LABOR MARKET AND IMPRISONMENT

LABOR MARKET AND IMPRISONMENTAuthor(s): Ivan JankovicSource: Crime and Social Justice, No. 8 (fall-winter 1977), pp. 17-31Published by: Social Justice/Global OptionsStable URL: http://www.jstor.org/stable/29766015 .

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LABOR MARKET AND IMPRISONMENT*

Ivan Jankovic**

I. INTRODUCTION

The prevalent conception of punishment treats it as an epiphenomenon of crime, a reaction by the state to the criminal's breach of the legal order. This conception was expressed by Immanuel Kant

(1&&7, 2:194): "Juridical punishment can never be administered merely as a means for promoting another good, either with regard to the criminal himself or to civil society, but must in all cases be

imposed only because the individual on whom it is inflicted has committed a crime." Alternately, punishment is seen as a measure to prevent crime

and as a weapon in the war against crime," but it is

always a part of a dyad, one side of the coin whose other face is represented by crime.

The underlying assumption here is that forms and intensity of punishment must be determined by forms and magnitude of crime, and that the

primary function of punishment is to revenge, prevent, contain and decrease crime. In sociology, this is reflected by the fact that the largest part of

sociological literature on punishment is concerned with its deterrent effects. At times, this theoreti? cal concept of punishment is also reflected in the

design of empirical studies, as when punishment (e.g., number of prison admissions) is used as an index of crime.

Among the first to successfully break the

supposed bond between crime and punishment by demonstrating how penal policies are shaped by economic and political considerations, were Georg Rusche and Otto Kirchheimer (1933, 1939). Their

theory of punishment, according to which "every system of production tends to discover punishments which correspond to its productive relationships," provides the starting point for the present essay. We shall try to further transcend the bond which is

supposed to exist between crime and punishment, and to examine the latter as an independent phenomenon in its manifold relationships to the social and economic structure.

To state that punishment is not a simple consequence of crime is not to deny that most

judges, when passing a sentence, sincerely believe themselves to be reacting to the defendant's crime. Nor is it to deny that theories of punishment reflect a sincere concern over crime and a belief that punishment is a necessary consequence of crime. The statement implies that theories, as well as practice, of punishment reflect prevailing ideologies which are, in turn, partly determined by economic requirements of concrete systems of material production.

So, for example, societies plagued by shortages of labor (e.g., sixteenth century Germany) develop ideologies which emphasize man's duty to work (the Protestant ethic); those faced with an oversupply of labor (e.g., nineteenth century England) resort to

ideologies which make work one's right, to be

* An earlier version of this paper was presented at the

meetings of the Pacific Sociological Association in

Sacramento, April 21, 1977. Both versions are adapted from an unpublished Ph.D. dissertation, "Punishment and

the Postindustrial Society: A Study of Unemployment, Crime and Imprisonment in the United States," Depart? ment of Sociology, University of California, Santa

Barbara.

** Ivan Jankovic is presently preparing and translating Marxist writings in Western criminology for the journal Marxism Today, which attempts to keep the Yugoslav

public informed about Marxist scholarship abroad.

Jankovic is scheduled to join the faculty at the

University of Kragujevac in 1978.

17 / Crime and Social Justice



fought for in the labor market (the laissez faire of liberal capitalism).

All theories of punishment must apply to con? crete penal systems in specific historical periods and socioeconomic systems. Rusche and Kirch heimer applied theirs to Western societies from the Middle Ages to the 1930s, but encountered some difficulties in explaining the continuing use of

imprisonment in advanced capitalist countries. The use of imprisonment could be explained by condi? tions of general labor shortages, when convicts could be profitably exploited. But it defies such

explanation in capitalist societies which are not

only faced with a permanent oversupply of labor but in which freedom of labor is the essential condition of its productivity, and, therefore, con? vict labor cannot be profitably exploited. Ulti? mately, Rusche and Kirchheimer were forced to dismiss imprisonment as an "irrational" penal meas?

ure in developed capitalist countries. The persistent use of imprisonment in the most

advanced capitalist societies in the final quarter of the twentieth century, however, demands a better explanation. If the proposition that "every system of production tends to discover (and use) punish? ments which correspond to its productive relation?

ships" is true, then imprisonment must be meeting some needs of advanced capitalist economies.

The main task of this essay will be to explore the applicability of the Rusche-Kirchheimer theory to contemporary Western societies. Specifically, the potential connections between use of imprison? ment and the conditions of the labor market will be examined.

II. A CRITIQUE OF THE RUSCHE-KIRCHHEIMER THEORY

In their treatment of modern penal systems, Rusche and Kirchheimer have opened themselves to two major criticisms. The first is that they fail to provide an explanation for the continued use of imprisonment

- a punishment which does not seem to correspond to productive relationships of ad? vanced capitalism. The second is that they overem? phasize the use of fines as the typically capitalist punishment.

The fine is, no doubt, better suited to a capitalist than to any other economy and it is most widely used in capitalist countries. However, it has not turned, as Rusche and Kirchheimer believe, into the dominant punishment of the twentieth century

- at least with reference to felonies and serious misdemeanors. The early twentieth century trend toward an increase in the frequency of fines has been replaced by increased use of probation.

For felonies and misdemeanors, a fine is by no means a typical punishment. In fact, the use of fines seems to be declining. Less than 1% of all felons sentenced in California superior courts in 1974 were fined (California Bureau of Criminal Statistics, 1976:14). Consistently, my analysis of 2,250 sentences (mainly for misdemeanors) in Sun? shine County, California (see below), showed only 18% to be fines. (In another 20% of all cases, fines

were imposed as a condition of probation.) More

interestingly, the use of fines in Sunshine County has been steadily declining over the years, from 24% in 1970 to 12% in 1974. The magnitude of fines has decreased as well, in spite of inflation, from a mean of $160 in 1970 to $130 in 1974.

The dominant (most common) punishments in

postindustrial societies - if we are to judge from

contemporary American data - are incarceration

(jail and prison) and probation, in that order, and often in various combinations. The most frequent single sentence passed by California superior courts in 1974 was probation with a jail term as a condition of the probation order (46.7%). Custodial sentences (with or without probation) were awarded to 81% of the persons sentenced, while probation (with or without incarceration and/or fine) accounted for 69% (California Bureau of Criminal

Statistics, 1976:14). In the Sunshine County sample, the most common single punishment was jail (25%). All custodial sentences (with or without probation and/or fines) accounted for 45% of the total, and

probation (with or without jail) accounted for 50%. Probation orders in California superior courts grew from 52% of all sentences in 1966 to 69% in 1974 (California Bureau of Criminal Statistics, 1976:14). Similarly, the percentage of probation sentences in Sunshine County increased from 23% in 1970 to 42% in 1974.

With regard to imprisonment, Rusche and Kirchheimer have argued that its early forms were introduced in order to provide needed forced labor. Forced labor has no economic justification, how? ever, within a capitalist system of production, where freedom of labor is the conditio sine qua non of its productivity. It might be noted, parentheti? cally, that prison labor, initiated solely as a source of profit, assumed a purely punitive character in the nineteenth century, and is justified in the twentieth century by its alleged educational and

therapeutic value. Reaching this impasse, Rusche and Kirchheimer are forced to explain solitary confinement, a typical feature of the nineteenth century prison, as an irrational punitive resp? onse - evidence of "a mentality which, as a result of surplus population, abandons the attempt to find a rational policy of rehabilitation and conceals this fact with a moral ideology (i.e. penitence)" (1939:137).

Fall-Winter 19771 18



By accepting the "irrationality" of imprison? ment, Rusche and Kirchheimer appear to have underestimated the heuristic value of their own

theory. In particular, they have not pursued two

hypotheses which are implicit in their work and which are formulated in more detail in the present essay.

The first of these is that there is a negative relationship between economic conditions and

severity of punishment: when economy is bad, the

punishments are more severe. The second hypo? thesis deals with the relationship between the labor market and forms of punishment. When labor is scarce, Rusche and Kirchheimer note, punishments are attempted which make greatest use of convict labor (the house of correction, transportation, etc.). Conversely, when labor is abundant, punish? ments which are wasteful of labor (e.g., death

penalty) may be used. It generally so happens that

punishments which preserve labor are also less severe than those which waste it, but this is only incidental to the primary purpose of exploitation of labor. This hypothesis works well when applied to

precapitalist societies in which labor could be forced and yet productive, but it breaks down when

applied to advanced capitalist countries. What Rusche and Kirchheimer have failed to do is to

provide an alternative connection between the labor market and imprisonment.

III. PUNISHMENT IN POSTINDUSTRIAL SOCIETIES

The two questions which have yet to be an? swered within the framework of the Rusche Kirchheimer theory are: 1) in what ways does

probation correspond to the productive relation?

ships of advanced capitalism? and 2) given the

persistence of incarceration, what functions, if

any, does this sanction serve for advanced

capitalist economies? Little more than speculation can be offered in

response to the first question. Postindustrial societies are characterized by a decisive shift from

manufacturing to service and information-process?

ing activities, with the concomitant development of appropriate technologies (Levitan et al., 1976:1).

Increasing use of probation is consistent with both these developments. The relationship between a

probationer and a probation officer is the epitome of a service relationship. In modern correctional literature, probationers are typically referred to as

"clients," probation officers as "agents," and proba? tion as "service" (cf. Remington et al., 1969:793 814). At the same time, probation supervision requires gathering and monitoring of extensive


information about the probationer, a task for which

cybernetic information-processing systems are

ideally suited.

By keeping the probationer in the community, probation does not interfere with his or her

employment and so does not disrupt the production process. In fact, maintenance of a steady employ? ment is a standard condition of probation orders. In this way, a part of the labor force is monitored and controlled by the state, while being actively engaged in the production process. Finally, proba? tion is cheaper than imprisonment, an argument which has historically carried much weight in all movements for penal reform. The cost to the state

per probationer in California in 1974 was $1,150, as

compared with $4,112 per jail inmate and nearly $10,000 per prison inmate (California Bureau of

Criminal Statistics, 1975:15, 17). Rusche and Kirchheimer's difficulties in

explaining the use of imprisonment in advanced

capitalist societies stem from their insistence on

the condition of exploitability of convicts' labor. The workhouses and some early forms of imprison? ment are easily explicable as attempts on the part of the state to mitigate periodic shortages of labor. Similarly, the United States Constitution in

1865 prohibited slavery and involuntary servitude

"except as a punishment for crime whereof the

party shall have been duly convicted." But ad? vanced capitalist societies are characterized by a

permanent oversupply of labor. In addition, convict labor cannot be exploited because forced labor cannot produce profit in capitalist economies (on the question of forced labor, see Evans, 1970).

It is possible, however, that the exploitability of

labor is not the crucial intervening variable in the

relationship between contemporary, postindustrial economic conditions and punishment.

One of the most striking features of capitalist economies is that they are always faced with an

oversupply of labor. A leading British economist, Lord Beveridge (1930:70), noting this peculiarity, asked a rhetorical question: "Why should it be the normal condition of the labor market to have more

sellers than buyers, two men to every job and not at least as often two jobs to every man?" His own

answer had to do with the fragmentation and

organizational imperfections of the market itself. But Marx had earlier given a structural answer

which seems superior to Beveridge's. In order to

survive, Marx noted, capitalist economies need to

maintain a permanent reserve of surplus labor which can at short notice be coupled with newly invested capital. This reserve army of labor is

created not by fiat or from any ulterior motives but by the technological processes essential to

capitalism. Thus, mechanization and automation of

established industries increase the capital-to-labor



ratio in favor of capital, minimizing the contribu? tion of labor. But labor is the only source of newly created value and, hence, of profit. Accordingly, capital has a tendency to move into new areas of production, likely at first to be labor-intensive, offering higher potential profits. When these areas are mechanized, the cycle is repeated. The reserve army of labor, then, is a necessary condition for the rapid movement of capital (Marx, 1867:628-40).

A reserve army of labor also places an effective limit on economic demands of employed workers. The very existence of an army of unemployed persons reminds the employed laborer of his expendability. An economist who argues that "minimum" unemployment is unavoidable in a "free" society, formulates this point with a disarm?

ing frankness: "The labor market should never be so

tight that workers have no incentive to be on their toes" (Copeland, 1966:3).

There is widespread agreement among econo? mists that the "officially" unemployed represent

LNS

only a part of the reserve army of labor. There is

disagreement, however, as to how large this part is. Some believe that the officially unemployed are

only the tip of an iceberg of surplus labor, which includes" the sporadically employed; the part-time employed; the mass of women who, as house workers, form a reserve for 'female occupations'; the armies of migrant labor, both agricultural and industrial; the black population with its extraordi? narily high rate of unemployment; and the foreign reserves of labor" (Braverman, 1974:386). Others refer to "discouraged workers" but tend to be vague about their numbers (Levitan et al., 1976:119).

Thus, under conditions which make it profitable to maintain a permanent oversupply of labor - what Marx (1967:630-31) called the "surplus population" or "reserve army of labor" -

imprisonment can be used to regulate the size of the surplus labor force. This surplus population depends directly on the state for its economic welfare. It is supported by a network of "projects and services which are

required to maintain social harmony - to fulfill the

state's 'legitimization' function" (O'Connor, 1973:7). These projects and services are "social expenses" of the state. Two main components of the state's effort to support, and thus control, the surplus population are the social welfare system and the criminal justice system. Given the persistence and the magnitude of the surplus population in ad? vanced capitalist countries, imprisonment may serve to contain a fraction of it and to manipulate its size.

IV. PRESENT RESEARCH

A. Hypotheses

The first hypothesis to be tested is that

imprisonment and unemployment co-vary directly. The independent variable is unemployment, and the

expectation is that a rise in unemployment will lead to an increase in prison commitments and

prison population. This is a restatement of Rusche and Kirchheimer's "severity" hypothesis: when the economy is bad, punishments are more severe.

Unemployment is taken as an index of the state of the economy, and imprisonment as an index of

severity of punishment. Imprisonment data indicate severity of punish?

ment in the following ways. First, imprisonment is taken to be the most severe form of punishment in contemporary American society. Second, the data on annual prison admissions indicate the frequency of punishment, which is one component of the

severity dimension. Finally, the data on number of

prisoners incarcerated on a given day every year serve as a very rough index of the magnitude of

punishment. Holding admissions constant, an in? crease in prison population indicates that the average sentences served are longer.

Punishment is expected to be more severe

during economic crises because the policy of deterrence dictates an intensification of punish? ment in order to combat the assumed increased

temptation to commit crime. Furthermore, intensi? fied punishments help preserve the socioeconomic order, threatened at times of economic crises, regardless of the stated penal policies.

The same hypothesis could be deduced from other theoretical models. Most notably, as we have seen, the widely held assumption that high unem?

ployment (and economic crises in general) results in increased criminal activity, also implies that high unemployment will result in more punishments, including imprisonment. It will be necessary, there? fore, to control for the influence of criminal

activity (as indicated by number of crimes known

Fall-Winter 1977/ 20



to the police and number of arrests) on the

relationship between unemployment and imprison? ment. This observation leads to the following refinement of the first hypothesis:

(1) Unemployment and use of imprisonment co

vary directly, regardless of the volume of crime. Or: (la) The correlation between unemployment and

imprisonment is significantly greater than zero, regardless of changes in the volume of criminal

activity. As stated, the hypothesis implies that imprison?

ment could increase even if crime were decreasing, provided that unemployment is rising. (The reverse is implied as well.)

The second hypothesis to be tested is that increased imprisonment functions to reduce unem?

ployment. This "utility" hypothesis asserts that the effect of changing penal policies is reflected in

changes in the conditions of the labor market. It is derived from Rusche and Kirchheimer's theory, which suggests that each socioeconomic system invents and uses punishments which correspond to its productive relationships. When applied to the use of imprisonment in advanced capitalist coun?

tries, this theory suggests the hypothesis that

imprisonment may be used to remove a part of the

surplus population from the labor market.

Recently, the same hypothesis has been indepen? dently advanced in at least two essays. (2)

In order to test this hypothesis it is first necessary to determine the magnitude of the potential impact of incarceration on unemploy? ment. This is an exploratory question, designed to

provide some idea of the relationship between the two variables. The next step is to reverse the

implied causal order between imprisonment and

unemployment and to test whether the size of

prison population (and admissions) at time ti has a

negative effect on unemployment rates at times t2, t3, etc. This hypothesis can be stated as follows:

(2) The size of the prison population co-varies

inversely with lagged unemployment rates. Or: (2a) The inverse correlation between the size of

the prison population and lagged unemployment rates is significantly greater than zero.

It should be emphasized again that the present argument is not concerned with the motivation of state officials who impose and administer punish? ments, or with their understanding of and rationali? zations for specific penal policies. What matters is the effect that penal policies may have on the national economy. If this effect is a reduction of

unemployment rates, the finding will give credence to the idea that imprisonment as a punishment in

capitalist economies functions, in part, to regulate the labor market. Imprisonment may function this


way regardless of the conscious goals of the state's

policies. Officials who commit persons to prison may be motivated to reduce crime, provide bed and board for destitute criminals, provide skilled mechanics for work in a particular prison industry or something else. Nevertheless, the effect of their actions may be the regulation of the labor market.

B. Data

Two different sets of data were used in testing the two hypotheses. The first set is nationwide statistics on imprisonment rates for the United States, 1926-1974, and on unemployment rates and certain other demographic data for the same

period. The second set is statistics obtained in a mid-sized California jurisdiction, called here Sun? shine County. These include unemployment and

imprisonment rates by month for an eight-year period (1969-1976).

The national statistics were obtained from the U.S. Statistical Abstracts and from the publication of the Bureau of Prisons, National Prisoner Statis? tics: Prisoners in State and Federal Institutions for Adult Felons. They include: 1) number of persons detained on a given day (usually December 31) each year; 2) number of persons entering prisons each year either after being sentenced to prison by a

court, or as violators of an earlier conditional release from prison; 3) number of persons released from prison during each year either conditionally (mainly on parole), or unconditionally (mainly at

expiration of sentence). Unemployment data were drawn from the U.S.

Statistical Abstracts and include size of the civil ian labor force (in thousands), number of persons unemployed (in thousands), and unemployment rate

(percent unemployed in total labor force). The

unemployment rates are annual averages, based on

seasonally adjusted monthly rates.

Population figures were also taken from the U.S. Statistical Abstracts. They refer to resident civilian population. Imprisonment rates (per 1,000) were computed on the basis of this population. The size of the armed forces was obtained from the same source.

Crime and arrest data came from the FBI

publication Uniform Crime Reports. Crime data include numbers reported annually to the FBI of "seven major crimes" (murder, assault, rape, rob?

bery, burglary, theft and vehicle theft). From 1937 to 1957, the figures are based on reports from 353 cities with 25,000 or more inhabitants; since 1957

they reflect a wider reporting base, with rural and

suburban, as well as urban communities included. No nationwide crime data exist prior to 1937. Arrest data from 1932 to 1952 are based on examination of fingerprint cards filed with the FBI



and, since 1952, on reports submitted to the FBI by local law enforcement agencies. Again, no nation?

wide arrest data are available prior to 1932. In Sunshine County, monthly labor force statis?

tics were obtained from the County Employment and Training Department. This department collects

weekly information on unemployment claims and on numbers of employed persons, and tabulates them

by month. On the basis of the employed and

unemployed categories, the size of the total (civilian) labor force and unemployment rates are

computed. The same department provided monthly estimates of the total county population.

Crime and arrest data were not available on a

monthly basis for the entire county. Only one law enforcement agency in the county, the Sheriff's

Department, could provide monthly counts of

reported crimes and arrests made within its juris? diction for the period under study. The Sheriff's

Department accounts for over one third of all crimes reported and arrests made in the county annually, and its share is fairly constant over time. However, the data base changed in June, 1971, when one of the cities policed under contract by the Sheriff's Department acquired its own police force. The effect of this change in reporting base was examined by use of a dummy variable, and was found to be nonsignificant.

Information about the Sunshine County jail population was obtained from jail records, and it consists of average daily population for each month, from January, 1969, to December, 1976, inclusive. Except for the first year (1969), the total population was broken down into sentenced and unsentenced categories.

These statistics were supplemented with data

previously gathered by this author for a study of

sentencing patterns in Sunshine County. The

sentencing study examined dispositions of six cate?

gories of offenses, of which four were misdemea? nors and two felonies. The total sample included 2,250 cases disposed of in the Sunshine County Municipal Court and Superior Courts over a five year period (from January, 1970, to December, 1974, inclusive). The sample was random, strati? fied by year (450 cases for each year) and by offense. The offenses included: robbery (N=75), burglary (N=625), misdemeanor drunk driving (N=500), drunk and disorderly (N=450), being under the influence of narcotics (N=450) and possession of marijuana (N=250).

C. Methods

The basic technique used in the present study was that of multiple linear regression. Regression analysis was used, with population included as an

independent variable, in order to standardize the

imprisonment and unemployment values. The rou?

tine procedure was to solve regression equations in which the dependent variable was some index of

imprisonment and the independent variables were

unemployment and population. In this way, it was

possible to measure the impact of unemployment on imprisonment, holding population constant.

The same technique was used to control for the

impact of crime on imprisonment. Crime was added to the regression equation as an independent variable, which made it possible to assess its relative contribution to an explanation of variance in imprisonment.

The multiple regression technique is described

by Blalock (1972:429-68) and by Nie et al. (1975:320-42). It is applicable to the present data, which are all represented by ratio-level variables. In the present context, it is used as a descriptive technique, intended to decompose and summarize the assumed linear dependence of imprisonment on

unemployment and crime. At the same time, inferential statistics (F ratios and T tests) are used to assess the statistical significance of observed linear relationships.

Since the present data represent time series, it was necessary to consider the autocorrelation

effect (see Pindyck and Rubinfeld, 1976:106-20). In the present study, the Durbin-Watson d statistic was used to test for the presence of serial (auto-) correlation. This statistic tests the null hypothesis that no serial correlation is present (Pindyck and

Rubinfeld, 1976:113). Whenever serial correlation was established, the standard procedure was to

purge its effects by using an AR(1) model and to

report the adjusted regression results.

D. Findings and Analysis

The first step in the analysis was to determine the correlation between unemployment and various indices of imprisonment, controlling for population growth. Six different regressions were run to examine the effect of unemployment on different indices of prison population. Results for the United

States, 1926-1974, are summarized in Table I, which suggests that as the total number of unem?

ployed persons increases, the total number of

persons present in and admitted to prisons also increases.

The partial correlation coefficient between total prison population and unemployment, with

population held constant, is .43. The unstandardized

regression coefficient indicates that an increase of

1,000 unemployed persons corresponds to an in? crease of 2 prison inmates. The coefficient of determination suggests that 32% of the variation in

prison population is accounted for by the joint action of unemployment and civilian population.




All these results are significant at p=.01 or better.

Essentially the same results are obtained when the

dependent variable is the number of persons actually admitted to prisons.

When the prison population is broken down into its two major jurisdictional components (state vs. federal institutions), it appears that the above correlation is due to the relationship between

unemployment and state prison population, and that no significant relationship exists between unem?

ployment and federal prison population. In fact, the regression and correlation coefficients asso? ciated with the impact of unemployment on federal

prisoners are negative, ranging from -.03 to -.05. The overall positive correlation is due to the fact that federal inmates account for only one tenth of the total prison population.

Analysis of different subperiods within the national sample showed that the hypothesized positive relationship did not obtain during the

period of the Great Depression (1930-1940). All

regression and correlation coefficients for this

period were negative (-.02 to -.42), although none was statistically significant. During World War II, unemployment and imprisonment both decreased, producing high positive correlations, most likely

due to the extraordinary shortage of labor created by the mobilization of the nation for the war effort.

In order to eliminate the effects of the Great Depression and World War II, a set of regressions were run on the time series of 28 years after the war (1947-1974). The results, reported in Table 2, show that prison admissions were particularly responsive to changes in unemployment. For every 1,000 additional unemployed persons, there were 4.36 additional admissions. The partial correlation coefficient between state prison admissions and

unemployment, with population held constant, was .49. Unemployment and population growth, opera? ting jointly, accounted for 50% of the variance in total prison admissions. Similar but somewhat weaker results were obtained for prisoners present in state institutions (R2=.26) and all institutions (R2=.27).

For the federal prison population, the regression and correlation coefficients were higher than the coefficients for the entire 49-year period. Although not statistically significant, they have the expected (positive) signs and approach the lower limits of statistical significance. Further investiga? tion revealed that, with the passage of time, the

23 / Oime and Socw/ Justice



relationship between unemployment and the popu? lation of federal prisons became much stronger. As shown in Table 3, for the 15-year period since 1960

(1960-1974), the correlation between unemploy? ment and total federal prisoners, and the correla? tion between unemployment and federal prison admissions, are both very high. Unstandardized

regression coefficients indicate that an increase of

1,000 unemployed persons corresponds to an increase of 1.69 and 1.31 federal prisoners present and received, respectively. Partial correlation coefficients are .81 and .82. The R(2) statistic indicates that almost 70% of the variation in federal prison population and federal prison admis? sions is attributable to the joint effect of unem?

ployment and population. In order to establish the effect of changing

patterns over time on the overall results, a

regression was run with two dummy variables

designed to distinguish the prewar and war years in

comparison with the postwar period. The results (Table 4) show that the differences, which are in the expected direction, are not statistically signifi? cant. This is consistent with the finding that

regression results for the 1926-1974 and 1947-1974

periods are not significantly different. The second step in the analysis was designed to

test the effect of the volume of crime on the

imprisonment-unemployment relationship. Two in? dices of crime were available: number of reported crimes, 1937-1974, and number of arrests, 1932 1974. Both were used in two sets of regressions,

and the results were very similar. However, arrests

were preferable because they provided a longer period for analysis. The basic procedure was to run

multiple regressions with total prison inmates and total prison admissions as dependent variables, and

arrests, unemployment and population as

independent variables. Results of these regressions, summarized in

Table 5, indicate that the unemployment-imprison? ment relationship obtains regardless of the volume of crime. Except for the two regressions in which

dependent variables are represented by federal

prisoners, regression coefficients associated with

unemployment are all significant at p=.01, while no

regression coefficient associated with arrests attains statistical significance. Partial correlation coefficients between unemployment and state

prison population, obtained when the effect of arrests on both variables was accounted for, are

still around .5 and significant at p=.01. Interestingly, regression and correlation coeffi?

cients associated with arrests are almost all nega? tive. A literal interpretation suggests that an

increase in arrests corresponds to a decrease in

prison population. This relationship between arrests

and prison population actually existed during seve

ral of the periods under study. For example,

throughout the decade of the 1960s (see Appen? dix 1), prison population was declining (as was

unemployment), while arrests were sharply

increasing. Similar results were established for the postwar

period, 1947-1974 (Table 6). Here the federal prison

population, too, correlates with unemployment (p=.05). However, some regressions presented in

Table 6 have low determinants and standardized

regression coefficients larger than 1 (for total

prison population), indicating presence of serious

multicolinearity. (This multicolinearity is due to

strong correlation between arrests and population

growth.) In addition, the low Durbin-Watson statist?

ics point to a strong serial correlation effect which was not fully purged by the AR(1) model. The

foregoing applies to regressions which use prison

population as dependent variables. Regressions which use prison admissions show better results.

Examination of the national sample thus demon? strates that the expected positive relationship between imprisonment and unemployment holds

regardless of the volume of criminal activity. The third step in the analysis correlated

monthly Sunshine County fluctuations in unemploy? ment and fluctuations in the total population of the

county jail. Table 7 shows that an increase of 1,000 in either population or unemployment corresponds to an increase of 4 jail inmates each month. The

partial correlation coefficient between unemploy? ment and jail population, with civilian population held constant, is .23 (significant at p=.01).

When controls are introduced for the changes in the volume of criminal activity (Table 8), it

appears that neither crimes nor arrests influence the relationship between unemployment and im?

prisonment. Both these variables have nonsignifi? cant regression coefficients and their partial correlation coefficients (with jail population) do not exceed .1 (p greater than .05).

Results obtained from the local sample are fully consistent with those from the national sample. Thus, both monthly and annual correlations between unemployment and incarceration are posi? tive, statistically significant and unaffected by fluctuations in the volume of crimes and arrests.

The fourth step in the analysis was to study the two apparent exceptions to the unemployment imprisonment relationship

- the 11-year period of the Great Depression and the federal prison population prior to 1960.

1. The Great Depression (1930-1940)

The deep crisis which struck America in the decade of the 1930s sent unemployment rates well above any "normal" or "acceptable" limits. The

Fall-Winter 1977/ 24



unemployment rate first rose to 8.9% in 1930, then

skyrocketed to 25.2% (37.6% for nonfarm laborers) in 1933, and stayed above or around 15% (20% for nonfarm laborers) until 1941. In absolute numbers, almost 13 million persons were unemployed in 1933, and for most of the decade this number stayed close to or above 10 million.

Neither prison population nor prison commit? ments kept pace with unemployment. Coefficients of correlation between imprisonment and unem?

ployment rates for this period are negative, ranging from -.02 to -.42, although none are statistically

significant. Arrest had a high negative correlation with unemployment rates (r=-.87; N=9; p=.001).

These findings do not contradict the labor

market-imprisonment hypothesis. The hypothesis postulates that imprisonment may be used to

absorb a part of the surplus labor, but this is

expected to hold under normal economic condi? tions. Under normal conditions, the number of

persons unemployed varies, but usually stays within definable limits (between 3% and 6% of the labor

force). Under such conditions, the prison population maintains a relatively stable relationship with the

number of unemployed persons. Expressed as a

proportion of the unemployed labor force, prison

population normally varies between 4% and 9%, with a mean of 5.2% for the years 1926-1974.

If the same relationship had continued during the Depression years, the number of prisoners would have varied between ^50,000 and 770,000, with a mean value of approximately 600,000. This

figure refers to state and federal prisons only. If

local jails and institutions for juvenile delinquents were included, the total would have exceeded one

million persons incarcerated on any one day during the year. The capacity of prisons, which is admit?

tedly an uncommonly flexible concept, could not

possibly have absorbed such a mass of inmates. (3) There is a general consensus that prisons

became overcrowded during the Depression, and

that the overcrowding was due to significant cuts

in correctional budgets and personnel (Sellin, 1937). The immediate response of criminal justice

agencies to the Depression was repressive - there

was an increase in prison admissions and population in the first two years (1930 and 1931). Then,

apparently, considerations of the costs of imprison? ment produced a trend toward more moderate

sentences, thus reducing both prison population and

prison admissions to levels below those expected on

the basis of the 1930-1931 policies. In 1931, prison commitments reached a peak

unsurpassed throughout Depression years. Except for 1940, when about 73,000 persons were admitted

to prisons, the number of admissions did not again reach the 1931 peak until 1953. The number of

prisoners present, however, averaged about 150,000

25/ Crime and Social Justice

between 1931 and 1940, while the average for

1926-1930 was about 113,000 and the average for

1941-1950 was about the same as in 1931-1940. This difference between admissions in 1931 and

total population in subsequent years suggests that

fewer people were admitted to prisons in the

Depression years, but those who were committed

stayed in prison for longer periods than formerly.

Longer sentences thus contributed to prison over?

crowding, thereby stemming the 1930-1931 trend

toward greatly increased admissions. Similar results were reported by Stern (1940),

who undertook a largely descriptive study of prison commitments and sentences in Pennsylvania from

1924 to 1933. Stern found that prison commitments rose in 1930 and peaked in 1931, after which time

they started to decline. He also found that average sentences, especially those meted out to recidi?

vists, were much longer in the Depression years (1930-1933) than in the pre-Depression years (1924 1929). Stern concluded that "allowing for the fact

that for years there has been a feeling in this

country for more severe punishment of criminals, it can be said the data show that in cases of

recidivists committed for serious property crimes

there was a definite tendency to greater severity. (This) study shows that the economic situation

apparently influences policies of court and penal administration" (1940:711).

After 1932, criminal justice expenditures declined at all levels of government, and all

criminal justice agencies suffered reductions in

personnel (see Historical Abstracts of the United

States, series 1012-1027). Although it has been

argued (Smith, 1935) that these reductions had a

beneficial long-term effect on criminal justice

agencies (by pruning the police force of the least

fit members, etc.), their immediate result was a

decline in arrests, prosecutions and prison commitments.

2. Federal Prisons

The second set of data which does not conform

to the hypothesis is represented by federal prison

population and admissions prior to 1960. It is

difficult to say why federal penal policies would

produce an exception in these years and why they would cease to produce it after 1960. The apparent anomaly may be due to a different composition of

the federal prison population, which might include

larger numbers of offenders with higher socio?

economic status (tax law violators, embezzlers) who are not typical of the marginal, reserve labor

force. This explanation also implies that, since

1960, the characteristics of federal prisoners have

been becoming increasingly similar to those of state prisoners. However, the data presently



available do not make it possible to test this or other plausible explanations of the exception.

Imprisonment and Unemployment

Considering that numbers of unemployed persons are usually in the millions and that prison population rarely exceeds 200,000 in any one year, what impact, if any, would incarceration have on

unemployment rates?

Prison population accounts for approximately one quarter of 1% of the total labor force, based on the 1926-1974 average (X = .26; S.D. = .02). Expressed as a percentage of the unemployed labor

force, it accounts for roughly 5% (X = 5.2; S.D. = 3.4).

The potential impact of prison population on

unemployment rates during the years 1926-1974 was considered under three different models: 1) all

prisoners are, before incarceration, in the labor force and all are unemployed; 2) all prisoners are, before incarceration, in the labor force and 50% are employed; 3) all prisoners are, before incar?

ceration, in the labor force and all are employed. Assumptions 1) and 3) are somewhat extreme, but

assumption 2) is probably not far removed from

reality. In other words, if all prisoners were to be

released, about half of them would be unemployed. In the first case, the unemployment rates from

1926 to 1974 would have increased by an average of .25% (one fourth of 1%). In the second case, the increase would have been .11%. Finally, in the third

case, there would have been a decrease in

unemployment rates of .02%. All three models, however, substantially under?

estimate the potential impact of incarceration on

unemployment rates. No reliable data are available

concerning national jail population on an annual basis. The Sunshine County data indicate that jail population behaves in the same way as does prison population in relation to unemployment. There? fore, if both jail and prison populations were considered simultaneously, the effect would be almost doubled.

The same argument should hold for the entire institutionalized population, but, because of lack of

data, it is here made in a tentative and speculative fashion. The total institutionalized population in the U.S. of about two million in 1970 would, if included in the labor force, affect labor force statistics in a dramatic way. After all, a rise in the

unemployment rate by even one half of 1% is in some circles a cause for alarm and for "action."

But, of course, it is not reasonable to expect all, or even a majority of the presently institutionalized

population to be economically active if not institu? tionalized. It may be not unreasonable, however, to assume that 50% of all those persons now institu

tionalized would be in the labor force if they were not under institutional care. This population includes prison and jail inmates, some mental

patients, some elderly citizens and some inmates of other institutions (e.g., unwed mothers). If one half of the total institutional population were in the labor force, and if all of them were unemployed, the unemployment rates would rise by an average of 1.3%. If only one half of those in the labor force (one quarter of the total institutionalized popula? tion) were unemployed, the impact on unemploy?

ment rates would be, on the average, seven tenths

of 1% (.7%).

UNEMPlOYMENr IHSURAMCfc

Very similar findings emerge when Sunshine

County data are examined. Expressed as a percen?

tage of the county labor force, jail population in 19691976 varied from .17% to .36%, with a mean value of .25%. When compared to the unemployed labor force, the jail population ranged between 2% and 5.3% with a mean of 3.6%.

Assessment of the potential impact of jail population on unemployment rates was based on the same three alternegative models used with refer? ence to the national data. The estimated change in

unemployment rates under the three conditions would be, respectively: 1) .21%; 2) .09%; 3) -.03%.

Our second ("utility") hypothesis predicts that

imprisonment will have a delayed negative effect on unemployment. The results of a test of this

hypothesis with the national data are shown in Table 9. When labor force variables are lagged by one year and correlated with prison variables in the

preceding years, the relevant correlation coeffi? cients have the expected (negative) signs, but fall short of statistical significance. For example, the coefficient of correlation between number of in? mates present in prisons and the unemployment rate in the year following is -.22. This result is not

improved when the relationship is tested in a

o > z

"Go home, I tell yt The recession is 01;




regression run with population as a standardizing variable.

The findings from the Sunshine County sample are even less encouraging. When unemployment is

lagged by up to six months, all correlations between jail population and unemployment rates are still positive.

Thus, the hypothesis that imprisonment acts to reduce unemployment rates was not supported by the present study. One possible explanation is that

prison population, taken by itself, is not suffi?

ciently large to produce an observable effect on

unemployment, and that future tests should inc? lude - at the minimum -

population of local jails and test the combined effects of jail and prison populations on unemployment.

V. CONCLUSIONS

Our main purpose was to test the applicability of the Rusche-Kirchheimer theory of punishment to

contemporary postindustrial societies. The focus of research was on the imprisonment and economic conditions in the United States from 1926 to 1974.

Two distinct hypotheses were identified as either explicitly or implicitly contained in the Rusche-Kirchheimer theory, which is best summa? rized with the phrase, "Every system of production tends to discover (and use) punishments which

correspond to its productive relationships." The first hypothesis refers to severity of punishment and predicts that criminal punishments will be more severe at times of economic crises. This was

operationalized in terms of the relationship between use of imprisonment and unemployment rates, and it was suggested that prison population will increase as unemployment increases, regard? less of the volume of recorded criminal activity.

The second hypothesis refers to the utility of

punishment and predicts a functional relationship between different punishments and the productive relationships inherent in different socioeconomic

systems. For our purposes, this hypothesis was

operationalized in terms of the impact of imprison? ment on unemployment, and it was predicted that the size of the prison population will be negatively related to lagged unemployment rates. The

hypothesis seemed reasonable on the assumption that postindustrial societies are permanently faced with an oversupply of labor, and that imprisonment removes a part of the surplus population from the labor market, thereby reducing unemployment rates.

The severity hypothesis was tested on two

samples of data: the national statistics for the

U.S., 1926-1974 and the monthly statistics for


Sunshine County, California, from January 1969 to December 1976. The findings were consistent with the hypothesis. The relationship between unemploy? ment and imprisonment was positive and statisti?

cally significant, regardless of the volume of criminal activity. There were two exceptions. This

relationship did not obtain during the Great

Depression (1930-1940), and federal imprisonment rates did not correlate with unemployment rates

prior to 1960. It was suggested that the extent of

unemployment during the Depression and the conci?

liatory policies of the New Deal prevented the

positive correlation between imprisonment and

unemployment. No satisfactory explanation was found for the aberrant behavior of the federal

prison population, but it was suggested tentatively that, prior to 1960, federal prisoners included

larger numbers of white-collar offenders, atypical of the marginal labor force members who populate the state prisons.

One's faith in these findings is bolstered by the fact that results from the national and local

samples correspond very closely. For instance, the

proportion of incarcerated population to the total civilian labor force in the two samples is almost identical (.26% and .25%, respectively).

The utility hypothesis was not supported by the data. The relationship between imprisonment and

lagged unemployment rates in the national sample was negative as predicted, but the coefficients were not statistically significant. In the local

sample, the predicted negative relationship could not be established.

The failure to confirm the utility hypothesis detracts from the significance of the confirmation of the severity hypothesis, for the meaning of the demonstrated relationship between unemployment and imprisonment remains somewhat ambiguous. It is suggested, however, that the available data base

was too narrow, and that the expected impact of incarceration policies on unemployment rates

might yet be demonstrated if the entire incarcer? ated population (not only the prison inmates, but also the inmates of local jails and juvenile homes) were used as an index of imprisonment.

Despite their shortcomings, the present findings do lend some support to the Rusche-Kirchheimer

severity hypothesis. Their greatest immediate con?

tribution, however, may be that of putting current

speculations about imprisonment and unemploy? ment rates on a solid foundation. By providing some

empirical estimates of the magnitude of the poten? tial impact of imprisonment on unemployment rates, the present study may help to remove discussions such as Quinney's (1977) and Reasons and Kaplan's (1975) from an almost metaphysical realm and place them on firmer empirical ground.

Another contribution made by the present study



is in its demonstration that the relationship between unemployment and imprisonment is a direct one, independent of the changes in criminal activity.

TABLES Table 1. Prison population regressed on number of

unemployed (U) and resident civilian population (P), U.S., 1926-1974.

unstandardized standardized

regression regression partial coefficient T-test coefficient coefficient

Total prisoners present

U 2.02 3.23a .39 P .76 3.53a .43

.43

.46

admitted r=.56 R2=.32 s.E.=5.75 F=10.79a DW=1.28 determinant=.99

U 1.71 3.08a .38 .41 P .78 3.50a .43 .46 r=.56 R2=.31 s.E.=5.13 F=10.40a DW=1.03 determinant=.99

State prisoners present

U 2.04 3.58a .43 .47 P .72 3.64a .43 .47 r=59 R2=.35 s.E.=5.25 F=12.27a DW=1.35 determinant=.99

admitted U 1.66 3.39a .40 .45 P .87 3.60a .43 .47 r=59 R2=35 S.E.=4.53 F=12.18a DW=1.06 determinant=1.0

Federal prisoners present

U -.04 -.35. -.05 -.05 P .06 2.27b .32 .32

r=.33 R2=,11 S.E. = 1.13 F=2.79 DW=1.63 determinant=.98 admitted

U -.03 -.29 -.04 -.04 P .06 .32 .002 .04

r = .07 R2 = .004 S.E. = 1.12 F=.11 DW=1.82 determinant=.97

a=p<.01 b=p<.05

Table 2. Prison population regressed on number of




Total prisoners present a

U 3.42 2.92a .51 P .06 .17 .03

.51

.03 r = .52 r2=.27 S.E. = 4.35 F=4.51b DW=1.25 determinant=.97

admitted U 4.58 2.77a .43 .49 P .64 2.62a .41 .47

r = .66 r2=.44 S.E.=5.89 F=9.63a DW=.77 determinant=.94


U 3.12 2.92a .51 .51 P .005 .01 .002 .003

r = .51 R2=.26 S.E.=4.06 F=4.39b DW=1.38 determinant=.97 admitted

U 4.36 2.81a .42 .49 P .64 3.15a .47 .54

r = .70 R2=.50 S.E. = 5.46 F=12.09a DW=.75 determinant=.93


U P

.34 1.62 .30 .31

.05 1.59 .29 .30 r=46 R2=.21 s.e.=.75 f=3.38 dw=.83 determinant = .94

admitted U P

a=p<.01

.33 1.64 .32 .31 -.03 -.83 -.16 -.16

r = .33 R2=.11 s.e. = .74 F=1.48 DW=1.48 determinant=.96

b = p<05

Table 3. Federal prison population regressed on number of


unstandardized

regression coefficient


U 1.69 P -.09

r=.83 R2=.69 admitted

U 1.31 P -.002

r=83 R2=.69

standardized

regression T-test coefficient

partial coefficient


unemployed (U) and resident civilian population (P), with dummy variables to distinguish prewar (D1) and war (D2) periods from the postwar period, U.S., 1926-1974.

4.86d -3.27a

S.E. = .99 F = 13.89i

5.043 -.08 = .74 F=

.80 -.54

1.27

s.e. 13.60'

.83 -.01 DW=1.44

.81 -.68

determinant^

.82 -.02

determinant^

.92

.92

unstandardized

regression coefficient

Total prisoners admitted

U 1.68 P .77

D1 .64 D2 -.39

standardized

regression partial T-test coefficient coefficient

2.78a 3.29a

.08 -.07

.37

.43

.01 -.01

.39

.44

.01 -.01

R=.56 R2=.31 s.E. = 5.25 F=5.03a DW=1.03 determinant = .42

a=p<.01 a=p<01

Fall-Winter!977/ 28




unemployed (U), resident civilian population (P) and number of arrests (A), U.S., 1932-1974.

unstandardized

regression coefficient T-test

standardized

regression partial coefficient coefficient


U P A

admitted R = .74

U P A

3.32 1.16

-4.77 R2 = .54

2.57 .76 -.33

S.E.

3.87 d

3.56a -1.53

= 5.81 F =

3.22a 1.86 -.10

.42 .88

-.33 15.383 DW =

.43

.33 -.01

1.51

.53 .50

-.21 determinant=.19

.46

.29 -.01

r=.61 R2=.37 S.E.=5.17 F=7.59a DW=1.08 determinant=.47


U P A

admitted R=.76

U P

3.12 1.18

-5.52 R2 = .58

2.33 .86

-.80

S.E.

4.05a 4.05a -1.74

= 5.26 F=

3.77 a

2.29 -.28

17.65a

.43

.99

.43 DW=

.43

.38 -.04

1.56

.54

.54 -.27

determinant=.18

.46 .34

-.04 r=63 R2=.40 S.E.=4.59 F=8.75a DW=1.09 determinant=.50


U P A

.17

.02

.45

1.20 .36 .73

.17

.12

.25

.19

.05

.11

admitted R=42 R2=.17 S.E. = 1.01 F=2.75 DW=1.15 determinant=.18

U P

.003

.015 -.13

.02

.28 -.22

.003

.14 -.11

.003 .04

-.03

a=p<.01

R = .05 R2=.002 S.E. = 1.08 F=.34 DW=1.44 determinant=.10

b = p<.05


unemployed (U), resident civilian population (P) and number of arrests (A), U.S., 1947-1974.




U P A

admitted

4.26 1.19

-7.03 r=70 R2 = .50

4.70 .98

-2.78 r = .67 R2=.44


admitted

3.75 1.05

-6.24 R=.69 R2=.48

4.50 1.03

-3.27 R = .71 R2 = .50

2.63a 2.37b -1.61

S.E. = 5.53 F=

2.79a 1.65 -.60

S.E.=5.94 F =

2.64a 2.36b -1.63

S.E.=4.86 F:

2.87?? 1.92b -.76

S.E. = 5.49 F =

.41 1.23 -.84

7.67a DW=.84

.45

.61 -.22

6.25a DW=.82

.48

.44 -.31

determinant=.07

.50

.32 -.12

determinant=.16

.42 1.20 -.84

= 7.22a DW=.91

.43

.73 -.29

=7.96a DW=.82

.48

.44 -.32

determinant=.07

.51 .37

-.32 determinant=.13


U P A

admitted U P A

a=p?.01

.58

.14 -.87

R = .65 R2 = .43

.31 -.09 .45

R=36 R2 = .13

b=p<.05

2.11 ?

1.78 b

-1.21 S.E. = .93 I

1.49 -1.11

.79 S.E. = .75 I

.36 1.19 -.87

= 5.79a DW= 82

.29 -.40 .29

= 1.18 DW=1.09

.40

.34 -.24

determinant = .04

.29 -.22 .16

determinant=.26

Table 8. Jail population regressed on number of unemployed (U) and total civilian population (P), controlling for arrests (A) and crimes (C), Sunshine County, January 1969-December 1976.

Table 7. Jail population regressed on number of unemployed (U) and total civilian population (P), Sunshine County, January 1969-December 1976.

unstandardized


standardized


unstandardized


standardized


Jail population

U P R = .60

a=p<.01

.004

.004 R2 = .36 S.E.

2.31 b 6.12a

= 21.59 F = 25.88?

.19

.52 1 DW=1.53

.23

.53 determinant =

b = p<.01

Jail population

U P A R = .62

U P c R = .64

a=p<.01

.004

.004

.000 R2=38

.004

.004

.010 R2 = .41

2.28 a

S.E.

S.E.

6.42 a

.05 = 21.71 F =

.19

.54

.00 19.00a DW = 1.52

2.20 a

.18 6.81a .57

.99 .06 = 21.60 F = 21.23a DW = 1.64

.23

.55

.006 determinant=.94

.22

.58

.10 determinant=.90

29 / Crime and SocialJustice



Table 9. Pearson's product-moment coefficients of correlation between prison population and lagged labor force variables, U.S., 1926-1974.

1 2 3 4 5 6 7 8 1 total

prisoners present .99 .79 .75 .85 .79 -.07 -.22

2 total

prisoners admitted .74 .76 .84 .79 -.06 -.21

3 state prisoners present .99 .83 .76 -.03 -.18

4 state

prisoners admitted .81 .74 -.01 -.16

5 total labor

force .97 -.23 -.38

6 number

employed -.46 -.59 7 number

unemployed .98

8 unemployment rate

critical values of r for two-tailed test of statistical significance:

p= .05 .01 r= .28 .37

FOOTNOTES

1. Recently, Quinney (1977) has developed a more

general theory of criminal justice based on the concept of "surplus population control." His book was published too late for detailed consideration in this essay.

2. Reasons and Kaplan (1975) included "reduction of

unemployment rates" among their eleven "latent func?

tions of prisons," but they did not advance or refer to any evidence. At the same time, some of their eleven functions seem positively frivolous (e.g.: prisons function as a means of birth control), so that it is not clear whether they have advanced the imprisonment-unem? ployment hypothesis in good faith or in jest. Quinney claims that "hundreds of thousands find economic support

through criminal offenses and economic security while

being confined in prison, at the same time lowering the

unemployment rate of the society" (1977:129; emphasis added), but he does not cite any evidence for this

statement.

3. An enlightening definition of prison overcrowding was

given by a veteran prison administrator (Clemmer, 1957:283): "A prison may be said to be crowded when it is

unable to perform its statutory duties of safeguarding,

providing decent care and engaging in the training and

instruction of inmates by virtue of the fact that the

obstacles thereto are brought about by insufficient living and work space, or insufficient budget or insufficient

personnel, according to acceptable minimal standards as, or when prescribed by the American Correctional

Association and as interpreted by a responsible administrator."

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Blalock, Hubert M.

1972 Social Statistics. 2nd ed. New York: McGraw

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of.Work in the Twentieth Century. New York:

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California Bureau of Criminal Statistics 1976 California Comprehensive Data Systems.

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Clemmer, Donald

1957 "Some Aspects of Crowded Prisons." Proceed?

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Copeland, Morris A.

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Evans, Robert

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Kant, Immanuel

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Marx, Karl

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Fall-Winter 1977130



Nie, Norman H., C. Hadlai Hull, Jean G. Jenkins, Karin

Steinbrenner and Dale H. Bert

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