health depreciation (the rate at which we age) could
 be increasing.
 Both of these factors would generate steepening mortality and disability
 age profiles.
 Importantly, these patterns cannot easily be explained by “temporary” condition
s; in the model they can only be the result of permanent changes in the
 parameters beginning early in adulthood (or even earlier in life).
 This is again the same conclusion that Case and Deaton reach from their
 non-parametric analysis.
\end_layout

\begin_layout Standard
To assess the likelihood of each of these hypotheses, I use evidence from
 the literature to speculate on the root causes of these changes in mortality.
 Stalling or falling real lifetime incomes, in combination with increasing
 costs of health inputs, could rationalize lower health resources.
 Increases in lifetime exposure to pollutants, or increases in stress (due
 to, for example, declining intergenerational mobility or greater inequality)
 could also potentially be linked to increases in the depreciation rate.
 While a full evaluation of the empirical validity of these hypotheses is
 beyond the scope of this comment, the discussion suggests several directions
 for future research.
\end_layout

\begin_layout Standard
I end by commenting on the results by education and reflecting on possible
 policy implications.
 
\end_layout

\begin_layout Section
A simple model of health and mortality estimated for the US
\end_layout

\begin_layout Standard
This section draws heavily on Lleras-Muney and Moreau (2017).
 In the baseline model, the population is born with a given level of health
 
\begin_inset Formula $H_{0}$
\end_inset

 which is normally distributed.
 Then every period health deteriorates, due to wear and tear.
 Importantly this deterioration is increasing with age, rather than constant.
 But individuals can enhance their health stocks by devoting resources to
 their health.
 These resources (
\begin_inset Formula $I$
\end_inset

) are identical for all individuals in a population and constant over the
 lifetime.
 But individuals within the population are subject to i.i.d.
 shocks 
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none

\begin_inset Formula $\varepsilon_{t}$
\end_inset

 every period: some get higher, and some get lower than average resources.
 Finally individuals die when their health stock reaches a lower bound 
\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit

\begin_inset Formula $\underline{H}$
\end_inset

.
 
\end_layout

\begin_layout Standard
More precisely, a cohort's health and mortality can be characterized by
 the following dynamic system
\end_layout

\begin_layout Standard
\begin_inset Formula 
\begin{equation}
\begin{array}{c}
H_{0}\sim N(\mu_{0},\sigma_{0}^{2})\\
\\
H_{t}=H_{t-1}-\delta t^{\alpha}+I+\varepsilon_{t}\\
\\
\varepsilon_{t}\sim N(0,\sigma_{\varepsilon}^{2})\\
\\
MR_{t}=P(H_{t}<\underline{H}|H_{t-s}>\underline{H},\forall s<t-1)\\
\\
\end{array}\label{eq:1}
\end{equation}

\end_inset


\end_layout

\begin_layout Standard
with 
\begin_inset Formula $\delta\in(0,\infty),\alpha\in(0,\infty),\text{and }I\in\mathbb{R}$
\end_inset

.
\end_layout

\begin_layout Standard
In this model, mortality falls rapidly at young ages because those with
 initially low levels of health die in the first periods.
 But if 
\begin_inset Formula $I$
\end_inset

 is sufficiently high (relative to the depreciation rate), then the distribution
 of health moves away from the threshold and causes mortality to plummet
 to very low levels by adolescence.
 Since the depreciation increases with age, eventually health starts to
 fall and mortality increases.
 After normalization,
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
Two parameters are not identified, we arbitrarily set 
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
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\uwave off
\noun off
\color none

\begin_inset Formula $\underline{H}=0,$
\end_inset

 and 
\begin_inset Formula $\sigma_{0}^{2}=1$
\end_inset

.
\end_layout

\end_inset

 this model describes health and mortality at every age using only five
 parameters: one for initial conditions 
\begin_inset Formula $(\mu_{0})$
\end_inset

, two that govern the aging process 
\begin_inset Formula $(\delta,\alpha)$
\end_inset

, and two that characterize the health resources provided by environment,
 in the form of average investments (
\begin_inset Formula $I$
\end_inset

) and the variance of these investments or shocks (
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none

\begin_inset Formula $\sigma_{\varepsilon}^{2}$
\end_inset


\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
).
 
\end_layout

\begin_layout Standard
This model is a very simplified version of reality.
 It does not account for accidents.
 It also does not allow for optimization: here 
\begin_inset Formula $I$
\end_inset

 is a constant provided by the environment (which is assumed to be stationary).
 In Lleras-Muney and Moreau we investigate many of these extensions.
 But here I use this model because it provides a remarkly good baseline:
 using only five parameters it can match the basic age profile of mortality
 we observe in the Human Mortality Database.
 Therefore this simple model is a good starting point for understanding
 the possible factors behind the deterioration in (white) Americans' health
 and longevity.
 
\end_layout

\begin_layout Section
Estimating the model for the US 
\end_layout

\begin_layout Standard
I validate this model by estimating the parameters for the 1940 birth cohort,
 using cohort tables provided by the Social Security Administration.
\begin_inset Foot
status open

\begin_layout Plain Layout
Data taken from https://www.ssa.gov/oact/NOTES/as116/as116_Tbl_7_1940.html#wp108127
4
\end_layout

\end_inset

 Because cohorts born after 1940 experienced robust GDP growth, I estimate
 a slightly extended version of the model above, with has a sixth parameter,
 
\begin_inset Formula $r.$
\end_inset

 
\begin_inset Formula $I$
\end_inset

 is assumed to be increasing every period at a constant rate 
\begin_inset Formula $r$
\end_inset

, also to be estimated.
 This model cannot be solved in closed form, so estimates are obtained using
 the simulated method of moments by minimizing the errors in predicted survival
 rates at each age.
\end_layout

\begin_layout Standard
Figure 1 shows the results of this exercise for US females.
 The left panel shows the (natural) log of the observed and the predicted
 mortality rate.
 The right panel shows the predicted and observed survival rates.
 Although the model does not perfectly predict some important features of
 the data (for instance the exact level of mortality during reproductive
 ages
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
This feature can also be improved upon.
 See Lleras-Muney and Moreau (2017).
\end_layout

\end_inset

), the model matches the basic shape of mortality very well.
 Moreover it predicts life expectancy (up to age 65
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
The cohort tables uses projections after 2005.
\end_layout

\end_inset

) of 60.5080 for this cohort, compared to the actual life expectancy of 60.5084.
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
The estimated parameters for men are 
\begin_inset Formula $I=0.0546$
\end_inset

, 
\begin_inset Formula $\delta=0.0012$
\end_inset

, 
\begin_inset Formula $\:\:\sigma_{\varepsilon}=0.1534,\:\alpha=1.3022,\:\mu_{0}=1.6078,\:r=1.0207$
\end_inset

.
 The fit is also good for men but not quite as good.
 This is because the 1940 male cohort has substantially higher mortality
 during reproductive ages that we cannot account for in the baseline model
 I am using here.
 In Lleras-Muney and Moreau (2017) we estimate models that succesfully account
 for the hump in mortality.
 
\end_layout

\end_inset

 The estimates show that initial health starts 1.74 standard deviations away
 from the 
\begin_inset Quotes eld
\end_inset

death threshold
\begin_inset Quotes erd
\end_inset

 and that the standard deviation of the annual shock is equivalent to 0.15
 standard deviations of the initial health distribution.
 The baseline health investment 
\begin_inset Formula $I$
\end_inset

 is equal to about 3% (0.0554/1.74) of the initial stock of health.
 Interestingly the rate of growth of 
\begin_inset Formula $I$
\end_inset

 is estimated as 2.24, which is remarkly close the the growth of US GDP over
 the last century (
\begin_inset CommandInset citation
LatexCommand citealp
key "jones_facts_2016"

\end_inset

).
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Mortality and survival profiles for US females born in 1940.
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename US1940F.jpg
	scale 30

\end_inset


\end_layout

\begin_layout Plain Layout
The estimated parameters are 
\begin_inset Formula $I=0.0554$
\end_inset

, 
\begin_inset Formula $\delta=0.0012$
\end_inset

, 
\begin_inset Formula $\:\:\sigma_{\varepsilon}=0.1515,\:\alpha=1.3049,\:\mu_{0}=1.7424,\:r=1.0224$
\end_inset

.
 
\end_layout

\end_inset


\end_layout

\begin_layout Section
Predicting and explaining the trends in white non-hispanic mortality profiles
 in the US
\end_layout

\begin_layout Standard
I now use this model to investigate whether changes in any of the parameters
 can generate the patterns documented by Case and Deaton.
 I simulate the effect of changes in the key parameters of interest starting
 at age 20 for both mortality and disease rates.
 I also simulate disease rates, by assuming that individuals are sick if
 they are alive but their health falls below some arbitrary (health) threshold.
 
\end_layout

\begin_layout Subsection
Simulation results
\end_layout

\begin_layout Standard
Figures 2 and 3 show the results for mortality and morbidity.
 Three types of changes can rationalize Case and Deaton's findings.
 A decrease in the baseline level of annual health investment, a decrease
 in their annual rate of growth, or larger depreciation, result in steeper
 age profiles for both mortality and disease rates (Figure 2).
 Note that in all cases, the effects of changing the parameters on mortality
 are almost imperceptible between ages 20 and 40.
 These effects materialize later in life and grow with age.
 
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Three factors generate steeper age profiles in mortality and disease
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename casedeatonI.pdf
	scale 50

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename casedeatonR.pdf
	scale 50

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename casedeatonD.pdf
	scale 50

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Changes in other parameters cannot explain the findings.
 Increasing the accident rate (from 0 to a positive number), the variance
 of resources, or the death thresholds result in patterns for mortality
 and morbidity that differ from what we observe (Figure 3).
 If we allow for an exogenous increase in random accidents, mortality increases
 but its slope is unchanged.
 And disease rates are identical (because accidents do not kill individuals
 on the basis of their health levels).
 If we increase the threshold for dying, mortality increases at all ages,
 but again the age-slope of mortality is unchanged.
 Moreover disease rates fall, because the frailest individuals are dying.
 Finally if we increase the variance of annual resources, then mortality
 becomes less steep and disease rates fall.
 
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Other factors cannot explain changes in both mortality and morbidity age
 profiles
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename casedeatonA.pdf
	scale 50

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename casedeatonT.pdf
	scale 50

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename casedeatonV.pdf
	scale 50

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
A few comments about these simulations are in order.
 First, I only simulate the effect of permanent changes starting at age
 20 and lasting until death, rather than temporary shocks at age 20.
 In Lleras-Muney and Moreau (2017), we simulate the effects of temporary
 changes (lasting 10 years and then ending) at age 20— the patterns we observe
 in these simulations differ substantially from those shown here: after
 the shock ends, mortality starts reverting to its counterfactual level.
 We cannot generate steepening age profiles with temporary shocks.
\end_layout

\begin_layout Standard
Second, although changes in these parameters at birth would cause similar
 patterns, the data suggest it is unlikely that conditions before age 20
 are responsible for the declines in adult mortality we observe.
 Infant mortality was falling for all these cohorts.
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
https://www.cdc.gov/mmwr/preview/mmwrhtml/mm4838a2.htm#tab1
\end_layout

\end_inset

 Educational attainment stalled for men and grew for women born after 1950,
 though at a much slower pace than for cohorts born before the war.
\begin_inset Foot
status open

\begin_layout Plain Layout
See Figure 5 of https://www.cbo.gov/publication/22010.
 Also see 
\begin_inset CommandInset citation
LatexCommand citet
key "goldin_long-run_2007"

\end_inset


\end_layout

\end_inset

 Heights increased throughout, although again at a decreasing pace for those
 born after 1950.
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
For white men height grew by more than 4cms for birth cohorts born between
 1910 and 1950, but only grew by 1 cm from 1950 to 1980 (http://www.tandfonline.co
m/doi/full/10.1080/03014460601116803).
 For women the increases are 2.1 and 1.3.
 Data from other sources suggest similar patterns (
\begin_inset CommandInset citation
LatexCommand citealp
key "bleakley_health_2014"

\end_inset

).
\end_layout

\end_inset

 These three measures (infant mortality, heights and education) are excellent
 indicators of initial conditions and early investments.
 And they are highly predictive of mortality in adulthood.
 These indicators did not decline after 1950 and thus early factors are
 not likely explanations for the increases in mortality.
 
\end_layout

\begin_layout Standard
Relatedly, the simulations assume that the entire profile of mortality is
 identical up to age 20, but this is not the case.
 
\begin_inset CommandInset citation
LatexCommand citet
key "currie_inequality_2016"

\end_inset

 report that from 1990 to 2010 
\begin_inset Quotes eld
\end_inset

For children and young adults below age 20, however, we found strong mortality
 improvements that were most pronounced in poorer counties
\begin_inset Quotes erd
\end_inset

.
 The fact that mortality rates before age 20 were falling for cohorts born
 after 1950 suggest that initial conditions are not constant across birth
 cohorts.
 In our model this would result in the entire profile of mortality shifting
 downwards, and thus lower mortality in middle and old age.
 A proper evaluation of any explanation needs to carefully consider changes
 in conditions before entry into the labor market.
 I expand on this issue below.
\end_layout

\begin_layout Subsection
What factors could be causing these changes?
\end_layout

\begin_layout Standard
Given these results I now discuss whether the existing literature provides
 any support for these possibilities.
\end_layout

\begin_layout Standard

\series bold
Decline in annual health investments.

\series default
 Could health resources be lower for more recent cohorts? Note that in the
 model 
\begin_inset Formula $I$
\end_inset

 does not correspond to current income, it is expressed in health units.
 But health cannot be directly consumed or increased—it must be produced.
 Consider then the simplest case where 
\begin_inset Formula $I$
\end_inset

 is produced using inputs 
\begin_inset Formula $x$
\end_inset

, which must be purchased at price 
\begin_inset Formula $p_{x}$
\end_inset

.
 Suppose that a constant share of one's lifetime income 
\begin_inset Formula $a$
\end_inset

 is spent on health at any given age: 
\begin_inset Formula $I=f(x)=f(aY/p_{x})$
\end_inset

.
 What this suggests is that resources could be going down if either (a)
 lifetime incomes are falling (holding prices constant), or (b) the price
 of health inputs is rising (holding incomes constant).
 Data on lifetime income by cohort is difficult to find.
 A very recent paper by 
\begin_inset CommandInset citation
LatexCommand citet
key "guvenen_lifetime_2017"

\end_inset

 uses data from the Social Security Administration tracking individuals'
 earnings over time.
 It reports that lifetime income stagnated or fell for men entering the
 labor market in 1967 and later, and this is mostly explained by a decline
 in incomes upon entry into the labor market.
 The lifetime incomes of women did rise, though starting from a lower baseline
 and never reaching the level of men.
 It is unclear then what has happened to lifetime family incomes, but it
 is possible they fell.
 More significantly, the price of health-related goods and services has
 increased over time very substantially, at a much faster pace than the
 the cost of other goods and services, starting in the late 1970s.
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
According to the Bureau of Labor Statitics, the cost of medical goods rose
 many times faster than the cost of other goods.
 See https://www.bls.gov/opub/mlr/2014/article/one-hundred-years-of-price-change-t
he-consumer-price-index-and-the-american-inflation-experience.htm
\end_layout

\end_inset

 Thus in real 
\begin_inset Quotes eld
\end_inset

health
\begin_inset Quotes erd
\end_inset

 terms, incomes could be much lower for those at the bottom of the income
 distribution or those with less education.
 
\end_layout

\begin_layout Standard
Case and Deaton downplay income as an explanation.
 But they consider only contemporary correlations in incomes and mortality,
 rather than correlations in lifetime resources and adult mortality.
 In our model of health and mortality, one year changes in the parameters
 have very small effects on contemporary mortality.
 Sustained changes have effects that are not visible immediately, but but
 become apparent after a substantial delay, as shown in Figure 2.
 Assessing whether lifetime resources fell for cohorts entering the labor
 market after 1970, particularly for those with low education, seems worthy
 of further exploration.
 It requires a much more in depth analysis than is provided here.
 It requires careful tracking of households (who is married to whom and
 how many dependents they have), of family and governmental transfers (taxes
 and subsidies), and of the prices of health inputs (like exercise and medical
 care).
 Equally important, one needs a model that allows for dynamic (delayed)
 effects of conditions at a point in time, and that accounts for differences
 in intial conditions.
 
\end_layout

\begin_layout Standard

\series bold
Increase in depreciation (aging) rates.
 
\series default
What might cause higher deterioration rates or faster aging? The medical
 literature suggests several hypotheses.
 For instance repeated exposure to stress cumulates and eventually leads
 to permanent changes in the functioning of the immune system (among others),
 a process known as 
\begin_inset Quotes eld
\end_inset

allostatic load
\begin_inset Quotes erd
\end_inset

 (
\begin_inset CommandInset citation
LatexCommand citealp
key "sapolsky_why_2004"

\end_inset

).
 These processes have been documented experimentally in animals.
 It is possible that cohorts entering the labor market in the 1970s and
 after would have experienced increasing levels of stress.
 This stress could be caused by their lower wages upon entry into the labor
 market.
 
\begin_inset CommandInset citation
LatexCommand cite
key "chetty_association_2016"

\end_inset

 show that cohorts born after the 1940s were less likely to do better than
 their parents.
 Perhaps these cohorts suffer stress by falling short of their expectations,
 as suggested by David Cutler in his comment on this paper.
 The changes in inequality that started in the late 1970s could also be
 hypothesized to lead to increased stress among these cohorts.
 The 
\begin_inset Quotes eld
\end_inset

stress
\begin_inset Quotes erd
\end_inset

 hypothesis also seems worth investigating, particularly given the 
\begin_inset Quotes eld
\end_inset

deaths of despair
\begin_inset Quotes erd
\end_inset

: alcohol and drug abuse suggest individuals are unhappy.
 
\end_layout

\begin_layout Standard
Pollution (air, water and food toxins) can also result in accelerated aging.
 This hypothesis is supported by animal models (eg 
\begin_inset CommandInset citation
LatexCommand citealp
key "sun_long-term_2005"

\end_inset

) but is difficult to demonstrate in humans.
 The use of fossil fuels has increased steadily since 1900, and while some
 pollutants have been regulated since the 1970s, there are more than a thousand
 toxins emitted into the air and the water, and most are not regulated.
 For instance PM 2.5 (particulate matter with a diameter of 2.5 microns or
 less) has recently been linked to many diseases, but has been regulated
 only since 2007.
 Mercury, another highly toxic pollutant, has only been regulated since
 2011.
 Thus more recent cohorts may have accumulated substantially higher lifetime
 exposure to pollutants than cohorts born before the war.
 Moreover exposure to pollutants is higher for those from lower SES backgrounds.
 For example poor individuals with low education are more likely to live
 close to highways and Superfund sites (
\begin_inset CommandInset citation
LatexCommand citealp
key "currie_pollution_2013"

\end_inset

).
 A careful analysis of the pollution hypothesis needs to account for differences
 in lifetime exposure by race, location and birth cohort.
 Because pollution has been shown to affect the cardiovascular system in
 the short term in humans, its long term effects seem worthy of futher investiga
tion, particularly in light of the fact that cardiovascular mortality rates
 are no longer falling among adults.
 
\end_layout

\begin_layout Standard
Case and Deaton point to the increases in obesity and diabetes as possible
 explanations, and indeed these are chronic conditions that could result
 in the type of effects we observe.
 There are many other possible factors that could affect aging, for instance
 physical activity.
 
\end_layout

\begin_layout Subsection
In Light of Education
\end_layout

\begin_layout Standard
The increase in age-adjusted mortality for white non-Hispanics as a whole
 is modest compared to the increase in mortality experienced by those with
 less than a college degree.
 Several papers have documented that the gap in life expectancy between
 those with college and those without has been rising since the 1960s (e.g.
 
\begin_inset CommandInset citation
LatexCommand citealp
key "meara_gap_2008"

\end_inset

, 
\begin_inset CommandInset citation
LatexCommand citealp
key "montez_trends_2011"

\end_inset

).
 Case and Deaton show that since 1998, mortality rates have fallen for those
 with college degrees, while increasing for those without.
\end_layout

\begin_layout Standard
This widening gap does not appear to be caused by a change in the composition
 of those with more education.
 Although there have been increases in the share of individuals holding
 a college degree, these increases have been small for cohorts born after
 1950, particularly for men.
 The share of college graduates has been roughly constant for men born after
 WWII, and for women born after 1970.
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
For working men born in 1950 years of completed education (measured at age
 25-29) were 13.4 compared to 13.2 for those born in 1980.
 For working women, average completed years of education were 13.3 for the
 1950 cohort, 13.9 for the 1970 cohort and 14.1 for 1980 cohort (www.cbo.gov/publica
tion/22010).
\end_layout

\end_inset

 Nevertheless the composition of the pool could be changing despite roughly
 constant shares.
 There could be increasing selectivity in college entrance on the basis
 of test scores for instance.
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
Case and Deaton repeatedly note that the share of college graduates has
 remained unchanged, and argue this rules out changes in composition or
 selection as an explanation for changes in mortality.
 While constant shares are suggestive, they are neither necessary nor sufficient
 to guarantee that the pool of college gradutes has remained similar over
 the last 50 years.
 For instance college slots could be given by lottery in one year but allocated
 according to entrance exams in another.
 The same fraction of people would be accepted into college in both years,
 but selection (the type of individuals in college) would be vastly different.
\end_layout

\end_inset

 But previous papers investigating this issue have concluded that changes
 in the composition or behaviors of this pool do not appear sufficient to
 explain the growing gap in life expectancy by education (eg 
\begin_inset CommandInset citation
LatexCommand citealp
key "cutler_rising_2011"

\end_inset

).
 Rather the 
\begin_inset Quotes eld
\end_inset

returns to college
\begin_inset Quotes erd
\end_inset

 in terms of health appear to be on the rise.
 
\end_layout

\begin_layout Standard
The labor market returns to college have also been steadily rising since
 the 1970s, when they reached their lowest point in the century (
\begin_inset CommandInset citation
LatexCommand citealp
key "goldin_race_2007"

\end_inset

).
 
\begin_inset CommandInset citation
LatexCommand citet
key "autor_skills_nodate"

\end_inset

 reports that 
\begin_inset Quotes eld
\end_inset

the earnings gap between college and high school graduates has more than
 doubled in the United States over the past three decades.
\begin_inset Quotes erd
\end_inset

 For men without a college degree, median wages have declined since 1979.
 Women without a college degree have seen improvements in their median wages,
 but they start at a lower level and again never catch up to men.
 
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
See Figure 3 of www.cbo.gov/publication/22010.
 Also see 
\begin_inset CommandInset citation
LatexCommand citet
key "autor_skills_nodate"

\end_inset

.
 
\end_layout

\end_inset

 The literature looking at the 
\begin_inset Quotes eld
\end_inset

college premium
\begin_inset Quotes erd
\end_inset

 has concluded its rise is likely due to the increase in demand for college
 workers, rather than changes in the composition of college workers.
 Again it is difficult to estimate the changes in lifetime resources by
 education, because this requires accounting for marriage and fertility
 patterns by education, as well as transfers and changing prices.
 But the evidence does suggest that lifetime resources of the less educated
 may have fallen, while increasing for those with college degrees.
 Altogether deteriorating wages upon entry into the labor market provide
 a parsimonious explanation for the findings.
 
\end_layout

\begin_layout Subsection
The importance of accounting for initial conditions
\end_layout

\begin_layout Standard
Case and Deaton contrast the experience of blacks and Hispanics with that
 of whites in their search for explanations.
 And they use the comparison as another piece of evidence against the income
 explanation.
 Though blacks’ changes in income tracked that of whites, black mortality
 was still decreasing while white mortality was increasing, at least until
 2010.
 But blacks, whites and Hispanics have markedly different levels and trends
 in childhoood mortality.
 This makes the comparisons across groups difficult to intepret, because
 improvements in health conditions have delayed effects.
 
\end_layout

\begin_layout Standard
Black infant mortality in 1940 was much higher than whites, and it fell
 much more in levels (though not in percentage terms).
 More generally, mortality before age 20 has fallen more for blacks than
 for other groups (
\begin_inset CommandInset citation
LatexCommand citealp
key "currie_inequality_2016"

\end_inset

).
 All else equal, these improvements early in life lower mortality throughout
 the lifetime, 
\shape italic
and particularly after age 40
\shape default
.
 In our model a population with higher initial health will have lower mortality
 throughout the lifetime.
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
See Lleras-Muney and Moreau (2017) for proofs of these statements.
\end_layout

\end_inset

 There is also ample empirical evidence showing that early conditions have
 long lasting consequences on health and mortality later in life.
 For instance it is well established that conditions in utero affect mortality
 after age 45 (
\begin_inset CommandInset citation
LatexCommand citealp
key "almond_killing_2011"

\end_inset

, 
\begin_inset CommandInset citation
LatexCommand citep
key "almond_childhood_2017"

\end_inset

).
 Perhaps middle-aged Blacks are still reaping the health benefits of improving
 conditions in childhood and adolescence, and these lasting gains overshadow
 the detrimental effects of declining economic conditions.
 
\end_layout

\begin_layout Standard
To illustrate this point I conduct another simulation.
 For whites I assume mean initial health in 1940 is 1.754 and 
\begin_inset Formula $I$
\end_inset

 is 0.0551–these parameters match the 1940 profile of mortality, as expained
 in section 2.
 The group labeled 
\begin_inset Quotes eld
\end_inset

low SES
\begin_inset Quotes erd
\end_inset

 has lower initial health (
\family roman
\series medium
\shape up
\size normal
\emph off
\bar no
\strikeout off
\uuline off
\uwave off
\noun off
\color none

\begin_inset Formula $\mu_{0}=1$
\end_inset


\family default
\series default
\shape default
\size default
\emph default
\bar default
\strikeout default
\uuline default
\uwave default
\noun default
\color inherit
) and lower initial annual resources (
\begin_inset Formula $I=0.051$
\end_inset

) in 1940, and thus has much greater infant and child mortality.
 For both groups the hypothetical cohort labeled 
\begin_inset Quotes eld
\end_inset

1980
\begin_inset Quotes erd
\end_inset

, has higher initial health, and higher annual investments up to age 20,
 which generate lower mortality for the more recent cohort up to age 20.
 But at age 20 both groups see their annual health resources fall by the
 same proportional amount (20%).
\begin_inset Foot
status collapsed

\begin_layout Plain Layout
These simulations are just illustrative–the parameters are not meant to
 match any specific mortality profile.
\end_layout

\end_inset

 
\end_layout

\begin_layout Standard
\begin_inset Float figure
wide false
sideways false
status open

\begin_layout Plain Layout

\end_layout

\begin_layout Plain Layout
\begin_inset Caption Standard

\begin_layout Plain Layout
Improvements in initial conditions and decreases in I at age 20
\end_layout

\end_inset


\end_layout

\begin_layout Plain Layout
\begin_inset Graphics
	filename initial.pdf
	scale 70

\end_inset


\end_layout

\end_inset


\end_layout

\begin_layout Standard
Figure 4 shows the results.
 For both groups mortality up to age 20 is markedly lower for the 1980 cohort,
 consistent with what we observe in the US.
 But despite the fact that both groups are hit at the same age (age 20)
  by the same adverse  shock, mortality increases at earlier ages for whites
 than for low-SES groups.
 In the time series one would observe for instance that mortality at age
 40 is falling for low-SES groups, but increasing for whites.
 This occurs because the improvements in early conditions have delayed effects
 on mortality later in adulthood.
 For the low-SES groups these greater improvements (in levels) partly mitigate
 the negative shock at age 20.
 This suggests that it is extremely difficult to draw conclusions about
 the effects of shocks without accounting for differences in conditions
 before the shock.
 
\end_layout

\begin_layout Standard
These early life improvements could explain why mortality of blacks is not
 falling at the same time as that of whites, despite their being hit by
 equally deteriorating conditions in the labor market at age 20.
 Interestingly Fig 1.2 in Case and Deaton's paper shows that the mortality
 of the black population also started to rise in 2010.
 So it is possible that, for blacks, adverse labor market effects are just
 begining to outstrip the benefits of improved childhood conditions.
\end_layout

\begin_layout Section
Concluding remarks
\end_layout

\begin_layout Standard
Health and longevity appear to be in decline in the US among white non-Hispanics
, particularly for those without a college education.
 
\end_layout

\begin_layout Standard
Case and Deaton (2017) show that current incomes and other contemporary
 short-term factors cannot adequately explain the patterns in the data.
 Instead “the data are consistent with long-run processes influencing outcomes,
 rather than contemporaneous shocks affecting health.” The analysis in this
 article, based on a cohort model of health and mortality, comes to very
 similar conclusions.
 There has been a permanent deterioration in one or more factors that affect
 health, starting around the time of labor market entry.
 This deterioration is visible for cohorts born after 1950, and likely started
 occuring around age 20 (rather than at birth).
 It is more visible for those without college.
 These affected cohorts entered the labor market in the 1970s.
 Changes in labor market conditions starting in the 1970s resulting in lower
 wages, and possibly lower lifetime real incomes for a substantial part
 of the population, are a likely explanation for the observed deterioration
 in health in middle age.
 But any factor affecting health to which cohorts are exposed for a long
 period of time starting around age 20 is a candidate explanation.
 Temporary changes on the other hand are unlikely to explain the findings.
 However a full accounting of the patterns we observe requires a careful
 consideration of how the entire set of lifetime circumstances has changed
 for more recent cohorts.
 
\end_layout

\begin_layout Standard
Deaths associated with prescription drug and alcohol abuse have increased
 very substantially.
 Policies that limit access to these drugs could save many lives, as could
 expansion of alternative non-lethal pain killers such as marijuana.
 But the data suggests that the underlying mental and physical health of
 a large fraction of the population is declining.
 Reducing access to alcohol and drugs will not reduce pain, nor reverse
 the underlying trend that is causing recent cohorts to be in worse health.
 Thus a deeper understanding of the underlying causes of these trends is
 necessary.
 
\end_layout

\begin_layout Standard
Mortality is declining particularly fast for those without college education.
 The returns to college in terms of lifetime wages and incomes, as well
 as in terms of longevity, is rising.
 If these wage and health returns are causal, then serious consideration
 should be paid to expanding college attendance.
 If education is not causing these, it would be extremely important to identify
 what, then, is causing the increasing gaps by education.
 Another possible policy response would be to consider wage subsidies, perhaps
 throuh programs like the EITC, which provide greater support for those
 with the lowest wages.
 If the trends identified by Case and Deaton continue, it is possible that
 future generations will be substantially worse off.
 
\end_layout

\begin_layout Standard
\begin_inset Newpage newpage
\end_inset


\end_layout

\begin_layout Standard
\begin_inset CommandInset bibtex
LatexCommand bibtex
bibfiles "My Library/My Library"
options "plainnat"

\end_inset


\end_layout

\end_body
\end_document
