The Relationship between Poverty and Recidivism in
Indiana
Matthew Channing
PA 706
December 10, 2014
Abstract
Recidivism is the instance of convict released from incarceration and returning to
incarceration within three years (Petersilia, 1999, p. 512). The causes of this are not universally
known nor agreed upon. This paper presents a literature to investigate the background research
of poverty’s effect on reoffending. As a caveat, poverty and recidivism are both difficult to
measure meaningfully, as the literature shows. Simple linear regression analyses were conducted
on four measures of poverty against recidivism rates for counties in Indiana. What was found
was that poverty as defined in this paper was not strongly associated with recidivism rates, but
indicators of real and potential economic need, unemployment rate and rates of receiving
federal benefits via SNAP, are.
Channing 1
Introduction
I.
Incarceration is usurping a larger share of state budgets than previously, while at the
same time, states have less money to dedicate to the process (Orrel & Blackwell, 2008, p. 1). Yet,
recidivism is likely a symptom of greater societal conditions. If all a state does is seek to treat the
symptom, then the trend of resource-drain demanded by the criminal justice system will
continue. The purpose of this paper is to determine if living in an area of poverty is related to
recidivism.
Literature Review
II.
The review of existing literature is divided into two main categories: review of material on
poverty and a review of material on recidivism. In regards to poverty, the literature spotlights the
method through which it is calculated and the controversies surrounding it. Also, other methods
for determining a poverty statistic were examined to create a comparison in their validity and
applicability. For the literature on recidivism, the articles centered around demographic statistics
and likely contributing factors. Additionally, this paper reviews literature about the reentry
process for ex-offenders.
Poverty
Poverty is measured in multiple forms, each with their own purpose. The most common
measurement is the Census Bureau’s Current Population Survey’s Annual Social and Economic
Supplement (CPS ASEC), which is the official measurement of poverty.
Channing 2
The Census Bureau produces its figures from “the Current Population Survey's Annual
Social and Economic Supplement (CPS ASEC)” (Institute for Research on Poverty, 2013). As
context, the poverty measurement was useful because in 1964, it coincided with President
Johnson’s War on Poverty (Fisher, 1992). These numbers came from “comparing pre-tax cash
income against a threshold that is set at three times the cost of a minimum food diet in 1963,
updated annually for inflation using the Consumer Price Index, and adjusted for family size,
composition, and age of householder” (Institute for Research on Poverty, 2013). In addition to
the pre-tax cash, other forms of income included are “cash benefits from the government…Social
Security and Unemployment Insurance…Supplemental Security Income (SSI), public assistance
benefits, such as Temporary Assistance for Needy Families… and workers compensation
benefits” (Short, 2013). Calculating poverty status is expressed in a simple equation, “if a family's
total income is less than the family's threshold, then that family and every individual in it is
considered in poverty” (United States Census Bureau, 2013).
An interesting note about the CPS ASEC is its definition of family and its insistence on that
unit for calculating the poverty measure. The poverty thresholds themselves are determined by
the size of the family and the age of the family members (United States Census Bureau, Poverty,
2013). As defined by the CPS portion of the census, a family is the related people within a
household and a family household is the total of all people within a (United States Census
Bureau, Definitions, 2013).
As stated earlier, the CPS ASEC is the barometer for poverty in America, but the measure
is not without its criticisms. There are several areas wherein the CPS ASEC falls short and may
Channing 3
not capture the true nature of poverty. The most striking shortcoming of the measure is that it
does not take account of taxes or noncash benefits aimed at improving the economic situation of
the poor” (Short, 2013). This means that medical and housing assistance do not factor into the
census’s poverty statistic and may over inflate the actual need that an individual experiences. For
example, if a family’s threshold is compared against the actual cost of the residence, they can be
found in poverty even though the difference between need and income is actually made up by a
housing assistance supplement from the government. While this may be considered good news
for poverty, the CPS ASEC can also deflate the poverty number.
One way the census’s account of poverty can misrepresent the number is by omitting
certain factors. By its own admission, the CPS ASEC does not adjust for the diversity of the
country, saying, “The official poverty thresholds do not vary geographically” (United States
Census Bureau, Definitions, 2013). Problematically, this tactic drastically discounts the
proportion of a person’s income that may go to housing costs across the nation. The effect of
this is that the ratio spent should be “the same whether a family lives in rural Mississippi or
Manhattan” (Renwick, 2009, p. 2). Self-evidently, there are obvious differences in what it costs
for a family of four to provide for housing across America. What this does is reduce the number
of households who should be considered in poverty, but who are unrepresented by the census’s
official statistic for their family size
Recidivism
The literature on recidivism is as equally varied as poverty’s. However, many articles are
consistent in their findings of the state of recidivism and incarceration in general. Broadly
Channing 4
defined, recidivism is the measurement of a released offender reoffending. A recidivism rate
would be calculated by the reentrants in a population against the number of them who have
reoffended. Traditionally, recidivism rates are calculated in three year intervals. Precedent for
this calculation was set by a survey done in Oregon which “found that, overall, 63 percent of
inmates were arrested for a felony or serious misdemeanor offense within three years of release
from prison” (Petersilia, 1999, p. 512).
A question remains as to what recidivism actually measures and if it approximates what
its users want it to. At its heart, recidivism gives a number that approximates the people who
have been found guilty of violating a law or condition of release (although it is possible to
calculate recidivism for people that have simply been arrested but not gone to court) against
those who have previously been found guilty of violating a law or condition of release. One
author characterizes the ambiguous situation thusly, “Recidivism is currently an unsophisticated,
dichotomous, terminal variable, incapable of measuring incremental progress toward postrelease success” (Gehring, 2000, pp. 511-512).
As a reentrant, the individual is not given many resources upon which to build their
successful life as a non-offender. What is termed gate money is the sum a reentrant is given to
start the process of being self-sufficient. The “‘gate money’” provided by the state is rarely
sufficient for more than a few days of subsistence” (Berk, Lenihan, & Rossi, 1980, p. 768). More
disparagingly, the Economist reported that “in Illinois, released prisoners receive $50, a set of
clothes, and a bus ticket” (as cited in Visher & Travis, 2003, p. 96). And Travis et al reports “one
third of all state departments of corrections report that they do not provide any funds upon
Channing 5
release” (as cited in Visher & Travis, 2003, p. 96). Without the requisite resources to generate a
non-offending lifestyle, the odds are stacked against the reentrant.
When the reentrant returns to the community, they are faced with a lack of
opportunities. The most notable problem for reentrants is their unemployment. “The more
urban the area, the higher the unemployment rate” (Harer, 1995, p. 99). “Unemployment is
closely correlated with drug and alcohol abuse. Losing a job has similar effects” (Petersilia, 2000,
p. 4). The employment opportunities are lower for a reentrant who returns to a more
disadvantaged neighborhood compared to one that returns to a more better off area (Morenoff
& Harding, 2011, p. 1). As reported above, for blacks, Hispanics, and whites, employment status
was one of the leading causes of recidivism, yet the literature habitually express the difficulties
reentrants face when attempting to find work.
What this demonstrates is that the environment a reentrant returns to has a large impact
on whether or not that reentrant recidivates. About their living situations, one study reported
that “most offenders (60%) lived in poverty earning less than $15,000 annually, with only 9%
having an annual income between $25,000 and $34,999 and 7% having an annual income of
$35,000 or more” (Stalans, Yarnold, Seng, Olsen, & Repp, 2004, p. 259). Echoing the above,
Travis et al says, “other research has shown that returning prisoners are increasingly
concentrated in our nation's central cities and within them, in a relatively small number of
neighborhoods that often are characterized by severe poverty, social disorganization, and high
crime rates” (as cited in Visher & Travis, 2003, p. 102). Additionally, criminologists argue “that
poverty represents a relative lack of opportunities for licit employment, making illicit
opportunities for economic gain…an attractive alternative” (Harer, 1995, p. 108). From these
Channing 6
articles, it is clear that the socioeconomic factors of a reentrants community are going to have a
significant impact on their ability to not reoffend. As distinguished by the literature,
opportunities for success in these neighborhoods are rare to begin with and so reentrants
returning who are already disadvantaged cognitively and have a criminal background, are going
to have a more arduous task of providing form themselves.
On the link between poverty itself and recidivism, the literature bore mixed conclusions.
By one account, “a higher percentage of persons below the poverty level…decreases the chances
of a repeat offense. This could be possibly due to the lower propensity to commit property
offenses among the probationers” (Sirakaya, 2006, p. 872).This article was the only one to make
that conclusion about the relationship between poverty and recidivism. Most other pieces either
determined that there was a slight effect, or that poverty was not a statistically significant
indicator for recidivism rates. In so far as to recidivate, a person must commit a crime and
“strong individual and aggregate level correlations between poverty and official measures of
crime are perhaps among the most firmly established of social science empirical generalization”
(Berk, Lenihan, & Rossi, 1980, p. 766). Another study found that amongst its data, “the strongest
observed correlation between the independent variables is between risk and poverty”
(Holtfreter, Resig, & Morash, 2004, p. 196). As so many studies are composed of different data
sets, across different time periods, variance in their conclusion is to be expected. Yet, the nature
of the conclusions is still similar in some fashions.
Finally, one study attempted to bridge the connection between the poverty measure and
recidivism rates. What was found was that “modest amounts of financial aid can reduce
Channing 7
recidivism among ex-felons. Experimentally induced unemployment can increase recidivism”
(Berk, Lenihan, & Rossi, 1980, p. 784).
Future Research
Future research should be done on the causal link between poverty and recidivism. As
the government is already taking steps to reduce poverty, it would benefit policy-makers to
know what external effects their policies have on the criminal justice system. This would
certainly factor into their decisions and empower them to take future opportunities to directly
diminish the rate of recidivism. So few studies have been done on this link because it is
problematic to meaningfully identify what poverty is in the environment. To this end, what
would be ideal for a study is one involving a control group of similar individuals. However, ethical
considerations should be stated due to the harmful effects of poverty and recidivism both at the
individual and at the community level. Intentionally subjecting a group of people to the
conditions which could lead to them committing crimes is in itself a crime, legally and morally.
III.
Methods
This paper’s research question was to determine the effect of poverty on recidivism
rates. The hypothesis was that they are positively related, that an increase in the poverty rate
will cause an increase in the recidivism rate. To explore this relationship, this paper selected the
available data at the county level for Indiana. Indiana provides a good example to be studied
because its poverty rate was about equal to the national average, lending it external validity, and
Channing 8
more practically, the data for recidivism and poverty at the county level were available to be
examined.
Variables
The following table expresses the methodological framework for the research question.
Included is the independent variable, the rate of poverty in a given county, and the dependent
variable, the adult recidivism rate for the same county. To test spuriousness, the table presents
variables which may be acting upon, or obscuring, the relationship between the independent
and dependent variable. A conditional variable is presented that could create a link between the
independent and dependent variable. Crime as a need to survive is a conditional variable
because it would be caused by increasing poverty rates, which could cause reentrants to
supplement their income with crime, thus recidivating. Table 1 displays the variables used
Table 1: Variables to Test
Independent variable
Dependent variable
Possible spurious variables
Conditional variable
Poverty Rate
Under 125% of Federal Poverty Level
Recidivism Rate of adults 18 and over
Unemployment Rate
Rate of SNAP reception
Crime as a need to survive
Data Sources
The data for the Poverty Rate and Unemployment Rate in Indiana counties were from the
US Census’s Small Area Income and Poverty Estimates. They are “single-year ACS direct survey
estimates from all counties and states regardless of population size” (United States Census
Channing 9
Burea, American Community Survey, 2013).The American Community Survey itself is a yearly
mandatory survey of about three million households in counties and metropolitan areas of a set
metropolitan size (United States Census Bureau, Description of Income, 2013). In this
experiment, the data were collected from the 2012 data set found at (United States Census
Bureau, State and County, 2013).
To augment the ACS information, data sets about SNAP recipients and Federal Poverty
Level information were taken from an altered set from the New York Times. Food Research and
Action Center applied, to the New York Times data, “county poverty data, using Census Bureau
estimates of the number of people in each county with incomes under 125% of the Federal
Poverty Level,” (Cooper 2010). This was done to account for changes in poverty levels based on
geography, as opposed to applying a blanket level to all regions. While these data may present a
more accurate picture of real poverty levels, it must be noted that the data were from 20062008 and “poverty has increased in most places” by the time the ACS was completed in 2012.
Therefore, mapping the FRAC data over the ACS data may still be unreliable.
The data for the Recidivism Rate in Indiana were taken from the 2012 recidivism report
of 2009 releases from the Indiana Department of Corrections (Garner, 2012). Indiana measures
recidivism as offender’s return to incarceration within three (3) years of their release date from a
state correctional institution. For the returning offense, there categories are “a new conviction
or a technical violation of post-incarceration supervision” (Garner, 2012, p. 2). Reentrants fall
into four categories “Community Transition Program (CTP), Probation, Parole, or Discharged”
(Garner, 2012, p. 2) in terms of the nature of their release from incarceration. Finally, if a
Channing 10
reentrant reoffends multiple times within the same calendar year, only the earliest release dates
impact the recidivism classification so that the data only reflect unique releases, not total
releases (Garner, 2012, p. 2).
Operationalization of measures
The level of analysis for this study will be the county level. County averages are more
reflective of population figures than the state level, and with ninety-two counties, there is a
sufficient sample size to capture the diversity of counties and make the experiment generalizable
to the entire population of counties. Therefore, there are ninety-two cases, corresponding with
the reported total recidivism rate for the ninety-two counties, measuring the entire population.
Using the SNAP and FPL measures does not give accounts of all counties as data were not
available. These two data sets only measured 72 of the 92 counties in Indiana
There are two things to say about the internal and external validity of the experiment.
The concept of internal validity is, for the most part, not an issue, as this study examines all
counties within the state of Indiana. On external validity, however, it should be noted that due to
the possible limitations in meaningfully measuring the poverty rate, it is possible that Indiana
may show that there is no correlation between poverty and recidivism rate, when in fact, a
different sample group with a different effective poverty rate may actually express correlation.
Channing 11
Research Design
Because the data sets for this research are all interval level, the appropriate test to run in
order to determine a relationship is a simple linear regression analysis. To test the goodness of
fit between the two variables, the regression analysis reveals two statistics that express how well
the independent variable explains variation in the dependent variable: the Adjusted R Squared
and the Significance Coefficient of the alpha level (Meier et al, 2010, pgs. 345-346).
The simple linear regression tests to run will use the Recidivism Rate (Recidivism) per
county as a dependent variable. Tests will be run with County Poverty Rate (Poverty), County
Unemployment Rate (Unemployment), Rate of County Population Under 125% of the Federal
Poverty Level (FPL Rate), and Rate of County Population Receiving SNAP benefits (SNAP).
There are many confounding variables not taken into account that could obscure any
relationship or the lack thereof. As identified from the literature, these could be demographic
data such as racial makeup of the reentrant population, and perhaps more importantly, the type
and intensity of social services they receive upon reentry into society that would mitigate
recidivating.
IV.
Results
After running a correlation test (see Appendix: Table 3), each independent variable
appears to correlate positively somewhat strongly with another independent variable.
Running the pairs together in a regression test, while they may have high explanatory power
for the dependent variable may lead to overlapping explanations, or multicolinearity.
Channing 12
Therefore, when running the regressions, there will only be one independent variable run at
a time.
The independent variables hypothesized to have the most significant relationship with
Recidivism did not. Below, Table 2 compares the relationship between the two variables
yielded by the regression analysis.
Table 2: Results of Linear Regression
Independent
Dependent
Adjusted R
Significance
Equation
Variable
Variable
Squared
Score
Poverty
Recidivism
.01
.165
=25.03+.364X
Unemployment
Recidivism
.025
.069
=16.216+1.324X
FPL
Recidivism
.022
.111
=24.311+.362X
SNAP
Recidivism
.031
.072
=24.668+.530X
These data show that while the four variables, as shown by the Adjusted R Squared,
explain between 1% and 3% of variation within Recidivism, only two of them have a
significant relationship with the dependent variable, Unemployment and SNAP. The
Significance scores, when compared to an alpha of 10%, indicate that with 90% confidence,
we can reject the null hypothesis that Unemployment and SNAP are not unassociated with
Recidivism, respectively. By itself, the regression tests do not prove causation, but by using
the equations build by the regression analyses, the relationship shows that an increase in
Unemployment and Snap will result in an increase in Recidivism.
Channing 13
V.
Conclusion
The regression analyses do not support the original hypothesis that Poverty as such would
have the strongest relationship with Recidivism. This is not to say that adverse economic
conditions don’t have a relationship with Recidivism, as evidenced by Unemployment and SNAP.
Interestingly, a difference between these variables and the other two is that they may more
likely indicate economic need, as shown by receiving benefits in the case of SNAP. One
conclusion to draw from is that people who are in positions of economic need may be more
likely, as supported by the literature. Also supported by one article in the literature review is that
poverty may lead to decreases in recidivism as potential recidivists may be less likely to commit
crimes against people living in poverty.
Implications of economic need contributing to increases in recidivism are mixed. It would be
fallacious to say that by increasing benefits, as SNAP shows, would increase recidivism. More
evidence needs to be gathered on the economic conditions of those who recidivate and the
motivations behind their crimes.
Channing 14
Appendix
Figure 1: Histogram of Recidivism Rate
Channing 15
Table 3: Correlations between all Variables
Percent of
Poverty Rate
Poverty Rate
Pearson Correlation
Recidivism Rate
1
Sig. (2-tailed)
N
Recidivism Rate
Population under
population on
125% Federal
Rate
SNAP
Poverty Limit
.146
.408**
.546**
.704**
.165
.000
.000
.000
92
92
92
74
74
Pearson Correlation
.146
1
.190
.210
.187
Sig. (2-tailed)
.165
.069
.072
.111
92
74
74
1
.538**
.200
.000
.087
N
Unemployment Rate
Percent of
Unemployment
Pearson Correlation
Sig. (2-tailed)
N
Percent of population on
Pearson Correlation
SNAP
Sig. (2-tailed)
N
Percent of Population under
Pearson Correlation
125% Federal Poverty Limit
Sig. (2-tailed)
N
**. Correlation is significant at the 0.01 level (2-tailed).
92
92
.408**
.190
.000
.069
92
92
92
74
74
.546**
.210
.538**
1
.545**
.000
.072
.000
.000
74
74
74
74
74
.704**
.187
.200
.545**
1
.000
.111
.087
.000
74
74
74
74
74
Channing 16
Poverty Rate & Recidivism
Figure 2: Scatter Plot - Poverty Rate and Recidivism Rate
Channing 17
Table 4: Linear Regression Analysis - Poverty Rate and Recidivism Rate
Variables Entered/Removeda
Variables
Model
Variables Entered
1
Poverty Rateb
Removed
Method
. Enter
a. Dependent Variable: Recidivism Rate
b. All requested variables entered.
Model Summary
Std. Error of the
Model
1
R
R Square
.146a
Adjusted R Square
.021
Estimate
.010
9.56198
a. Predictors: (Constant), Poverty Rate
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Poverty Rate
a. Dependent Variable: Recidivism Rate
Std. Error
25.030
3.859
.364
.260
Coefficients
Beta
t
.146
Sig.
6.486
.000
1.399
.165
Channing 18
Unemployment & Recidivism
Figure 3: Scatter Plot - Unemployment Rate and Recidivism Rate
Channing 19
Table 5: Linear Regression Analysis - Unemployment Rate and Recidivism Rate
Variables Entered/Removeda
Variables
Model
Variables Entered
1
Unemployment
Removed
Method
. Enter
Rateb
a. Dependent Variable: Recidivism Rate
b. All requested variables entered.
Model Summary
Std. Error of the
Model
1
R
R Square
.190a
Adjusted R Square
.036
Estimate
.025
9.48883
a. Predictors: (Constant), Unemployment Rate
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Unemployment Rate
a. Dependent Variable: Recidivism Rate
Std. Error
18.216
6.617
1.324
.720
Coefficients
Beta
t
.190
Sig.
2.753
.007
1.838
.069
Channing 20
Percent of Population on SNAP & Recidivism
Figure 4: Scatter Plot – Percent of Population on SNAP and Recidivism
Rate
Channing 21
Table 6: Linear Regression Analysis - Percent of Population on SNAP and
Recidivism Rate
Variables Entered/Removeda
Variables
Model
Variables Entered
1
Percent of
Removed
Method
population on
. Enter
SNAPb
a. Dependent Variable: Recidivism Rate
b. All requested variables entered.
Model Summary
Std. Error of the
Model
1
R
R Square
.210a
Adjusted R Square
.044
Estimate
.031
8.14394
a. Predictors: (Constant), Percent of population on SNAP
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Percent of population on SNAP
a. Dependent Variable: Recidivism Rate
Std. Error
24.668
3.182
.530
.291
Coefficients
Beta
t
.210
Sig.
7.753
.000
1.824
.072
Channing 22
Percent of Population under 125% of Federal Poverty Level &
Recidivism
Figure 5: Scatter Plot - Percent of Population under 125% of Federal
Poverty Level and Recidivism
Channing 23
Table 7: Linear Regression Analysis - Percent of Population under 125% of
Federal Poverty Level and Recidivism Rate
Variables Entered/Removeda
Variables
Model
Variables Entered
1
Percent of
Removed
Method
Population under
. Enter
125% Federal
Poverty Levelb
a. Dependent Variable: Recidivism Rate
b. All requested variables entered.
Model Summary
Std. Error of the
Model
R
R Square
.187a
1
Adjusted R Square
.035
.022
Estimate
8.18303
a. Predictors: (Constant), Percent of Population under 125% Federal Poverty
Level
Coefficientsa
Standardized
Unstandardized Coefficients
Model
1
B
(Constant)
Percent of Population under
125% Federal Poverty Level
a. Dependent Variable: Recidivism Rate
Std. Error
24.311
3.774
.362
.224
Coefficients
Beta
t
.187
Sig.
6.442
.000
1.615
.111
Channing 24
References
Berk, R. A., Lenihan, K. J., & Rossi, P. H. (1980, October). Crime and Poverty: Some Experimental
Evidence From Ex-Offenders. American Sociological Review, 45(5), 766-786. Retrieved from
http://www.jstor.org/
Fisher, G. M. (1992, May). The Development of the Orshansky Poverty Thresholds and Their Subsequent
History as the Official U.S. Poverty Measure. Retrieved from United States Census Bureau:
https://www.census.gov/
Cooper, R (2010, January). County-by-County Review of SNAP/Food Stamp Participation. Retrieved from
Food Research and Action Center: http://frac.org/pdf/ny_times_snap_poverty_formatted.pdf
Garner, A. (2012). Indiana Department of Corrections 2012 Recidivism Report (2009 Releases). Retrieved
from State of Indiana: http://www.in.gov/
Gehring, T. (2000, June). Recidivism as a Measure of Correctional Education Program Success. Journal of
Correctional Education, 51(2), 197-205. Retrieved from http://www.jstor.org/
Harer, M. D. (1995, September). Recidivism Among Federal Prisoners Released in 1987. Journal of
Correctional Education, 98-128. Retrieved from http://www.jstor.org/
Institute for Research on Poverty. (2013). How is poverty measured in the United States? Retrieved from
Institute for Research on Poverty: http://www.irp.wisc.edu/index.htm
Meier, K., Brudney, J., & Bohte, J. (2010). Applied statistics for public and nonprofit
administration (8th ed., pp. 345-346). Belmont, CA: Wadsworth
Morenoff, J. D., & Harding, D. J. (2011, November). Final Technical Report: Neighborhoods, Recidivism,
and Employment Among Returning Prisoners. Retrieved from National Criminal Justice
Reference Service: https://www.ncjrs.gov/
Orrel, B. R., & Blackwell, A. G. (2008). The Poverty Forum: Community Factors Reducing Recidivism.
Retrieved from The Poverty Forum: http://www.thepovertyforum.org/
Petersilia, J. (1999). Parole and Prisoner Reentry in the United States. Crime and Justice, 26, 479-529.
Retrieved from http://www.jstor.org/
Petersilia, J. (2000, November). When Prisoners Return to the Community: Political, Economic, and
Social Consequences. Sentencing and Corrections, 1-8. Retrieved from https://www.ncjrs.gov/
Renwick, T. (2009, August). Alternative Geographic Adjustments of U.S. Poverty Thresholds: Impact on
State Poverty Rates. Retrieved from United States Census Bureau: http://www.census.gov/
Short, K. (2013, November). The Research Supplemental Poverty Measure: 2012. Retrieved from United
States Census Bureau: http://www.census.gov/
Sirakaya, S. (2006, September). Recidivism and Social Interactions. Journal of the American Statistical
Association, 101(475), 863-877. Retrieved from http://www.jstor.org/
Channing 25
Stalans, L. J., Yarnold, P. R., Seng, M., Olsen, D. E., & Repp, M. (2004, June). Identifying Three Types of
Violent Offenders and Predicting Violent Recidivism while on. Law and Human Behavior, 253271. Retrieved from http://www.jstor.org/
United States Census Burea. (2013). American Community Survey. Retrieved from United States Census
Bureau: http://www.census.gov/
United States Census Bureau. (2013). Current Population Survey (CPS) - Definitions. Retrieved from
United States Census Bureau: http://www.census.gov/
United States Census Bureau. (2013). Description of Income and Poverty Data Sources. Retrieved from
United States Census Bureau: http://www.census.gov/
United States Census Bureau. (2013). How the Census Bureau Measures Poverty. Retrieved from United
States Census Bureau: http://www.census.gov/
United States Census Bureau, Small Area Income and Poverty Estimate. (2013). State and County Data
File 2012. Retrieved from http://www.census.gov/
Visher, C. A., & Travis, J. (2003). Transitions from Prison to Community: Understanding Individual
Pathways. {Annual Review of Sociology, 29, 89-113. Retrieved from http://www.jstor.org/