Cumulative Disadvantage and the Role of Transportation in Community Supervision

Abstract

Drawing from cumulative disadvantage theory, we are the first to examine the role of transportation disadvantage among other known challenges for women on community supervision. We create a composite measure of transportation disadvantage using factor analyses and data for 362 women on probation and parole in one Midwestern state: It is used to predict arrest and conviction using multiepisode event history analysis and conditional logistic regression. Consistent with cumulative disadvantage theory, the results suggest each additional disadvantage makes women more vulnerable, over and above the other disadvantages. Transportation disadvantage is a significant and entrenched feature in criminal justice-involved women’s lives. The import of modeling all available recidivism events, given the entrenched nature of criminal justice system involvement, cannot be overstated.

Keywords

transportation justice-involved women reentry community supervision transportation disadvantage recidivism multiepisode event history analysis

Cumulative disadvantage theory posits that individuals’ lives are shaped by decisions and events that are both positive (advantages) and negative (disadvantages). Advantages and disadvantages, occurring at various levels, shape an individual’s path (Foster & Hagan, 2007; Merton, 1968; Sampson & Laub, 2004). Advantaged individuals experience opportunities and benefits that propel them toward college, professional degrees, and successful careers; they readily shake off the occasional disadvantage. However, for the disadvantaged, who face an accumulation of constraints and deficits, shaking off the occasional problem is less easily accomplished; each additional hardship serves to accumulate and severely restrict future opportunities (Sampson & Laub, 2004).

Highly relevant to women on community supervision, the cumulative disadvantage approach advises that to understand how women arrived at a particular moment in time, accumulating past events, occurring at multiple levels, must be examined. Empirical research supports the need to examine transportation disadvantage at multiple levels (Kubrin & Stewart, 2006). At the individual level, women on probation and parole face challenges such as finding employment, completing education or job training, addressing substance abuse issues, or tending to physical and mental health concerns. At the higher levels, women more often than men care for children and other family members. Women often live in unsafe or inaccessible neighborhoods that limit both their options for travel as well as their ability to move safely (Bloom, Owen, & Covington, 2003; Koons, Burrow, Morash, & Bynum, 1997; Richie, 2001; Schram, Koons-Witt, Williams, & McShane, 2006).

Successfully completing community supervision, managing all the dynamic and multilayered tasks, has been described as the process of baking a cake in which several key ingredients are required both to be present and to align properly (O’Brien, 2001). For many justice-involved women, several of these “key ingredients” are deficits, rather than advantages, that serve as obstacles to exiting criminal justice system involvement. Evidence of this is that, in 2012, approximately 25% of state and federal prison admissions were for supervision violations (Carson & Golinelli, 2013); this indicates individuals were unable to “find the right recipe” to successfully complete community supervision.

A disadvantage rarely addressed in community supervision literature is the role of transportation. Highlighting the importance of transportation, Morash (2010, p. 11) in a study of women on probation and parole in one state explains that, to meet their needs, women must “navigate a wide array of geographically scattered county, regional, and state services . . . in which the programs may be distant and fragmented . . . and [the appointments] often interfere with keeping supervision appointments.” Getting to all these places is challenging; it requires either dependable access to a car, reliable bus services, or living within walkable distance to amenities (Bloom et al., 2003). Richie (2001) has found that many women on supervision must travel for several hours each day because they lack adequate transportation. Lacking adequate transportation forces women to make difficult choices. Morash (2010) finds that, among women with substance abuse issues, driving without a license is common. Disadvantages such as the revocation of a driver’s license seem easily surmountable for the advantaged, but for the disadvantaged, revocation can accumulate quickly into, for example, employment or financial problems (Dewan, 2015). Using statewide survey data (n = 362) from one Midwestern state, we examine the relationship between transportation disadvantage and other known challenges of community supervision.

Challenges for Justice-Involved Women

An important cornerstone for success on community supervision, access to both affordable and dependable transportation, may be more critical for women than men. Although women and men successfully complete probation and parole at similar rates (70%-72%; Gould, Pate, & Sarver, 2011), women’s pathways into the criminal justice system, and the needs that must be addressed for them to exit, vary from men. Women have more frequent or more severe experiences, compared with men, in a number of areas including substance abuse, mental health issues, poor physical health, unemployment, financial hardship, caretaking responsibilities for minor children, lack of social support, and residence in unsafe or inaccessible areas (Chesney-Lind, 1989; Chesney-Lind & Pasko, 2004; Daly, 1992, 1994; Salisbury & Van Voorhis, 2009). Deficits in these areas, according to cumulative disadvantage theory, can quickly accumulate.

Individual-Level Factors

Prior research on justice-involved women has found that substance abuse and mental health problems, when left unaddressed, can increase recidivism (Petersilia, 2004; Scroggins & Malley, 2010). A 2006 Bureau of Justice Statistics report found, in state prisons, 73% of females and 55% of males had mental health problems such as depression (James & Glaze, 2006) . About 74% of those with mental health problems met the criteria for substance dependence or abuse. Women more often than men in the criminal justice system have experienced sexual abuse (Blackburn, Mullings, & Marquart, 2008). In response, as a means for coping with past trauma, these women commonly abuse alcohol or drugs (Widom, 2000).

Women on probation and parole have very high levels of substance abuse, estimates range from 45% to as high as 80% (Bloom et al., 2003; Covington & Bloom, 2006; Mumola & Karberg, 2006). For drug-involved women, punitive state laws aimed at reducing drug offenses become intertwined with substance abuse issues and transportation problems. For example, 16 states require automatic 6-month suspension of a driving license for drug possession charges (Cauchon, 2014).

Poor health is another disadvantage common to women on probation and parole. Research has found that female prisoners and jail inmates, more often than males, report chronic health conditions such as cancer, high blood pressure, stroke, diabetes, heart-related problems, kidney-related problems, arthritis, asthma, and cirrhosis of the liver (Maruschak & Berzofsky, 2015). Further, women are less likely than men to have received treatment for these needs while in prison (Belknap, 2007). Serious health conditions impact transportation when women are unable to walk or drive. Transportation problems, such as walking long distances or extended time seated on buses, may accumulate into larger health problems for those who are unwell. Transportation problems can exacerbate health problems by interfering with women’s abilities to get to necessary medical appointments. On the other hand, better access to transportation can increase access to medical services (Cvitkovich & Wister, 2001).

Economic marginalization, highly correlated with recidivism, is a major concern of community supervision (Holtfreter, Reisig, & Morash, 2004). Improving financial situations can be challenging for women in the criminal justice system. Women exiting prison are less likely than men to have benefited from job training or education programs in prison; lack of programming in these areas is a barrier to obtaining employment (Belknap, 2007; Morash, Haarr, & Rucker, 1994; Salisbury & Van Voorhis, 2009). Further, many women reside in poverty-stricken neighborhoods with limited opportunities for employment in well-paying jobs (Cobbina, Morash, Kashy, & Smith, 2014; Owen & Bloom, 1995; Richie, 2001).

Employment is an essential ingredient to successfully reducing recidivism and substance use (Davis, Bahr, & Ward, 2012; Uggen & Staff, 2001). A key factor in both obtaining and maintaining a job is reliable transportation. Studies of the general population have found that, for those with better access to transportation, employment outcomes are better. Sanchez (1999), using Census data for two large U.S. cities, analyzed the impact of access to public transit on workers’ employment characteristics and locations. Transportation access was operationalized as nearness to a bus or subway stop as well as transit frequency. The results suggest that those with greater access to public transit had significantly higher rates of labor participation within both cities studied. A 2014 Urban Institute Study, Driving to Opportunity (Pendall et al., 2014), recommends increasing levels of automobile ownership for low-income households to increase positive employment outcomes.

The relationship between transportation and both employment and income is bidirectional—first, income matters for access to transportation because it translates into one’s ability to pay for fuel, bus passes, or needed car repairs. In many states, inability to pay fines for traffic, or any criminal offense, results in license suspension (Dewan, 2015); in this way, financial hardship exacerbates transportation access. However, transportation access also impacts income primarily by providing, or inhibiting, ability to search for jobs, to go to job interviews, get to work reliably, and to earn wages.

Social-Level Factors

At the social level, which includes family and friends, women experience several challenges. Justice-involved women are more likely than men to have, and be primary caretakers of, children (Belknap, 2007; Owen & Bloom, 1995). Approximately 66% of women in prison, compared with less than 50% of men, are primary caregivers of children living with them, prior to incarceration (Belknap, 2007). Being the primary caretaker of children increases women’s economic and time-management responsibilities.

Women on probation and parole often rely on family and friends for help with childcare, housing, and employment (Morash, 2010; O’Brien & Harm, 2002). Cobbina (2010) found that women on probation and parole perceived family and social support as the most critical factor in successful community reentry. Women with a supportive family relationship do better on community supervision than those without such support in terms of recidivism, substance abuse, and mental health outcomes (Dowden & Andrews, 1999; Slaght, 1999). Support from family or friends can translate into rides to job interviews, grocery stores, medical appointments, supervision appointments, and other required appointments (e.g., urine drops, substance abuse treatment, court dates, etc.). Support from family or friends may also mean money for a bus ticket or someone to walk with you through an unsafe neighborhood.

Prisoners who have social support from family during imprisonment, compared with those without, do better on release (Hairston & Rollin, 2006). However, women’s support networks may have been severed from lack of contact while in prison. Women’s prisons are often located further away from their homes in remote areas inaccessible to family members (Coughenour, 1995; La Vigne, 2005; Shollenberger, 2009; Travis & Visher, 2005). Due to scarcity of federal prisons for women, an average female inmate is more than 160 miles farther from her family than a male inmate (Coughenour, 1995). Being imprisoned farther from family, women may experience fewer visits; these visits have been shown to lower recidivism (Duwe & Clark, 2013).

Those with felony records, in general, but particularly women on probation and parole, commonly live in neighborhoods characterized by high crime rates, unemployment, family disruption, and poverty (Cobbina et al., 2014; Petersilia, 2003; Richie, 2001). Sampson and Loeffler (2010) refer to these neighborhoods as hot spots for incarceration; these spaces, with high rates of residents going to prison and returning from prison, are demarcated by a collision of disadvantage, crime, and high incarceration. Residing in these places is destructive for a variety of reasons.

Residing in these unsafe and inaccessible neighborhoods, women lack appropriate services to address their constellations of needs (Morash, Bynum, & Koons-Witt, 1998; Richie, 2001). They report feeling unsafe walking from place to place or standing at bus stops (Cobbina et al., 2014); thus, the neighborhood environment further limits transportation options. However, transportation problems also contribute to residence in poor neighborhoods: individuals are unable to get reliable bus service, out of these neighborhoods, to other areas where more- and better-paying jobs are located.

While it would be hard to change someone’s neighborhood, better transportation access, for example access to a car, can overcome the negative impacts of disadvantaged neighborhoods. Research on dual-labor market theory (Bluestone, 1972; Crutchfield, 1989; Piore, 1970) supports the assertion that traveling to primary-sector employment outside these disadvantaged areas, or secondary-labor markets, is key to improving economic stability. Transportation enables individuals to escape low-wage jobs with high instability and poor work conditions, characteristics of secondary sector jobs, and reach better-paying and more stable jobs. Research supports the link between transportation and better jobs. For low-income single mothers, car ownership is an even stronger predictor of gaining and maintaining employment than education or work experience (Lichtenwalter, Koeske, & Sales, 2006). One factor is that a reliable car translates into fewer days of missed work (Lambert, 1998). Recent research, utilizing data from the Department of Housing and Urban Development, found that low-income individuals with cars, compared with those without, lived in higher opportunity neighborhoods. These neighborhoods were characterized by lower poverty rates, higher social status, stronger housing markets, and lower health risks (Pendall et al., 2014).

Gains in employment, due to better access to transportation, are not isolated to car ownership. Research from New York University’s Rudin Center for Transportation (2014) examined the link between mass transit and employment. After categorizing neighborhoods by accessibility to mass transit (i.e., sufficient transit; some but insufficient transit; no transit), the researchers examined employment rates and incomes for each area. The first group had the lowest unemployment rate (8.3%), the highest median household income ($79,148.83), and the lowest reliance on cars (10.9% commuted by car). The researchers found that, as access to quality transit decreases, employment and incomes commensurately suffer. In fact, mass transit was more important to economic mobility than education.

Present Study

Consistent with cumulative disadvantages theory, the previous literature shows that justice-involved women face many challenges during the time they’re on community supervision. These include substance abuse, mental health issues, poor physical health, unemployment, financial hardship, caretaking responsibilities for minor children, lack of social support, and residence in unsafe or inaccessible areas. Added to these other factors, cumulative disadvantages theory would suggest that dependable and affordable transportation also impacts recidivism. Because transportation access has not been routinely examined in previous research, this study must first create a scale for assessing levels of transportation access. Then, the study can examine the theory, that is, the impact of transportation disadvantage, with other traditional predictors of recidivism, on arrest and conviction events using multiepisode event history analysis followed by conditional logistic regression.

Method

Sample

The sample for the data analysis¹ included 402 drug-involved women on probation or parole convicted of a felony offense in the state of Michigan. Drug-involved women account for the most common subgroup of women offenders (Harer & Langan, 2001; Morash, 2010; Peters, Strozier, Murrin, & Kearns, 1997), and as such represent the typical female offender. The sample encompassed 16 counties that include more than 68% of the 2011 state population, all major population centers (e.g., Detroit, Grand Rapids), and a mix of rural and suburban areas. Women were recruited from agent’s caseloads and interviewed after approximately 2-, 5-, and 8 months of supervision had passed. When transportation problems arose as a common theme in two earlier waves of data collection, measures of transportation disadvantage were included in the third wave of data collection. Data for this study come from the third interview, at which time 379 (94.3%) women were reinterviewed, as well as subsequent tracking of rearrest and conviction records (described below). At the third interview, 12 women were institutionalized (i.e., in jail, prison, or inpatient substance abuse treatment) and one woman was too physically ill to leave her home. As a result, these 13 cases were excluded; the sample was restricted to only women who could appropriately answer questions about transportation access (n = 366). Finally, four women moved out-of-state at an unknown date; their time to rearrest could not be considered censored survival time without an accurate move date. Their removal yielded a final sample size of 362.

Measures

Transportation disadvantage

To create the Transportation Disadvantage Scale, several items were examined for inclusion (see Table 1 for a complete list). Litman (2011) wrote that transportation deprivation is indicated by expenditures greater than 20% of annual income and by greater than 90 min of travel time per day. Following Litman’s (2011) measurement approach, women’s reports of travel time and costs in a typical week were collected. Other researchers have noted the importance of considering safety, ease (Solomon & Titheridge, 2006), and stress related to travel (Gottholmseder, Nowotny, Pruckner, & Theurl, 2009). Thus, for each trip in a typical week, women rated safety, ease, and stress of traveling. Further, women provided a self-reported access to dependable transportation (i.e., a likert scale indicating their agreement that they had money for bus fare, gas for a car, or a dependable car when they need it). Finally, the number of people providing transportation-related support was available from women’s ratings of agreement that they could count on family and friends to (a) help them get to places and to (b) give them money to get to places. Other items examined were related to access to a car, access to public transit in their area, neighborhood accessibility, physical health, and anticipated transportation-related help from a broader social network.

Table 1.

Transportation Access Indicators for Exploratory Factor Analysis.

Variable	Range	M	SD
Does not own/lease car	0-1	0.679	0.466
Does not have access to licensed car	0-1	0.366	0.482
Does not have valid license	0-1	0.579	0.492
Poor overall health	1-6	3.175	1.181
Difficulty walking	0-1	0.232	0.420
Poor vision	0-1	0.294	0.452
Number family/friends who can help	0-13	3.052	2.156
Agrees has help from family/friends	1-4	3.144	0.854
Agrees receives money from family/friends	1-4	2.851	0.905
WalkScore: Neighborhood accessibility score	0-91	41.531	21.024
Livability: Neighborhood accessibility score	53-84	69.52	5.185
Livability: Proximity to amenities	1-9	6.132	2.376
Livability: Safety of neighborhood	1-9	3.299	2.787
Overall, self-report of transportation access^a	1-4	3.196	0.891
Ease of travel, on average^a	1-5	4.346	0.727
Stress of travel, on average^a	1-4	2.140	0.552
Time spent in travel, on average^a	2.5-120 min	17.905	14.638
Cost of travel, on average	$0-51	5.126	5.422
Safety of travel, on average^a	0-1	0.9666	0.156

Note. Minimum N is 366.

Indicates items included in final access score.

In all, 19 items were evaluated through a combination of exploratory and confirmatory factor analysis (see Table 1). Following Bollen’s (Bollen, 1989, p. 301) prescriptions, we randomly split the sample into an exploratory half and a confirmatory half. The exploratory half was subjected to an exploratory principal factor analysis using orthogonal rotation to discover underlying dimensions in the items. Based on the scree plot of eigenvalues, and a comparison of the eigenvalues to the roots of a random correlation matrix (Gorsuch, 1983; Longman, Cota, Holden, & Fekken, 1989), a five-factor model was indicated.

Seven items that appeared to best capture transportation access loaded on the first factor and were retained for further analysis. The seven items on the first factor included self-reported travel time in a typical week; self-rated safety, ease, and stress of traveling and self-reported access to dependable transportation; and actual financial or transportation-related support from family and friends. Using the confirmatory half-sample, we ran one- and two-factor confirmatory factor analysis models on these seven items. The two-factor model had a good fit to the data (χ² = 21.575, df = 13, p = .062; goodness of fit index [GFI] = .966; root mean square error approximation [RMSEA] = .061) and was a significant improvement in fit over the one-factor model (Δχ² = 24.383, df = 1, p < .001). For the two-factor model, five items loaded on the first factor and two loaded on the second. On the first factor were all the individual-level items; on the second factor were two social-level items related to support from family and friends. Although statistically the two-factor model was advisable, it made more conceptual sense to use only the individual-level items loading on the first factor. Table 1 shows that, for the final scale, we used the five items that loaded together on the first factor: self-reported travel time, which was approximately 17 min per trip, on average; self-rated safety of travel (.9, indicating safe neighborhood); self-reported ease of travel (4.34, indicating high agreement that travel is easy); stress of travel (2.14 out of 4); and self-reported access to dependable transportation (3.19 of 4, indicating high levels of access). Two items, travel time and stress of travel, were reverse coded so that high scores on the scale indicate greater access to transportation.

Focal variables

Measures included in the recidivism analysis include Substance Abuse Scale, Depression and Anxiety Scale, physical health items, employment status, financial status, caretaking of minor children, neighborhood crime, and community accessibility. Two scales are adapted from the Women’s Risks and Needs Assessment (Van Voorhis, Salisbury, Wright, & Baumann, 2008). First, the Substance Abuse Scale was formed by summing together six items regarding recent convictions related to drug/alcohol use, missing treatment appointments, associating with or living with individuals who drink heavily or use drugs, and current or recent drug or alcohol use (Cronbach’s α = .431). Second, six Depression and Anxiety Scale items measure whether women, in the past 7 days, have experienced problems staying focused, mood swings, loss of appetite, fears about the future, or trouble sleeping or getting things done (Cronbach’s α = .778).

Following measures used by Dupuis, Weiss, and Wolfson (2007), questions about physical health limitations focused on women’s ability to walk and see and their overall health. Each item was standardized and then the three questions were summed into a scale, with higher scores indicating poorer health. Employment status was an interval-level variable indicating women were employed full-time, part-time, or unable to work, or unemployed but able to work. Financial hardship is a dichotomous variable indicating that the participant has “had problems like bankruptcy, calls from collection agencies, cut-off utilities, problems with getting child support payments, repossession of property, or other things like that” since their first interview, which captures approximately the past 6 months. Women were asked whether they are the caretakers of minor children, that is, whether children 18 or younger live with them or if they have monthly contact.

Two measures were available for community accessibility and safety of neighborhoods. Women’s residential address was linked to two sources of publicly available data on accessibility—Walkscores and Livability scores (Area Vibes, 2014; Walk Score, 2014). These accessibility measures utilize a variety of data sources (e.g., Google, Open Street Map) to rate neighborhoods by availability of goods and services as well as safety, cost of living, education, employment, housing, and weather. Walkability scores, which range from zero to 100 with a higher score indicating a better location, were used to measure community accessibility. Livability scores provide a measure of overall accessibility as well as subscores for each dimension; the subscore for crime was utilized for this study as a proxy for neighborhood safety. The crime dimension was derived from Uniform Crime Report data on two main categories of crime: violent crime (murder, rape, robbery, and assault) and property crime (burglary, theft, and vehicle theft). The Livability website calculates the total crime index based on all crimes; higher weights are given to violent crimes, and the score is based on comparisons to both state and national averages.

Recidivism

Recidivism is operationalized as an arrest or conviction for a new offense (i.e., a rearrest or reconviction). Data on rearrests and reconvictions were obtained electronically from the Michigan State Police (MSP) criminal history database. The main limitation of these data is that they are limited to arrests and convictions that occurred in Michigan, therefore the findings likely underestimate the true recidivism rates (i.e., rearrests and reconvictions). This limitation is considered minor because most women, while on probation and parole, must reside in Michigan and must ask permission before traveling outside the state, greatly reducing the likelihood of being arrested or convicted in other states. Another limitation of these data was that incarceration information was not available. To accurately measure the amount of time that offenders are actually at risk to recidivate, for survival analyses, researchers should account for, and adjust “at-risk periods” for time spent in jail/prison by deducting the amount of time spent in jail/prison from a person’s total at-risk period. Failure to deduct time spent away from the community artificially increases the length of the at-risk period for these offenders.

The recidivism at-risk period was calculated as number of days between when the women were interviewed about transportation access and the date the recidivism data were obtained from MSP, July 1, 2015. Survival times were adjusted for five deaths and three women who moved out-of-state. For these women, their “time at risk” was calculated as the time from their Wave 3 interview until the end of the risk period, which occurs when they moved or died. Since interviews were conducted from May 2012 to May 2013, the follow-up period was 793 to 1,128 days (2 to 3 years) with a mean of 973.3 days (2.66 years) and standard deviation of 75.9 days. In this time period, 109 (30%) women experienced one to seven rearrests and 92 (25.4%) experienced one to five new convictions.

Missing data

Fewer than 3% of sample respondents had missing data on any given variable, most often due to the respondent skipping a question during the interview. Hence, we employed listwise deletion to preserve only cases with valid data on all variables as the analytic sample. Although multiple imputation was considered, Allison (2002) has shown that listwise deletion is the missing-data technique that is most robust to violation of the missing at random (MAR) assumption for the independent variables.

Analytic Strategy

Two different statistical models were employed for the two main response variables in this study. First, we were interested in modeling the hazard of rearrest, while taking account of the fact that several of the women had more than one arrest. For this purpose, we employed multiepisode event history analysis (DeMaris, 2004) which can account for a series of arrests. Multiepisode event history analysis is well-suited for a correctional population. Because many individuals recidivate, the use of logistic regression (i.e., modeling simply whether or not an event occurs) is not as informative as modeling the time elapsed before an event occurs. Further, many justice-involved individuals have experienced a series of arrests and convictions: multiepidsode event history modeling, in contrast to more commonly used models, is appropriate for utilizing the additional data provided by the series of events.

Data on the 362 respondents were reconfigured into a 422-case person-period file. Each woman contributed as many observations to this file as she had rearrests. So if she had just one rearrest, she contributed one observation. If she had two rearrests she contributed two observations, and so forth. For each risk period for rearrest, a duration variable recorded the number of days until rearrest. If a woman was lost to follow-up or died before rearrest, her duration (survival) in the non-arrest state was considered censored at that point. A woman’s survival time was also considered censored as of the end of the study if she had not been rearrested by that time. For each rearrest for the same woman, the “clock” began again at time zero, and tracked the duration until that rearrest. Once the data set was configured in this manner, we ran Cox regression models to estimate the hazard of rearrest as a function of model covariates. Due to multiple observations being taken from the same respondent, there was a potential for dependence among observations in the data set. To assess this, we employed Allison’s (2010, p. 265) test for duration dependence, which turned out to be significant. Therefore, as recommended by Allison, we utilized robust standard errors to correct for the non-independence of survival times in the data set.

The second response of interest was whether or not a woman was convicted, given that she was rearrested. For this part of the analysis, we employed logistic regression on the 109 women experiencing at least one rearrest to examine the predictors of conviction. Because this analysis is conditional on having been arrested, we refer to it as a conditional logistic regression. For both the hazard analysis and the conditional logistic regression, we report the scaled generalized R² (“generalized R²” in the table) to assess the models’ discriminatory power (DeMaris, 2004).

Results

Table 2 presents the descriptive statistics for the sample. As can be seen, on average, women in the sample were 19 to 61 years old with the average age being 34 years old. Women had an average of 4.9 felony arrests before their participation in this interview. The number of arrests ranged from 1 to 15. Sixty-one percent of women were in romantic relationships, 46% took care of minor children, 68% had a high school diploma or equivalent, and 19% were employed full-time. Thirty-three percent of the sample had experienced financial hardship in the past 6 months.

Table 2.

Descriptive Statistics for Study Variables.

Variable	Range	M	SD
Arrested	0-1	0.304	0.461
Number of arrests	0-7	0.492	0.957
Convicted	0-1	0.254	0.436
Transportation access	−3.348-0.998	−0.006	0.619
Age at T3	19-61	34.754	10.638
Number of arrests prior to T3	1-15	4.912	3.707
Partnered	0-1	0.610	0.488
Has dependent children	0-1	0.461	0.499
High school graduate	0-1	0.681	0.467
Employed	0-1	0.191	0.393
Economic hardship	0-1	0.337	0.473
Substance abuse	0-4	0.967	1.159
Depression	0-6	1.862	1.890
Health limitations score	−3.037-5.750	0.007	2.165
Neighborhood crime score	1-9	3.306	2.808
Neighborhood accessibility score	0-91	41.481	21.177

Note. Minimum N is 359.

Women scored low on both the Substance Abuse Scale (mean of .967 out of 4 possible points) and the Depression Scale (1.862 out of a possible 6 points). With an expected mean of zero and a larger than expected standard deviation of 2.165, we can see women in the sample had a wide range of physical health. Scores range from −3 standard deviations below, to almost 6 standard deviations above, average physical health limitations for the sample. Looking at neighborhood characteristics, we can see that women lived in high-crime areas (3.306 out of 9) and in undesirable, or inaccessible, areas (41.48 out of 100 possible).

Women experienced a range of zero to seven arrests; just over 30% of the sample experienced at least one arrest during the follow-up period. As we would expect, fewer women were convicted; 25.4% of the sample experienced one conviction during the follow-up period. Because the transportation access score is the mean of standardized items, the mean is approximately zero. One can see the range for transportation access scores in this sample extends to −3.348. This indicates that some women in the sample are quite disadvantaged, compared with other women.

Multiepisode Event History Analysis

Model 1 in Table 3 shows estimates for the regression of the log hazard of rearrest on transportation access and other model covariates. As can be seen in Model 1, economic hardship (−.511), being partnered (−.324), and being older (−.042) are associated with lower hazards of rearrest. On the other hand, having more health limitations (.118) and a greater number of prior arrests (.204) are associated with an increased risk of rearrest. Controlling for these other factors, transportation access apparently has no measureable effect on recidivism, with a coefficient (−0.005) that is close to zero.

Table 3.

Cox Multiepisode Hazard Estimates for the Hazard of Arrest (Model 1) and Conditional Logistic Regression Estimates for the Probability of Conviction, Given Arrest (Model 2).

Variable	Model 1	Model 2^a
Transportation access	−0.005 (0.152)	−1.517^† (0.849)
Age at T3	−0.042*** (0.011)	−0.031 (0.033)
Number of arrests at T3	0.204*** (0.020)	0.278** (0.095)
Partnered	−0.324^† (0.179)	−0.548 (0.628)
Has dependent children	0.009 (0.207)	1.301* (0.635)
High school graduate	−0.160 (0.192)	1.283* (0.620)
Employed	0.173 (0.258)	1.033 (0.767)
Economic hardship	−0.511* (0.222)	0.734 (0.732)
Substance abuse	0.039 (0.076)	0.008 (0.279)
Depression	0.033 (0.050)	−0.228 (0.187)
Health limitations score	0.118* (0.047)	0.372* (0.178)
Neighborhood crime score	0.048 (0.036)	0.415** (0.145)
Neighborhood accessibility score	0.002 (0.005)	0.017 (0.016)
Likelihood ratio χ²	135.508***	35.791***
df	13	13
Generalized R²	.277	.416
Hosmer–Lemeshow χ²		2.534
df		8
Area under the curve (AUC)		.851

Note. n = 422 person-periods for Model 1 and 109 persons for Model 2. Standard errors are in parentheses.

Intercept omitted for consistency with Cox regression results.

†

p > .10. *p < .05. **p < .01. ***p < .001.

Conditional Logistic Regression Analysis

Model 2 in Table 3 presents the results of the conditional logistic regression of conviction on transportation access and model covariates. As we see in Model 2, transportation access has a marginally significant negative effect on the risk of conviction (−1.517). For every unit increase in the transportation access score, the log odds of conviction declines by about a unit and a half. This is in line with the expectation that better transportation access should lower the odds of recidivism events.

All the other significant effects on the probability of conviction are positive. Thus, the risk of conviction is enhanced by having dependent children (1.301), being a high school graduate (1.283), having greater health limitations (.372), having more prior arrests (.278), and living in a higher crime neighborhood (.415). Note that the model appears to have a decent fit to the data judging by several criteria. First, the generalized R² of .416 suggests this model accounts for 41.6% of the variation in conviction rates. Also, the non-significant Hosmer–Lemeshow statistic suggests that the model has a good fit to the data. Finally, the area under the curve, a measure of classification accuracy, at .851, is considered quite good as well.

Discussion

The purpose of this study was to examine whether cumulative disadvantage theory helps explain the role of transportation access on recidivism events. That is, for disadvantaged women, does transportation access combine with other common challenges of community supervision to create insurmountable challenges. And, for women with more advantages, transportation concerns will be easily surmountable. The first step in this endeavor was to create a scale that indicates women’s access to transportation. We also had a secondary aim of utilizing stronger, and more appropriate, statistical methods, such as conditional logistic regression and survival analysis, to capture more than just the first recidivism event and to include time until the event occurs. The use of these more appropriate methods emerged as a concern during prior work (Northcutt, 2014) which revealed that less appropriate methods for examining the impact of criminogenic factors on only initial recidivism events, although commonly used in criminal justice research, highlights shorter term factors as significant and conceals longer term factors impacting recidivism. The import of modeling all available recidivism events, given the entrenched nature of criminal justice system involvement, cannot be overstated. A major contribution of this work is to illustrate a more suitable method for modeling recidivism events.

This study found that transportation access significantly predicts the occurrence of conviction, given that an individual is first arrested. Transportation access is a significant predictor even when other common predictors of conviction are included in the model. However, transportation is not a significant predictor of the hazard of rearrest. This is the first study to examine the impact of transportation alongside other common predictors of recidivism and to find that transportation emerges as an important challenge among other cumulative disadvantages.

Consistent with the prior literature, this study finds support for several commonly utilized predictors of recidivism. First, consistent with research on the age–crime curve, being younger was a significant predictor of the hazard of arrest, though not of conviction, given arrest (Loeber & Farrington, 2014). Second, more extensive criminal histories were significantly predictive of the hazards of both arrest and conviction (Gendreau, Little, & Goggin, 1996). Third, women who live in higher crime neighborhoods have greater risk of conviction, given arrest (Cobbina et al., 2014; Sampson & Loeffler, 2010). However, living in a high-crime neighborhood did not significantly predict the timing of arrest. Fourth, consistent with previous literature, poor health is predictive of patterns of arrest and conviction (Thomas, Spittal, Taxman, & Kinner, 2015).

This study finds several perplexing effects that are not easily reconciled with prior literature. First, women who care for children have a greater risk of conviction, given arrest. Perhaps for women who have children, consistent with cumulative disadvantage theory (Sampson & Laub, 2004), the presence of children brings additional economic and emotional stressors which can deplete women’s resources beyond their abilities to cope non-criminally (Agnew, 1992). Second, inconsistent with cumulative disadvantage theory, women with greater economic hardship had significantly lower risk of rearrest, but not conviction. Previous literature has found economic hardship to be related to higher levels of crime (Crutchfield, 1989; Holtfreter et al., 2004; Morash, 2010). Perhaps women in this sample who had greater levels of financial hardship reached the threshold for applying for social services and therefore, despite lack of money, had food and housing available. Third, and counterintuitively, women with high school diplomas, compared with those without, had higher hazard of conviction, given arrest. Research has consistently shown that higher levels of education reduce crime (Chiricos, 1987; Freeman, 1996; Lochner & Moretti, 2004). However, these studies also identify that the greatest impact is for murder, assault, and motor vehicle theft and for men (Lochner & Moretti, 2004). Perhaps women differ from men; greater education is not a protective factor for women who tend to engage in higher levels of identity theft and fraud which require greater mental acumen. This highlights the need for future research to, as others have acknowledged (Hannah-Moffat, 1999; Van Voorhis, 2012), identify that criminogenic needs common to men may differ from those common to women. Finally, discordant with the previous literature, this study does not find substance abuse, depression, or unemployment to be important predictors of hazards of either arrest or conviction (Bloom et al., 2003; Davis et al., 2012; Scroggins & Malley, 2010). Of course, non-significant coefficients should not be interpreted to say these factors did not matter at all. Rather, the findings may suggest that, when examining patterns of recidivism events, as opposed to just the initial event, these factors may play less of a role. Further, when considering which factors contribute to an accumulating wave of disadvantage, these findings suggest that the factors that traditionally accumulate faster or with greater frequency may not be the correct focal factors. Or, perhaps, this research suggests that, for recidivism outcomes, it is not the presence or combination of Factors A, B, and C that matter as much as any three factors accumulating.

The study is limited in terms of generalizability. The study focuses on substance-involved females with felony convictions in one Midwestern state. Because many women offenders are substance involved, the study is generalizable to most women in the criminal justice system. The present study improves existing research in several important ways. First, the longitudinal nature of the study, examining transportation access over time and situating it within the broader concerns in women’s lives, establish the extent to which transportation impacts the lives of women offenders. Second, the sample sizes and high retention of women in those samples (366 of the original 402) provide confidence in the external validity of the study—that the women who were retained represent the larger population from which they were sampled. Third, the diversity of the sample included women living in both rural and urban areas which allowed for examination of a variety of levels of neighborhood crime, access to public transportation, and proximity to needed resources.

These findings, taken together, suggest two avenues for research. First, this study is the first to identify transportation access as a significant predictor of conviction, given arrest. As such, access to transportation has emerged as a new factor impacting women’s success on community supervision. Second, the study finds several perplexing factors that impact recidivism in ways that differ from prior studies: presence of children, economic hardship, education, substance abuse, depression, and unemployment. Perhaps these traditional predictors of recidivism are typically used to predict only the first recidivism event may highlight shorter term factors as significant and conceal longer term factors impacting recidivism. These findings underscore the need for additional research that considers the multiple factors, often disadvantages, impacting individuals who are embedded in communities that vary in terms of employment, crime, social services, and change over time (Hagan & Coleman, 2001).

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institute of Justice under Grant Number 2013-IJ-CX-0041 and the National Science Foundation under Grant Numbers SES-1323461 and SES-1126162. The opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect those of the funding agencies.

Author Biographies

Miriam Northcutt Bohmert is an assistant professor of Criminal Justice. Her work focuses on race, class, gender, and other social justice issues related to prisons and community supervision. Her research has been funded by the National Science Foundation and the National Institute of Justice. Her published work has appeared in the British Journal of Criminology, Social Forces, The Prison Journal, and Criminal Justice Policy Review.

Alfred DeMaris is a professor of sociology and statistician for the Center for Family and Demographic Research at Bowling Green State University in Bowling Green, Ohio. His substantive interests are focused on romantic relationships, including issues surrounding premarital cohabitation, the prediction of marital instability, the antecedents and consequences of violence in heterosexual dyads, the antecedents and consequences of extramarital sex, and the operation of equity/inequity in marriage. His work in these areas has appeared in Social Forces, Social Psychology Quarterly, Journal of Marriage and Family, Journal of Family Issues, Family Relations, and Journal of Sex Research. He is also the author of Logit Modeling: Practical Applications (1992, Sage), Regression With Social Data: Modeling Continuous and Limited Response Variables (2004, Wiley), and Converting Data Into Evidence: A Statistics Primer for the Medical Practitioner (2013, Springer).

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