Drugs and Crime in the US:
Evidence from a Shock in the Local Production of Crystal
Meth
VERY PRELIMINARY AND INCOMPLETE – WORK IN PROGRESS – NOT TO BE QUOTED
WITHOUT AUTHOR’S PERMISSION
By ROCCO D’ESTE *
(8 FEBRUARY 16)
I investigate the effects of illicit drugs on crime, focusing on US state’s policies
restricting the access to methamphetamine’s precursors chemicals. A diff-in-diff
design reveals 10% to 15% reduction in property and violent crimes. I investigate
the mechanisms showing: 34% and 16% drop in the arrests for possession and
sale of synthetics drugs; 43% decrease in clandestine meth-labs seizures; 0.2 to
0.3 elasticity of crime to meth-labs; heterogeneous effects in states farther away
from Mexico, the major “meth-exporter” into the United States. A separate
analysis on hate crimes shows an increase only in violent episodes motivated by
victims’ sexual preferences. I reconcile this finding with the psychosis from methwithdrawal and the widespread consumption of this substance within gay and
bisexual communities.
Keywords: Methamphetamine, Drugs, Crime, Hate-Crimes
JEL Codes: K14, K42
•University of Warwick, Department of Economics, (e-mail: r.d-este@warwick.ac.uk). Special thanks for the support
to Robert Akerlof, Dan Bernhardt, Mirko Draca, Rocco Macchiavello and Chris Woodruff. I gratefully acknowledge the
financial support from the Economic and Social Research Council (ESRC), the International Association of Applied
Econometrics (IAAE) and the Royal Economic Society (RES). All errors and omissions remained are my own
responsibility.
1
I.
Introduction
The total cost connected to the trafficking and abuse of illicit drugs in the
United States is immense and hard to quantify, with an estimated annual
economic loss for society of nearly $200 billion (ONDCP, 2007). The damage is
reflected in an overburdened justice and healthcare system, lost productivity,
environmental destruction and crime, with nearly 50% of all prison inmates being
clinically addicted to some type of drug (NCADD, 2014).
The proliferation of illicit drugs can lead to an increase in criminal offenses
mainly through three different channels: 1) economic, due to the users’ need to
support drug-habits; 2) systemic, due to the violence connected to the production
and distribution of the drug itself and 3) pharmacologic, due to the psychotic
behavior arising from drug’s effects (Goldstein, 1985).1
Nonetheless, while the shaping of cost-effective policy interventions needs a
solid understanding and quantifying of these channels, very few studies have
distinctly identified the existence of a causal link between the proliferation of
illicit drugs and the propagation of crime.2
This paper contributes to the existing literature by focusing on crystal
methamphetamine, an extremely addictive and neurotoxic substance, perceived to
be as the most dangerous drug in the United States.3
I examine the effects on crime of US state level policies implemented as a
reaction to a rapid proliferation in small toxic labs, where this illicit drug is
produced. These policies restricted the access to methamphetamine precursor
chemicals, ephedrine and pseudoephedrine, easily obtained through the chemical
1
2
3
Add note on other two indirect mechanisms
Please read the “Relate Literature” subsection for more information.
Add note on 500 sheriffs.
2
manipulation of cold tablets, which were freely sold over the counter prior to
2005.
I build a comprehensive panel dataset for the analysis: 1,400 counties in 30 US
states, from 1997 to 2010, including in the analysis a wide set of county, timevarying, socio-economic controls.4 I use a difference in differences design
focusing on the effects of meth precursors states’ legislation on FBI reported
crimes.
I divide the empirical analysis into three main sections: 1) the direct analysis of
the impacts of the laws on crime; 2) the exploration of the underlying mechanisms
and 3) a separate analysis on hate crimes.
I show a decline of 10% to 15% in both theft and violent crimes. These
findings are robust to extensive checks, weighting the regression by a measure of
the quality of the information on reported crimes, using different functional
forms, adding to the analysis potential confounding controls and state time trends.
I then explore the mechanisms behind these results in several ways.
I detect a reduction of 34% in the arrests for possession and 16% in the arrests
for sale of synthetic drugs. These findings suggest that precursors’ regulation is
disrupting the market for those substances that are entirely created in clandestine
laboratories, only through manipulations of chemical components. Falsification
tests on marijuana, cocaine and heroin-related arrests do not reveal the presence
of any spillover effects on these different drugs.
I include data on clandestine labs seized by the police, a measure of the latent
clandestine production of crystal meth in a county. Results show 43% decrease in
clandestine meth-labs seizures. I then use this specification as a first stage
regression in a two stage least square framework, where I estimate the effects of
meth-labs on crime. I show an elasticity of both property and violent crimes to
4
Please refer to the Data section for a detailed description of all controls used in the analysis.
3
meth labs in the range of 0.2 to 0.3, with IV estimates 3 to 7 times higher in value
than the Ordinary Least Square (OLS). I reconcile this difference to the presence
of random noise and systematic measurement error in the county-level measure of
the latent clandestine production of crystal meth.
I hence test for the presence of heterogeneous effects on crimes across US
states, employing a triple difference in differences strategy. The third interaction
term is the distance of each US state from Mexico: the largest supplier to the U.S.
illicit drug market, with Mexican drug traffickers earning approximately 25
billion USD each year in wholesale U.S. drug markets (U.N. World Drug Report,
2011). In particular, Mexican drug cartels accounts for as much as 80 per cent of
the meth sold, suggesting that small clandestine labs do not fulfill the entire
demand for this drug in the United States (DEA, 2010).
While the lack of precise information related to the structure of drugtrafficking networks impose caution in the interpretation, I find some evidence
suggesting the reduction in crime being stronger in states where drug users might
rely more on the internal production of meth, (the one disrupted by the laws),
rather than the Mexican one.
I finally conduct a separate analysis on hate crimes: episodes of violence
motivated by any sort of ethnic, religious, gender, sexual or other bias. I perform
this analysis due to the possible connection between these somehow “irrational”
violent crimes and the psychotic effects deriving from the abuse of crystal meth.
Results show a controversial increase in violent episodes motivated by victims’
sexual preference, in particular against homosexuals, with no effect detected on
any other hate crime. I reconcile these results with evidence related to the
psychosis from meth withdrawal and the widespread consumption of this
substance among gay and bisexual men, mainly as sexual stimulant.
Overall, the results uncover a strong causal link between meth proliferation
and the rise in criminal activity, suggesting that the economic, systemic and
4
pharmacologic channels, through which illicit drugs could cause crime, are all
playing a role in this context.
This work has the power to inform policy. Policymakers should take into
account the extra benefit deriving from a reduction in crime, when contemplating
cost and benefits of measures designed to disrupt the supply side of the market for
drugs.
Nevertheless, this study also suggests the need of carefully considering the
demand side of the market, with a particular focus on communities where the
abuse of the illegal substance is extremely acute. Violent side effects, in fact,
might arise due to a drop in meth’s availability, not anticipated by a relative drop
in users’ level of addiction.
This paper unfolds as follows: section II describes the related literature; section
III provides background information on methamphetamine effects, production and
precursors’ legislation; section IV presents all the datasets used in the analysis,
providing relative summary statistics; section V shows the results on crime and
further explores the mechanisms; section VI offers the analysis on hate crimes;
section VII concludes.
II.
Related Literature
This paper adds to the existing literature on the determinants of crime in
several ways.
To the extent of my knowledge, this is one of the first papers offering a
systematic empirical investigation on the effects of illicit drug propagation on the
proliferation of criminal activity. The existing evidence supporting the theory that
more drugs lead to more crime is somewhat inconclusive. Corman and Mocan
(2000) using high frequency data for New York City show that drug usage has
only a small effect on some property crimes. Despite the important of this study
5
as one of the first in this area, the time series dimension, coupled with the lack of
a clear identification strategy, seems to represent a limit to the quality of this
study. De Mello (2011) looks at the role of crack cocaine in explaining the
aggregate dynamics in violence, using a fixed effect framework and adding timevarying controls. He hence relies on the exogeneity of the proportion of crackcocaine as a determinant in explaining crime, showing that crack explains 30% of
the time series variation in the homicides in the state of Sao Paolo.5
By focusing on crystal methamphetamine, this study is closely related and
complements two different works. The first, by Dobkin and Nicosia (2009),
focuses on the effects in California of a different government effort to reduce the
supply of methamphetamine precursors in 1995.6 While showing that
methamphetamine price tripled, purity declined from 90 percent to 20 percent,
amphetamine related hospital and treatment admissions dropped 50 percent and
35 percent, they do not find substantial reductions in property or violent crime.
The second study, by Dobkin et al (2014), focuses on the same over the counter
regulations studied in this paper, using a wide set of rich administrative datasets to
detect the impact of the regulation of the number of labs, price, quality and
consumption of the drugs. Consistent with my analysis, they show a decrease of
meth-labs of 36%, but no effects on price, purity or consumption of
methamphetamine. My study, while adding support to the results related to the
disruption of methamphetamine market, look at the effects of precursors
5
Cracoland studio da citare, con risultati. Ma l’identification strategy e l’utilizzo di la predetermined sembrano a mio
avviso un limite dello studio e rendono difficile la corretta interpretazione dei riultati
6
The Domestic Chemical Diversion Control Act (DCDCA) removed the record-keeping and reporting exemption for
distributors of single-entity ephedrine products and empowered the DEA to deny or revoke a distributor’s registration
without proof of criminal intent. In May 1995, the DEA shut down two suppliers that appear to have been providing more
than 50 percent of the precursors used nationally to produce methamphetamine. This is probably the largest “supply” shock
that has occurred in any illegal drug market in the United States and was made possible by the substantial concentration in
the supply of methamphetamine precursors (Dobkin and Nicosia, 2009).
6
legislation from a different perspective, with an almost exclusive focus on the
causal link between meth-proliferation and criminal activity.7
This study also adds to the literature related to the effects of drugs policy
intervention. In a similar fashion, Melissa Dell (2012) examines the direct and
spillover effects of Mexican policy towards the drug trade. In particular, she uses
a regression discontinuity design to show that drug-related violence increases
substantially after close elections of PAN mayors. Empirical evidence suggests
that the violence reflects rival traffickers' attempts to usurp territories after
crackdowns have weakened incumbent criminals.8
Finally, this paper adds to the existing literature by offering one of the first
rigorous analyses of the determinant of hate crimes, linking them to the effects of
crystal methamphetamines.
Becker's seminal paper (1968) was the first to consider crime in an economic
framework of rational behavior: agents maximize utility by comparing the
benefits of crime with the relative costs. In this model, harm or loss to the
individual is considered an externality, essentially an unintentional side effect of
the offender's actions. In the case of a hate crime, however, it has been suggested
that loss to the victim is the intention of the crime (Gale, Heath, and Ressler,
2002; Craig, 2002).
The presumed irrationality of hate crimes could be explained by several factors
that alter individual’s preferences. Along these lines, Machin et Hanes (2014) find
significant increases in hate crimes against Asians and Arabs that occurred almost
immediately in the wake of London and New York error attacks. They
hypothesize that attitudinal changes resulting from media coverage may act as an
underlying driver of the spike in hate crimes.
7
8
I also use county-year variation in the estimates, while their study exploits data grouped at the state level.
Other similar studies are:
7
An alternative view is offered by contributions from behavioral economics.
This suggests there may be some element of group interaction, such as peer
pressure or the removal of social barriers, which causes individuals to commit
hate crimes.
My study adds to this literature an generally overlooked element: the abuse of
highly addictive psychotic drugs in specific groups. This might lead feeling of
anger and rage to dominate the individual's rational decision-making process,
especially in closed communities, (in my case homosexuals and bisexuals), where
the addiction is more severe.
III.
Background Information
Methamphetamine Effects
Methamphetamine is a powerful, highly addictive stimulant that affects the
central nervous system. Also known as meth, chalk, ice, and crystal, it costs
between $20-25 for ¼ of grams.9 The drug takes the form of a white, odorless,
bitter-tasting crystalline powder that easily dissolves in water or alcohol.10 It can
be smoked, snorted, injected, or ingested orally to produce a release of high levels
of dopamine and neurotransmitters into the brain, generating sensations of self-
9
Amphetamine type stimulants (ATS, excluding ecstasy) remain seconds only to cannabis, with an estimated prevalence
of 0.3-1.2 per cent in 2010, between 14.3 to 52.5 million users (UNODC, 2012). The United States government reported in
2008 that approximately 13 million people over the age of 12 have used methamphetamine—and 529,000 of those are
regular users. It is a drug widely abused in the Czech Republic. There it is called Pervitin and is produced in small hidden
laboratories and a limited number of larger ones. Consumption is primarily domestic but Pervitin is also exported to other
parts of Europe and Canada. The Czech Republic, Sweden, Finland, Slovakia and Latvia reported amphetamines and
methamphetamine as accounting for between 20% and 60% of those seeking drug abuse treatment. In Southeast Asia, the
most common form of methamphetamine is a small pill—called a Yaba in Thailand and a Shabu in the Philippines. More
info at: http://www.drugfreeworld.org.
10
The U.S. Drug Enforcement Administration has classified methamphetamine as a Schedule II stimulant, which makes it
legally available only through a no refillable prescription. Medically it may be indicated for the treatment of attention
deficit hyperactivity disorder (ADHD) and as a short-term component of weight-loss treatments, but these uses are limited
and it is rarely prescribed.
8
confidence, energy, alertness, pleasure, and sexual arousal. The high from
methamphetamine lasts from 8 to 24 hours while, in comparison, the high from
cocaine lasts from 30 minutes to one hour. With repeated use, meth exhausts
accumulations of dopamine in the brain, simultaneously destroying the wiring of
dopamine receptors.11 This process is what makes crystal meth extremely
addictive, leading frequent users towards the physical impossibility of
experiencing pleasure, (a condition known as anhedonia), and the consequent
intense craving for the drug itself.
Chronic abuse can lead to psychotic behavior, hallucinations, paranoia, violent
rages, mood disturbances, suicidal thoughts, insomnia, psychosis, poor coping
abilities, sexual dysfunction, dermatological conditions and "meth mouth", a
dental condition characterized by severe decay and loss of teeth, fracture and
enamel erosion (NIDA, 2002). The termination of use can result in depression,
fatigue, intense craving for methamphetamine, anxiety, agitation, vivid or lucid
dreams, suicidal temptation, psychosis resembling schizophrenia, paranoia and
aggression (ONDCP 2003).
Since meth withdrawal is extremely painful and difficult, most abusers revert:
93% of those in traditional treatment return to abusing methamphetamine (Drug
Free World, 2014).
Methamphetamine Production and Precursors’ Legislation
Unlike heroin or cocaine, methamphetamine is a synthetic product can be
easily and inexpensively manufactured within the U.S. with little equipment, few
11
Although both methamphetamine and cocaine increase levels of dopamine, administration of methamphetamine in
animal studies leads to much higher levels of dopamine, because nerve cells respond differently to the two drugs. Cocaine
prolongs dopamine actions in the brain by blocking the re-absorption (re-uptake) of the neurotransmitter by signaling nerve
cells. At low doses, methamphetamine also blocks the re-uptake of dopamine, but it also increases the release of dopamine,
leading to much higher concentrations in the synapse (the gap between neurons), which can be toxic to nerve terminals
(National Institute of drug abuse, 2014). More info at http://www.drugabuse.gov.
9
supplies, and almost no expertise in chemistry. 12 Methamphetamine’s main
ingredient is ephedrine or pseudoephedrine that, if not already in pure powder
form, it must be separated from the tablets of cold medicine that contain it. For
this purpose, cold tablets are mixed to sodium hydroxide, anhydrous ammonia,
iodine, matches containing red phosphorus, Drano (a drain cleaner product), ether,
brake and lighter fluid and hydrochloric acid. All these are legal products, which
can be easily bought in different stores.
The entire chemical process, usually undertaken in self-made chemical labs
hidden in flats, caravans, garages or hotel rooms, generally takes about two days'
time and can result in hundreds of thousands of methamphetamine doses. The
production process involving a dangerous mixture of highly corroding and
inflammable chemicals creates a set of unique problems for the environment and
the community (Weisheit et al, 2010).13
Because of the concentration in the meth production of ephedrine and
pseudoephedrine, the federal government has passed, in the last 25 years, several
laws intended to cut the diversion of ephedrine and pseudoephedrine to illegal
drug labs.14 This paper examines the effects of state-level policies implemented,
12
Insert here a bit of story of meth production citing the paper.
13
Methamphetamine laboratories pose environmental and health risks that transcend the effects of the drug on the user.
Apartment residents may be killed or injured by a meth lab explosion in the adjoining apartment, children in homes where
meth is cooked may be exposed to toxic chemicals and to meth itself, hotel guests may be injured by toxic chemical residue
from the previous tenant’s meth lab, children may be burned or seriously injured by the meth trash dumped along the
roadways near their homes, and emergency responders may be sickened when they enter a lab site. Further, producing one
pound of methamphetamine generates five to six pounds of toxic waste (Hargreaves 2000), waste that may contaminate the
ground or water supplies. Thus, meth labs pose a type of threat to innocent citizens that simple drug use does not.
14
The first of these was the Chemical Diversion and Trafficking Act of 1988 (CDTA), which regulated ephedrine and
pseudoephedrine in bulk powder form, but left processed forms unregulated. This was followed by the Domestic Chemical
Diversion Control Act of 1993, which placed restrictions on OTC ephedrine products (e.g. tablets) and increased DEA
oversight of suppliers. Then, the Methamphetamine Control Act of 1996 tightened regulations on the sale of products
containing methamphetamine precursors over 24 grams, but contained an exception for “blister packs”. Shortly thereafter,
the Methamphetamine Anti-Proliferation Act of 2000 lowered the thresholds from 25 to 9 grams, but blister packs
remained exempt. Each of these federal efforts induced methamphetamine producers to switch to sources of precursors that
remained unregulated (Dobkin et al., 2013).
10
in the year 2005, in reaction to a rapid increase in small toxic labs.15 These
policies focused on controlling access to the methamphetamine precursor
chemicals ephedrine and pseudoephedrine through retail transaction quantity
restrictions, sales environment restrictions, purchase and possession penalties, and
agency responsible for enforcing precursor policies. 16
[Table 1]
Table 1 shows the effective date of enactment of the laws for each state
included in the analysis.
IV.
Data and Summary Statistics
In my main analysis, I use county level data on crime rates, arrests and sociodemographic variables, from 1997 to 2006. I exclude the last 4 years of the
sample due to the implementation of the federal Combat Methamphetamine
Epidemic Act, with the last provision of the law taking effect on the 30th of
September 200617. This law did not pre-empt more restrictive state policies.18 I
use several data sources that I separately describe in this section.
15
Policy activity directed at limiting access to methamphetamine precursor chemicals has not been limited to the state level
Federal legislation took place in 2006 through the Combat Methamphetamine Epidemic Act that became effective the 8th of
April 2006 (Federal purchase quantity limits) and the 30th of September 2006 (Federal clerk intervention requirements).
Importantly, federal intervention did not pre-empt more restrictive state policies.
16
Data are available for 32 US States and are obtained trough McBride et al. 2011. The States included in the analysis are:
Alabama, Arizona, Arkansas, California, Colorado, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas,
Kentucky, Michigan, Mississippi, Missouri, Montana, Nebraska, Nevada, New York, North Carolina, North Dakota, Ohio,
Oklahoma, Oregon, Pennsylvania, South Dakota, Virginia, Washington, Wisconsin, and Wyoming. Given the scope of the
empirical exercise, I do not differentiate across specific types of legislation. Jean et al. (2009) provide a detailed analysis of
the legislations enacted by each state.
17
While this law does not allow for the analysis of the long run effect of the state laws on crime, it can be seen as a further
natural experiment where the treatment states become control states and vice versa. The results of this specification are
quite inconclusive and are shown in the appendix of the paper.
18
The Combat Methamphetamine Epidemic Act of 2005 (CMEA) was signed into law on March 9, 2006, to regulate,
among other things, retail over-the-counter sales of ephedrine, pseudoephedrine, and phenylpropanolamine products. Retail
provisions of the CMEA include daily sales limits and 30-day purchase limits, placement of product out of direct customer
access, sales logbooks, customer ID verification, employee training, and self-certification of regulated sellers. The CMEA
is found as Title VII of the USA PATRIOT Improvement and Reauthorization Act of 2005 (Public Law 109-177).
11
Reported Crimes And Data on Arrests
County level data on reported crimes, arrests and number of sworn police
officers and civilian employees19 is accessed through the National Archive of
Criminal Justice Data.20
The eight different types of reported crimes are: larceny, burglary, robbery,
motor-vehicle theft, murder, aggravated assault, rape and arson.21 I also analyze
data on arrests for sale and possession of drugs: synthetic drugs, opium/cocaine,
marijuana, and other dangerous non-narcotics. I have collected these data from
1997 to 2010.
Hate Crimes
The Federal Bureau of Investigation’s Hate Crime Statistics (HCS) provides
incident-level data on hate crimes in which each case represents a single incident
report, from which I construct county-level hate-crime measures. The Hate Crime
Statistics Act of 1990 brought these data into existence. That Act requires the
Attorney General to collect annual data on “crimes that manifest evidence of
prejudice based on race, religion, disability, sexual orientation, or ethnicity,
including where appropriate the crimes of murder, non-negligent manslaughter;
19
Sworn police officers are law enforcement employees with arrest powers. Civilian employees include personnel
employed by each local agency who do not have arrest powers and include job classifications such as clerks, radio
dispatchers, meter maids, stenographers and accountants.
20
Data are downloadable at: http://www.icpsr.umich.edu/icpsrweb/content/NACJD/guides/ucr.html#desc_cl.
21
County-level files are created by NACJD based on agency records in a file obtained from the FBI that also provides
aggregated county totals. NACJD imputes missing data and then aggregates the data to the county-level. The FBI
definition of the eight types of crime, as well as the explanation of the hierarchy rule, can be found in the data appendix. In
the FBI’s Uniform Crime Reporting (UCR) Program, property crime includes the offenses of burglary, larceny-theft, motor
vehicle theft, and arson. The property crime category includes arson because the offense involves the destruction of
property; however, arson victims may be subjected to force. Because of limited participation and varying collection
procedures by local law enforcement agencies, only limited data are available for arson. In the FBI’s Uniform Crime
Reporting (UCR) Program, violent crime is composed of four offenses: murder and non-negligent manslaughter, forcible
rape, robbery, and aggravated assault. Violent crimes are defined in the UCR Program as those offenses that involve force
or threat of force.
12
forcible rape; aggravated assault, simple assault, intimidation; arson; and
destruction, damage or vandalism of property”. I have collected these data from
1997 to 2010.
Meth Labs Seizures
The National Clandestine Laboratory Register, provided by the US department
of Justice, contains – from 2004 onwards – the dates and addresses of the
locations where law enforcement agencies reported they found chemicals or other
items that indicated the presence of either clandestine drug laboratories or
dumpsites. I use this information to create a county annual measure of the number
of meth labs seized by the police.
TEDS – Treatment Episode Data Set
The Treatment Episode Data Set (TEDS) is maintained by the Center for
Behavioral Health Statistics and Quality, Substance Abuse and Mental Health
Services Administration (SAMHSA). The TEDS system includes state level
records for some 1.5 million substance abuse treatment admissions annually.
While TEDS does not represent the total national demand for substance abuse
treatment, it does comprise a significant proportion of all admissions to substance
abuse treatment, and includes those admissions that constitute a burden on public
funds. Data are collected from 1997 to 2010.
Socio-Economic Controls
I add a wide set of county time-varying socio-economic controls, obtained
from the US Census Bureau22 and from the Bureau of Labor Statistics-Current
22
I use http://censtats.census.gov/usa/usa.shtml.
13
Population. I include income per capita, percentage of people below the poverty
line, percentage of unemployment, the number of social security recipients and
the average monthly payment per subsidy. I also add the number of commercial
banks and saving institutions in the county, the number of pawnshops,23 the
amount of banking and saving deposits, the population density and the
racial/ethnic composition in the county24
Summary Statistics
Summary statistics for all the crime and drugs-related variables are shown in
table 2A. I define all these variables in terms of rates per 100,000 inhabitants.
[Table 2A]
Larceny is most frequent property crime, with a mean of 1,981 and a standard
deviation of 3,121, followed by burglary, motor-vehicle theft and arson. For what
concerns violent crimes instead, aggravated assault is the most frequent, followed
by robbery, rape and finally murder.
I also show summary statistics for possession and sale of drugs, separately.
These will be another outcome of the analysis, and include marijuana, crack
cocaine, synthetic and other drugs. Top of the list in both cases is marijuana
followed by crack cocaine. I can observe the same type pattern for the number of
hospital admissions, with the top substance being alcohol.
Nevertheless, these numbers hide the enormous growth that methamphetamine
has had overtime. Table 2b shows the annual growth rate of a pooled measure of
the arrests, both for sale and possession, of marijuana, crack cocaine and
synthetics.
23
Infogroup provided the overall number of pawnshops by county, per year. See d’Este (2014).
24
The racial origin is defined according to four categories: White, Black, Asian and Indian American. Moreover each race
is divided into Hispanic or Not Hispanic ethnic origin.
14
[Table 2B]
Before 2004, synthetic-related arrests displayed an impressive increase of more
than 100%, with the arrests for the other type of drugs displaying a more stable
pattern.
Finally, I report the ranking of all the 50 US States related to the normalized
number of methamphetamine related hospitalization. Hawaii, Iowa, Montana,
South-Dakota being first in the list, with the problem being more widespread in
the south and in the center of the United States rather then in the coast.25
[Table 3]
Table 3 reports the descriptive all socio-economic controls used in the analysis.
For brevity, I omit discussion of summary statistics of these variables.
V.
Empirical analysis
In this part of the paper I will proceed as follows: first, I will present the
baseline difference in differences approach on the effects of the states’ law on
crime; then I will start to explore the mechanism showing: the results on drugsrelated arrests; the result on meth labs seizures and the instrumental variable
approach; a triple difference in differences to investigate the differential impact of
the law on crime in US states farther away from Mexico.
25
FBI evidence on Hawaii
15
Difference in Differences Approach
I start to use a standard difference in differences approach using the following
estimating equation:
′
𝑦𝑖,𝑠,𝑡 = 𝛼𝑖 + 𝜇𝑡 + 𝑋𝑖,𝑠,𝑡
𝛽0 + (𝑡𝑟𝑒𝑎𝑡𝑒𝑑 ∗ 𝑝𝑜𝑠𝑡)𝛽1 + 𝜖𝑖,𝑠𝑡,
(1)
where i indicates the county, s the state and t the year. The outcome of interest
is the number of reported crimes, with a separate regression for each type of
crime. Reported crimes are expressed as a 𝑙𝑜𝑔(1 + 𝑥) where 𝑥 is the number of
crimes normalized per 100,000 people. The analysis focuses on 𝛽1, the interaction
between a dummy indicating a treated state and a dummy indicating the period
after which the law was enacted. In this case, the variable post takes the value 1 in
2005. Standard errors are clustered at the state-year level.
The inclusion of county fixed effects 𝛼𝑖 control for time-invariant unobserved
characteristics both related to the changes in crime and the states’ decision of
enacting the law. Year fixed effects 𝛾𝑡 capture common shocks across the entire
sample. I finally add a vector of county time-varying socioeconomic controls
′
𝑋𝑖,𝑠,𝑡
, described in the previous section.
[Table 4]
Results for this specification are shown in Table 4 panel A, where I include a
column for each crime. This specification detects a differential reduction in
murders and robberies in treated states, after the enactment of the law. I detect a
reduction of 12% and 18% of murders and robberies, both significant at the 1%
level. While I do not detect any effect on any other crime, the negative coefficient
on burglaries has a p-value of 13%.
16
Table 4 panel B shows the results of the same specification, adding as a control
the number of sworn police officers and civilian employees, the number of public
hospitalization for crack-cocaine, marijuana, heroin and medicines bought over
the counter. The decision of analyzing the results separately depends by the fact
that these controls might be, for different reasons, potential outcomes of the law.26
Adding these controls barely change the point estimates of the baseline
regression, increasing the precision for burglaries at a 10% significance level.
Table 4 panel C shows the results where potential outcomes and state linear
trends are included. The inclusion of state linear trends increases the precision of
the estimates. Using this specification, a 13% reduction of larcenies, a 14%
reduction of burglaries and robberies, all significant at the 1% level. While the
coefficient on murders looses its significance I detect a drop in aggravated assault
of 9% significant at the 10% level.
Table 5 shows the results of the baseline specification with the inclusion of the
interaction between the variable treated multiplied by a dummy for each year,
excluding the dummy for the first year of the sample.
[Table 5]
While the inclusion of all these interaction creates collinearity across
regressors, I do not observe any particular trend in specifications for larceny,
burglary, robbery and murder.
In Table 6 I restrict the analysis from 2003 to 2006, creating a balanced
analysis of two years before and after the laws’ enactment.
[Table 6]
I detect strong negative results for larceny, burglary, murder and robbery,
oscillating from 6% to 17%, all significant at the 1% level. I also detect an 8%
26
Discuss the reasons.
17
reduction in aggravated assaults and 6% drop in motor-vehicle thefts, significant
at the 5% and 10% level.
Analysis of Drugs-Related Arrests
I investigate the mechanism performing a further analysis on a different set of
outcomes: arrests for sale and arrests for possession of crack-cocaine, marijuana,
synthetic and other drugs.
Results for this specification are shown in table 7.
[Table 7]
I detect a 34% and a 16% drop in the arrests for possession and sale of
synthetic drugs but no effects on marijuana, crack-cocaine and heroin arrests.
These results, while showing a strong effect of the laws only on synthetic-related
arrests, strengthen the validity of the hypothesis presented in the paper. Moreover,
the lack of spill over effects across different type of drugs, seems to suggest the
uniqueness of methamphetamine production process and its diversity from the
production and, most likely, distribution of all the other drugs.
Meth-Labs Seizures and IV approach
I extend the analysis introducing a county measure of the latent local
production of methamphetamine. For this reason, I include in the analysis the
number of clandestine meth-labs, seized by the police, available from 2004
onwards.
For each county in every year I instrument the log of the normalized number of
meth-labs seized by the police, using the interaction of the treated*post dummy.
The baseline IV model is given by the following two equations system where (3)
is the first stage and (2) is the second stage.
18
′
̂ 𝑖,𝑠,𝑡 𝛽1 + 𝜀𝑖,𝑠,𝑡
𝑦𝑖,𝑠,𝑡 = 𝛼𝑠 + 𝛿𝑡 + 𝑋𝑖,𝑠,𝑡
𝛽0 + 𝑚𝑒𝑡ℎ_𝑙𝑎𝑏𝑠
(2)
′
𝑚𝑒𝑡ℎ_𝑙𝑎𝑏𝑠𝑖,𝑠,𝑡 = 𝛾𝑠 + 𝜂𝑡 + 𝑋𝑖,𝑠,𝑡
𝛽2 + (𝑡𝑟𝑒𝑎𝑡 ∗ 𝑝𝑜𝑠𝑡)𝛽3 + 𝜁𝑖,𝑠,𝑡 (3)
Reported crimes and methamphetamine labs are expressed in the form
𝑙𝑛(1 + 𝑥), where 𝑥 is the number of crimes or the number of labs expressed per
100,000 inhabitants. Due to data availability and the implementation of the
federal law, I include in the analysis 3 years of the sample, from 2004 to 2006.
For this reason, I also omit from the baseline specification county fixed effects,
which will be added back as a robustness check together with state-linear trends.
[Table 8]
Table 8 shows the results of the first stage. The sign of the coefficient is
negative as expected. The introduction of the law reduces by 43% more the
number of labs seized by police in treated states. This reduction is significant at
the 1% level. Moreover, the Kleinbergen-Paap Wald F-statistic on the excluded
instrument has a value of 67.26 an extremely reassuring value that excludes the
bias arising from the use of a weak instrument.
Table 9A and 9B show the OLS and IV result for property and violent crimes.
[Table 9A – 9B]
For the case of property crimes, the elasticity on larceny and burglary is
positive 0.2 and 0.3 significant, in both cases, at the 5% level. The elasticity in
case of motor-vehicle thefts is 0.15 with a p-value of 13%.
For violent crimes, I detect a 0.27 elasticity on robbery, significant at the 10%
level, and an elasticity of 0.23 for murder with a p-value of 15%. The IV
estimates are 3 to 4 times higher than the OLS estimates. A reason for that can be
related to the presence of idiosyncratic measurement error in the count of
methamphetamine labs and to a plausible systematic over representation of seized
19
meth labs in treated states where police could have become tougher on this
problem.27
Distance from Mexico
In this part of the paper I investigate for the possible presence of a
heterogeneous effect of the laws, taking into account the distance from Mexico,
the major exporter of methamphetamine into the United States.
Mexican drug cartels are filling the void in the nation's drug market created by
the long effort to crack down on American-made methamphetamine, flooding
U.S. cities with exceptionally cheap, extraordinarily potent meth from factory like
"super labs." Although Mexican meth is not new to the U.S. drug trade, it now
accounts for as much as 80 per cent of the meth sold here, according to the Drug
Enforcement Administration.
To test for the possibility of a differential impact of the US States’ laws on
crime, I employ a triple difference in differences strategy, using the triple
interaction given by the treated and post dummy, and a measure of the distance of
the State from Mexico. Results for larceny, burglary, robbery and murder are
shown in Table 10. This specification captures a negative and significant result of
the triple interaction term on larcenies and burglaries.
[Table 10]
The crime reducing effect of the law is higher in US States further away from
Mexico. While I do not want to over-emphasize the significance of these results
do to the lack of precise information related to the composition of the networks of
drug trafficking, I consider this as an interesting venue for future research.
27
Description systematic measurement error and mccrary findings.
20
VI.
Hate Crimes and Crystal Meth
In this section of the paper I try to further explore the link between
methamphetamine proliferations and hate crimes. These crimes are biasmotivated, which the Hate Crime Statistics Act of 1990 defines as “offenses
against a person or property motivated by bias toward race, religion,
ethnicity/national origin, disability, or sexual orientation”
Table 11 shows the distribution of episodes in the United States from 1997 to
2010, by type of bias. Hate crimes’ episodes are mainly divided in crimes against:
black 36%, white 10%, Jewish 11%, Hispanic 7%, Homosexuals 10%.28
[Table 11]
Hate Crimes are mainly violent crimes. The distribution of these crimes by
type of offense is shown in Table 12 with destruction/vandalism representing 33%
of episodes, intimidation 30%, simple assault 18% and aggravated assault 10%.
[Table 12]
Crystal Meth in the Gay and Bisexual Community
Methamphetamine use and risky sexual behavior have been connected in
several studies of gay and bisexual men (Colfax et al., 2005; Drumright et al.,
2006; Fernández et al., 2007; Plankey et al., 2007; Purcell et al., 2005; Vaudry et
al., 2007). Methamphetamine use is associated with numerous sexual risk factors
including behavioral disinhibition, enhanced sexual desire, low rates of condom
use, high rates of sexually transmitted infections (STIs), increased desire for high
28
All the categories and the relative distribution are shown in Table 11.
21
risk activities, prolonged sexual activity, multiple partners, and casual or
anonymous sexual partners (Colfax et al., 2005; Molitor, Truax, Ruiz, & Sun,
1998; Shoptaw, 2006). In a recent review article, Drumright, Patterson, and
Strathdee (2006) posited that a causal relationship between methamphetamine use
and HIV-positive status is likely to exist, which implicates methamphetamine use
as a major public health concern.
Table 13 shows the results of a difference in differences specification. The first
column reports the results on the overall number of hate crimes while, from
column 2, I display the results for the most frequent hate crimes: Anti-White,
Anti-Black, Anti-Jewish, Anti-Islamic, Anti-Homosexual, Anti-Heterosexual,
Anti-Physical Disability.
[Table 13]
Panel A reports the results from the baseline diff-in-diff specification, in Panel
B I add state trends, In Panel C I log transform the outcome variables, always
including state linear trends in the specification.
Results in each panel show a positive effect, significant at the 1%, on violent
episodes against homosexuals and against heterosexuals (only insignificant in the
first specification with a p-value of 15%). No significant result, across all the
different specifications, is detected on other hate crimes.
Despite the difficulty to explain the mechanisms behind these findings, the
analysis clearly indicates the presence of some positive response after the
enactment of the law related to the meth abuse and the sexual activity. These
results suggest the importance of considering side effect to the implementation of
laws restricting the production of an extremely addictive drug, without shifting as
well the level of addiction of drug users condensed in certain communities.
22
VII.
Conclusions
This paper offers one of the first systematic empirical investigations of the
effect of the proliferation of illicit drugs on criminal behavior.
Motivated by the richness of anecdotal evidence, I look at this issue through
the lens of crystal methamphetamine, a neurotoxic illicit substance widely diffuse
in the United States and in the rest of the world. I use as a source of variation
state-level policies restricting the access methamphetamine precursor chemicals.
A mixture of diff-in-diff and IV approaches shows: 1) a reduction of property
and violent crimes of 10% to 15%; 2) a 43% decrease in clandestine meth-labs
seizures and 3) an underlying crime’s elasticity of around 0.3; 4) a 34% and a
16% drop in the arrests for possession and sale of synthetic drugs; 5) no spill-over
effects on arrests for sale or possession of other drugs. I also test for the presence
of differential effects in states farther away from Mexico, and I conduct separate
analysis on hate crimes, showing a sharp increase only on violent episodes related
to the sexual orientation of the victims.
This paper suggests new directions for future research. A direct spin off of this
work would be the analysis of shocks on the supply, distribution or consumption
of other dangerous drugs such as crack-cocaine and heroin. Moreover, entering
the “black box” of the mechanism linking proliferation of drugs and criminal
activity is critical for the understanding of criminal behavior. Three main
mechanisms seem in fact to play an important role in this context: economic,
systemic and psychological. Disentangling these three channels might help to
shape specific policy interventions that seek to reduce the impact that the
proliferation of drugs can have on criminal behavior. This and other interesting
aspects are left for future research.
23
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27
TABLES
TABLE 1: STATES' LEGISLATION (MCBRIDE ET AL., 2011)
States
Date Enacted
Date Effective
ALABAMA
ARIZONA
ARKANSAS
CALIFORNIA
COLORADO
FLORIDA
GEORGIA
HAWAII
IDAHO
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
MICHIGAN
MISSISSIPPI
MISSOURI
MONTANA
NEBRASKA
NEVADA
NEWYORK
NORTHCAROLINA
NORTHDAKOTA
OHIO
OKLAHOMA
OREGON
PENNSYLVANIA
SOUTHDAKOTA
VIRGINIA
WASHINGTON
WISCONSIN
WYOMING
05/24/2005
05/20/2005
03/30/2001
10/10/1999
05/27/2005
06/01/2005
04/19/2005
07/05/2005
None
08/24/2004
05/10/2005
03/22/2005
04/15/2005
03/18/2005
None
07/01/2005
06/15/2005
05/02/2005
05/31/2005
None
None
09/27/2005
04/22/2005
None
04/06/2004
08/16/2005
None
02/25/2005
None
05/11/2005
06/07/2005
03/15/2005
05/07/2005
08/12/2005
03/30/2001
01/01/2000
07/01/2005
07/01/2005
07/01/2005
07/01/2005
None
01/01/2005
07/01/2005
05/21/2005
06/01/2005
06/01/2005
None
07/01/2005
06/15/2005
07/01/2005
09/05/2005
None
None
09/27/2005
04/22/2005
None
04/06/2004
08/16/2005
None
07/01/2005
None
10/01/2005
06/21/2005
07/01/2005
28
TABLE 2 – CRIMES AND DRUGS DESCRIPTIVE STATISTICS
(1)
Mean
(2)
Standard Deviation
28.98
29.72
11.96
19.11
74.99
43.38
31.22
137.7
58.28
226.1
24.80
46.40
214.3
210.4
50.90
226.9
Larceny
Burglary
Robbery
Motor/Vehicle Theft
Murder
Aggravated Assault
Rape
Arson
1,984
659.0
61.98
215.2
4.516
223.0
27.35
19.25
3,121
991.2
396.8
790.9
28.88
417.3
40.80
52.61
Sworn Police Officers & Civilian Employees
81.2
54.6
28640.5
2366.5
556.5
13626.2
15719.3
4486.2
76.6
38214.03
2542.4
1685.4
23539.4
16984.2
12804.7
105.7
Arrests for Sale
Cocaine
Marijuana
Synthetic
Others
Arrests for Possession
Cocaine
Marijuana
Synthetic
Others
Reported Crimes
Measures of addiction
Alcohol
Methamphetamine
Amphetamine
Cocaine
Marijuana
Heroin
Over the Counter
29
TABLE 2 B – ARRESTS FOR SALE AND POSSESSION
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Marijuana
Synthetic
Cocaine
3.34%
0.65%
0.34%
-5.56%
-0.34%
5.32%
3.97%
0.98%
2.52%
-4.45%
-9.00%
1.15%
-3.60%
16.14%
5.26%
12.87%
20.60%
15.67%
3.62%
12.41%
4.99%
-12.34%
-7.85%
-10.57%
20.46%
3.17%
3.61%
-8.91%
-1.67%
-5.34%
4.42%
3.52%
6.22%
9.49%
6.69%
-11.64%
-19.24%
-12.85%
-10.48%
Note: This table shows the percentage change of the number of arrests from t-1. Year 1998 is computed using
1997, omitted from the table
30
TABLE 2C – METH-RELATED HOSPITALIZATION, BY STATE
HAWAII
IOWA
MONTANA
SOUTHDAKOTA
CALIFORNIA
WYOMING
IDAHO
UTAH
NEBRASKA
NEVADA
KANSAS
WASHINGTON
OREGON
MINNESOTA
COLORADO
OKLAHOMA
MISSOURI
NORTHDAKOTA
ARKANSAS
ARIZONA
INDIANA
KENTUCKY
ALABAMA
NEWMEXICO
ALASKA
MISSISSIPPI
GEORGIA
LOUISIANA
ILLINOIS
VIRGINIA
SOUTHCAROLINA
VERMONT
WESTVIRGINIA
TEXAS
MICHIGAN
MAINE
WISCONSIN
FLORIDA
OHIO
NEWHAMPSHIRE
PENNSYLVANIA
DELAWARE
NEWYORK
NORTHCAROLINA
MASSACHUSETTS
MARYLAND
NEWJERSEY
CONNECTICUT
RHODEISLAND
TENNESSEE
318.88
316.69
269.85
237.80
199.62
198.23
191.34
179.38
162.60
161.62
150.65
150.08
142.36
133.03
130.37
122.68
120.44
90.12
57.63
56.31
43.44
36.93
32.99
31.66
31.06
26.95
24.80
21.14
17.95
15.62
12.49
12.47
12.46
11.54
8.77
8.48
7.91
7.64
7.10
6.89
6.16
5.96
5.64
5.56
3.71
3.49
3.29
2.25
2.09
1.24
Note: Number of public hospitalization due to meth, normalized per 100,000 inhabitants.
31
TABLE 3 – SOCIO ECONOMIC CONTROLS DESCRIPTIVE STATISTICS
% White not Hispanics
% White Hispanics
% Black Hispanics
% Black not Hispanics
% Asian not Hispanics
% Asian Hispanics
% Indian Hispanics
% Indian not Hispanics
% Unemployment
Income per capita
People below the poverty line
Number of Banks and Saving Institutions
Percentage of poverty
Number of Social Security Recipients
Density
Number of Pawnshops
(1)
Mean
(2)
Standard Deviation
0.818
0.0431
0.00195
0.106
0.0104
0.000425
0.00150
0.0185
5.158
25,341
13,919
41.39
0.141
19,074
350.8
6.339
0.168
0.0688
0.00477
0.153
0.0283
0.00146
0.00269
0.0601
1.968
6,458
40,443
30.45
0.137
42,203
2,436
7.462
32
TABLE 4 – DIFFERENCE IN DIFFERENCES
Panel A - Baseline
Treated*Post
(1)
Larceny
(2)
Burglary
(3)
M/V Theft
(4)
Arson
(5)
Murder
(6)
Assault
(7)
Rape
(8)
Robbery
-0.0483
(0.0529)
-0.0801
(0.0530)
0.0153
(0.0480)
0.109
(0.119)
-0.121***
(0.0311)
-0.0111
(0.0510)
-0.0490
(0.0617)
-0.179***
(0.0384)
Panel B – Baseline + Sensitive Controls (Law Enforcement & Hospital Admissions for Other Addictions)
Treated*Post
-0.0162
(0.0480)
-0.0684*
(0.0356)
-0.0179
(0.0384)
0.0722
(0.111)
-0.123***
(0.0444)
-0.00729
(0.0435)
-0.0604
(0.0619)
-0.149***
(0.0249)
Panel C – Baseline + Sensitive Controls+ State Linear Trends
Treated*Post
-0.133***
(0.0392)
-0.144***
(0.0527)
-0.0685
(0.0449)
-0.105
(0.0886)
-0.0591
(0.0458)
-0.0969*
(0.0508)
-0.00369
(0.0568)
-0.143***
(0.0501)
YEAR FE
COUNTY FE
Controls
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the state/year level. I use the xtivreg2 command in Stata. Each column represents a different
regression, one for each type of crime. All the specifications in each panel include year FE, county FE and all county-varying observables. Panel A is the baseline diff-in-diff
specification. In panel B I add controls that might be potential outcomes and, hence, confounding factors in the analysis. In Panel C I add State Linear Trends to Baseline and
Sensitive Controls.
33
TABLE 5 - PRE TRENDS
Lead7*Treated
Lead6*Treated
Lead5*Treated
Lead4*Treated
Lead3*Treated
Lead2*Treated
Lead1*Treated
Treated*Post
(1)
Larceny
(2)
Burglary
(3)
Robbery
(4)
Murder
-0.0167
(0.0269)
0.0396
(0.0424)
0.0889
(0.0567)
0.0618
(0.0652)
0.0695
(0.0657)
0.115
(0.0731)
0.114
(0.0788)
-0.0165
(0.0295)
0.0455
(0.0410)
0.0670
(0.0546)
0.0292
(0.0822)
0.0492
(0.0889)
0.0922
(0.0886)
0.0857
(0.106)
-0.162**
(0.0638)
-0.0762
(0.0845)
-0.0783
(0.0585)
-0.0786
(0.0627)
-0.0802
(0.0587)
-0.0520
(0.0617)
-0.152
(0.0987)
-0.120
(0.0876)
-0.0903
(0.0722)
-0.0347
(0.115)
-0.131
(0.0948)
-0.211***
(0.0561)
-0.0480
(0.105)
-0.157
(0.102)
0.0145
(0.0926)
-0.0333
(0.104)
-0.265***
(0.0834)
-0.223**
(0.108)
Observations
12,715
12,715
12,715
12,715
Number of fips
1,393
1,393
1,393
1,393
YEAR FE
YES
YES
YES
YES
COUNTY FE
YES
YES
YES
YES
Controls
ALL
ALL
ALL
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the state/year level. This table shows
the analysis of the pre-trends only for the crimes affected by the change in the laws. I omit the interaction between the
dummy for the year 1997 and the treated dummy.
TABLE 6 – ANALYSIS AROUND THE LAWS’ THRESHOLD (2003 - 2006)
Treated*Post
(1)
Larceny
(2)
Burglary
-0.108***
(0.0319)
-0.132***
(0.0425)
(3)
M/V
Theft
-0.0657*
(0.0364)
(4)
Arson
(5)
Murder
(6)
Robbery
(7)
Assault
(8)
Rape
0.0203
(0.0913)
-0.130***
(0.0472)
-0.168***
(0.0525)
-0.0889**
(0.0359)
-0.0356
(0.0633)
Observations
5,398
5,398
5,398
5,398
5,398
5,398
5,398
Number of fips
1,384
1,384
1,384
1,384
1,384
1,384
1,384
YEAR FE
YES
YES
YES
YES
YES
YES
YES
COUNTY FE
YES
YES
YES
YES
YES
YES
YES
Controls
ALL
ALL
ALL
ALL
ALL
ALL
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the state/year level.
5,398
1,384
YES
YES
ALL
34
TABLE 7 – DRUGS ARRESTS
(1)
Cocaine
Treated*Post
0.0164
(0.0626)
(2)
(3)
Sales
Marijuan Synthetic
a
-0.00251
-0.166*
(0.0132) (0.0971)
(4)
(5)
Others
Cocaine
0.0365
(0.146)
-0.0193
(0.0725)
(6)
(7)
Possession
Marijuana Synthetic
0.0221
(0.0532)
(8)
Others
-0.347***
(0.105)
-0.00898
(0.0968)
Observations
12,715
12,715
12,715
12,715
12,715
12,715
12,715
Number of fips
1,393
1,393
1,393
1,393
1,393
1,393
1,393
YEAR FE
YES
YES
YES
YES
YES
YES
YES
COUNTY FE
YES
YES
YES
YES
YES
YES
YES
Controls
ALL
ALL
ALL
ALL
ALL
ALL
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the state/year level.
12,715
1,393
YES
YES
ALL
TABLE 8 - FIRST STAGE
(1)
Crystal Meth Labs
Treated*Post
-0.435***
(0.0519)
(Kleibergen-Paap Wald F statistic)
67.26
Observations
3,798
R-squared
0.259
YEAR FE
YES
STATE FE
YES
Controls
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the county level.
TABLE 9A– OLS/IV PROPERTY CRIMES
(1)
(2)
Larceny
OLS
IV
Meth Labs
0.0555**
(0.0152)
*
0.203**
(0.0693)
*
(3)
(4)
Burglary
OLS
IV
0.0447**
(0.0133)
*
0.291**
(0.0851)
*
(5)
(6)
M/V Theft
OLS
IV
0.0482**
(0.0167)
*
0.151
(0.0995)
(7)
(8)
Arson
OLS
IV
0.0317
(0.0223)
-0.142
(0.148)
Observations
3,798
3,798
3,798
3,798
3,798
3,798
3,798
YEAR FE
YES
YES
YES
YES
YES
YES
YES
STATE FE
YES
YES
YES
YES
YES
YES
YES
Controls
ALL
ALL
ALL
ALL
ALL
ALL
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the county level.
3,798
YES
YES
ALL
35
TABLE 9B – OLS/IV VIOLENT CRIMES
(1)
(2)
Robbery
OLS
IV
Meth Labs
0.0802***
(0.0245)
0.271*
(0.159)
(3)
(4)
Murder
OLS
IV
0.0474***
(0.0180)
(5)
(6)
(7)
(8)
Assault
OLS
IV
Rape
0.235
(0.165)
OLS
IV
0.0316
(0.0219)
-0.118
(0.139)
0.0481***
(0.0162)
0.0807
(0.0897)
Observations
3,798
3,798
3,798
3,798
3,798
3,798
3,798
3,798
YEAR FE
YES
YES
YES
YES
YES
YES
YES
YES
STATE FE
YES
YES
YES
YES
YES
YES
YES
YES
Controls
ALL
ALL
ALL
ALL
ALL
ALL
ALL
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the county level.
TABLE 10 – TRIPLE DIFFERENCE IN DIFFERENCES (DISTANCE FROM MEXICO)
Dist. Mexico*Post*Treated
(1)
Larceny
(2)
Burglary
(3)
Murder
(4)
Robbery
-0.08*
(0.04)
-0.07***
(0.02)
0.32
(0.23)
-0.19
(0.18)
Observations
12,715
12,715
12,715
12,715
Number of fips
1,393
1,393
1,393
1,393
YEAR FE
YES
YES
YES
YES
COUNTY FE
YES
YES
YES
YES
Controls
ALL
ALL
ALL
ALL
Note: *** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the State/Year level.
36
TABLE 11 - HATE CRIMES BY TYPE OF BIAS - UNITES STATES 1997-2010
Bias
Number of Episodes
Percentage
Anti-White
Anti-Black
Anti-American Indian
Anti-Asian
Anti-Multi-Racial
Anti-Jewish
Anti-Catholic
Anti-Protestant
Anti-Islamic
Anti-Other
Anti-Multi-Religious
Anti-Atheism/Agnosticism
Anti-Hispanic
Anti-Other Ethnicity
Anti-Male Homosexual
Anti-Female Homosexual
Anti-Homosexual (both)
Anti-Heterosexual
Anti-Bisexual
Anti-Physical Disability
Anti-Mental Disability
10,184
36,190
800
3,032
2,707
11,903
745
672
1,721
1,765
573
76
6,978
6,279
10,086
2,277
3,126
269
252
240
400
10.16
36.09
0.8
3.02
2.7
11.87
0.74
0.67
1.72
1.76
0.57
0.08
6.96
6.26
10.06
2.27
3.12
0.27
0.25
0.24
0.4
Number of Episodes
100,275
37
TABLE 12 – HATE CRIMES BY TYPE OF OFFENSE – UNITED STATES 1997 - 2010
UCR CODE
Type of Offense
Episodes
Percentage
09A
09B
100
11A
11B
11C
11D
120
13A
13B
13C
200
210
220
23A
23B
23C
23D
23E
23F
23G
23H
240
250
26A
26B
26C
26E
270
280
290
35A
35B
36A
36B
370
40A
520
Murder/non-negligent manslaughter
Negligent manslaughter
Kidnapping/abduction
Forcible rape
Forcible sodomy
Sex assault
Forcible fondling w/object
Robbery
Aggravated assault
Simple assault
Intimidation
Arson
Extort/blackmail
Burg/B &
Pocket-picking
Purse-snatching
Shoplifting
Theft
Theft from building
Theft from coin-op machine
Theft of motor vehicle
All other mv parts
Motor vehicle larceny
Counterfeit/forgery theft
FALSE pretenses/swindle
Credit card/ATM game
Impersonation fraud
Wire fraud
Embezzlement
Stolen property
Destruct/vandalism
Drug/Narc violations offenses
Drug equip
Incest
Statutory rape violations
Pornography/obscene material
Prostitution
Weapon law violations
122
1
45
82
29
9
53
1,661
10,599
18,776
30,630
573
20
1,678
9
12
192
245
7
288
92
1,125
176
85
110
44
25
5
23
43
33,109
229
27
6
4
13
10
118
0.12
0
0.04
0.08
0.03
0.01
0.05
1.66
10.57
18.72
30.55
0.57
0.02
1.67
0.01
0.01
0.19
0.24
0.01
0.29
0.09
1.12
0.18
0.08
0.11
0.04
0.02
0
0.02
0.04
33.02
0.23
0.03
0.01
0
0.01
0.01
0.12
100,275
100
Total
38
TABLE 13 – HATE CRIMES
Panel A - Baseline
Treated*Post
(1)
Total Hate
Crimes
(2)
Anti-White
(3)
Anti-Black
(4)
Anti-Jewish
(5)
Anti-Islamic
(6)
AntiHomosexual
(7)
AntiHeterosexual
(8)
Anti-Physical
Disability
0.0843
(0.188)
-0.0907
(0.135)
-0.00
(0.0403)
0.0336
(0.0218)
0.00865
(0.0113)
0.0443***
(0.0130)
0.00833
(0.00551)
-0.0158
(0.0176)
0.206***
(0.0755)
0.0120
(0.102)
-0.0446
(0.102)
0.0546
(0.0344)
0.0252*
(0.0148)
0.0582***
(0.0216)
0.0188***
(0.00509)
-0.0135
(0.0196)
Panel B – Baseline + State Trends
Treated*Post
Panel C – Baseline + State Trends Log Linear Specification
Treated*Post
0.0415
(0.0419)
-0.0182
(0.0271)
-0.00593
(0.0342)
0.0212
(0.0185)
0.00880
(0.00593)
0.0271***
(0.00742)
0.00819***
(0.00129)
-0.00182
(0.00775)
YEAR FE
COUNTY FE
Controls
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
YES
YES
ALL
*** p<0.01, ** p<0.05, * p<0.1. Standard errors are double clustered at the State/year level.
39
Figures
40
41
42
43
44