Mass probation: Toward a more robust theory of state variation in punishment

Abstract

Scholarship on the expansion of the U.S. carceral state has primarily focused on imprisonment rates. Yet the majority of adults under formal criminal justice control are on probation, an “alternative” form of supervision. This article develops the concept of mass probation and builds a typology of state control regimes that theorizes both the scale and type of punishment states employ. Drawing on Bureau of Justice Statistics data from 1980 and 2010, I analyze whether mass probation developed in the same places, affecting the same demographic groups and driven by the same criminal justice trends, as mass imprisonment. The results show that mass probation was a unique state development, expanding in unusual places like Minnesota and Washington. The conclusions argue for a reimagining of the causes and consequences of the carceral state to incorporate the expansion of probation.

Keywords

carceral state imprisonment probation state variation

Introduction

The rapid expansion of mass imprisonment in the U.S. stands as one of the most important social transformations of the past 40 years and an integral part of racialized social control (Alexander, 2010; Tonry, 1995; Western, 2006). As researchers continue to explore the mechanisms that propelled mass imprisonment, it is clear that the expansion had two proximal determinants driven by criminal justice actors (including police, prosecutors, judges, bureaucrats, and policy-makers): decisions that sent more individuals to prison and kept them incarcerated for longer periods (Raphael and Stoll, 2013). Much of this research focuses on state variation, attempting to explain why these processes were so much more pronounced in Southern and Sunbelt states, which tend to be more racially diverse and politically conservative, as compared to Midwestern and Northeastern states, which tend to be less diverse and more progressive (Campbell and Schoenfeld, 2013).

Throughout this literature, it is often assumed that imprisonment rates—as the most extreme form of supervision—represent the expansion of criminal justice control more broadly. Yet inmates in state and federal prisons are a minority of those under formal criminal justice supervision nationwide; the majority (56% in 2014) are under probation supervision, serving their sentences in the community. At its peak in 2007, nearly 4.3 million—or one in every 53 U.S. adult residents—were on probation, compared to just under 1.6 million incarcerated in state and federal prisons (Kaeble et al., 2015).

While probation is defined as an “alternative” to prison, research suggests that it serves as a “net-widener” that increases overall supervision (Aebi et al., 2015; Phelps, 2013). Probation also imposes substantial harms on supervisees (Durnescu, 2010), including onerous conditions that give probation officers tremendous power and discretion (Doherty, 2016). Failure to meet these demands can lead to revocation, sending probationers into jails and prisons (Klingele, 2013). Thus, as Doherty argues, probation is less a “potential solution to the problem of overincarceration” than “part of the continuum of excessive penal control” (2016: 291). Yet with few exceptions, scholarship on the causes and consequences of the carceral state has only indirectly explored this parallel buildup, skewing representations of the penal field (McNeill, 2013).

To address this gap, I develop the concept of mass probation, examining the expansion of this form of social control in the U.S. Relying on Bureau of Justice Statistics (hereafter BJS) data from 1980 to 2010, my primary question is whether mass probation developed in the same places, affecting the same demographic groups and driven by the same criminal justice trends (increasing felony convictions and longer time served), as mass imprisonment. I pay particular attention to state-level variation, developing a typology of control regimes that considers both the scale and form of criminal justice supervision. I also analyze whether criminalization and sentence length patterns explain differences across the regimes. To the extent that states' imprisonment and probation trajectories diverge, accounts of the sociopolitical causes and consequences of the carceral state cannot be complete without incorporating probation.

The results demonstrate that mass probation was a unique state development. First, its expansion affected a less racially skewed population than imprisonment; today, there are more white probationers than prisoners of any racial/ethnic category. Second, probation rates exploded in some low imprisonment states, like Minnesota and Washington, which have been commended as progressive outliers that resisted the punitive turn. The results suggest that the effects of increasing criminalization in these states were funneled into probation, with imprisonment growth restrained. Yet exceptionally high rates of probation supervision failed to appear in other low imprisonment states, while developing in most high imprisonment states, suggesting that it is neither a simple alternative nor pathway to prison. Finally, low probation, high imprisonment states appear to be an artifact of the BJS data, with these states systematically underreporting misdemeanor probationers. Together, these findings argue for more research—and better data collection efforts—on the development of mass probation. The discussion outlines the possibilities for this literature, speculating in particular on how we might better understand states' diverse pathways to mass punishment.

The punitive turn and probation

The causes and consequences of mass imprisonment have become a central concern of criminologists (Garland, 2013). A prominent trend in this literature is to compare imprisonment rates across U.S. states, noting that punishment is often structured by regional, demographic, and political factors (Lynch, 2011). Quantitative researchers analyzing state-level time-series data have found that expansions of imprisonment are correlated with crime rates and drug arrest rates, racial diversity, state revenues and spending patterns, and dominance of the Republican party (Campbell et al., 2015; Phelps and Pager, 2016). Much of this research highlights the punitive nature of punishment in the South and the Sunbelt, where imprisonment rates are high and punishment is “cheap and mean” (Lynch, 2009). It is these states—particularly those with a history of slavery—that provide the most compelling picture of contemporary punishment as the “New Jim Crow” (Alexander, 2010).

Scholarly attention has begun to shift beyond mass incarceration. New accounts, for example, explore expansions in policing and “surveillance” more broadly (Young and Petersilia, 2016), the proliferation of misdemeanor convictions (Kohler-Hausmann, 2013; Natapoff, 2011), the political effects of criminal justice contact (Lerman and Weaver, 2014), and the fines and fees levied by courts on (primarily poor) defendants (Harris et al., 2010). Another strand considers the expansion of the “shadow state,” including administrative sanctions (Beckett and Murakawa, 2012). Building off pioneering work on parole (e.g. Simon, 1993) and intermediate sanctions (Morris and Tonry, 1990), scholars are beginning to investigate “mass social control,” particularly the expansion of community sanctions (DeMichele, 2014). Critical to this movement has been work on “mass supervision” in Europe and beyond (Robinson et al., 2012). Probation is central to this endeavor as the most common form of community supervision.

Probation in the U.S. was initially championed as the “exemplary penal form” of the penal-welfarist model (Simon, 2013). With the abandonment of the rehabilitative ideal and the rise of the “lock ‘em up” strategy, we might have expected probation rolls to empty (Robinson et al., 2012). Instead, we saw a rapid expansion. Feeley and Simon (1992) posit that probation became a “cost-effective” risk-management solution, complete with “tough” innovations including intensive supervision and electronic monitoring. Unlike parolees (who are, by definition, felons), probationers are under supervision for a wide variety of offenses and supervision and support can vary dramatically. The average probation officer supervises caseloads of more than 100 (Taxman, 2012) and the estimated annual costs of one year of community supervision range from $300 to $7000 (compared to $15,000–$50,000 for prisoners) (Vera, 2013).

Although probation is routinely described as an “alternative” sanction designed to divert cases down from prison, research suggests that it often functions as a “net-widener,” diverting cases up from fines and other nonsupervisory punishments (Aebi et al., 2015; Blomberg, 2003; Morris and Tonry, 1990). Further, probationers experience supervision as a punitive intervention with its own pains, including deprivations of autonomy, family life, and time—and the very real fear of revocation, or return to jail or prison for violating the terms of supervision (Doherty, 2016; Durnescu, 2010).¹ As a result, probation can increase imprisonment rates through “back-end net-widening” (Caplow and Simon, 1999; Klingele, 2013; Tonry and Lynch, 1996). Indeed, among probationers leaving supervision in 2014, over a third failed to successfully complete their supervision (Kaeble, Maruschak, & Bonczar, 2015). In earlier work (Phelps, 2013), I find that this relationship between probation and imprisonment is mediated by states' sentencing and supervision practices, with probation serving as more of a net-widener in states where probation is frequently used for misdemeanor offenses and revocation rates are higher.

The scale of punishment: A control regimes typology

In an early text on the prison boom, Zimring and Hawkins (1991) argue that scholars should theorize the “scale of imprisonment,” or the varied rate at which states imprison their residents. Building on this work, I argue for considering the scale of punishment, examining multiple forms of criminal justice supervision in theorizing both how and how much states punish.² The typology in Figure 1 highlights two critical dimensions: states' imprisonment and probation rates. While these do not encompass all forms of state control, they represent the two largest forms of criminal justice supervision and opposing ends of the carceral continuum (from least to most restrictive of physical liberty).³ Nor are they mutually exclusive; probationers often experience a stint in jail or prison at the start of their supervision, either while awaiting judgment and/or as part of a “split” sentence,⁴ and after revocation. Thus, at the individual level, supervisees experience multiple and overlapping forms of control.

Figure 1.

Control regimes typology.

I divide the probation–prison space into four control regimes, distinguishing between states above or below the median rate for each form of supervision. Each quadrant can be thought of as an “ideal type,” rather than a rigidly bounded empirical category. The regimes on the main diagonal are concordant, with either low–low or high–high distribution of probation and imprisonment rates. The upper right-hand corner of Figure 1 represents the “punitive control” regime or high supervision rates. This most fully represents the carceral state ideal, with a very high portion of the population under both forms of supervision. Shifting across the diagonal brings us to low imprisonment and low probation rates, or the “sparing control” regime in which neither form of punishment is utilized extensively. The regimes off this diagonal are discordant. States in the “managerial control” regime have restrained imprisonment rates yet very high probation rates. The final regime is “incapacitative control,” characterized by high imprisonment rates and low probation supervision rates.

In the concordant regimes (i.e. states where restrained or overgrown probation and imprisonment rates developed in tandem), the expansion of mass probation can likely be tied to similar causes—and may have similar macro-level consequences—as mass imprisonment. In contrast, if states fall within the discordant regime spaces, it suggests that these forms of supervision have different antecedents and impacts.

Data and methods

The primary data are national and state-level counts of probation and prison populations collected by the Bureau of Justice Statistics and reported in the “Prisoners,” “Probation and Parole in the United States,” and “Correctional Populations in the United States” series. The “Felony Sentences in State Courts” reports provide data on sentencing trends. Estimated state property and violent index crime rates are from the Federal Bureau of Investigation Uniform Crime Report's online data tool;⁵ population data are from the U.S. census.

The analyses begin in 1980, a standard starting point for the carceral buildup and the relevant data series. I select states as the lowest unit of analysis because sentencing policy is set at the state level and prisoner totals are only available at this level of analysis. Note, however, that substantial local-level variation exists within states (e.g. Ball, 2011) and that most states' correctional populations are disproportionately drawn from urban counties. The prison population includes adults sentenced to serve one year or more under a state's jurisdiction (including inmates housed in local jails or other states). Probation totals include all adults reported as under supervision by state and/or local probation departments to BJS.⁶ To control for population differences across state-years, I estimate supervision rates (per 100,000 in the resident population).⁷

After calculating overall imprisonment and probation supervision rates, I estimate the supervision rate for probationers convicted of felony-level offenses in 2010.⁸ Due to substantial missing data on the composition of states' probation population, I collate data from 2007 to 2013, drop data points where more than 20% of the probation population has an unknown conviction level (felony, misdemeanor, or other), and select the remaining year with the highest percent of complete data.⁹ I calculate the percent of probationers convicted of felonies among probationers with known conviction for the relevant year and multiply this ratio by the current probation rate to estimate the felony probation supervision rate. This provides an estimate of the percent felony supervision for 46 states.¹⁰

The results are organized into three sections. First, following Garland's (2001) definition of mass incarceration, I outline the scale and racial composition of mass probation nationally. Second, I trace whether mass probation was proximally driven by the same trends as mass imprisonment. The “iron law” identifies two key forces determining prison size: how many people are sentenced and how long they remain under supervision (Clear and Austin, 2009). Following the methodology of imprisonment rates analyses (e.g. Raphael and Stoll, 2013), I use a decomposition to estimate the influence of these two components on probation rate growth. Because individual-level sentencing and release data do not exist for probationers, I estimate length of supervision using the reciprocal of the exit rate adjusted for population growth (for details on this estimate, see Patterson and Preston, 2007). I also examine felony sentencing trends to understand whether the expansion was driven by felony or misdemeanor-level criminalization. I focus on the national level for these analyses because data on probationers' race and sentencing trends are only available at this aggregate level.

The second section considers the where of probation's expansion. I analyze states' supervision rates for each decade between 1980 and 2010, using bivariate regressions to summarize how the correlation between states' imprisonment and probation rates changed over time. Using scatterplots for data visualization, I evaluate whether probation expanded most rapidly in states that embraced imprisonment or those that maintained more moderate rates. I map state variation in 2010 onto the typology presented in Figure 1, deciphering whether states are arranged across the typology space or clustered within the regimes.¹¹

Last, I analyze the proximal drivers of probation rates across regimes to understand how states developed divergent profiles of control. As is clear from the typology, it is important to consider this expansion in tandem with incarceration rates: probation increases in low-incarceration states may have been propelled by very different factors than in traditionally punitive states. I compare each concordant regime with its paired discordant regime (i.e. sparing versus managerial control and incapacitative versus punitive) to examine how the drivers of probation rates differ across low and high incarceration rate states.

The analyses consider five relevant compositional differences across regimes: the probation admission rate (per 100,000 residents), average sentence length, percent of probationers under supervision for a felony, and violent and property index crime rates. Together, these variables unravel whether differences in crime, misdemeanor and felony criminalization, admission patterns, and/or length of probation term drive state variation in probation rates. I analyze the means of these characteristics by states' regime type for the year 2010. For each, I use t-tests for group means to evaluate the significance of observed differences. With the exception of percent felony, I expect each of these factors to be higher in high probation states. However, I use two-tailed tests for all comparisons as a conservative estimate.

As described below, I find that incapacitative control states have an unusually low percent of misdemeanor probationers. Prior research suggests that probation totals are more ambiguous than prisoner tallies, with jurisdictions variously including or excluding individuals under parallel forms of community control, including diversion programs, private probation, and drug courts (Taxman, 2012).¹² This is particularly true for misdemeanor probation, which generally entails less supervision; what might be treated as a suspended sentence in one locale might instead be treated as “informal,” “inactive,” “court monitored,” or “pay only” probation in another. This led me to hypothesize that these states were underreporting misdemeanants. To explore this possibility, I used informational interviews, calling each state's department of corrections to inquire about the presence of such programs.

Results

National expansion of mass probation

Garland (2001) coined the term “mass imprisonment” to reflect the historically and internationally unprecedented scale of U.S. imprisonment rates as well as its concentration among young men of color in urban neighborhoods, such that imprisonment became “a regular, predictable experience” (pp. 1–2). Thus, for probation to have hit “mass” proportions, it should meet these criteria. The unprecedented scale is easy to establish: the state and federal probation population soared from one million in 1980 to a peak of nearly 4.3 million by 2007. In addition, the U.S. probation rate is more than five times the average across European countries (Alper et al., 2016). As with prison conditions, there is evidence that probation supervision is also experienced as uniquely punitive in the U.S. (Rhine and Taxman, forthcoming).

Probation supervision is disproportionately concentrated among men of color.¹³ At its peak in 2007, I estimate that one in every 21 black adults (and one in 12 black men) was under probation supervision (compared to the national average of one in 53 adults, one in 65 white adults, and one in 41 white men).¹⁴ The disproportionality of probation is less severe than for imprisonment: in 2014, 54% of probationers were white (versus 33% of prisoners), 30% black (36%), 13% Hispanic or Latino (22%), and 75% male (93%). These results are consistent with a large body of research that suggests white and female defendants are more likely to be sentenced to probation rather than imprisonment (e.g. Steffensmeier et al., 1998; Sutton, 2013). Given the massive scale of probation, this means that there are more white probationers under supervision today than prisoners of any racial/ethnic background.

The second question for the national analyses is whether mass probation was driven by the same proximal drivers as mass imprisonment, namely increasing admissions and longer time served. The probation expansion can be largely explained by increases in admissions. Between 1981 and 2007, entries to probation increased by 214%, from 753,500 to over two million, explaining most of the 250% increase in the probation population. In the same time period, I find that the estimated average time served on probation fluctuated, starting at 1.8 years in 1981, reaching 1.7 in 1990, 1.9 in 2000 and 2007, and 1.8 by 2014. (This same methodology estimates that the average time served for prisoners increased from 2.1 years in 1981 to 2.4 by 2014.) This suggests that the probation expansion was more closely tied to increasing criminalization than to punitive policy-making or courtroom decisions that lengthened sentence terms. However, with only an aggregate estimate of time served available, it is possible that more individuals could have cycled rapidly in and out of probation, while long-term probationers served increasingly long terms (e.g. Neal and Rick, 2014).

Taking a step back, what explains the increase in probation admissions? As with prison rates, these increases cannot be primarily explained through crime rates. Index crimes began a sharp descent in the 1990s while punishment rates continued to climb (although rising crime rates in the 1960s and 1970s likely generated a receptive context for punitive policy-making) (Western, 2006). Instead, mass probation was likely driven by increasing criminalization—a complex process of redefining crime, increasing police presence, and hardening prosecutorial and judicial decisions (Stuntz, 2011). While data on these various processes are limited (Raphael and Stoll, 2013), we can trace national changes in felony sentencing: in 1986, an estimated 600,000 persons were convicted of felony offenses in state courts, compared to over 1.1 million by 2006. New research suggests this was largely due to prosecutors' increasing likelihood of filing felony charges (Pfaff, 2014). During the same period, the estimated percent of felons in state courts sentenced to prison declined from 46 to 41%, while sentences to “straight” probation (i.e. no jail or prison time) slightly increased from 26 to 27% (Gaskins, 1990; Rosenmerkel et al., 2009).

Whereas imprisonment rates are driven solely by felony-level sentencing, probation is a sentencing outcome for both misdemeanor and felony offenses. Throughout the past two decades, the percent of probationers under supervision for a felony hovered around 50% (Phelps, 2015), inching up to 56% in 2014 as the probation population was scaled back (Kaeble, Maruschak, & Bonczar, 2015).¹⁵ Thus, mass probation was driven not just by the proliferation of felony sentences but also by the expansion of misdemeanor justice (Kohler-Hausmann, 2013; Natapoff, 2011). There are no reliable national data on changes in misdemeanor sentencing, but a recent survey found that state courts process four times as many misdemeanor filings as felony cases on average (Kohler-Hausmann, 2013).

To summarize, at the national level, the probation expansion was clearly mass. The population affected by this expansion overlaps with—but is distinct from—that of mass imprisonment. In particular, the probation population has generally been convicted of less serious offenses and is more demographically representative of the noninstitutionalized population. This expansion was clearly tied to increasing criminalization, but in contrast to imprisonment trends, there is no evidence that substantial increases in average time served contributed.

Expansion across states

Did mass imprisonment and mass probation emerge in tandem, growing the same places at the same time, or did the trajectories diverge, suggesting that they represent two very different (perhaps alternative) modes of state control? Figure 2 presents the cross-sectional relationship between probation and imprisonment rates in the first year of each decade: 1980, 1990, 2000, and 2010, with a bivariate regression line plotted along with each scatterplot. The first result is unsurprising: rates of supervision rose dramatically across each decade. Between 1980 and 2010, the median state probation rate increased from 384 to 995 probationers (per 100,000 residents), while the median imprisonment rate increased from 105 to 386 prisoners (per 100,000 residents).

Figure 2.

Second, in each decade, the relationship between probation and imprisonment rates declined, from a moderate and statistically significant correlation in 1980 (r = 0.4, p < .01) to effectively zero by 2010 (r = 0.1, n.s.).¹⁶ After three decades of expansion, probation and imprisonment were decoupled. This decoupling was primarily due to expansions in probation, which followed a more unpredictable path (and produced more outliers) than imprisonment. A relevant metric is the consistency in states' relative supervision rate. Ranking states from lowest (1) to highest (50) supervision rate, I find the correlation between states' relative rate ranking in 1980 and 2013 is much stronger for imprisonment rates (0.7, p < .001) than for probation rates (0.3, p < .05). Some states nearly flipped in the probation rankings, from low to high or vice versa.¹⁷

Mapping contemporary variation

We can now map the variation in states' supervision rates onto the typology presented in Figure 1. Figure 3 overlays states' 2010 supervision rate rankings onto the regime quadrants. Rather than raw rates, I plot states' relative rankings to reduce clustering and improve legibility. States' supervision rates are ranked from lowest (1) to highest (50) along the two axes (from left to right and bottom to top). Figure 3 shows again that by 2010, probation and imprisonment rates were decoupled, with states spread across the control regimes. States like Oklahoma, South Carolina, and Virginia populate the incapacitative control regime (high imprisonment and low probation rates), while Minnesota and Rhode Island emblemize the managerial control regime (low imprisonment and high probation). Another way to summarize this disconnect is through regional variation: whereas imprisonment rates in the South far surpass other regions, probation rates in 2010 were more equivalent,¹⁸ and in fact were highest in the Northeast as recently as 2008.

Figure 3.

States' probation and incarceration rate rankings in 2010. Note: States are ordered according to their supervision rates from lowest (1) to highest (50) for each axis.

At the national level, we saw that mass probation was driven primarily by criminalization processes that led to greater probation admissions for both felony and misdemeanor crimes. I turn now to how these drivers vary across regimes. Table 1 presents the regime means for the five criminal justice characteristics. As expected, states in the high probation regimes (managerial and punitive control) have, on average, substantially higher probation admission rates than low probation regimes. Variation across estimated time served on probation is less clearly patterned, with the shortest term length average in the punitive regime and the longest in the incapacitative regime. The percent of probationers under supervision for a felony-level offense was highest in the low probation regimes, particularly incapacitative control. Finally, crime rates were highest in the high incarceration regimes (incapacitative and punitive control) and lowest in sparing control. As noted, however, comparing across high versus low supervision rates for either form of supervision is incomplete without considering the other dimension of variation.

Table 1.

Proximal determinants of mass probation: Average state characteristics by control regime in 2010.

	Sparing control	Managerial control	Incapacitative control	Punitive control
	Mean (std. dev.)	Mean (std. dev.)	Mean (std. dev.)	Mean (std. dev.)
Probation admission rate (per 100,000 residents)	383 (205)	722 (306)	320 (74)	1200 (685)
Estimated probation time served (in years)	2.2 (1.3)	2.2 (1.1)	2.8 (1.4)	1.8 (0.6)
Percent on probation for felony offense^a	59% (23.2%)	47% (17%)	87% (15%)	42% (18%)
Violent crime rate (per 100,000 residents)	281 (119)	322 (116)	475 (153)	392 (114)
Property crime rate (per 100,000 residents)	2526 (479)	2772 (531)	3082 (616)	3074 (586)
N	13	12	12	13

All variables have complete data for 2010 except percent felony (as described in “Data and methods” section).

To understand the unique drivers of mass probation independent of imprisonment trends, I compare across regime type pairs (comparing sparing versus managerial control and incapacitative versus punitive). One clear difference emerges for low imprisonment states: the average probation admission rate in managerial control states is nearly double that of sparing control states (t = 3.3, p < .01). For example, in 2010 the probation entry rate was over 1200 (per 100,000 residents) in Minnesota, yet just 175 (per 100,000) in New York. In contrast, there is no difference in average supervision length across the regimes (2.2 years for both groups). As expected, the percent of probationers under supervision for felony-level offenses is higher in sparing control states (59% versus 47%), but this association does not reach statistical significance due to the high variance in both groups (t = 1.4, n.s.). Thus, for low-imprisonment states, the decoupling of imprisonment and probation rates was driven by a difference in criminalization—the number of cases funneled into probation supervision. Importantly, this criminalization appears to be the result of policy choices, not differences in overall crime patterns. Both violent and property crime rates were slightly higher in managerial control states, though neither relationship reached statistical significance (t = 0.9 and 1.2, respectively).

Turning to the high imprisonment states, we see that compared to the punitive control regime, incapacitative control states have a much lower average admission rate (t = 4.4, p < .001), a longer average term length (t = 2.2, p < .05), and a strikingly high percent of probationers convicted of felony-level offenses (87% versus 42%; t = 6.7, p < .001). As with the low imprisonment states, however, these differences are not driven by large crime differentials; violent crime rates are slightly higher in incapacitative control states, although this difference is not significant (t = 1.5, n.s.). These patterns suggest that the decoupling of probation and imprisonment rates is explained, in part, by incapacitative control states providing infrequent probation supervision for misdemeanants.

Yet, as explained above, the probation population—especially for misdemeanor offenses—is in many respects an ambiguous total. This led me to hypothesize that rather than showing leniency, these trends may reflect underreporting of the misdemeanor population. Indeed, nearly all of the incapacitative control states I contacted by phone reported having local community supervision programs for misdemeanants that were not included in the probation counts reported to BJS.¹⁹ States referred to such supervision as city (or county) probation, bench probation, or private probation. This underreporting of misdemeanors explains why incapacitative states have a low admission rate coupled with a greater mean time served: only more serious felony-level sentences are counted.

The BJS probationer totals are thus an undercount (or “floor” estimate) of the relevant population. As misdemeanor reporting is not consistent across states, Figure 3 presents a misleading portrait of state variation. Figure 4 follows the same logic but only uses the estimated felony probation rates. Note that the correlation between felony probation rates and imprisonment rates is greater than for overall probation rates (as expected), although the relationship is only moderate (r = 0.4, p < .05). With the exception of Nevada, all of the states in the most extreme corner of the incapacitative regime, including Oklahoma, Mississippi, Missouri, and Louisiana, shift to the right. While a handful of states remain in the boundaries of this control regime, their location in the typology moves much closer to the 45 ° line that represents a perfect correlation between probation and imprisonment rates.²⁰ Note that states' locations within the other three regimes remain more consistent, suggesting that state variation for these three regimes is less distorted by the felony–misdemeanor difference.

Figure 4.

States' felony probation and incarceration rate ranking in 2010. Note: States are ordered according to their supervision rates from lowest (1) to highest (50) for each axis.

As with the national analyses, the state-level results suggest that mass probation overlapped with—but was distinct from—mass imprisonment. As states developed mass punishment, imprisonment and probation rates increasingly diverged. States' probation rates changed quickly and unpredictably. Some low imprisonment states restrained growth in both forms of supervision (the sparing control regime), while others developed mass punishment through probation (managerial control), driven by criminalization trends that increased the flow of probationer entries. In addition, it initially appeared that some high imprisonment states restrained probation growth (while others expanded both forms of supervision), but probing reveals these states systematically underreport misdemeanor probationers.

Conclusions and discussion

Sociologists and others are deeply concerned about the dramatic expansion of mass imprisonment. Yet in terms of sheer scale, this expansion is eclipsed by mass probation, which brings state agents into individuals' neighborhoods (Cohen, 1985). This article provides a first step toward explaining this development, creating a typology to categorize states' control strategies in late modernity. The results support three conclusions. First, mass probation exploded between 1980 and the late 2000s, driven primarily by an increase in the number of felony and misdemeanor convictions and affecting a more demographically representative swath of Americans than mass imprisonment. Second, as mass imprisonment and mass probation both expanded, states' supervision rates decoupled. Third, this decoupling is due in part to the massive increase in probation admissions in some low imprisonment states and in part by the tendency of some high imprisonment rate states to underreport misdemeanor probationers.

This variation in approaches to punishment—and, in particular, the unpredictable nature of low imprisonment states' probation rates—reveals that comparative research focused primarily on imprisonment rates fundamentally misconstrues state variation. If we take probation as a serious exercise of state control, complete with its own deprivations and harms (Durnescu, 2010), the rise of mass probation in states like Minnesota, Washington, and Delaware suggests that we have much to learn about the causes and consequences of the carceral state.²¹ Probation is neither a simple alternative nor complement to imprisonment, but a unique form of state control. Rather than a monolithic expansion, states followed diverse trajectories, likely driven by local social, political, and economic conditions, producing a multifaceted array of control strategies.

Thus, a full account of the carceral state requires us to understand each of the various mass punishments. The story of state variation in probation complicates many of our established truths about the rise of the carceral state, especially that punishment is most concentrated in Southern states with a history of conservative politics and large minority populations (Campbell and Schoenfeld, 2013). For example, if Barker's (2009) study of state variation in punishment had included probation, she would find that Washington's inclusive democratic traditions did not prevent the state from developing mass punishment, but channeled that growth into probation, generating a substantially higher overall supervision rate than the punitive exemplar California. Relatedly, a focus on probation expands the scale of control observed among white Americans, a demographic group often presented as simply “collateral damage” in accounts of the carceral state as a form of racial domination (Forman, 2012).

The reasons behind the expansion of probation across states remain a puzzle, the full unraveling of which will likely require the same level of analysis as the deep (and still developing) history of mass imprisonment. However, I offer a few preliminary thoughts, focusing in particular on what might explain the differences across low imprisonment regimes. First, there are few obvious social, economic, and political differentials between sparing and managerial control states; both include predominantly progressive Midwestern and Northeastern states without a legacy of slavery (and, in many cases, a relatively racially homogenous population). This suggests that the differences in probation rates are unlikely to be explained by the usual social, economic, and political correlates of imprisonment rates.

It seems likely that probation rates were inadvertently shaped by the broader judicial and correctional structures that frame criminal justice outcomes, particularly those that shape court actors' decisions and the routine operation of probation supervision. These structures include the overall sentencing structure and the organization of funding and oversight for probation services (Phelps, 2013). Cunniff and Shilton (1991), for example, find that judges operating in determinant sentencing states are more likely to assign probation, perhaps because individuals cannot be released early onto parole. Indeed, a higher percent of managerial control states have determinant sentencing structures as compared to the sparing control regime, although indeterminant sentencing is common among all low-imprisonment states.²² In addition, states where probation is organized through the court tend to spend more on probation supervision (Anderson Economic Group, 2013), possibly shaping the success of probationers in avoiding revocation and judges' willingness to sentence serious cases to probation (Petersilia, 2002). Again, there is some descriptive evidence—managerial control states are more likely to operate probation through the judiciary—but probation departments managed through the Department of Corrections are more common in both regimes.²³

In addition, the trajectory toward mass probation across states was shaped by policy efforts to curb the imprisonment rate that ignored (if not encouraged) growth in probation. For example, both progressive sentencing guidelines and community corrections acts²⁴ tend to explicitly restrict the use of imprisonment and length of sentence, but do nothing to restrict probation (Dailey, 1998; Frase, 2005). Overburdened judicial actors are able to lean on probation to manage court dockets without substantially increasing prison rolls or correctional costs—a practice familiar since the Progressive Era (Rothman, 2002). The focus on imprisonment as the problem blinded observers to the reality of expanding probation totals, with policy-makers treating expansions in community corrections as an unqualified “good” rather than as another form of state control to be used parsimoniously.

Much as we have spent the past two decades trying to understand the rise of mass incarceration and deploring its consequences, this article intends to spur researchers to investigate mass probation. Better estimates of probation totals and other alternative sanctions will help, as will historical scholarship that considers the development of multiple forms of punishment across states. In addition, states in the sparing control regime provide ideal testing grounds for how probation can be used as an alternative sanction without widening the net of control (Phelps, 2013). Finally, while this article focuses on the state level of analysis, we still have much to learn about the consequences of mass probation for individuals, families, and communities. Together, such scholarship will bring probation into the mainstream of punishment research, providing a more robust conceptualization of the state's capacity to punish.

Footnotes

Acknowledgements

Special thanks to Devah Pager, Kim Scheppele, Amy Lerman, and Miguel Centeno for helping to guide the project from its inception. Thanks also to Christopher Uggen, Joachim Savelsberg, Penny Edgell, Sara McLanahan, German Rodriguez, Michael Schlossman, Christopher Mueller, Joshua Kaiser, Amy Cooter, Jennifer Carlson, Matthew DeMichele, Philip Goodman, Alex Ewald, Letta Page, and the anonymous reviewers and journal editors, who provided helpful comments and critiques. Veronica Horowitz provided excellent research assistance.

Notes

Michelle S Phelps is an assistant professor of sociology at the University of Minnesota (with affiliations at the University of Minnesota Law School and Minnesota Population Center). Her research is in the sociology of punishment, focusing in particular on the punitive turn in the U.S., and has been published in Law & Society Review, The ANNALS of the American Academy of Political and Social Science, Law & Policy, and Theoretical Criminology. Together with Philip Goodman and Joshua Page, she has a forthcoming book with Oxford University Press on the process of penal change.

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