Over the last decade, scholars interested in the punitive shift in U.S. criminal justice policy in the 1980s and 1990s amassed an extensive literature on the social and structural processes that influence decisions to incarcerate, and subsequent sentence length. Consequently, there is a variety of information available about policy changes over time, the factors that led to the shift in punitive policies, and the ways these policies influenced the likelihood of incarceration for people from various sociodemographic groups (Reference Beckett and SassonBeckett and Sasson 2000; Reference DusterDuster 1997; Reference Pettit and WesternPettit and Western 2004). Despite the abundance of literature available on sentencing patterns (Reference Kramer and UlmerKramer and Ulmer 1996; Reference Steffensmeier and DemuthSteffensmeier and Demuth 2000; Reference Steffensmeier, Kramer and StreifelSteffensmeier, Kramer, and Streifel 1993; Reference Steffensmeier, Kramer and UlmerSteffensmeier, Kramer, and Ulmer 1995; Reference Steffensmeier, Ulmer and KramerSteffensmeier, Ulmer, and Kramer 1998; Reference TonryTonry 1996; Reference Ulmer, Kurlychek and KramerUlmer, Kurlychek, and Kramer 2007) and prison reentry (Reference BushwayBushway 2004; Reference KushelKushel et al. 2005; Reference Langan and LevinLangan and Levin 2002; Reference Lynch and SabolLynch and Sabol 2001; Reference PetersiliaPetersilia 1999, 2003), scholars and policymakers have yet to explore patterns of release over time and across various socio-demographic groups. Moreover, the available literature on the shift in U.S. criminal justice policy tends to neglect the influence of history, which suggests that punitive policy in the period under study had a uniform effect on sentencing and incarceration across racial/ethnic groups.
The study used data from the National Corrections Reporting Program (NCRP) to examine patterns of release from 1985 to 2009 in California. In addition to incorporating the factors that we know impact sentencing, I specified temporal boundaries for the cohorts studied, based on theoretical and historical factors that may have influenced the release of individuals admitted to prison in California between 1985 and 2000. Thus, this article examines cohorts sentenced between 1985 through the end of 2000, and follows them through 2009. It then decomposes the length of stay into the maximum sentence given at the time of sentencing and the percentage of the maximum sentence actually served. The product of these two pieces equals the length of stay; however, one is determined by a judge at the time of sentencing, and the other is influenced by factors outside the bounds of the courtroom.
This inquiry examines the relationship between the critical features that introduce inequity in sentencing and the components of release—maximum sentence, percentage of maximum sentence served, and total time served. I also investigate if there are differences between racial and ethnic groups in the components of release. Differences do exist between racial groups for each component, and the differences enable the masking of disparities in sentencing. Essentially, the findings implicate the role of the people and events that occur outside of the courtroom as playing a significant role in the continuation of racial disparities in time served. Such dynamics can play an equally, if not more, important role in the actual time served than the sentence delivered by the judge.
The next section of the article, the background, provides an overview of the rise of mass incarceration and the punitive shift in crime control in the United States and California in the 1980s and 1990s. I then address the relevant literature on sentencing trends in the United States, describe the data sources, and outline the two prison-entry cohorts used in the analysis. This includes descriptions of the incarceration policies and practices that shaped the experiences of individuals who entered prison in those two time periods—1985–1993 and 1994–2000. I then describe the methods used, followed by the analysis, and conclude with a discussion of the findings.
Background
The Punitive Shift in Crime Control
Reference GarlandGarland (2001) first coined the term “mass incarceration” to describe the enormous rise in the incarcerated population in the United States since the 1970s. In fact, the United States currently has the largest incarcerated population in the world (International Centre for Prison Studies 2013). Between 1978 and 2005, there was a fivefold increase in the number of people sentenced to more than 1 year in a state or federal correctional facility (Reference Hill and HarrisonHill and Harrison 2005). By 2009, America's jails and prisons were home to almost 2.3 million people (Reference GlazeGlaze 2010). Although the vast majority of scholars agree that the rise in the prison population in the 1980s and 1990s had more to do with a turn toward harsher and longer sentences than an increase in criminal behavior, scholars differ in their explanations for the markedly more punitive shift in the U.S. penal system (Reference Conklin and JacobsonConklin and Jacobson 2003). Some scholars examining the economic characteristics of this punitive shift point to falling wages and rising income inequality in the early 1980s as a reason for rising incarceration rates (Reference Western, Kleykamp and RosenfeldWestern, Kleykamp, and Rosenfeld 2006) while other scholars argue that the prison system expanded as a mechanism for managing surplus labor (Reference GilmoreGilmore 2007; Reference Wacquant and TonryWacquant 2010; Reference WilsonWilson 2007).
The political climate may also be implicated in the punitive shift. A number of studies support the notion that “tough-on-crime” policies are particularly salient when the Republican Party is dominant (Reference BeckettBeckett 1999; Reference Jacobs and HelmsJacobs and Helms 1999; Reference MarionMarion 1994). Republican presidential platforms throughout the last two decades of the twentieth century leaned heavily on tough-on-crime rhetoric, culminating in programs like Reagan's infamous War on Drugs and President George H. W. Bush's televised statement that crime was the “nation's most serious problem” (Reference BeckettBeckett 1999; Reference MarionMarion 1994). Still, when examining trends in the criminal justice system it is important not to focus solely on the party in power. In California, Governor Arnold Schwarzenegger, a Republican, was committed to prison reform and endorsed rehabilitation and drug treatment programs (Petersilia 2010). Democrats in turn participated in the punitive shift; at the end of the twentieth century, no politician wanted to be characterized as being weak on crime, and many Democratic politicians were quick to endorse tough punitive legislation on the local, state, and federal level (Reference BeckettBeckett 1999; Reference PettitPettit 2012).
In 1986, President Reagan formalized his political agenda that featured a harsh crackdown on drugs and crime by passing the Anti-Drug Abuse Act, which targeted drug users as a criminal population and substantially lowered the threshold for imprisonment. The War on Drugs was an important factor in the rise of inequality in imprisonment in the late 1980s and 1990s, with blacks and Latinos experiencing higher drug-related incarceration rates than whites, despite evidence that whites had higher rates of substance abuse (Reference Blumstein and BeckBlumstein and Beck 1999; Reference TonryTonry 1996). The prison population embodied a strikingly disproportionate representation of race and class, and this inequality in incarceration has only increased over time (Reference ClearClear 2008; Reference Daly and TonryDaly and Tonry 1997; Reference Pettit and WesternPettit and Western 2004; Reference Wakefield and UggenWakefield and Uggen 2010). According to the recent report published by the National Research Council, blacks were three times more likely to be incarcerated than whites in the 1930s, but that figure had risen to seven times more likely by the 1990s (Travis, Western, and Redburn 2014). Furthermore, Reference Pettit and WesternPettit and Western (2004) found that, among men born between 1965 and 1969, 3 percent of whites served time in prison by their early thirties, compared to 20 percent of blacks.
Sentencing in California
In California, the passage of a determinate sentencing law (DSL) in 1977 and the “Three Strikes” Law in 1994 were two important contributors to the rise of mass incarceration and subsequent prison overcrowding (Reference Ehlers, Schiralde and ZiedenbergEhlers, Schiralde, and Ziedenberg 2004; Reference GilmoreGilmore 2007; Reference LightLight 1988; Petersilia 2010). DSLs are designed to standardize sentencing and thereby limit the discretion judges and parole boards have in sentencing procedures. Determinate sentencing was accompanied by a new parole experiment. When the law was passed, it effectively eliminated the parole board's capacity to make release decisions, expanded the supervisory power of the criminal justice system, and significantly increased parole officers' authority over a large number of former prisoners. Since passage of the DSL, every prisoner released in California has been on parole and under the supervision of a parole officer. These officers do not operate with the same transparency, and are not held to the same accountability, as parole boards (Reference Travis and WaulTravis 2003). As a result, California hosts nearly one-fifth of all parolees in the United States, and parolees' rate of return to prison increased thirtyfold between 1980 and 2000 (Travis and Lawrence 2002b).
Although DSLs aimed to standardize the sentencing procedure, evidence from various studies indicates that disparities in sentencing practices remain, and entry into prison is as much socially determined as it is legislatively mandated (Reference Ulmer, Kurlychek and KramerUlmer, Kurlychek, and Kramer 2007; Reference Wakefield and UggenWakefield and Uggen 2010). Much of the literature on sentencing examines the influence of race, ethnicity, age, and gender on sentencing outcomes (Reference Steffensmeier and DemuthSteffensmeier and Demuth 2000, 2006; Reference Steffensmeier, Kramer and StreifelSteffensmeier, Kramer, and Streifel 1993; Reference Steffensmeier, Kramer and UlmerSteffensmeier, Kramer, and Ulmer 1995). Ultimately, young black men experience harsher sentences than any other group (Reference Steffensmeier, Ulmer and KramerSteffensmeier, Ulmer, and Kramer 1998).
Disparities in sentencing remained as a result of prosecutorial decisions to apply mandatory sentences to eligible offenders. In a study of prosecutorial decisions to enforce a mandatory minimum among eligible offenders in Reference Ulmer, Kurlychek and KramerPennsylvania, Ulmer, Kurlychek, and Kramer (2007) found prosecutors more likely to give a mandatory minimum to Hispanic males than to white and black males. They found further that black–white disparities in mandatory sentencing increased as the percentage of the black population in counties increased. Consequently, minorities were doubly disadvantaged under DSLs, in that these laws target offenses for which minorities are disproportionately arrested, and that prosecutors are more likely to request the mandatory minimum to minorities. California's DSL system disadvantages minorities but through a different mechanism.
California's DSL requires the judge to classify the crime as low-term, medium-term, or high-term. The classification is a result of judicial discretion, severity of the offense, criminal history of the defendant, and other mitigating or aggravating factors. Specific sentence lengths are accompanied by that decision. In other words, the sentences are exact. For example, for burglary, the low sentence is 24 months, the medium sentence is 48 months, and the high sentence is 72 months. Second degree attempted murder carries a sentence of 60 months in the low-term, 84 months for the middle-term, and 108 months in the high-term. This type of schedule is not imposed on misdemeanors, where the maximum sentence is a fine and up to one year in jail. Prison is reserved for sentences greater than 1 year. There are some crimes that can be classified as a misdemeanor or a felony, such as spousal abuse. The decision is in the hand of the judge. The other piece of legislation that differentially impacted individuals was California's Three Strikes Law.
California was the second state, after Washington, to pass this law, which doubled sentences for second-time felons and automatically gave 25-to-life sentences to third-time felony offenders. Moreover, offenders sentenced under this law were ineligible for parole until they had served at least 80 percent of their sentence. The Three Strikes Law was designed to standardize the sentencing process for felony offenders, even those who committed nonviolent felonies, and to demonstrate a tough-on-crime stance. Since the end of discretion in sentencing due to DSL, the California prison system has exploded; the DSL had already made California's criminal justice system dysfunctional. As such, there was ample discretion on the part of prosecutors and judges in their application of the Three Strikes Law; that is, actors found ways to avoid and not invoke its usage (Reference Feeley, Kamin, Shichor and SechrestFeeley and Kamin 1996).
By 2004, one in four prisoners in California was serving a doubled or 25-to-life sentence, the majority of whom had committed nonviolent felonies (Reference Ehlers, Schiralde and ZiedenbergEhlers, Schiralde, and Ziedenberg 2004). In fact, Ehlers and coworkers' 10-year study of the law in California found that the Three Strikes Law disproportionately affected nonviolent offenders, blacks, and Latinos (Reference Ehlers, Schiralde and ZiedenbergEhlers, Schiralde, and Ziedenberg 2004). In fact, the incarceration rate for black third-strikers is now 12 times higher than for white third-strikers; the rate for Latino third-strikers is nearly twice as high as for whites. The discretional application and invoking of the Three Strikes Law mostly affected the population already experiencing the heaviest arm of punishment, non-Hispanic blacks (Reference SuttonSutton 2013).
Incarceration Crisis in California
In the early 2000s, overcrowding in U.S. jails and prisons reached a crisis point when tough-on-crime rhetoric failed to map onto the realities of state budgets. By 2003, California prisons were operating at 200 percent of their intended capacity, resulting in substandard medical care, deplorable living conditions, increased gang violence, and frequent extended lockdowns (Petersilia 2003). All over the country, states began overhauling their prison systems and rolling back punitive sentencing to reduce prison populations and save money.
Overcrowding in prisons and, budgetary crises led to a movement to reform California state prisons in the early 2000s. In November 2000, the California legislature passed the Substance Abuse and Crime Prevention Act, which allowed petty drug offenders to attend community-based treatment programs in lieu of incarceration. The new law also provided treatment rather than incarceration for probationers and parolees who violated a drug-related condition of release. This early wave of sentencing reform signaled a much larger shift in the criminal justice system, both in California and across the United States, throughout the first decade of the twenty-first century, heralding a renewed interest in rehabilitation and judicial discretion (Reference BermanBerman 2005).
In the mid-2000s, states throughout the United States, including California, began to roll back punitive sentencing initiatives and California began to release petty criminals early, due to overcrowding in prisons and jails all over the state. Beginning in 2003, the Los Angeles County sheriff, Lee Baca, began releasing hundreds of petty criminals daily to save money and create prison space. In 2003 and 2004, he released over 47,500 prisoners, most of whom had been convicted for petty theft, car theft, stalking, and other nonviolent crimes (Reference Guccione, Winton and GormanGuccione, Winton, and Gorman 2004). Nevertheless, in 2005, California prisons were still operating at nearly twice their intended capacity. In 2009, a federal court ordered the state to produce a plan for the release of 46,000 prisoners within 2 years, due to substandard living conditions in the state's prisons (Reference MooreMoore 2009). Thus, an oscillating process of reform and the enforcement of punitive legislation from previous decades characterized the 2000s.
The current study examines the patterns of release in California during the period of significant changes in the sentencing practices and shifts in underlying punishment ideologies that led to California having the largest state correctional population. As a fundamental component of social processes and change, I incorporated demographic dimensions of release, similar to work done in studies of population studies of fertility and divorce. I specified prison-entry cohorts in accordance with theoretical-historical factors (Reference Lutz, Wils and NieminenLutz, Wils, and Nieminen 1991; Reference RyderRyder 1983). The theoretical and historical reasoning behind the temporal boundaries of the cohorts is closely tied to the historical overview and literature review. Rather than examining the patterns of release year after year, I take a theoretically informed approach by creating cohorts of admission that are based on political, economic, and policy-related factors. I argue that, based on these factors and the historical context outlined in the previous sections, inmates' release patterns should vary according to the time of their admission to a state correctional facility. More specifically, the later period of study (1994–2000) should be more punitive in nature than the earlier period (1985–1993). In addition, I expect that studying release patterns through the components of release—maximum sentence, percentage of maximum sentence served, and total time served—will reveal a much more complex depiction of the presence of racial inequalities beyond the sentencing process.
Data and Methods
Previous studies have firmly established the punitive shift away from rehabilitation and toward retribution and punishment in the 1980s and 1990s. Although policy and political context can present a homogeneous punitive message, the fluctuation of crime rates and economic processes indicate the possibility for changes in release patterns throughout this punitive era in the U.S. criminal justice system. Thus, one of the challenges was creating a dataset that allowed for a cross-temporal comparative examination; in other words, I wanted to test for both homogeneity (similarities) within and heterogeneity (differences) between the two periods of admission. Given the increase in severity of punishment in the second period, I expected to see changes in time served and release. Policies in place at time of sentencing impact the duration one can expect to serve behind bars, and the mean length of stay in prison is directly related to the sentencing regimes in place at the time of sentencing (Reference Patterson and PrestonPatterson and Preston 2008). Nevertheless, there is the possibility for within group differences in the mean length of stay given the many stops and interactions with authorities between sentencing and release from prison.
Data Source: NCRP
The data used for this study are from the NCRP Web site (http://www.icpsr.umich.edu/NACJD/ncrp). The NCRP data are collected by the Bureau of Justice Statistics to provide comprehensive information on entrance to and departure from correctional facilities in 38 states, which covers 80–90 percent of the total U.S. prison population. The NCRP provides individual-level data on all people admitted to and released from federal prisons and from prisons in states that choose to submit information. Each record includes demographic variables, variables pertaining to the crime for which the offender was charged, variables pertaining to length of stay in prison and time on parole (if applicable), and type of exit from prison and conditions of parole (if applicable).
This study primarily used files pertaining to the release of prisoners between 1985 and 2009 in the state of California. It follows two distinct prison-entry cohorts of new court commitments to compare the patterns of release over time. I focused on California for a number of reasons. First, California is often cast as the quintessential example of the punitive shift away from rehabilitation toward incapacitation in the 1980s and 1990s (Reference GoodmanGoodman 2012). California is also the only state that received a federal court order to release tens of thousands of prisoners, and thus provides an interesting case for an examination of release patterns over time. California collects information on the ethnicity of prisoners, which is a critical component in this analysis. The literature on sentencing indicates that Hispanics have a disproportionately greater likelihood of imprisonment and face longer terms than white and black offenders. Furthermore, the immigration policies enacted between 1985 and 2009, along with the criminalization of illegal immigrants, had an unprecedented effect on the arrest and incarceration of Hispanics in the United States. California has had the largest correctional population for many years during the study period. While analyzing additional states would also add to the literature, it would be a difficult task because each state has its own correctional history and set of policies. Lastly, studies have shown the NCRP data regarding admissions and releases to be reliable in California (Reference Neal and RickNeal and Rick 2013; Reference PattersonPatterson 2010).
The California data allowed me to parse different conditions of release and length of sentence served by race and ethnicity, which ultimately contributes to a more nuanced understanding of the state's criminal justice system. Finally, by focusing on California I was able to control the conceptualization of an “event.” In many states, the conditions of release can vary qualitatively; for example, individuals can be released unconditionally or released on parole. In California, the type of exit is uniform: prisoners are released to parole, which is not decided by a group of people but by a standard rule. I, therefore, did not have to disentangle parole board decisions, which reduced the likelihood of unobserved heterogeneity in the results. Nevertheless, programs that reward or penalize inmates' behavior still had an impact on the study of release patterns. California does allow reduced sentences as a reward for good behavior but also imposes increased sentences as a disciplinary measure.
Cohort 1: Sentenced Between 1985 and 1994
Individuals who entered prison between 1985 and 1993 experienced the first years of the punitive shift in crime control and punishment policy. In the political sphere, this cohort falls within Reagan's presidency and George Deukmijian's governorship in California. Both politicians ran for office on a platform characterized by a tough-on-crime stance, which resulted in the national War on Drugs and, in California, enforcement of the new DSL and expansion of the state's prison infrastructure with the construction of new facilities. The California legislature lowered the threshold for prison-eligible crimes to include residential burglary and domestic assault as felonies requiring prison terms (Reference GilmoreGilmore 2007).
In 1984, an act known as Street Terrorism Enforcement and Prevention, which required longer sentences for identified gang members, passed in California. New laws designed to criminalize deviant behavior disproportionately targeted racial minorities and resulted in longer sentences for black and Hispanic offenders than for white offenders. The late 1980s also marked the rise of “crimmigration,” or the criminalization of immigrants, through federal bills like the Immigration Reform and Control Act of 1986 and the 1988 Anti-Drug Abuse Act, which raised criminal penalties for unlawful reentry to the United States after deportation. In 1990, the Immigration Act raised sentences for concealing undocumented immigrants and identified a new felony, “immigration-related entrepreneurship fraud” (Reference SklanskySklansky 2012).
On the economic front, the nationwide recession in the early 1980s and the budget deficit in California contributed to both a rise in incarceration and extended sentencing. The notion that low-skill minorities unable to find work were increasingly turning to drugs and crime to mitigate the financial chasm they faced created a culture of fear among the general public. Despite evidence that crime rates were falling, politicians latched onto this fear of increased criminal behavior and introduced a flurry of punitive bills in the legislature. Politicians in California also cast the building of new prison facilities in a positive light; they appealed to prison guard unions and rural populations by arguing that these new facilities created jobs (Reference Beckett and SassonBeckett and Sasson 2000; Reference GilmoreGilmore 2007).
Cohort 2: Sentenced between 1994 and 2001
The second cohort was admitted to prison in 1994 through the yearend 2000. At the time, the federal government passed a $30 billion crime bill in 1994, which called for 100,000 new police officers, increased the number of prisons across the United States, and expanded which crimes qualified for the death penalty. An early version of the bill allotted at least $3 billion for crime prevention programs. It also contained an element known as the Racial Justice Act, which aimed to minimize racial disparities in capital punishment sentencing. Republican opposition blocked the bill's passage until the crime prevention programs and the Racial Justice Act were removed. The political rhetoric surrounding the bill's passage reinvigorated the discourse about the ineptitude of preventive and rehabilitative programs, and rekindled public misconceptions about the relationship between race, crime, and public safety (Reference Wheelock and HartmannWheelock and Hartmann 2007).
The federal government also passed the Violent Crime Control and Law Enforcement Act in 1994, which raised sentences for immigration-related entrepreneurship fraud, passport and visa fraud, and illegal entry into the United States In 1996, Congress passed the Illegal Immigration Reform and Immigrant Responsibility Act, which significantly expanded the list of “aggravated felonies” to include shoplifting, speeding through immigration checkpoints, battery, and other crimes formerly classified as misdemeanors (Reference MorawetzMorawetz 2000; Reference SklanskySklansky 2012; Reference StumpfStumpf 2006). Finally, California governor Pete Wilson signed the Three Strikes Law in 1994, which had a profound effect on the length of sentences and the likelihood of early release for people sentenced between 1994 and 2000. This cohort was incarcerated in an era of economic prosperity and falling crime rates in the United States. In fact, beginning in 1994, rates of violent and property crimes declined dramatically, which was an encouraging sign to the supporters of deterrence theory and determinate sentencing (Reference Travis and WaulTravis and Waul 2003).
Overview of Analysis
One of the primary ways to study patterns of release is to look at the components that contribute to total time served such as the maximum sentence and the percentage of the maximum sentence served. As such, I limited the main analysis to adults who enter state correctional facilities for a new conviction. On the release side of the equation, I omitted certain types of releases because they did not reflect a “normal” sentence. People who escaped, transferred, or left prison due to injury or death were not included in the study; however, this group only accounts for two percent of those exiting prison. In addition, there was a smaller analysis restricted to people who were serving time due to a parole violation.
Although the release files provided the core information used in the study, the admission files allowed confirmation that the majority of persons that entered between 1985 and 2000 were represented. The two cohorts were followed for equal amounts of time, 10 years, and almost all people who entered prison between 1985 and 2000 were released within 10 years. More specifically, approximately 95 percent of both cohorts were released within 10 years. Table 1 presents the population used in the analysis. Most of the population is male, although the proportion of females significantly increased over time. In fact, unless otherwise noted, each of the categories within the variables shows a significant increase or decrease between the defined periods. The proportion of people identified as Hispanic increases during the time under investigation; however, the proportion of non-Hispanic blacks decreases during the period of study and the proportion of non-Hispanic whites remains rather constant. The majority of the population is less than 45year old, and most people served 50–74 percent of their sentences.
Table 1. Descriptive Statistics of Releases from California State Correctional Facilities by Period of Entry, 1985–2009
Data Source: Bureau of Justice Statistics
Note: Unless otherwise indicated, each category is significantly different between all periods, where α = 0.01.
a Periods are not significantly different from one another.
Again, I hypothesized that the second period would be more punitive than the first because of the increasing pressure and actions taken by the nation and state of California to maintain and even elevate their stance of being tough on crime. The first table provides some evidence to support that hypothesis. The category of people serving more than their maximum sentence is greatest in the second period, and the proportion of people entering state correctional facilities for a drug-related offense peaks at that time. Below I describe the two cohorts and the dominant policies that shaped their contact with the criminal justice system.
Results
Describing the Components of Release by Period
Focusing only on the process of release misses other pieces of the incarceration process that occur after sentencing and prior to release. For example, the maximum sentence to be served is known at sentencing, and the total time served is assessed after an individual is released. Therefore, rather than focusing solely on the event of release, I investigated the predictors of (1) the maximum sentence and (2) the percentage of the maximum sentence served. Potential inequalities in time served resulting from events that occur at earlier points in the sentencing process could be masked if such measures are not considered both independently of and together with the actual time served.
Table 2 presents the means of the three main components—maximum sentence, percentage of maximum sentence, and total time served—by race and ethnicity for both periods studied. The table includes whether there are significant differences in the means of each component across periods, using t-tests to determine the difference in means. The sample was restricted to men age 25–44 who had no prior convictions, so as to remove expected variability in each component. I also limited my examination to the following offenses: murder, armed robbery, burglary, petty theft, marijuana trafficking, and crack or cocaine trafficking. While I address racial differences below, I sought to remove the possibility that race would mask differences between the two periods, given the extensive literature on differences in sentencing by race. Nevertheless, Table 2 provides evidence that they are meaningful in their breakdown and that they reveal visible differences in the behavior of the legal system.
Table 2. Comparing Mean Maximum Sentence (Months), Mean Percentage of Maximum Sentence Served, and Total Time Served between Periods for Selected Offenses, New Court Commitments
* Data Source: Bureau of Justice Statistics: p < 0.05; **p < 0.01; ***p < 0.001.
Note: The sample is limited to men aged 25–44 who were released from California State Correctional Facilities. Period 1 persons admitted in 1985 through yearend 1993, and Period 2 corresponds to persons admitted in 1994 through yearend 2000.
Regardless of the crime committed and largely racial group, the percentage of the maximum sentence served significantly increased between the two periods under study. For example, the percentage of time served by Hispanic men imprisoned for murder rose from 78.9 percent to 100.9 percent; the percentage for white men climbed from 75 percent to 93.8 percent; and for black men it rose from 76.8 percent to 102.4 percent. These figures support the hypothesis stated in the data section: If the maximum sentence remained unchanged across the two periods, then the increase in the percentage of the maximum sentence served would certainly result in an increase in the total time served. However, the maximum sentence actually declined in some cases between the two periods; regardless of the direction of change, in several instances, the change in maximum sentences from the first period to the second period was not significantly different. This occurred most frequently for non-Hispanic white men, where the maximum sentence for four of the six crimes did not change significantly. Maximum sentence for the remaining two crimes, petty larceny and cocaine or crack trafficking, went in opposite directions for all groups. The maximum sentence for petty larceny increased 11 percent in the second period, from 28.3 months to 31.6 months, whereas the maximum sentence for trafficking cocaine/crack actually declined for all racial groups; however, the decline between the two periods was only significant for non-Hispanic white men.
Hispanic men received significantly different maximum sentences from their white and black counterparts for all but one offense, murder. The increase was significant only for armed robbery and petty larceny, and the maximum sentence for the three remaining crimes showed a significant decline. Non-Hispanic black men, however, present a different story. The mean maximum sentence for three of the six offenses declined for non-Hispanic black men between the two periods, but the decline was not significant. However, the mean maximum sentence for the other three offenses—armed robbery, burglary, and petty larceny—showed a significant increase. In short, although the mean percentage moved in the predicted manner, the mean maximum sentence changed in some unexpected ways.
Given the long-standing war on drugs in the United States, one might expect the maximum sentences for trafficking marijuana, cocaine, and crack to increase significantly between the two periods. However, this was not the case for any racial category. There was an insignificant increase in the maximum sentence for trafficking marijuana for non-Hispanic white men, from 31.3 months to 31.9 months. Non-Hispanic black men experienced a nonsignificant decrease for trafficking marijuana and crack or cocaine, whereas Hispanic men experienced a significant decrease.
What the lack of significant change does not reveal, specifically in the case of trafficking cocaine/crack, is the large increase in the number of people sentenced to serve time for the offense. In the first period, 572 Hispanic men, 739 non-Hispanic white men, and 1,245 non-Hispanic black men were admitted to California state correctional facilities for trafficking cocaine/crack. Between 1994 and 2000, this number more than tripled for Hispanic men, to 2,033, while it almost doubled for non-Hispanic white men. Furthermore, the earlier period is 2 years longer, and if we pair this fact with the total time served, which is significantly higher in period two than in period one for trafficking marijuana and cocaine or crack, the impact of the war on drugs is evident.
To help contextualize this analysis, let us consider the makeup of the U.S. population, starting with what percentage of men age 25–44 are Hispanic, non-Hispanic white, and non-Hispanic black. In 2000, Hispanics made up 33.3 percent of California's male population, non-Hispanic white men made up 47 percent, and non-Hispanic black men made up 6 percent. However, from 1985 through 2000, Hispanic men accounted for 53.7 percent of the people admitted to state correctional facilities for trafficking marijuana; non-Hispanic white men made up only 19.8 percent, and non-Hispanic black men made up 26.5 percent. The percentages and numbers, however, might be more meaningful if applied to a more positive social institution—education.
Statistics show that prisons are full of men who did not graduate high school, and many male inmates have kids in primary or secondary school. The view through this lens reveals the many years these incarcerated men spend away from their communities, schools, and families due to being convicted of crimes. The standard number of days in a school year is 180 days or 36 five-day weeks. Adjusting for the mean time served in the two periods under study, this is the equivalent of approximately 10,942 years of primary and secondary education for Hispanic men, 4,312 for non-Hispanic white men, and 5,952 for non-Hispanic black men. When we add in the time served for trafficking cocaine/crack, the equivalent years of schooling jumps to 25,700 years for Hispanic men, 21,628 for non-Hispanic white men, and 23,420 for non-Hispanic black men.
Decomposing Time Served: The Role of Maximum Sentence
The findings shown in Table 2 provide a window onto the complex and dynamic relationship between periods of entry, maximum sentence, percentage of maximum sentence served, and total time served. Using a regression model, a more nuanced picture emerges of the independent contributions of sociodemographic characteristics, the specific offense, and the period of admission to prison. Table 3 presents the relationship between the three main study components—maximum sentence, percentage of maximum sentence served, and total time served—and the standard covariates associated with sentencing. The predictor variables include age at prison entry, felony history, race/ethnicity, gender, and the type of crime committed. The models include essentially the same variables theorized to be important in the sentencing process, as well as a variable indicating the period of admission to prison. Although the NCRP does ask states to submit information regarding education level, states sometimes do not collect the information often enough for it to be used in analyses. Unfortunately, this is the case with California during the years of study; thus, there is no measure of socioeconomic status.
Table 3. Least Squares Regression of Maximum Sentence (in Months), Percentage of Sentence Served in California, and Total Time Served among New Court Commitments, 1985–2009
Data Source: Bureau of Justice Statistics, NCRP.
* p < 0.05; **p < 0.01; ***p < 0.001.
Each component was investigated in a series of three models. The first model examined the relationship between the component and the factors that “should” impact prison release—type of crime committed and prior felony conviction. The next model added the period of entry component, testing the value of my cohort-era construction. The final model added characteristics that are typically considered important in sentencing—race/ethnicity, age at prison entry, and gender—although such characteristics should not be important. In other words, these attributes should not influence the sentencing processes.
The first three models in Table 3 refer to the maximum sentence, measured in months. The initial model shows the expected results. Given the reference category “violent offense,” the coefficients for the other offense types—property, drug, and other—were negative and significantly different. The magnitude of the difference did vary, but not significantly among people sentenced for nonviolent crimes; drug offenders spent approximately 12 fewer months and people sentenced for property-related offenses spent 15 fewer months than those incarcerated for violent offenses. Having a prior felony conviction increased the expected maximum sentence by 8.5 months. Given an intercept of about 50.5, this would mean that a person with a felony on their record who was convicted of a violent crime could expect to have an average maximum sentence of 59 months.
Including the two time periods did not much alter the offense and felony coefficients; however, the intercept increased to 51.4, and the later period was significantly different from the first. The maximum sentence decreased by 1.9 months between the first and second periods. Including the factors that “should” not matter—race/ethnicity, age at entry, and gender—demonstrated, as in the existing literature, that such factors do influence the maximum sentence. The middle age group, ages 25 to 44, had significantly lower maximum sentences than the youngest and oldest age groups; however, the sentencing for people age 18–24 was only 1 month more than for those ages 25–44; it was not significantly different for people age 45–64. People age 65 and older actually experienced a longer sentence (penalty) of approximately 6 months. In fact, the penalty for the oldest age group was greater than the gender penalty—that is, those men's maximum sentences were almost four months longer than women's. As the findings shown in Table 2 indicate, the maximum sentence for Hispanics was shorter than for non-Hispanic whites, but the difference was less than one month. Far more surprising, based on the literature, is that there was no significant difference between the maximum sentences for non-Hispanic blacks and non-Hispanic whites.
Decomposing Time Served: The Role of Percentage of Maximum Sentence Served
The second group of models, presented in Table 3, progress in the same manner, but the dependent variable is the percentage of the maximum sentence served. People with a prior felony conviction on average served 43 percent more of their sentence than those who had no prior felony. The person from the prior example who committed a violent crime and had at least one prior felony could expect to serve approximately 123 percent of their maximum sentence. As shown in the models for maximum sentence, the penalty for having a prior felony remained relatively stationary for each model. The coefficients for the type of crime go in the expected direction; people convicted of a property offense could expect to serve 14.5 percent less time than those sentenced for a violent crime. Those convicted of a drug-related offense could expect to serve slightly less (1.2 percent) time than those convicted of a property crime.
The period coefficients for the models were significant and positive. That is, while the period coefficients indicated a lower maximum sentence in the second period than the first, the period coefficients indicated that people entering prison between 1994 and 2000 served about 8 percent more of their maximum sentence than persons admitted to prison during the first period. Being older, on the other hand, often meant a person served a lower percentage of their maximum sentence. Men continued to serve more of their maximum sentence than women (2.3 percent), and non-Hispanic blacks served 3.4 percent more of their maximum sentence than their non-Hispanic white counterparts. Hispanics could expect to serve 2.2 percent more of their sentence than their non-Hispanic white counterparts. This is a perfect example of how significant differences in the maximum sentence and percentage of sentence served can vanish: although Hispanics were given significantly lower maximum sentences than non-Hispanic whites, they served a higher percentage of their maximum sentence. Thus, for some groups, receiving a lower maximum sentence was possibly countered by the fact that they served a higher percentage of that sentence.
Taken together, the maximum sentence and the percentage of the maximum sentence served provide us very different pieces if considered alone, particularly in the case of race and ethnicity. Taken alone, the maximum sentence shows evidence that we have crossed into a different stage of sentencing, where traditionally marginalized populations do not receive harsher punishment. In fact, they receive less punishment. But if we instead look only at the percentage of the maximum sentence served, we find that there are still significant differences between non-Hispanic whites, non-Hispanic blacks, and Hispanics, where non-Hispanic whites serve a lower percentage of their maximum sentence. Taken together, we have processes at odds with one another. Furthermore, the process that shows evidence of continued disparities, percentage of maximum sentence served, explains a greater percentage of the variance (R-squared of 0.116 vs. 0.052).
Obscuring Differences: Blurring Reality Through Combining Maximum Sentence and Percentage of Sentence Served
The last set of models in Table 3 examines the relationship between the total time served and the covariates. The inclusion of this set of results provides a sense of the cumulative effect of shortened sentences and longer percentages served. First, the models explain very little of the variation; in fact, even the last model explains less than 1 percent of the variation in total time served. While gender and type of offense maintain their significance, the significant difference between Hispanics and non-Hispanic whites dissipates, and there is no significant difference between non-Hispanic blacks and whites.
Undeniably, the intersection of maximum sentence and percentage of the maximum sentence served changes the results. In the models of both maximum sentence and percentage of the maximum sentence served, at least two of the three age groups were significantly different than the reference age group (persons aged 18 to 24). However, when the total time served is the dependent variable, it appears that age does not matter. Furthermore, the cost of the prior felony increases from that seen in the maximum sentence, and the relative importance of the period sentenced attenuates. In sum, each of the components provides a different account of sentencing in California. Additionally, looking at only time served removes many of the inequalities and the influence of the political atmosphere that surfaced during the individual investigation of the two components that constitute total time served.
The Illusion of Vanishing Inequalities
Table 2 displays the maximum sentence, percentage of maximum sentence served, and total time served. It was arranged in a way that would reveal whether significant changes occurred in the three measures between the two periods. In Table 4, the focus shifts from period changes to changes between the three racial and ethnic groups under studyFootnote 1.The analysis is also restricted to men age 25 to 44 with no prior felonies for six offenses—murder, armed robbery, burglary, petty larceny, trafficking marijuana, and trafficking cocaine/crack. The regression models make it possible to discern the contribution of selected variables in explaining the variation in the components, but one of the more significant conclusions that come out of this inquiry is that a significant difference in total time served alone does not mean that racial inequality in the judicial process is no longer present.
Table 4. Examining the Role of Components in Masking Racial Disparities for Selected Offenses, New Court Commitments
Data Source: Bureau of Justice Statistics.
Note: The sample is limited to men aged 25–44 who were released from California State Correctional Facilities. Values greater than one indicate a higher value for the group first mentioned.
* p < 0.05; **p < 0.01; ***p < 0.001.
In Table 4, I examine the three possible racial comparisons: Hispanic versus non-Hispanic white men, Hispanic versus non-Hispanic black men, and non-Hispanic black men versus non-Hispanic white men. The simplest way to communicate the differences between the groups was by calculating the ratios for each of the three measures. After making those calculations, I tested if the value was significantly different than one. If it was significantly different than one, it meant the values between the racial categories were significantly different. In addition, a ratio greater than one indicated that the first group mentioned carried a higher value in the measure than the second group, and values less than one indicated that the second group had a greater value. In other words, the table displays the results of the tests for significant differences in the means within each period for racial groups.
For the crime of murder, the total time served by Hispanic men and non-Hispanic white men did not significantly differ in either period. In fact, there were only two crimes during the first period for which the two groups did differ significantly—armed robbery and trafficking cocaine/crack. For both of these crimes, the ratios were less than one, which indicates that non-Hispanic white men served more time on average than Hispanic men. Although not apparent in the total time served, there are significant differences for three out of the four other crimes, in other components. The maximum sentence for burglary for non-Hispanic white men was significantly higher than for Hispanic men, yet the difference in the percentage of the maximum sentence served, while not significant, led to there being no statistical significant difference in time served between the two groups. For trafficking marijuana, Hispanic men were given a significantly higher maximum sentence than non-Hispanic white men, but the significantly lower percentage of the maximum sentence served by Hispanic men resulted once more in a total time served that was not significantly different.
The same phenomenon is present between Hispanic and non-Hispanic black men with respect to trafficking marijuana. We see no significant difference between them in the total time served; however, the two components, maximum sentence and percentage of sentence served, are significantly different and in opposite directions, which masks the existing inequality. This occurs throughout the table and among all the group comparisons. For example, in the first period, non-Hispanic white men received a significantly lower maximum sentence for trafficking marijuana; yet because on average they served a significantly longer percentage of their sentence, we see no difference in the inequality present at the time of sentencing. To be more precise, the maximum sentence is a measure of the judicial system's ability to issue fair and equal sentences, while the percentage of the sentence served reflects the incentives and the disciplinary process in prisons, such as good time and loss of good time.
What About Parole?
Scholars have done a considerable amount of research on “back-end” sentencing, or sentencing that gets added on due to parole violations (Reference Lin, Grattet and PetersiliaLin, Grattet, and Petersilia 2010; Travis 2007; Reference Travis and LawrenceTravis and Lawrence 2002a). This is significant, given that all California prisoners are under parole supervision after they are released, and the majority of admissions to California prisons during the past few decades have been the result of parole violations (Petersilia 2003; Reference TravisTravis 2002, 2004).
My final analysis, presented in Table 5, examined people during the same period, 1985–2009 who returned to prison as a result of a parole violation rather than committing a new offense. The models examine the relationship of the percentage of the maximum sentence served and the predictors used in the other models—gender, age, race and ethnicity, type of crime, period of re-entry to prison. In the second period, a person received a much heavier penalty if they were readmitted to prison due to a parole violation. The difference between the two periods in the percentage of the maximum sentence served increased to 27 percent for those readmitted. In the second period there was still a gender penalty, but Hispanic men served 3.5 percentage points less of their maximum sentence than non-Hispanic white men and non-Hispanic black men served 5 percent more. Finally, men aged 45 to 65 served a higher percentage of their sentence than men of other ages. More analyses should be performed in this growing area of research, which is just a small part of the full picture of racial disparities in sentencing and resentencing in U.S. correctional facilities.
Table 5. Least Squares Regression of Percentage of Maximum Sentence Served in California, Parolees, 1985–2009
Data Source: Bureau of Justice Statistics, NCRP.
*p < 0.05; **p < 0.01; ***p < 0.001.
Conclusion
Issues related to crime and crime control are socially constructed through a number of contextual factors that shift and are molded over time (Reference BeckettBeckett 1999). Previous studies have determined that the type of crime, race, gender, and age are all key factors in the decision to incarcerate a person and the length of sentence imposed. Comparatively little research has examined the other end of the spectrum; to date we know almost nothing about the release process. Understanding this process is important for a number of reasons, aside from gaining broader knowledge of the prison experience. It is critically important for policymakers to know if and how release is experienced differently across demographic groups. If we still see disparities in mandatory sentencing despite DSLs designed to standardize the sentencing process, it is likely that there are disparities in the release process as well.
This study examined three major factors related to release: the predictors of the maximum sentence given, the percentage of the maximum sentence served, and the differences in total time served. The results highlight a number of important factors at work in the amount of time individuals spend in prison. First, the results show what we would expect to see when it comes to factors like the type of crime and having a prior felony conviction; clearly we would expect these factors to increase a person's sentence, and they did exactly that. The results also reveal that men of color are consistently at a disadvantage when it comes to the amount of time served in prison. Non-Hispanic blacks served more time in prison than both non-Hispanic whites and Hispanics for all crimes during the second period studied and for a fair number of the crimes in the first period. However, the significant differences were not always in the total time served. They sometimes included significantly higher maximum sentences and significantly higher percentages of the maximum sentence served.
The analysis also reveals that the maximum sentence given is not enough information to determine the differential experiences in prison time served. When non-Hispanic whites are given a longer sentence than non-Hispanic blacks and Hispanics, they typically spend less total time behind bars and serve a lower percentage of the maximum sentence than the other two groups. The experience of Hispanics is the opposite; when they are given a lower average maximum sentence than non-Hispanic whites, they tend to serve a greater percentage of their sentence. The only group that simultaneously experienced the highest maximum sentence, highest percentage of the sentence served, and the most total time served was non-Hispanic blacks.
The current study differs from prior research by defining and including a measure of contextual factors that differentiate the period of entry, whereas previous studies tend to examine sentencing processes that implicitly assume homogeneity in punitive policies. By creating prison-entry cohorts and comparing components of release, I was able to parse out the various factors that influence release patterns. Differences in maximum sentence by themselves tell one story, and differences in the percentage of the maximum sentence served tell another; when separated, they are not the same story. Nevertheless, considering the two components in tandem tells an eerily familiar tale of how the implementation of policies and their enforcement over time continue to defy the ultimate goal of equality and justice under the law. The inherent decoupling of these components in practice and research only further demonstrates the many blind spots that shape inequality in the U.S. criminal justice system.
The experiences of inmates behind bars are not uniform; the judicial process in fact involves several factors that determine the real time served. This calls into question our prior understanding of racial disparities in the U.S. penal system, and the number of places and spaces that make a contribution to disparities. The numerous steps that could contribute to inequalities could not be detailed in this article or through use of the NCRP data alone because there are several layers of potentially systematic differences that could affect the timing of release.
For example, California prisons award good behavior; but the potential for an inmate to show good behavior is different depending on the level of security in the prison. Maximum security level inmates have little to no opportunities to demonstrate good behavior, while inmates in lower security prisons may have several ways to demonstrate good behavior. Furthermore, the level of security and type of prison are based on one's offense and the judge's assessment of the offender's risk to create complications. Thus, a person with a prior felony risk level might be higher than a new court commitment. In addition, new legislative policies such as the 2011 realignment law pose challenges in the ability to see inequality since it affects the admissions and releases of persons when they are admitted, released, and returned (parole violation).
Each of these pieces should not be considered solely limitations to the current study, but rather the beginning of exploring the “in between” in further detail. The current article established the presence of an in between and a measure to assess the processes between sentencing and release are uniform. For instance, using the 2004 Survey of Inmates in State and Federal Correctional Facilities, I found that of the men in California ages 18 to 64 found guilty of committing a violation, 86 percent of Hispanic men were disciplined, 85 percent of non-Hispanic white men were disciplined, and 92 percent of non-Hispanic black men were disciplined. These areas require the same vigor of investigation as time served because we cannot understand time served or the true role of sentencing without a deeper understanding of these processes. In other words, the road to racial disparities in the actual time inmates serve has many twists and turns. Moreover, it does not end with the sentencing process; rather, sentencing is where legislative influence ends and the justice dealt by other actors such as wardens, correctional officers, and parole officers begins.
Appendix A: Examining the Role of Components in Masking Racial Disparities for Selected Offenses, New Court Commitments in Both Periods
Data Source: Bureau of Justice Statistics: *p < 0.05; **p < 0.01; ***p < 0.001.
Note: The sample is limited to men aged 25–44 who were released from California State Correctional Facilities. Period 1 persons admitted in 1985 through yearend 1993, and Period 2 corresponds to persons admitted in 1994 through yearend 2000.
