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WEB NOTES
Sixth Edition
The following are the web notes for the sixth edition of Law and Economics by Robert D.
Cooter and Thomas S. Ulen. Our intent in these notes is to extend the material in the text by describing some additional issues, articles, cases, and books. Because the fields of law, economics,
and law and economics are not standing still – because, that is, scholars are adding interesting
new material all the time, we may supplement, alter, and add to these notes from time to time.
Each note begins with a copy of the material from the text about the content of the web note
and the page on which that web note can be found. We will from time to time insert new material, update some of the entries, and add some additional material. You should be able to download pdf versions of each chapter’s web notes and of the entire set of web notes for all 13 chapters.
We have found that the very best students and their instructors from all over the world pay
close attention to these web notes. They often have good ideas about how to add to the entries
already here and suggestions about articles, cases, books, and topics that would be instructive to
add. We would be grateful for any comments or suggestions about any of the notes.
Chapter 13
Web Note 13.1 (p. 489)
On our website we provide up-to-date statistics on crime in the United States and other
countries, links to websites with further information, and some comparative explanations
of differences in the amount of crime in various countries.
In the United States, crime rates for most types of offenses have declined steadily over the
past two decades. This Note provides an overview of some key statistics and trends. For those
interested in more detailed analyses and data, we encourage reference to the websites of the
FBI’s Uniform Crime Report (http://www.fbi.gov/stats-services/crimestats/crime_statistics) and
the Bureau of Justice Statistics (http://bjs.ojp.usdoj.gov/). Together, these sources provide a
wealth of data on violent and nonviolent crime, arrest rates, incarcerations, and many other topics.
The homicide rate in America is at its lowest level since the 1960s, at 5 homicides per
100,000 people. In 2009, at present the most recent year for which final information is available,
the homicide rate was nearly 50 percent lower than it was in 1990. The rate declined quickly in
the late 1990s, and continues to go down, albeit at a slower pace.
Crime rates have also dropped significantly in the other three violent crime categories that
the FBI tracks; rape, robbery, and aggravated assault. In all three categories, 2009 saw the lowest
commission rates in at least 40 years. For example, the rape rate was roughly 88 percent lower in
2009 than it was in 1973.
Property crime is down significantly as well. The 2009 combined rate of burglary, larceny,
and motor vehicle theft is about 40 percent lower than it was as recently as 1990. Motor vehicle
theft alone is down over 60 percent in the last two decades.
Web Notes – Sixth Edition
Cooter & Ulen
Drug offenses provide a glaring exception to the rule of declining crime. Between 1970,
when the Comprehensive Drug Abuse Prevention and Control Act was passed, and 2007, the
drug offense rate has risen over fivefold. Even when other crime rates began to drop significantly
in the 1990s, drug offenses continued to rise. The 2007 rate was 20 percent higher than the 1997
rate.
If you ask 100 lawyers, economists, and criminologists for the reasons behind declining
crime rates, and the increase of drug offenses, you’ll probably get at least 101 theories. A vast
body of literature examines this very question. One of the best recent summaries is Philip Cook
& Jens Ludwig, “The Economist’s Guide to Crime Busting,” Wilson Quarterly (Winter, 2011).
Historically, crime rates have risen in the United States during economic recessions. In the
coming years, it will become apparent whether crime rates during the Great Recession followed
a similar trend. So far, crime rates seem to have continued their decline, lending credence to the
idea that crime is more than just a symptom of economic decay.
Reliable statistics on international crime rates are tough to come by, and data collection is
compounded by many difficulties. For starters, the law varies from country to country; some acts
are criminal in one place but not another. Different governments have shown varying levels of
commitment to the collection and analysis of crime statistics. Furthermore, political, economic,
and social circumstances affect the rate at which certain crimes such as rape and domestic abuse
are reported.
Still, trends can be inferred from sources such as the International Crime Victims Survey,
which has compiled statistics for several common types of crime in over 70 countries since 1989.
Most developed countries participate, as do a smaller number of developing countries. Although
not every country participates every year, useful comparative and longitudinal data exists. A
summary
of
the
2004/2005
survey
can
be
found
online
(http://rechten.uvt.nl/icvs/pdffiles/ICVS2004_05summary.pdf).
The 2004/2005 summary showed that in developed countries, overall crime rates peaked in
the mid-1990s and have declined slowly but steadily since then. This roughly mirrors trends seen
in the United States beginning a few years earlier. Unfortunately, the crime rates remain high in
many crowded urban centers, especially in the developing world.
Web Note 13.2 (p. 497)
We provide some additional information on the behavioral analysis of crime and punishment on our website.
There are several recent articles that you might wish to read about the behavioral analysis of
criminal law. A good place to begin is a survey article by Richard H. McAdams & Thomas s.
Ulen, “”Behavioral Criminal Law and Economics,” in Nuno Garoupa, ed., Criminal Law and
Economics 403 (2009). You should also see Lee Anne Fennell, “Willpower and Legal Policy,” 5
Ann. Rev. Law & Soc. Sci. 91 (2009), a superb summary of an important topic. See also Richard
H. McAdams, “Present-Bias and Criminal Law,” U. Ill. L. Rev. (forthcoming, 2011). There is
some older literature that is relevant, too, in that it has stressed the difficulty that criminals appear to have with self-control. See, for instance, Michael R. Gottfredson & Travis Hirschi, A
General Theory of Crime 85 – 129 (1990) and James Q. Wilson & Richard J. Herrnstein, Crime
and Human Nature 430 – 37 (1985).
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Cooter & Ulen
Web Note 13.3 (p. 501)
For more on the Donohue and Levitt hypothesis, critiques of that hypothesis, an extension of the hypothesis that looks at the behavior of teenage girls, and links to other literature on the causes of the decline of crime in the 1990s, see our website.
We are great admirers of the work of Professors Donohue and Levitt – that is, of their work
individually, with other co-authors, and with one another. Their famous article on the causal relationship between the legalization of abortion and the subsequent decline in crime is a classic. But
the article has been subjected to a great deal of criticism.
Much of the criticism of the Donohue-Levitt finding has to do with technical econometric or
empirical issues, some of which will be beyond our ability to explain competently here. We have
examined this critical literature and taken extensive notes on it. What we propose to do here is to
tell you, first, what articles we have consulted, then to give you our edited notes on those readings, then to give you our assessment of the debate, and finally to conclude with a brief summary
of a recent extension of the hypothesis by Donohue, Grogger, and Levitt.
We would like to thank Justin McCreary of Boalt Hall for his help with this matter and
Amitai Aviram, Dhammika Dharmapala, Nuno Garoupa, Bob Lawless, and Jen Robbennolt for
very helpful discussions on this literature.
The articles that we examined to get a sense for the state of the debate were these:
John J. Donohue, III, & Steven D. Levitt, “The Impact of Legalized Abortion on Crime,” 116 Q.
J. Econ. 379 (2001).
Ted Joyce, “Did Legalized Abortion Lower Crime?,” 39 J. Hum. Resources 1 (2004).
John J. Donohue, III, & Steven D. Levitt, “Further Evidence That Legalized Abortion Lowered
Crime: A Reply to Joyce,” 39 J. Hum. Resources 29 (2004).
Christopher L. Foote & Christopher F. Goetz, “The Impact of Legalized Abortion on Crime: A
Comment,” Federal Reserve Bank of Boston, Working Paper No. 05-15 (January 31, 2008; orig.
2005). Published at 123 Q. J. Econ. 407 (2008).
John J. Donohue, III, & Steven D. Levitt, “Measurement Error, Legalized Abortion, and the Decline in Crime: A Comment on Foote & Goetz,” (January, 2006). Published at 123 Q. J. Econ.
425 (2008).
John Lott & John Whitley, “Abortion and Crime: Unwanted Children and Out-of-Wedlock
Births,” 45 Econ. Inq. 304 (2007).
William Anderson & Martin T. Wells, “Numerical Analysis in Least Squares Regression with an
Application to the Abortion-Crime Debate,” 5 J. Emp. Legal Stud. 647 (2008).
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Cooter & Ulen
Ted Joyce, “A Simple Test of Abortion and Crime,” 9 Rev. Econ. & Stat. 112 (2009).
Ted Joyce, “Abortion and Crime: A Review,” NBER Working Paper 15098 (June, 2009). To
appear as a chapter in BRUCE BENSON & PAUL ZIMMERMAN, EDS., HANDBOOK ON THE ECONOMICS OF CRIME (2010).
William Anderson & Martin T. Wells, “A Bayesian Hierarchical Regression Approach to Clustered and Longitudinal Data in Empirical Legal Studies,” 7 J. Emp. Legal Stud. 634 (2010).
And here are out notes on those articles:
1. Donohue & Levitt.
a. The central contention in the Donohue & Levitt piece is that the legalization of abortion in Roe v. Wade in January, 1973, led to a very large increase in abortions (from
zero in 1972 to 1.5m per year in 1980). This increase in abortions led to a decrease in
live births, particularly of unwanted children. The reduction in the number of births
and of unwanted children, beginning in 1973, meant two things – (1) fewer 18-yearold young men beginning in 1991, and (2) fewer crime-prone 18-year-old young men
beginning in 1991.
b. Because young men between the ages of 14 and 24 account for approximately half of
all crime in most societies, the reduction in the number of young men led to less
crime.
c. Further, because the young men (and women) who were born after the legalization of
abortion were wanted – or at least the women who had them had greater resources to
care for and supervise them – they were less likely to commit crime.
d. Donohue & Levitt call these two effects the “cohort size” and “cohort quality” effects.
e. They attribute 50 percent of the decline in crime between 1991 and 1998 to these
combined effects; 25 percent to each effect.
f. Violent crime fell by 40 percent in the 1990s. Non-violent crime, by 30 percent. The
declines have continued through the first decade of this century, as Donohue & Levitt
predicted.
g. They offer five central pieces of evidence for their hypotheses. Two of them are descriptive; three are the result of regression analysis.
i. “The first shows that total crime rates, not age-specific crime rates, fell earlier
in the [five] pre-Roe states relative to states that legalized abortion with Roe.”
From Joyce, “Abortion and Crime: A Review.”
ii. “The second demonstrates that crime rates fell further in states with greater
abortion rates in the 1970s. The next three pieces of evidence involve regression analysis, but their main results involve regressions of total crime rates at
the state-year level from 1985-1997 on a proxy for cohort exposure to abortion also at the state-year level. The latter they term the ‘effective abortion
rate’ (EAR). It is critical to emphasize that Donohue and Levitt cannot identify cohorts with these state-year regressions because the crime rate is not agespecific.”
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h.
i.
j.
k.
l.
m.
Cooter & Ulen
iii. “Donohue and Levitt use no data on abortions prior to 1973 and assume the
abortion rate to be zero.”
What DL then do is regress the crime rate in a particular state in a particular year on
the EAR and a vector of independent variables. They also attempt to capture state
and year fixed effects in this panel data. “Donohue and Levitt (2004) estimate the coefficient on ß [the coefficient on EAR] to be -0.166 [with a t-ratio of 3.01] when the
dependent variable is the murder rate. An increase in the EAR of 100, approximately
one standard deviation in the EAR in 1997, is associated with a 16.6 percent decline
in the murder rate. Since the murder rate fell by 31 percent between 1991 and 1997,
legalized abortion can account for over half the decline. Regressions of the violent
crime rate and the property crime rate on the respective EARs yield roughly similar
results.”
DL also do a regression of age-specific arrests as a function of lagged abortion rates.
The equation specification is of the natural log of the arrests or homicides for age
group a, in state j, in year t. They include fixed effects for age, state, and year. “The
strength of the analysis is that each birth cohort is associated with the abortion rate
that existed roughly in the year in which the cohort was in utero. This represents a
major improvement over regressions of the total crime rate on the EAR, since the link
between the ‘exposure’ (legalized abortion) and the outcome (the crime rate) is more
direct.”
Donohue and Levitt find that “abortion is associated with a decline of between 10 and
14 percent in age-specific arrest rates.”
DL claim that this effect observable between 1991 and 1997 will extend into the future:
i. “Roughly half of the crimes committee in the United States [as of approximately 2000] are done by criminals born prior to the legalization of abortion.
As these older cohorts age out of criminality and are replaced by younger offenders born after abortion became legal, we predict that crime rates will continue to fall. When a steady state is reached roughly twenty years from now,
the impact of abortion will be roughly twice as great as the impact felt so far
(p. 415).”
ii. Another of Joyce’s criticisms is that this prediction has been proven wrong for
the first decade of this century. [This seems to me to be too much. Crime has,
in fact, continued to decline over the last 10 years.]
Joyce dislikes getting at the result this way because he prefers the “more transparent
presentation of the data” that occurs by simply doing a time-series plot of age-specific
crime and arrest rates. “Such figures are at odds with the timing of legalization and
the downturn in crime.” [See his Figure 2.]
“Lott and Whitley (2007) [using a model developed by Akerlof et al. in 1996 about
out-of-wedlock births] reach the following conclusion: “The basic specification [of
the regressions] includes measures of the prison population, execution rate, arrest
rate, right-to-carry laws, unemployment rate, poverty rate, per capita income, population density as well as state and year fixed effects. An increase in the abortion rate
from zero to its 1980 level is associated with an approximately 25 percent increase in
the homicide rate. They perform extensive sensitivity tests and report that the coefficient on the abortion rate was negative only once in over 6,000 regressions.”
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Cooter & Ulen
2. Ted Joyce, “Did Legalized Abortion Lower Crime?,” 39 J. Hum. Resources 1 (2004).
a. Abstract: “In this paper I compare changes in homicide and arrest rates among cohorts born before and after the legalization of abortion to changes in crime in the
same years among similar cohorts who were unexposed to legalized abortion. I find
little consistent evidence that the legalization of abortion in selected states around
1970, and then in the remaining states following Roe v. Wage, had an effect on recent
crime rates. I conclude that the dramatic association as reported in a recent study is
most likely the result of unmeasured period effects such as changes in crack cocaine
use.”
b. “A 50 percent increase in the mean abortion ratio is associated with an 11 percent decrease in violent crime, an 8 percent decrease in property crime, and a 12 percent decrease in murder.”
c. “They conclude that the full impact on crime of Roe v. Wade will not be felt for another 20 years. To quote, ‘Our results suggest that all else equal, legalized abortion
will account for persistent declines of 1 percent a year on crime over the next two
decades.’” 1-2.
d. “Donohue and Levitt regress crimes rates between 1985 and 1996 on abortion ratios
lagged 15 to 25 years adjusted for state and year fixed effects. However, the study
period coincides with the rise and decline of the crack cocaine epidemic, which many
observers link to the spread of guns, and the unprecedented increase in youth violence. … Thus, even in models with state and year fixed effects, the relationship between abortion and crime may be biased by differences in within-state growth in cocaine markets over time, a classic problem of omitted variables.” 2.
e. “I use the early legalization of abortion in selected states prior to Roe v. Wade and
then national legalization after Roe in the remaining states to identify exogenous
shifts in unintended childbearing. Specifically, I estimate a reduced-form equation in
which changes in arrest and homicide rates among cohorts before and after exposure
to legalized abortion are compared to changes among cohorts that are unexposed. …
I then use a difference-in-difference estimator based on a within-state comparison
group to net out changes in crime associated with hard-to-measure factors that vary
by state and year, such as the spread of crack cocaine and its spillover effects. In
these analyses I find no effect of abortion legalization on crime regardless of the years
analyzed.” 2.
f. “the difference-in-difference strategy has two other advantages in an analysis of abortion and crime. First, Donohue and Levitt use the ratio of abortions to births as an inverse proxy for unwanted births. However, abortion is endogenous to sexual activity,
contraception, and childbearing. A rise in abortion may have relatively little effect on
unwanted childbearing. It is noteworthy that the abortion rate rose from 16.3 abortions per 1,000 women ages 15 to 44 in 1973 to 29.3 in 1980, an increase of 79 percent. Over the same period, however, the number of births per 1,000 women ages 15
to 44 was essentially unchanged from 69.2 to 68.4. … This change in fertility is a
more plausible source of exogenous variation with which to identify a decline in unwanted births than within-state changes in reported legal abortions between 1973 and
1985.” 2-3.
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Cooter & Ulen
g. “The other advantage of the difference-in-difference approach is that it obviates the
need to measure illegal or unreported abortion in the years before legalization.
Donohue and Levitt use no data on abortion prior to 1973. … Demographers have
concluded that most legal abortions in the early 1970s replaced illegal abortions.” 3.
h. “The primary difference between Donohue and Levitt’s approach and mine is one of
identification. We all agree that the impact of crack and its spillover effects had a
dramatic influence on crime and that its impact varied by state, year, and age. The
problem, therefore, is how to identify a cohort effect, such as the legalization of abortion, amidst strong age and period effects. What I have tried to show is that the comparison of changes in crime across states, the essence of the state fixed effects methodology, is flawed because the period effects vary by state and year. … To minimize
this problem I first used a difference in differences-indifferences (DDD) estimate
based on a DD within repeal states to eliminate period effects and a second DD in
nonrepeal states to net out age-crime differences.” 25.
i. “First, the actual number of unintended births averted, although significant, was an
order of magnitude less than the number of reported legal abortions in the early
1970s. … Second, analysts, I being one have tended to overestimate the selection effects associated with abortion. A careful examination of studies of pregnancy resolution reveals that women who abort are at lower risk of having children with criminal
propensities than women of similar age, race, and marital status who instead carried
to term. For instance, in an early study of teens in Ventura County, California, between 1972 and 1974, researchers demonstrated that pregnant teens with better
grades, more completed schooling, and not on public assistance were much more likely to abort than their poorer, less academically oriented counterparts.” 26.
3. John J. Donohue III & Steven D. Levitt, “Further Evidence that Legalized Abortion Lowered
Crime: A Reply to Joyce,” 39 J. Hum. Resources 29 (2004).
a. Abstract: “Donohue and Levitt (2001) suggest there is a causal link between legalized abortion and reduction in crime about two decades later when the cohorts exposed to legalized abortion reach their peak crime years. Joyce (2003) examines
crime committed in the period 1985-90 for the cohorts born immediately before and
after abortion legalization. He finds little impact of legalized abortion. In this paper,
we demonstrate that Joyce’s failure to uncover a negative relationship between abortion and crime is a consequence of his decision to focus almost exclusively on one
nonrepresentative six-year period during the peak of the crack epidemic. We provide
empirical evidence that the crack cocaine epidemic hit the high-abortion earlylegalizing states earlier and more severely than other states. When we simply replicate his analyses, but extend the sample to cover the entire lives of these exact same
cohorts, abortion is just as negatively related to crime as in our original analysis.
Joyce’s results appear to be purely an artifact of omitted variable bias due to focusing
on the peak crack years without including adequate controls for crack.”
b. “First, his claim that legal abortions simply replaced illegal abortions I shown to be
directly at odds with the existing evidence. Second, although Joyce is critical of the
shortcomings in our abortion data, we demonstrate both theoretically and empirically
that the biases due to measurement error in the abortion proxy unambiguously lead
our reported coefficients to understate the true impact of abortion. Joyce makes a
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Cooter & Ulen
basic econometric error in arguing to the contrary. Third, most of the empirical findings that contradict our original results are revealed to simply be an artifact of his decision to focus his analysis on the smaller subset of the data that coincides with the
peak of the crack epidemic (without including controls for crack in the regressions).
We present evidence that crack hit early-legalizing, high-abortion states earlier and
harder than the rest of the country. We then demonstrate that if one simply takes
Joyce’s identification strategy regarding the early-legalizing states, but follows these
same cohorts over their entire lifetime rather than just the six-year window 1985-90,
abortion exposure is in fact associated with lower criminal involvement.” 30.
c. “DL present five pieces of evidence consistent with the hypothesis that cohorts born
after the legalization of abortions have lower crime rates because legalized abortion
reduces the number of unwanted children, who are at higher risk of engaging in criminal conduct when they grow up. First, the five states that legalized abortion in
roughly 1970 (as opposed to the national legalization resulting from the January 1973
U.S. Supreme Court decision in Roe v. Wade) experienced a somewhat earlier drop in
crime. Second, higher abortion states (based on the rates of legal abortion in the
1970s) showed much greater drops in crime during the 1985-97 period. In contrast,
the crime trends in high and low abortion states were similar over the period from
1973 to 1985, when the children born after legalization were too young to be influencing crime rates. Third, this relationship between legal abortions in the 1970s and
lower crime over the period 1985-1997 persisted in panel data regression models that
controlled for prisoners and police per capita, state economic conditions, and state
and year fixed effects. … Fourth, the link between abortion and crime was only present for those born after legalization (roughly those younger than age 25 when our arrest data ends in 1998), and not for those older than 25 as of 1998 (and, therefore,
born prior to Roe v. Wade). … Fifth, the pattern of lower rates of crime in states
with higher rates of abortion held true when we linked the abortion rates in a particular state in a particular year with the crime committed by the cohort born in that year,
even controlling for state-year specific interactions.” 30-31.
d. “Joyce does not challenge any of these findings directly, and indeed, confirmed a
number of them in previously circulated drafts of his paper. Rather, Joyce presents
five different arguments as to why he believes the link between legalized abortion and
crime is not causal:”
i. “He claims that demographers have concluded that most legal abortions in the
early 1970s replaced illegal abortions, so there should be no impact of legalized abortion. He further argues that measurement error in our abortion proxy
may cause our original estimates to overstate the true impact of abortions on
crime rates.”
ii. “For the six-year period 1985-90, there is no measurable impact of state abortion rates on state crime rates; only after 1991 does the strong negative relationship between abortion and crime emerge.”
iii. “A comparison of cohorts born just before or after the early legalizations in
1970 in legalizing and nonlegalizing states does not yield negative impacts of
abortion on crime for the years 1985-90.”
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iv. “In states where abortion only became legal in 1973 with the passage of Roe
v. Wade, those born after legalization do not have systematically lower crime
rates than those born before.”
v. “He finds that states that legalized abortion in 1970 experienced initial reductions in crime consistent with a causal impact of legalized abortion, but the
fact that these early legalizers continued to experience greater reductions in
crime even after abortion became legal nationwide argues against causality.”
e. DL go on to refute each point.
4. Christopher L. Foote & Christopher F. Goetz, “The Impact of Legalized Abortion on Crime:
Comment,” 123 Q. J. Econ. 407 (2008).
a. Abstract: “This comment makes three observations about Donohue and Levitt’s
(2001) paper on abortion and crime. First, there is a coding mistake in the concluding
regressions, which identify abortion’s effect on crime by comparing the experiences
of different age cohorts within the same state and year. Second, correcting this error
and suing a more appropriate per capita specification for the crime variable generates
much weaker results. Third, earlier tests in the paper, which exploit cross-state rather
than within-state variation, are not robust to allowing differential state trends based
on statewide crime rates that pre-date the period when abortion could have had a
causal effect on crime.”
b. “The strongest evidence in favor of DL’s hypothesis comes from comparing changes
in crime rates across U.S. states. The prevalence of abortion differed markedly across
states in the years following abortion’s legalization. In the District of Columbia, New
York, and California, more than one-third of pregnancies ended in abortion, on average, from 1970-1984. In North Dakota, Idaho, and Utah, however, abortion was used
in less than 10 percent of pregnancies over the same period. In the 1990s, highabortion states experienced bigger declines in crime than low-abortion states, suggesting that abortion reduces crime.”
c. “Yet statewide crime rates are influenced by other factors besides abortion. Crime in
New York is determined by different factors than crime in Utah, so it should not be
surprising that crime in the two states diverges over some period. The best ways to
isolate the true effect of abortion on crime is to use within-state rather than cross-state
comparisons.” [Note that this is an alternative means of dealing with the fixed effects
problem.]
d. “The best way to determine if abortion has a causal effect on crime is to compare two
people who are in a similar environment today, but who had differing probabilities of
being wanted at birth.”
e. “In this comment, we offer two reasons why these regressions were implemented incorrectly. The first flaw in DL’s concluding regressions is that they are missing a key
set of regressors because of a computer coding error. The missing regressors would
have absorbed variation in arrests on the state-year level, insuring that the abortion
coefficient was identified using within-state comparisons only. Second, unlike the
other tests in their paper, the concluding regressions do not model arrests in per capita terms. Instead, the dependent variable is the total number of arrests attributed to a
particular cohort of young persons. Only by using per capita arrest data, however,
can we test whether abortion has a selection effect on crime. In Section 2 of this
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comment, we run the concluding regressions on a per capita basis with the appropriate regressors, and we find that compelling evidence for a selection effect of abortion
on crime vanishes. A reader may ask whether the concluding regressions at least
show that abortion reduces crime by reducing the number of young people (the cohort-size channel, as opposed to the selection channel). However, we argue below
that the concluding regressions do note even provide this partial kind of evidence,
owing to the way in which the abortion variable is defined.”
f. “It is reasonable to assume that state-specific factors jointly determine both abortion
and crime. … First, we show that state-level abortion and crime rates were strongly
correlated before 1985, when it was impossible for abortion to have had a causal effect on crime. We then show that accounting for this correlation has damaging consequences for the abortion coefficient in the cross-state regressions that SL use to
quantify abortion’s effect on crime. In fact, the abortion coefficients in these crossstate regressions are no long significantly different from zero when a potential proxy
for omitted state-year factors is added. Finally, Section 4 concludes with a test that is
robust to many of the econometric issues we discuss throughout this comment. This
test also provides no evidence that abortion reduces crime.”
5. John J. Donohue III & Steven D. Levitt, “Measurement Error, Legalized Abortion, and the
Decline in Crime: A Comment on Foote & Goetz,” (January, 2006). Published at 123 Q. J.
Econ. 425 (2008).
a. Abstract: “Donohue and Levitt (2001) argue that the legalization of abortion in the
United States in the 1970s played an important role in explaining the observed decline in crime approximately two decades later. Foote and Goetz (2005) challenge
the results presented in one of the tables in that original paper. In this reply, we regretfully acknowledge the omission of state-year interactions in the published version
of that table, but show that their inclusion does not alter the qualitative results (or
their statistical significance), although it does reduce the magnitude of the estimates.
When one uses a more carefully constructed measure of abortion (e.g., one that takes
into account cross-state mobility, of doing a better job of matching dates of birth to
abortion exposure), however, the evidence in support of the abortion-crime hypothesis is as strong or stronger than suggested in our original work.”
b. “In this reply, we address in turn the two issues raised by Foote and Goetz (2005).
While it is with great embarrassment that we acknowledge that state-year interactions
were omitted from four of the eight regressions in the published version of Table of
our original paper, the mistake in the table, as we show, has a relatively minor impact
on the results. With respect to the second challenge raised by Foote and Goetz, we
show that the absence of effects when including state-year interactions and using per
capita arrest rats is an artifact of the combination of a very crude abortion proxy and
empirical specifications that remove an enormous amount of the true signal in the data by controlling for state-age interactions, age-year interactions, and state-year interactions. When building on our work in Donohue and Levitt (2004), we more carefully construct the abortion measure so that it 91) better corresponds to the actual month
and year of birth of the individual (2) incorporates cross-state mobility between birth
and adolescence, and (3) reflects the state of residence of those having abortions (as
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opposed to the state in which the abortion is performed), the results we obtain are as
strong or stronger than the original results [].”
6. Ted Joyce, “A Simple Test of Abortion and Crime,” 91 Rev. Econ. & Stat. 112 (2009).
a. Abstract: “I first replicate Donohue and Levitt’s results for violent and property
crime arrest rates. I apply their date and specification to an analysis of age-specific
homicide rates and murder arrest rates. The coefficients on the abortion rate have the
wrong sign for two of the four measures of crime and none is statistically significant
at conventional levels. I then use the legalization of abortion in 1973 to exploit two
sources of variation: between-state changes in abortion rates before and after Roe, and
cross-cohort differences in exposure to legalized abortion. I find no meaningful association between abortion and age-specific crime rates.”
b. DL contend (in a summary contained in Freakonomics) that “a 1 standard-deviation
increase in the abortion rate lowers homicide rates by 31 percent and can explain upwards of 60 percent of the recent decline in murder. If one accepts these estimates,
then legalized abortion has saved more than 51,000 lives between 1991 and 2001, at a
total savings of $105 billion.”
c. “I argue that the magnitude of the association between age-specific arrest rates and
the abortion rate is small, if appropriately scaled, and I show that the coefficients on
the abortion rat are statistically insignificant if corrected for serial correlation. I
demonstrate that there is no association between abortion and age-specific homicide
rates or age-specific arrest rates for murder. I contend that Donohue and Levitt’s attempt to instrument the abortion rate against measurement error is of questionable
value because the instrument is likely correlated with the error term by construction.
Finally, I argue that the abortion rate is endogenous. States with greater abortion
rates are assumed to have lower rates of unwanted births. Yet the availability of legalized abortion affects the decision to have sex, to use contraception, and to carry to
term if pregnant. Without a demonstrable inverse association between state abortion
rates and state fertility rates, there is no way to distinguish state variation in abortion
that is causally elated to lower rates of unintended childbearing from variation in
abortion due to changes in sexual activity and contraception.”
7. Ted Joyce, “Abortion and Crime: A Review,” NBER Working Paper 15098 (June, 2009). To
appear as a chapter in BRUCE BENSON & PAUL ZIMMERMAN, EDS., HANDBOOK ON THE ECONOMICS OF CRIME (2010).
a. Abstract: “Ten years have passed since John Donohue and Steven Levitt initially
proposed that legalized abortion played a major role in the dramatic decline in crime
during the 1990s. criminologists largely dismiss the association because simple plots
of age-specific crime rates are inconsistent with a large cohort affect following the legalization of abortion. Economists, on the other hand, have corrected mistakes in the
original analyses, added new data, offered alternative tests and tried to replicate the
association in other countries. Donohue and Levitt have responded to each challenge
with more data and additional regressions. Making sense of the dueling econometrics
has proven difficult for even the most seasoned empiricists. In this paper I review the
evidence. I argue that the most straightforward test given available data involves agespecific arrest and homicide rates regressed on lagged abortion rates in the 1970s or
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c.
d.
e.
f.
g.
h.
i.
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indicators of abortion legalization in 1970 and 1973. Such models provide little support for the Donohue and Levitt hypothesis in either the US or the United Kingdom.”
“They [criminologists] make the first-order observation that the timing between
changes in age-specific crime rates and exposure to legalized abortion is inconsistent
with the Donohue and Levitt story.”
“Homicides fell from 24,703 in 1991 to 15, 586 in 2000.”
I have included Joyce’s summary of DL in the first entry in this section.
“The statistical work [] may have done more to obfuscate what appears obvious from
time-series of age-specific crime rates: cohorts exposed to legalized abortion committed crimes at roughly the same rate as those who were unexposed.”
“In the end, the simple time-series plots of age-specific arrest and homicide rates tell
the story: the crime rates of cohorts born just before abortion was legalized follow the
same time path as the crime rates of those born just afterwards. There is no discontinuity in crime rates associated with the early legalization of abortion in New York or
California, nor is there a break in crime rates in the rest of the states after Roe.”
“There is a large difference in abortion rates between pre-Roe and Roe states that persists well after 1973 with no corresponding difference in fertility rates. One would
expect higher abortion rates to be associated with lower fertility rates I, in fact, abortion reduced the rate of unwanted childbearing. One explanation is that greater acceptance of abortion and greater availability of abortion services in the pre-Roe states
led to more sex and more pregnancies, but not necessarily to fewer unwanted births.”
“If there is no association between state abortion and state fertility rates, then abortion
improves well-being by lowering the prevalence of mistimes as opposed to unwanted
births. The association between mistimed births and adverse child development,
however, is much weaker than the association between unwanted births and child
well-being.”
“Akerlof et al. (1996) develop a model in which the legalization of abortion leads to a
rise in out-of-wedlock childbearing, since it frees men from having to legitimize a
pre-marital conception. The model fits the basic time-series well. The ration of
births to unmarried women rose from 7.7 percent of births to 17.8 percent between
1965 and 1980 (Ventura et al. 1995).”
“Thus, it is useful to step back for a moment and ask whether there are features of an
empirical test of abortion and crime that criminologists and economists might agree
upon. I would offer the following:
i. “The crime measure must be age-specific in order to identify cohorts.”
ii. “The outcome should be a rate of crime and not a level.”
iii. “The hypothesis should be consistent with the timing of abortion legalization
and should be evident or not contradicted by basic time-series plots.”
iv. “The abortion rate should be measured by state of residence.”
v. “The abortion rate should be inversely related to fertility rates.”
vi. “Regressions of age-state-year crime rates should include state-year fixed effects.”
vii. “The number of observations with no measure of abortion should be minimized.”
viii. “Statistical tests should take account of the auto-correlation in crime-rate residuals.”
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k. There is a section on the application of the DL hypothesis to data from other countries.
i. “The relationship between abortion and crime in Canada is of particular interest given its proximity and cultural similarity to the U.S. In addition, Canada
legalized abortion in 1969 and then expanded its availability again in 1988.
Third, Canada was not affected as much as the U.S. by crack, gangs, and guns,
and thus, an important source of confounding in the U.S. data is probably absent in Canada.”
ii. Amidya Sen (2007) did a DL-like study for Canada for 1983-1998. He generally finds support for the DL hypothesis in the Canadian data. [Check on this
and get details.]
iii. Pop-Eleches (2006) did a study of an interesting example of the possible connection between abortion and crime in Romania. “In 1967, the Romanian dictator, Nicolae Ceausescu, banned all abortion in the country. Birth rates doubled that same year, since abortion had been the most common form of fertility control. Outcomes among children born immediately after the ban improved. Educational achievement increased and employment in high-skilled
jobs rose among the cohort exposed to the ban.” Pop-Eleches compared crime
rates of cohorts born before and after the ban. He cannot adequately control
for all the relevant factors but “he finds that the crime rate of cohorts born in
the first few years after the ban falls, the opposite of the DL hypothesis.”
l. ‘Reyes [Jessica Wolpaw Reyes, 2007, “Environmental Policy as Social Policy?: The
Impact of Childhood Lead Exposure on Crime,” The B.E. Journal of Economic Analysis & Policy, 7 (1): 1-41] argues that reduction in childhood lead exposure in the
1970s led to lower crime rates in the 1990s. Her research design is similar to DL’s
state-year regressions. Total crime rates are regressed on lagged measures of lead exposure at the state level. Reyes includes the effective abortion rate as an additional
regressor. Her results suggest that lead can explain 56 percent and abortion 29 percent of the decline in violent crime between 1992 and 2002. In other words, 85 percent of the recent drop in crime can be attributable to factors never considered by
criminologists.”
8. William Anderson & Martin T. Wells, “Numerical Analysis in Least Squares Regression
with an Application to the Abortion-Crime Debate,” 5 J. Emp. Legal Stud. 647 (2008).
a. Abstract: “Concepts of numerical analysis with applications to least squares problemsare introduced in a manner the practitioner can readily apply to his or herown research problems, especially in the social sciences. Numerical analysisis mainly concerned with the accuracy and stability of numerical algorithms.We frame these concerns in terms of forward and backward error, twoimportant concepts in helping understand the quality of the computedanswers. The goal of numerical computing is to
obtain correct, approximateanswers to the true solution. We extended this forward
and backward errorframework to issues in least squares problems and check the condition ofthe regression problem via condition numbers. The more ill-conditionedthe
data are, the more sensitive the computed solution is to perturbationsin the data, and
the more unstable the computed solutions become. Conditionnumbers can also be
used to signal the presence of solution degradingcollinearity in regression problems.
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b.
c.
d.
e.
f.
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We apply the various numericalanalysis tools outlined with some model diagnostics
to the abortion-crimedebate, and show the regression analysis used in various papers
addressingthe abortion-crime debate cannot be trusted.”
“In 2001, Donohue and Levitt published a controversial article entitled “The Impact
of Legalized Abortion on Crime” and claimed to have found evidence that shows legalized abortion in the 1970s is causally related to a decline in crime in the 1990s.
Donohue and Levitt offer regression models as well as other types of data analysis to
support their conclusion, which led to a flood of papers both validating and contradicting (including the highly cited article by Foote and Goetz (2005)) their findings.
In this article we apply the numerical analysis techniques from the subsequent sections to show that the results of the regressions run by Donohue and Levitt (2001) and
Foote and Goetz( 2005) are problematic. Fixed effects modeling, as used by
Donohue and Levitt (2001) and Foote and Goetz (2005), is pervasive in empirical legal studies and in social sciences in general. The methods discussed in this article
should be routinely used to assess the degree of computational stability of the collected data before any statistical analysis.”
“The goal of numerical computing is to get approximately correct answers to the true
solution; that is, close solutions to the true solution. Unfortunately, this important
goal is largely ignored today in least squares applications. We extended this forward
and backward error framework to issues in least squares problems to check the condition of the regression problem, in the form of condition numbers. The more illconditioned the data are, the more sensitive the computed solution is to perturbations
in the data, and the more unstable the computed solutions become.”
“Condition numbers can also be used to signal the presence of solution degrading collinearity in the covariates. We used two methods based on condition numbers to diagnose collinearity in the data. One was the variance decomposition and the other
was collinearity indices. Variance decomposition has the added benefit of detecting
multiple near dependencies in the data.”
The authors go on to say that “there is no statistical way to overcome the problems
caused by collinearity for regression analysis. … One of the most commonly used
ways of dealing with ill-conditioning and collinearity is to exclude the collinear explanatory variables from the regression model, but this strategy is extremely ill advised. This approach can bias coefficient estimates and undermine the validity of inferential statistics for the remaining covariates. Such is the case when continuous
population covariates used in DL are not used in FG and are replaced by a single
measure of population size. The imprecision in a coefficient estimate is usually better
than bias.”
“We applied the numerous data and model diagnostics outlined in this article to the
data and models proposed in the controversial article of DL and the working paper of
FG. The results of these diagnostics show that data used for the full regression model
suffer from ill-conditioning, collinearity, and the linear model specified for the reduced regression model is problematic. As a result, the data and statistical methods
used in DL, FG, and their progeny cannot compute reliable answers to the question of
the relationship between abortion and crime.”
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9. William Anderson & Martin T. Wells, “A Bayesian Hierarchical Regression Approach to
Clustered and Longitudinal Data in Empirical Legal Studies,” 7 J. Emp. Legal Stud. 634
(2010).
a. Abstract: “The various forms of regression are a dominant feature of modern data
analysis. This is hardly surprising since the basic premises of regression are well understood in many different areas of research, and basic regression analysis is a standard component in many statistical software packages. However, researchers do not
have to venture very far in their applications of regression analysis to run into trouble
from a computational and modeling point of view. This is especially apparent when
modeling longitudinal or repeated measures data using classical regression. We introduce a Bayesian hierarchical modeling approach to clustered and longitudinal data.
These hierarchical models overcome many of the limitations of classical regression
and are well suited to handle longitudinal data. The intuitive concepts of hierarchical
models are introduced via the Donohue and Levitt (DL) abortion crime data set, using
the statistical software package R. We show that when properly modeled, there is no
empirical relationship between abortion and crime using the DL data set.”
b. “Often, these basic regression formulations suffer from ill-conditioning, collinearity,
and the problems associated with these effects. In our paper on numerical analysis
and regression, Anderson and Wells (2008), we delineate several diagnostics that
should be routinely run on regression data. One diagnostic is looking at the condition
number of the problem, which helps determine if the problem is ill-conditioned. If
the problem is ill-conditioned, a computed solution may not exist and if one does, the
solution could be far away from the true answer, which may lead to false conclusions.
In addition, ill-conditioning may lead to extreme sensitivity in the computed solution,
thus slight perturbations in the data lead to different computed solution, such as sign
changes and orders of magnitude changes in the regression coefficients. The other
diagnostics covered, such as variance decomposition, collinearity indices, and variance inflation factors, should be routinely run as well. Researchers should always be
concerned about the quality of the computed answer. So, the question arises, is there
a modeling paradigm that allows the modeling of the features of interest without the
potentially harmful effects of ill-conditioning and collinearity? One answer lies in the
general class of hierarchical models. We will show that the use of hierarchical models serves as a regularization method to turn ill-conditioned (ill-posed) problems into
well-posed problems through shrinkage, thus creating stable computed answers.”
i. A footnote explains that “shrinkage is a topic in statistics where an estimator
is improved (e.g., more efficient) by combining it with other information. …
Shrinkage is implicit in Bayesian inference via prior distribution, and penalized likelihood inference. … Most simple types of maximum likelihood and
least squares estimation procedures do not have shrinkage effects.”
So, after all that what do we make of the debate – does the Donohue-Levitt hypothesis withstand these criticisms or not? Our sense is that the critics make some very good points. We also
acknowledge that we are not particularly good at assessing the strength of those points, but there
seems to be enough smoke here to allow one to conclude that there is a fire and that the
Donohue-Levitt hypothesis is not proven.
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The extension of the Donohue and Levitt hypothesis that looks at the effect of legalized abortion on the behavior of teenage girls is John J. Donohue, III, Jeffrey Grogger, & Steven D.
Levitt, “The Impact of Legalized Abortion on Teen Childbearing,” 11 Am. Law & Econ. Rev. 24
(2009). Here is the abstract of that article:
After 41 consecutive years of increase, out-of-wedlock teen childbearing unexpectedly reversed
course in 1991 and by 2002 was 20 percent below its peak. Explanations for that reversal have
proven elusive. In this paper, we present evidence that exposure to legalized abortion in utero for
the cohort of women that became teenagers in the 1990s is one factor contributing to this decline.
We estimate that the legalization of abortion in the 1970s changed the composition of women at
risk of bearing children out of wedlock some 15-24 years later. This composition effect reduced
out-of-wedlock teen birth rates by 6 percent, which accounts for roughly 25 percent of the observed decline in unmarried teen birth rates over this period. It also lowered rates of unmarried
births for women aged 20-24. At the same time, it increased the number of married births to
women 20-24, so that there is only a small reduction in total fertility over the ages 15-24. The detailed information available on birth certificates enables a more direct identification of in utero
abortion exposure than prior studies looking at other outcomes such as crime.
To our knowledge there has not been a literature critical of this article. Therefore, we cannot
say whether this article’s conclusion should be read with the same caution that we recommended
for the original article on the effects of legalized abortion on crime.
Web Note 13.4 (p. 511)
The dramatic findings of the conviction of innocent people have caused several states, including Illinois, to rethink the procedures by which courts impose the death penalty. To
learn more about the new procedures and find additional information and links to articles
about wrongful convictions, see our website. See also SCOTT TUROW, THE ULTIMATE
PUNISHMENT: A LAWYER’S REFLECTIONS ON DEALING WITH THE DEATH PENALTY (2003).
In recent years, much has been made about the number of innocent people being convicted of
serious crimes. While false convictions are a concern at all levels, they are especially worrisome
in capital cases, where execution ends all hope of remedying the situation.
Technological advances, particularly in the field of DNA analysis, have led courts and politicians to overturn or commute many sentences, especially those stemming from rape and murder
charges. By one count, hundreds of people, including over 100 on death row, have been exonerated and freed in the last 30 years by DNA evidence and other means.
Many articles and media stories have been written on the topic; we present abstracts and
summaries of a few of the most compelling.
Samuel R. Gross, “Convicting the Innocent,” 4Annual Review of Law & Social Science, 173
(2008).
In this 2008 paper, Prof. Samuel Gross of the University of Michigan Law School, argues
that false convictions are far more common than most people realize. The Introduction of his paper follows:
False convictions have gotten a lot of attention in recent years, and for good reason. In the
past three decades, more than 200 innocent American defendants have been exonerated and freed
by DNA tests, and over 200 others have been exonerated without the benefit of DNA evidence,
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including more than 100 who had been sentenced to death. In addition, we have learned about
several major scandals in which police officers systematically framed dozens, or in one case
hundreds, of innocent defendants who were ultimately exonerated en masse.
The message is clear: Innocent people are convicted of serious crimes in the United States on
a regular basis. There is no disputing the importance of this news. What is surprising is that it is
news.
We are not a modest nation.We frequently insist that the American criminal justice system
(or in other contexts, the American medical system) is “the best in the world.” Convicting the
guilty and clearing the innocent has got to be the central operational goal of any system of criminal justice, so one reason for our superlative superiority must be that we never (well . . . , hardly
ever) convict the innocent—as prominent Americans forcefully assert with no evidence in support. In 1923, Judge Learned Hand wrote in a federal district court opinion that “[o]ur [criminal]
procedure has always been haunted by the ghost of the innocent man convicted. It is an unreal
dream” (United States v. Garrison 1923). At the time, with no systematic data one way or the
other, this could be taken as a statement of faith, an expression of red-blooded self-confidence
and optimism. Eighty-three years later, Justice Antonin Scalia was more specific in a concurring
opinion in the Supreme Court, if less eloquent. He claimed that American criminal convictions
have an “error rate of 0.027%—or, to put it another way, a success rate of 99.973%” (Kansas v.
Marsh 2006). Given what we knew by 2006, the charitable explanation for such an assertion is
self-deception.
The recent exonerations have been highly influential. They are responsible for a spate of new
laws that make post-conviction DNA testing more readily available (Garrett 2008, Natl. Conf.
State Legis. 2008). They have sparked moves to reform basic aspects of criminal investigation,
including eyewitness identification and custodial interrogation procedures; testimony by jailhouse informants; and the preservation, testing, and use of physical evidence (Connors et al.
1996, Moore 2007). In January 2000—two weeks after the thirteenth innocent inmate had been
released from Illinois’s death row—Governor George Ryan imposed a moratorium on executions
in Illinois, which is still in effect (Johnson 2000). Three years later, Governor Ryan commuted
the death sentences of all prisoners then on death row in Illinois, in large part because of the
danger of executing innocent defendants (Ryan 2003). And across the country, concern about
executing the innocent has been the major cause for a substantial reduction in support for capital
punishment—from about 75% in 1995 to about 65% since 2000 (Gross & Ellsworth 2002)—and
a much sharper drop in new death sentences, from 323 in 1996 to 115 in 2005 (Snell 2007).
In theory, we should have known all along that false convictions happen and that they are
caused by false or misleading evidence from eyewitnesses, police officers, forensic scientists,
and other witnesses. That has to be true. No system of adjudication is error free, and ours is hardly a candidate for perfection. But knowing that something must be true is not the same as seeing
that it is true; knowing abstractly that innocent people are convicted is a far cry from knowing
their names and faces and learning how their lives were destroyed. That is what the hundreds of
exonerations in the last few decades have accomplished: teaching us not only that innocent people are convicted, but who some of them are and how it happened to them. These are highly important lessons.
What else have we learned about false convictions? That is the subject of this review. The
answer, in brief, is that we have learned a few important things, but only a few. Our ignorance
still vastly exceeds our knowledge.
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Richard A. Leo & Deborah Davis, “From False Confession to Wrongful Conviction: Seven Psychological Processes,” 38 J. Psychiatry & Law 9 (2010).
Abstract: “A steadily increasing tide of literature has documented the existence and causes of false confession as well as the link between false confession and wrongful conviction of the innocent. This literature has primarily addressed three issues: the manner in
which false confessions are generated by police interrogation, individual differences in
susceptibility to interrogative influence, and the role false confessions have played in
documented wrongful convictions of the innocent. Although the specific mechanisms
through which interrogation tactics can induce false confessions, and through which they
can exert enhanced influence on vulnerable individuals have been widely addressed in
this literature, the processes through which false confessions, once obtained by police,
may lead to wrongful conviction have remained largely unaddressed.
This article addresses this gap in the literature, examining seven psychological processes linking false confession to wrongful conviction and failures of post-conviction relief: (1) powerful biasing effects of the confession itself, including incorporated "misleading specialized knowledge" (inside crime-relevant knowledge displayed by the suspect in
the false confession, but acquired through outside sources (such as the interrogator) rather
than in the course of the commission of the crime); (2) tunnel-vision and confirmation biases, (3) motivational biases, (4) emotional influences on thinking and behavior; (5) institutional influences on evidence production and decision-making; and inadequate context
for evaluation of claims of innocence, including (6) inadequate or incorrect relevant
knowledge, and (7) progressively constricting relevant evidence. We discuss reciprocal
influences of these mechanisms and their biasing impact on the perceptions and behaviors
of suspects, investigators, prosecution and defense attorneys, juries, and trial and appellate judges.
“Texas: DNA Evidence Clears Man After 30 Years,” The New York Times, p. 13, January 4,
2011.
Thirty years after Cornelius Dupree Jr. was imprisoned for rape and robbery, prosecutors in
Dallas declared him innocent on Monday in light of new DNA evidence. Mr. Dupree, 51, has
served more years in a Texas prison for a crime he did not commit than any of the other 41 people exonerated in the state in recent years. In 1980, Mr. Dupree was convicted along with a second man, Anthony Massingill, of robbing a couple and then kidnapping and raping the woman.
But DNA tests completed last year on traces of semen showed that neither man committed the
rape. Mr. Dupree was released on parole last summer, weeks before the DNA tests were done.
Mr. Massingill, who was convicted in another sexual assault, remains in prison.
Brandon Garrett & Peter J. Neufeld, “Invalid Forensic Science Testimony and Wrongful Convictions,”95 Va. L. Rev. 1 (2009).
Abstract: This is the first study to explore the forensic science testimony by prosecution
experts in the trials of innocent persons, all convicted of serious crimes, who were later
exonerated by post-conviction DNA testing. Trial transcripts were sought for all 156 exonerees identified as having trial testimony by forensic analysts, of which 137 were located and reviewed. These trials most commonly included testimony concerning serological analysis and microscopic hair comparison, but some included bite mark, shoe print,
soil, fiber, and fingerprint comparisons, and several included DNA testing. This study
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found that in the bulk of these trials of innocent defendants - 82 cases or 60% - forensic
analysts called by the prosecution provided invalid testimony at trial - that is, testimony
with conclusions misstating empirical data or wholly unsupported by empirical data. This
was not the testimony of a mere handful of analysts: this set of trials included invalid testimony by 72 forensic analysts called by the prosecution and employed by 52 laboratories, practices, or hospitals from 25 states. Unfortunately, the adversarial process largely
failed to police this invalid testimony. Defense counsel rarely cross-examined analysts
concerning invalid testimony and rarely obtained experts of their own. In the few cases in
which invalid forensic science was challenged, judges seldom provided relief. This evidence supports efforts to create scientific oversight mechanisms for reviewing forensic
testimony and to develop clear scientific standards for written reports and testimony. The
scientific community can through an official government entity promulgate standards to
ensure the valid presentation of forensic science in criminal cases and thus the integrity
and fairness of the criminal process.
Web Note 13.5 (p. 526)
In More Guns, Less Crime, John R. Lott, Jr., has attempted to show that when a state
passes a “concealed carry” law—a law allowing registered gun owners to carry concealed
weapons—there is a discernible subsequent decline in crime in that state. Lott argues that
criminals are rational and that if they know that either their victims or those nearby the
scene of a crime may have concealed handguns and that, therefore, the possibility of serious injury or death to the criminal is high, they are less likely to commit crime. On our
website we review Lott’s arguments and survey the critique of his work.
In 1997 John R. Lott, Jr., and David Mustard published an article – “Crime, Deterrence, and
Right-to-Carry Concealed Handguns,” 26 J. Legal Stud. 1 (1997) – that first articulated the theory that we mentioned in the Web Note. In addition, the article – and a subsequent book, John R.
Lott, Jr., More Guns, Less Crime: Understanding Crime and Gun-Control Laws (3d ed. 2010) –
gathered a large amount of data to explore whether the theory was borne out by actual events.
The first dataset contained information on crime in all U.S. counties from 1977 to 1992. Additionally, the dataset included information on demographic and socioeconomic variables for each
county and state and, importantly, information on when, if at all, each jurisdiction had passed
laws easing the ability of people to gain a permit to carry a concealed weapon.
Lott and Mustard did regressions with this data. The dependent variables (in a series of regressions) were the level of various crimes in each county and state in the dataset for the 15 years
in the sample. The independent or control variables were the demographic and socioeconomic
variables and dummy variables for the years in which the “shall issue” laws were passed. Their
prediction was that crime rates would decrease in the years following the passage of “shall issue”
laws, all other things held equal. And that is what they found – the passage of concealed-carry
laws in a given state were associated with significant declines in property and violent crimes in
that state during the 1977-1992 period. The subsequent editions of More Guns, Less Crime have
presented newer, more recent data and new tests that, according to Lott, confirm his and Mustard’s earlier findings.
The Lott-Mustard results excited a great deal of public, political, and scholarly interest. Everyone recognized the extraordinarily important public policy implications of the findings. The
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fairest commentators based their criticisms not on the hypothesis itself but on the statistical evidence that Lott and Mustard presented.
The notes that follow are from three articles that appeared in an issue of the 2003 Stanford
Law Review on the Lott-Mustard hypothesis. If you read the first and third articles – about research by Ian Ayres and John Donohue – you will get a sense for the sophisticated econometric
and statistical criticisms that have been made. The middle article, by Plassmann and Whitley,
attempted, not very successfully, to buttress the Lott-Mustard findings.
Ian Ayres & John J. Donohue III, “Shooting Down the ‘More Guns, Less Crime’ Hypothesis,”
55 Stan. L. Rev.1193 (2003).
“We opined [in an earlier review in the American Law and Economics Review] on the potential theoretical and empirical infirmities in [Lott’s] analysis, and noted the value in further study
given that more state adoptions and the passage of time would likely either strengthen Lott’s case
if it were true or weaken it if it were false. Having extended the state data through 1999 and the
county dataset through 1997, we are now able to test that prediction. We conclude that Lott and
Mustard have made an important scholarly contribution in establishing that these laws have not
led to the massive bloodbath of death and injury that some of their opponents feared. On the other hand, we find that the statistical evidence that these laws have reduced crime is limited, sporadic, and extraordinarily fragile. Minor changes of specifications can generate wide shifts in the
estimated effects of these laws, and some of the most persistent findings – such as the association
of shall-issue laws with increases in (or no effect on) robbery and with substantial increases in
various types of property crime – are not consistent with any plausible theory of deterrence. …
[I]t may well be the case that the scattered negative coefficients for various violent crime categories, which on their face suggest that crime decreases with passage of shall-issue laws, should be
thought of as statistical artifacts. While we do not want to overstate the strength of the conclusions that can be drawn from the extremely variable results emerging from the statistical analysis, if anything, there is stronger evidence for the conclusion that these laws increase crime than
there is for the conclusion that they decrease it.” 1201-02. .
Footnote 3 of this article has an excellent list of the academic articles that support and criticize the Lott-Mustard hypothesis.
Florenz Plassmann& John Whitely, “Confirming ‘More Guns, Less Crime,’” 55 Stan. L. Rev.
1313 (2003).
Plassmann and Whitely write to support the Lott-Mustard hypothesis. They begin with the
observation that “[m]ost studies have found significant benefits, with some finding reductions in
murder rates twice as large as [those reported in] the original [Lott-Mustard] research. Even the
critics did not provide evidence that such laws [the concealed-carry laws] have increased violent
crime, accidental gun deaths, or suicides. … The most detailed results [reported by Ayres and
Donohue] – following the change in crime rates on a year-by-year basis before and after right-tocarry laws were adopted – clearly show large drops in violent crime that occur immediately after
the laws were adopted. Their hybrid results showing a small increase in crime immediately after
passage are not statistically significant and are an artifact of fitting a straight line to a curved
one.” 1315-16.
Ian Ayres & John J. Donohue III, “The Latest Misfires in Support of the ‘More Guns, Less
Crime’ Hypothesis,” 55 Stan. L. Rev. 1371 (2003).
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The authors say that they had two major points in their initial article. “First, that there was no
credible statistical evidence that the adoption of concealed-carry (or ‘shall-issue’) laws reduced
crime; and second, that the best, although admittedly quite imperfect, data suggested that the
laws increased the costs of crime to the tune of $1 billion per year (which is a relatively small
number, given the total cost of FBI index crimes of roughly $114 billion per year).In their response to our article, Florenz Plassmann and John Whitley offer two sets of evidence in support
of their view that concealed-carry laws are beneficial: First, they argue some of our regression
specifications really buttress their position; and second, they analyze some new county data for
the period 1977-2000.
“Their first method of proof fails because it simply overlooks – without a single word of
commentary! – the entire thrust of our papers: that aggregated specifications of the effects of
these laws are badly marred by ‘jurisdiction selection’ effects. [Fn. 3: “Section effects can mar
statistical analyses when the selected sample is taken as representative of a larger group even
though it differs systematically from the larger group. Our paper showed that the aggregated regressions that Lott and Mustard prefer are frequently marred because they confuse effects that
apply in a few early-legalizing states with the effects that occur in all adopting jurisdictions.
Therefore, in these aggregated regressions, there is a selection problem because some unrepresentative jurisdictions bias the estimated effects intended to capture the effects for all jurisdictions. We refer to this phenomenon as the ‘jurisdiction selection’ effect.”] …
“Their second method of proof fails because [Plassmann and Whitley] seriously miscoded
their new county dataset in ways that irretrievably undermine every original regression result that
they present in their response.” 1371-73.
These notes from the articles published in the Stanford Law Review in 2003 and Lott’s response in the third edition of his book fairly represent the depth of the controversy that this hypothesis and the attempts to find statistical evidence to support it have spawned. (To show that
the issue is still very much alive, see the links at the end of this Web Note.) Indeed, the issue was
so important for public policy to get right and had become so heated that the National Academy
of Sciences appointed a committee of 15 distinguished scholars – including doctors, statisticians,
crime control experts, political science specialists (notably James Q. Wilson) and some economics professors (Steve Levitt, Peter Reuter, and Joel Waldfogel) – to study and report on the issues. The Committee to Improve Research Information and Data on Firearms issues its report in
2005 – National Research Council of the National Academy of Sciences, Committee to Improve
Research Information and Data on Firearms (Charles F. Wellford, John V. Pepper, & Carol V.
Petrie, eds.),Firearms and Violence: A Critical Review (2005). The study is available on-line at
http://books.nap.edu.
One of the central findings of the report is that the “committee found that answers to some of
the most pressing questions cannot be addressed with existing data and research methods, however well designed. For example, despite a large body of research, the committee found no credible evidence that the passage of right-to-carry laws decreases or increases violent crime, and
there is almost no empirical evidence that the more than 80 prevention programs focused on gunrelated violence have had any effect on children’s behavior, knowledge, attitudes, or beliefs
about firearms.”
The committee’s recommendations were for the systematic gathering of data about such fundamental issues as gun ownership [but see the notes below from the Cook, Ludwig, and Samaha
article], firearms markets, comparative data between the U.S. and other countries [the committee
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reported, intriguingly, that “in the United States, suicide appears to be positively associated with
rates of firearms ownership, but homicide is not. In contrast, in comparisons among countries,
the association between rates of suicide and gun ownership is nonexistent or very weak, but there
is a substantial association between gun ownership and homicide.”], defensive versus offensive
uses of firearms, and the real effects of restricting access to firearms.
The United States Supreme Court entered the troubled issue of gun control in an important
case handed down in 2008 – District of Columbia v. Heller, 554 U.S. 570 (2008). To quote
Cook, Ludwig, and Samaha (see below): “The case involved a police officer who wanted to keep
an operable handgun in his home and to ‘carry it about his home in that condition only when
necessary for self-defense.’ … One District law [of 1976] prohibited possession of handguns by
private citizens, with only narrow exceptions. A second regulation required all firearms to be either unloaded and disassembled or trigger-locked at all times. Exceptions were made for law enforcement officers, places of business, and otherwise lawful recreational activities, but the regulation reached people’s homes. A third regulation involved firearms licensing by the chief of police. The Heller majority left unaddressed the issue of firearms licensing, but it concluded that
the first two regulations infringed the plaintiff’s right to have a handgun in his home for selfdefense.” [To quote Cook, Ludwig, and Samaha again: “But, by the late 1980s, the notion that
the ban had achieved anything useful seemed unlikely, given common references to [Washington, DC] as the ‘murder capital of the country.’”]
There is, of course, some controversy about exactly what the Heller court actually held. But,
at a minimum, this is now the law: “Heller establishes that the current Supreme Court will not
tolerate comprehensive handgun bans when such laws are challenged by citizens that the Court
believes are otherwise entitled to possess handguns for the purpose of self-defense in the home.”
(Cook, Ludwig, & Samaha, 1071.)
The UCLA Law Review published a symposium issue on Heller in 2009 -- “Symposium: The
Second Amendment and the Right to Bear Arms After D.C. v. Heller,” 56 UCLA L. Rev. (June,
2009). The articles in that issue are by some of the most distinguished constitutional scholars in
the United States. Of those many articles, one that we very, very highly recommend is Philip J.
Cook, Jens Ludwig, & Adam M. Samaha, “Gun Control After Heller: Threats and Sideshows
from a Social Welfare Perspective,” 56 UCLA L. Rev. 1041 (2009). As you will see from our
notes below, this is a tour de force on gun ownership, gun violence, and gun control regulation.
What follows is the abstract of the article and reading notes.
Philip J. Cook, Jens Ludwig, & Adam M. Samaha, “Gun Control After Heller: Threats and Sideshows from a Social Welfare Perspective,” 56 UCLA L. Rev. 1041 (2009).
Abstract: What will happen after District of Columbia v. Heller? We know that five justices of the Supreme Court now oppose comprehensive federal prohibitions on home
handgun possession by some class of trustworthy homeowners for the purpose of, and
maybe only at the time of, self-defense. Perhaps the justices will push further and apply
Heller’s holding to state and local governments via the Fourteenth Amendment. But the
majority opinion in Heller offered limited guidance for future cases. It did not follow a
purely originalist method of constitutional interpretation, nor did it establish a constraining doctrinal framework for evaluating firearms regulation – although the opinion did
gratuitously suggest that much existing gun control is acceptable. There is significant
room for judges to maneuver after Heller. In the absence of more information from the
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Supreme Court, we identify plausible legal arguments for the next few rounds of litigation and assess the stakes for social welfare.
Based on available data, we conclude that some salient legal arguments after Heller
have little or no likely consequence for social welfare. For example, the looming constitutional fight over local handgun bans – an issue on which we present original empirical
data – seems largely inconsequential. The same can be said for a right to carry a firearm
in public with a permit. On the other hand, less prominent legal arguments could be quite
threatening to social welfare. At some point judges might draw on free speech doctrine
and presumptively disfavor taxation or regulation targeted especially at firearms. This
could have serious consequences. In addition, and perhaps most important, Second
Amendmentdoctrine might deter innovative regulatory responses to the problem of gun
violence.The threat of litigation may inhibit useful policy experimentation ranging
frompersonalized firearms technology and the microstamping of shell casings, to premarketreview of gun design, social-cost taxation, gun-owner insurance requirements,and
beyond.
The remainder of the article has sections on the data on gun ownership in the United States, a
survey of gun regulations in the cities and states, an interpretation of Heller, and speculation on
what the effects of Heller are likely to be on gun violence and on the ability of the cities and
states to regulate handguns in the future. We will focus on the first, second, and fourth of these
sections.
The section entitled “Guns, Risks, and Regulation in the United States” begins with a remarkably informative summary of information about gun ownership in the U.S. (Contrary to
what the National Academy of Sciences committee held, this information – largely updates from
Philip J. Cook & Jens Ludwig, Guns in America: Results of a National Comprehensive Survey
on Firearms Ownership and Use (1996) – seems to us to be comprehensive and valuable.)
“In America, gun ownership is concentrated. Our best estimate is that there are 200-250 million firearms in private circulation, meaning that there are nearly enough guns for every adult to
have one. But about 75 percent of all adults do not own any guns. Recent survey data suggests
that about 42 percent of males, 9 percent of females, and 35 percent of all households have at
least one gun. It seems that the prevalence of gun ownership by household has been in long-term
decline, partly because households are becoming smaller and less likely to include an adult male.
On the other hand, most people who own one gun, own many. In 1994, about 75 percent of all
guns were owned by those who owned four or more, and this slice of gun owners amounted to
only 10 percent of the adult population.” 1045-46.
“The prevalence of gun ownership differs widely across regions, states, and localities, as well
as across different demographic groups. For example, while it appears that about 13 percent of
Massachusetts households own a gun, a full 60 percent of Mississippi households own one. Residents of rural areas and small towns are far more likely to own a gun than residents of large cities, partly because of the importance of hunting and sport shooting in those communities. And
this geographic skew is consistent with a concentration of ownership among middle-aged, middle-income households. These attributes are associated with relatively low involvement in criminal violence, and it is reasonable to suppose that most guns are in the hands of people who are
unlikely to misuse them. Still, gun owners as a group are more likely than other adults to have a
criminal record.” 1046.
“Of the subset of Americans who own firearms, handguns are somewhat popular but by no
means the dominant type of weapon. Around 33 percent of America’s privately held firearms are
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handguns, which are more likely than long guns to be kept for defense against crime. In the
1970s, about 33 percentof new guns were handguns, a figure which grew to nearly 50 percent by
the early 1990s and then fell back to around 40 percent by the end of that decade. Despite the
long-term increase in the relative importance of handgun sales, a mere 20 percent of gun-owning
individuals have only handguns; 44 percent have both handguns and long guns. … Less than 50
percent of gun owners say that their primary motivation for having a gun is self-protection
against crime.” 1046-47.
“The majority of guns acquired in a recent two-year period were obtained by their owners directly from a federally licensed firearm dealer (FFL). However, the 30 to 40 percent of all gun
transfers that do not involve licensed dealers – the so-called secondary market – accounts for
most guns used in crime. Despite the prominence of gun shows in contemporary policy debates,
the best available evidence suggests that such shows account for only a small share of all secondary market sales. Another important source of crime guns is theft. Over 500,000 guns are stolen each year.” 1047.
The authors then turn to the issues of gun violence.
“Including homicide, suicide, and accidental deaths, 30,694 Americans died by gunfire in
2005. This amounts to a gun-related mortality rate of 10.4 deaths per 100,000 people for the
year. The mortality rate is down substantially from 1990, when it was 14.9 per 100,000, but is
still much higher than the observed rate in, say, 1990.” 1047-48.
“More Americans die each year by gun suicide than gun homicide. However, more people
suffer nonfatal gun injuries from crime than from unsuccessful suicide attempts. … [A]bout 800
people per year die from unintentional gunshot injuries.” 1048.
“[T]he shooters and victims [of gun violence] are not a representative slice of the population.
In 2005, the gun homicide victimization rate for Hispanic men ages 18-29 was six times the rate
for non-Hispanic white men of the same age. And the gun homicide rate for black men in this
age group – 99 per 100,000 – was a remarkable 24 times the rate for white males in the same age
group.” 1048.
“The large majority of both groups have prior criminal records. The demographics of gun suicide look somewhat different. … Gun suicides are more common among whites than blacks, and
more common among the old than among young or middle-aged adults. Men are vastly
overrepresented in all categories.” 1049.
The authors then turn to a survey of gun regulations.
“The [federal] Gun Control Act of 1968 established the framework for the current system of
controls. … [That] Act specifies several categories of people who are denied the right to receive
or possess a gun, including illegal aliens; people convicted of a felony or an act of domestic violence; people under indictment, illicit drug users, and those who have at some time been involuntarily committed to a mental institution. In addition, federally licensed dealers may not sell handguns to people younger than age 21, or long guns to those younger than 18. Dealers are required
to ask for identification from all would-be buyers, have them sign a form indicating that they are
not within a proscribed category, and initiate a criminal history check. … Notably omitted from
federal regulation are gun sales by people not in the business. … This private sale loophole is a
major gap in federal regulation and helps the used-gun market thrive.” 1050-51.
“Twelve states require handgun buyers to obtain a permit or license before taking possession,
a process that typically entails a fee and a waiting period. All but a few of these transfer-control
systems are permissive, however, in that most people are legally entitled to obtain a gun. … Chicago and Washington, DC, have largely prohibited handgun ownership as a matter of formal law
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since 1982 and 1976, respectively. … California, Maryland, and Virginia bar dealers from selling more than one handgun a month to any one buyer.” 1051.
“Federal law also imposes some restrictions on gun design, and certain types of firearms are
effectively prohibited. The National Firearms act of 1934 (NFA) was intended to eliminate Prohibition-era gangster firearms, including sawed-off shotguns, hand grenades, and automatic
weapons capable of continuous rapid fire with a single pull of the trigger. … In 1994, Congress
temporarily banned the importation and manufacture of certain assault weapons (military-style
semi-automatic firearms). [This 1994] Crime Control Act … also banned manufacture and import of magazines holding more than 10 rounds. However, then-existing assault weapons and
large-capacity magazines were grandfathered. And in 2004, the ban was allowed to expire.”
1052-53.
“[F]irearms and ammunition are excluded from the purview of the Consumer Product Safety
Commission.” 1053.
“Every state except Alaska and Vermont places some restriction on carrying a concealed
firearm. … Since 2005, federal law has required all handguns sold by licensed dealers to come
equipped with a secure storage device. Eleven states and the District of Columbia have laws concerning firearm locking devices. Massachusetts and the District require that all firearms be stored
with a lock in place. And the Maryland legislature recently adopted a pioneering requirement:
All handguns manufactured after 2003 and sold in the state must be ‘personalized’ with a built-in
locking device that requires a key or combination to release.” 1054.
“Over 40 states preempt at least some local laws affecting firearms.” 1056
“In the 1990s, several cities facing tremendous costs from gun-related crime tried an alternative. Frustrated by their inability to change gun regulations through legislation, they filed mass
tort lawsuits that had the potential to impose higher standards of conduct on the gun industry.
These suits asserted unsafe and defective gun design under state law, or claimed that the industry
was creating a public nuisance through failure to police the supply chain by which guns were
marketed and often found their way into dangerous hands. … [The] cities’ arguments did not fare
well in court. … Of the city lawsuits, the ‘great majority have been dismissed or abandoned prior
to trial, and of the few favorable jury verdicts obtained by the plaintiffs, all but one have been
overturned on appeal. A handful of claims have been settled prior to trial.” [Citing Timothy D.
Lytton, “Introduction: An Overview of Lawsuits Against the Gun Industry,” in Timothy D. Lytton, ed., Suing the Gun Industry, 1, 1-35 (2005).] 1056.
“Then, on October 26, 2005, President George W. Bush signed the Protection of Lawful
Commerce in Arms Act (PLCAA). It provided an important degree of legal immunity to the firearms industry, while preserving the possibility of traditional tort actions against the industry. For
example, injuries from defects in design or manufacture can be compensated in private suits. But
the industry is now exempt from liability for injuries resulting from criminal misuse of its product.” 1057.
The authors note that handgun bans are among the least popular forms of gun control. “In a
2007 Gallup Poll, 68 percent of respondents opposed a handgun ban.” 1072.
“Research on the effects of gun prevalence has been facilitated by the discovery of a useful
proxy: the percentage of suicides committed with guns. It allows us to analyze how gun use relates to the prevalence of gun ownership across states, or even counties. This proxy has been
used to document a strong positive relationship between county gun prevalence and each of the
following outcomes: the fraction of robberies involving guns; the fraction of homicides with
guns; the likelihood that young men carry a gun; and, most important, the overall homicide rate.
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… The bulk of the evidence at this point suggests more prevalent handgun ownership engenders
more widespread use of guns in crimes as well as higher social costs of crime.” 1074-75.
“Let us review the chain of logic. To the extent that Heller and subsequent Court decisions
make handguns cheaper and more readily available in some jurisdictions, those jurisdictions will
likely experience an increase in demand for handguns and ultimately an increase in the prevalence of ownership. An increase in ownership prevalence will in turn make guns more readily
available to criminals, thereby increasing gun use in violent crime and suicide, resulting in an
increased death rate from intentional violence. Burglary rates are also likely to increase as burglary becomes more lucrative. But as it turns out, the first link in that chain – the connection between invalidating handgun bans and increased prevalence of handgun ownership – is the weakest empirically.” 1076.
And it is to that subject that the authors turn, with a fascinating result.
“In an influential article published in the New England Journal of Medicine, criminologist
Colin Loftin and his colleagues showed that, following the [1976 handgun] ban, homicides and
suicides declined in Washington, DC, and by a greater margin than in the city’s Maryland and
Virginia suburbs. A challenge to the use of affluent suburbs as a control group for the city
prompted additional research using Baltimore data. Like the District, Baltimore experienced a
reduction in firearm homicides around 1976. But unlike the District, Baltimore experienced a
reduction in bothnon-gun and gun homicides, suggesting some general change in Baltimore during this time period that was not specific to guns. Further, Baltimore did not experience a decline
in gun suicides.” 1077.
“Gun ownership has declined in the District since the early 1990s, and in recent years has
dropped lower than when the ban was initiated in 1976 (and far lower than the national average).” 1077.
(Incidentally, the only other major U.S. city that has banned handguns is Chicago, which did
so in 1982.)
“In sum, the effect of these local handgun bans on the prevalence of gun ownership is uncertain, although there is some indication that it has not been large. … But available data leads us to
question whether judicial invalidation of (weakly enforced) handgun bans would seriously
threaten social welfare. … It is therefore plausible that the most obvious implication of Heller for
formal law has little significance for sound and politically feasible gun control.” 1078.
“Currently, criminals use guns in only about 25 percent of noncommercial robberies and 5
percent of assaults. If increased gun carrying among potential victims causes criminals to carry
guns more often themselves, or become quicker to use guns to avert armed self-defense, the end
result could be that street crime becomes more lethal.” 1081.
“Donohue’s re-analysis of the Lott data indicates that states that eventually ended restrictive
concealed-carry laws had systematically different crime trends from the other states even before
these law changes went into effect. The tendency to adopt the law under study following an unusual spike in crime – which would ordinarily be followed by a reduction regardless of whether a
new law were passed – makes the analysis problematic. Indeed, Donohue finds much evidence in
support of the view that these laws increased crime rates in the 1990s, when crime was generally
declining. …
“Whether the net effect of relaxing concealed-carry laws is to increase or reduce the burden
of crime, there is good reason to believe that the net is not large. One study found that in twelve
of the sixteen permissive concealed-carry states studied, fewer than 2 percent of adults had obtained permits to carry concealed handguns. … [M]any of those who obtain permits were already
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carrying guns in public. Moreover, the change in gun carrying appears to be concentrated in rural
and suburban areas where crime rates are already relatively low, among people who are at relatively low risk of victimization – white, middle-aged, middle-class males. The available data
about permit holders also imply that they are at fairly low risk for misusing guns, consistent with
the relatively low arrest rates observed to date for permit holders.
“Based on available empirical data, therefore, we expect relatively little public safety impact
if courts invalidate laws that prohibit gun carrying outside the home, assuming that some sort of
permit system for public carry is allowed to stand. The result would most likely be a modest
change in gun carrying rates among a subset of the population that is itself at relatively low risk
of either committing gun crimes or being victimized by them.” 1082.
The horrific events in Tucson, Arizona, on January 8, 2011, in which a lone gunman shot and
killed six people and wounded 13, including Representative Gabriele Giffords, caused a great
deal of soul-searching about whether revised or strengthened gun control laws could have prevented this horror. The New York Times invited short responses by two of the principals in the
gun-control debate. You can read their thoughts by following the links below.
John J. Donohue III, “It Takes Laws to Control the Bad Guys,” The New York Times,Jan. 12,
2011.
http://www.nytimes.com/roomfordebate/2011/01/11/more-guns-less-crime/we-need-laws-tocontrol-the-bad-guys?scp=1&sq=Lott%20Donohue&st=cse.
John Lott, “The Case for Arming Yourself,” The New York Times, Jan. 12, 2011.
http://www.nytimes.com/roomfordebate/2011/01/11/more-guns-less-crime/the-case-for-armingyourself.
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