The effect of available capacity on jail incarceration: An empirical test of Parkinson's law

Journal of Criminal Justice. Vol. 25, No. 4, pp. 279-288.1997 Copyright 0 1997 Elsevier Science Ltd Allrights reserved Printed in the USA.

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THE EFFECT OF AVAILABLE CAPACITY ON JAIL INCARCERATION: AN EMPIRICAL TEST OF PARKINSON’S LAW

STEWARTJ. D’ALESSIO and LISA STOLZENBERG School of Policy and Management Florida International University North Miami, Florida 33 18 1

ABSTRACT The strong positive association between available capacity and incarceration rates has been interpreted by social scientists as consistent with one of two competing hypotheses: (I) that available capacity affects rates of incarceration or (2) that capacity levels respond to variations in conjinement populations. Although these two alternative hypotheses imply difSerent causal mechanisms, it has been d@cult to adjudicate between them, in part because of the data and methodologies employed in prior research. This study investigates whether a large increase in jail capacity in Orange County, Florida increased daily jail incarceration levels above that expected on the basis of preexisting incarceration trends and police activity. Results are consistent with the hypothesis that available capacity influences incarceration levels. The number of daily arrests made by police, however, is of little consequence in predicting levels of jail incarceration. Taken in total, these findings suggest that adding additional capacity may act to accelerate the growth of confinement populations. 0 1997 Elsevier Science Ltd

INTRODUCTION A current controversy in research on incarceration concerns the effect of available capacity on incarceration levels. Although it is readily acknowledged that capacity and incarceration rates are related, the temporal order of this relationship has not been adequately established. Does capacity determine incarceration levels, or do capacity levels merely respond to fluctuations in confinement populations? Previous research that has attempted to resolve this causal dilemma 279

has produced conflicting findings. One study (Abt Associates, 1980) reported that available prison capacity affected levels of incarceration regardless of crime rates; another study (Blumstein, Cohen, and Gooding, 1983) found little evidence that available capacity was associated with prison populations, once other salient factors were controlled. Methodological and conceptual shortcomings probably account for these discrepant findings. Particularly serious problems included heteroscedasticity, serial autocorrelation, and measurement error.

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In this study, the relationship between available capacity and incarceration is further investigated, correcting for some of the methodological problems encountered in earlier research. This study does not, however, aim to replicate previous research that used prison population as the dependent variable. Rather, for analytical and practical reasons, this article contributes to the ongoing debate by using data calibrated in daily intervals and an interrupted time-series ARIMA design to determine the extent, if any, of the effect of available capacity on jail incarceration. To reduce the possibility of spurious results, the number of daily arrests made by police is included in the analysis as a statistical control. The policy implications of this study are noteworthy. If available capacity is unrelated to jail incarceration, once other relevant factors are controlled, then one can conclude that policy efforts directed at building more confinement facilities may be effective in alleviating overcrowding. Yet if enlarging capacity merely acts as a catalyst to further increase levels of incarceration, then inmate overcrowding may be addressed more effectively through alternative, nonincarcerative mechanisms.

BACKGROUND Although many social scientists have noted a strong positive association between available capacity and incarceration rates, the extant literature is quite ambiguous about the causal direction of the relationship. One prominent view suggests that incarceration rates determine capacity levels. The crux of this argument is that crime rates, economic factors, and population characteristics affect incarceration rates, which in turn affect capacity levels (Young and Brown, 1993). A second position suggests the opposite: that the number of incarcerated offenders grows in direct relation to capacity levels, regardless of changes in rates of crime or other factors (Nagel, 1973). Adherents to this view maintain that if a market is created for incarceration by building more prisons and jails, then the supply of inmates inevitably will rise to meet this demand. The theoretical rationale for this position derives largely from the literature on bureaucratic

organizations (Borcherding, 1977; Breton and Wintrobe, 1982; Niskanen, 1971). In monitoring the daily operations of public-sector bureaucratic organizations, for example, Parkinson (1957) observed that public administration departments during peacetime typically increased their staffs between 5.17 and 6.56 percent per year regardless of any variation in work load. He attributed this growth in personnel to an elaborate process whereby civil servants, imagining themselves to be overworked, appointed two subordinates rather than one to avoid the creation of rivals. These subordinates eventually followed suit, thereby causing the organization to grow in size. Parkinson (1957) also observed that work assigned to employees in bureaucratic organizations, no matter how trivial, invariably took the total time allotted by supervisors for its completion. In interpreting his observations, Parkinson (1957) advanced the thesis that growth was elastic in its demands on space. He theorized that bureaucratic growth was determined not by increased work load or by other factors commonly thought to engender expansion, but rather by factors that limited growth, such as funding for positions. He used expressions such as “work expands so as to fill the time available for its completion” and “expenditures rise to equal income” to help explain the causal mechanisms underlying the processes associated with bureaucratic expansion. Anecdotal evidence supporting this view may be found in the criminal sentencing literature, which suggests that judges are more reluctant to incarcerate offenders when space is not available in confinement facilities. The recent work of D’Alessio and Stolzenberg (1995) provides a good illustration of how available capacity influences judicial decision making. Specifically, they reported that the effect of mitigated dispositional departures on jail use in Minnesota was salient only when prison population levels were high. They theorized that judicial concern about prison overcrowding motivated judges to circumvent Minnesota’s sentencing guidelines in order to shift the burden of incarcerating offenders from the state to the local level. Their study tacitly supports the thesis that judges are less likely to incarcerate offenders when confinement facilities are overcrowded.

An Empirical Test of Parkinson’s Law

The thesis that the number of incarcerated offenders grows in direct relation to capacity has been subjected to only a few empirical tests, and results have been inconsistent. Two major studies that analyzed the same data set reached different conclusions about the effect of available capacity on prison incarceration. In a study funded by the National Institute of Justice, Abt Associates (1980) examined changes in capacity and imprisonment levels between 1955 and 1976 for the fifty states and the District of Columbia. Using bivariate and multiple regression that included lagged measures of capacity and inmate population, they revealed a consequential effect of available capacity on prison incarceration: They found that a one-unit increase in prison capacity was filled approximately two years later. On the basis of their findings, the authors questioned “the value of adding correctional capacity . . as a means of redressing the problems of prison and jail crowding (Abt Associates, 1980:27). While Abt Associates concluded that the causal influences operated in only one direction-from capacity to incarceration-another important empirical work suggested that lagged levels of incarceration rather than available capacity were related significantly to current incarceration levels. In an often-cited study, Blumstein, Cohen, and Gooding (1983) questioned the validity of Abt Associates’ research on methodological grounds. They argued that if capacity determined prison population levels, then why was the excess in prison capacity during the late 1950s and early 1960s not filled?’ By reanalyzing Abt Associates’ data, Blumstein and his associates demonstrated that the strongest predictor of current prison population was not capacity but the number of offenders previously incarcerated. A computational error resulting from “incorrectly assigning missing values to the lagged variables in the control statements for using the SPSS program” supposedly led to Abt Associates’ unwarranted findings (Blumstein, Cohen, and Gooding, 1983:21). Although Abt Associates (1980) and Blumstein, Cohen, and Gooding (1983) analyzed the same data set, they offered rather different predictions about the effect of available capacity on prison incarceration. According to one view-

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point, incarceration rates should rise after an increase in capacity because available capacity influences inmate population levels. According to the other, factors such as the number of offenders previously confined in a facility are more salient than available capacity in determining levels of incarceration. These contradicting predictions are probably due to several methodological problems that plagued both analyses. One shortcoming involved the use of prison data aggregated at the national level. Analyzing national-level data has the unfortunate consequence of commingling incarceration rates, which generally vary considerably from one region or jurisdiction to another (Selke and Andersson, 1992; Zimring and Hawkins, 1991). Another problem relates to the availability and reliability of prison capacity figures from different states and across time periods (Blumstein, Cohen, and Gooding, 1983). A third weakness concerns the substantial variation in defining available capacity by individual states (e.g., number of available beds, space per inmate), which reduces the comparability of data. Because of these methodological problems, it is difficult to evaluate the relative merits of each position without more systematic analysis. As Blumstein, Cohen, and Gooding (1983: 1) concluded, “Isolating the unique influence of prison capacity on prison population . . . requires much more reliable data and careful formulations than have yet been displayed.” The purpose of the present study is to advance the understanding of the relationship between available capacity and levels of incarceration. Specifically, precise daily data over a 366-day period and an interrupted time-series ARIMA study design are used to conduct a relatively straightforward analysis of this relationship. The central question behind this inquiry is whether a large increase in available capacity in Orange County, Florida increased jail incarceration above the level expected on the basis of preexisting incarceration trends. In addition to analyzing this effect, an attempt is made to determine whether the number of daily arrests made by police has an effect independent of the influence of available capacity. The type of data and the analytic strategy used here have obvious methodological advan-

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tages over previous research in this area. First, although no research design guarantees correct inferences, the interrupted time-series design is considered among the strongest of the nonexperimental designs for drawing causal inferences (Cook and Campbell, 1979). Second, the analysis of data calibrated in precise daily intervals reduces the potential confounding of history effects that may threaten internal validity. Third, because this analysis compares changes in incarceration levels within a single jurisdiction over time and does not predict changes in incarceration levels across jurisdictions, biases resulting from regional or state differences in reporting practices are eliminated.

METHODS Data The daily data used in this analysis were obtained for a one-year period (July 1, 1991 to June 30,1992) for Orange County, Florida. The city of Orlando, which is located in this county, is one of the largest urban centers in the state. Several relevant factors were considered in selecting Orange County. First, county-level data are most appropriate for this analysis because jails are constructed and operated at the local level. Second, Orange County houses a variety of defendants, including pretrial defendants, convicted offenders, offenders awaiting sentencing, sentenced offenders awaiting transportation to federal or state prisons, and juvenile defendants. Third, Orange County not only gathers and maintains comprehensive and reliable jail data, but it also experienced only one change in capacity during the observation period: the Orange/Horizon Correctional Facility, with an operational capacity of 768 beds, opened on January 22, 1992. This facility increased the county’s overall capacity (i.e., number of jail beds) by approximately 34 percent. The availability of precise incarceration data, combined with a diverse jail population and a single large increase in capacity, provide optimal conditions for empirically analyzing the relationship between available capacity and levels of incarceration.

The endogenous variable, jail incarceration, is operationalized as the daily number of individuals incarcerated in Orange County’s seven jails as determined by a 6:00 P.M. head count.* Rates were not used in this analysis because accurate county population data were not available on a daily basis. The effect of the capacity change is analyzed with a dummy variable coded zero before January 22,1992 and one otherwise. Because police activity is thought to be correlated strongly with jail incarceration (Shelden and Brown, 1991; Welsh et al., 1990), specification of any independent effect of available capacity on jail incarceration requires the inclusion of arrest data in the analysis. Accordingly, in an effort to avoid spurious results, the number of daily arrests made by police was incorporated in the analysis as a statistical control. Although arrest data are often confounded by factors unrelated to actual crime, such data “provide a useful measure of that portion of crime which affects directly the local criminal justice system, especially jails” (Welsh et al., 1990:354). Figure 1 depicts the jail incarceration series over time. The vertical line represents the opening of the new 768~bedfacility. Even a cursory glance at Figure 1 reveals striking differences between the pre- and postintervention periods. The mean level of the jail incarceration series before the capacity change was 3,393; and the mean level of the series after the capacity increase was 3,623. The difference between pre- and postintervention means is statistically significant at the .OOl level of analysis. Because simple comparisons of mean changes are only suggestive, an interrupted time-series analysis was used to determine more accurately whether the observed rise in jail incarceration, as depicted in Figure 1, actually resulted from the increase in capacity. Intervention Analysis The intervention analysis began with the construction of the univariate ARJMA model for the jail incarceration series for the 205-day period preceding the capacity change. The univariate ARJMA model, which is developed through an iterative model-building strategy, accounts for the stochastic processes associated with a series (McCleary and Hay, 1980). Several

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Number of Defendants Capacity Change >

I

3500 3400 3300 3200 3100 3000 1

21

41

61

81

101

121

141

161

181

201

221

241

261

281

301

321

341

361

Note: New facility opened on January 22,1992.

Figure 1. Daily Levels of Jail Incarceration in Orange County, Florida From July 1, 1991 to June 30, 1992. factors must be considered in selecting an appropriate univariate ARIMA model. One important consideration is whether the series has a single constant variance throughout its course. A nonstationary variance is engendered by dramatic fluctuations in variation between observations in a series. To determine whether the jail incarceration series was stationary in variance, a rule-based expert system in the statistical software program Forecast Pro wasconsulted (Goodrich, 1989). This rule-based system, which uses a goodness-of-fit measure (BIC) to compare competing models, recommended a natural logarithm transformation to stabilize the variance of the jail incarceration series. Another salient consideration is whether a series has a single constant level throughout its course. That is, a series should not trend or drift upward or downward over time. A commonly used test for the presence of an unstable level is the “augmented” Dickey-Fuller test (Dickey, Bell, and Miller, 1986). This test, which assesses whether a series has a unit root, indicated that the jail incarceration series was trended and required first-order differencing.3

A third consideration is whether a series has any cyclical or periodic fluctuation that repeats itself each time at the same phase of the cycle or period (McCleary and Hay, 1980:80). This repetitive variation, commonly known as seasonality, is most likely to occur at weekly intervals with daily data. An examination of the jail incarceration series autocorrelation function (ACF) at lags of seven days, fourteen days, twenty-one days, twenty-eight days, and thirty-five days did not indicate the existence of a seasonal process. Once the jail incarceration series was determined to be stationary in variance and level, an examination of its autocorrelation function (ACF) and partial autocon-elation function (PACF) was undertaken in order to check for autoregressive and for moving-average processes. In an autoregressive process, the current value in a series is influenced by an exponentially weighted sum of one or more previous values. That is, the effect of one or more prior observations (i.e., the order of the autoregressive parameter) on the current observation diminishes over time: (Y, = $,Y,_, + . . . + $Y,_, + a,). In contrast, each value in a moving-average process is deter-

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mined by the average of the current disturbance and one or more previous disturbances. The effect of a moving-average process lasts for a finite number of periods (i.e., the order of the moving-average parameter) and then vanishes abruptly (Y, = a, - @,a,_, - . . . - Cl,a,_,). Visual inspection of these plots revealed an Ln(O,1,2) ARIMA process. The residuals for this model were white noise (Ljung and Box, 1978). Once the univariate ARIMA model was constructed, the null hypothesis of no statistical difference between the pre- and postintervention jail incarceration series was tested while controlling for the daily number of arrests made by police. Because extant theory provides little insight as to the appropriate functional form of the intervention component, the decision was made to model the capacity change with a zero-order transfer function. This decision was based on a visual examination of Figure 1 and on findings from a preliminary analysis.4 The zero-order transfer function models the capacity change as having an abrupt and permanent effect on jail incarceration levels (Y, = o,,Z, + N,). For example, if the intervention coefficient for the capacity change is positive and statistically significant, it would indicate support for the proposition that available capacity increases jail incarceration levels.

RESULTS Table 1 presents the maximum-likelihood coefficients along with t-values to evaluate statistical significance.5 This table shows that the capacity change increased daily levels of jail incarceration above that expected from preexisting trends. The intervention parameter (o), which is interpreted as the increase or decrase in the level of the series that is due to the intervention, indicates that the mean level of the jail incarceration series increased by more than 4 percent on the day immediately after the new facility opened.6 The effect of the capacity change on jail population levels persists even when the number of daily arrests made by police is taken into account. Table 1 also shows little evidence that the number of daily arrests made by police affected jail incarceration levels. Although the arrest variable

TABLE 1 MAXIMUM-LIKELIHOOD COEFFICIENTSFORJAIL INCARCERATION EQUATION b First-order moving average process Second-order moving average process Capacity change Arrests Constant

SE(b)

.362*

,051

.179* .043* .770E-05 .207E-03

,052 ,013 .209E-04 .348E-03

?? p < ,001 (one-tailed test).

was related positively to jail incarceration, its effect was not statistically significant. This finding runs counter to the prediction that police activity influences levels of jail incarceration. Taken together, these results suggest that the capacity increase rather than preexisting trends or the number of daily arrests made by police was most salient in determining levels of jail incarceration. Having established the importance of available capacity in determining levels of jail incarceration, the mean changes between the preand postintervention periods were compared for several different offender classifications. A comparison was made between the 205 days immediately preceding the intervention (July 1, 199 1 to January 21, 1992) and the last ninetyone days for which data were available (April 1, 1992 to June 30, 1992). The results of this comparison are presented in Table 2. In the general picture that emerges from visual inspection of Table 2, misdemeanor rather than felony defendants are more likely to be confined when jail space is available. The number of incarcerated parole violators and undocumented aliens (+97.1 percent), pretrial misdemeanor/presentenced defendants (+5 1.1 percent), sentenced misdemeanor offenders (+23.0 percent), and pretrial misdemeanor probation violators (+ 17.2 percent) all increased dramatically after the capacity change. The number of incarcerated sentenced felony offenders (+4.3 percent) and felony probation violators (+ 1.5 percent) also increased, but less markedly. Unexpectedly, the numbers of incarcerated pretrial/presentenced felony defendants (- 8.6 percent) and offenders held for other jurisdictions (-2.6 percent) showed small but statistically significant declines after the intervention. The

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TABLE 2 MEANS AND STANDARDDEVIATIONSFOR INCARCERATED DEFENDANTSFOR PRE- AND POSTINTERVENTION PERIODS, BY LEGALCLASSIFICATIONS Pre (N =

Felony Sentenced PretriallPresentenced Violation of probation Misdemeanor Sentenced Pretrial/Presentenced Violation of probation Held for state/Other jurisdicGonsb OtherC

205)

Post (N = 91)

Mean

SD

Mean

SD

579.7 1260.3 543.6

22.0 49.6 28.5

604.9* 1151.9’ 551.6’

25.0 38.9 23.4

329.1 229.4 378.6 58.0 13.9

21.5 28.1 19.0 4.3 3.0

404.9’ 346.7’ 443.9’ 56.5* 27.4’

18.9 25.0 12.2 5.5 2.4

aFigures are mean values on each variable. An asterisk indicates that the mean difference is statistically significant at the .Ol level (one-tailed f-test). bThis category includes state, federal, and other county jail inmates. CThis category includes parole violators and undocumented aliens.

reasons for these decreases are not readily apparent. It is clear, however, that when jail space was made available in Orange County, it tended to be filled with minor law violators.

DISCUSSION

AND CONCLUSION

The evidence presented here suggests that available capacity has a strong positive effect on levels of jail incarceration. The interrupted time-series analysis demonstrated the effect of available capacity, even after accounting for preexisting trends in jail incarceration and the number of daily arrests made by police. Another interesting finding was that when jail beds were made available, they tended to be filled with minor law violators. Additionally, the finding that the number of incarcerated misdemeanor probation violators rose markedly after the increase in jail capacity is particularly disturbing, especially for proponents of rehabilitation. Although drug testing and other conditions often associated with probation are ostensibly meant to ensure rehabilitation, it appears that such requirements may actually be acting to accelerate the growth of confinement populations (Blomberg and Lucken, 1993; Zimring and Hawkins, 1991). Such a finding also reinforces a variant of Feeley and Simon’s (1992) new pe-

nology thesis: that the purpose of probation and parole is no longer to rehabilitate or punish individual offenders, but to maintain state control over problem populations in society. The results displayed in Table 1 also show that the daily number of arrests made by police is inconsequential in determining jail incarceration levels. Although previous research assumed that police activity was important for understanding variations in jail incarceration levels, the findings presented in this study undermine that assumption. The weak influence of police activity is probably attributable to the use of ARIMA, which is generally considered to be a more robust (McCleary and Hay, 1980) and a more conservative statistical procedure (Pierce, 1977) than regression time-series analysis. Although the results of this study have important theoretical and policy implications, it would be premature to accept them as definitive and final. First, the data used in this analysis were drawn from only one large urban county and may not apply to other counties, particularly rural counties with relatively small jails. Future investigators should consider replicating this analysis in other jurisdictions. The more frequently such research is conducted, the greater the confidence one can place in the generalizability of this study’s findings. Second, because jails are generally considered to be historically separate and operationally distinct from prisons,

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this analysis offers only a limited basis for drawing inferences about the effect of available capacity on levels of prison incarceration. Third, while the evidence presented here supports the thesis that capacity influences levels of incarceration, it does not provide an explanation of that effect. As pointed out by Young and Brown (1993:35), “Even if prison capacity did have a significant bearing on the prison population, it would still not explain why capacity was fixed at a particular level or varied from one jurisdiction to another.” At this preliminary stage, only two possible explanations can be offered for variations in capacity levels that merit future exploration. One possibility, as mentioned previously, is that capacity may be fixed at a particular level to maintain control over problem populations in society. The jail is recognized not only as the major intake center for the criminal justice system, but also as a place where the rabble of society are controlled (Goldfarb, 1975; Irwin, 1985). The rabble class comprises junkies, petty hustlers, and society’s other deviant groups who are not typically involved in serious criminal activity. Irwin (1985) argues that jail confinement is determined primarily by the disorderliness and offensiveness of members of these outcast groups rather than by their actual criminal behavior. If the jail is a place where punishment is inflicted on people principally for their social status or circumstances rather than for any particular criminal act, then it stands to reason that jail capacity may be fixed at a level necessary to maintain state control over problem populations. A second intriguing possibility is that variations in available capacity reflect processes associated with self-interest rather than state interest. In his seminal work, Downs (1967) advanced the thesis that nonprofit, public-sector organizations, which he labeled bureaus, have an endemic tendency to expand in size.’ He argued that although bureaucrats in the public sector are unable to maximize profit, as do their counterparts in private industry, they still can reap numerous personal benefits by expanding the size of their agency. The benefits acquired from such expansion include increased status and power, personal income, greater opportunities for career advancement, and increased ability

on the part of the agency to secure government subsidies. The expansion of public-sector organizations is not unbridled, however, but is mediated by the actions of what Downs (1967) terms functional and allocational rivals. Functional rivals are distinct government agencies with similar, competing functions; and allocational rivals are agencies, with different functions, that compete against each other for finite government resources. These two types of rivals have the same effect on public-sector organizations as do competing businesses on private firms. Downs’ (1967) thesis is noteworthy because if one is willing to assume that state-operated jails and prisons are similar in character to nonprofit public-sector organizations, it is quite reasonable to expect that they also are predisposed to expand in size and that the extent of such expansion is determined in competition with functional (e.g., diversion programs, probation) and allocational rivals (e.g., the police, social service agencies). Another interesting aspect of Downs’ (1967) work is his thesis that newly created public-sector organizations will grow rapidly after inception in order to achieve a “survival threshold.” Defined briefly, a survival threshold is the size a fledgling bureau must reach to enable it to fend off competitors (i.e., functional and allocational rivals) and thereby assure its continued survival. If the survival threshold argument has merit, one also might expect that the number of offenders incarcerated in a newly created confinement facility will grow rapidly in order to guarantee its continued survival. Furthermore, it stands to reason that if a newly created jail must rapidly reach a survival threshold, the best strategy for achieving this objective would be to fill the available capacity, at least initially, with offenders least able to resist incarceration. The perceived dangerousness of individuals would play only a secondary role in determining jail confinement. This possibility may explain why misdemeanor defendants accounted for most of the increase in jail incarceration after the capacity change. Yet although both the self-interest account and the problem population thesis seem plausible and in accord with this study’s findings, much more research is necessary before the specific causal mechanisms underlying fluc-

An Empirical

Test of Parkinson’s

tuations in capacity levels over time and across jurisdictions can be identified. With these qualifications in mind, the implications of this study for public policy are important. Because adding capacity may have a positive and substantive effect on jail incarceration, apart from the influence of police activity and beyond the effect of preexisting incarceration trends, policymakers should remain cautious about adding capacity to reduce inmate overcrowding. Additionally, because available capacity does appear to increase incarceration levels, especially for minor defendants, inmate overcrowding may be addressed more appropriately by greater reliance on nonincarcerative remedies. It is important to emphasize, however, that the findings generated from this study are only preliminary. Until further research is conducted, it would be premature to entirely dismiss the influence of incarceration levels on capacity levels. It is hoped that this study not only stimulates more empirical research, but also encourages theoretical work for a fuller understanding of the association between available capacity and incarceration levels.

ACKNOWLEDGMENT This research was partially supported by a grant from the School of Public and Environmental Affairs at Indiana University, Bloomington.

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Law

This transfer function, which has two effect parameters, models the effect of the capacity change on jail incarceration as gradual and permanent rather than abrupt and permanent. The intervention parameter (0) measures the degree of change in the level of the incarceration series: the delta parameter (6) estimates the amount of time required for this change to be realized. The larger the value of the delta parameter, the more gradual the impact of the capacity change on jail incarceration. Conversely, a small delta coefficient would indicate that the change in the level of the dependent variable was actualized much more rapidly. Results showed that although the intervention coefficient was statistically significant at the .05 level of analysis, the delta coefficient was relatively small and of minor import. Taken together, these findings indicated that the first-order transfer function was inappropriate for modeling the capacity change. 5. The ARIMA procedure in SPSS/PC+ Trends (1990) was used to estimate the jail incarceration equation. SPSS Trends uses the traditional Box-Jenkins methodology to generate maximum-likelihood estimates for a standard linear regression model with an error structure that is not independently and identically distributed. The two moving-average terms and the number of daily arrests made by police were included in the maximum-likelihood equation as explanatory variables. 6. The intervention coefficient, which is stated in the natural logarithm, was transformed into a percent-change score by using the following formula: percent change = ((@“I - l)lOO). 7. Traits of bureaus include a hierarchical organizational structure, complexity of administrative tasks, impersonality of operations, extensive use of rules, secrecy, and employment of specially trained personnel, generally on a full-time basis (see Weber [I9621 for a general discussion of the characteristics of bureaucratic organizations). In addition, output is not evaluated in markets external to the bureau. That is, a direct relationship does not exist between the services provided by a bureau and the income it receives for providing them (Downs. 1967:2&25).

NOTES REFERENCES I. A recent analysis conducted by Lessan and Sheley (1992) may partially answer this question. They found that although police activity increased durmg the Korean and Vietnam War eras, levels of incarceration remained virtually unchanged. To account for the stability in incarceration rates, they theorized that many criminal cases were minor and did not warrant incarceration, that many defendants were encouraged to join the military instead of being incarcerated, and that many defendants were ,funneled into the work force. 2. For the purposes of this study, a local jail is defined as a facility that holds inmates beyond arraignment, usually for more than forty-eight hours, and is administered by local officials (Bureau of Justice Statistics, 1990). This definition excludes drunk tanks, lockups, and similar holding facilities. 3. The arrest series was stable in both variance and level. 4. In a preliminary analysis, a first-order transfer function ((w,/l - 6,B)I,) was used to model the intervention.

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