SAFE SEX AND CRIME

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates
SAFE SEX, SAFE COMMUNITIES:
ANALYZING THE LINK BETWEEN
CONTRACEPTIVE USAGE AND
CRIME RATES
Nicholas J. Hill, Jackson State University
Mxolisi Siwatu, Texas Southern University
Maury Granger, Jackson State University
ABSTRACT
This research investigates the link between contraceptive use in the
1970s and the decline of crime rates observed during the 1990s. A theoretical
framework is adopted in which individual contraceptive use reflects a decision
to delay parenthood until a family network can be established to increases the
costs associated with criminal behavior. In this context, contraceptive use affects
the crime rate by reducing the number of individuals with low opportunity costs
and high potential for engaging in criminal activity. The theoretical predictors
of the model are tested with data on contraceptive use from the Centers for
Disease Control Family Growth Survey, using the crime rate in the 1990s and
contraceptive use in the 1970s. Empirical evidence shows that the use of
contraceptive methods does have statistically significant effects on the crime
rates. This extends the existing literature on crime and abortion by considering
the effects of a wide array of reproductive choice technologies on the crime rate.
JEL Classifications: J12, J13, K42
INTRODUCTION
The role that abortion plays in criminal activity has been debated.
Donohue and Levitt (2001) linked the decrease in crime rates observed in the
1990’s to the number of abortions after Roe v. Wade legalized active abortion in
1973. The validity of the link between abortion and crime rates has been
challenged, most notably by Joyce 2001, 2004a, 2004b; Foote and Goetz 2005;
Cook and Laub 2003; Miron and Dills 2006. They argue that the findings by
Donohue and Levitt (2001) were not compelling once the key statistical
measurement errors were corrected. However, Donohue and Levitt (2005)
addressed their concerns and still find evidence of a significant link between
abortion rates in the 1970 and crime rates in the 1990s. .
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Southwestern Economic Review
If there is a significant link between abortion and crime rates, then there
may be also a link between contraceptive usage and the crime rates. This article
investigates the link between crime and contraceptive use. Just as abortion
reduce the number of unwanted children who, according to Donohue and Levitt,
were more likely to engage in crime as teens and young adults, contraceptive use
would generate the same effect through the reduction in the number of unwanted
children. The cumulative effect of social/economic, cultural, and socio
psychological factors heavily influences the likelihood of a child becoming a
juvenile delinquent (Shulman, 1949). In general, children are more likely to
become juvenile delinquents and participate in criminal activities when they are
born into single-parent or other non-intact families. (Painter and Levine, 2000).
This article investigates whether contraceptive use lowers the future
crime rate by reducing the size of the birth cohort most likely to enter a future
criminal cohort. Donohue and Levitt (2001) report that the drop in the U.S crime
rate in the early 1990s can be explained by the increase in abortion rates in the
1970s shortly after the Supreme Court ruling in Roe v. Wade. This research
extends the work of Donohue and Levitt (2001) by considering the full menu of
reproductive technology choices beyond abortion, and their effect on the crime
rate in the early 1990s.
UNDERSTANDING CRIME, UNWANTED CHILDREN AND
CONTRACEPTION USAGE
The economic modeling of crime begins with the works of Becker
(1968), Stigler (1970), and Ehrlich (1973). It was first suggested by Becker
(1968) that improvements in legitimate labor market opportunities or a strong
economy makes crime less attractive. However, Freeman (1995), Machin and
Meghir (2000), Gould, Weinberg and Mustard (1997), Donohue and Levitt
(2001) and Raphael and Winter-Ebmer (2001) suggest that there is a statistically
significant but weak relationship between unemployment rates and property
crime. This may be due to the phenomenon of hysteresis in criminal behavior, as
first suggested by Mocan et. al., (2005). In this seminal work, they explain that
criminal participation may be express in a dynamic model where individuals
utilize both legal and criminal human capital to increase potential earnings. This
suggests that higher criminal participation in not necessarily linked to higher
unemployment.
Becker (1968) attempts to find the optimal level of public and private
policies to decrease the crime rate. Becker (1968) looks at the social value of a
crime, but Stigler (1970) investigates various ways to decrease or limit the
supply of criminals. Additionally, he examines the best policies to deter
criminals from committing various offenses. Stigler analyzes factors that affect
the supply of criminals. He conjectures that the laws designed to prevent crimes
are highly influenced by public policy and that public opinion is not adequate
for the prevention of crime.
In addition to establishing the economic rationale for the crime rate, it
is necessary to understand what costs will deter people from committing crimes.
Ehrlich (1973) tries to explain the crime level by measuring the value of the time
allocated between illegitimate activities versus legitimate activities. Within this
90
Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates
analysis, the focus is directed on the environmental factors contributing to
criminal activities. He advocates more spending on law enforcement where
expenditures depend on the effectiveness of the expected punishment the cost of
deterring crime compared with
alternative methods of combating crime. And investigates how increasing the
cost certain factors such as law enforcement can deter an individual from
participation in illegal activity.
The environment and social interactions are also factors contributing to
crime. Glaeser, Sacerdote, and Scheinkman (1996) argue that there are strong
peer-effects that contribute to an individual being a participant or a catalyst in
criminal activity.
Some of the deterrents that influence negative social
interaction according to this study are: strong parents, formal schooling, and
information that counters peer influences. This give us the opportunity for our
research to adopt a theoretical framework in which family planning decisions
condition the number of unwanted children who may later join a criminal
cohort, as an approach to exploring the possible causal nexus between crime rate
and contraceptive use.
While the previous literature indicates the role that public policy and
environmental factors has in criminal participation, the central aim of this
research is to investigate the possible causal link between the reductions in the
crime rate observed during the 1990s and the wide array of reproductive choices
used during the 1970s. While there are many arguments why crime rate fell
during the 1990s, Donohue and Levitt’s (2001) findings have attracted attention
to the role of reproductive choice technologies and their effects on future cohorts
of criminals. In addition, reproductive choice technologies permit individuals
some control over the timing of conception and provide an opportunity to bear
children under more favorable conditions.
Donohue and Levitt (2001) attribute the decrease in crime due to the
decrease of unwanted children from the legalization of abortion. Unwanted
children are likely to receive smaller human capital investments by their parents
and are more likely to get into trouble when they get older (Lott and Whitley,
2007; Bouza, 1990 and Morgentaler, 1998). These finding are supported by the
works of Hay and Evens (2006) who find that children of an unwanted or
unplanned pregnancy had significantly higher delinquency rates when they were
ages 11 to 17. They find the correlation between unwanted pregnancy and
general delinquency, status offenses, drug offenses, and serious offenses (Hay
and Evens, 2006). An opportunity to reduce the number of unwanted pregnancy
through contraception could lead to lower delinquency rates especially if women
younger women use contraception. Any study that links increase in abortion in
the 1970s with decrease in crime in 1990s must come to grips with the racial
gaps in both abortion and crime. (Trent et. al, 1991 and Jones et.al., 2002) It is
well known that young black men are over represented in US prisons. (Bureau
of Justice Statistics).
We also know that black women are more likely to
terminate pregnancies versus white women. (Jones et al. 2002). This would lead
to a decrease in crime rate observed.
THEORY AND METHODOLOGY
91

Southwestern Economic Review

The economic theory of choice can be extended to making the decision
to use contraceptives during sexual activity (Donohue and Levitt, 1999: Akerlof
et. al., 1996). Assume that contraceptive cost associated with sexual activity is
classified as either low or high. Low cost activity carries the highest probability
or risk of unintended pregnancy because no birth control is used. Call this the
high risk strategy (H). Conversely, high cost activities carry with it the lowest
probability of unintended pregnancy and has a positive birth control cost. Call
this the low risk strategy (L). Choosing the low risk strategy has a cost of “C”,
due to lost purchasing power by expenditures on contraception. The probability
of conception (CN) is equal to PH (the probability associated with using high
risk strategy) and PL (the probability associated
with low risk strategy). Let the probability of CN given H and CN
given L respectively be: ) | ( H CN P PH
 and ) | ( L CN P PL
 and
) | ( H CN P > ) | ( L CN P .
Let the utility of a newborn child be “B”, if the net utility of B is
positive (B>0) then an individual’s utility increases with a newborn child. B<0
corresponds to a case where an individual is better off without the baby at that
given time. A child born to parents when B < 0 is deemed unwanted. This child
that satisfies (B<0) is unlikely to be the beneficiary of parental human capital
investments that increases the opportunity cost of participating in crime.
The expected utility from engaging in sexual activity can be modeled as
follow:

C BP C BL CN P EU L L
    ) | (
(1)

BP BH CN P EU H H
  ) | (
(2)

The expected utility specified in (1) and (2) are based on the risk level taken at
the time of the sexual act. It is assumed that the utility of sexual activity is the
same regardless of the associated risk; therefore our interest is in what
determines the equilibrium sexual activity strategy on unwanted newborns.
Setting equation (1) and (2) equal and solving for B yields the
equilibrium condition:

) ( H L
L H P P
C B C BP BP     
(3)
It is plausible that the low risk strategy is adopted if and only if (Donohue and
Levitt, 1999):
) ( H L
P P
C B  
(4)

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

Since PH>PL, then PL-PH < 0, and the low risk strategy may be exercised if B<0.
This establishes that for all individuals engaging in sexual activity for which
children are unwanted, the low-risk strategy is the equilibrium choice. In other
words, an individual will prefer to use contraception when a baby is not wanted
As such, in a population where unwanted newborns are more likely to engage in
criminal activity, there will be a causal and inverse relationship between
contraceptive use today and crime rates tomorrow. Some women adopt a high
risk strategy, even though B < 0. In this case, the pregnancy may be unwanted
and they may opt to have an abortion.1
Why might “unwanted” children select to engage in criminal activity
later on in life? Prior research supports the theory that unwanted children are
more likely to engage in criminal behavior due to that lack of human capital
investment. (Lott and Whitley, 2007; Bouza, 1990 and Morgentaler, 1998)
Moreover, if the choice of activities is governed by self-selection processes
conditioned on differential costs and productivity (Roy, 1951), then relative to
wanted children, unwanted children may have lower costs associated with
engaging in criminal activity. For example, the

absence of a nuclear and extended family networks at the time of birth could
result in low stocks of human and social capital. Human and social capital are
important for success later in life. Therefore, unwanted children may face
unfavorable schooling/labor market opportunities that reduce the amount of
earnings available for them in the legitimate labor market. To illustrate this
point, a selection model can be used to explain the rational of why an individual
will choose to participate in crime due to the earning potential.
A Roy-type selection model (Roy, 1951) is well-suited when
considering how unwanted children decide to engage into criminal activity.
Selection model allows us to determine the probability of participating in either
a legal or an illegal occupation. Let the log of earnings from legal and illegal
activities respectively be indexed by 0 and 1. The log earnings from each
profession are denoted by the following:

0 0 0
  w & 1 1 1
  w
(5)

In equation (5) 0
 and 1
 are interpreted as one’s mean earnings in a
particular activity with ) ,0( ~ 2
0 0
  N and ) ,0( ~ 2
1 1
  N . 0
 and 1
 are
the mean value of an individual’s skills in a particular profession.2 There is an
associated cost (C) with the choice of each activity that includes the related cost
of time. The choice to participate in illegal activity depends on the sign of the
index function below:

) ( ) ( )) /( ln( 0 1 0 1 0 1
            C w w I
(6)

Southwestern Economic Review

Borjas (1987) accounts for time by the term
0
wC . The variable is a
constant; which establishes that, C is directly proportional to 0
w .
Assume that a person knows their own C, 0
, and 1
 and their own 0

and 1
. The researcher can only observe an individual’s choice to participate in
legal or illegal activity. Assume further that the cost of a particular choice is
highly sensitive to the nuclear and/or extended family network that is present,
this network serves to condition the behavior by creating a system of rewards
and punishments, and provides a source of social capital that expand the set of
legitimate opportunities for a child. The use of contraception allows one to time
pregnancy affording an opportunity to put into place the nuclear and/or extended
family networks that make possible for the future success of a child.
Established nuclear and/or extended family networks can also increase
the costs associated with illegal activities by imposing upon a child higher moral
costs. Similar to the costs associated with the “shaming” to members of one’s
family network. These costs can be viewed as a “stigma-cost”. This stigma-cost
associated with a family network is increased when resources, such as maturity,
time, income, etc., are available. The use of contraception gives a women
control over the timing of her pregnancy. This control over timing can
potentially allow a woman an opportunity to give birth where a more supportive
family network is present. Arguably, this stigma cost would impact crime
through increasing the opportunity cost.
If we assume that stigma-cost are higher when a family network is
available, then a child whose birth was planned would have a higher stigma-cost
associated with participating in criminal activity. Conversely, a child born at an
inopportune time may not have the necessary family network in place, thereby
creating a lower stigma-cost associated with participating in criminal activity.
Building on equation (6), the correlation between illegal and legal
earning can be defined as follows:

1 0
01

  ,where 01
 is the cov ( 0
, 1
)
(7)

According to Bojars (1987) implementing this model requires knowledge about
, however we do not need to know 0
 and 1
. It is plausible that an
individual will participate in criminal activity when the index function, I > 0 or:

0 ) ( ) ( 0 1 0 1
         
(8)

To clarify, a person will engage in criminal activity if the earning is greater than
the earning they would receive from legitimate employment and the cost
associated with participating in this illegal activity. Also, in the second set of
parentheses, the difference between the values of the skills to participate in legal
Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

and illegal activity has to be positive in order for this condition to be true.
Meaning, the skills used for each activity are assumed to be equal.
If follows from equation (8) that the probability of choosing an
individual at random who will participate in illegal activities instead of legal
activities can be derived. Let 0 1
     , then the probability of a child
choosing to participate in criminal activity can be stated as follows:

 ) ( Pr 1 0
       P  

 
    
 

  

 ) ( Pr 1 0
(9)
 


 

    


   ) ( 1 1 0  z 1 where z =  


 

  


   ) ( 1 0 .

In equation (9)  is equal to the standard normal distribution (Borjas,
1987). It is shown that as z increases, the probability of participating in criminal
activity decreases. If we analyze z, the greater the cost of , the greater z
becomes. It is maintained in this research that the stigma-cost associated with
criminal behavior is greater when a family network is present. This suggests that
the probability of an individual participating in a criminal activity would be
lower when pregnancies are timed through the use of birth control. Conversely,
an untimely pregnancy attributed to high risk sexual behavior increases the
probability of a child involvement in criminal activity.

This approach sets forth a theoretical framework which explains why
children born in a particular cohort are more likely to participate in crime.
Furthermore, this affords an opportunity to incorporate contraceptive use as a
variable that empowers individuals to better influence the environment (i.e.
family network) around which children are reared. The theory of optimal
contraceptive use and selection into criminal activity suggests that empirically,
the crime rate will be a function of contraceptive usage sufficiently lagged to
account for the timing of the entry of a birth cohort into the criminal cohort.
To formalize this idea, a utilization rate is constructed from the sample
to understand the total amount of contraceptives used in a state. Because the use
of contraceptives does not immediately affect the crime rate, utilization rate is
gathered from a period sufficiently lagged from the crime rate. In this research, a
15 – 17 year difference is selected between the use of contraceptives and the
crime rate. This 15 – 17 year difference is supported by Hay and Evens (2006)
and Donohue and Levitt (2001). For clarification, if a person uses birth control
in 1973, its effect on crime will not be felt until 17 years later in 1990. Juvenile
delinquency can occur earlier than age 17 however; this age group represents the
high crime participation group. A specification of the process generating crime
is as follows:

Southwestern Economic Review

) , ,2(
) ( ) 17 ( 1
n i for
X ive Contracept Crime st t s st i t s st
 
     
    
(10)

where “s” indexes states in the region, and “t” reflects time. s
 and t
 are
used to represent state and time fixed effects. The crime variable will analyze
crime rates for not only the states but also arrest for individuals under 25 and
individuals that are 15 – 17 years old. The contraceptive variable is the
utilization rate calculated from a sample from the National Family Growth
Survey. The utilization rate is calculated as follows:

population female of number Total
ive contracept g u population female of number Total Rate n Utilizatio sin 
(11)

This utilization rate shows what proportion of the female population reported
using contraception, during the selected period. The variable denoted as “X”, is
a vector that includes the incarceration rate, police per capita, the unemployment
rate, per capita income, the poverty rate, presence of concealed handgun laws,
and per capital beer consumption. These variables are the same ones supported
by prior research (Donohue and Levitt 2001).3 This resulting model is adjusted
for population differences.

Data
Data on individual contraceptive usage are from the National Survey of
Family Growth (NSFG).4 The NSFG, sponsored by the Center for Disease
Control (CDC), which provides an opportunity to verify if variation in the use of
contraceptives technologies by individuals in the 1970s across states, had an
effect on crime rates in the 1990s. However, the data only covers states until
1976 after which regional data is only accessible. This resulted in a panel of 204
observations covering the 50 states and the District of Columbia. The state level
control variable will be lagged to ensure the effects of the selected variables on
criminal activities are observable. From the NSFG, the following contraceptive
methods were reported: 1.) birth control pill, 2.) douche, 3.) foam, 4.) jelly
cream suppository, 5.) IUD coil loop, 6.) condom rubber, 7.) diaphragm, 8.)
rhythm calendar, 9.) rhythm temperature, 10.) abstinence, 11.) withdrawal coitus
interruptus, 12.) sterilization wife, 13.) no method, 14.) sterilization husband and
15.) other method. For the purpose of this research, each contraceptive
technology will not be examined individually. All technology is grouped
regardless of which method was used. Even though specified, combination of
technologies will not be examined in this study, only single used technologies.5
Second, crime statistics are tabled from the FBI Uniform Crime Report.
This report has data on various violent and property crimes from 1960 to 2004
for every state in the US. Since this study is looking at the crime rate after the
1990s, the only years that will be examined will be the 1990 to 1993. Third,
information on the police and incarceration rate is collected from the
Correctional Population in the United States published by the Bureau of Justice
Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates
Statistics (BJS). Lastly, population characteristics such as the poverty rate,
unemployment rate, and the per capita state personal income will be gathered
from the Census Bureau United States Statistical Abstract.6
RESULTS
Table 1 reports the mean and standard deviation of the relevant
variables for the entire sample of 50 states and Washington, DC. The following
variables were constructed: the number of violent crimes per 100,000
(VIOLENT), the number of property crimes per 100,000 (PROPERTY), number
of violent arrests per population for individuals under the age of 25 (VIOLENT
under 25), number of property crime arrests per population for individuals under
the age of 25 (PROPERTY under 25), number of violent arrests per population
for individuals 17 years of age (VIOLENT arrest 17), number of property crime
arrests per population for individuals 17 years of age (PROPERTY arrest 17)
percentage of unemployed per population (UNEMPLOY), percentage of people
below the poverty line per population (POVERTY), a binary variable indicating
whether or not the state has the presence of a concealed gun law (GUNLAW),
the per capita income (INCOME), the amount of beer consumption per
population (BEER), the number of police per capita (POLICE), the number of
people incarcerated per capita (INCARCERATE) and the number of females
using any specified form of contraceptive technology per female population
(CONTRACEPTIVE).
Table 1 reveals that approximately 80% of the sample female
population during the periods of 1973-1976 in a state used some form of
contraception. This sample also reveals that the average income in the United
States is around 22,000 for the period of 1990 -1993 and the percentage of
unemployment in the United States is 6%. The variable of interest for this study,
examines the utilization rate of contraception by the population. Utilization can
also be specified by each individual contraceptive technology.
Table 2 reports on the mean and standard deviation of the various
contraceptive selected for the sample of females in the population that using
each of the major contraception methods including, PILL, FOAM CONDOMS,
etc.
Table 2 reveals that the majority female population preferred the pill
and foam as the contraception of choice as opposed to other technologies.
According to this sample, approximately 32 % of the female population used
both Pill and Foam as a contraceptive choice. Furthermore, the least preferred
method of contraceptive practice over the period was rhythm temperature and
abstinence, with the maximum utilization rate by a state at 7 percent and 15
percent of the population.
Table 1
Sample Mean, Standard Deviations, Minimums, and Maximums
Variable
Mean
Std. Dev.
Min
Max
UNEMPLOY
0.0622
0.01527
0.02142
0.113
97

Southwestern Economic Review

POVERTY 13.8363 4.16704 6 26.4
GUNLAW 0.34804 0.47752 0 1
BEER 23.6578 3.80833 12.7 40.2
INCARCERATE 266.74 166.332 62 1287
VIOLENT 596.552 412.098 65.5608 2921.8
PROPERTY 4648.19 1166.84 2324.18 8839.28
POLICE 277.957 93.5396 178.942 907.947
INCOME 22058.7 3514.34 15438.3 33165.7
CONTRACEPTION 0.79242 0.10382 0.28239 1
VIOLENT under 25 6369.71 10729.9 77 63016
PROPERTY under 25 22763.9 27904.7 685 168882
VIOLENT arrest 17 628.926 1045.38 9 5862
PROPERTY arrest 17 2396.3 2875.16 75 16741
VIOLENT arrest 16 512.874 923.005 4 5245
PROPERTY arrest 16 2477.37 3077.99 91 18170
VIOLENT arrest 15 354.4 673.625 0 3997
PROPERTY arrest 15 2207.23 2816.86 24 17305

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

Variable Mean Std. Dev. Min Max
PILL 0.3211919 0.0997025 0 0.5962672
FOAM 0.3211919 0.0997025 0 0.5962672
DIAPHRAM 0.0068822 0.0151805 0 0.1725997
JELLY 0.0069722 0.0201816 0 0.1524305
D and J 0.0301333 0.0729917 0 0.758118
DOUCHE 0.0061021 0.0098909 0 0.0821751
IUD 0.0954336 0.0844195 0 0.5396144
F_STERILIZATION 0.0836949 0.0633907 0 0.3649648
M_STERILIZATION 0.0936815 0.0793295 0 0.4436441
CONDOMS 0.0606053 0.0746707 0 0.7200888
RHYTHM_TEMP 0.0025843 0.00813 0 0.0782185
RHYTHM_CAL 0.0244761 0.0354112 0 0.2551622
WITHDRAWL 0.0160093 0.0348805 0 0.3438732
ABSTINENCE 0.0052525 0.0158019 0 0.1541676
ALL_OTHER 0.0066102 0.0316395 0 0.382412
N 204——–
Table 2
Contraceptive Methods’ Means, Standard Deviation,
Minimum, and Maximum

The following model estimates the Fixed Effect parameter for the
following model7:

t s x IVE CONTRACEPT B E INCARCERAT B POLICE B BEER B
INCOME B GUNLAW B POVERTY B UNEMPLOY B B CRIME
    
     
) ( 8 ) ln( 7 ) ln( 6 ) ( 5
) ln( 4 3 ) ( 2 ) ( 1 0 ) ln(
(12)

Table 3 reports the estimates for the empirical specification for violent and
property crimes. To fully understand the effect contraceptive use may have on
criminal activity, this research investigates violent and property crime rates,
violent and property arrest for individuals under the age of 25 and violent and
property crime arrest for individuals 17 years of age. Each column in Table 3
represents different areas of criminal participation. The first column expresses
the impact on violent crime rates. From the variables estimated UNEMPLOY,
INCOME and CONTRACEPTIVE are statistically significant. Specifically
looking at the variable of interest, CONTRACEPTIVE, has a coefficient (
.1744) which suggest an inverse relationship to violent crime. Column 2
estimates the relationship to violent arrest for individuals under the age of 25. It
is argued that contraceptive use only influences juvenile behavior or criminal
participation below the age of 25. In this model the variables GUNLAW,

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100

INCOME and CONTRACEPTIVE are statistically significant. Furthermore, the
coefficient for the variable CONTRACETIVE, (-.3305), is negative

and has a value with a larger magnitude of influence than the estimate for states
violent crime rates. Lastly, the effect of contraception on violent crime for a
specific group is estimated. Column 3 estimates the impact on violent arrest for
individuals 17 years or age. The variables INCOME, BEER, and
CONTRACEPTIVE are statistically significant. The main variable of interest,
CONTRACEPTIVE, has a coefficient of (-.4684) that has a larger magnitude
value than the previous coefficients estimates in Column 1 and 2. Although
columns 2 and 3 have less statistical significance than the first column, it does
suggest that contraception uses has impacted violent crime rates and increases in
magnitude when examining only 17 years of age criminal activity.

                                                                                    Table 3 
                         Fixed Effects Models for Violent and Property Crimes using Equation (10)

Specification Fixed Effects Model

Fixed Effects Model

Violent Violent Violent

Property Property Property
Crime under 25 arrest 17

Crime under 25 arrest 17

UNEMPLOY 1.540571b 0.7381 3.0861
-0.104 0.03368 -0.74838

(.688) (1.75) (2.54)

(.568) (1.34) (1.55)
POVERTY -0.00163 0.0013 0.0036
-0.00464 -0.00862 -0.00131

(.004) (.010) (.014)

(.003) (.008) (.008)
GUNLAW -0.655 0.4268b 0.23881
-0.01107 0.07456 0.0389

(.730) (.193) (.279)

(.060) (.148) (.170)

INCOME 1.7768c 2.4739a 2.8378b
-0.6247b -0.55924 0.35518

(.310) (.847) (1.22)

(.256) (.651) (.748)
BEER
-0.00243 -0.01539 -0.0349c

0.00754 0.0052 -0.01194

(.005) (.014) (.021)

(.005) (.011) (.012)
POLICE
-0.0668 -0.1776 -0.1145

0.06087 -0.02348 0.02102
(t-1)

(.085) (.217) (.314)

(.071) (.167) (.192)
INCARCERATE 0.0002 -0.02932 -0.03425

0.00322 0.01564 0.01418
(t-1)

(.010) (.026) (.037)

(.008) (.020) (.022)

CONTRACEPTIVE -0.174430b -0.33057c -0.4684c

0.11742b -0.23046c -0.3232b
(t-17)

(.068) (.176) (.255)

(.056) (.135) (.155)
CONSTANT -15.96435a -30.06a -32.486a

9.959a 1.2649 0.51396

(3.06) (8.38) (12.1)

(2.53) (6.43) (7.40)

204 204 204

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

101

R_squared 0.1261 0.028 0.181

0.038 0.02 0.045

aSignificant at the .01 level, b Significant at the .05 level, c Significant at the .10 level,
Standard errors in parentheses, N = Number of observation
Using the same logic as applied for violent criminal activity above,
Property crime is estimated for total, under 25 and individuals 17 years of age.
These estimates are represented in column 4, 5, and 6, respectively. In Column
4, the estimate for property crime show INCOME and CONTRACEPTIVE
being statistically significant. The coefficient for CONTRACEPTIVE also
suggests an inverse relationship with property crime (-.1174). When estimating
property crime arrest for individuals under the age of 25, CONTRACEPTIVE is
significant at the 10% level with a coefficient of (-.2304). Once again, the value
of the coefficient becomes larger in magnitude than the previous estimate for all
property crime. When estimating the relationship to Property crime arrest for
individuals 17 years of age, CONTRACEPTIVE is statistically significant at the
10% level. It is also shown that that the value of this coefficient (-.3232) is
larger in magnitude than all previous estimate for property crime. This further
suggests and supports the argument that contraception use has some impact on
criminal participation.

Table 4
Fixed Effects Models for Violent and Property Crimes using
Equation (10) with t-16 and t-15 (Coefficients for Contraception only)
T-16

Contraceptive S.E.
Violent Crime -0.1532253c (0.085079)
Violent crime under 25 -0.3331908b (0.166469)
Violent arrest 16 -0.7031635a (0.247162)

Property crime -0.0844337 (0.053502)
Property crime under 25 -0.2129082c (0.117154)
Property arrest 16 -0.1694893 (0.144273)

T-15
Violent Crime -0.0156282 (0.08856)
Violent crime under 25 -0.0969999 (0.140845)
Violent arrest 15 -0.4860699c (0.248004)
Property crime -0.0819031c (0.047814)
Property crime under 25 -0.2454359a (0.085936)
Property arrest 15 -0.1195171
(0.117033)

aSignificant at the .01 level, b Significant at the .05 level, c Significant at the .10
Southwestern Economic Review
level Standard errors in parentheses, N = Number of observation
Table 4 reports the Coefficients for contraceptive use for individuals in
the 15 and 16 year cohorts. Here we estimate the effect of contraceptive on the
years 15 and 16 years after the use for contraceptives. The coefficient for
considerations CONTRACEPTIVE is the only one shown on this chart. The
signs of each test reveal an inverse relationship as suggested with the theory.
However, it is largely significant when looking at the 16 year cohorts, especially
for violent crime and only significant slightly for property crime at the 15 year
cohorts.
CONCLUSION
To the extent that the availability of contraceptives affects birth
cohorts, policy makers find strength when arguing for an increased in spending
on family planning incentives. While on the forefront of this debate is the
constitutional right to abortion, Roe v. Wade, various amounts of choices may
provide an avenue to increase the general public use of noninvasive
contraceptives, thereby decreasing long run societal cost. Certain state level
controls are very expensive and requires a large portion of the state budget to
finance. Utilizing a cost effective funding of contraceptive usage, could lead to
decrease state expenditures later. The cost associated with the process of
incarcerating an individual is expensive, for example court cost, processing fees,
housing, and etc. Expenditures on readily available low cost contraceptive
technology, could be instituted which would decrease the cost that envelops
incarceration.
Human and social capital investment plays an important role in
determining the outcomes of individuals. More importantly, this capital
investment helps develop children to become more functional in society. Society
has often stated that it “takes a village to raise a child.” This is very evident from
this research. The stigma cost associated with behavior is increased when
individual have enough time to establish a family network. This network helps
to mold an individual into a more productive citizen. In many instances, the
family network will deter a child from participating in criminal activities. For
society, being involved in community and neighborhood programs could
increase the cost associated with children participating in crime. Children, as
shown in the literature, which possess low social capital investment, have a
higher probability of participating in criminal activity. Adopting specific
technology, may allow an individual more time to set up a family network that
will deter their child from participating in criminal activity.
102
Safe Sex, Safe Communities: Analyzing the Link Between
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105
Southwestern Economic Review
ENDNOTES
1
According to Donohue and Levitt (1999), if a woman chooses to abort a
pregnancy, it may cost her the amount of “A” which includes both
emotional and monetary cost as well as the possibility of complication with
future child bearing. A woman will choose to abort if and only if B<-A, or
the utility of having the baby is more negative than the cost of an abortion.
The negative value of “A” is expressed because it could be misleading with
respect to cost being a positive number and the utility of a baby being less
than zero. Because abortion as an option is costly, “no wanted baby will
ever be aborted, and some unwanted babies will be born.” (Donohue and
Levitt, 1999).
C

A

(
P
L

P
H
Then choosing the low level of pregnancy
)
risk is more efficient means of preventing birth than abortion. In the above
mentioned equation, the usage of contraception will be preferred as a
precautionary choice. For all women the level of risk is identical to the level
chosen when the abortion is illegal. If
)

A

C
(
P
L

P
H
then the abortion
becomes a more efficient method of birth control than precaution.
2
3
4
This model will continue the assumption of Roy’s Model that assumes
jointly lognormal distribution with means of 0


and 1

. These mean
values are considered socioeconomic variables that are observable. Also,
the value of 0
and 1
 socioeconomic variables that are unobservable
which are the same as discussed in Borjas (1987)
Further description/definition of variables is located in the appendix under
definition of terms.
The data was provided by the Centers for Disease Control. The NSFG
conducts the national samples of women 15-44 years of age, interviewed in
person in their own households. Sample sizes were 9,797 in 1973, and
8,611 in 1976. In Cycles 1, 2, and 3, only the conterminous United States
was included. In Cycles 4 and 5, Alaska and Hawaii were included.
Analysis can be done for the four major census regions (Northeast,
Midwest, South, and West) and for metropolitan and non-metropolitan
areas. Estimates cannot be made for individual States or for smaller areas
106
Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates
after 1976 .Therefore the selected dates of 1973-1976 provide state
observations that determine the sample size. The data following 1976 were
not estimated for this research.
5
6
7
For example, in this data set, condom/foam and diaphragm/sponge, was
specified both as used together and separate. This study will not examine
combinations such as this for individual birth control technology usage.
However, as specified earlier, total use of all birth control technologies will
be analyzed.
Any information that the census does not have, such as beer consumption
and the presence of a concealed handgun law, will be gathered from
Donohue and Levitt’s data set that they have made readily available.
Given the possibility of unobserved heterogeneity, which introduces a bias
in the OLS parameter estimates, a Random and Fixed Effects parameter
estimates maybe more effective. Random Effect parameter estimates
assume individual specific constant terms as randomly distributed across
cross–sectional units (Green, 2005) Fixed Effects parameter estimates
assume that differences across units can be captured in the differences in the
constant term. It is also possible to allow the slopes to vary across i, but this
method introduces some new methodological issues, as well as considerable
complexity in the calculations. A study on the topic is Cornwell and
Schmidt (1984). Also the assumption of a Fixed T is only for convenience.
The more general case in which Ti varies across units (Green, 1995a). It is
suggested to use a Fixed Effects Model as an appropriate estimation model
(Green, 2001 and Gujarati 1998) A Hausman test to discriminate between
the Random and Fixed Effects parameter specification indicates that the
Fixed Effects specification is consistent with the data. This conclusion can
be gleamed from the significant value of the chi2 statistic for both violent
(Chi2 = 71.85) and property crimes (Chi2 = 21.04).
107Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates
SAFE SEX, SAFE COMMUNITIES:
ANALYZING THE LINK BETWEEN
CONTRACEPTIVE USAGE AND
CRIME RATES
Nicholas J. Hill, Jackson State University
Mxolisi Siwatu, Texas Southern University
Maury Granger, Jackson State University
ABSTRACT
This research investigates the link between contraceptive use in the
1970s and the decline of crime rates observed during the 1990s. A theoretical
framework is adopted in which individual contraceptive use reflects a decision
to delay parenthood until a family network can be established to increases the
costs associated with criminal behavior. In this context, contraceptive use affects
the crime rate by reducing the number of individuals with low opportunity costs
and high potential for engaging in criminal activity. The theoretical predictors
of the model are tested with data on contraceptive use from the Centers for
Disease Control Family Growth Survey, using the crime rate in the 1990s and
contraceptive use in the 1970s. Empirical evidence shows that the use of
contraceptive methods does have statistically significant effects on the crime
rates. This extends the existing literature on crime and abortion by considering
the effects of a wide array of reproductive choice technologies on the crime rate.
JEL Classifications: J12, J13, K42
INTRODUCTION
The role that abortion plays in criminal activity has been debated.
Donohue and Levitt (2001) linked the decrease in crime rates observed in the
1990’s to the number of abortions after Roe v. Wade legalized active abortion in
1973. The validity of the link between abortion and crime rates has been
challenged, most notably by Joyce 2001, 2004a, 2004b; Foote and Goetz 2005;
Cook and Laub 2003; Miron and Dills 2006. They argue that the findings by
Donohue and Levitt (2001) were not compelling once the key statistical
measurement errors were corrected. However, Donohue and Levitt (2005)
addressed their concerns and still find evidence of a significant link between
abortion rates in the 1970 and crime rates in the 1990s. .
89
Southwestern Economic Review
If there is a significant link between abortion and crime rates, then there
may be also a link between contraceptive usage and the crime rates. This article
investigates the link between crime and contraceptive use. Just as abortion
reduce the number of unwanted children who, according to Donohue and Levitt,
were more likely to engage in crime as teens and young adults, contraceptive use
would generate the same effect through the reduction in the number of unwanted
children. The cumulative effect of social/economic, cultural, and socio
psychological factors heavily influences the likelihood of a child becoming a
juvenile delinquent (Shulman, 1949). In general, children are more likely to
become juvenile delinquents and participate in criminal activities when they are
born into single-parent or other non-intact families. (Painter and Levine, 2000).
This article investigates whether contraceptive use lowers the future
crime rate by reducing the size of the birth cohort most likely to enter a future
criminal cohort. Donohue and Levitt (2001) report that the drop in the U.S crime
rate in the early 1990s can be explained by the increase in abortion rates in the
1970s shortly after the Supreme Court ruling in Roe v. Wade. This research
extends the work of Donohue and Levitt (2001) by considering the full menu of
reproductive technology choices beyond abortion, and their effect on the crime
rate in the early 1990s.
UNDERSTANDING CRIME, UNWANTED CHILDREN AND
CONTRACEPTION USAGE
The economic modeling of crime begins with the works of Becker
(1968), Stigler (1970), and Ehrlich (1973). It was first suggested by Becker
(1968) that improvements in legitimate labor market opportunities or a strong
economy makes crime less attractive. However, Freeman (1995), Machin and
Meghir (2000), Gould, Weinberg and Mustard (1997), Donohue and Levitt
(2001) and Raphael and Winter-Ebmer (2001) suggest that there is a statistically
significant but weak relationship between unemployment rates and property
crime. This may be due to the phenomenon of hysteresis in criminal behavior, as
first suggested by Mocan et. al., (2005). In this seminal work, they explain that
criminal participation may be express in a dynamic model where individuals
utilize both legal and criminal human capital to increase potential earnings. This
suggests that higher criminal participation in not necessarily linked to higher
unemployment.
Becker (1968) attempts to find the optimal level of public and private
policies to decrease the crime rate. Becker (1968) looks at the social value of a
crime, but Stigler (1970) investigates various ways to decrease or limit the
supply of criminals. Additionally, he examines the best policies to deter
criminals from committing various offenses. Stigler analyzes factors that affect
the supply of criminals. He conjectures that the laws designed to prevent crimes
are highly influenced by public policy and that public opinion is not adequate
for the prevention of crime.
In addition to establishing the economic rationale for the crime rate, it
is necessary to understand what costs will deter people from committing crimes.
Ehrlich (1973) tries to explain the crime level by measuring the value of the time
allocated between illegitimate activities versus legitimate activities. Within this
90
Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates
analysis, the focus is directed on the environmental factors contributing to
criminal activities. He advocates more spending on law enforcement where
expenditures depend on the effectiveness of the expected punishment the cost of
deterring crime compared with
alternative methods of combating crime. And investigates how increasing the
cost certain factors such as law enforcement can deter an individual from
participation in illegal activity.
The environment and social interactions are also factors contributing to
crime. Glaeser, Sacerdote, and Scheinkman (1996) argue that there are strong
peer-effects that contribute to an individual being a participant or a catalyst in
criminal activity.
Some of the deterrents that influence negative social
interaction according to this study are: strong parents, formal schooling, and
information that counters peer influences. This give us the opportunity for our
research to adopt a theoretical framework in which family planning decisions
condition the number of unwanted children who may later join a criminal
cohort, as an approach to exploring the possible causal nexus between crime rate
and contraceptive use.
While the previous literature indicates the role that public policy and
environmental factors has in criminal participation, the central aim of this
research is to investigate the possible causal link between the reductions in the
crime rate observed during the 1990s and the wide array of reproductive choices
used during the 1970s. While there are many arguments why crime rate fell
during the 1990s, Donohue and Levitt’s (2001) findings have attracted attention
to the role of reproductive choice technologies and their effects on future cohorts
of criminals. In addition, reproductive choice technologies permit individuals
some control over the timing of conception and provide an opportunity to bear
children under more favorable conditions.
Donohue and Levitt (2001) attribute the decrease in crime due to the
decrease of unwanted children from the legalization of abortion. Unwanted
children are likely to receive smaller human capital investments by their parents
and are more likely to get into trouble when they get older (Lott and Whitley,
2007; Bouza, 1990 and Morgentaler, 1998). These finding are supported by the
works of Hay and Evens (2006) who find that children of an unwanted or
unplanned pregnancy had significantly higher delinquency rates when they were
ages 11 to 17. They find the correlation between unwanted pregnancy and
general delinquency, status offenses, drug offenses, and serious offenses (Hay
and Evens, 2006). An opportunity to reduce the number of unwanted pregnancy
through contraception could lead to lower delinquency rates especially if women
younger women use contraception. Any study that links increase in abortion in
the 1970s with decrease in crime in 1990s must come to grips with the racial
gaps in both abortion and crime. (Trent et. al, 1991 and Jones et.al., 2002) It is
well known that young black men are over represented in US prisons. (Bureau
of Justice Statistics).
We also know that black women are more likely to
terminate pregnancies versus white women. (Jones et al. 2002). This would lead
to a decrease in crime rate observed.
THEORY AND METHODOLOGY
91

Southwestern Economic Review

C BP C BL CN P EU L L
    ) | (
(1)

BP BH CN P EU H H
  ) | (
(2)

) ( H L
L H P P
C B C BP BP     
(3)
It is plausible that the low risk strategy is adopted if and only if (Donohue and
Levitt, 1999):
) ( H L
P P
C B  
(4)

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

0 0 0
  w & 1 1 1
  w
(5)

) ( ) ( )) /( ln( 0 1 0 1 0 1
            C w w I
(6)

Southwestern Economic Review

1 0
01

  ,where 01
 is the cov ( 0
, 1
)
(7)

0 ) ( ) ( 0 1 0 1
         
(8)

Southwestern Economic Review

) , ,2(
) ( ) 17 ( 1
n i for
X ive Contracept Crime st t s st i t s st
 
     
    
(10)

population female of number Total
ive contracept g u population female of number Total Rate n Utilizatio sin 
(11)

Southwestern Economic Review

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

The following model estimates the Fixed Effect parameter for the
following model7:

Southwestern Economic Review

100

INCOME and CONTRACEPTIVE are statistically significant. Furthermore, the
coefficient for the variable CONTRACETIVE, (-.3305), is negative

                                                                                    Table 3 
                         Fixed Effects Models for Violent and Property Crimes using Equation (10)

Specification Fixed Effects Model

Fixed Effects Model

Violent Violent Violent

Property Property Property
Crime under 25 arrest 17

Crime under 25 arrest 17

UNEMPLOY 1.540571b 0.7381 3.0861
-0.104 0.03368 -0.74838

(.688) (1.75) (2.54)

(.568) (1.34) (1.55)
POVERTY -0.00163 0.0013 0.0036
-0.00464 -0.00862 -0.00131

(.004) (.010) (.014)

(.003) (.008) (.008)
GUNLAW -0.655 0.4268b 0.23881
-0.01107 0.07456 0.0389

(.730) (.193) (.279)

(.060) (.148) (.170)

INCOME 1.7768c 2.4739a 2.8378b
-0.6247b -0.55924 0.35518

(.310) (.847) (1.22)

(.256) (.651) (.748)
BEER
-0.00243 -0.01539 -0.0349c

0.00754 0.0052 -0.01194

(.005) (.014) (.021)

(.005) (.011) (.012)
POLICE
-0.0668 -0.1776 -0.1145

0.06087 -0.02348 0.02102
(t-1)

(.085) (.217) (.314)

(.071) (.167) (.192)
INCARCERATE 0.0002 -0.02932 -0.03425

0.00322 0.01564 0.01418
(t-1)

(.010) (.026) (.037)

(.008) (.020) (.022)

CONTRACEPTIVE -0.174430b -0.33057c -0.4684c

0.11742b -0.23046c -0.3232b
(t-17)

(.068) (.176) (.255)

(.056) (.135) (.155)
CONSTANT -15.96435a -30.06a -32.486a

9.959a 1.2649 0.51396

(3.06) (8.38) (12.1)

(2.53) (6.43) (7.40)

204 204 204

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

101

R_squared 0.1261 0.028 0.181

0.038 0.02 0.045

Table 4
Fixed Effects Models for Violent and Property Crimes using
Equation (10) with t-16 and t-15 (Coefficients for Contraception only)
T-16

Contraceptive S.E.
Violent Crime -0.1532253c (0.085079)
Violent crime under 25 -0.3331908b (0.166469)
Violent arrest 16 -0.7031635a (0.247162)

Property crime -0.0844337 (0.053502)
Property crime under 25 -0.2129082c (0.117154)
Property arrest 16 -0.1694893 (0.144273)

Southwestern Economic Review

C BP C BL CN P EU L L
    ) | (
(1)

BP BH CN P EU H H
  ) | (
(2)

) ( H L
L H P P
C B C BP BP     
(3)
It is plausible that the low risk strategy is adopted if and only if (Donohue and
Levitt, 1999):
) ( H L
P P
C B  
(4)

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

0 0 0
  w & 1 1 1
  w
(5)

) ( ) ( )) /( ln( 0 1 0 1 0 1
            C w w I
(6)

Southwestern Economic Review

1 0
01

  ,where 01
 is the cov ( 0
, 1
)
(7)

0 ) ( ) ( 0 1 0 1
         
(8)

Southwestern Economic Review

) , ,2(
) ( ) 17 ( 1
n i for
X ive Contracept Crime st t s st i t s st
 
     
    
(10)

population female of number Total
ive contracept g u population female of number Total Rate n Utilizatio sin 
(11)

Southwestern Economic Review

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

The following model estimates the Fixed Effect parameter for the
following model7:

Southwestern Economic Review

100

INCOME and CONTRACEPTIVE are statistically significant. Furthermore, the
coefficient for the variable CONTRACETIVE, (-.3305), is negative

                                                                                    Table 3 
                         Fixed Effects Models for Violent and Property Crimes using Equation (10)

Specification Fixed Effects Model

Fixed Effects Model

Violent Violent Violent

Property Property Property
Crime under 25 arrest 17

Crime under 25 arrest 17

UNEMPLOY 1.540571b 0.7381 3.0861
-0.104 0.03368 -0.74838

(.688) (1.75) (2.54)

(.568) (1.34) (1.55)
POVERTY -0.00163 0.0013 0.0036
-0.00464 -0.00862 -0.00131

(.004) (.010) (.014)

(.003) (.008) (.008)
GUNLAW -0.655 0.4268b 0.23881
-0.01107 0.07456 0.0389

(.730) (.193) (.279)

(.060) (.148) (.170)

INCOME 1.7768c 2.4739a 2.8378b
-0.6247b -0.55924 0.35518

(.310) (.847) (1.22)

(.256) (.651) (.748)
BEER
-0.00243 -0.01539 -0.0349c

0.00754 0.0052 -0.01194

(.005) (.014) (.021)

(.005) (.011) (.012)
POLICE
-0.0668 -0.1776 -0.1145

0.06087 -0.02348 0.02102
(t-1)

(.085) (.217) (.314)

(.071) (.167) (.192)
INCARCERATE 0.0002 -0.02932 -0.03425

0.00322 0.01564 0.01418
(t-1)

(.010) (.026) (.037)

(.008) (.020) (.022)

CONTRACEPTIVE -0.174430b -0.33057c -0.4684c

0.11742b -0.23046c -0.3232b
(t-17)

(.068) (.176) (.255)

(.056) (.135) (.155)
CONSTANT -15.96435a -30.06a -32.486a

9.959a 1.2649 0.51396

(3.06) (8.38) (12.1)

(2.53) (6.43) (7.40)

204 204 204

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

101

R_squared 0.1261 0.028 0.181

0.038 0.02 0.045

Table 4
Fixed Effects Models for Violent and Property Crimes using
Equation (10) with t-16 and t-15 (Coefficients for Contraception only)
T-16

Contraceptive S.E.
Violent Crime -0.1532253c (0.085079)
Violent crime under 25 -0.3331908b (0.166469)
Violent arrest 16 -0.7031635a (0.247162)

Property crime -0.0844337 (0.053502)
Property crime under 25 -0.2129082c (0.117154)
Property arrest 16 -0.1694893 (0.144273)

Southwestern Economic Review

C BP C BL CN P EU L L
    ) | (
(1)

BP BH CN P EU H H
  ) | (
(2)

) ( H L
L H P P
C B C BP BP     
(3)
It is plausible that the low risk strategy is adopted if and only if (Donohue and
Levitt, 1999):
) ( H L
P P
C B  
(4)

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

0 0 0
  w & 1 1 1
  w
(5)

) ( ) ( )) /( ln( 0 1 0 1 0 1
            C w w I
(6)

Southwestern Economic Review

1 0
01

  ,where 01
 is the cov ( 0
, 1
)
(7)

0 ) ( ) ( 0 1 0 1
         
(8)

Southwestern Economic Review

) , ,2(
) ( ) 17 ( 1
n i for
X ive Contracept Crime st t s st i t s st
 
     
    
(10)

population female of number Total
ive contracept g u population female of number Total Rate n Utilizatio sin 
(11)

Southwestern Economic Review

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

The following model estimates the Fixed Effect parameter for the
following model7:

Southwestern Economic Review

100

INCOME and CONTRACEPTIVE are statistically significant. Furthermore, the
coefficient for the variable CONTRACETIVE, (-.3305), is negative

                                                                                    Table 3 
                         Fixed Effects Models for Violent and Property Crimes using Equation (10)

Specification Fixed Effects Model

Fixed Effects Model

Violent Violent Violent

Property Property Property
Crime under 25 arrest 17

Crime under 25 arrest 17

UNEMPLOY 1.540571b 0.7381 3.0861
-0.104 0.03368 -0.74838

(.688) (1.75) (2.54)

(.568) (1.34) (1.55)
POVERTY -0.00163 0.0013 0.0036
-0.00464 -0.00862 -0.00131

(.004) (.010) (.014)

(.003) (.008) (.008)
GUNLAW -0.655 0.4268b 0.23881
-0.01107 0.07456 0.0389

(.730) (.193) (.279)

(.060) (.148) (.170)

INCOME 1.7768c 2.4739a 2.8378b
-0.6247b -0.55924 0.35518

(.310) (.847) (1.22)

(.256) (.651) (.748)
BEER
-0.00243 -0.01539 -0.0349c

0.00754 0.0052 -0.01194

(.005) (.014) (.021)

(.005) (.011) (.012)
POLICE
-0.0668 -0.1776 -0.1145

0.06087 -0.02348 0.02102
(t-1)

(.085) (.217) (.314)

(.071) (.167) (.192)
INCARCERATE 0.0002 -0.02932 -0.03425

0.00322 0.01564 0.01418
(t-1)

(.010) (.026) (.037)

(.008) (.020) (.022)

CONTRACEPTIVE -0.174430b -0.33057c -0.4684c

0.11742b -0.23046c -0.3232b
(t-17)

(.068) (.176) (.255)

(.056) (.135) (.155)
CONSTANT -15.96435a -30.06a -32.486a

9.959a 1.2649 0.51396

(3.06) (8.38) (12.1)

(2.53) (6.43) (7.40)

204 204 204

Safe Sex, Safe Communities: Analyzing the Link Between
Contraceptive Usage and Crime Rates

101

R_squared 0.1261 0.028 0.181

0.038 0.02 0.045

Table 4
Fixed Effects Models for Violent and Property Crimes using
Equation (10) with t-16 and t-15 (Coefficients for Contraception only)
T-16

Contraceptive S.E.
Violent Crime -0.1532253c (0.085079)
Violent crime under 25 -0.3331908b (0.166469)
Violent arrest 16 -0.7031635a (0.247162)

Property crime -0.0844337 (0.053502)
Property crime under 25 -0.2129082c (0.117154)
Property arrest 16 -0.1694893 (0.144273)

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Articles, books and commentary by Joel Snell

SAFE SEX AND CRIME

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