Download life tenure reconsidered: term limits for the supreme court

Politicized Departure from the United States Supreme Court*
Running Head: Supreme Court Departures
May 14, 2007
Total Word Count 11,611
Ross M. Stolzenberg**
Department of Sociology
University of Chicago
[email protected]
Telephone: 847 408-0168
and
James Lindgren
School of Law
Northwestern University
[email protected]
Telephone: 312-503-8374
*We thank Albert Yoon for making available to us some of the data analyzed herein, and Lee
Epstein for advice.
**Corresponding author.
Politicized Departure from the United States Supreme Court
ABSTRACT
Nearly seven decades of quantitative analysis and narrative historical research investigate the
hypothesis that political factors influence the probability and timing of resignations and deaths-in-office
of U.S. Supreme Court justices (the Politicized Departure Hypothesis). Narrative historical studies mostly
report support for the hypothesis, quantitative analyses mostly report the opposite, and neither method
yields consistent findings. Reasons for these differences are uncertain because prior studies differ
dramatically in the data they examine and the methods they apply. Previous quantitative analysts criticize
each others’ methods and generally regret the lack of appropriate health (vitality) measures for most
justices. Here, we review prior research, reformulate hypotheses, and reconsider conceptual, measurement
and analytic issues. We include a widely-accepted vitality measure. We analyze data on every justice
from 1789 through 2006, using robust, cluster-corrected, discrete time, censored, event history methods.
We apply previously-used techniques to compare our results to previous studies. Findings are consistent
with the Politicized Departure Hypothesis and suggest that its effects are substantial. Most non-political
factors have nonlinear effects on resignations, some of which reverse direction as justices age.
2
Supreme Court Departures
May 14 2007
Page 1
I. INTRODUCTION
Of U.S. Supreme Court Justices, two things are certain: They are appointed to the Court, and they
subsequently depart from it. By law, Court appointment is explicitly political, involving nomination and
appointment by the President “with the Advice and Consent of the Senate” (U.S. Const., Art. II, sec. II).
But law dictates no role for Presidents, politics, or public scrutiny in selecting the times at which justices
vacate the Court: Except in cases of treason, bribery, or other serious crimes, justices leave office only by
death, or when they themselves alone, individually, resign. Nonetheless, observers have long asserted that
the timing of justices’ resignations from the Court, and even the probability that they die in office, reflect
a highly politicized process that does, like their nominations to the Court, revolve around political
compatibility between the individual jurist and the incumbent President of the United States (Ward 2003;
Calabresi and Lindgren 2006; Zorn and Van Winkle 2000; Biskupic 2004). We call this assertion the
Politicized Departure Hypothesis, and we develop and test it below.
The Politicized Departure Hypothesis asserts that the timing of resignation from active service on
the Supreme Court tends to be influenced by political as well as personal circumstances of justices.
Personal circumstances likely would include the same factors that influence retirements and voluntary job
terminations of all workers -- vitality, age, personal finances, and job tenure (i.e. length of service on the
Court) (see, e.g., French 2005). Political circumstances are hypothesized to impinge on personal
considerations because the retirement of a justice – particularly if it occurs early in a President’s term of
office – allows the sitting President to nominate the replacement for that justice, and thereby prevents a
future President of the opposing party from doing so. The Politicized Departure Hypothesis reasons that,
regardless of differences in political philosophy, justices tend to be loyal to the political party of the
President who nominated them to the Court and that justices consequently tend to adjust the timing of
their resignations to give a President of that same party the opportunity to appoint their judicial successor
(see Epstein, Segal, Spaeth, and Walker 1996, 368).1 In particular, the Politicized Departure Hypothesis
asserts that:
1) Other things equal, if the incumbent President is of the same party as the President who
nominated the justice to the Court, and if the incumbent President is in the first two years of a
four-year Presidential term, then the justice is more likely to resign from the court than at times
when these two conditions are not met.
1
Supreme Court Departures
May 14 2007
Page 2
By removing live justices from office, accelerated departure reduces their opportunity to die in office.
Retarded departure does the opposite. Thus, the Politicized Departure Hypothesis implies that,
2) ) Other things equal, if the incumbent President is of the same party as the President who
nominated the justice to the Court, and if the incumbent President is in the first two years of a
four-year Presidential term, then the justice is less likely to die in office than at times when these
two conditions are not met.
This paper tests these two implications of the Politicized Departure Hypothesis. Section I considers
previous research and historical material that is related to the Politicized Departure Hypothesis. Sections
II and III consider methodological and conceptual issues, data, and measurement. Section IV reports
analytic results. And Section V discusses findings.
II. EXISTING EVIDENCE REGARDING POLITICIZED DEPARTURE
Table 1 summarizes key features of some previous considerations of the Politicized Departure
Hypothesis, starting with Fairman’s (1938, 406) Harvard Law Review article that considers both
quantitative data and narrative historical evidence. Subsequent narrative studies tend to be highly
selective, focusing on specific justices whose Court departures appear to have been timed politically. For
example, Goff (1960, 96), argues that a senile and incompetent Justice William Cushing remained in
office until death in 1810, solely in an attempt to stave off the appointment of a Democratic-Republican
replacement. Van Tassel (1993), Atkinson (1999), Garrow (2000) and Ward (2003) provide historical
evidence of politicized departure by particular justices. Farnsworth reports that Justices William
Rehnquist (2005, 448) and Sandra Day O’Connor (2005, 375) stated preferences that their replacements
be nominated by a President of the same party as the President who appointed them. Hutchinson (1998)
reports statements of similar preferences by Justice Byron White. Justices Black, Douglas, Marshall and
Brennan are all reported to have delayed departure in unsuccessful attempts to give a Democratic
President the opportunity to name their successors (Oliver 1986, 806-08; Woodward and Armstrong
1979, 161).
Although some narrative historical studies provide evidence that several justices acted according
to the predictions of the Politicized Departure Hypothesis, they leave three sources of uncertainty. First,
some narrative studies contradict the Politicized Departure Hypothesis. For example, Brenner (1999)
argues that politicized departure occurs rarely or never (but see Ward’s [2004] critique of Brenner).
2
Supreme Court Departures
May 14 2007
Page 3
Others cite the case of Justice Warren to assert that political philosophy, rather than party identification
per se, affects the timing of some Court resignations; Justice Warren was appointed by Republican
President Eisenhower, but is reported to have tried unsuccessfully to time his resignation so a Democrat
could nominate his successor (Oliver 1986, 805-06, citing White 1982, 306-08, Schwartz 1983, 680-83,
720-25). Second, most of these narrative accounts rely on justices’ recollections of their thoughts and
emotions, their subjective explanations of their own past behavior, and sometimes even their predictions
of their future behavior. Contemporary social science evidence suggests skepticism about the validity of
such reports (see Fazio 1986; Fishbein and Ajzen 1975; Cannell, Miller, and Oksenberg 1981;
Eisenhower, Mathiowetz, and Morganstein 1991; Kalton and Schuman 1982; Sudman and Bradburn
1982). Third, and perhaps most important, existing historical narrative studies disproportionately focus
on justices whose behavior is hypothesized or reputed to be dramatically consistent with the politicized
departure hypothesis. By selecting and focusing on judges whose departures were highly politicized, these
studies provide no evidence whatsoever concerning departures of the great majority of Supreme Court
justices. In short, historical studies provide nuanced understanding of specific cases, and they are a useful
basis for formulating the Politicized Departure Hypothesis, but they are too selective to test that
hypothesis, or to indicate if Politicized Departure is a general pattern, an occasional aberration, or a
hypothesized event that has never actually occurred.
In contrast to fine-grained, narrative historical research that seeks certainty about the personal
motives and retirement behavior of particular individuals, existing quantitative analyses consider
probabilistic assertions about patterns of behavior by Supreme Court justices in general, examine
observable, publicly available data; and consider hypotheses that observable political circumstances have
had, on average, some effect on the timing of all departures from the Court. Quantitative analyses of
Supreme Court departures vary widely in substantive conclusions and methodology. Substantively, Yoon
(2006), Brennner (1999) and Squire (1988) report finding no evidence of a general pattern of politicized
departure of justices. In contrast, Hagle (1993) and King (1987) report findings of political effects on
annual numbers of departures from the Supreme Court. Zorn and Van Winkle (2000), and BoxSteffensmeier and Zorn (1998) present some statistical analyses that are consistent – and some that are not
consistent – with the hypothesis of politicized departure.2
While the contradictory findings of prior research permit no firm substantive conclusions,
3
Supreme Court Departures
May 14 2007
Page 4
methodological disagreements between previous analysts clearly delineate critical methodological issues.
These issues include the lumpability of death-in-office and retirement, aggregation of individual data into
frequency data for the Court as a whole, measurement and control of the vitality (health) of justices, and
the calculation of effect measures and statistical significance in analyses involving multiple indicators and
statistical interactions. We describe those issues briefly:
Lumpability. Some previous statistical studies (e.g. King 1987; Yoon 2006) lump resignations
and deaths-in-offices into undifferentiated departures from the Court. Hagle (1993), Spriggs and
Wahlbeck (1995, 575), and Nixon and Haskin (2000, 462) complain that lumping confuses interpretation.
These complaints are logical: Retirement is a voluntary action by justices who have not yet died, but
death-in-office is an involuntary biological event that befalls justices who have not yet chosen to retire.3
Thus, using Departure-by-any-means as a dependent variable appears to confound positive and negative
mortality risks, and to conflate processes that promote and discourage retirement. Logical problems
notwithstanding, the lumping problem is well-known in statistics to be sometimes disastrous, but
sometimes benign (Ledoux, Rubino, and Sericola 1994; Cotterman and Peracchi 1992). Accordingly, we
take an empirical approach to lumping: we estimate separate models of departures, resignations, and
deaths-in-office. If these models produce similar results for retirement and death-in-office, then they
demonstrate that lumping is permissible. If they produce different results for retirement and death-inoffice, then they show that separate analyses are required.
Aggregation. In some previous research, retirements and deaths-in-office of individual justices
are aggregated into annual frequencies of these events for the Court as a whole (e.g., Wallis 1936; Callen
and Leidecker 1971; Ulmer 1982; King 1987; Hagle, 1993). These annual frequencies are informative
descriptions of historical trends that permit appropriate tests of organization-level hypotheses about the
Court as a whole. However, departure from the Court is an individual-level phenomenon determined by
the decisions and deaths of individuals. Testing hypotheses about individual-level phenomena with
aggregated frequency data would commit the “ecological fallacy” under most circumstances, and so is
unacceptable (see King 1997). Therefore, we examine individual-level data. When it is useful to consider
frequency distributions for the Court as a whole, we can use probability laws to aggregate individual-level
results into frequency distributions for the court as a whole.
Vitality. Many previous labor force studies hypothesize and find that as workers’ subjective or
4
Supreme Court Departures
May 14 2007
Page 5
objective health declines, their probability of retirement increases (see French 2005; Bound 1991; Dwyer
and Mitchell 1999; Parsons 1982). Virtually all previous historical narrative studies of Supreme Court
departures consider the retirement effects of vitality, or its opposite, decrepitude. In quantitative analyses,
Squire (1988) includes a measure of justices’ poor health, but Hagle (1993, 35) criticizes Squire’s vitality
measure. Zorn and van Winkle (2000: 162) complain that Squire’s measure “is so narrow as to almost
reach the certainty of leaving office.” Previous analysts despair of the possibility of obtaining adequate
health data for quantitative analysis of Supreme Court departures: Citing Greenhouse (1984), Hagle
(1993:46) asserts that Supreme Court Justices are confirmed fibbers concerning their health. Zorn and van
Winkle (2000) use justices’ productivity (written opinion production per year) to measure their health, but
productivity is obviously different from health (see Green and Baker 1991), and seemingly subject to a
wide range of political, psychological and personal influences. To the best of our knowledge, no
subsequent quantitative analysis of Supreme Court departures (e.g., Yoon 2006) includes a direct measure
of vitality. Because previous research finds that health is an important determinant of retirement in the
general working population, the absence of an accepted health indicator in previous quantitative Supreme
Court studies remains a source of uncertainty.
Ironically, it appears that a standard, useful and available health measure exists for Supreme
Court justices, but has not been used previously. This measure is the number of remaining years of life for
each justice, measured in each year of each justice’s service on the Court. Remaining years of life has
been used as an objective health indicator in a long line of previous health research (e.g. Idler and Kasl
1991; Kaplan 1987; Mossey and Shapiro 1982) and studies of health effects on retirement of the general
population (e.g. Parsons 1982; Bound 1991; Baker, Stabile and Deri 2004; French 2005). Further,
remaining years of life has been used as an objective health indicator for establishing the validity of selfreported, subjective health measures (Davies and Ware 1981; Mossey and Shapiro 1982; Ross and Wu
1995). In addition, Han, Phillips, Ferrucci, Bandeen-Roche, Jylha, Kasper, and Guralnik (2005: 216) find
significant association between increased mortality and changes in subjective feelings of vitality. In short,
future longevity is an empirically validated indicator of “objective” and “subjective” vitality (health). As
we write, all justices (except those now serving on the Court and Justice Sandra Day O’Connor) are now
dead, making it possible to calculate the years of remaining life for all but one former justice in each and
every year of their service on the Court.
5
Supreme Court Departures
May 14 2007
Page 6
Multiple Indicators. Previous studies of Supreme Court departures consider various hypotheses
involving the party of the sitting President, the party of the President who appointed the justice in
question, and additional variables combining various party affiliations. Even if there is no ambiguity in
the substantive hypotheses that motivate these variables, the variables themselves sometimes appear in
several interaction terms, each with its own coefficient, standard error, and error covariance with other
included terms. Calculation of effect measures and significance tests for each variable must therefore
accommodate all terms that involve the variable.
III. METHODS
Strategy. The Politicized Departure Hypothesis asserts that, other things equal, justices are more
likely to retire from the Court, and less likely to die in office, when the incumbent President is of the same
party as the President who nominated them to the Court, and when the President is in the first two years
of a four-year Presidential term. To test these assertions, we estimate discrete-time event-history analyses
of the probabilities of retirement and death-in-office by Supreme Court justices. Compared to continuous
time methods, discrete time analyses better accommodate the rounding of retirement dates and cooccurrence of retirements -- the tendency of justices to withhold announcement of their retirement until
the end of a term, and the simultaneous retirement of more than one justice (see Yamaguchi 1991).
Dependent variables are the hazards of retirement and death-in-office for each justice in each year that the
justice served on the Court.4 Thus, the unit of analysis in this research is the justice-year. Because each
justice-year represents data pertaining to one justice sitting on the Court for one calendar year (or part of
it), time is intrinsic to all of our analyses. All variables are permitted to vary over time. Some independent
variables in these analyses represent the hypothesized causal variables: i.e., political parties of the current
President and President who appointed the justice, for the particular justice and specific year that defines
the justice-year. Remaining independent variables in these models are controls for justices’ age, tenure on
the Court, eligibility for Federal retirement pension benefits, secular trends in probabilities of retirement
and death-in-office and, in some models, the future longevity of the justice.
Nonlinear Probability Models. Although 44.5 percent of all justices have died in office and 47.3
percent have retired from office, death-in-office occurs in 2.6 percent of the justice-years, and retirement
occurs in 2.8 percent of the justice-years.5 The proportions of justice-years ending in death-in-office and
the proportion of justice-years ending in retirement are small enough to require nonlinear probability
6
Supreme Court Departures
May 14 2007
Page 7
models – linear models would predict nonsensical negative probabilities of retirement and death-in-office
for some justice-years. Therefore, most of our analyses use logistic regression (logit) analysis, which
constrains probability estimates to the interval (0,1). To permit comparisons to some previous analyses,
we also use multinomial probit analysis. Our analyses are estimated on a complete enumeration of each
and every year served on the Supreme Court by each and every justice ever appointed to the Court,
through 2006.
Right Censoring. At any moment while they serve on the Supreme Court, justices are “at risk” of
both retirement and death-in-office. That is, they have nonzero hazards of both retirement and death-inoffice. These hazards can be positively correlated, but the actual events of retirement and death-in-office
are mutually exclusive: Although death and retirement occur probabilistically – and the probabilities of
these events are the subject of our analyses – justices who actually retire no longer have offices in which
to die; and justices who have died in office no longer can retire. Thus, death-in-office right censors data
on retirement, and retirement right censors data on death-in-office (Allison 1995). Right censoring of
discrete time event history data is an easily accommodated commonplace of survival analysis and eventhistory analysis: we include in our analyses the justice-years of each and every justice for each and every
year in which that justice serves any time on the Supreme Court. This method makes full use of all
available information on retirement and death-in-office, avoids bias, and produces consistent estimates of
model parameters (Allison 1995).
Competing Risks. We accommodate a previous argument that retirement and death-in-office of
Supreme Court justices should be conceptualized as “competing risks” (Zorn and Van Winkle 2000).
Box-Steffensmeier and Jones (1997: 1450) observe that a competing risks conceptualization implies the
use of a multinomial logit (MNL) analysis or, if computationally tractable, multinomial probit (MNP)
analysis. In retirement analyses of the U.S. labor force, the competing risks formulation with MNL is
commonplace (e.g. Blau and Gilleskie 2001; Cahill, Giandrea and Quinn 2006). In application of the
competing risks formulation to departures from the Supreme Court, there is no censoring. Rather,
continuation-in-office, death-in-office and retirement are conceived as alternative choices. Although we
apply the competing risks model for comparison and completeness, we note that continuation-in-office
and death-in-office are not different choices, but are both results of the choice to continue in office rather
than to retire. In the case of death-in-office, that choice is followed within a year by death, which is
7
Supreme Court Departures
May 14 2007
Page 8
experienced but not chosen.
Clustering. Persistent, unobserved and unmeasured characteristics of justices undoubtedly affect
their annual probabilities of retirement and death-in-office. For example, justices differ in their tastes for
work, family circumstances, and so on. Thus, the observations (justice-years) pertaining to a particular
justice constitute a cluster of observations with possibly correlated errors; the variance of errors also may
vary across justices. To accommodate these possible effects of clustering, we calculate robust standard
errors and significance tests, adjusted for sample clustering (Wooldridge 2002, 57).
Independent Variable Nonlinearities. Our analyses test hypotheses about the effects of political
variables on justices’ probabilities of retirement and death-in-office, after controlling for the confounding
effects of other variables. Previous studies suggest that these control variables may have nonlinear effects
on retirement, job termination and mortality. Lest we underestimate the impact of a control variable – and
thereby overestimate the effect of the political circumstances variables that are the subject of our
hypotheses – our models permit, but do not require, control variables to have nonlinear effects. We follow
an iterative algorithm to apply log-fractional polynomial transformations of these variables, and we retain
whatever transformations most improve the fit of control variables to the data (Royston and Altman 1994;
Gilmour and Trinca 2005).
Effects of Variables. Effects of variables follow from the logistic regression model. For brevity,
we state that model for retirement only. Let Yij equal 1 if the ith justice retires in the jth year, and let Yij = 0
if he or she does not retire in that year. Let Pij equal the probability that Yij equals 1. Let Oij = Pij/(1-Pij) be
the odds that Yij equals 1. Let Xij be a vector of independent variables, Xij = (xij1, xij2, …, xijK) , measuring
hypothesized causes and control variables for the ith justice in the jth year. ij is the error. ^ indicates an
estimate. The logistic regression model is:
Ln(Pij/(1-Pij)) = Ln(Oij) = Xij +ij
Exponentiating equation (1) transforms the left side from the logit to the odds, Ôij , which has
(1)
greater intuitive appeal:
P̂ /(1- P̂ ) = Ô = e X ij
(2)
ij
ij
ij
For more intuitive appeal, we solve (2) for the probability, as follows:
P̂ = Ô /(1+ Ô ) =e X ij / (1+ e X ij)
(3)
ij
ij
ij
The effect of Xk on P̂ij , is the difference in the probability that Y=1 that is associated with some
8
Supreme Court Departures
May 14 2007
Page 9
convenient difference in Xk , on average and other things equal. If convenient differences in Xk are
minutely small, and the effect is expressed as a rate of change in probability per unit change X k , then the
effect measure is the partial derivative of the probability with respect to Xk  P̂ij / Xk . These partial
derivatives (or marginal effects) obtain as follows: Differentiating (3) with respect to Xk indicates that the
marginal effect of any particular Xk on the odds is a constant proportion of the odds:
(4)
 Ôij /  X..k = k Ôij
And, dividing both sides of (5) by Ôij yields:
(5)

Ô ij
Ô ij
/ x
..k
 βk
Differentiating (4) yields the marginal effect of any particular x.k on the probability that Yij=1 :
(6)
 P̂ij / Xk = k P̂ij (1- P̂ij )
If a “convenient difference” in Xk is not minutely small, then it is necessary to calculate the change in Pij
associated with that difference in Xk.
Methodological Strategy in Sum. Our statistical analyses estimate the effects, in each year, of
independent variables on the probabilities that each justice who sits on the Supreme Court retires that
year, dies in office that year or remains in office for all of that year. These probabilities are unobservable,
so we estimate them statistically, from observations of whether each justice retires, dies in office, or does
neither, in each year in which the justice serves at least some time on the Court. To test hypotheses, we
use statistical estimation to measure the effects of independent variables on these probabilities.
Independent variables in analyses described here are: 1) an indicator of whether or not the sitting U.S.
President is of the same political party as the President who appointed the justice (hereafter SameParty);
2) an indicator of whether or not the sitting U.S. President is in the first two years of his term (hereafter
Year12); 3) the combination of SameParty and Year12 (see discussion below); and 4) various control
variables, possibly correlated with the previous three, that are known or believed to affect probabilities of
mortality or retirement in the general working population.
To accommodate differences between the structures that produce probabilities of retirement and
death-in-office, we estimate separate models of retirement and death-in-office. To recognize that actual
retirement precludes future death-in-office, and to recognize that death-in-office precludes future
retirement, our analyses specify that actual retirement censors future observations of the probability of
9
Supreme Court Departures
May 14 2007
Page 10
death-in-office, and actual death-in-office censors future observations of the probability of retirement. To
avoid nonsensical probability estimates that are made likely by the low mean probabilities of death-inoffice and retirement (each about 2.5 percent per justice-year), we estimate model parameters with
logistic regression. To avoid confounding SameParty and Year12 with the other known or suspected
determinants of retirement and mortality, we include as independent variables in our analyses measures
justices’ age, tenure on the Court, pension-eligibility, calendar year, and, sometimes, future years left to
live, all in each justice-year. To further accommodate nonlinearities in effects of control variables, we use
log-fractional polynomial methods to find appropriate functions of control variables. To accommodate the
effects of unobserved differences between justices that could create heteroskedasticity and correlations of
estimation errors from the same judge, we compute robust, clustering-corrected standard errors and
significance tests. To overcome the intuitively unappealing metric in which logistic regression and probit
coefficients are measured, we find it useful to translate some of those effects into changes in the odds or
probabilities of retirement and death in office. Finally, to permit comparisons of our findings to those of
previous analysts, we perform additional analyses with multinomial probit analysis, and we perform
additional analyses in which deaths-in-office and retirements are lumped together as departures from the
Court, in spite of our expressed concerns for the appropriateness of doing so.
IV. DATA
We examine data on all justices of the United States Supreme Court from 1789 through 2006.6
Summary statistics for variables used in our analyses are described briefly in Table 2. These variables and
the reasons for including them are as described more fully as follows:
Retire. A dummy variable equal to 1 for a justice-year if the corresponding justice retired,
resigned or accepted “senior status” during the corresponding year, or retired, resigned or accepted senior
status before commencing service as a Supreme Court justice during the following year. Otherwise, Retire
equals 0. Justices who accept senior status no longer hear Supreme Court cases.
Death-in-office. A dummy variable equal to 1 for a justice-year if the corresponding justice began
the year sitting on the Supreme Court, and died without retiring before commencing service during the
subsequent year. Otherwise, Death-in-office equals zero.
Calendar year, Year1788, ln(Year1788). We include calendar year to hold constant trends in the
probabilities of retirement and death-in-office, as well as trends in the causes of retirement and death-in-
10
Supreme Court Departures
May 14 2007
Page 11
office. Subtracting 1788 from calendar year preserves all information and avoids rounding problems that
occurred in initial analyses with STATA version 8 that used calendar year. Year1788 is the calendar year
minus 1788. ln(Year1788) is the natural logarithm of Year1788; the logarithmic transformation improves
the fit of some models.
Age, Age-cubed, Age-squared. Age is the age of the justice in years. Probabilities of death and
retirement increase with age. In some analyses, we find it useful to add Age-squared and Age-cubed to the
analysis, to fit nonlinear age effects.
Tenure, Tenure-cubed, Tenure-cubed x ln(Tenure). Tenure is years of service on the Supreme
Court. The annual probability of job quitting in the working population is known to change nonlinearly
with tenure, first declining as tenure increases, and then increasing with additional tenure (Stolzenberg
1989). Tenure-cubed and Tenure-cubed x ln(Tenure) prove useful transformations of tenure.
Pension Eligible. A dummy variable equal to 1 if the justice is eligible for a Federal judicial
pension in the relevant year, and equal to 0 otherwise.
Future longevity, Future longevity-squared. Future longevity indicates the justice’s remaining
years of life. Future longevity for each justice-year is the difference between the calendar year of the
justice-year and the calendar year in which the justice ultimately dies. Future longevity-squared proves to
be a useful transformation of future longevity.
Political Circumstances Variables: These variables measure political circumstances hypothesized
to affect retirement and death in office probabilities.
SameParty. A dummy variable equal to 1 if the U.S. President who appointed the justice and the U.S.
President in the current year are affiliated with the same political party.7
Year12. A dummy variable equal to 1 if the U.S. President is in the first two years of a four-year
Presidential term. Because of a consistent pattern of midterm losses of Congressional seats to the
President’s party, Presidents tend to have more control over judicial appointments in the first two years of
their terms in office than in the third and fourth years (see e.g. Campbell 1991; Calabresi and Lindgren
2006b).
SameParty*Year12. The product of SameParty and Year12. SameParty*Year12 equals 0 unless
SameParty and Year12 both equal 1.
Together, the three variables SameParty, Year12 and SameParty*Year12 indicate all possible
11
Supreme Court Departures
May 14 2007
Page 12
combinations of values of SameParty and Year12.
In some models, the following alternative parameterization of political circumstances simplifies
interpretation. With no loss of information, we define NoSameParty as 1 minus SameParty, and Year34
as 1 minus Year12, and we parameterize political circumstances with the variables SameParty*Year12,
SameParty*Year34 and NoSameParty*Year12.8 Together, the three variables SameParty*Year12,
SameParty*Year34 and NoSameParty*Year12 indicate all possible combinations of values of SameParty
and Year12.
V. RESULTS
Historical Trends. Figure 1 presents historical trends in the mean age of sitting Supreme Court
justices, the mean age at which these sitting justices took the oath of office, the mean age at which they
eventually leave the Court and the mean age at which they ultimately die. Age-at-oath increased in the
19th Century and declined slightly (but insignificantly) in the 20th Century. Mean age of justices, mean age
at eventual departure from the Court, and mean age at ultimate death all have increased since 1789. At
any particular year, the gap between mean age at oath and mean age of justices indicates justices’ average
tenure on the Court. At each year, the gap between mean age and mean age at retirement indicates the
mean future years of service on the Court of justices serving in that year. Note that mean tenure and age
have increased in the 20th Century as consequences of the combination of declining age at oath and
increasing age at departure from the Court.
Analyses of Retirement: Table 3 presents five logit analyses of retirement from the Supreme
Court. The unit of analysis in these analyses is the justice-year; the dependent variable is the log of the
odds that the justice retires. Analyses 1, 2, 4 and 5 parameterize President’s political party and term year
with dummy variables SameParty*Year12, SameParty*Year34 and NoSameParty*Year12. Coefficients
of these variables indicate effects of combinations of values of SameParty and Year12, relative to the
condition that both SameParty and Year12 equal zero. Analysis 3 parameterizes Presidential party and
term year with dummy variables SameParty, Year12 and SameParty*Year12.
To dispel doubts that nonlinear transformations of control variables mask something unfavorable to
our argument, Analysis 1 includes only linear forms of independent variables. In column 1, Row 10 of
Table 3, the coefficient of SameParty*Year12 is 0.9850, with a robust, cluster-corrected z-statistic of
2.09 (≤0.02, 1-tailed test, 1879 d.f.). Thus, other things equal, the odds of retirement increase by 168
12
Supreme Court Departures
May 14 2007
Page 13
percent (=e0.9850 - 1) when the sitting President is from the same political party as the President who
nominated the justice and a Presidential administration is in its first or second year (compared to the odds
when the sitting President is from a different political party and a Presidential administration is in its third
or fourth year of a four-year cycle).
At the cost of some additional complexity, we translate this coefficient from an effect on odds to
an effect on the probability of retirement: Suppose a hypothetical justice had a retirement probability of 1
percent, the sitting President was of a party different from the party of the President who nominated the
justice; and the President was in the last two years of his four-year term (i.e., SameParty=0 and
Year12=0). Changing both SameParty and Year12 to 1 would increase the justice’s expected probability
of retirement by a multiple of slightly more than 2.6, from 1 percent to 2.6 percent, on average and other
things equal. If, in this same example, the justice’s initial probability were 10 percent, then the justice’s
expected retirement probability after the change of Presidents would increase by a multiple of about 2.3,
to 22.9 percent, on average and other things equal.
In Analysis 2 of Table 3, we use an iterative algorithm to fit log fractional polynomial
transformations of control variables. As control variables are better fitted to the data, they absorb more of
the variance in the dependent variable, leaving less variance to be explained by the political variables that
we hypothesize to affect the timing of retirement decisions and deaths in office. After estimating and
comparing 42 different sets of nonlinear transformations of calendar year, justice’s age, and justice’s
tenure on the Supreme Court, we replace Year1788 with ln(Year1788) and we replace Tenure with
Tenure-cubed and the product of Tenure-cubed and ln(Tenure). These transformations raise the pseudoR2 from 0.1254 in Analysis 1 to 0.1581 in Analysis 2.
The coefficient for SameParty*Year12 in Analysis 2 is 0.9555 (significant, ≤0.02, 1-tailed test).
Thus, if both SameParty and Year12 changed from 0 to 1 (thereby also changing SameParty*Year12 from
0 to 1 and leaving all other political circumstances indicators unchanged), then the expected odds of
Retirement would increase by a multiple of 2.6. In short, effects of political circumstance variables found
in Analysis 2 are virtually the same as the effects obtained in Analysis 1 and discussed above.
In Analysis 3 of Table 3, we reparameterize the variables representing Presidential party and term
year. Rather than using three dummy variables to represent four combinations of these conditions, we use
one dummy for each condition (SameParty, Year12), and one dummy for the product of the two
13
Supreme Court Departures
May 14 2007
Page 14
(SameParty*Year12). The effect of having both conditions present is the sum of the coefficients for all
three dummies. The sum of those three coefficients is 0.955, which is identical to the coefficient for
SameParty*Year12 in analysis 2. Note that all other results for Analysis 3 are precisely identical to those
of Analysis 2.
Pension Benefits and Retirement. Coefficients of the pension benefits eligibility indicator range
from 2.0904 in Analysis 1 to 2.4514 in Analysis 5. The smallest of these estimates indicates that, other
things equal, pension benefit eligibility changes the odds of retirement in a justice-year by a multiple of
8.09. In the metric of probabilities, these effects are as follows: Other things equal, if a justice without
pension benefit eligibility had a retirement probability of 1 percent, then the addition of pension benefits,
would increase that probability to 7.55 percent, on average. If that justice had a 5 percent probability of
retirement before receiving benefits, then the addition of pension benefits eligibility would increase the
probability to 29.9 percent. Effects of this magnitude are remarkable by social science standards and need
no embellishment.
Tenure and Retirement. To interpret coefficients of Tenure-cubed and Tenure-cubed*ln(Tenure),
we use them to calculate the effect of an additional year of Supreme Court tenure on the odds of
retirement, other things equal. These effects are stated as proportional changes in odds, and are calculated
from Analysis 4 and graphed below in Figure 2(a). That Figure shows that these effects are not linear.
Figure 2(a) shows that for those with one year of service on the Supreme Court, the effect of an additional
year of tenure on the odds of retiring is negative but nearly zero. The effect of an additional year of tenure
becomes more strongly negative through the first 15 years on the Court. At 15 years of tenure, an
additional year of tenure decreases the odds of retirement by 12.5 percent. Thereafter, the retirementinhibiting effect of an added year of tenure decreases annually. At 25 years of service, tenure effects
become positive. At 28 years of tenure, an additional year would increase the odds of retirement by 11.2
percent. At 29 years, the increase would be 15.8 percent.
Age and Retirement. In Analysis 4, the coefficient of Age is .0548 (significant, ≤0.05, one-tailed
test). Thus, on average and other things equal, a difference of one year of age is associated with a 5.5
percent increase in the odds of retirement. In Analyses 2 and 3, which do not hold constant Future
longevity, the odds of retirement increase a rate of about 6.5 percent per additional year of age.
Future Longevity and Retirement. In Analysis 4 of Table 3, we add control variables based on the
14
Supreme Court Departures
May 14 2007
Page 15
years that a justice has to live: Future longevity-squared and Future longevity-squared x ln (future
longevity). Each future longevity term is statistically significant by itself (each ≤0.01, two-tailed test);
both terms are jointly significant ((2 d.f.)=7.88, ≤0.02, 2-tailed). Notice that controlling for Future
longevity does not substantially modify the estimated effect of SameParty*Year12 on the probability of
retirement. The coefficient of SameParty*Year12 in Analysis 4 is 0.9706, which trivially higher than the
estimate obtained in Analysis 2, trivially lower than the estimate obtained in Analysis 1, and not
significantly different from either at any meaningful  level.
Because future longevity is known only for dead justices, Analysis 4 is not estimated for justiceyears of the nine living justices and one living former justice. Thus, Analysis 4 is computed over 161
fewer justice-years than Analyses 1, 2 and 3. To consider the hypothesis that results in Analysis 4 are
substantially affected by the loss of these 161 justice-years, we re-estimate as Analysis 5 the same model
that we estimated as Analysis 2, after excluding the 161 justice-years that are dropped to permit
computation of Analysis 4. If Analysis 5 produces estimates similar to those that we produced as Analysis
2, then there is evidence that removal of justice-years for surviving justices has not biased results reported
as Analysis 4. Looking at Analysis 5, notice coefficients are not meaningfully different from those in
Analysis 2. In particular, coefficients and z-statistics of Political Circumstances Indicators are virtually
identical in Analyses 2 and 5.
To interpret coefficients of Future longevity-squared and Future longevity-squared*ln(Future
longevity), we use them to calculate the proportional effect of a one-year increase in Future longevity on
the odds of retirement. We calculate these effects from Analysis 4 and graph them in Figure 2(b).
Looking at Figure 2(b) notice that if justices with less than 22 years of remaining life were granted
another year to live, then their odds of retirement would decrease, other things equal. That decrease is
weakest for justices with 21 years remaining to live, for whom the odds of retirement are reduced by 0.5
percent, on average, other things equal; the decrease is strongest for those with 8 years of remaining life,
for whom an additional year of remaining life would decrease the odds of retirement by 7.5 percent, on
average, other things equal. These results are consistent with the interpretation of Future longevity as an
indicator of health – health declines more rapidly at the end of life than in middle age.
Secular Trend in Retirement Probability. Analysis 4 reports a coefficient of -.4951 for
ln(Calendar year – 1788). Thus, on average and other things equal, annual odds of retirement of Supreme
15
Supreme Court Departures
May 14 2007
Page 16
Court justices change at a constant elasticity of about one-half percentage point of decrease in the odds of
retirement for each percentage point of increase in the number of calendar years since 1788. Figure 2(c)
states these effects as the impact of 10-year increases in the calendar year. In 1789, an additional decade
would lower the annual odds of retirement by 69.5 percent. In 1879, an additional decade would bring a
5.0 percent reduction. In 2005, the effect of an additional decade was a 2.2 percent reduction in the odds
of retirement.
Analyses of Death-in-Office. Because death-in-office occurs when a justice both chooses to not
retire and dies, we expect negative effects on the probability of death-in-office from independent
variables that promote retirement, and positive effects from independent variables that mark elevated
mortality risk. If an independent variable increases the probabilities of both retirement and mortality, then
its negative effects (via retirement) and its positive effects (via mortality) would offset each other to some
degree, depending on the strength of each effect and the association between the probabilities of
retirement and mortality. Some of this offsetting seems likely for Age, Tenure and Pension eligibility.
However, we know of no suggestion from any source that Presidential party affiliation or the timing of
Presidential elections could alter a Supreme Court justice’s mortality risk – justices do not live longer just
because the President is a Republican (or a Democrat). So we expect negative effects on the annual
probability of death-in-office of the political circumstances indicators that show positive effects on the
annual probability of retirement.
Table 4 presents two logit analyses of the hazard of death-in-office. These analyses are analogous
to analyses of retirement probability shown in Table 3, except for two differences: First, we search for but
do not find nonlinear transformations of control variables that improve their fit to the hazard of death-inoffice. Second, we cannot use Future longevity as a control variable in analyses of death-in-office,
because future longevity is conceptually and empirically confounded with mortality. That is, death occurs
in the current year if and only if future longevity is zero. Consequently, models of death-in-office are
computationally intractable when future longevity is included among the independent variables.
Political Circumstances and Death in Office. In Table 4, Analysis 1 follows the parameterization
of political circumstances used in most of the Table 3 retirement analyses. The coefficient of
SameParty*Year34 is -1.1026 (significant, z = 2.30, <0.025, one-tailed). Thus, a change in value of
SameParty*Year34 from 0 to 1 is associated with a reduction by about two-thirds (2/3 ≈ 1 - e-1.1026) of the
16
Supreme Court Departures
May 14 2007
Page 17
odds of dying in office. (The reference category for this effect is NoSameParty*Year34=1.) Other results
are less clear in this parameterization: The coefficient of SameParty*Year12 is not significantly different
from zero (z=1.03), but it is also not significantly different from the coefficient of SameParty*Year34
(≤0.05, two-tailed).
Because we examine data on the entire universe of Supreme Court justices, it is at least arguable
that significance tests can be ignored in these analyses. If coefficients are interpreted without significance
tests, then Analysis 1 leads to the following conclusion: Other things equal, the coefficient of -.4229 for
SameParty*Year12 indicates that the odds of death-in-office for a justice in a year are reduced by about
one-third (.34=1- e-0.4229) if the sitting President is of the same party as the President who first nominated
the justice to the Court, and the sitting President is in the first two years of his term. The coefficient of 1.1026 for SameParty*Year34 indicates that if the sitting President is instead in the third or fourth year of
his term, then this reduction in the odds of death-in-office doubles. But, by itself, the number of years left
in the sitting President’s term has virtually no effect on the probability of death-in-office.
Without abandoning significance testing, Analysis 2 clarifies political circumstances effects by
reparameterizing. In Analysis 2, the reference category is changed to SameParty*Year34 = 1. The
coefficient of NoSameParty*Year12 is 1.0824 (significant, z = 2.35, <0.01, one-tailed). The coefficient
for NoSameParty*Year34 is 1.1025, significant (z = 2.30, <0.015, one-tailed), almost identical to the
coefficient of NoSameParty*Year12 and not significantly different from it (at any meaningful
significance level, 2 < .01, 1 d.f.). We must reject the null hypothesis that both of these coefficients are
zero ( 2 =6.57, 2 d.f., ≤0.05, two-tailed). In contrast, the coefficient for SameParty*Year12 is not
distinguishable from zero at any meaningful significance level (i.e. insignificant, z=1.43, one- or twotailed test).
Interpretation of the political circumstances effects in Analysis 2 is straightforward: A change in
value of either NoSameParty*Year12 or NoSameParty*Year34 from 0 to 1 approximately triples the odds
of dying in office (3≈2.95 = e1.0824; 3≈3.01 = e1.1025) , other things equal. Thus, the odds of death in office
are about three times higher when the incumbent President is not of the same party as the President who
appointed the justice (compared to when the incumbent President is of the same party, other things equal),
but there is no statistically significant effect of Presidential term year on death in office.
These coefficients can be stated as probability effects: Other things equal, if a hypothetical justice
17
Supreme Court Departures
May 14 2007
Page 18
had a 1 percent probability of dying in office in a given year, then a change of NoSameParty*Year12
from 0 to 1 would increase that probability to 2.9 percent. If the hypothetical justice had an initial 10
percent probability of dying in office, then a change of NoSameParty*Year12 from 0 to 1 would increase
that probability to 24.7 percent. Effects would be nearly identical for changes in NoSameParty*Year34.
Effects of Control Variables on Death-in-Office. The odds of death-in-office decline at a rate of
1.17 percent per calendar year, on average, other things equal. Over a decade’s time, these annual
reductions cumulate to an 11 percent reduction in the odds of death-in-office. In contrast to our analysis
of retirement, nonlinear transformations of Year1788 do not improve its fit to death-in-office data. Thus,
the secular decline in these odds continues unabated. Other things equal, as time passes, death-in-office is
increasingly improbable. Each additional year of Age increases the odds of death-in-office by about 6.5
percent (the coefficient is 0.0646; significant, <.001, one-tailed), on average and other things equal.
However, the effects of Tenure and Pension Eligibility are not distinguishable from zero (i.e., not
significant, <.05, one-tailed). The absence of Tenure and Pension Eligibility effects stand in sharp
contrast to our findings for retirement hazard.
Analyses of Departure by Any Means. Table 5 presents two logit analyses of the odds of
Departure versus continuing in office. Again, the unit of analysis is the justice-year. Censoring cannot
occur in this analysis. Future Longevity is not included as an independent variable, because it is
confounded with death-in-office, a component of Departure. Other independent variables are those that
appeared in our analyses of retirement and death-in-office. We again use iteratively-fitted log-fractionalpolynomials to find transformations of control variables that maximize their fit to the data. Results for
control variables are discussed below, following discussion of political circumstance variables.
In Analysis 1, we measure political circumstances with dummy variables SameParty, Year34 and
their product, SameParty*Year34. Coefficients for these variables are all negative, and none is by itself
statistically significant. However, their joint effect is substantial and statistically significant: For a
particular justice-year, if the political party of the current U.S. President were the same as the political
party of the President who appointed the justice, and if the current President were in his third or fourth
term year of a Presidential term, then each of these dummy variables would equal one, and their
combined effect would be the sum of their coefficients. That sum is -0.7142 (statistically significant,
Z=2.44, <0.01, one-tailed test), and it corresponds to a 51 percent reduction in the annual odds of
18
Supreme Court Departures
May 14 2007
Page 19
vacating office, other things equal. Analysis 2 yields identical results, although it reparameterizes the
effects of Presidential party and term year with three interaction effects rather than two main effects and
one interaction: the coefficient for SameParty*Year34 in Analysis 2 is -0.7142 (statistically significant,
Z=2.44, <0.01, one-tailed test), and it corresponds to a 51 percent reduction in the annual odds of
vacating office, other things equal.
Three time-related control variables affect Departure in Table 5: First, the coefficient of
Year1788 is -.01020, indicating that the odds of vacating decline by about one percent with each
additional calendar year. Second, for justices with one year of Tenure on the Supreme Court, the effect of
an additional year of Tenure is a 10.0 percent reduction in the odds of Departure. Thereafter, the effect of
one additional year of Tenure increases by 0.6329 percent per year. After justices have accumulated 17
years of Tenure on the Court, the effect of an additional year of Tenure becomes positive. At 35 years of
Tenure, an additional year of Tenure increases the odds of Departure by 14.2 percent, on average and
other things equal. Third, effects of Justice’s Age are nonlinear and fitted with 1/Age-squared and Agecubed. As shown in Figure 2 (d) below, the effect of an additional year of Age on the odds of Departure is
highest at the very youngest ages (14.7 percent at age 35), declines to its minimum (6.8 percent at age
59), and rises thereafter, reaching 8.7 percent at age 80.
Competing Risks Analyses. Having stated above some limitations of the competing risks
conceptualization of departures from the Supreme Court, we apply it nonetheless, to permit comparison to
our own results in Tables 3 and 4. Table 6 presents results of a multinomial probit analysis of the
probability that in a given year a justice dies in office, remains in office, or retires from the Supreme
Court. Again, the unit of analysis is the justice-year. The multinomial probit yields two equations, one
predicting the probit of the hazard of Death-in-office (versus Continuation-in-office), the other predicting
the probit of the hazard of Retirement. The omitted reference category is Continuation-in-office. The same
independent variables must appear in all equations of the multinomial probit model, so we must omit
Future Longevity. We also add nonlinear transformations of control variables, but, because we have no
basis to evaluate transformations that improve the fit of one equation while weakening the fit of the other,
we cannot use automated algorithms to fit combinations and permutations of transformations.
Results of the Competing Risks model (Table 6) are similar to the single-equation results we
reported in Tables 3 and 4. The Table 6 retirement analysis finds a positive, statistically significant probit
19
Supreme Court Departures
May 14 2007
Page 20
coefficient of 0.5383 (significant, ≤0.03, one-tailed) for SameParty*Year12. To permit comparison of
this probit coefficient to the logit coefficient for the same variable in Table 3, we consider the same
hypothetical scenario that we applied to the logit coefficient: Suppose a hypothetical justice had a 5
percent probability of retirement in the current year, and suppose also that the President was in the last
two years of his term and of a different party than the justice (Year12 = 0, and SameParty = 0). Then, on
average, and other things equal, if Year12 and SameParty were both changed to 1 (it was the first two
years of a President of the same party as the President who first appointed the justice to the Court),
thereby making SameParty*Year12=1, the probit result indicates that the probability of retirement this
year by the hypothetical justice would rise from 5 to 13.4 percent, on average, other things equal. In the
logit analysis of Retirement in Column 2 of Table 3, the same hypothetical scenario indicates a
probability increase to 12.0 percent. Thus, the two retirement analyses estimate political climate effects
that are both statistically significant, of identical direction and similar magnitude.
Analyses of death-in-office yield much the same conclusion: In Table 6, the probit coefficient of
SameParty*Year34 is -.6053 (significant, ≤0.03, one-tailed). If a hypothetical justice has a five percent
probability of Death-in-office in the current year, and SameParty=0 and Year34 = 0, then changing
SameParty to 1 and Year34 to 1 would reduce the probability of Death-in-office from 5 percent to 1.22
percent, on average and other things equal. In Analysis 1 of Table 4, the hypothetical scenario just
described would reduce the probability of dying in office to 1.72 percent, on average and other things
equal. Again, in spite of our theoretical objections to the competing risks approach, it leads to the same
conclusions as our separate logit analyses of retirement and death in office.
VI. DISCUSSION
Can Retirement be lumped with Death-in-Office? Retirements and deaths in office both produce
opportunities for the President to nominate new justices to the Supreme Court. However, we argued
conceptually and logically that the hazards of retirement and death-in-office are different phenomena
produced by different processes, albeit with the same result. Our empirical findings are consistent with
those arguments. First, we find that political circumstances and control variables have different effects on
retirement hazard and the hazard of death-in-office. For examples:

SameParty reduces the hazard of death-in-office, but increases the hazard of retirement. These
20
Supreme Court Departures
May 14 2007
Page 21
SameParty effects interact with the effects of the President’s year in office, but, again, differently: If
Year12 =1, then the absolute size of the effect of SameParty on Retirement hazard increases; if
Year12 = 0 (that is, Year34=1), then the absolute size of the effect of SameParty on the death-inoffice hazard increases

Pension benefit eligibility increases the odds of retirement more than eight-fold, on average and other
things equal. But the effect of pension benefit eligibility on the odds of death-in-office is not
distinguishable from zero.

The odds of death-in-office declines since 1789. In contrast, the odds of retirement decline very
rapidly in the 18th Century, but taper off quickly, and now recede at a small annual rate.

Tenure shows no effect on the odds of death-in-office, on average and other things equal. But Tenure
shows negative effects on the odds of retirement for justices with less than 25 years of experience on
the Court, and increasing, positive effects thereafter.
In short, empirical evidence suggests different patterns of causation for the hazards of death-in-office and
retirement.
Second, our analyses also indicate that the hazard of retirement is empirically distinct from the hazard
of death-in-office. For each justice-year, we use Analyses 3 and 4 from Table 3 to calculate the estimated
the hazard of retirement.9 We use Analysis 1 from Table 4 to calculate the estimated the hazard of deathin-office for each justice-year. Distributional characteristics of these hazards over all justice-years are
shown in Table 7. Figure 3(a) plots the logarithms of these hazards against each other and shows the
regression of ln(retirement hazard) on ln(death-in-office hazard): Ŷ = -2.7441 + 0.3405 X (R2 = 0.14).
The standardized coefficient of ln(death-in-office hazard) is 0.37 (statistically significant, <.001, Robust
t=7.70, corrected for clustering, two-tailed test). For comparison, in status attainment models, the
standardized effect of father’s occupational status on son’s occupational status is about 0.3 and the
standardized effect of son’s schooling on his occupation is about 0.4 (Blau and Duncan 1967). Thus, the
hazards of retirement and death-in-office are substantially related, as expected, but they are no more
identical than father’s and son’s occupations, and no more identical than son’s education and son’s
occupational status.
Because the Supreme Court is a functioning organization as well as a collection of individuals, it is
useful to consider variation in the distributions of the hazards of retirement and death-in-office for sitting
21
Supreme Court Departures
May 14 2007
Page 22
justices on the Court.10 Figure 3(b) shows the time trend in the means of these hazards for sitting justices,
smoothed by Cleveland’s method of locally-weighted regression (LOWESS). Observe that mean hazards
of retirement and death-in-office tend to move in opposite directions over time, offsetting each other to
some extent. The standard deviations of these distributions also move in opposite directions over time. In
brief, trends in the mean and standard deviations of retirement hazards and death-in-office hazards for
sitting justices are well differentiated from each other, and lead to the same conclusion indicated by the
distributions of these hazards over justice-years and the effects of independent variables on these hazards:
the hazards of death-in-office is empirically different from the hazard of retirement, and hypotheses about
the causes each should be tested with methods and data that permit that difference to manifest itself.
How Big are the Effects of Political Circumstances? We find the variable SameParty*Term12
multiplies the odds of retirement by a factor of more than 2.6. Although it seems unreasonable to call this
effect anything less than “big,” or even “very big,” if the hazards of retirement are very small, they will
still be small after being doubled or tripled. In fact, the average hazard of retirement is less than three
percent. Yet those hazards are sufficient to have dispensed with 103 justices of the Supreme Court in the
218 years from 1789 to 2006. It seems appropriate to consider the size of these annual probabilities in
relation to the likelihood of retirement and death-in-office over the four or eight year periods that
constitute the minimum and maximum lengths of a President’s tenancy in office. When integrated over
the interval of one or two Presidential terms, seemingly-small hazards of retirement and death-in-office
become substantial. For example, over an eight year period, a 1 percent annual hazard of retirement
cumulates to 7.7 percent – about one chance in 13 – a 3.53 percent annual hazard cumulates to 25
percent, and an 8.3 percent cumulates to 50 percent.11 Further yet, these cumulative hazards also cumulate
over the nine justices who have served on the Supreme Court at the same time,12 with each justice
separately subject to the hazards of retirement and death-in-office. When integrated over all nine justices
and the four or eight years of a presidency, the hazard of retirement is not small at all, and the
consequence of doubling or tripling that hazard seems to be appropriately described as a “big” effect.
Analyses of death-in-office lead to similar conclusions: When SameParty equals 1, the odds of death-inoffice are approximately tripled. Again, it seems appropriate to describe these effects as “big.”
Why is retirement behavior consistent with the Political Departure Hypothesis? The Politicized
Departure Hypothesis grows out of commonplace thinking that justices make rational decisions that
22
Supreme Court Departures
May 14 2007
Page 23
further their own wishes to serve the political party of the President who appointed them. However, the
research reported here includes no findings whatsoever on political wishes of justices, their perceptions of
political circumstances, or the rationality of their retirement decisions. Without such findings, we can
report evidence in favor of the Politicized Departure Hypothesis, but we cannot dismiss alternative
explanations of Politicized Departure. For example, perhaps justices’ departures are politicized by the
application of external social pressure (rather than their own wishes) to repay their political debt to the
party of the President that nominated them to the Court. Alternatively, perhaps social pressure for
politicized retirement is not actually applied to justices. Perhaps instead justices anticipate that negative
social consequences would result from a retirement that denies political advantage to the party of the
President who appointed them. It seems that new data might be required to choose among these
alternative explanations. Because all but one former justice is now dead, the necessary data might well be
impossible to gather.
VII. CONCLUSION
Research on departures of justices from the Supreme Court spans seven decades and several
academic disciplines. Previous analysts are highly critical of each other’s conclusions and research
methods. One might despair of such dissensus, but we find it a useful guide to methodological and
substantive issues that must be accommodated to appropriately formulate and test the implications of the
politicized departure hypothesis. The analyses presented here attempt to accommodate the mutual
criticisms of earlier scholars of each others’ work on politicized departure from the Supreme Court. We
also attempt to evaluate those criticisms by comparing systematically a number of different methods that
have been used to study the Politicized Departure hypothesis. Very briefly, our analyses provide reasons
to agree that it is probably an error to lump together deaths in office and retirements, and it is probably an
error to use aggregate data on departure rates (i.e., the number of departures per year) to test hypotheses
about individual justices. However, we did not find evidence to support the claim that omission of a
vitality measure does fatal damage to estimates of the effects of political circumstances. Finally, we find
that length of tenure on the Court, future longevity, and calendar year all have markedly curvilinear
effects on retirement probability. Even if ignoring those nonlinearities would not alter our conclusions
about the effects of political circumstances, they are of substantive interest in their own right, and have
not been considered in previous research. In brief, if time matters for departures from the court, it seems
23
Supreme Court Departures
May 14 2007
Page 24
to matter in opposite ways, depending on how much time has passed already.
Finally, the main substantive news here is that our results are consistent with the Politicized
Departure hypothesis: In particular, we consider the annual probabilities of resignation from office and
death in office of each and every justice who served on the U.S. Supreme Court from 1789 through 2007.
Other things equal, and on average, we find that

If the incumbent President is of the same as the party of the President who nominated a Supreme
Court justice to the Court, and if the incumbent President is in the first two years of a four-year
Presidential term, then the annual resignation probability is about 2.6 times higher than at times
when these two conditions are not present. And,

If the incumbent President is not of the same as the party of the President who nominated a Supreme
Court justice to the Court, then the odds of death-in-office in any given year are approximately three
times higher than at times when the this condition is present.
In closing, we observe that although the Supreme Court has a uniquely important place in
American government, the behavior of Supreme Court justices may inform more general thinking about
the behavior of persons who serve on other governmental and nongovernmental boards and courts.
Further, research on other boards and courts may also inform thinking about the behavior of Supreme
Court justices. For example, Mace’s (1986) consideration of corporate boards of directors describes
behavior much like that predicted by the Politicized Departure Hypothesis. It remains to be seen if there is
much to be gained from connecting research on the Supreme Court to studies of other organizations.
However, connecting these different lines of research is certainly possible, and it seems a fruitful
direction for future studies. In the mean time, the analyses described here provide considerable empirical
support for the belief that the Politicized Departure Hypothesis does indeed describe the behavior of U.S.
Supreme Court justices, whether that hypothesis is uniquely applicable to the Supreme Court or more
generally descriptive of behavior of persons who are appointed to service on other organizations.
24
Supreme Court Departures
May 14 2007
Page 25
REFERENCES
Abraham, Henry J. 1999. Justices and Presidents and Senators: A History of the U.S. Supreme Court
Appointments from Washington to Clinton (revised ed.). New York: Oxford University Press.
Allison, Paul D. 1995. Survival Analysis Using the SAS System: A Practical Guide. Cary, North Carolina:
SAS Institute Inc.
Atkinson, David N. 1999. Leaving the Bench: Supreme Court Justices at the End. Lawrence: Kansas
University Press.
Baker, Michael; Mark Stabile and Catherine Deri. 2004 What Do Self-Reported, Objective, Measures of
Health Measure? Journal of Human Resources 39 (Fall, 4)) 1067-93.
Barrow, Deborah J., and Zuk, Gary. 1990. “An Institutional Analysis of Turnover in the Lower Federal
Courts, 1900-1987.” Journal of Politics 52: 457-476.
Barrow, Deborah J., Gryski, Gerard S., and Zuk, Gary. 1996. The Federal Judiciary and Institutional
Change. Ann Arbor, MI: University of Michigan Press.
Bazzoli, G.J. (1985): The Early Retirement Decision: New Empirical Evidence on the Influence of
Health, The Journal of Human Resources, 20 (2), pp. 214-234.
Belsley, David A. , Edwin Kuh and Roy E. Welsch. 1980. Regression Diagnostics: Identifying Influential
Data and Sources of Collinearity New York: Wiley.
Biskupic, Joan. 2004. “The Next President Could Tip High Court.” USA Today, September 29.
Blau, Peter and Otis Dudley Duncan. 1967. The American Occupational Structure. New York:
Wiley.
Blau, D, Gilleskie, DB 2001“Retiree health insurance and the labor force behavior of older men in the
1990's.” Review of Economics and Statistics, 2001, Vol. 83 pp. 64-80.
Bound, J. (1991): Self-Reported versus Objective Measures of Health in Retirement Models. The Journal
of Human Resources, 26, 1, 106-138.
Box-Steffensmeier, Janet M., and Zorn, Christopher J. W. 1998. “Duration Models and Proportional
Hazards in Political Science.” Midwest Political Science Assn. Annual Meeting, April 23-25.
Box-Steffensmeier, Janet and Bradford Jones 1997 “Time is of the essence: Event history models in
political science.” American Journal of Political Science, 41: 1414-61
Brenner, Saul. 1999. “The Myth that Justices Strategically Retire.” Social Science Journal 36: 431-439.
25
Supreme Court Departures
May 14 2007
Page 26
Cahill, Kevin, Michael Giandrea and Joseph Quinn 2006 “Retirement Patterns From Career
Employment” The Gerontologist Vol. 46, No. 4, 514–523.
Calabresi, Steven G., and Lindgren, James. 2006a. “Term Limits for the Supreme Court: Life Tenure
Reconsidered.” Harvard Journal of Law and Public Policy 29: 770-877.
Calabresi, Steven G., and Lindgren, James. 2006b. “The President: Lightning Rod or King?” Yale Law
Journal 115: 2611-2622.
Caldeira, Gregory A., Wright, John R., and Zorn, Christopher J. W. 1999. “Strategic Voting and
Gatekeeping in the Supreme Court.” Journal of Law, Economics and Organization 15: 549-572.
Callen, Earl and Henning Leidecker, Jr. 1971. “A Mean Life on the Supreme Court.” American Bar
Association Journal 57: 1188-1192.
Campbell, James E. 1991. “The Presidential Surge and its Midterm Decline in Congressional Elections,
1868-1988.” Journal of Politics 53:477-87.
Cannell, Charles, Peter Miller and Lois Oksenberg. 1981. “Research on Interviewing Techniques.” In
Samuel. Leinhardt (ed.), Sociological Methodology, 389–437. San Francisco: Jossey-Bass.
Cotterman, Robert and Franco Peracchi. 1992. “Classification and Aggregation: An Application to
Industrial Classification in CPS Data.” Journal of Applied Econometrics 7: 31-51.
Davies, Allison, and Ware, John. 1981. Measuring Health Perceptions in the Health Insurance
Experiment. Document R-2711-HHS. Santa Monica, CA: Rand Corporation.
Dwyer, D.S. and Mitchell, O.S. (1999): Health problems as determinants of retirement: Are self-rated
measures endogenous? Journal of Health Economics 18 (1999), p. 173-193.
Eisenhower, Donna, Nancy Mathiowetz and David Morganstein. 1991. “Recall Error: Sources and Bias
Reduction Techniques.” Pages , 127–144 in Paul P. Biemer, Robert M. Groves, Lars E. Lyberg,
Nancy A. Mathiowetz and Seymour Sudman (eds.), Measurement Errors in Surveys. New York:
Wiley.
Fairman, Charles. 1938. “The Retirement of Federal Judges.” Harvard Law Review 51: 397-443.
Farnsworth, Ward. 2005. “The Regulation Of Turnover On The Supreme Court.” University of Illinois
Law Review 2005: 407-453.
Fazio, Russell H. 1986. “How Do Attitudes Guide Human Behavior?” In Richard M. Sorrentino and E.
Tory Higgins (eds.), Handbook of Motivation and Cognition, vol. 19. New York: Guilford.
26
Supreme Court Departures
May 14 2007
Page 27
Federal Judicial Center. 2006. Biographical Directory of Federal Judges.
http://www.fjc.gov/public/home.nsf/hisj (last accessed April 5, 2006).
Fishbein, Martin, and Ajzen, Icek. 1975. Belief, Attitude, Intention and Behavior: An Introduction to
Theory and Research. Reading, MA: Addison-Wesley.
French, Eric. 2005 The Effects of Health, Wealth, and Wages on Labour Supply and Retirement
Behaviour. Review of Economic Studies 2005 72:2 39
Garrow, David J. 2000. “Mental Decrepitude on the U.S. Supreme Court: The Historical Case for a 28th
Amendment.” University of Chicago Law Review 67: 995.
Gilmour, Steven G., and Trinca, Luzia A. 2005. “Fractional Polynomial Response Surface Models.”
Journal of Agricultural, Biological and Environmental Statistics 10 (March): 50-60.
Goff, John S. 1960. “Old Age and the Supreme Court.” Journal of American History 4: 95-106.
Green, Gareth and Frank Baker. 1991. Work, Health and Productivity. New York: Oxford
Greenhouse, Linda. 1984. “Taking the Supreme Court’s Pulse.” New York Times. Section 1, p. 8, col. 3.
Hagle, Timothy M. 1993. “Strategic Retirements: A Political Model of Turnover on the United States
Supreme Court.” Political Behavior 15: 25-48.
Han, Beth, Phillips, Caroline, Ferrucci, Luigi, Bandeen-Roche, Karen, Jylha, Marja, Kasper, Judith, and
Guralnik, Jack M. 2005. “Change in Self-Rated Health and Mortality Among Community-Dwelling
Disabled Older Women.” Gerontologist 45: 216-221.
Hutchinson, Dennis. 1998. The Man Who Once Was Whizzer White. New York: Free Press.
Idler, Ellen, and Kasl, Stanislav. 1991. "Health Perceptions and Survival: Do Global Evaluations of
Health Status Really Predict Mortality?" Journal of Gerontology 46: 55-65.
Kalton, Graham,. and Schuman, Howard.. 1982. “The Effect of the Question on Survey Responses: A
Review.” Journal of the Royal Statistical Society 145: 42–57.
Kaplan, Sherrie. 1987. "Patient Reports of Health Status as Predictors of Physiologic Health Measures in
Chronic Disease." Journal of Chronic Disease 40 (suppl.): 27-35.
King, Gary. 1987. “Presidential Appointments to the Supreme Court, Adding Systematic Explanation to
Probabilistic Description.” American Politics Quarterly 15: 373-386.
King, Gary. 1997. A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior
from Aggregate Data. Princeton, NJ: Princeton University Press.
27
Supreme Court Departures
May 14 2007
Page 28
Ledoux, James, Rubino, Gerardo, and Sericola, Bruno 1994. “Exact Aggregation of Absorbing Markov
Processes Using the Quasi-Stationary Distribution.” Journal of Applied Probability 31: 626-634.
Mace, Myles L. 1986. Directors: Myth and Reality. Revised Edition. Boston, MA: Harvard Business
School Press.
Mossey, Jana, and Shapiro, Evelyn. 1982. "Self-Rated Health: A Predictor of Mortality Among the
Elderly." American Journal of Public Health 72: 800-808.
Neumann, Richard K., Jr. 2003. “Conflicts of Interest in Bush v. Gore: Did Some Justices Vote
Illegally?” Georgetown Journal of Legal Ethics 16: 375-443.
Nixon, David C., and Haskin, J. David. 2000. “Judicial Retirement Strategies: The Judge’s Role in
Influencing Party Control of the Appellate Courts.” American Politics Quarterly 28: 458-489.
Oliver, Philip D. 1986. “Systematic Justice: A Proposed Constitutional Amendment to Establish Fixed,
Staggered Terms for Members of the United States Supreme Court.” Ohio State Law Journal 47: 799834.
Parsons, Donald O. (1982) The Male Labour Force Participation Decision: Health, Reported Health, and
Economic Incentives. Economica, New Series, Vol. 49, No. 193. (Feb.) 81-91.
Ross, Catherine, and Wu, Chia-ling. 1995. "The Links between Education and Health." American
Sociological Review 60: 719-45.
Royston, Patrick, and Altman, Douglas G. 1994. “Regression Using Fractional Polynomials of
Continuous Covariates.” Applied Statistics 3: 429–467.
Schmidhauser, John R. 1962. “When and Why Justices Leave the Supreme Court.” In Wilma Donahue
and Clark Tibbitts (eds.), Politics of Age, 117-34. Ann Arbor: University of Michigan Press.(1962)
Schwartz, Bernard. 1983. Super Chief: Earl Warren and his Supreme Court. New York: New York
University Press.
Schwarz, Norbert, and Knäuper, Barbel. 2000. “Cognition, Aging, and Self-Reports.” Denise Park and
Norbert Schwarz (eds.), Cognitive Aging. A Primer, 233-252. Philadelphia: Psychology Press.
Spriggs II, James F., and Wahlbeck, Paul J. 1995. “Calling It Quits: Strategic Retirement on the Federal
Courts of Appeals, 1893-1991.” Political Research Quarterly 48: 573-597.
Squire, Peverill. 1988. “Politics and Personal Factors in Retirement from the United States Supreme
Court.” Political Behavior 10: 180-190.
28
Supreme Court Departures
May 14 2007
Page 29
Stolzenberg, Ross M. 1989. "Job Quits in Empirical and Theoretical Perspective." In ed. Arne. L.
Kalleberg (ed.),. Research in Social Stratification and Mobility, 99-130. Greenwich, CT: JAI Press.
Sudman, Seymour and Bradburn, Norman M. 1982. Asking Questions: A Practical Guide to
Questionnaire Design. London: Jossey-Bass.
Taubman, Paul, and Rosen, Sherwin. 1982. "Economic Aspects of Health." In Victor R. Fuchs (ed.),
Economic Aspects of Health, 121-40. Chicago: University of Chicago Press.
Ulmer, S. Sidney. 1982. “Supreme Court Appointments as a Poisson Distribution.” American Journal of
Political Science. 26: 113-116.
United States Bureau of the Census. 2002. Current Population Survey. Technical Paper 63RV. Design
and Methodology. Washington, D.C.: U.S. Government Printing Office.
United States Constitution. Article II. 1789.
United States Supreme Court. 2006. Members of the Supreme Court (1789 to Present).
http://www.supremecourtus.gov/about/members.pdf (last accessed April 5, 2006).
Van Tassel, Field. 1993. “Resignations and Removals: A History of Federal Judicial Service—and
Disservice—1789-1992.” University of Pennsylvania. Law Review 142: 333.
Walker, Thomas G., Segal, Jeffrey A., Spaeth, Harold J., and Epstein, Lee (eds). 2006. Supreme Court
Compendium: Data, Decisions, And Developments (3d ed.).
Walker, Thomas G., Segal, Jeffrey A., Spaeth, Harold J., and Epstein, Lee (eds). 1996. Supreme Court
Compendium: Data, Decisions, And Developments (2d ed.).
Wallis, W. Allen. 1936. “The Poisson Distribution and the Supreme Court.” Journal of the American
Statistical Association 31: 376-80.
Ward, Artemus. 2003. Deciding to Leave: The Politics of Retirement from the United States Supreme
Court. Albany, NY: SUNY Press.
Ward, Artemus. 2004. “How One Mistake Leads to Another: On the Importance of
Verification/Replication,” Political Analysis 12: 199-200.
White, G. Edward. 1982. Earl Warren: A Public Life. New York: Oxford University Press.
Woodward, Bob and Scott Armstrong. 1979. The Brethren: Inside the Supreme Court. New York: Simon
and Schuster.
Wooldridge, Jeffrey M. 2002. Economic Analysis of Cross Section and Panel Data. Cambridge, MA:
29
Supreme Court Departures
May 14 2007
Page 30
MIT Press.
Yalof, David Alistair. 1999. Pursuit of Justices: Presidential Politics and the Selection of Supreme Court
Nominees. Chicago: University of Chicago Press.
Yamaguchi, Kazuo. 1991. Event History Analysis. Newbury Park: Sage.
Yoon, Albert. 2006. “Pensions, Politics, and Judicial Tenure, An Empirical Study of Federal Judges,
1869-2002.” American Law and Economics Review 8: 143-180.
Zorn, Christopher J. W. and Van Winkle, Steven R. 2000. “A Competing Risks Model of Supreme Court
Vacancies.” Political Behavior 22: 145-166.
30
Supreme Court Departures
May 14 2007
Page 31
Figure 1 - Means of Sitting Justices' Age at Oath, Age, Eventual Age at Departure
from Court and Eventual Age at Death (in order listed), vs. Calendar Year
90
Years of Age
80
70
60
50
40
1800
1850
1900
Calendar Year
Note: Lines fitted and smoothed by Cleveland's locally weighted regression (LOWESS).
31
1950
2000
Supreme Court Departures
May 14 2007
Page 32
Figure 2 – Curvilinear Effects of Time Variables
Proportional Effect on Annual Odds
Figure 2a - Proportional Effect of an Additional Year of Tenure
on Annual Odds of Retirem ent, by Current Tenure
25%
20%
15%
10%
5%
0%
-5%
-10%
-15%
0
5
10
15
20
25
30
Tenure in Years
Figure 2b - Proportional Effect of an Additional Year of Future Longevity on Odds
of Retirement in Current Year, by Future Longevity
Proportional Effect on Annual Odds
6%
4%
2%
0%
-2%
-4%
-6%
-8%
-10%
0
5
10
15
20
25
Future Longevity (Years Left to Live beyond Current Year)
Figure 2c - Proportional Effect of an Additional Calendar-Year Decade
on Annual Odds of Retirement, by Current Calendar Year
Proportional Effect on Annual Odds
0%
-10%
-20%
-30%
-40%
-50%
-60%
-70%
1789
1809
1829
1849
1869
1889
1909
1929
1949
1969
1989
Calendar Year
Proportional Effect on Odds
of Vacating Court
Figure 2d - Proportional Effect of An Additional Year of Age
on Annual Odds of Vacating Court, by Current Age
15%
10%
5%
35
40
45
50
55
60
65
70
Justice's Current Age in Years
32
75
80
Supreme Court Departures
May 14 2007
Page 33
Figure 3 – Estimated Hazards of Court Departure by Death and Retirement
Figure 3a - Hazard of Retirement vs. Hazard of Death-in-Office, with Regression Line
.001
.003 .005
.01
.025
.05
.10
.20 .30
.50
1
(n=1895 justice-years)
.001
.003
.005
.01
.025
.05
.10
.20
.30
.50
1
Hazard of Death-in-Office (log scale)
.04
0
.02
Probability
.06
.08
Figure 3b - Smoothed Mean Hazard of Leaving Supreme Court in Current Year,
by Year, by Any Means -, by Retirement ... , or by Death - - -
1800
1850
1900
Current Year
33
1950
2000
Supreme Court Departures
May 14 2007
34
Page 34
Supreme Court Departures
May 14 2007
35
Page 35
Supreme Court Departures
May 14 2007
36
Page 36
Supreme Court Departures
May 14 2007
37
Page 37
Supreme Court Departures
May 14 2007
38
Page 38
Supreme Court Departures
May 14 2007
39
Page 39
Supreme Court Departures
1
May 14 2007
Page 40
Political party identification is a meaningful but crude dimension of political orientation. Obviously,
justices and political parties may change their political orientations over time. Further, political views
vary among supporters of the same political party. Nonetheless, political party identification as described
here is a meaningful indicator of political orientation to researchers who have investigated the Politicized
Departure Hypothesis, beginning at least with Fairman (1938) and continuing as recently as Yoon (2006).
2
In related analyses, Nixon and Haskin (2000); Barrow, Gryski, and Zuk (1996); Spriggs and Wahlbeck
(1995); Barrow and Zuk (1990) and others examine departures from Federal district and appellate courts.
3
Suicide appears to be unprecedented among Supreme Court justices.
4
The hazard of an event is the probability that the event occurs to an individual in a period of time, given
that the individual has survived to the start of that period. The probabilities of retirement and death-inoffice examined here are hazards. In a continuous time framework, the hazard is the probability that the
event occurs per unit time, in the limit as time approaches zero, subject to the condition that the event has
not occurred before the start of the time period in question.
5
From the year 1789 through June, 2006, 110 Supreme Court justices had served all or part of 1,895
justice-years. 52 of these justices retired alive, 49 died in office, and 9 currently-serving justices had
neither. Two justices were appointed twice to the Court and retired from it twice.
6
We started with database kindly supplied by Professor Albert Yoon (see Yoon 2006), based on
information he obtained from the Administrative Office of the U.S. Courts (Federal Judicial Center 2006).
We checked some of those data against various sources, corrected errors and added more data from the
Federal Judicial Center (2006) and the U.S. Supreme Court (2006) for the 1789-1868 and the 2003- 2006
periods.
As is conventional, Whigs, Federalists and Republicans are coded as if members of the same
party.
7
8
Other variables in the second parameterization are linear transformations of variables in the first
parameterization, as follows:
40
Supreme Court Departures
May 14 2007
Page 41
SameParty*Year34 = SameParty – SameParty*Year12
NoSameParty*Year12 = Year12 – SameParty*Year12
Thus, it makes no difference which parameterization is used. In any particular analysis, we report results
with the parameterization that simplifies the calculation and presentation of effects and significance tests.
For the unconvinced, Table 3 includes two renditions of the same analysis, each done with a different
parameterization; all results are absolutely identical. A footnote to that table shows how the results of
either analysis can be translated algebraically to the results of the other.
9
Because retirement and death-in-office are disjoint events, the hazard that a justice retires or dies-in-
office is the sum of the hazards of death-in-office and retirement. To some extent, these differences in
predicted hazards are a consequence of the different coefficients just discussed. However, those
coefficients are not sufficiently identified to permit comparisons between different equations. However,
probabilities and odds that are estimated from different equations are identified and can be compared.
Finally, comparisons of individual coefficients leaves open the possibility that the effect of one
independent variable offsets the effect of another; unless otherwise indicated, the estimated probabilities
and odds reported here include effects of all independent variables at once.
10
Descriptive statistics for the annual estimated hazards of retirement, death-in-office, and either
outcome, 1789-2006 are:
Hazard of
Retirement
Mean
Standard Deviation
Minimum
Maximum
n (justice-years)
11
0.0303
0.0468
0.0007
0.4743
1895
Hazard of
Death-inOffice
0.0259
0.0306
0.0006
0.2553
1895
Hazard of
Either
0.0562
0.0615
0.0028
0.5873
1895
If the hazard of retirement in a year is p, then the probability of retirement in an n-year period is
1 – (1-p)n.
41
Supreme Court Departures
12
May 14 2007
Page 42
The number of Supreme Court seats has fluctuated, but was fixed at nine seats in 1869 (some of which
have been vacant from time to time).
42