Unemployment and Crime Research Paper

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Because criminal sources of income, such as theft and fraud, are alternatives to legitimate earnings, they tend to be associated with unemployment. This linkage, however, is far from clear and consistent.

Dependence on Labor Force Attributes

One variable that greatly affects crime and employment relationships is the age of persons who are unemployed. Government statistics regularly indicate that juveniles and young adults greatly exceed older persons in rates of arrest for burglary, robbery, and other crimes of taking property belonging to others. A 1959 pioneer study found that this inverse relationship of such crimes to unemployment was most pronounced for young persons, and less evident for older persons out of work (Glaser and Rice). In 1968 a British study yielded similar findings, showing that the relations between crime and unemployment are most intense for youths who were out of school as well as work (Farrington et al.). The old adage that ‘‘idle hands are the devil’s workshop’’ seemed to be confirmed.

These findings also appear to support the 1999 assertion by Bruce Western and Katherine Beckett that the U.S. penal system is ‘‘a labor market regulating institution.’’ They justified this statement by pointing out that confinement institutions remove able-bodied but idle young men from the workforce, and that once these men have a record of imprisonment, their subsequent job prospects are greatly diminished (imprisoned women are not sufficiently numerous to affect the total female labor force significantly).

Unfortunatly, any statistical generalizations on the linkage of crime to unemployment, as well as to age or other personal attributes of offenders and nonoffenders, can only be tested with imperfect data. The completeness of our knowledge on lawbreakers necessarily varies with the extent to which they are caught, and with the use of imprisonment rather than alternative penalties for those convicted. Data on employment, age, and various other attributes of persons committing crimes is usually reported for those offenders who are arrested, but their total number, and information on them is somewhat diminished (although presumably made more accurate) if one studies only those arrestees who are subsequently convicted of the crimes for which they were arrested. Furthermore, data on the personal attributes of those convicted are often not compiled in as much detail for those fined or released on probation as for those who are imprisoned.

In 1939 two European scholars, Georg Rusche and Otto Kirchheimer, refugees from Hitler’s Germany, published what proved to be a classic volume of historical scholarship, Punishment and Social Structure. In it they treated the variations in reaction to crime from ancient to contemporary periods, and generalized that the types of penalties used—such as executions, transportation to distant colonies, torture, mutilation, confinement in idleness, of forced labor— depended greatly on economic conditions, particularly on the current value of labor. The cruelest penalties became most frequent, they asserted, when unemployment was extensive, making labor cheap.

These rather vague assertions were subsequently formalized and tested statistically by others, with diverse data, as can be illustrated by summarizing a few of the most methodologically sophisticated studies. Mathematician David Greenberg concluded from multivariate analysis in the 1970s, that ‘‘oscillations in the rate of admissions to prison in Canada in recent years have been governed almost entirely by changes in the unemployment rate. The same relationships appear to hold in the United States as well’’ (p. 651). Sociologists Andrew Hochstetler and Neal Shover, however, found in the 1990s that the intensity of this relationship in the United States varied greatly in different historical periods, and in various regions.

Hochstetler and Shover note that the incarceration rate in the United States, defined as the number of imprisoned adults per 100,000 population, was 462 for the southern states in 1994, but only 291 for the northeastern states. From a regression analysis of 1990 data for a sample of 269 U.S. counties that they showed were highly representative of all counties, they conclude that R-square (the variance explained) was only 0.14. But regressing 1990 imprisonment data for these counties with the 1980 unemployment rates of the same counties yielded an R-square of 0.74. They interpret this finding of a lag in the impact of unemployment on crime rates by pointing to the fact that the highest rates of known offenses, particularly unspecialized street crimes, occurs among teenagers, and in those whose childhood was spent in the most poverty-stricken urban areas, the slums. Their main conclusion is:

Change in violent street crime, in the proportionate size of the young male population, and in labor surplus, contribute to change in the use of imprisonment, while changing levels of property crime do not. These relationships persist even when street-crime rates and other presumed correlates of imprisonment are controlled. . . . The criminal justice system grows increasingly punitive as labor surplus increases. The fact that our findings were achieved using both a unit of analysis more appropriate theoretically than measures employed by most investigators, and a longitudinal design, only strengthens confidence in them.

Other Factors in Crime-Unemployment Relationships

Of course, unemployment rates, age, and offense do not operate alone in determining sentences of imprisonment. In 1966 David Jacobs and Ronald E. Helms showed, in their multivariate analysis of historical and geographical fluctuations of incarceration rates in the United States, that unemployment’s impact on crime seemed to have a close linkage with other conditions not noted in prior studies. They point out that rates of state plus federal imprisonment of adults per 100,000 population in the United States changed only from 43.4 to 50.9 from 1918 to 1965, dropped to 35.9 by 1968, then nearly doubled in 13 years to 69.7 in 1981, and rose by 84 percent to 127.9 in 1989. There was no such large movement in rates of crime known to the police, which increased only 12.1 percent in 1975–1991. They found that because of this small range in reported crime rates for this long time span, a multivariate regression only yielded very strong results if crime rates were squared. Also, in their regression analysis, out-of-wedlock births—an index of the rate of breakdown in family relations—was one of the best predictors of imprisonment rates, especially when using birthrate data for five years before the prison figures, to allow for the time before such family conditions could promote higher imprisonment rates. Furthermore, rather than unemployment rates, income inequality, and votes for the Republican Party—an index of conservative political trends—were among the strongest predictors of crime rates.

In 1999 Michalowski and Carlson ascribed the diversity of findings in tests of ‘‘the RuscheKirchheimer hypothesis’’ to variations in the historical periods covered by these studies. Their analysis is based on theories about stages in what they call ‘‘social structures of accumulation,’’ or ‘‘SSAs.’’ The first stage is ‘‘Exploration,’’ which occurs when new institutional arrangements emerge to cope with high levels of unemployment and with the displacement of both farm and industrial populations. The second is ‘‘Consolidation,’’ an effort to maintain whatever new arrangements seem to help capital to preserve its profit margins. The third stage is ‘‘Decay,’’ when conditions develop that impede consolidation policies, thus increasing unemployment and preventing children of workers from achieving upward status mobility.

The Michalowski and Carlson analysis of unemployment and imprisonment for crime focuses on what they and some others call the ‘‘Fordist’’ SSA of 1933–1992, for which they differentiate the three phases indicated. During Exploration, from 1933 to 1947, new types of ‘‘capital-labor accords’’ and welfare policies ‘‘socialized the costs of labor force displacement.’’ Thus, unemployment insurance and old-age pensions, both adjusted for changes in the cost of living, shifted burdens of dealing with economic

distress in this period from businesses to governments, and helped to maintain social order. In Consolidation, from 1948 to 1966, periods of unemployment were shortened by the bargaining strength of organized labor and its central position in the Democratic Party, which transferred government tax income to the poor, the disabled, and the elderly. These ‘‘profit-eroding’’ developments ‘‘made it difficult for capital to protect profit margins in the face of growing foreign competition,’’ these authors claim, so that Decay occurred in 1967–1979, during which unemployment and inflation rose while profits declined and the United States lost the Vietnam War despite sending 3 million workers there.

These authors identify a new Exploration phase from 1980 until 1992, in which ‘‘cybertechnology’’ accelerates labor displacement, and also increases earning inequality between workers in digital information systems and those at ‘‘hamburger jobs.’’ The latter were disproportionately from minority groups, and in this period there was a ‘‘shift away from placative social welfare strategies . . . toward repressive control strategies based on increased use of imprisonment’’ (p. 226). Consequentially, even with rates of arrest and of unemployment in the 1990s usually below those of prior decades, the rate of imprisonment nearly tripled, confining predominantly young men who had dropped out of school and work. The latter were disproportionately African Americans, whose rates of unemployment exceeded what one would predict for white workers of similar education and work experience. Although their higher rates of joblessness are doubtless due in large part to prejudice against them, it is also because they are below the growing number of Latinos in the labor market in their ‘‘reservation wage’’—the lowest pay rate for which they would be willing to work (Moss and Tilly, p. 9). Another major factor in their unemployment rates is that they exceed other racial and nationality groups in the proportion who have entered military service, or are enrolled full-time in schools, which probably removes those with the highest earning potential from the labor market (Mare and Winship). But still another influence was their ready opportunity, in the segregated slum areas in which most of the housing available to them was located, to gain much greater income from drug dealing than from the jobs available to them.

In summary, evidence and analysis indicate that unemployment is predictive of crime, but disproportionately for youth, the least educated, those in broken or disorganized families, and those segregated in poor minority residential areas. Also, these relationships of unemployment to crime are likely to continue unless unsegregated housing, special education, family unity, work experience, and appealing career jobs become more readily available to those who are unemployed.


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