The Effect of Presidential Service on Life Expectancy
Mark Borgschulte∗
UC Berkeley
January 2014
Abstract
Presidents of the United States of America appear to suffer a significant loss of natural life from their service. Using runner-up presidential candidates as the counterfactual, presidents lose an average of 3.8 years of natural life expectancy; including
assassinations increases the lost life to 6.7 years. I argue the rigors of the office
are the most likely explanation for the difference in natural lifespan. This finding
complicates our understanding of the health effects of labor supply and rank-based
explanations of the socioeconomic gradient in life expectancy.
JEL No.: I14; J14
∗
PhD Candidate, Department of Economics, University of California, Berkeley. I would like to thank
David Card, Angus Deaton, Ronald Lee, and Ken Wachter for their comments, and Natalie Goldberg
and Allen Gurdus for their excellent research assistance. Email: mark at econ.berkeley.edu.
1
Introduction
The health effects of rank within a social and bureaucratic hierarchy have been proposed
as an explanation for the socioeconomic gradient in health and life expectancy, most
famously in the Whitehall study.1 Despite the extensive subsequent literature on social
inequalities and health,2 causal evidence on the relationship between rank and lifespan
remains scarce, especially as it is difficult to disentangle the effects of working conditions
from selection into rank and occupation.3 A fundamental feature of the socioeconomic
gradient in health and life expectancy is that it extends throughout the distribution, a
fact which motivates much of the interest in rank-based explanations, and away from
income and poverty as the causal forces behind health inequalities. Studies on primates
find negative effects of low rank; however, they also suggest the effects of high rank may
be inversely related to stability in the social hierarchy.4 To date, we have no systematic
evidence on life expectancy at the top of human hierarchies, despite the importance of
these issues for our understanding of inequality, bequests and estate taxation, and the
labor supply of the most productive workers in the economy.5
In this paper, I investigate the reported rapid aging of US presidents, an apparent
counterexample to rank as the cause of superior life expectancy. If promotion to high
rank leads to longer life (as the rank-lifespan theory predicts), then presidents should
exhibit superior life expectancy to both the general population and other political officeholders, since they are the highest ranking member of a number of hierarchies in the
US government. On the other hand, conventional wisdom based on observation holds
that presidents age more rapidly than other men of their age; in particular, they exhibit
gray hairs, wrinkles and other age-related changes to facial features.6 The office of US
president is uniquely well-suited for such an investigation, not only due to its prominence
1
From Cutler et al. (2006): “Outside of economics, the currently dominant theory of health differentials
is that the poor health of low status individuals is caused by ‘psychosocial stress’—the wear and tear
that comes from subordinate status and from having little control over ones own life.” See Marmot et al.
(1991), Marmot (1994) and Marmot (2004) on Whitehall.
2
For example, see McEwen (1998) on allostatic loads, the proposed mechanism behind rank-based
explanations.
3
Recent work in economics on the causal mechanisms of Whitehall include Anderson and Marmot
(2011) and Case and Paxson (2011); also, see Stringhini et al. (2010).
4
As in Sapolsky (2005).
5
For example, Kopczuk and Saez (2004) use the life expectancy of college-educated whites (estimated
in Brown et al. (2002)), in the absence of detailed data on life expectancy at the top of the income
distribution.
6
A Google search of “presidential aging in office” uncovers articles and photo series documenting this
visual effect published by Time magazine, the Washington Post and CNN, among others. See Olshansky
(2011) and Hanna (2011) for discussions of this evidence.
1
in the US political hierarchy, but also the long history of contested democratic elections
and stable 4-year term of service. These features allow me to establish counterfactual life
expectancy through the comparison of US presidents to electoral runners-up.7
Against the predictions of the theory, but consistent with conventional wisdom, I find
a shorter natural life expectancy of US presidents relative to runners-up in presidential
elections. Runners-up outlive winning candidates by an average of 3.8 years, treating
assassinated and still-living presidents as censored observations. Expanding the sample
to include deaths by assassination increases the estimated effect to 6.7 years, meaning
the loss of natural life expectancy has historically been larger than the loss to the risk of
assassination. Winning candidates have survived an average of 16 years, so these estimates
corresponds to a 20% reduction in natural life expectancy. The statistical significance of
the results depends on the full sample, but is otherwise robust to a variety of controls
and alternative specifications. In sum, I find the historical evidence is consistent with the
observed rapid aging of presidents.
Presidential runners-up may outlive presidents due to either the effects of presidential
service or the (s)election of shorter-lived candidates in the final round of the electoral process. Previous studies on the socioeconomic gradient struggled to disentangle the causal
effect of rank from the unobservable factors that predict it, as most promotion processes
select for qualities associated with longer life. Crucially for my comparison, the selection
effect works against the finding: if electors prefer longer-lived candidates, the comparison
of winners to losers will understate the true costs of service. Historical accounts and previous research suggest healthier candidates possess an advantage in the election—whether
through the campaign process or a direct electoral preference for health8 —implying the
estimates are biased towards finding a life expectancy advantage for presidents. To be
precise regarding the size of this bias, the estimates here should be adjusted by the (inherently unobservable) pre-election advantage in life expectancy of winning candidates.
Overturning the monotonicity finding of previous research on the socioeconomic gradient complicates rank-based explanations of the socioeconomic gradient in life expectancy,
but may also provide support for the hypothesized causal mechanism. As a causal pathway from rank to health, the Whitehall literature has focused on increased job control and
predictability that comes with higher rank, diminishing work-related stress, and hence,
7
I define the runner-up to be the highest popular vote finisher who did not become president, or the
in absence of a popular vote, the highest electoral vote-getter. Usually, but not always this candidate
came in “second-place.”
8
See Acemoglu (2005) and Besley and Persson (2009).
2
a decreasing the risk of cardiovascular and other diseases.9 Although the medical literature has not conclusively shown stress to reduce lifespan, research has demonstrated
the short-term damage caused by allostatic loads and repeated exposure to stress-related
hormones.10 The job of US president may be particularly exposed to decreased job control and low predictability, especially during times of national emergencies. Motivated by
the observed signs of aging among US presidents, previous research has investigated the
hypothesis of a link between presidential service and life expectancy attributed to exposure to stress.11 In Section 4, I draw on evidence from secondary analyses and historical
accounts to argue that work-related stress is the most likely explanation for these findings.
Although work-related stress is an important hypothesis for the shorter life expectancy
of US presidents, exposure to other risk factors may confound the relationship between
service and aging-related deterioration of health and lifespan. While the detrimental
health effects of presidential service correspond to effects observed at the top of primate
hierarchies, I find little support for a corresponding relationship between the stability of
the president’s position and the magnitude of the effect. I find no relationship between
the margin of victory and life expectancy of the candidates, and no smaller effect for reelection (which should represent more stable political environments). Another possibility
is that presidents travel and interact with many people, and may come into contact
with infectious diseases at higher rates than had they lost the election. As well, the
rigors of the office may lower immunity, and hence, make presidents more susceptible to
infectious disease.12 I find little evidence of an effect of moving to Washington, D.C.,
as vice-presidents show no loss of life expectancy relative to runner-up vice-presidential
candidates, but I cannot rule out all work-related exposures. An analysis of cause of death
would require a larger sample size, and throughout the paper I measure the combined effect
of service on longevity.
The remainder of the paper is organized as follows. Section 2 discusses the selection
of presidential candidates, and previous research on life expectancy at the top of hierarchies. Section 3 reports the main results. Section 4 discusses prospective mechanisms and
robustness. Section 5 concludes.
9
See Vaananen et al. (2003), Anderson and Marmot (2011), Moslehi et al. (2012) for evidence on the
links between job control, stress and life expectancy.
10
For example, see McEwen (1998), Epel et al. (2004).
11
I discuss Olshansky (2011) and selection into candidacy in Section 2.
12
Indeed, two of the four presidents to die of natural causes while in office fell victim to infectious
disease, William Henry Harrison (pneumonia) and James K. Polk (gastroenteritis).
3
2
Presidents and Life Expectancy at the Top
2.1
Previous Work on Superstar Life Expectancy
Using an alternative counterfactual, Olshansky (2011) estimates a near zero effect of
presidential service on aging by comparing presidents to other men of their birth cohort,
conditional on survival to the age of election. In essence, this strategy assumes presidents
can be compared to the average man of their birth cohort, conditional on survival to the
age of election. This assumption would hold if presidents were chosen at random from the
population, or more generally, the process of selection into the presidency were unrelated
to factors that predict longer life. The comparison of presidents to the average man of their
age and cohort ignores the socioeconomic gradient and attendant advantages possessed by
presidents; to take a simple example from the US Census, literacy rates did not reach 90%
for men age 50-60 until 1930. There are other obvious presidential characteristics that
distinguish these men from the general population in ways that have been associated with
differential life expectancy: marriage rates are around 75% for men age 50-60 before 1950,
while 84% of presidential candidates have been married; the foreign-born comprised 30%
of the US population in 1900, while presidents are natural born citizens by law; and around
10% of men aged 50-60 have identified as black since Emancipation, while no candidate
before Barack Obama acknowledged African ancestry. Presidential candidates have been
noted for their relative height, another predictor of high income and long life through
much of US history.13 Put simply, the class of men from which the president is drawn has
much higher life expectancy than the average man of their age in the population.
The potential for negative effects of fame, superstardom and tournaments have also
been studied, including the potential for effects on lifespan. Superstars, according to
Rosen (1981), arise from systems with highly skewed distributions of income, market
share and public attention. The tournament structure discussed by Rosen is reflected by
the multiple rounds of election over a politician’s lifetime. Historically, winning presidential candidates are dramatically distinguished from losing candidates and other “near
presidents,” receiving far greater prestige and celebrity after the election, as well as the
returns in social and political capital to serving as chief executive. Malmendier and Tate
(2009) study superstar CEOs and find negative effects of celebrity on company performance. Similar in spirit to this paper, Rablen and Oswald (2008) and Becker et al. (2008)
study the effects of elections and prestige on longevity. Rablen and Oswald (2008) compares Nobel prize winners to a matched group of similar nominees, finding small (1-1.5
13
See Persico et al. (2004), Fogel (2004).
4
year) gains in life expectancy from winning a Nobel.14 Becker et al. (2008) study the
life expectancy effect of election to the Baseball Hall of Fame by comparing those just
elected to those just under the threshold. Becker et al. finds those who are elected live
longer than both those who narrowly miss election and those not considered, but those
who narrowly miss election have shorter life expectancy than those not considered. In
sum, previous literature has explored linkages between high rank and lifespan without
producing a consistent account of the causal effects of promotion.
2.2
Data and the Counterfactual Experiment
In contrast to the previous work on presidential life expectancy, I focus my analysis on
the difference between presidents and their closest comparison group, the “runners-up”
in presidential elections. This comparison implicitly adjusts for the characteristics which
predict selection into candidacy, as all members of the sample have been selected in
this way. The comparison of presidents to runners-up allows me to reduce the potential
explanations for the shorter life expectancy of presidents to either the effects of service, or
an electoral advantage of shorter lived-candidates. I return to the possibility of an electoral
preference for characteristics associated with shorter life expectancy in Section 4.
In the analysis, I focus on the outcome of the election as the random event; where
ambiguities arise, I take the runner-up from the candidates eligible for the presidency
on the popular ballot.15 Data on the birth and death dates of presidential candidates
is available from numerous sources, and an Online Appendix reports the runners-up and
dates of birth and death of the candidates. Table 1 summarizes the population of candidates, treating each candidacy as an individual observation. US presidential candidates
are 56.02 years old on average. Winners (irrespective of cause of death) are 56.67 years
years old, 1.31 years older than runners-up. A t-test fails to reject the null of no difference in mean age at election between winners and runners-up (t = 0.94; p = 0.35). The
full sample size is 112 candidacies, composed of 72 individuals, 57 of whom have died of
natural causes, with 4 assassinations representing 6 candidacies; no losing candidate has
been assassinated. The sample statistics include individuals who appear multiple times
as candidates; in other words, the sample is candidacies, not individuals. For example,
Andrew Jackson narrowly lost the 1824 election, an election in which he actually garnered
14
This paper surveys the medical and social science literature in greater depth than I do here.
Thus, I exclude “technical” presidential candidacies such as Aaron Burr in 1800, and include Horace
Greeley, the losing presidential candidate in 1872, who died before the electoral college could meet.
Greeley has the shortest survival of any candidate, winner or runner-up.
15
5
more electoral and popular votes than the winner, John Quincy Adams, but failed to take
a majority of electoral votes. This sent the election to the House of Representatives,
which elected Adams. Following the defeat, Jackson went on to win the 1828 and 1832
elections, and serve a total of 8 years. In the empirical model, I treat Jackson as three
times a candidate, losing once and winning twice.16 In the analysis, I use weighting and
clustered standard errors to address the multiple candidacies problem; however, summary
statistics and figures report raw data.
Although the age differences between the candidates at the time of election are not
statistically significant, selection on health and age requires additional discussion. The
process of selection into candidacy explicitly considers the expected survival of the candidate; specifically, selection on the health of candidates increases with age, meaning the
age-specific hazard rate will be a function of the age of the candidate at the time of the
election. I call this the “Ronald Reagan problem,” after the oldest president at election.17
It is natural to believe that candidate health matters most over the term of service, so
that candidates will be selected on a high probability of survival for an additional 4 years.
For example, compare the expected age at death (at the time of selection into candidacy)
of Ronald Reagan, elected for the second time at age 73, to Barack Obama, elected for the
first time at age 47. For Reagan, an additional 4 years of life expectancy placed him close
to the expected survival of a man of Barack Obama’s age (not accounting for the fact
that Obama is or was a cigarette smoker). As a result, Reagan’s age (and health) was an
important campaign issue.18 On the other hand, it is unlikely the electorate placed high
weight on Obama’s survival from age 73 to 77. Due to the context, we should not expect
Obama and Reagan to have the same age-specific mortality rates or life expectancy. In the
empirical analysis with age at death as the outcome, I account for this by controlling for
age at election, a term which is significantly different from 0 (no Ronald Reagan problem)
and 1 (perfect selection on remaining life expectancy) at p < 0.01 in all specifications.
16
In a setting (US House Elections) with many more observations, Dal Bo et al. (2009) restricts attention
to candidates standing for their first re-election.
17
Thank you to Ken Wachter for suggesting this name.
18
Reagan famously neutralized the issue of his age in a debate with Walter Mondale, saying “I will
not make age an issue of this campaign. I am not going to exploit, for political purposes, my opponent’s
youth and inexperience.” Mondale later cited this quip as the moment his chance of winning came to an
end.
6
3
3.1
Effects of Presidential Election and Succession
Graphical Results
As the primary visual test of the hypothesis that presidents age faster than electoral
runners-up, I plot the survival of US presidents and runners-up using Kaplan-Meier survival curves. This method permits the inclusion of still-living and assassinated presidents
as censored observations, so the figures represent all 112 candidacies. Figure 1 displays
the primary result. In the upper panel, the survival curves of presidents and runners-up
reveal the superior survival of runner-up candidates at all time horizons. Both curves
display a S-shape consistent with selection on health at the time of election, and resemble
a normal-distribution survival function. Survival does not appear sharply differentiated
until after the 10th year, and the majority of the lost life years appear in the second and
third decades following election. Such an effect is consistent with selection on a minimum
survival time for candidates. A proportional hazards assumption appears reasonable.
The lower panel plots survival estimates with a cubic age adjustment. To construct
this, I first run a regression of survival on a cubic polynomial in age for candidates
who have died of natural causes, then predict survival based on age for all candidates
(note the sample is balanced on year of election; results are quite similar when including
additional controls or using alternative polynomials in age). I plot the residual between
this prediction and realized survival, with living candidates truncated as in the KaplanMeier estimates above; the smallest residual is set to zero. This regression adjustment has
only a small effect on the survival curves, but does serve to narrow the distance between
the curves by several years among those surviving longest. It does not appear that the
small gap in age at election between candidates affects the qualitative conclusion that
runners-up have outlived presidents.
3.2
Censored Regression
My preferred regression specifications use the Tobit model, which can handle the Ronald
Reagan problem through the inclusion of age controls, as well as the censoring of stillliving candidates’ survival times. The age controls serve to increase the precision of the
estimates and correct for the (not significant) difference in age at election. Expected
survival in Tobit follows a normal distribution, the tail of which closely matches the
empirical distribution of expected human survival from the beginning of the Gompertz
years of mortality through age 90, and the empirical pattern of presidential candidate
7
survival. Tobit has the additional advantage of producing linear estimates interpretable
as lost years of life. In practice, these assumptions do not play a large role, and the results
from a hazard model coincide with these estimates.
The general specification for individual i, running for election in year t is
2
AgeAtDeathi,t = β0 + β1 [W ini,t ] + β2 Agei,t + β3 ElectY eari,t + β4 ElectY eari,t
+ [AgeAtDeathi,t − Agei,2013 |Ii = 1] + �i,t ,
(1)
where Ii is an indicator for survival until January 1, 2013, the point of right-truncation
of the sample. The expectation, [AgeAtDeathi,t − Agei,2013 |Ii = 1], follows the assumed
normal distribution, and is estimated in a first-stage that includes the same variables as
the main specification. The linear age term and quadratic in election year were chosen by
adding higher-order terms until the highest-order term became statistically insignificant;
results are similar when using birth cohort in place of election year. The sample is
weighted by the inverse of the candidate’s appearances to avoid placing undue weight on
the individuals who appear multiple times. Standard errors are clustered by individual.
Table 2 reports the main regression results. Column 1 reports the basic specification
that does not control for previous terms served, finding an gap in conditional life expectancy of 4.5 years between winning and runner-up candidates. If service does exact
a toll, we should prefer specifications which control for previous exposure; so, columns 2
through 5 add controls for previous terms served. The preferred specification is Column
2, in which winning candidates suffer a loss of 3.8 years of life expectancy. Previous terms
served are associated with 2.6 years shorter life expectancy, consistent with the effects of
presidential service cumulating over terms served. Although the previous terms served
coefficient is not significant, we cannot reject the equality of the previous terms served
coefficient and the main effect.19 Column 3 drops still-living candidates, finding a similar
loss of life years (4.3 years), meaning the inclusion of still-living presidents (the censored
observations) are not crucial to the result. Column 4 drops the sample weights, which
leads to an improvement in precision that more than offsets a small decrease in the estimated effect, to 3.2 years of life lost. In the unweighted specification, previous terms
reduce life expectancy by 3.0 years, slightly less than the estimated effect of winning the
election. In all specification, the estimated effect of presidential service is significantly
negative, with life expectancy reduced by around 4 years.
The specifications reported here summarize the broad patterns in the data, and are
19
We would expect the previous terms coefficient to be smaller than the treatment effect, because the
presidents with the worst health outcomes do not run for president again.
8
reasonably robust to alternatives. Election fixed effects increase the magnitude and significance of the effects. The effects are also quite similar when censoring the Ronald
Reagan and Theodore Roosevelt observations as the time these presidents were shot (in
failed assassination attempts). Attempts to add controls have had varying impacts on the
main result. For example, controls for parent’s life expectancy are themselves far from
significant (p > 0.5), but their inclusion does compromise the statistical significance of
the main effect.20 On the other hand, controlling for height increases the magnitude of
the main effect and its statistical precision in any model in which it is included. Perhaps
surprisingly, height itself predicts a shorter life at half-a-year per inch (p < 0.01), almost
exactly according to the estimates for healthy, modern males.21 Adding a dummy for military service leaves the main point estimate unchanged at -3.6 years (p = 0.067), though
again, the dummy itself is not significant. I have also explored expanding the sample to
candidates who finish 3rd and below in the electoral college, and vice-presidents. This
changes the counterfactual experiment to one in which presidents are compared to men
and women from lower in the political hierarchy, and point estimates fall to around 3 years
of lost life. It is not clear if this decrease in the estimated effect is because the newly
added sample members have lower life expectancy (i.e. are a poor counterfactual), or if
the estimate is truly improved. Studying the extremes of distributions means dealing with
small samples, and it is probably unwise to read too much into these patterns. What is
clear is that the average survival of men who have been elected president has been shorter
than those who have finished as runners-up, and this historical precedent is large enough
to meet traditional levels of statistical significance.
4
4.1
Discussion
Might the Electorate Prefer Shorter-lived Candidates?
I have documented the shorter life expectancy of US presidents in comparison to electoral runners-up. A primary concern with interpreting these results as a causal effect of
20
There are two reasons this model loses statistical power: parent’s life expectancy is unrealized for
a number of modern presidents, decreasing the sample size; as well, life expectancy is endogenous for
parents still-living at the time of election.
21
More on height: Height is the only covariate I have uncovered which is itself significant. Historically,
height does not significantly predict electoral success in presidential elections; in my data, each inch of
height is associated with 0.62% increase in the probability of victory, but this is far from statistically
significant (p = 0.38). Nevertheless, recent research in political science argues such an association exists
(Murray and Schmitz, 2011). See, for example, Samaras and Storms (1992) for evidence on the modern
height-life expectancy relationship in well-nourished populations.
9
service is the selection effects in the final round of voting. In other words, are the lives
of losing presidential candidates truly a good counterfactual for winners? In the introduction, I suggested we can put these issues aside due to an assumed electoral advantage
for healthier candidates, which would imply the results are biased against the finding.
Political economy models have emphasized the desirable incentives for long-lived rulers
(Acemoglu (2005), Besley and Persson (2009)). Other research shows groups elect healthy
members to lead them, and the performance and perception of leaders is related to their
health (Little et al., 2007). The electorate’s preference for the candidate with superior
life expectancy seems a reasonable assumption for the selection directly on health and life
expectancy, however, it is possible that differences in other dimensions of the selection
process into candidacy and the selection process in the general election can explain this
result.
There are a number of plausible reasons for an electoral preference for characteristics
that are associated with shorter life expectancy. In general, it could be that selection
along dimensions associated with longer life expectancy occurs in the nomination process,
i.e. “class,” but the general electorate prefers candidate characteristics associated with a
shorter lifespan i.e. “grit.” For example, the electorate may prefer candidate who have
served in the military, or were born poor. Alternatively, it is possible the general election
involves candidates competing on commitments to the electorate. If the candidate’s probability of winning is increasing in his commitments, and commitments have some power,
candidates may end up competing on life-expectancy-reducing campaign promises: “I
will do (a lot) in my first 100 days,” or “I will pass this controversial piece of legislation.”
Controlling for previous terms served should answer most concerns regarding incumbency
advantage, however, complex stories may be constructed. Strategically, parties might
nominate longer-life expectancy candidates against incumbents, as they have the greatest
chance of re-appearing.
Based on this list, there are plausible reasons to suspect the electorate may favor
characteristics associated with shorter-lived candidates, however, we must weigh these
secondary electoral motives against the the direct factors favoring healthy candidates.
Presidential candidates certainly desire to appear healthy, for example, releasing the results of a medical exam is standard in modern elections. A frequently-cited turning point
in the 1960 presidential election occurred during a televised debate, when Richard Nixon
appeared haggard and pale (i.e. less healthy) than the younger John F. Kennedy, Jr.
To take another historical example, during the 1900 campaign William Jennings Bryant
traveled 19,000 miles and gave 546 speeches, while his opponent, Theodore Roosevelt,
10
traveled 21,000 miles and gave 673 speeches (Cherney (1985); the difference in number
of appearances likely resulted from the superior fund-raising successes of the Republican
candidate). Even though retail campaigning did not become standard until the late 19th
century, the health of a candidate could only help gather support through travel and
longer work-days. History is replete with examples of men who were denied nomination
due to health or age. Although Franklin Delano Roosevelt was elected 4 times while in a
wheelchair, the truth is that he did not reveal his affliction to the public until late in his
presidency. I interpret the weight of the historical evidence as supporting the assumption
that health, particularly conditioned on age, predicts electoral success.
Another important aspect of the counterfactual that I have not discussed is the effect of losing. Becker et al. (2008) find a negative effect on life expectancy of narrowly
missing election to the Baseball Hall of Fame. While it is possible to construct a story
in which losing presidential candidates experience a windfall gain in life expectancy (in
some sense, this is the finding of the paper), it is more plausible that candidates make
significant investment in the nomination and campaign process that yield smaller returns
upon losing. To the extent that the experience of losing negatively affects life expectancy,
the comparison made in this paper understates the loss of life resulting from service.
Finally, even if the findings are explained by electorate’s preference for candidates with
characteristics correlated with shorter life expectancy, the result remains a counterexample
to the rank-lifespan correlation. If democratic elections favor candidates with shorter life
expectancy, then the correlation is reversed in this setting. Might certain hierarchies
promote leaders with ex-ante shorter life expectancy? As far as I know, this hypothesis
has not been explored in the literature.
4.2
Mechanisms
If the negative health effects can be assigned to service, which aspects of presidential
service are most likely to explain them? Based on Figure 1, the loss of presidential life to
natural causes may begin soon after service, however, the majority of deaths occur in the
years after the term served. This suggests the effect occurs through a drawdown of health
capital in the process of service, consistent with reports of increased aging of presidents.
Based on the evidence from other primates, I investigated the role of margin of victory
in the estimates. If instability of the political hierarchy counteracts the effects of rank,
we would expect candidates with smaller margins of victory to experience more lost life
years. This is not the case: a higher vote share predicts a shorter life, and some of the
shortest-lived presidents won by considerable margins. The sample size is far too small
11
to use a regression-discontinuity design, but what evidence we have does not support a
strong role for stability in the political system on lifespan.
These effects would also be consistent with environmental risks associated with service,
such as increased exposure to disease. Presidents travel around the country and world,
and meet with many people. Before the germ theory of disease, travel entailed exposure to
water-borne disease and other infections. William Henry Harrison contracted pneumonia
in his first weeks in office, when presidents solidify support by meeting with the political
elites in the capital. As discussed, one potential mechanism I have ruled out is the physical
move to the capital. This hypothesis is of particular interest, given the prevalence of
malaria in the capital. If this was the primary mechanism, we would expect to see vicepresidents experience a loss of life. Nevertheless, it is likely that some portion of the effect
can be explained by the exposure to additional risk factors beyond the rigors of the office,
or represent the combination of such factors with work-related stress.
Although I lack the statistical power to distinguish between mechanisms, in my view,
the hypothesis of work-related aging remains the most plausible explanation for the findings. Two exemplars are the presidencies of Woodrow Wilson and Franklin Delano Roosevelt, the presidents who served during the world wars in the twentieth century. Both
men traveled extensively as a result of the conflicts and worked themselves to the point
of exhaustion. Near the conclusion of each wars, both presidents’ health failed, leading
to their death. This anecdotal evidence is consistent with a model in which presidents
face a variable demand for their services, and whether through altruistic motivations or
unbreakable commitments, respond with an increase in work effort leading to a loss of
life expectancy. The potential to select men willing to make these sacrifices seems to be
exactly what democratic elections are intended to accomplish.
5
Conclusion
Jimmy Carter on the Curse of Tippecanoe (presidents elected in years
divisible by 20 between 1840 and 1960 died while in office): “I’m not afraid.
If I knew it was going to happen, I would go ahead and be president and do
the best I could, for the last day I could.” -October 2, 1980, at a presidential
campaign rally in Dayton, Ohio
In this paper, I establish a stylized fact: on average, losing presidential candidates
outlive presidents who die of natural causes by around 4 years. I argue this difference arises
from the causal impact of presidential service, based on the assumption that the electorate
12
prefers longer-lived candidates. To the extent this assumption holds, these quantitative
estimates understate the negative effects of presidential service on life expectancy. I
cannot assign the effect to one particular mechanism, but emphasize the likely role of
work-related stress and aging. That presidents are willing to sacrifice life expectancy in
exchange for the returns to service is not a surprise, given the known risks of assassination.
What is novel about this result is the loss of natural life expectancy. To my knowledge,
this is the first paper to corroborate the reversal of the natural life expectancy gradient
at the top of a human hierarchy.
This finding complicates rank-based explanations for the socioeconomic gradient in
life expectancy, by both supporting the proposed mechanism in this literature, while also
providing a clear counterexample at the logical extreme of the theory. From the evidence
presented here, I conclude that the mechanism of work-related stress may have sufficient
force to explain large-scale patterns in life expectancy, but that the patterns of exposure
to work-related stress do not support the reduction to direct relationship between social
status and life expectancy, as the rank-lifespan theory implies. It is important to note
that a defining characteristic of the socioeconomic gradient is that it extends throughout
the socioeconomic distribution, so that the extension to the upper tiers of hierarchies is
implied. While the US presidency is clearly an unusual job, we might expect the same
effects to be found among other high-profile, high-stress occupations. Given the difficulties
in gathering data on the very top of the distribution, some of the best evidence on the
lives of these individuals is likely to come from historical examples, such as this one.
The broadest findings of this investigation speak to a larger role for health in models
of labor supply at older ages, particularly for high-achieving individuals. In their labor
supply decisions, presidents choose to sacrifice not just leisure, but also a portion of their
time endowment. This is obvious from the risk of assassination; however, this investigation
finds that risk of assassination has accounted for less than one-half of the years lost
to presidential service. The modeling conveniences of assuming a fixed lifespan or an
infinite horizon cannot capture the tradeoff documented here, suggesting that for certain
categories of workers, models of the retirement decision must account for the interaction
of career, extensive-margin behavior and lifespan. As well, the returns to presidential
service may or may not include increased consumption, but consumption differentials
between winners and losers are unlikely to compensate presidents for the years of lost
life. If presidents expect to lose 5 out of 20 remaining life years, and we assume log
utility over consumption and no discounting (i.e. quite conservative assumptions), then
this sacrifice would require to a 212% increase in consumption in the remaining years to
13
leave an individual indifferent. This compensating differential suggests we look beyond
strictly consumption-based utility, to elements such as prestige and ego rents, risk-seeking
behavior, dynastic concerns and altruism as the motivations of presidents. I conclude that
research on the labor supply behavior among high-achieving older workers should treat
health, lifespan, extensive margin choices, selection into occupations and careers, and
beyond-consumption utility as essential objects. Finally, to the degree that work-related
stress is truly concentrated in the lower-tiers of the income distribution, there may be
scope for a more general role for health in the modeling of labor supply.
References
Acemoglu, D. (2005). Politics and economics in weak and strong states. Journal of
Monetary Economics 52 (7), 1199 – 1226.
Anderson, M. and M. Marmot (2011). The Effects of Promotions on Heart Disease:
Evidence from Whitehall. The Economic Journal 121, 1–35.
Becker, D. J., K. Y. Chay, and S. Swaminathan (2008). Mortality and the Baseball Hall
of Fame: An Investigation into the Role of Status in Life Expectancy. Working Paper.
Besley, T. and T. Persson (2009). The Origins of State Capacity: Property Rights,
Taxation, and Politics. American Economic Review 99 (4), 1218–44.
Brown, J., J. Liebman, and J. Pollet (2002). Estimating Life Tables That Reflect Socioeconomic Differences In Mortality, Chapter Appendix, pp. 447–458. University of
Chicago Press. National Bureau of Economic Research.
Case, A. and C. Paxson (2011). The Long Reach of Childhood Health and Circumstance:
Evidence from the Whitehall II Study. The Economic Journal 121 (554), F183–F204.
Cherney, R. W. (1985). A Righteous Cause. Little, Brown and Company.
Cutler, D., A. Deaton, and A. Lleras-Muney (2006). The Determinants of Mortality.
Journal of Economic Perspectives 20 (3), 97–120.
Dal Bo, E., P. Dal Bo, and J. Snyder (2009). Political Dynasties. The Review of Economic
Studies 76 (1), 115–142.
14
Epel, E. S., E. H. Blackburn, J. Lin, F. S. Dhabhar, N. E. Adler, J. D. Morrow, and R. M.
Cawthon (2004). Accelerated Telomere Shortening in Response to Life Stress. Proceedings of the National Academy of Sciences of the United States of America 101 (49),
17312–17315.
Fogel, R. W. (2004). The Escape from Hunger and Premature Death, 1700-2100. Cambridge University Press.
Hanna, J. (2011, August). Do Presidents Age Faster in Office? CNN Online Article;
Accessed January 7, 2014.
Kopczuk, W. and E. Saez (2004). Top Wealth Shares in the United States, 1916-2000:
Evidence from Estate Tax Returns. National Tax Journal 57, 445–487.
Little, L. M., B. L. Simmons, and D. L. Nelson (2007). Health Among Leaders: Positive
and Negative Affect, Engagement and Burnout, Forgiveness and Revenge. Journal of
Management Studies 44 (2), 243–260.
Malmendier, U. and G. Tate (2009). Superstar CEOs. The Quarterly Journal of Economics 124 (4), 1593–1638.
Marmot, M. (1994). Social Differentials In Health Within and Between Populations.
Daedalus 123:4, 197–216.
Marmot, M. (2004). The Status Syndrome: How Social Standing Affects Our Health and
Longevity. New York: Holt.
Marmot, M., S. Stansfeld, C. Patel, F. North, J. Head, I. White, E. Brunner, A. Feeney,
M. Marmot, and G. Smith (1991). Health Inequalities among British Civil Servants:
the Whitehall II Study. The Lancet 337 (8754), 1387 – 1393. Originally published as
Volume 1, Issue 8754.
McEwen, B. S. (1998). Protective and Damaging Effects of Stress Mediators. New England
Journal of Medicine 338 (3), 171–179.
Moslehi, J., R. A. DePinho, and E. Sahin (2012). Telomeres and Mitochondria in the
Aging Heart. Circulation Research 110 (9), 1226–1237.
Murray, G. R. and J. D. Schmitz (2011). Caveman Politics: Evolutionary Leadership
Preferences and Physical Stature. Social Science Quarterly 92 (5), 1215–1235.
15
Olshansky, S. J. (2011). Aging of US Presidents. JAMA: The Journal of the American
Medical Association 306 (21), 2328–2329.
Persico, N., A. Postlewaite, and D. Silverman (2004). The Effect of Adolescent Experience
on Labor Market Outcomes: The Case of Height. Journal of Political Economy 112 (5),
pp. 1019–1053.
Rablen, M. D. and A. J. Oswald (2008). Mortality and Immortality: The Nobel Prize
as an Experiment into the Effect of Status upon Longevity. Journal of Health Economics 27 (6), 1462 – 1471.
Rosen, S. (1981). The Economics of Superstars. The American Economic Review 71 (5),
pp. 845–858.
Samaras, T. and L. Storms (1992). Impact of Height and Weight on Lifespan. Bulletin
of the World Health Organization 70 (2), 259.
Sapolsky, R. M. (2005). The Influence of Social Hierarchy on Primate Health. Science 308 (5722), 648–652.
Stringhini, S., S. Sabia, M. Shipley, E. Brunner, H. Nabi, M. Kivimaki, and A. SinghManoux (2010). Association of Socioeconomic Position With Health Behaviors and
Mortality. JAMA: The Journal of the American Medical Association 303 (12), 1159–
1166.
Vaananen, A., S. Toppinen-Tanner, R. Kalimo, P. Mutanen, J. Vahtera, and J. M. Peiro
(2003, September). Job Characteristics, Physical and Psychological Symptoms, and
Social Support as Antecedents of Sickness Absence among Men and Women in the
Private Industrial Sector. Social Science & Medicine 57 (5), 807–824.
16
Table 1: Summary Statistics
Age at Election
Mean SD
N
All
56.02 7.38
112
Winners
56.67 6.56
56
Win, Natural Cause 57.25 6.52
50
Runners-up
55.36 8.13
56
t-test (N =112)
1.31
1.40 t=0.94
Age at Death
All
72.95 11.56
97
Winners
70.93 11.60
49
Win, Natural Cause 73.25 10.29
43
Runners-up
75.01 11.26
48
Notes: Age at Election and Death, Presidential Candidates in 1789-2008 Elections. Ages separately reported for presidents who died of natural causes, the primary sample of interest.
Winning candidates (irrespective of cause of death) have been 1.31 years older than losers, on
average; a t-test reveals this difference is not significant.
17
Table 2: Age at Death
(1)
Winner
-4.47*
(2.09)
Previous Terms
Age at Election
Tobit
Weights
Censor Assassinations
Candidacies
Individuals
Deaths
-0.52**
(0.14)
X
X
X
112
72
57
(Survival Past Election)
(2)
(3)
(4)
-3.81* -4.31* -3.21*
(1.89)
(2.02) (1.61)
-2.63
-2.15
-2.97*
(1.77)
(1.94) (1.50)
-0.55** -0.51** 0.49**
(0.14)
(0.15) (0.12)
X
X
X
X
X
X
X
112
91
112
72
57
72
57
57
57
(5)
-6.66**
(2.22)
-1.93
(1.71)
-0.69**
(0.16)
X
X
112
72
61
Notes: Top 2 finishers, US Presidential Candidates in 1789-2008 Elections. Specifications (1)-(4)
consider deaths from natural causes (treating assassinated and still-living candidates as censored
observations); (5) adds assassinations (still-living candidates remain censored). Tobit model (all
except (3)) accounts for right-censoring due to still-living candidates. Identical results on the
main effect would be obtained taking survival past election as the outcome, due to linear control
for age at election; all models also includes quadratic in election year. Observations weighted
by the inverse of number of appearances in samples (1)-(3) and (5); standard errors clustered
by individual. Significance levels: ** p<0.01, * p<0.05, † p<0.1.
18
Figure 1: Kaplan-Meier and Age-adjusted Survival
Kaplan−Meier survival estimates
1.00
Presidents
Runners−up
0.75
0.50
0.25
0.00
0
10
20
30
Years Since Election
40
50
Age−adjusted survival estimates
1.00
Presidents
Runners−up
0.75
0.50
0.25
0.00
0
10
20
30
Years Survived, age−adjusted
40
50
Notes: The figures plot the empirical probability a president or losing candidate survives the
given years. Assassinated presidents and still-living candidates are treated as censored observations. Cubic age-adjustment accounts for slightly older age of winners, and selection on health
as age rises.
19
Data Appendix (For Online Publication)
20
Table A.1: Presidential Candidates Birth and
Name
Year Outcome Birth Date
Washington 1789
Winner
22feb1732
Adams
1789 Runner-up 30oct1735
Washington 1792
Winner
22feb1732
Adams
1792 Runner-up 30oct1735
Adams
1796
Winner
30oct1735
Jefferson
1796 Runner-up 13apr1743
Jefferson
1800
Winner
13apr1743
Adams
1800 Runner-up 30oct1735
Jefferson
1804
Winner
13apr1743
Pinckney
1804 Runner-up 25feb1746
Madison
1808
Winner
16mar1751
Pinckney
1808 Runner-up 25feb1746
Madison
1812
Winner
16mar1751
Clinton
1812 Runner-up 02mar1769
Monroe
1816
Winner
28apr1758
King
1816 Runner-up 24mar1755
Monroe
1820
Winner
28apr1758
Adams
1820 Runner-up 11jul1767
Adams
1824
Winner
11jul1767
Jackson
1824 Runner-up 15mar1767
Jackson
1828
Winner
15mar1767
Adams
1828 Runner-up 11jul1767
Jackson
1832
Winner
15mar1767
Clay
1832 Runner-up 12apr1777
VanBuren
1836
Winner
05dec1782
Harrison
1836 Runner-up 09feb1773
Harrison
1840
Winner
09feb1773
VanBuren
1840 Runner-up 05dec1782
Polk
1844
Winner
02nov1795
Clay
1844 Runner-up 12apr1777
Taylor
1848
Winner
24nov1784
Cass
1848 Runner-up 09oct1782
Pierce
1852
Winner
23nov1804
Scott
1852 Runner-up 13jun1786
Buchanan
1856
Winner
23apr1791
Fremont
1856 Runner-up 21jan1813
Lincoln
1860
Winner
12feb1809
Breckinridge 1860 Runner-up 16jan1821
21
Death Dates
Death Date
14dec1799
04jul1826
14dec1799
04jul1826
04jul1826
04jul1826
04jul1826
04jul1826
04jul1826
16aug1825
28jun1836
16aug1825
28jun1836
11feb1828
04jul1831
29apr1827
04jul1831
23feb1848
23feb1848
08jun1845
08jun1845
23feb1848
08jun1845
29jun1852
24jul1862
04apr1841
04apr1841
24jul1862
15jun1849
29jun1852
09jul1850
17jun1866
08oct1869
29may1866
01jun1868
13jul1890
15apr1865
17may1875
Table A.2: Presidential Candidates
Name
Year Outcome
Lincoln
1864
Winner
McClellan 1864 Runner-up
Grant
1868
Winner
Seymour
1868 Runner-up
Grant
1872
Winner
Greeley
1872 Runner-up
Hayes
1876
Winner
Tilden
1876 Runner-up
Garfield
1880
Winner
Hancock
1880 Runner-up
Cleveland 1884
Winner
Blaine
1884 Runner-up
Harrison
1888
Winner
Cleveland 1888 Runner-up
Cleveland 1892
Winner
Harrison
1892 Runner-up
McKinley 1896
Winner
Bryan
1896 Runner-up
McKinley 1900
Winner
Bryan
1900 Runner-up
Roosevelt 1904
Winner
Parker
1904 Runner-up
Taft
1908
Winner
Bryan
1908 Runner-up
Wilson
1912
Winner
Roosevelt 1912 Runner-up
Wilson
1916
Winner
Hughes
1916 Runner-up
Harding
1920
Winner
Cox
1920 Runner-up
Coolidge
1924
Winner
Davis
1924 Runner-up
Hoover
1928
Winner
Smith
1928 Runner-up
Roosevelt 1932
Winner
Hoover
1932 Runner-up
Roosevelt 1936
Winner
Landon
1936 Runner-up
Roosevelt 1940
Winner
Willkie
1940 Runner-up
22
Birth and Death Dates (con’t)
Birth Date Death Date
12feb1809
15apr1865
03dec1826
29oct1885
27apr1822
23jul1885
31may1810 12feb1886
27apr1822
23jul1885
03feb1811
29nov1872
04oct1822
17jan1893
09feb1814
04aug1886
19nov1831 19sep1881
14feb1824
09feb1886
18mar1837 24jun1908
31jan1830
27jan1893
20aug1833 13mar1901
18mar1837 24jun1908
18mar1837 24jun1908
20aug1833 13mar1901
29jan1843
14sep1901
19mar1860
26jul1925
29jan1843
14sep1901
19mar1860
26jul1925
27oct1858
06jan1919
14may1852 10may1926
15sep1857 08mar1930
19mar1860
26jul1925
28dec1856
03feb1924
27oct1858
06jan1919
28dec1856
03feb1924
11apr1862 27aug1948
02nov1865 02aug1923
31mar1870
15jul1957
04jul1872
05jan1933
13apr1873 24mar1955
10aug1874 20oct1964
30dec1873
04oct1944
30jan1882
12apr1945
10aug1874 20oct1964
30jan1882
12apr1945
09sep1887
12oct1987
30jan1882
12apr1945
18feb1892
08oct1944
Table A.3: Presidential
Name
Year
Truman
1948
Dewey
1948
Eisenhower 1952
Stevenson
1952
Eisenhower 1956
Stevenson
1956
Kennedy
1960
Nixon
1960
Johnson
1964
Goldwater 1964
Nixon
1968
Humphery 1968
Nixon
1972
McGovern 1972
Carter
1976
Ford
1976
Reagan
1980
Carter
1980
Reagan
1984
Mondale
1984
Bush
1988
Dukakis
1988
Clinton
1992
Bush
1992
Clinton
1996
Dole
1996
Bush
2000
Gore
2000
Bush
2004
Kerry
2004
Obama
2008
McCain
2008
Candidates
Outcome
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
Winner
Runner-up
23
Birth and Death Dates (con’t)
Birth Date Death Date
08may1884 26dec1972
24mar1902 16mar1971
14oct1890 28mar1969
05feb1900
14jul1965
14oct1890 28mar1969
05feb1900
14jul1965
29may1917 22nov1963
09jan1913
22apr1994
27aug1908 22jan1973
02jan1909 29may1998
09jan1913
22apr1994
27may1911 13jan1978
09jan1913
22apr1994
19jul1922
21oct2012
01oct1924
14jul1913
26dec2006
06feb1911
05jun2004
01oct1924
06feb1911
05jun2004
05jan1928
12jun1924
03nov1933
19aug1946
12jun1924
19aug1946
22jul1923
06jul1946
03mar1948
06jul1946
11dec1943
04aug1961
29aug1936