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Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UM I directly to order. U n ive rsity M icro film s Intern ation al A Bell & Howell Inform ation C o m p a n y 3 0 0 N o rth Z e e b Road. Ann Arbor, Ml 48 10 6 -1 3 4 6 U SA 3 1 3 /7 6 1 -4 7 0 0 8 0 0 /5 2 1 -0 6 0 0 O rder N u m b er 9 3 0 3 0 5 0 The relationship betw een em ploym ent, jail adm ission, and social welfare: A n assessm ent o f tw enty-four urban counties in M ichigan, 1980-1986 Senese, Jeffrey David, Ph.D. Michigan State University, 1992 UMI 300 N. Zeeb Rd. Ann Arbor, MI 48106 THE RELATIONSHIP BETWEEN EMPLOYMENT, JAIL ADMISSION, AND SOCIAL WELFARE: AN ASSESSMENT OF TWENTY-FOUR URBAN COUNTIES IN MICHIGAN 1980-1986 By Jeffrey D. Senese A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY The School of Criminal Justice 1991 ABSTRACT THE RELATIONSHIP BETWEEN EMPLOYMENT, JAIL ADMISSION, AND SOCIAL WELFARE: AN ASSESSMENT OF TWENTY-FOUR URBAN COUNTIES IN MICHIGAN 1980-1986 By Jeffrey D. Senese This study attempts to shed light on the impact labor force changes have on criminal justice system behavior. Specifically, the research conducted in this study examines the relationship between the fluctuations in employment rates and incarceration rates of local jails in twenty-four Michigan urban counties, with differing structures of commerce, between 1980 and 1986. The fluctuation in social welfare payments for those counties for that time period has also been included to test the possible impact social welfare payments may have on mitigating the impact of economic fluctuations and possible subsequent jail incarceration rat e s . The major findings of this research indicate that there is in fact a relationship between jail and social welfare rates and the fluctuation of the labor force at the local level, although it is different across certain lltypes,, of counties. Dedicated in loving memory to my brother Christopher Abraham Senese I see your smile, and feel your presence . . . but miss your company. ACKNOWLEDGMENTS I would like to acknowledge the guidance, patience, and valuable time which Dr. Tim Bynum, Dr. Bruce Pigozzi, and Dr. June Thomas have given toward the completion of this project. I would like to especially acknowledge the chairperson, friend, and mentor of this project Dr. Dave Kalinich, without whom this project would not have been possible. I would also like to thank Susan Herman of the Michigan Department of Corrections for facilitating access to the jail data. In a similar vein, I would like to thank Mr. George Mecham and Mr. Ronald McGraw of the Michgan Employment Securities commission for their assistance with accessing the employment data. I would also like to acknowledge the support of the School of Criminal Justice and its staff for their assistance and patience. Last, and certainly not least, I would also like to thank my parents Fred and Mary Senese for their unending support throughout the process of my education. TABLE OF CONTENTS LIST OF FIGURES.............................................. vi LIST OF TABLES............................................. viii CHAPTER 1: INTRODUCTION............. 1 Historic Viewpoints on Criminality and Economic C o n d i t i o n s ............................................... 3 Contemporary Perspective: Criminality and Economic C o n d i t i o n s ................ „ ...........................14 Theoretical Debate . . ................................ 19 Purpose of the S t u d y ............... CHAPTER 2 : . . 28 LITERATURE R E V I E W ......... ..................... 32 The Economic Perspective ............................ 33 Classical Economic Theory and Cri m e............... 36 Urban Labor Markets............................... 51 Human Capital and Urban Labor Markets. . . . . .54 Business Cycles and Cri m e.........................55 Unemployment and C r i m e ........................... 57 Criminology and Sociological Perspective............... 60 Criminological and Sociological View of Unemployment and Crime ......................... 66 Radical Criminology and Economic Theory . . . . 7 0 Unemployment and Imprisonment ................. 73 S u m m a r y ........................................... 79 Conclusion and Conceptual Framework ............... 82 CHAPTER 3: M E T H O D S .......................................... 89 iii iv Introduction .......................................... Research Questions 89 . . ................................ 90 Definition of T e r m s .....................................90 Jail Admission Rates . ..........................90 Employment R a t e ........... Social Welfare Rate 91 ....................... 92 A n a l y s i s ................................................ 95 Study S a m p l e ........................................... 96 Data C o l l e c t i o n ....................................... 98 S u m m a r y ................................................ 99 CHAPTER 4: R E S U L T S ......................................... 102 Individual County Results. .......................... 102 Barry C o u n t y .......................................... 103 Bay County ................................ Berrien C o u n t y ........................................ 112 Calhoun County ........................................ 116 Clinton C o u n t y ........................................ 121 Eaton C o u n t y .......................................... 124 Genesee C o u n t y ........................................ 128 Ingham County.......................................... 131 Ionia County ....................................134 Jackson County ........................................ 137 Kalamazoo County ...................................... 140 Kent County.............................................142 Lapeer County.......................................... 145 Livingston County...................................... 147 Macomb County.......................................... 151 107 V Muskegon County........................................ 153 Oakland County . ......................................156 Oceana County.......................................... 158 Ottawa County .......................................... 161 Saginaw C o u n t y ........................................ 164 Shiawassee County...................................... 167 St. Clair C o u n t y ......................................170 Van Buren C o u n t y ......................................173 Washtenaw County ......................................176 Summary of Individual County Results .................... 179 Comparisons of Counties by Ecomoraic Base ............... 180 Suburban Primary Industry Counties................... 181 Urban Diversified Counties ........................... 183 Urban Factory Counties .......... 185 Summary of F i n d i n g s ................................. 186 CHAPTER 5: SUMMARY AND CONCLUSION..........................218 Summary...................................................... 218 Conclusions.................................................. 224 Recommendations........... 229 FOOTNOTES.................................................... 232 B I B L I O G R A P H Y ............. 236 A P P E N D I X .................................................... 266 LIST OF FIGURES Map 1 Urban Counties in the State of M i c h i g a n ........... 101 Figure 1 Line Graphs for Barry C o u n t y ..................... 189 Figure 2 Line Graphs for Bay C o u n t y ....................... 190 Figure 3 Line Graphs for Berrien C o u n t y ................... 191 Figure 4 Line Graphs for Calhoun C o u n t y ................... 192 Figure 5 Line Graphs for Clinton C o u n t y ................... 193 Figure 6 Line Graphs for Eaton C o u n t y ..................... 194 Figure 7 Line Graphs for Genesee C o u n t y ................... 195 Figure 8 Line Graphs for Ingham C o u n t ..................... 196 Figure 9 Line Graphs for Ionia C o u n t y ..................... 197 Figure 10 Line Graphs for Jackson County...................198 Figure 11 Line Graphs for Kalamazoo County................ 199 Figure 12 Line Graphs for Kent C o u n t y ..................... 200 Figure 13 Line Graphs for Lapeer C o u n t y ...................201 Figure 14 Line Graphs for Livingston County ............ Figure 15 Line Graphs for Macomb C o u n t y ...................203 Figure 16 Line Graphs for Muskegon County .............. Figure 17 Line Graphs for Oakland County...................205 Figure 18 Line Graphs for Oceana C o u n t y ...................206 Figure 19 Line Graphs for Ottawa County . . . . . . . . Figure 20 Line Graphs for Saginaw County................... 208 vi 202 204 207 vii Figure 21 Line Graphs for Shiawassee County ........... Figure 22 Line Graphs for St. Clair C o u n t y .............. 210 Figure 23 Line Graphs for Van Buren C o u n t y .............. 211 Figure 24 Line Graphs for Washtenaw County ............ 209 212 LIST OF TABLES Table 1 Employment in The Twenty-Four Urban Counties: The Civilian Labor Force in Ten Sectors of the Economy.............................. 213 Table 2 Summary of the Regression Models with Standardized Coefficients for All Counties 1980-1986: Jail Admission as the Dependent Variable ......................................... 214 Table 3 Summary of the Regression Models with Standardized Coefficients for All Counties 1980-1986: Social Welfare as the Dependent Variable . . ................... 215 Table 4 Summary of the Regression Models with Standardized Coefficents for All Counties 1980-1986: Employment Rate As the Dependent V a r i a b l e ........................................... 216 Table 5 Classifcation of Counties: Member Counties for Each T y p e ...................................... 217 viii CHAPTER 1 INTRODUCTION This study attempts to examine the extent to which changes in employment rates impact upon the utilization of criminal justice system resources. Specifically, the research conducted in this study examines the relationship between the fluctuations in employment rates and incarceration rates of local jails in twenty-four Michigan urban counties, with differing structures of commerce, between 1980 and 1986. The fluctuation in social welfare payments for those counties for that time period has also been included to test the possible impact social welfare payments may have on mitigating the impact of economic fluctuations and possible subsequent jail incarceration rates. In addition to the research conducted in this thesis, an examination of the extensive literature which examines the impact of the economic conditions on crime is provided to furnish an overview of the theories and debate on this topic. Over the past centuries there has been a recurring concern among scholars with the fluctuations of the economy and its potential effects on the labor force and society. 1 2 For example, economists have approached the study of labor markets from a production or capital standpoint, whereas others have examined alienation, or motivation in the labor force. Additionally, there has been a consistent concern with the relationship between legitimate economic activity and criminality. One might assume that this concern began during the industrial revolutions in Europe and North America. The literature, however, clearly shows the issue predates that period. The present work is a study in the relationship between employment and incarceration rat e s . It is general in the sense that it provides a very comprehensive analysis of the literature including the research examining the connection between economic factors and incarceration. It is specific in the sense that the relationship between employment, welfare and jail incarceration rates in individual counties are examined, over time, in the State of Michigan. Curiosity about the effect of economic conditions on crime and imprisonment i s , of course, not new. The following section briefly reviews the historical thinking and inquiry into the effects of economic conditions on criminality. The review is also followed by a brief discussion of contemporary work on this subject. The brief historical sojourn and current look at scholarly thinking on this topic set the stage for a review of the literature. The pertinent literature from the areas of economics, crime, and 3 incarceration produced by economists, sociologists, and criminologists will be considered. Historic Viewpoints on Criminality and E c o n o m ic Conditions. The history of studies of the relationship between crime and economic conditions has been lengthy despite recent suggestions (Parker and Horowitz, Chiricos, 1987) to the contrary. 1986; and One can trace these considerations to Plato's "Republic" in which he maintains that in every society where there is poverty, there also will be criminals (Hamilton, 1951). The earliest trace of broad scholarly concern or argument that employment fluctuations lead to similar changes in criminality can be found in the late 1840's . This roughly coincides with the beginnings of the industrial revolution in the United States, with large increases in urbanization, and the beginnings of consideration by scholars who were interested in social problems. Bonger claims that Ducpetiaux is the first scholar to discover the relationship between hard times and crime (Bonger, 1916). Ducpetiaux did not publish his work until 1850 and there were a few authors before that time who suggested there was a relationship between economic conditions and criminality. Most of the early literature deals with European rather than United States populations. Although there may have been similar theoretical musings in the United States, reports of empirical tests of the relationship were specific 4 to Europe, and primarily in England. One scholar (Fletcher) suggested that the connection between the economic situation, shown by the cost of living, or surviving, could be directly in proportion to the response to government controls (through penal sanctions). Fletcher, relying on data for 1810 through 1847 (in England), discovered "an immediate connection . . . between the price of food and the amount of commitments, every access to the former being followed by an access to the latter" (Fletcher, 1849, p . 167). As the prices for food increased commitment rates to penal institutions increased, and therefore, he concluded that the two measures were associated. It is significant that the conceptual strength of these earlier works, not to mention their use of statistical analysis, is appropriate given the development of research methodology at the time. Early in the theoretical specification there was a foreshadowing of the future conceptual conflict. For example a few years after Fletcher reported his results, Clay supposed that economic downturns "may add a few cases to the sessions calendars and that 'good times' greatly aggravate summary convictions; that the increase to the sessions consists of the young and thoughtless who, when thrown into idleness, are liable to lapse into dishonesty; and that the increase in summary cases arises from the intemperance which high wages encourage among the ignorant and sensual" (Clay, 1855, p. 79). Clay suggested that in 5 effect good times increase the court calenders at a greater rate than bad for minor crime convictions. Thus, Clay was suggesting that the connection between crime and the economic situation is not wholly inverse. This argument also holds that the relationship between favorable economic times and criminality can be direct in certain instances. Walsh correctly pointed out that Clay's use of total commitments was an inaccurate measure of criminality and showed there was an inverse relationship between crime and economic conditions. Clay's rebuttal, however, shows no change in understanding (Walsh, 1857, p. 37). T h u s , the basic interpretation of empirical results into a dichotomy of theories of an existence/non-existence of a relationship between economic change and criminality can be found at this early date. Another early theorist posited: "During periods of industrial depression crime of almost all grades is increased in volume. The difficulty of demonstrating this . . . To any full extent lies in the fact that our criminal statistics are given for periods and not year by year . . . We should find that the lines of crime rise and fall as the prosperity of the country falls and rises (Wright, 1893, p. 774). The data were collected every ten years across the period that Wright examined, although the data were occasionally collected for shorter cycles. Wright also 6 asserts " . . . a l l idleness, whether induced by economic conditions or by a lack of inclination to work . . . leads directly to crime - not, of course, in all cases but such conditions aggravate and irritate and drive men to criminal courses" (Wright, 1893, p. 776). In 1907 Mayo-Smith suggested "Hard times increase the number of crimes, especially of crimes against property. A general rule has been laid down that as the price of food increases, crimes against property increase, while crimes against person decrease . . . " (Mayo-Smith, 1907, p. 277). Many of the early studies suggest the relationship between crime and economic change was specific to property crime. The theoretical basis of these early scholars seems specific to a micro-economic approach than is the case in more recent research of this topic. These early studies focused on the effects of factors like the price of food on criminality instead of the general indicators of employment or unemployment. In 1916 Bonger suggested "When we sum up the results that we have obtained it becomes plain that economic conditions occupy a much more important place in the etiology of crime than most authors have given them" (Bonger, 1916, p. 667). Although the basic premise about the causes of the connection between criminality and economic conditions seem Marxian, these theorists were more akin to the positivistic approach to changing social 7 conditions rather than radical socio-political changes. It is significant to note at this point that although many early theorists suggest such a radical perspective, they clearly offer positivistic solutions. For example, Bonger suggested that the solutions are to widen employment opportunities within the context of the current society (Bonger, 1916). The study of the relationship between crime and economic factors in the United States was largely ignored until urbanization and rises in crime rates occurred. The development of the study of criminal justice and criminology also created an interest in such studies. An early attempt to examine the relationship between crime and economic conditions in the United States was undertaken by Davies (1922). Davies contended there is a direct relationship between the costs of goods and the response of the government to those pushed out of the economy due to reductions in the demands for labor. Therefore, to test his propositions, he correlated for the 1896-1915 period the indexes of wholesale prices published by the Bureau of Labor Statistics with the annual admissions to the state prisons of New York. At the turn of the century it may have been plausible to suggest a price index prison admission relationship, given the proportion of daily individual budget required for survival during that time. In contemporary society such an argument may be tenuous at best 8 and is likely to be negligible. For most people individual expenditures seem to have declined for basic cost of living items (e.g., costs of food), as a proportion of individual budgets, although such a relationship may still hold true for certain "marginal" groups in contemporary society. Ogburn, and Thomas also examined the issue when they analyzed several economic indicators with the number of convictions for criminal offenses in the State of New Yor k . Their observation of the curve of crime statistics and its fluctuations around the trend line shows that in most depressions the number of convictions is above the normal and most of the periods of prosperity the number of convictions is less. They conclude that although the crime indexes they relied on are not sufficient, there "does appear to be some negative correlation between convictions for crime and the business cycle" 340). (Ogburn & Thomas, 1922, p. In addition, other scholars determined "much, if not the majority, of the thievery, forgery, robbery, and other crimes against property . . . (is) committed under the pressure of absolute economic necessity" (Simpson, 1923, p. 316-317). The early criticisms of scholarly work were based on the presumption that the data were not amenable to showing the theoretical relationship. Thomas suggested: The criminologists seem to me to be the worst offenders in their treatment of economic influences on 9 social life. They have made no real attempt to measure the relative effect of economic influences upon crime. They use and abuse statistics outrageously, presenting short series, frequently of less than ten years, and claiming general causality from such comparisons as could be made with these short series. A review of the literature on the subject suggests that discussions of the relations of crime and economic conditions are still in the realm of metaphysics (Thomas, 1927, p. 37) . Winslow softens, but does not negate, Thomas's attack when she suggests that her findings " . . . are fairly conclusive with reference to the tendency for crimes against property, and vagrancy, to increase during period of economic depression and decrease during prosperity . . . Other groups of offenses are apparently affected only slightly or irregularly" (Winslow, 1927, p. 269). Thomas's study was " . . . Sellin suggests that the first anywhere to apply to the fullest extent modern statistical techniques . . . 11 to the relationship between economic conditions and criminality (Sellin, 1937, p. 37). been a dissenting voice. However, there seems to have always For example, in 1929, Woytinsky provided contradictory evidence to Thomas's study (Woytinsky, 1929). He found a correlation between non­ violent property crimes and changes in a price index, which he attributes to his economic index. 10 Another examination in 1929, studied the cycles of crime (Phelps, 1929). This analysis grouped the commitment rates by the types of offense, and a poverty index based on the use of both private and public sources of welfare. Phelps also suggested that a rise in welfare would show hard times, and vice versa (Phelps, 1929). He further suggests that although his results are not conclusive there is a need for further research into " . . . the social and economic conditions which operate to make these cycles" (Phelps, 1929, p. 120). During the depression the Report of the Crime Problem Advisory Committee of California asserted: "All over the world it is found that the crime wave goes up when unemployment is prevalent. become desperate. children go hungry" Hungry men, women, and children Men will steal rather than watch their (Report of the Crime Problem Advisory Committee of California, 1932, p. 62). Simpson found "If unemployment causes crime one would expect to find consistent increases in the number of yearly admissions to prison during or immediately following periods of marked business depression" (Simpson, 1932, p. 907). In 1933 a national study of prostitution determined "When economic depression came to be felt generally throughout the country, several rather unusual types of prostitutes cropped up in the larger cities . . . The prostitution underworld called these new comers 7depression girls'" (Bascom & Kinsie, 1933, 11 p. 484). In addition Bichham claimed "unemployment sets the stage for mercenary crime . . . It creates the economic urge, it develops the social resentments at the current economic injustices and the presence of abundance of wealth and food in the community, and offers the incentive and the opportunity for the worker to embark upon his crime career" (Bichham, 1933, p. 70). Simpson provides further contradictory evidence in 1934 when his results suggested that crime and good times may be positively related (Simpson, 1934). In 1935, he determined "the evidence presented here seems to suggest at least that the depression has tended to stop the so-called crime wave rather than to initiate it" (Simpson, 1935, p. 128). Therefore, even in 1937, Sellin could postulate "The assumption is old that the criminality of a community is to some degree influenced by economic conditions." Many arguments to that effect might be culled from the literature of the last four centuries and especially from the studies pursued in the last hundred years with statistical tools and other diagnostic instruments known to the social scientist (Sellin, 1937, p. 1). These studies still have been limited to examining the criminality of certain "classes" of people, rather than the effects of economic changes on individuals or groups of individuals. In addition, Sellin also argued "Outside of the studies of the last-mentioned type the question of the effect of economic crises on criminality has 12 received scant attention" (Sellin, 1937, p. l). Sellin also claims that studies of the relationships between economic fluctuations and crime: . . . Should aim to show the effect of economic crises on the conduct norms and the penal sanctions of the law, and their relationship, whether harmonious or conflicting, to the conduct norms of various social groups within the state studied . . . and to the extent to which the introduction of new norms with old labels or the modification of prior legal norms affect the offense rates requires investigation (Sellin, 1937, p. 18). In the late 1930's Sellin suggested that there were several different ways to study the effects the depression had on criminality: 1. A comparison of the rise and fall of crime with the business cycle (Sellin, 1937). 2. Comparison between areas which, while similar in most respects, are different in terms of the degree of effects the economic downturn may have had. 3. One can study the effects an economic crises has on different social classes. 4. The degree of economic fluctuation can be assessed over time. An interesting claim which Sellin suggested was that these questions can be "secured" only through quantitative 13 analysis and specifically by correlating indexes of crime with indexes of economic conditions (Sellin, 1937, p. 20). Hobbs also suggested the relationship between criminality and economic fluctuations could be Definitely established or definitely rejected if the total amount of crime, or the total amount of certain types of crime, could be compared with the vibrations or cycles in actual economic conditions or in certain aspects of economic conditions, but neither criminality nor economic phenomenon are measured directly (Hobbs, 1943, p. 5). As is true of the earlier research Hobbs questioned the reliability of the data relied on to test the existence of the relationship between crime and economic phenomenon. Hobbs studied 10,386 cases for the years 1791-1810 in Philadelphia. He found a very weak correlation between court cases and consumer price indexes he relied upon. He suggests "there are many obvious weaknesses involved in formulating conclusions based on such data, yet do not these same weaknesses attach to many of the other studies which seem to fortify the belief that the amount of criminality, or of certain types of criminality, economic conditions?" is a function of (H o b b s , 1943, p. 10). Although his point is that his findings are similar to those in the past, it does not address the issue of whether all the past analyses were reliable or valid. 14 Contemporary Perspective: Criminality and Economic Conditions. It is significant that there is a wide gap in the literature that corresponds to the second world war and the post war period. During these somewhat stable economic periods of general and sustained growth, and nearly full employment, and the intense focus of energy on world wide conflict, the study of the connection between crime and economic conditions w a n e d . In addition, the unemployment rates were at their lowest rates in history during the war (Duboff, 1975). It was not until Becker's seminal article in 1968 that the interest in the relationship resumed. Although Fleisher studied the influences of unemployment before Becker, it was the latter work that revived the study of the relationship between employment and criminality (Fleisher, 1963). Becker presumed an economic approach to the relationship between crime and employment. For instance, he attempted to show the quantity of punishment that should be used to enforce different statues. He also believed one could gain insights into apprehension, punishment, and the responses of offenders to the criminal justice system through an economic approach. From a purely theoretical approach he attempted to show that crime is influenced by criminal justice system resource (punishment) allocation rather than external economic conditions. There is a ground-swell of literature directly linked to Becker and Ehrlich's (A student of Becker) work. The 15 commonality in much of this literature is a level of quantification that obscures the essence of the relationship to those lacking competence in advanced mathematics or econometrics. Therefore, their retrofitting of these "high- tech" approaches to the same basic relationships, and data, seems to provide limited further, or improved, understanding of the relationship. In addition, the rebirth of the concern for the relationship between economic factors and criminality followed the same theoretical line as in the past. During the 1970's the trend in studying the crime and economics relationship moved toward prescriptive analyses of the problem of unemployment and criminality, deterrent capacity being a primary focus. Although there were certain analyses of the relationship between crime and economic change (Hemley & McPheters, 1974; Phillips & V o t e y , 1974; Phillips, Vot e y , Maxwell, 1972), the orientation of this research was toward the development of specific programs. These theories also simultaneously considered employment opportunities and the concept of deterrence through legal sanctions. Inequality in society was also of general concern in the literature on the relationship between crime and economic factors. Another area which experienced something of a resurrection was the Marxist perspective on the relationship between crime and economic change. Again, it is significant to note that in the earlier literature 16 (1800's and early 1900's) the theoretical arguments for a Marxist approach pursued positivistic (functional), change through existing social conditions, or approaches; whereas in the 1970's the Marxist perspectives suggested more radical changes in social structure. In the 1980's there has been renewed debate of the economic factors and crime relationship, although little change from the conceptual approaches of the past. For example Joubert, P i c o u , and McIntosh determined that social structure predicts crime that in turn predicts criminality (Joubert, Picou, and McIntosh; 1981). Crime is related to the current economic and social conditions. Another contemporary scholar repeats the suggestions made in the 1800's using modern econometric language: Longitudinal economic cycles or regional capitalism shifts mandate that the state must control marginalized populations while simultaneously facilitating the labor force entry, or re-entry of cheap labor to meet new investment needs (Wallace, 1981, p. 59). Wallace also suggests that penal practices fluctuate according to changes in the late capitalist economy's need for labor, indexed by labor force participation rates (Wallace, 1981). In addition, the work of Becker and Ehrlich receives continued attention. Shavell, for example, builds on Becker's analysis through examining the use of fines and imprisonment and their deterrent abilities, and 17 concludes that an individual will engage in the activity if his/her private gain exceeds the expected sanction (Shavell, 1984, p. 91). Another interesting aspect of the study of the relationship in the 1980's is the considerations of the federal government of the United States. In a National Institute of Justice supported project, Thompson et a l ., suggested that through careful consideration of "both theoretical work and empirical data" they could: Clarify the theoretical assumptions that may or may not support a policy emphasis on employment initiatives as part of a crime control strategy. Identify more clearly the types of people in high crime neighborhoods and in the criminal justice system for whom enhanced employment would be likely to avert crime. Identify periods in the individual's life cycle during which this form of intervention would be more likely to succeed. Identify more clearly the kinds of economic and social-psychological processes through which enhanced employment would have to work on the community and individual levels in order to be effective as a crime control mechanism. Describe more fully the kinds of work that are valued and the process by which such work is found and work histories are established in high crime neighborhoods. Describe how information of this kind can be used to shape the design, planning, 18 conduct and evaluation of employment programs in such communities. (Thompson, et a l ., 1981). In the end, they suggest that their study reviews social science theories and empirical findings to summarize what is known, or the "state-of-the-art," concerning employment and crime relationships. The claims of these researchers appear overly broad in reference to a more complete exploration of the literature and research that has been conducted in the past. There have also been recent studies that guestion the results of past analyses. It is apparent that the claim of no relationship between employment and crime is not based on a wide search of the literature before Becker's article in 1968. McDonald suggests that Becker did not "consider alternative notions of the nature of the function describing social welfare" and he suggested a number of extensions of Becker's theory involving an appropriate social welfare function (McDonald, 1987, p. 245). It is also significant that Ehrlich, developed approaches that moved beyond Becker's basic theories to consider the relationship in a more realistic way (Ehrlich, 1981: also Harris, 1970). In addition, there have been several of theorists who have questioned the "utilitarian" basis of Becker's theories (Nozick, 1974; Stigler, 1970). Parker and Horowitz, based on a less than complete study of the literature suggest "despite the widespread acceptance of the notion that during 19 periods of economic downturn, higher levels of unemployment lead to higher levels of crime and imprisonment, the research literature reveals very little consistent support for the existence of such a relationship" (Parker & Horowitz, 1986). The problem with Parker and Horowitz's approach is that although it is empirically sound as many other analyses, and their conceptual basis is rather weak. Their conceptual basis that there is no reason to believe there is a relationship between employment and crime rates also seems implausible, based on a wider examination of the literature on the t o p i c . In the end, it is impossible to deduce any definitive conclusions regarding economic crisis and criminality and incarceration. The general impression one can gain from the literature is that crime has steadily increased despite economic changes. The reason it has been impossible to establish any clear relationship between crime and the economic situation may of course be due to the reality that such a relationship does not exist. However, the explanation also could be that the data in question may be differentially unreliable, or the analytical methods employed have been unsuitable for showing a relationship. Theoretical Debate. The theoretical arguments can be classified into two distinct perspectives. On the one hand there are authors (such as Becker, 1968; Ehrlich, 19763; and to some extent Block & Lind, 1975; Sjoquist, 1973; Block & 20 Heineke, 1975; etc.) that argue there is a relationship between economic factors (income and employment), and the phenomenon of crime (crime and incarceration rates). Under the guidance of the economic paradigm and the concept of "rational man" these theorists assert that the relationship between economic cycles and criminality is the result of rational decision making by law breakers. These theories suggest that the utilitarian arguments of balancing a punishment with a crime and the premeditation of the criminal results in either a criminal or a non-criminal depending on the decision consciously undertaken to commit a crime or to remain law abiding. Recently, as Osberg suggests, there is some argument that these "utilitymaximizing" models may apply across several generations rather than merely to individual choices (Osberg, 1984, p. 259). There are also a group of scholars that are opposed to the former group. This second group of authors asserts there are no demonstrated relationships between economic conditions and crime rates (Parker & Horowitz, 1986; Tittle, 1969; Gasmick, Jacobs, & McCollom, 1983). This latter group suggests that the empirical investigations to date have not shown, with any degree of consistency, there is a relationship between economic cycles (or employment) and crime or incarceration rates. Although the group which contends there is no relationship is dominated by 21 sociologists and criminologists (whereas the former is dominated by economists and political scientists) it is egually plausible given there is an inconsistency in the attempts to provide empirical validation to the relationship. Given that this theoretical classification is the overriding theme in the literature there are several attendant issues that should also be considered. First, there are a few theorists, mainly "radical" political scientists & criminologists, who suggest that the "critical" or "new" criminology best explains the relationship between the economic situation and crime in the United States. This argument, although rarely supported by empirical evidence, is nonetheless conceptually sound, and could be considered in the examination of the relationship between economics and crime. The so-called "radical" approach may aid in the explanation of the relationship between the economic situation and criminality or incarceration. Second, there are also theorists who discuss the issue of deterrence and the use of the criminal justice system as a means to that end. These theorists postulate that the "rational" man, in the economic sense, can be controlled via the sanctions imposed by the criminal justice system. This idea is derived from the classical school of criminology of the late eighteenth century (Bentham, 1789; and Beccaria, 1963) in which they postulated that criminals would consider 22 the consequences of their actions before committing an act. Besides these early utilitarians, Karl Marx, and William Bonger have also discussed the relationships between economics and crime, but have been largely ignored by recent generations of economists (most likely due to their radical orientations) (Sullivan, 1973, p. 139). Although this issue seems tangential to the relationship between unemployment, social welfare and jail rates it is central to any discussion of the relationship between economic factors and criminal indicators. This perspective holds that the mind set of criminals is both rational and considered. Third, it is suggested here that unemployment is simply one relevant factor in the economic situation. Crime rates are similarly limited indicators of the alleged social harms that are the supposed results of economic downturns and their effects on individuals. It is possible that an assessment of the relationship between the changes in the labor market situation and criminality and/or incarceration would be more enlightening. Moreover, based on the realistic limits of time or cognitive ability, one limits the number and types of "other" social factors that may be considered. One can, therefore, suggest that a more holistic approach to the examination of the relationship between labor force changes over time and criminality or incarceration should consider changes in the "correlates" of criminality such as education, income, age, and gender 23 relationships as those factors may be affected by economic conditions and labor market structure. Most of the past research in these areas focuses on both official unemployment and crime rates. The study of the relationship between crime and unemployment by both researchers and government officials has been tested by those measures. There has been a persistent fascination with the economy, especially the way it structures employment opportunities for different individuals. However, the typical approach has merely examined the intercorrelations between the official measures of crime and unemployment, and has rarely considered other issues. Although in the national context this is almost the only way to complete such an analysis, it provides a somewhat limited depiction of both local areas and the actual concepts measured. Both official crime and unemployment rates may not be accurately measuring what one supposes. For instance the official crime rates simply measure what crimes have been labelled by the police. What can be said of crimes that escape their knowledge? It is entirely possible that the crimes not counted may be the crimes that are most influenced by changes in employment or economic cycles. Moreover, given that the police are typically engaged in (eighty to ninety percent of the time) service activities, it behooves one to question the reality of these crime measures (Newman, 1986). Similarly, the official 24 unemployment index does not measure unemployment, but a temporary and/or relative quality of those out of work (Thurow, 1975). Only those who are still searching for work, and reporting to an official agency, are considered to be unemployed. This raises questions about where those discouraged from collecting unemployment or those who have begun illegitimate or other legitimate means of generating income fit into the relationship. In the early 1960’s the federal government of the United States readdressed the issues surrounding unemployment in this country. Many of these programs focused on those populations that were most likely to come into contact with the criminal justice system. To that end, the National Institute of Justice (NIJ) decided to look closely at the relationships between employment and crime and to develop a context of knowledge within which to assess accomplishments and future policies and programs in this area (Thompson, et a l ., 1981, p. 2). The Vera Institute was contracted to carry out a long-term study of the problem. The general objective of the study was to: . . . Consider carefully the empirical and theoretical reasons for the contention that experiences of employment and unemployment are related to criminal behavior, and to increase understanding of the various ways in which these relationships may be manifested (Thompson, et al., 1981, p. 3). 25 Consistent with most of the studies in the realm of the economic perspective (below) the NIJ study presumes that criminal actors behave rationally, and consciously weight the costs and benefits of criminal actions. The specific focus of the Vera study was to address the practical issues of policy making and to address the problems of employment and the criminality of the unemployed.2 The general approach to the study was a multidisciplinary search of the literature. Although the specific theoretical focus may suffer the same difficulties as the strict economic approaches, it does provide a more extensive orientation to the concepts of human capital and its influence on the relationship between employment and crime. The literature cited in the NIJ study seems to show there are many arguments both supporting and disputing the relationship between unemployment and crime from several different perspectives. In the most general sense most of the different perspectives seem to show there is almost no conclusive evidence that a cogent relationship exists. Within each perspective there are those who contend that such a relationship does exist and there are those who contradict this assertion. First, the typical theory of the economic perspective assumes that criminals behave in a rational and calculated manner, in that they consider the punitive results, and risks associated with criminal activities. The general assumption of rational man pervades 26 every economic study, but there has been no conclusive evidence that the relationship is clearly demonstrated. The empirical evidence from the economic studies leans toward supporting the relationship between crime and unemployment. Monzingo argues: Of all the social scientists the economists have been most successful in selling the idea that they are the most scientific: their methods of model-building, their adaptations of mathematical and statistical analyses, and their procedures for hypothesis-testing more closely approximate those of the physical sciences, and where they do not, economists design analysis and predictive techniques that facilitate our understanding of how society works and of how government policies should be changed to achieve specific goals (Monzingo, 1977, p. 261). Although this argument seems plausible, the conceptual basis for the economic theories is no better, nor w o r s e , than any other in the social sciences. The validity of the economic argument, therefore, remains to be s e e n , but one must acknowledge the quantity of direct effort at discerning the relationship between crime and employment that has been done. The basis for the interface between criminal justice and criminology and the study of the labor force have been suggested on three grounds: philosophy, theory, and methodology. Orsagh (1981) suggests that the basic 27 philosophy of the economists has a rich tradition, a high quality of theories, and innovative empirical methodologies that have made substantial contributions to the understanding of the relationships between crime and economic issues. There are several sociological theories that presume to examine the relationship between crime and unemployment. Although the results of these studies a r e , again, a mixedbag, they apparently show a lack of support for the relationship. A key issue that is apparent in the literature is that most of the empirical studies to date have examined the connection between crime and employment variables by relying on the "official" indicators of unemployment and crime. Although this analysis attempts to move beyond the consideration of unemployment through examining the social welfare system and its relationship to imprisonment at the county level, it largely relies on similar types of d a t a . There is limited work in the consideration of social welfare as an indicator of social problems (Jankovic, 1977).3 It is somewhat apparent that the issue of social welfare is crucial to the relationship between unemployment and crime and/or incarceration. The use of social welfare may compensate for the increases in unemployment, possibly showing why the relationship has been unclear to d a t e , and it also allows one to develop alternatives to the economic 28 models of Becker and others that suggest that the decision to be criminal is one that is rational. It is plausible to suggest that when the economy is in a recession and the employment levels are decreasing over time the unemployed have limited choices. Among these choices, after unemployment benefits expire, are the social welfare system (ie., food stamps, general welfare, e t c .), the secondary labor market (ie., McDonald's, laborers, etc.), and the illegal/opportunistic labor market (ie., running numbers, theft, e t c .). If the last option is selected, crime would increase, and, after some time period, incarceration rates should also increase. Purpose of the Study. Instead of iterating the traditional research on unemployment and crime rates this research attempts to determine the current change, if any, in governmental responses to unemployment, specifically in the supportive social control mechanism of social welfare payments and the coercive social control mechanism of incarceration. In addition, this research focuses on individual counties rather than using aggregate state or national data. Further, the counties selected, while all urban, differ in degree and kind of industry and commerce, allowing a consideration of the possible varied response to unemployment between greater and lessor industrialized areas. 29 This analysis proceeds in two general areas. First, there is a consideration of the labor force indicators and their relationship to jail populations. This is accomplished through the assessment of "official" employment statistics that can be selected on a county basis and matched with similar jail incarceration rat e s . Several theorists have suggested that as the economy experiences downturns the criminal justice system will respond with a proportional rate of incarceration (Fletcher, 1849; Davies, 1922; Ogburn & Thomas, 1922; Simpson, 1932; and Wallace, 1981). First, changes in the labor force would be reflected in changes in the local correctional population in a more apparent manner than would be the case from a wider (state level) consideration. Second, the relationship between jail commitment and social welfare data also will be assessed. This latter analysis represents somewhat of a departure from most prior research in that it presumes the specific function of social control also may be accomplished through the greater application of social welfare programs besides the more formalized means of control through criminal justice sanctions. Although Phelps suggested, as early as 1929, that social welfare can be an indicator of penal commitments at the local level (Phelps, 1922), this relationship has been neglected in contemporary studies. The basic issues to be addressed in this study focus on incarceration rates, social welfare, and labor force 30 participation in an urban context in the State of Michigan (See Map 1 for Urban Counties in the State of Michigan), with the intention of expanding the knowledge the relationship between economic change and the use of incarceration and social welfare. This study provides a comprehensive analysis of the fluctuations in social welfare, labor force, and jail population rates for seven years, by mon t h , and for all urban counties (Bureau of the Census definition), in the State of Michigan. It is further anticipated that a classification schema will be developed given the population differences, in the urban counties selected, in which the comparisons can be specified for similar jail populations in other counties or in other areas of the country. The logical connection between these specific indicators and the empirical analyses are that they allow one to assess the fluctuations in jail populations and the reliance on the welfare payment system, and to some extent, the secondary labor markets. The practical outcome of this type of analysis would be to assist local jail administrators to understand the linkages between the labor force fluctuations and their effects on the jail population. Moreover, through such an analysis one can develop a much clearer understanding of the relationship between the social welfare system and the local correctional system. Ultimately, the product of this 31 research is to both define and extrapolate the conceptual linkages between participation in legitimate economic system through both labor force and social welfare participation and the resulting rates of incarceration at the local level. CHAPTER 2 LITERATURE REVIEW The literature review is a detailed assessment of the theoretical debate that has been discussed above. The purpose of this chapter is to develop the conceptual framework of this research. Second, the criminological and sociological theories will be discussed. Finally, it will discuss the literature on employment and the use of incarceration. In recent times many social scientists, government officials and other criminal justice and social service administrators have focused considerable attention on the interrelationships between the economy and the persistence and control of crime. Although many traditional avenues of research pursued by sociologists and criminologists (i.e., The study of the family, the peer group, or mental capacity, e t c .) have been considered, one area of particular current interest is the study of the characteristics and relationships of crime and employment. Moreover, much of the theoretical and empirical work in this area can be attributed to the development of statistical technologies that can accommodate complex econometric questions. 32 33 The Economic Perspective. A key perspective in examining the relationship between employment, social welfare, and local incarceration is the economic perspective. The economic perspective maintains that the behavior of criminals is the direct result of rational and considered thought about the potential consequences, both negative and positive, of anticipated criminal actions. Under this perspective a criminal is logical and contemplates his/her actions, as well as the likelihood of apprehension and conviction before the actual commission of a crime. T h u s , criminals are normal and rational people who calculate and attempt to maximize their preferences subject to given constraints of specific situations (Sullivan, 1973, p. 140). Moreover, economists do not claim that offenders are "sick" (in a psychological sense), only that they pursue activities that yield the most satisfaction to them within their available alternatives (Reynolds, 1985, p. 7), and their psychological or social status. In addition, the economic perspective implies that the search in the criminological literature for causes of crime that are assumed to result from deviant behavior, and the search for reasons for criminal activity in deviant factors, is misdirected (Ehrlich, 1973, p. 521). The laws of society will not be universally maintained if there are those who see advantage in not following their direction. One also can argue there will always be these 34 types of people. Besides the inevitability of individuals who do not conform to society's rules there are those who specifically maintain their livelihood by such deviance. The result of these two general forms of deviance necessarily must be some form of action by society. This action typically takes the form of sanction as manifest in either incarceration, fin e , or a variety of community dispositions to remunerate the practice of deviance. In addition to these general precepts there is an inference of hedonism that is suggested by the economic perspective. Stated simply the economic perspective suggests that people are hedonistic and therefore, they attempt to maximize their pleasures and minimize pain on an individual basis. Although there are clear differences between individuals in terms of desires and tastes, there is a general motivation to maintain pleasurable activities. The economic explanation of criminality asserts that the individual contemplates: . . . all practical opportunities of earning legitimate income, the amounts of income offered, the incomes offered by various illegal methods, the probability of being arrested if he acts illegally, and the probable punishment should he be caught (Sullivan, 1973, p. 141). T h u s , the criminal is considered somewhat rational, normal, and calculating individual. Therefore, the solution to 35 crime is to increase the cost by increasing probability of being caught as well as the severity of punishment. The key point of the basic assumptions of the economic perspective is that in the contemporary market system, such as that of the United States and many other Western industrialized nations, there is an unequal distribution of resources, and therefore, all desires for economic "goods" or pleasures are not equally available. If these assumptions are accurate it would be evident that when legitimate employment is less available one should see increases in both the jail and welfare populations at the local level. Sullivan asserts that the economic argument overrides the sociological theories of deviance and socialization and should be forsaken for increased allocations of resources to apprehend and incarcerate offenders. Although many political economists and theorists in the 18th and 19th centuries such as Adam Smith, Jeremy Bentham, Caesar Beccaria, and Karl Marx addressed the connection between employment and cri m e , they have not been recognized or considered in contemporary research. In addition, it is unlikely that the link between employment and crime and/or imprisonment could have been widely studied before the industrial revolution in the Western world. It also would be unlikely given the structure and function of labor before that time. It is even more significant that the reanalysis of many early theories by contemporary scholars has 36 unwittingly occurred. There also is little reference to these primary sources in contemporary research. During the harsh economic times in the late 1930's and early 1940's there was a somewhat renewed interest in the potential relationship between crime and employment, but World War II and the resulting "full-employment" apparently pushed such concerns and analyses from the forefront. Beginning with the publication of Becker's key research article in 1968 on crime and employment, there has been a substantial increase in contemporary interest in the relationships between the labor force and cri m e . Classical Economic Theory and C r i m e . The application of traditional economic theory to crime and the explanation of criminal behavior has increased in the past two decades. A primary contention of this perspective is that " . . . if, in a given period, the two activities (legal and illegal employment) were mutually exclusive, one would choose between them by comparing the expected utility associated with each alone" (Ehrlich, 1967, 1973; Becker, 1968). It does not seem likely that the choice to engage in either criminal activity or to maintain legitimate ties is simply a choice between these two, but is complex. The arguments offered by the economists are also somewhat similar to the subcultural perspective (See Wolfgang & Ferracutti, 1967; G a s t i l , 1971). They express the choice for criminal over legitimate employment in terms 37 of a set of opportunities and preferences that are unique to the criminal group and neighborhood. Ehrlich contends "rather than resort to hypotheses regarding unique personal characteristics and social conditions affecting respect of the law, a penchant for violence, a preference for risk, or a general preference for crime, one may separate the latter from measurable opportunities and see to what extent illegal behavior can be explained by the effect of opportunities given preferences" (Ehrlich, 1973, p. 522). This perspective also seems to borrow conceptual wisdom from Cohen's opportunity theory or Sellin's hypotheses about neighborhoods and c r i m e . Although the economic perspective is a restatement of these theories in monetary values, the essence of each is apparent. The relationship between employment and crime or incarceration can be framed in the "classical" perspective. The individual calculates a balance between legitimate and illegitimate sources of income. If opportunity for one source of income decreases, the other will be used more readily. There have been various attempts to construct models that test the idea that crime is rational behavior. In specific, Sjoquist tests the hypothesis "that under some conditions, criminals can be treated as rational economic beings, assumed to behave in the same economic manner as any other individual making an economic decision under risk" (Sjoquist, 1973, p. 439). Although his test is limited to 38 property crimes, it seems to consider the economic and crime theories' main arguments of choice between legitimate and illegitimate work activities. However, he errs when he assumes that the gain from illegitimate activity is "generated with certainty" and that the "wage rate for crime is constant" (Sjoquist, 1973, p. 439). Thus, a key problem with the economic theorists' perspective is the use of unrealistic assumptions. If one decided to commit a crime it is not a certainty that a wage will be forthcoming and the "wages" of certain crimes are significantly different from each other. For example there is a clear difference in the returns to someone who chooses to embezzle as opposed to a street robber. Becker's theory, although basic in explaining crime, provided the preliminary forum for examining the linkages between economic theory and crime in a contemporary context. He suggests that the main purpose of his essay was to answer certain questions about the relationship between the law and crime, and to discover " . . . how many resources and how much punishment should be used to enforce different kinds of legislation?" (Becker, 1968, p. 170). Becker contended there is some balance, or that one can discern whether there is such a balance, between the crime that is committed and the necessary level of punishment instilled. not recognize that when " . . . Yet, he does punishment exceeds what are considered reasonable levels or is applied without 39 selectivity, criminal acts may become politicized and thereby increase in frequency" (Harris, 1970, p. 167). The Becker-Ehrlich model also has little to say regarding the losses from unjust punishment given what has been shown to be "typical" police behavior of directing extra effort at low-income, poorly dressed individuals who are ethnic minorities (Harris, 1970, p. 170). The Becker-Ehr1ich model is consistent with conservative position that government is capable of providing fair governance despite the social status or spatial location of its citizens. In addition, their model may only consider a "rational perspective" based on limited empirical observations instead of the broader social considerations. Davies (1922) argued over forty years a g o , that the reason economists neglect sociological and criminological variables is that these variables do not lend themselves to economic modeling as do the economic indicators, and that they may not lead to immediately practical conclusions or applications. It is also appropriate to point out that local responses, in jail and welfare payments, to changing employment patterns have not been widely examined. Becker also suggests that an economic theory of criminal behavior could: . . . dispense with special theories of anomie, psychological inadequacies, or inheritance of special 40 traits and simply extend the economist's analysis of choice (Becker, 1968, p. 170). For crimes that can be proved to be strictly economic and premeditated in nature it is plausible to suggest that may be the case. However it is also unlikely that the sociological and the criminological theories are totally inapplicable as Becker suggests. For example how would such a model explain the recent increases in the mentally ill being handled by the criminal justice system instead of the mental health system, or that some people are simply . . . more emotional than others, or that people often are unsure of the consequences of their behavior, or that they do not always make careful calculations about their next action. It seems unlikely that individuals choose, explicitly or implicitly, in crime as they do in other activities (Reynolds, 1985, p. 7). Conversely, situations which elicit violence are not beyond choice, and substitute actions exist for the settlement of disputes (Wolpin, 1978, p. 815). There are many situations where the individual does not, or is incapable, of exercising a choice on whether to commit a criminal act. The model Becker proposed was one that supposed there is some "optimal" level of crime control based on behavior and costs of actions.5 These costs range from the costs of the offense to the attendant cost of apprehension and conviction. Clearly, he was referring to a utilitarian 41 schedule by which the effects of criminal actions are to have attendant societal costs and thereby require directed actions. Becker begins his analysis with a discussion of the concept of crime. The contention is presented that crime is simply the net loss to society minus the gain to the individual criminal. He also asserts that the effects of most crime can be measured in monetary terms. The problem is that this aspect of the model does not account for the mental anguish that is often an effect of many physical crimes, as well as the lack of a definable cost for acts that are "victimless." Whether or not a utilitarian punishment schema could be developed, is moot, for it is unlikely that it would have wide application or remain static over time. In further specification of his model Becker asserts that punishment is a supply-side manipulator. It seems apparent that punishment determines, in whole or in part, how much further criminality will be committed by the individual. His theory, as well as that of many scholars of employment and cri m e , is that criminals simply weight the utility of committing an offense against the likelihood of punishment as well as the degree of punishment. This theory also holds that criminals only consider the degree and likelihood of the effects of the criminal justice system and little else before committing a criminal act. Although 42 Becker's insights seem profound and somewhat accurate in certain situations, there are many criminal cases that cannot be construed to conform to a cost benefit analysis in the mind of the offender. A considerable proportion of homicides are intra-familial in nature, and occur in the "heat-of-passion," and that many violent crimes such as rape, and robbery are based on the exploitation of an immediate opportunity of the situation rather than on preconceived consideration of any long-term consequence of said criminal actions (Newm a n , 1986). This theory would allow one, as in the p r e s e n t .stu d y , to suppose that both local corrections and welfare allocations are responses applied to influence the decisions of offenders. Ehrlich (a student of Becker) claims to go beyond Becker's theories in suggesting a model that considers the costs and gains from both legitimate and illegitimate activities, rather than punishment alone, and attempts to identify their empirical counterparts. He also considers occupational choice of illegitimate and legitimate activities. In addition, he purports to examine the interaction between crime and the enforcement of the law through the courts and the police (Ehrlich, 1973, p. 522523). Like the Becker m o d e l , choice is presumed to be rational and based on some utilitarian consideration of future consequences of current criminal activity. Ehrlich's model also maintains a similar presumptive restriction of a 43 constant wage from crime. Ehrlich, however, refines the question through the consideration of both legitimate and illegitimate employment and the interactions of enforcement and criminal choice. T h u s , Ehrlich7s developments would allow one to suggest that a relationship should exist between not only employment and crime but also between employment and incarceration and welfare. Besides the consistency with the economic perspective, there are problems with the conceptual application of Ehrlich's theory. One assumption offered by Ehrlich proposes to establish a link between crimes against person and choice. He contends that it is appropriate to consider crime against the person as nonmarket activities that directly meet certain non-pecuniary needs. He also argues that crime against persons can be examined in a similar context to the probability of the severity of punishment as can the property crimes. Clearly many crimes against persons are not readily amenable to the severity of punishment hypothesis in a similar manner to property crimes. There is often little, if any, consideration of the consequences in most personal crimes in that many are not premeditated acts. The assumption of rationality is most inappropriate for personal crimes, or any criminal activity that is characteristic of non-cognitive input (i.e., Victimless crimes such as drug use, child molestation, voyeurism, etc.). 44 Another argument that is presented (in the BeckerEhrlich model) is the suggestion that the criminal justice system can be made more rational through the application of monetary fines to specific crimes. Although Becker strongly recommends the use of fines instead of imprisonment, he contends that the main contribution of his work is "to demonstrate that optimal policies to combat illegal behavior are part of an optimal allocation of resources." (Becker, 1968, p. 209) Becker suggests, therefore, that the cost of punishments can be made comparable by converting them into their monetary equivalent or wor t h . However, it is unlikely that this can be substantiated for the wide number of criminal acts both personal and property in addition to the individual differences in offenders. It also is unlikely that any, but limited static, monetary conversions can be accomplished, and where they could be established it would be difficult to apply these to individual crime situations and victims. The structure of monetary conversion seems inapplicable unless it contemplates each criminal as an individual and her/his perceived gains and losses as well as the monetary equivalent of the pain caused to the victim and his/her immediate associations and the damage to society. Clearly even if such a schema could be developed its application would be extremely cumbersome and static. A general concept that pervades the economic studies is that criminals consider the possibility of apprehension as 45 well as the probable attendant punishment (Ehrlich, 1967, 1973; Ehrlich & Becker, 1967; Becker, 1968). Whether current sentencing relies on probation or counseling or incarceration is not the issue, but the probable outcome. Although this acknowledgement of both potential apprehension and disposition is necessary, it is not clear that offenders' base decisions on such factors, or there is a broad consistency with the "optimality analysis" 1968, p. 190). Becker states that " . . . (Becker, if the costs of apprehending, convicting, and punishing offenders were nil and if each offense caused more external harm than private g a i n , the social loss from offenses would be minimized by setting punishment high enough to eliminate all offenses" (Becker, 1968, p. 191). Yet, it is apparent that these attitudes are individualistic perceptions by both the criminal and the victim of an incident. concepts be legislated? How could such It also seems unlikely that poor offenders who steal for legitimate economic g o o d s , or illegitimate goods such as drugs, would fit into this hypothesis. Can they be effected by either level of apprehension or conviction? One could argue that they would not, given their general perception that they will not be caught and their generalized need which "must" be met. Ehrlich concludes that "the basic thesis underlying our theory of participation in illegitimate activities is that offenders, as a gro u p , respond to incentives in much the 46 same way that those who engage in strictly legitimate activities do as a group” (Ehrlich, 1973, p. 559). It is apparent that beyond the aggregate test of his model, he generalizes to individual behavior. Is choice based on aggregate fluctuation as shown by multiple regression analyses? Block and Lind offer another economic alternative to the Becker-Ehrlich model s . They claim to proceed similarly when they rely on economic theory to explain crime and they also cite several inconsistencies in the Becker-Ehr1ich m od e l . Specifically, they state that the Becker-Ehrlich m od e l : . . . is not always t r u e , as Becker asserts, that the effects of crime and punishment can be represented in terms of monetary equivalents. Second, we indicate why, even if a fine equivalent to a prison sentence exists, his result that suggests that prison terms always be set is misleading. Finally, we prove that the limits of the criminal sanction discussed by Becker (and Ehrlich) do not depend on risk preference or institutional considerations but follow directly from the boundedness of the utility function and the expected-uti1ity theorem (Block & Lind, 1975, p. 242). Apparently there has been a less than strict interpretation and application of the utility models. Block and Lind argue for a variation of the Becker-Ehrlich model that is 47 essentially similar, although there are refinements in the derivative formulations. is However, they do contend that it not always possible to attach a monetary equivalent to criminal acts as does the earlier model. They suggest that their model is a major alteration of the Becker-Ehrlich model that adjusts for less than rational consideration of the contemplation of criminal acts. They cite the following as an example: Suppose, for example, that a multimillionaire is contemplating the murder of a hated possible rival. It is quite that he would be unwilling to accept any amount of additional wealth as a substitute death of his rival. for the However, if confronted with a choice between murder accompanied by a simultaneous reduction in wealth to the level of subsistence and foregoing murder and retaining his present wealth, he might well choose the latter. This behavior is not inconsistent (Block & Lind, 1975, p. 244). Therefore, individuals may select the behavior that may not necessarily be the most economical, but is the most desired. A clarification of the issue of the substitution of monetary fines for incarceration is also suggested by Block and Lind. They suggest that, it is possible to avoid the payment of a fine (given lack of resources) whereas, it is difficult to avoid incarceration (barring dea t h , and lack of apprehension) (Block & Lind, 1975). One could suppose that 48 there are social costs attendant to the collection of fines. There is also a problem with attaching fines instead of incarceration given the high incidence of lower income individuals committing crime. Additionally, Block and Lind suggest that for any punishment in which the penalty is not preferred to the legal alternative, there exists a probability of conviction less than unity and it is sufficiently high to deter all crimes (Block & Lind, 1975, p. 246). Intuitively our results suggest that there does not exist a punishment severe enough to deter completely any crime, be it pickpocketing or treason. On the other hand, there very likely exists a probability of punishment less than one that will completely deter any crime. Moreover, for any crime, beyond some point in terms of the severity of punishment, it is not possible to keep deterrence constant by trading off harsher punishments against a lower probability of punishment (Block & Lind, 1975, p. 246). They are suggesting that the rational application of punishment cannot be either static or applied to effect crime rates through deterrence. Block and Lind conclude that the use of more effective apprehension is most likely to lead to a greater deterrent effect, as well as adequate punishment. The typical economic argument of the deterrent power of potential 49 punishments is not sufficient to deter, but effective apprehension is also a necessary component of a more effective criminal justice system. Many economic theories assert that they can provide an explanation for the deterrent effect of punishment on the commission of criminal acts. Although Becker attempts to assert that criminals are normal, in their decision making and in anticipation of committing a criminal act, and that they prefer taking risks, others assert that criminals are similar to the remainder of the population. There have also been several criticisms of the BeckerEhrlich model from a basic disagreement about the nature of the underlying social function (McDonald, 1987) to problems with the data relied on (Brier & Fienberg, 1980). Therefore, these theorists suggest that major limitations of these theories are the basic assumptions upon which the theory rests. First, the assumption that the individual selects between only legitimate and illegitimate labor markets is somewhat simplistic. It is likely that people who live their lives at the margins of economic stability would be free to participate in both the labor market and collect social welfare, as well as receive subsidized training. Although legitimate opportunities are more restricted for formerly convicted offenders, one cannot uphold the contention that these restrictions are the sole determinate of future criminal activity.6 The present study 50 examines the effects of these rational theories. The approach assesses whether a change in employment level results in a similar change in the use of local incarceration or the granting of welfare. Another more specific examination of theory of employment and crime was provided by Brier & Fienberg (1980). Although their study centers on the question of whether "marginal deterrence" effects shifts in subsequent criminal behavior, it is directed at the economic assessments of the relationship between deterrence and rational criminal choice. The tact that is taken is a critique of the misspecifications in concepts and empirical verifications that the economists have suggested to d a t e . One primary criticism that Brier and Fienberg offer is that the aggregation assumptions typically rest on the contention that the parameters used to specify the relationship between crime and employment are constant across individuals, or that the parameters are stochastic coming from some common distribution (Brier & Fienberg, 1980, p. 155). Given the statistics relied on by most of the studies done to date, it is unlikely that either of these contentions can be maintained with any degree of confidence. Although the Uniform Crime Reports summarize the individual criminal acts to arrive at an "index" of crime, it is clearly inappropriate to apply such a total count to individual decisions. 51 Urban Labor Markets. In the general realm of economic theory another variant that seems particularly cogent to this analysis is the concept of urban labor markets. In a general sense this theory postulates there are several sectors of the labor markets within the urban scene. Each labor market refers to a specific group or population and there are barriers to the entry into each. Thompson et a l ., (1981) suggest that these theories hold that a person, when confronting a range of choices about alternative behavior, will select that mix of activities that maximizes his or her utility, and that utility can be either in the form of maximizing monetary or non-pecuniary income (Thompson et a l ., 1981, p. 22). The general theme of this theory is a basic decision between work and non-work. For instance, the economic argument suggests that balance between work and non-work is direct and considered. The costs of non-work are reflected by work participation in the form of wages. The theories that have been developed to date assert that there are two distinct markets: The primary market offers jobs which possess several of the following traits: high wag e s , good working conditions, employment stability and job security, equity and due process in the administration of work rules, and chances for advancement. The other, or secondary sector, has jobs which, relative to those in the primary sector, are decidedly less attractive. 52 They tend to involve low wages, poor working conditions, considerable variability in employment, harsh and arbitrary discipline, and little opportunity to advance (Piore, 1977, p. 94). Therefore, one could imply that there are barriers to the entrance into the primary market from the secondary market based on qualifications, work history, or other situational factors. The segmented labor market theorists also assert that the barriers to entry into the primary market can be based on racial or sex discrimination, or other forms discrimination. Harrison has developed an application of this theory that moves beyond the simple "either-or" model of primary versus secondary labor markets to assert there are five different sectors of the labor market.7 He proposes a "synthesis according to which the urban economy is stratified into a 'core' and a 'periphery', with the latter in turn segmented into four interacting sectors" (Harrison, 1972, p. 6). He asserts that individuals move among various activities in the economic "periphery" with relative ease and frequency, while mobility into the primary sector is severely constrained (Thompson, 1981, p. 62). A primary advance in the theory of economics and crime is the consideration of the existence of a collection of labor possibilities, or markets. Ehrlich postulates that: 53 Multiple-job holding also entails various costs of movement between jobs that may offset potential gains due, say, to the increased returns on time spent in each. Specialization in a single market activity may thus be optimal, at least during periods of intensive training. Nevertheless, in the case of market activities involving a large measure of risk, there may be an incentive for diversifying resources among several competing activities (Ehrlich, 1973, p. 524 (note 2)). Thus the choice of illegitimate activities does not seem to limit the individual to exclusive participation in that market. In addition, there may be simultaneous participation in many different income generating activities, within the restrictions of time available to develop in each market or simultaneous markets. The segmented labor market theory seems to conform to and support the general tenets of the traditional approach, although it further specifies the relationship between employment and crime. Thompson et a l ., state that although labor market theories emphasize the role of organizational features of the economy, they concur with the notion of a predominantly economic motivation (Thompson et a l .,., 1981, p. 67). The difference between the two labor market theories and traditionally economics is in their means to economic success not in the competitive or rational ideals 54 of the theory. Labor market theory also suggests that welfare is another source of "labor." Although the present study does not address the use of illegitimate labor markets it does provide an examination of the relationship between the legitimate and welfare sectors. Human Capital and Urban Labor Markets. Within the labor market formulation there are distinct and identifiable urban labor markets that consider the level of economic contribution that is required for entry into each. There are certain requirements for entry into employment in the various labor markets. Although the theories of human capital were not developed to explain the relationship between crime and employment, they provide a forum for such an assessment. Moreover, in the perpetration of crime there are certain skills that are prerequisite to the commission of particular criminal acts (i.e., Knowledge of the opportunity to commit a cri m e , how to go about committing a certain crime, e t c .), and that "qualifications" to entry do not only apply to legitimate labor markets. In an attempt to clarify the application of choice theory to the study of illegal or criminal activities Block and Heineke summarily explain the economic perspective. They purport that the commission of crimes requires an expenditure of effort, which may in turn increase the criminal's relative wealth, that also could result in criminal sanction (Block & Heineke, 1975). In Becker's formulation of human capital 55 theory self-investment decisions are oriented toward expected changes in income over a lifetime (Becker, 1975). Thus the notion of self-improvement embedded in the theory of human capital attempts to explain the productivity (and lack of productivity) of workers, as well as the rewards (and lack of rewards) of the labor market. Thompson et a l ., suggest that: A simple human capital model is a schooling model which hypothesizes a direct, positive relationship between the extent of schooling and the level of earnings (Thompson et al., 1981, p. 27). Business Cycles and Crime. There have also been studies of relationships between crime and economic indicators from a macro-economic perspective. The main distinction here is an analysis of aggregate economic "cycles" and the potential interactions with aggregate crime rates. Although many theorists above have approached their analyses similarly, these theorists assert there is some order to the mutual fluctuations of each the crime rates and the employment over t i m e . In one early attempt to assess the relationships between social problems and fluctuations in the business cycles Davies examined several issues. Although he specified a very general objective of analyzing " . . . the economic data most closely related to human motives and well being," he provides a strong conceptual discussion of the 56 relationships. In his conclusion he determined "While there are certain crimes which are intensified by prosperity, yet crime as a whole is decidedly an accompaniment of depression" (Davies, 1922, p. 114). In a more recent analysis Cook and Zarkin suggest that the business cycle has a pervasive effect on the structure of economic opportunity and on behavior, and that its seems unlikely that crime is excluded from such a relationship (Cook & Zarkin, 1985, p. 115). They suggest that in a general sense the fluctuations in the economy have a direct effect on social behavior, and given the nature of the market system in the United States, it is unlikely that such a major aspect of American life (crime) should not be linked to these cycles. They also list the possibilities for the explanation of the connection between crime rates and the economic cycles; these being legitimate opportunities are procyclical and that recessions reduce criminal opportunities, that the use of certain criminogenic commodities (alcohol, handguns, e t c .) increases in good times, and that changes in the economic cycle could have wide effects on criminal justice system budgets. On both a non-parametric and parametric basis Cook and Zarkin allege to substantiate the connection between robbery and burglary and the recession and recovery cycles of the economy. They also determined that for homicide there is little consistent connection between the business cycles and the crime rate. 57 Their conclusions are more than a bit pessimistic; however, they suggest that the only "clear" answers researchers can produce are concerning short-run fluctuations in economic conditions alone (Cook & Zarkin, 1985, p. 128). The present study does not assess the relationship of general economic fluctuations and crime. However, the patterns between employment and local incarceration and welfare allocation allows an examination of the similarities and differences in utilization over t i m e . Unemployment and Crime. In a very general sense the theorists in this specific economic theory assert that unemployment influences the crime rate. Gurr suggests that: Mild unemployment will motivate a few to crime, moderate unemployment will push more across the threshold, and very high unemployment is likely to cause large segments of society to become involved in crime (G u r r , 1970). T h u s , the suggestion here is that there are direct responses in terms of crime to increasing, and by inference, decreasing levels of unemployment. Swisher also contends that there are several explanations that clarify the link between unemployment and crime: 1. Unemployed persons turn to crime to meet pressing economic needs. 58 2. Crime offers greater reward and requires less effort than being employed and represents an acceptable risk to otherwise unemployed individuals. 3. Unemployed juveniles and youths turn to crime for "kicks" and "pocket" m o n e y . 4. Unemployment tends to precipitate criminal behavior of persons who have a predisposition or prior history of delinquency or crime. 5. Unemployed persons are subjected to additional stress which exacerbates other interpersonal conflicts, and leads to an increase in the probability that arguments or despondency will erupt into violent offenses against family members or other acquaintances. 6. Unemployment undermines the stability of participation in primary social and economic institutions reducing the capacity of such institutions for instilling and reinforcing self­ esteem and social values that tend to be associated with lower crime rates (Swisher, 1975). Spector examined the relationship between violent crime and unemployment in 103 Standard Metropolitan Statistical Areas through a multiple regression design, and determined that there was no "significant relationship between unemployment rates and violent crime rates" (Spector, 1975). This finding does not seem at odds with the typical economic 59 theory in that it does not examine the use of crime to generate an economic gain, but specifically examines irrational violent crime, which one should not anticipate (at least in a direct way) to show a relationship with employment changes. In a study done in Atlanta, Georgia, Kvalseth (1977) found that urban unemployment was positively related to burglary and larceny, that the male unemployment rate is also positively associated with robbery, and that both the male and female unemployment rates were positively associated with the rate of r a p e . Nagel also determined that unemployment may result in minor crimes that may not necessarily result in imprisonment. Given the determination of a positive correlation of (.517) between unemployment and crime rate there was "little or no relationship between a state's crime rate and its incarceration rate (.214)" (Nagel, 1977). A significant problem with Nagel's study is that he used point-in-time data instead of than the more appropriate use of longitudinal data. In their study Walsh and Viets determined that less than half the pre-trial offenders surveyed were unemployed (46%), and that the unemployed were most likely to be young, black, or female (Walsh & Viets, 1977). Although it is not clear whether unemployment contributed to the criminality 60 cited in their study, it does suggest that pre-trial offenders are often unemployed. Criminology & Sociological Perspective. Many early theories in criminology and sociology have considered the relationship between economic fluctuations and crime in the United States. For example Durkheim suggested "severe economic problems could generate widespread social instability and therefore anomie" (Durkheim, 1950). Thus he implies that a generalized economic downturn and the attendant disorganization, manifested in changes in housing tenure, decreased vacancy rates, urban blight, e t c . , could expedite a more criminogenic environment. Merton emphasized Durkheim's concept of anomie, and its relationship to the economic situation when he explained that: The emphasis in American society on goals (e.g., possession of material goods) and not means (e.g., access to high-paying jobs) causes strain when the means are unavailable for achieving the prevailing success goals . . . crime i s , then, a reaction to culturally induced strains and not a product of rational choice (Merton, 1957). Merton asserts that the basic premise of the economists are inaccurate; criminals do not make rational choices considering the costs and benefits of committing cri m e , but are cajoled into crime by their situational needs and the environmental constraints. The strain toward innovation 61 (developing alternative sources of income) is strongest according to this theory among those of lower socioeconomic status because their means to achieve success goals are so limited that they are often tempted to employ culturally proscribed means (such as crime) in the pursuit of the culturally prescribed success goals (Tittle, 1983, p. 337). In the end certain assumptions must be met to allow for anomie theory to explain the relationships between crime and socioeconomic status. Tittle suggests that these assumptions can be summarized into three basic areas (Tittle, 1983, p. 339): 1. Lower class people are a result of less effective socialization. 2. Lower class people envy the upper class and want to behave like them. 3. Upper class people will rarely aspire beyond their means. Similarly Guttentag claims that anomie, the lack of stable neighborhood norms , lack of stable values, leads to higher criminality. For instance, she postulates that economic conditions, as reflected in the employment levels, play a direct role in the population shifts that lead to anomic conditions: If we examine conditions of employment throughout the wo r l d , we will see that depending on the differential effects on population mobility and social change, 62 employment patterns are related to both rises and declines in the rate of delinquency in different countries. It is possible to predict the direction of the effect by following the consequences of the employment pattern on the stability of the population . . . High delinquency rates follow conditions of unemployment, when, for example, job patterns change so that the poor must shift from place to place, often from urban center to urban center, in search of work. The resulting instability and anomie of the poor under these conditions will be reflected in high delinquency rates (Guttentag, 1968, p. 112). Thus she implies that one can specifically assess, or possibly monitor, the changes in an area that lead to the situation of anomie. Unemployment then, is an indicator of social problems that influences the tendency to commit crime to meet basic economic nee d s . There have also been theorists who focus on the defective psychological development issue of the anomie theory. These theorists suggest that certain familial traits produce criminogenic environments (Alexander & H e a l y , 1935; Cortes & Gatti, 1972; Glueck & Glueck, 1950, 1968; Healy & Bonner, 1931; Nettler, 1984). The natural result of psychologically defective socialization, according to these theories, is the use of crime to satisfy perceived or actual needs. 63 In "Delinquency and Opportunity," Cloward and Ohlin expanded on this "environmental influence" theme when they suggested: "A person recognizing that he does not have access to legitimate opportunity structure, such as vocational training or lucrative job, which are necessary to satisfy institutionalized success goals, might reject legitimate means in favor of an illegitimate opportunity structure open to him" (Cloward & Ohlin, 1961). Therefore, given the environmental constraints to legitimate employment, the delinquent pursues an illegitimate career to obtain a desired standard of living. Even these early theories suggest that although the environment is the major cause of criminality it applies to each perceptual situation offered to explain the relationship between crime and the economic situation differently. Holzman postulates that even these theorists did not believe crime to be freely chosen. They did implicitly acknowledge property crime as livelihood, albeit a livelihood well apart from the marketplace of the greater society (Holzman, 1982, p. 238). The subcultural theories of crime causation also can be claimed to assist in the explanation of the relationships between crime and the employment situation. In a very general sense these theories suggest that certain groups or areas have distinctly different sets of values and norms which sanction criminality for survival. Miller suggests that the social classes are differentiated with respect to 64 their crime relevant "values" or "focal concerns," with the lower classes being characterized by "a distinctive tradition many centuries old with an integrity of its own" featuring focal concerns about trouble, toughness, smartness, excitement, fate, and autonomy (Miller, 1958). Similarly Sutherland proposed that differential association theory is a variant on the subcultural perspective. DeFleur and Quinney state: Overt criminal behavior has as its necessary and sufficient conditions a set of criminal motivations, attitudes, and techniques, the learning of which takes place where there is an exposure to criminal norms in excess of exposure to corresponding anti-criminal norms during symbolic interaction in primary groups. (DeFleur & Quinney, 1966). Another variant on the subcultural argument is the community ecological (Tittle, 1983) theory. In a similar conceptualization to the subcultural theory, this perspective suggests there are distinct areas within urban areas that are concentrations of lower class attitudes, physical appearance, and value systems. Shaw and McKay postulate there are two main features in these areas: Physical deterioration, and economic depression (Shaw & McKay, 1969). Thus, given these features certain areas are unable or cannot control the influences of successful criminality in perpetuating future criminality. 65 In addition, labeling theory also fits this conceptualization of criminality. Once the environment encourages a crime-as-a-career choice, society reinforces that choice with certain restrictions to other labor markets. The likelihood of legal processing and labeling is thought to be greatest for those individuals with the least power and resources to resist labeling ” . . . the powerless, or disadvantage, include, especially, members of minority groups and those of lower socioeconomic status" (G o v e , 1980). In addition, Lemert suggests that the labeling process "restricts but does not destroy free choice," in effect conferring a certain marginality on the individual (Lemert, 1951, p. 193-195). Labeling theory, therefore, asserts that although there are restrictions to moving from illegal activities, the choice is not a matter of rational choice. The approach of both the criminologists and sociologists have been to postulate that crimes are linked with an individual's position in the social structure. The most frequently hypothesized relationship is negative. Criminal behavior is seen to vary inversely by social status with particular concentration in the lowest socioeconomic levels of society (Tittle, 1983, p. 335). Many general attributes of society that are indicative of poor environments are linked to the existence of the lower socioeconomic classes. Tittle argues "Discussion of the sad 66 state of the poor, with allegation that such undesirable consequences as sickness, vagrancy, crime, and hopeless despair go far back into antiquity” (Tittle, 1983, p. 335). Additionally, Schafer provides a review of the literature that shows "Hardly any of the thinkers of the causes of criminality omitted poverty or economic conditions from their catalog of crime factors, and thus an endeavor to present those who have treated this issue would mean to list almost all who treated the problem of crime" (Schafer, 1969, p. 255). There are also theorists, who posit that unemployment can be used as and indicator of incarceration rates (Cox, 1975; Waldron, 1975, e t c .). claim that there " . . . Although Parker and Horowitz have been few studies that have specifically focused on the impact of unemployment on crime and imprisonment . . . ," they have not adequately searched the literature (See Cox, 1975; Waldron, 1975; Polinsky & Shave11, 1984; Bowker, 1981). In addition, Parker and Horowitz further their fallacious argument by citing only the work of Radzinowicz from the 1930's (Radzinowicz, 1939). This is particularly inappropriate in that government expenditures on social control agencies are not as sensitive to the fluctuations of the economy as they may have been when Radzinowicz's formulated his theory. Criminological and Sociological View of Unemployment and Crime. Similar to the economists, several 67 criminological have examined the relationship between the criminal justice system and employment in the economy (Bonger, 1916; Rushe & Kirchheimer, 1968; Turk, 1969; Chambliss, 1964, 1976; and Quinney, 1977). The results of the analyses in this area have been mixed. Several studies that have reported no significant relationships between labor force participation and crime (Jones, 1974; Wellford, 1974; Pogue, 1975; and Loftin & McDowall, 1982; Swimmer, 1974; and Land & Felson, 1975). More specifically (Danziger & Wheeler, 1975; Spector, 1975; and Block, 1979) did not find support for a relationship between unemployment and crime. The use of time-series techniques to examine fluctuations in employment and criminality have been similarly accused of showing a lack of relationship when other factors are controlled (Fox, 1978; Bartel, 1979; Orsagh, 1981). Conversely, there have been researchers that have reported a positive relationship between the economic conditions and crime (Greewood & Wadycki, 1973; and Fox, 1979; Cook & Zarkin, 1985). Parker and Horowitz suggest that there is little consistency in what type of crime is or is not associated with unemployment, with the exception that burglary rates often likely follow elevated rates of unemployment (Parker & Horowitz, 1986, p. 752). Although Parker and Horowitz assert that there is little evidence of a relationship between crime and/or imprisonment and 68 unemployment, the evidence in the literature seems otherwise. It is possible that crime and imprisonment are practically related, if not empirically substantiated. A recession always provides police chiefs with a comfortable explanation for their failure to prevent increases in the crime rate (Cook & Zarkin, 1985, p. 115) . Although Parker and Horowitz claim to go beyond the limitations of the past work on the relationship between unemployment and crime, they do not significantly alter the data that has been relied on in the past. They assert that the aim of their study is to overcome, in part, the limits of most previous research on unemployment, crime rates, and imprisonment rates through examination of state level data from 1974 through 1979 (Parker & Horowitz, 1986, p. 754). Yet they do not recognize the inherent problems associated with "officially" measured unemployment, or the Uniform Crime Reports. One can easily argue that their analysis has merely mirrored the specification errors of the past in that both official unemployment and official crime rates are problematic measures of either crime or unemployment. In addition, they also fail to recognize that yearly reports of crime for large aggregate areas (states) may tend to mask intra-year fluctuations in the d a t a . Economists have argued (Thurow, 1975) that employment has a considerable intra-year variation and that specific areas may disproportionately influence the state levels. It is puzzling that Parker and 69 Horowitz cite that most researchers have not heeded their criticisms about differences across social groups, or crime types and yet have specified a state-level model. Certain theorists, in a more general sense, posit that the business-cycle has a pervasive effect on the structure of economic opportunity and therefore on behavior. Cook and Zarkin suggest that it would be "surprising indeed if crime rates were immune to general business conditions, and certainly the conventional wisdom asserts that 'street' crime is countercyclical" (Cook & Zarkin, 1985, p. 115). addition, Cook and Zarkin provide a clear conceptual explanation for the possible linkages between business conditions and crime. These possibilities are summarized under four main headings: 1. Legitimate Opportunities: The quality and quantity of legitimate employment opportunities are procyclical. 2. Criminal Opportunities: Recession may discourage crime by reducing the quality of criminal opportunities. 3. Use of Criminogenic Commodities: Alcohol consumption, handgun sales may increase in good times, and therefore crime will tend to increase. 4. The Criminal Justice System's Response to Crime: There may be a reduction of budgets and therefore effort in recessions. In 70 In the early part of this century Bonger, Van Dan, Sellin and others attempted to assess the applicability of these postulates. In that regard Thomas determined that the detrended measures of crime with an indicator of business conditions for the period of 1857 to 1913 was correlated with certain property crimes (burglary & larceny) and the economic climate of the time (Thomas, 1927). Henry and Short find similar correlations at the metropolitan level (Henry & Short, 1954) . Radical Criminology and Economic Theory. Another cogent perspective that considers the relationship between general economic conditions and the amount of crime present in society is what has been variously called "New Criminology, Radical Criminology, Critical Criminology" (Tittle, 1983). An early theorist who specifically discussed the relationships between economic conditions and crime (Marx did not directly address this issue) was Bonger. He maintained that a capitalist economic system is necessarily based on competition and exploitative exchange, the inherent product of which is demoralization of humans and rampant egoism (in Turk, 1969). Bonger further asserts that the proletariat are "inclined" toward crime for several reasons: the actions of the bourgeoisie are not defined as criminal, the bourgeoisie have legitimate means of income generation, and the social disorganization in the environments of the proletariat renders them less attuned to 71 the values of lawful behavior set by the bourgeoisie (Bonger, 1916). Therefore, a capitalistic system creates a disproportionate amount of crime among the lower classes because their desperate economic circumstances provides strong incentives for committing criminal acts. The employment situation provides a demoralization of these classes through competition and exploitation and the lower classes have no legitimate alternative. This perspective hypothesis that current criminality is based on past and present economic conditions, and that the response to crime (by the criminal justice system) will be based on these past economic relationships (Surette, 1985, p. 47). Quinney states that: The criminal justice system, on the other hand, serves more explicitly to control that which cannot be remedied by available employment within the economy or by social services for the surplus population. The police, the courts, and the penal agencies - the entire criminal justice system - expands to cope as a last resort with the problems of surplus population. criminal justice system The as well as a control and corrective agency (Quinney, 1977, p. 109). The conflict criminological or perspective has been the only sociological theory that has acknowledged the existence of rational economic choice by criminals, however, they blame the socio-political environment for this 72 choice. Reckless suggests that poverty is generated by the economic exploitation inherent in capitalism and is fundamental in an individual's choice to commit crimes (Reckless, 1961, 42-45). Thus for these theorists, criminality is a specific response to the exploitation encouraged by society. In addition, the behavior that is deemed criminal by the ruling class may be perceived as legitimate by those without power (Void, 1958). Liska, Chamlin, and Reed suggest that the conflict perspective presumes a variety of conceptualizations linking crime control to group conflict, stratification, and power (Liska, Chamilin, & Reed, 1985, p. 121). In a more general sense there have been theorists who argue that economic inequality also may stress economic conflict and by that influence the need for social control techniques (law enforcement, incarceration, welfare, e t c .). Chambliss and Seidman assert, "The more economically stratified a society becomes, the more it becomes necessary for the dominant groups in society to enforce (through coercion) the norms of conduct which guarantee their supremacy" (Chambliss & Seidman, 1982). In addition, Jacobs asserts that the political power is based on a claim that income inequality increases the relative power of dominant groups; and that the punishment of crime, especially property crime, is more in the interest of dominant than other groups, and that income inequality should increase the level of crime control 73 (Jacobs, 1978). Tittle's conclusion that these "analysts are not all of one cloth and most eschew a general theory about criminal behavior in favor of specific analyses which attempt to link crime in particular historical or cultural epoches to variations in the relationship between categories of persons and the means of production" seems apparent (Tittle, 1983, p. 345). Unemployment and Imprisonment. Most of the research in this area concentrates on state and federal imprisonment rather than local jail u s e . Although the primary focus of this research is to assess the correlations between the populations of correctional institutions and unemployment, a secondary goal is to be able to forecast future trends in the prisons' populations. During a conference on the "Economics of Crime and Punishment" it was stated that the rational assumptions of economic theory are not intended to describe the world; but as analytical instruments to generate testable implications (Public Policy Research, 1973, p. 2). This report goes further to postulate that: . . . the search for statistical association and for sequences of probabilities is not useless. People rationalized that eating spoiled food was often followed by distress long before they knew anything about bacteria or toxins (Public Policy Research, 1973, p. 13). 74 In a study done for the Subcommittee on Penitentiaries of the Senate Judiciary Committee requested by the Congressional Research Service it was determined there was some indication of a correlation between prison populations and unemployment rates (when unemployment rates are up, prison populations are up) (Robinson, Smith, & Wolf, 1974). This time-series analysis further showed that the changes in the prison populations lagged about one year behind the changes in the unemployment rates. In a similar study by Frank he found that the lags between unemployment and prison populations were about fifteen months (Frank, 1975). Cox also examined the relationship between unemployment and imprisonment. He explains that: . . . using the time period of January, 1967 to December, 1974, monthly prison populations figures show a pattern of annual fluctuations until early in 1972, when the population began to grow presently overcrowded size. steadily to its During these same two years, the number of unemployed showed a departure from annual cycles and paralleled the dramatic upsurge in the number of state offenders (Cox, 1975, p. i ) . The approach taken by Cox is similar to those of the other theorists, although he only examines the problem for the State of Georgia. In addition Cox's analysis departs from most research in that he determined there was almost no lag between onset of unemployment changes and state prison 75 population changes. One could suggest that his results, although methodologically sound, may be an artifact of the State of Georgia. In other similar studies (Armbust and Deloney, 1977; Brenner, 1976; Crago & Hromas, 1976; Greenberg, 1977) determined that changes in the national employment rates are consistently related with the number of admission to prisons. Waldron, Pospichal and Briggs have determined there are significant positive relationships between unemployment rates and incarceration rates over the 1971 to 1978 period; however, when they used point-in-time analysis by year they found no significant correlation (Waldron, Pospichal & Briggs, 1975, p. 16). In a study that was done in Iowa that examined the relationship between unemployment rates and prison populations. Their analyses showed that age was more significant than unemployment as an indicator (or predictor) of prison populations. Turpin, Fisher and Powers did suggest that, One objective factor probably stands out above the others as a predictor of criminality; failure to become satisfactorily and permanently established in the working world early in life. Powers, 1975). (Turpin, Fisher, & 76 In an analysis that was both national and regional, Brenner evaluated the relationship between economic indicators and unemployment. He determined there was a significant relationship between unemployment and imprisonment across the nation as well as within each region. He postulated that for each state a one percent change in employment rat e s , which were continuous over a six year period, would result in an attendant increase in state prison admissions of 3,340 inmates across the country (Brenner, 1976). Crago and Hromas analyzed the relationship between unemployment and imprisonment in an attempt to forecast future trends in prison populations. They assessed the relationship between inmate populations, unemployment rates, population at risk (males 18-24 years of age ) , and court commitments. They concluded that when unemployment is lagged about three months before court commitments there is a high correlation between the two (Crago & Hromas, 1976). In analyzing the relationship between unemployment and prison admissions in a Canadian sample Greenberg determined there is a very high correlation between the two (r = .92) for the period 1945-1959. He also found some evidence that prison admissions lag behind unemployment, as was found in the American studies (Greenberg, 1977). Jankovic determined that for the period 1926-1974 the were significant positive correlations between unemployment 77 rates and both prison population levels and prison admissions (at the state level) (Jankovic, 1977). In an analysis of federal prison populations Waldron determined there was a moderate correlation between monthly court commitments to the Federal Bureau of Prisons and monthly unemployment rates (r = .44) (Waldron, 1977). He further suggested that court commitments may be effected before the repercussions on prison populations. The social events that lead to unemployment, first seem to have an influence on court commitments to the Federal Prisons, instead of a direct relationship with the prison populations themselves. In an interesting analysis that attempted to disaggregate the national and state economic cycles or trends Marenin, Pisciotta, and Juliani (1983) examine the interaction between unemployment and incarceration during the 1958-1978 period. Although they claim that the relationship between employment and crime is undefined or that the " . . . precise impact of changes in business cycles on crime has not been clearly established." (p. 43). They contend that the relationship between unemployment and incarceration has been empirically supported. Their specific purpose was to examine further the relationship between incarceration and unemployment with the intention of uncovering significant fluctuations at the state level that may not appear in the national (aggregate) analyses. They 78 conclude that the disaggregated relationship between unemployment and incarceration " . . . reveals the interaction of unemployment, crime, and incarceration is less direct and more complex than many empirical studies and theoretical interpretations suggest" (Marenin, Pisciotta & Juliani, 1983, p. 46). Finally, they make four specific recommendations for further study that seem particularly on point to what is anticipated in the present analysis (see methods below). They suggest that the research on the relationship between economics and crime should be "context specific" (in concentrating on a discrete geographic area). Furthermore, the indices of unemployment, crime and social control need to be improved, imprisonment should not be the only measure of social control, and "reductionist" and "simplistic formulations" and specifications (as happens to be the case to date) should be reconsidered instead of the myriad of interactions in the relationship between economics and crime (Marenin, Pisciotta & Juliani, 1983). The literature suggests that the relationship between unemployment and incarceration exists. The local unemp1oyment rate will first be reflected in the local jail admissions and welfare rates, and subsequently at the state prison level which reflects unemployment and criminality. When an individual is first arrested they will be initially housed in jails (jail admission rate). 79 Summary The economic perspective posits that crime is a rationally considered behavior. Within this perspective it is further suggested that the offender considers the costs and benefits involved in the commission of a criminal act and that the goal is to achieve the most advantageous balance between the potential gains from criminality and the potential loss for being apprehended and incarcerated. The underlying assumption is that people are hedonistic as they calculate the potential pleasure from criminal activity and simultaneously attempt to avoid the pain of apprehension and conviction. In the end, the solution to the problem of criminality for the economists is to increase the costs of engaging in crime. The rational economic perspective also maintains that the wage for crime is constant; therefore, there are little variations in the benefits side of the equation. Although this theory seems v i able , there is some question about its practical significance. There are costs that are not measurable (i.e., victims loss of sense of security, society's loss, e t c .). The "utilitarian" presumptions of the economists have questionable application to understanding criminality. The economic analyses simply claim there is a relationship between unemployment and criminality, based on the reasoning of "classical" criminological theory. 80 The economic perspective does discuss the urban labor market theory in a specific way. It purports that the urban labor market can be subdivided into the primary and secondary labor markets. The primary market consists of "good jobs at good wages," whereas the secondary market is one in which the quality of the employment is largely of mere subsistence levels. The secondary market jobs typically pay minimum wag e s , provide little job security, and little chance for advancement. In an extension of the labor market theory Harrison supposed that the labor market could be further divided into four sectors. The labor market theory allows one to examine the basic structure of employment in the urban area. It also permits one to suggest that criminality results from lack of participation in the primary labor sector. Certain theories that presume to examine the relationship between business cycles and crime have also been examined. These analyses study the interrelationships between the macro-economic conditions and criminality. In a very general sense these analyses appear to have found similarities in patterns of crime and business cycles in the United States. The criminologists have relied largely on deterrence theory, and suggest there is no exclusive empirical relationship between criminality and economic conditions, as it has currently been tested. Most of the early theorists 81 suggested that criminality is the result of several factors, labor force participation merely being one of these. The social problems that lead to crime are broader, for these scholars, than what the economic perspective would presume. For example Durkheim believed that economic depression may lead to individual and collective "anomie" that in turn may lead to greater levels of criminality. The relationship between the labor market and criminality, at least for these theorists, seems indirect. In addition there have been several theorists that have examined the relationship between the level of labor force participation, most typically unemployment rates, and the imprisonment rates. The overwhelming majority of the studies in this area suggested that there is a relationship between the unemp1oyment and imprisonment. Although there is no consensus about the response time of the incarceration rate (e.g., some suggest no lag period whereas others suggest as much as a 15 month lag), it is clear that most of these studies have found a significant relationship. In summary then, the majority of the literature acknowledges that the relationship between criminality and the economic situation is possible, although for different reasons. The economic perspective is clearly of a "classical" orientation, whereas as the criminological perspective is "positivistic" in orientation. The 82 differences come in terms of approach to analysis or empirical verification. Conclusion and Conceptual Framework The results of this review of the literature demonstrates that there has been consistent disagreement among scholars on the relationship between economic factors and crime. Economists reason that there is a relationship between employment and crime. classical economic theory. This contention is based on This theory assumes that individual criminals consider the costs and benefits of committing a criminal act, and offenders balance the likelihood of both apprehension and punishment against the benefit of committing an offense. It is important to note that the costs of committing a crime vary for different individuals. For those who are unemployed, or marginally employed, the costs would appear to be less (they have less to lose) than for those who are employed. Although this point is generally ignored in the literature it may be responsible for the inconsistencies in much of the crime and unemployment rate literature. However, criminologists assert that any relationship between employment and criminality is limited or non­ existent. These theorists suggest that although employment may be a factor in the relationship it is not the only, or most important, factor. As stated above, these theorists claim that crime is caused by a number of factors. 83 This analysis examines the relationship between employment and incarceration rates. The research literature on unemployment and incarceration has consistently found a relationship between incarceration and unemployment. There has been virtually no conflicting results in the research which has examined the relationship between incarceration rates and unemployment rates. Additionally, the examination of the relationship between unemployment rates and incarceration rates seems important from a policy perspective. The relationship between employment and incarceration does have specific implications for correctional practice. One could argue that if the jail admission rate increases proportionately with the unemployment rate that during labor market atrophy jail administrators may need to develop alternative programs to reduce the utilization of their jails. Jails are also the main processing agency in local criminal justice systems. Increased demands placed on such agencies and facilities, without altering their capacities, will also directly affect the ability of the courts to sentence. In addition, incarceration rates may be a more appropriate unit of analysis than UCRs as crime rates can vary across jurisdiction and are often more a function of quirks and needs of police organizations than actual crime rates (McCleary, Nienstedt, and Erven, 1982). Crime rates are not accurate measures of criminal justice system 84 response to changing conditions or changes in the economic situation. Jail admission rates, however, are a specific indicator of how a major component of the criminal justice system responds, in an official way, to changes in its environment. The research literature examining unemployment and incarceration rates focused almost entirely on prison populations. Jail populations, specifically jail admission rates have never been used as the unit of analysis in similar research. The focus of research on jails has been neglected in much of the research literature in criminal justice and criminology (Klofas, 1987; Welch, 1989; Mays and Thompson, 1988; Stojkovic, P o p e , and Feyerherm, 1987). Klofas quite perceptively argues "despite the obvious social significance of the jail, few explanations of its neglect in the criminal justice research are available" (Klofas, 1987, p. 403) . This oversight is an important one. Given that a greater number of people are processed through our nation's jails than through the prison system (Kalinich, 1990), it seems rather ill-advised to ignore these agencies and facilities. It is also important to note the significant differences between jails and prisons. the state and incarcerate felons. Prisons are run by Moreover, the typically term of incarceration in state prisons is one year or m o r e . Prisons draw their budgetary support from the state and have 85 a number of programs and activities. hand, are local facilities. Jails, on the other Jails house both individuals who have been sentenced and those who are awaiting trial. The typical jail does not house an individual over one year and provides a physical location for incarceration sentences for misdemeanants. Jails draw their financial support from the local county governments and, therefore, generally have few programs or activities for inmates. Moreover jails have been suffering severe overcrowding and demands during the recent return to a more punitive ideology in sentencing (Goodstein & Hepburn, 1985). The number of citizens that are processed through jails in this country is quite large. In 1984, the average daily population in this nation's jails was estimated to be 223,552 but over 8 million offenders were admitted to jails that year (Bureau of Justice Statistics, 1984). prisons had a population of 419,820 in 1984, Although (Bureau of Justice Statistics, 1984) the rate of release from prisons is much less than jails due to determinate sentencing and parole policies (Duffee, 1989, p. 38) turnover in inmates as in jail. In addition, jails tend to have a more rapid turnover in their population because of shorter sentences and releases after booking. The distinction between jails and prisons is crucial to the analysis of the relationship between criminal justice response and employment and social welfare. Jails process 86 individuals prior to their long-term prison incarceration, and they also process misdemeanants who would not normally go to prisons. Hence, the jail admission rates are a more direct measure of the criminal justice system response for the majority of individuals who come in contact with the criminal justice system. In fact, prisons reflect only a small part of crime due to the criminal justice screening process (Cole, 1990). On the other hand almost all individuals arrested will be booked into the local jail. Moreover, the key indicator of the criminal justice system response are the jail admission rates. Although criminal justice system response will eventually be reflected in the prison populations, it is more direct and immediate at the jail level. These rates show the response to both felons and misdemeanants in a dynamic manner. Total jail population counts would not provide a response measure as they are constrained by the capacity of the jails and any court orders which restrict such populations. This analysis also attempts to contribute to the literature which has been developed in the past by adding the considerations of labor market theory. The use of social welfare rates adds another important dimension to the relationship between jail incarceration and employment. social welfare rate may also contribute to the possible social response mechanisms available to the state. The The 87 legitimate labor market responds to changes in demand for labor by reducing production through unemployment. This surplus labor market is then more available for illegal and quasi-legal labor as well as having more leisure time to engage in domestic violence. The local government and criminal justice system, through the use of incarceration in the local jail and the payment of social welfare, would also respond to the growth of the surplus labor market. Therefore, one can argue that both jail incarceration and social welfare rates should vary inversely with the employment rates across different areas. The state can respond to unemployment, at the local level, through either increasing its incarceration, possibly increasing its attempt to deter, and/or it can provide an increased level of social welfare benefits and payments. The present research utilizes a time-series design. This approach is necessary given that the focus here is to examine the effects of changes in jail admission and social welfare rates and whether they coincide with changes in employment rates across twenty-four urban counties from 1980 through 1986. counties. The sample selected is twenty-four urban Urban counties were selected given that the jail populations and social welfare rates were sufficiently variable, and the sample sizes were large enough to provide meaningful results. In addition, these counties, although all defined as urb a n , are clearly different in terms of 88 their employment bases. They allow an examination of the hypothesis that there are differential jail and social welfare utilizations (in response to employment changes) across different types of areas. This allows one to consider the potential differences or similarities in employment basis in different types of county criminal justice system responses to changes in the labor market. This research, therefore, will examine the relationship between fluctuations in employment rates, social welfare rates, and jail admission rates in 24 urban counties in Michigan between 1980 and 1986. The counties selected are all urban and all have some level of industrial production. Utilization of counties with differing commerce and industrial qualities also will allow the research to examine the extent to which the degree and kind of industry and commerce may effect the change in employment or the criminal justice response. The relationships that are anticipated are that as employment rates decrease, social welfare will increase. jail admissions and CHAPTER 3 METHODS Introduction. This analysis examines the interrelationships between labor force participation rates and jail admission and social welfare rates. employment, The jail, and social welfare rates for each of twenty-four urban counties in the State of Michigan will be examined in a qualitative, graphical, and statistical manner. The qualitative information is based on the "types" of economic base in each county, from manufacture based economies to diverse economies with manufacture, government, and services based on economics. Once these characteristics have been assessed, each of the three indicators will be examined graphically. Finally the interrelationship of the employment rate with both the jail and social welfare rates will be assessed. The relationships that are anticipated are that as the labor force participation increases jail admission rates will decrease. Similarly, the social welfare index would also respond in an inverse fashion to the labor force changes. Thus, the jail admission and social welfare rates are being examined as dependent upon the employment rate over time in twenty-four urban counties 89 90 in the State of Michigan. This analysis also examines the similarities and differences in the relationship across a classification of counties. Research Questions. The literature and conceptual framework result in the following research questions: Question O n e : To what extent is there a relationship between employment rates and iail admission rates in twenty-four urban Michigan counties between 1980-1986? Question T w o : To what extent is there a relationship between employment rates and social welfare rates in twenty-four urban Michigan counties from 1980 through 1986? Question Three: To what extent is there a relationship between iail admission raites and social welfare rates in twenty-four urban Michigan counties from 1980 through 1986? Definition of Ter m s : The following section describes the relevant terms from the research questions listed above. Jail Admission R a t e s : The jail admission rate is the number of individuals that are admitted each month to the local jail as the result of being arrested and formally processed to the number of all individuals that have been incarcerated in the jail during each mon t h . The admission rates were divided by the total jail populations in order to control for overall jail utilization (i e ., number admitted/total jail population = jail admission rate). Individuals that are formally process may be released on bond status immediately, or shortly after being formally processed, and hence, do not necessarily become a part of the incarcerated population. The division of the number 91 admitted by the total jail population also allows a more appropriate comparison of the admission rates across the twenty-four jails given that the total jail utilizations are somewhat different. The incarcerated jail populations were stable about their means from 1980 through 1986 for all counties in the study. The use of the incarcerated jail population as the base for the rate, therefore, provides a consistent base over t i m e . Although the total jail populations were consistent over time, the differences across the twenty-four counties was controlled for through the calculation of the jail admission rates. In addition, the United States Bureau of the Census yearly county population estimates show that the general populations in the counties studied increased by between 2 and 3 percent during the time period of the study. Therefore, due to the stability in the incarcerated jail populations and the general populations, it can be assumed that the number of individuals brought into the local jails and formally processed is not substantially affected due to changes in the general populations of the counties. Employment R a t e : The employment rate is the proportion of individuals in the civilian labor force of each county that are employed. Individuals are employed if they are contributing to unemployment compensation benefits. The civilian labor force is the total number of adults over the 92 age of sixteen which make up the county's potential labor force. The number of employed individuals is then divided by the total number of members of the civilian labor force, for each month. The employment rate is the proportion of the civilian labor force that is employed each month in each county from 1980 through 1986. Employment rates are relied on instead of unemployment rates. Employment is assumed to be a more appropriate measure of the labor force than official measures of unemployment. Official unemployment simply measures those individuals who have signed-up for unemployment benefits. It does not include those who have stopped looking for work, or fail to sign-up (discouraged workers). The employment figures on the other hand, are based on the rate of compensation paid to the Michigan Employment Securities Commission by the businesses for workmen's compensation and reports the MESC (Michigan Employment Securities Commission Report, 1988). Therefore, the latter indicator is much more precise than unemployment. Although the county employment rate could be affected by in-migration, cohort a g e s , or populations that reside outside the county, one can argue that it is a much more valid indicator than the unemployment rate. Social Welfare R a t e ; The social welfare rate is a combination of general assistance and Aid to Families with Dependent Children Unemployed (A F D C U ). These programs are 93 measures of the proportion of the people receiving general assistance and/or AFDCU out of the total number of individuals in the civilian labor force. General assistance is given to single individuals who have no income, whereas AFDCU is given to families with children where the provider(s) are unemployed. Therefore, the social welfare rate is a measure of the proportion of individuals in each of the twenty-four counties, by mon t h , that are collecting either general assistance or A F D C U . Question Four: Are there differences in the relationships between employment rates, iail admission r a t e s . and social welfare rates between counties with differing commerce and industrial bases? This question examines whether the relationship is viable in the very urban heavy manufacturing counties and less so in the more diversified, and less dense, counties. The counties that have been included in this analysis, although all urban under the United States Bureau of Census Definition, can be further classified through an assessment of their population densities and economic activities. The classification of the twenty-four counties results in a three level typology (See Analysis Section). Moreover, although most counties in the State of Michigan derive their economic production from manufacturing, there are differences in the intensity of that activity including less manufacture based, or "service," activities. The first type of counties are the highly u r b a n , heavy manufacturing 94 counties. These counties are labeled "Dense Urban Factory Counties." In these counties there is a very high population density (relative to the other counties in the sample), the primary economic activity is that of manufacturing, although there are other economic activities. These "Dense Urban Factory Counties" have over 3% of the state population each. population is 9%. The total percent of the state The second type of counties are those that have moderate to high population densities, and have somewhat more diverse economic activities than in the Dense Urban Factory counties. This second group of counties are labeled "Urban Diversified Counties." Although this second type of county possesses substantial manufacture activities, they have several oth e r , and more diverse, employment as well. Additionally, this second group of counties have between 2% and 3% of the state population. The third type of counties in this study are those counties with relatively lower population densities (within the sample). These counties have been labeled "Suburban Primary Industry Counties." Although these counties also have some manufacturing, their economies are not always based primarily on that activity. These counties have a primary economic activity and a few lessor industries or activities. This third group of counties has less than 2% of the state population. 95 Ana lysis. The analysis that will be used in this study is both qualitative and quantitative. Basic descriptive assessments of the counties involved in this study will be done in addition to a time-series modeling of the relationships. The first step in this analysis is to describe each county involved in the sample. This is done to bind the graphical and statistical analyses in the practical reality of the economic phenomenon of labor force participation. Through an assessment of the jail admission and social welfare rat e s , each county will be studied for a relationship between the two measures of state social response and labor force participation. Second, the initial data analysis step with be to identify the past patterns in the data. This can be accomplished, initially, through visual presentation of the past series. Although visual inspection alone may not always be sufficient to discern underlying patterns in the data, it provides nonetheless a logical starting point in the analysis. Third, the series will be estimated to minimize error and estimate the parameters over time. The observed series will be modeled, to control for spurious influences and to examine the ’’pure” relationships between the labor market participation and the jail admission. Fourth, regression models will be estimated based on the observed values, and the fit of the m o d e l . The values 96 will be generated based on the formula (model) and certain assessments can then be made about the relative contribution or effects of labor force participation on social welfare, and jail incarceration. Last, the accuracy of the dynamic process also will be evaluated and estimated. This analysis will use a procedure that corrects the ordinary least squares linear regression approach for the effects of serial correlations in the observations. The Yule-Walker method, relied on herein, is selected because it performs an estimation of the parameters with an application of the generalized least squares method that is more appropriate to time-series data than the linear approach (Gottman, 1984). The years selected were for practical and conceptual purposes. In order to construct a consistent data set across the three governmental sources 1980 was selected as the initial year in which all three data sets were consistently recorded. In addition, the urban counties were selected because the jail populations in less urban counties would not provide a sufficient number or variation to examine their potential response to employment over time. Study Sample. The sample of counties that is used in this analysis is based on the United States Bureau of the Census definition of an urban aggregation called the Standard Metropolitan Statistical Area (See Map 1). concept is the most discrete level of aggregation for This 97 classification of the 83 counties at the state level, into urban and non-urban areas.7 Moreover, two urban counties were excluded due to the lack of data. Wayne County was excluded because the jail population data was unavailable for the period studied. In addition, Monroe County was also excluded because of its proximity and inclusion in the Toledo metropolitan area, in addition to the lack of data for social welfare and jail rates across the study period (See Map 1). The urban counties were selected because they contain 80% of the population of the State of Michigan. Although an urban versus non-urban comparison would be informative, and perhaps an extension of the examination of the social response theory constructed above, it does not further the analysis of local corrections populations or social welfare rates and their function with local labor force participation rate s . Insert Map 1 In summary then there is a diverse selection of urban counties in the study sample. The counties are a mixed ranging from agricultural, to manufacturing, to service based economies. It is particularly useful at this point to focus on the description of the sample, in the attempt to summarize the different types of counties involved in the sample. 98 Data Collection. The data employed in this analysis were from three different agencies in the State of Michigan. To link jail populations with both employment and social welfare rates over-time the jail populations 1980 through 1986, by month, were selected. First, the jail rates were collected from the Michigan Department of Corrections. Each jail in the State of Michigan is required to supply monthly population reports (since 1979) to the State Department of Corrections. Although these counts contain various statistics regarding categories of the jail populations (e.g., general housing counts, holding counts, e t c .), the most relevant statistics in the present analysis are the monthly admission counts and the total monthly counts. These are the main indicators of intra-month change in these populations. That i s , they are purported to allow assessments of the fluctuations in jail utilization and the relationship to employment in the various urban counties. These rates were standardized using the total monthly jail population of each jail across time. The employment data that is relied on are taken from the Michigan Employment Securities Commission. The total civilian labor force as well as the unemployment and employment rates were collected for the twenty-four counties in the sample. Finally, the social welfare data was provided by the Michigan Department of Social Services Data Reporting Section. The data included here consisted of "Aid 99 to Families with Dependent Children: Unemployed" "General Assistance" (GA). (ADCU), and The ADCU program component includes families in which both parents are present but the children are deprived because of the parent's unemployment. The ADCU transfer payments are issued bimonthly after a twoweek period. One final note regarding the ADCU payments is that it is an indicator of both state and national economic transfer in that 50% is from the federal financial sources and 50% is from the state level sources. General Assistance is a program intended to provide financial aid primarily to unemployed single adults, widows and childless couples. In some situations; however, needy families may not qualify for A F D C U , e . g . , the children are neither deprived or living with a specified relative, and may be eligible for GA. These family cases account for less than 10% of the GA caseload. The Michigan GA transfer payments are also bimonthly allotments. The count that is relied on here is the "official" GA caseload used for departmental budgeting and legislative appropriation. Last, GA payments are 100% state funded programs. Summary. Given the somewhat limited data that has been relied on in the p a s t , this analysis improves the examination of the aggregate relationships between incarceration and the economic situation. As stated above, the reliance on the Uniform Crime Reports has been somewhat universal in the past, yet the potential problems, or 100 possibilities for improvement, of the measures of criminality have not been considered. This analysis focuses on the relationship between changes in the emstudy ployment rate and changes in jail admissions. This study examines the influences, and counter-influences, of the labor market situation and the relationship with the need for social response, in the form of local incarceration and social welfare payments is assessed between 1980-1986 in twenty-four urban counties in Michigan. of Michigan State MUSKEGON the OCEANA OTTAWA SAGINAW r KENT IONIA SHIAWA­ SSEE GENESEE LAPEER ST. CLAIR Urban Counties in CLINTON BARRY EATON INGHAM LIVINGSTON OAKLAND VAN BUREN KALAMAZOO BERRIEN CALHOUN JACKSON WAYNE ll/ASHTENAW MONROE MACOMB INSET CHAPTER 4 RESULTS The results of this study will be presented at this point. The plots of the individual county rates, for jail admittance, employment, and social welfare, will be discussed followed by a test of the hypothesized relationships which will allow the assessment of the relationship between the patterns in the jail and social welfare rates and labor force participation. For each individual county the initial presentation of the results will be a qualitative description of the county. That i s , each of the twenty-four counties will be described in terms of its labor force. This section will be followed by a graphical presentation of the employment, social welfare and incarceration rates, and finally a description of the results of the regression analyses for each county. Individual County Results. Prior to a general discussion of the modeling approach utilized, each county will be examined individually. This is deemed necessary in order to establish a more cogent presentation of the types of counties involved in this analysis. In addition to the basic description, the counties are also classified based on 102 103 their main economic activities in order to more discretely describe each county and the relationships between the three major indicators in this study. The actual reports on which much of the information is gained are listed in Appendix A. Table 1 was also constructed to provide a summary of the following more specific descriptive analyses of the sample of twenty-four counties. Insert Table 1 about here Barry County. Barry County was organized in 1839 and was named for William T. Barry of Kentucky who was the Postmaster General under President Andrew Jackson. The primary agricultural activities in Barry County are dairying and the production of poultry, sheep, cattle, as well as the farming of corn, hay, whe a t , and oats. Although Barry County is landlocked it has many lakes and resorts and has one of the largest state natural recreational are a s . While Barry County is included in the sample (given that the United States Bureau of the Census defines it as an urban county), it is clearly not a densely populated county. In fact the total population of the county in 1980 was only 45,781. Table 1 demonstrates that a large proportion of the economic activity in Barry County is manufacture in nature, followed by service, retail trade, government, farming, finance, construction and forestry & fishing respectively. 104 The principal employers in Barry County range from automotive parts to archery products (See Appendix A). Although there is apparently a wide range of economic activity in Barry County, it ranks fourth in the sample in farming activities as well as fourth in the percentage employed in finance and fifth in the sample in forestry & fishing activities. Barry County is a Suburban Primary Industry County in that its economic activities are primarily farming and manufacture and the population is relatively less dense (there are fewer cities) than the other counties in the study. Figure 1 shows the plots for the jail admission, employment, and social welfare rates for the 84 study months (January 1980 through December 1986) for Barry County. Insert Figure 1 about here The general patterns indicated by this graph are that they meanders about a mean rate. There appears to be little overall seasonal, cyclic, or -4;rend variations in these rates. The pattern in the jail admission rate appears to fluctuate about an average of approximately 8% of the total monthly population. Although the overall perceptions one gleans from an examination of the jail admission plot seem to suggest little in terms of significant trends there is clearly a cyclical fluctuation based on yearly cycles. That 105 is, there appears to be, within each year, a rise to mid­ year and then a subsequent decrease to years-end. Although the magnitude is dissimilar across the seven years, this pattern is somewhat consistent. The employment rate, interestingly enough, appears to have two basic trends. First, the employment rate, in Barry County, is decreasing from approximately 92% (of the Civilian Labor Force) employed in 1980 to 79% employed in April of 1984. The second trend can be described as a rise from 79% in April of 1984 to approximately 92% in 1986. It is very interesting to note that although the jail rates appear to be somewhat stable over the study period, the largest peak in the rates is shortly after the largest drop in employment (in 1983) in the study period. The social welfare rates provide virtually a mirror image of the employment rate, although they seem to be a bit more stationary than the employment rate. The response (or the changes in the patterns) in the social welfare rate seems to be similar to the decrease in employment in 1983. The trend in the social welfare clearly compensated for the drop in employment between month 22 and 50, yet it was approximately stable about the mean beyond that period. The overall impression of Figure 1 is that in Barry County the patterns in the jail admittance rate and the employment and social welfare rates are somewhat similar. In addition, relative to other counties Barry county appears to have lower than average jail admission 106 (JAIL), employment (EMP), and social welfare (SW) rates. The regression analyses of each the jail admission (JAIL) and social welfare (SW) rates will be discussed. Equation 1 indicates that although the data fit the model, the employment rate only explains approximately 1% of the variation in the jail admittance rate (See Table 5 for summary of all regression equations). (1) JAIL = -1.64 + (-.4841 EMP lag 1) + (-.3023 EMP lag 2)* (.1144) (.1279) *JAIL = Jail Admission R a t e . EMP lag 1 = Employment Rate at a one month lag. EMP lag 2 = Employment Rate at a two month lag. In addition there are significant negative parameters at one and two month lags. The relationship demonstrated here is that when employment rates increase the jail rate decreases at both one and two months. The model i s , therefore, consistent with the postulated social response relationship. That is, employment is negatively associated with local incarceration rates. For social welfare rates in Barry County the employment rate explains nearly 14% of the variation, over time (See Table 5). (2) SW = -.21 + (-.2683 EMP) + (-1.1545 EMP lag 1)* (.0772) (.1135) *SW = Social Welfare Rate. EMP = Direct Employment Rate parameter. EMP lag 1 = Employment Rate a one month lag. 107 This result indicates that there is a direct response in the social welfare rate and employment. That is, when employment increases social welfare decreases slightly, and in the second month of increases in employment, social welfare decreases dramatically. Once again this result is consistent with the state social response function postulated above. The analysis of the influence of empolyment rates and social welfare rates indicates that there is a direct influence of social welfare on employment rates such that the model explains approximately 18% of the variation in the employment rate. In addition, there is a significant lag parameter at one m o n t h , which indicates that as employment increases the combination of both measures will decrease at a one month interval. (3) EMP = .76 + (-.5759 SW) + (-.9613 JAIL/SW lag 1)* (.1540) (.1153) *EMP = Employment R a t e . SW = Social Welfare Rate JAIL/SW lag 1 = Interaction Effect of Jail Admission and Social Welfare Rates at a one month lag. Based on this result one could state that changes in state social response may influence the employment rate over time. Bay County. Bay County was organized in 1857, and named after the fact that it encircles the lower Southwestern portion of Saginaw Bay (Michigan Department of Commerce, 1986). In addition the county is know as one of 108 Michigan's two major potato producing counties and one of the top four for sugar beets. In addition, Bay County also ranks high in the production of beans, and the major grain crops are corn and w h e a t . Bay City (the largest city in Bay County) has a economic base which is rather diverse which includes: manufacturers of petroleum, cement, chemicals, potato chi p s , beet sugar, automotive parts and heavy machinery in addition to being an international seaport (Michigan Department of Commerce, 1986). The principal economic base employers in the Bay County area appear to be of five basic types; automotive, specialized machinery, health, chemical, and clothing. In addition, based on the employment by sector it is clear that Bay County is largely a service employment area followed by manufacture, retail t r a d e , government, finance, wholesale tra d e , farming, construction, forestry & fishing, and mining respectively. Bay County ranks third in the percentages in all counties in this study for both retail and wholesale trade employment, as well as second for the percentage employed in mining. Hen c e , the general employment characteristics of Bay County are apparently oriented toward its location on Saginaw Bay and the availability of the opportunity for service and trade which that location provides. Last, Bay County ranks fifteenth in the sample in total percentage employed. Bay County is a Suburban Primary Industry County in that it has a relatively low population 109 density and a somewhat not overly diverse, and the population density is low relative to the other counties in the sample. The patterns for the jail admittance, employment and social welfare rates for Bay county are shown in Figure 2. The jail admittance rate indicates that there is a general downward, albeit wildly variable, trend, although it is difficult to assert with any confidence that the visual pattern is significant. However, there does appear to be a cyclical pattern in the admittance rate in that during the study period (excluding 1981) the rate rises to a peak at mid-year and then decreases in the second half of the year. Insert Figure 2 about here The employment rate over the study period appears to be consistent with the pattern demonstrated in the jail admittance rate for 1982 to 1985. There is a cyclical similarity in the employment rate with the jail admittance rate. The general trend in the employment rate for Bay county demonstrates a consistent upward trend over the past seven years with an apparent stabilization of employment in 1986. The social welfare rate in Bay County, however, is rather interesting in that it is inconsistent with the patterns of jail admission rates or employment rates. jail admittance and employment demonstrate a repeating While 110 cyclical pattern, the social welfare rate decreases from 1980 to 1981, fluctuates wildly from 1981 to about 1985, and stabilizes 1985 through 1986. The overall patterns for the three rates in Bay county are somewhat unclear in a relative sense. Given the somewhat consistent fluctuations in each of the three rates from 1980 through 1986, one cannot readily discern, by ocular examination whether the changes in either employment or social welfare are reflected in similar changes in the jail admittance rates. Relative to the remaining 22 counties in the study, however, Bay county has a higher than average jail admittance rat e , a higher than average social welfare r a t e , and a lower than average employment rate. The results of the regression analyses for Bay County is shown below. The model which specifies jail admission rates as a dependent variable demonstrates that only .02% of the variation in the jail admission rate can be explained by the employment rate (See equation 4). There is a significant lag parameter at one month which indicates that as employment increases the jail admission rate decreases. (4) JAIL = 3.91 + (-.4519 EMP lag 1)* (.1145) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. Therefore, for every unit increase in the jail admission rate there is a -.4519 decrease, at one month lag, in the Ill employment rate. The theory that employment rates influence state social response is therefore supported by this result. The response of the local criminal justice correctional system, as measured by jail admission, is consistent with the direction of the relationship found and postulated in theory. The model of the social welfare rate and employment indicates that employment has two significant parameters. Equation 5 also explains approximately 7% of the variation in the social welfare rate to be explained by the employment rate. Unlike Barry County, the relationship for Bay County indicates that there is a direct relationship with employment followed by a negative relationship at a one month lag. That i s , as employment rates increase social welfare increases, followed in the second month by a significant negative relationship between the two. (5) SW = 6.39 + (.2034 EMP) + (-1.2052 EMP lag 1) (.0857) ( .1121) *SW = Social Welfare Rate. EMP = Employment Rate. EMP lag 1 = Employment Rate at a one month lag. The model in this case is somewhat inconsistent with what is presumed conceptually. The social welfare rate in this case appears to be similar to the employment rate in good times, and then responds with approximately a six times greater rate of service at a one month lag. One would expect both partial correlations to be negative in direction. 112 The model with employment dependent upon the two measures of state social response allows the explanation of 4% of the variation in employment. There is also a significant lag parameter at one m o n t h , which indicates that as employment increases the use of state social response decreases. (6) EMP = -3.65 + (-.8072 JAIL/SW lag 1) (.1175) *EMP = Employment R a t e . JAIL/SW = Interaction effects of Jail Admission and Social Welfare Rates at a one month lag. This model indicates a consistency with that proposed by the theory of state social response: as employment increases jail admission, and social welfare rates decrease. In addition, it makes intuitive sense that the jail admission rates and the social welfare rates lag behind the employment changes by at least one mon t h . Berrien County. Berrien county was organized in 1831 and was named after John M. Berrien of Georgia, who was the Attorney General under President Andrew Jackson. The climate advantages of the Berrien County area, near Lake Michigan and its Southern location in the State have made it a prime location for the growing of fruit. According to the Michigan Department of Commerce Berrien County ranks in the top two in Michigan for grapes, the top three for apples and top four for tart cherries, and is among the state's leading producers of blueberries and sweet cherries. Berrien County 113 also has a rich historical tradition of prominence in the region in that it was under the rule of four nations (Spanish, French, British, and the United States). Of particular note is that the Southern part of the county has at its doorstep the cultural (sic) and entertainment facilities of the entire South Bend-Mishawaka area with its four universities and lecture, dra m a , m u s i c , and arts activities (Michigan Department of Commerce, 1986). The principal economic base employers are dominated by appliances, automotive par t s , and electronic equipment (computers). Table 1 demonstrates that employment in Berrien County is largely in manufacturing, followed by service, retail trade, government, farming, financial, wholesale trade, construction, forestry & fishing and mining respectively. Berrien County ranks sixth in the sample for forestry & fishing employment in addition to ranking fifth in mining and fourth in wholesale trade employment. Finally, Berrien County ranks tenth in the sample in the proportion of the civilian labor force in the entire sample in that coun t y . A final comment about Benton Harbor (one of the principal cities in Berrien County) is that it has suffered somewhat severe economic setbacks in recent times given the movement of industry to more "attractive" climates and areas. Although the city of Benton Harbor has been the cite of intense revitalization efforts, the effects of these 114 activities have yet to materialize in substantive changes in the bleak economic outlook for that city. a Suburban Primary Industry County. Berrien County is The main urban area, Benton Harbor/St. Joseph, is primarily a manufacturing center in the county with the remainder of the county engaged in lessor economic activities. The population density of the county is also low relative to the other counties in the sample. Figure 3 shows the patterns in the three rates for Berrien county. The jail admittance rate for the study period indicate that there appears to be both a cyclical and a trend pattern in the d a t a . There is a rise from the beginning of each year to a peak at mid-year followed by a decrease to years end. Although this pattern is not as clearly demonstrated as in Bay and Barry counties, it is nonetheless appears to be present. In addition, the cyclical patterns seem to be decreasing in magnitude over the seven year period from a high rate of approximately 7% in 1980 to a low of 5% in 1986. The employment rate rises through October 1981, decrease through January 1983, and increases through December 1986. Although the employment rate does not seem to be similar to the jail admittance ra t e , in terms of cyclical patterns, the general trends are inverse; as employment has risen, in general, since 1980 the jail admittance rate has decreased, which is consistent with the postulates of the economic and incarceration theories. 115 The social welfare rate indicates a very stable pattern over the study period. Although there is a drop in 1981, coinciding with the rise in employment in that same year, it is a stable downward trend over the past seven years. However, the social welfare rate appears to be somewhat high relative to the Civilian Labor Force (C L F ) . Insert Figure 3 about here The three patterns seem to indicate the employment and incarceration rate relationship. That is, as employment generally increases jail admission decreases, and social welfare decreases. Relative to the other counties in this study Berrien county has a higher than average jail admittance and social welfare rate and a lower than average employment rate. The results of the regression analyses indicate that while the model fits the d a t a , employment explains merely .03% of the variation in the jail admission rates. Although the model fits the data one cannot be reassured by such less than robust indicators. (7) JAIL = 5.27 + (-.3378 EMP lag 1) (.1138) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. 116 This equation indicates that for every unit increase in the employment rate there is a -.3 378 decrease in the jail admission rate one month latter. Clearly the jail admission rate is the dependent variable, in this case, and therefore it would appear that the model is consistent with the social response hypothesis. state In addition, The model for social welfare and employment was not significant. If would appear that the state social response function for Berrien County is somewhat weak. The reciprocal relationship between employment and the two measures of state social response explains approximately 3% of the variation in labor force participation. (8) EMP = -.80 + (-.8847 JAIL/SW lag 1) (.1156) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. For every unit increase in social response there is a -.8847 decrease in employment. This result also appears to be consistent with the theory, although somewhat weakly. Calhoun County. Calhoun County was organized in 1833 and was named for Vice President John C. Calhoun. Calhoun county ranks in the third twenty percent in the United States for the total value added by manufacture. There are only 87 counties in the country which are in this class (Michigan Department of Commerce, 1986). The primary manufacture of note is in the Battle Creek area. In 117 addition to its being the center of the nation's cereal industry it is the home of various other manufacturers. The principal economic base employers are quite diversified (See Appendix A ) . The main employer is the Kellogg Company of Battle Creek followed by Defense Logistics Service Center of Battle Creek, and a number of service and manufacturing activities. Calhoun County ranks twelfth in the sample in percentage of employment as well as sixth in financial employment. The major activities in Calhoun county are manufacture, followed by service, government, retail trade, finance, farming, construction, wholesale tra d e , forestry & fishing, and mining respectively. Although the Kellogg Corporation is the main employer, a very diverse economic base is clearly present. Along with the Defense Logistics Service Center there are a number of major hospitals, Kellogg Community College, and the headquarters for State Farm Insurance. Calhoun County is a Suburban Primary Industry County by virtue of both its population density and the extent of limited diversified industry. Although Calhoun County possesses the urban area of Battle Creek, the majority of the county is more of a rural nature, therefore, this county is a suburban type as opposed to a urban county. The three patterns for Calhoun County are demonstrated in Figure 4. The pattern of the jail admittance rate appears to demonstrate an overall downward trend from 1980 through 1986. In addition, there appears to be a cyclical 118 nature in the yearly admittance rates, although somewhat different from that of the counties described above. There appears to be sub-cycles within each year although the seven years conform to the rise to mid-year and decrease to yearsend, there are sub-cycles of increase and decrease within the years. The patterns in the employment rate indicates that the employment rates decrease through approximately 1982 and then steadily increased through 1986. It is interesting to note that during the 24 (1981) through 36 (1982) month period there is a similarity in the increases in jail admittance with decreases in employment. The social welfare rate in Calhoun County appears, on the face to be rather symmetrical to employment. It increases through 36 months (1983) and then decreases through 84 months (1986). Although it somewhat mirrors the employment rate, it appears to be much more stable for 1980 through 1981, and 1985 through 1986. Insert Figure 4 about here The overall patterns in the three rates indicate, albeit weakly, a consistency with the theory that the pattern in employment rates correlates with the pattern in jail admission rates. There is a general downward trend in the jail admittance rates and an upward trend in the employment rate over the seven years examined. Relative to 119 the other counties in the study Calhoun County has a lower than average jail admission rate, a lower than average employment rate, and a higher than average social welfare rate. The regression results for Calhoun County is shown below. The result of the analysis of the employment rate and the jail admission rate allows one to explain .44% of the variation in the latter measure based on the former measure. In addition there are significant parameters at one month and four month lags in the employment r a t e . Subsequent to an increase in the jail admission rate there is a increase in the employment rat e , and at four months after an increase in the jail admittance rate there is a decrease in the employment rate. (9) JAIL = -4.19 + (.0874 EMP lag 1) + (-.2742 EMP lag 4) (.1532) (.1148) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. EMP lag 4 = Employment Rate at a four month lag. There appears to be an inconsistency with the theory that jail admission rates are inversely related to the employment rates in a social response function. The jail admissions are positively associated with employment at a one month lag, and then four months latter the relationship is negative. One would expect that the relationship would be inverse in both cas e s . 120 The result of the modelling of social welfare and employment rates explains 2% of the variation in state social response. In this instance the result is consistent with the state social response theory in that for every unit increase in employment there is a significant decrease in the social welfare rate. (10) SW = 2.36 + (-1.2933 EMP lag 1) (.1134) *SW = Social Welfare Admission Rate. EMP lag 1 = Employment Rate at a one month lag. This demonstrates that social welfare i s , in Calhoun County, dependent upon the employment rates. The relationships between employment and the two measures of social response are illustrated in equation 11. The two measures explain only approximately .5% of the variation in the employment rate. (11) EMP = .57 + (-.6448 JAIL/SW lag 1) (.1198) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Although the model is significant, it accounts for very little of the variation of the employment rate. Once again there is a consistency with the proposed model in that state social welfare response is a negative predictor of employment. 121 Clinton County. Clinton County was founded in 1839 and was named in honor of DeWitt Clinton, the governor of New York and the builder of the Erie Canal. The "flat-to- rolling" topography appear to be well suited for dairy and farming which occurs (Michigan Department of Commerce, 1986). Clinton County is another county which is an anomaly of the U.S. Bureau of Census definition of urban county. There are a few urban areas in the county; moreover, the majority of the county appears to be rural or somewhat suburban. It is significant to note that Clinton County has the sixth highest median household income in the State of Michigan as well as one of the lowest percentages of poverty. Although the major economic activity is in the service sector, Clinton County is similar to Barry County in that a large percentage of its employment is generated through farming. Table 1 demonstrates that Clinton County's employment is in farming, followed by service, government, retail trade, manufacture, construction, finance, and forestry & fishing. Clinton County ranks twenty-second in the sample for total employment yet it ranks second in the percentage of farming employment, third in forestry & fishing and second in construction employment. County is a Suburban Primary Industry County. Clinton Clinton County is primarily given the designation of urban by virtue of its being adjacent to Ingham County. It also appears to 122 have a few "primary" economic industries although some diversity also exists. The patterns for Clinton county are shown in Figure 5. In a very general sense they seem somewhat inconsistent with the counties which have been described to this point. All three rates are extremely stable over the study period. The jail admittance rate appears decrease over the study period, albeit with fluctuation. The jail admittance rates decrease from a high of approximately 22% in 1980 to a low of about 8% in 1986. The employment rate for the time period studied presents a rather curious finding. They are consistently high, that is at or above 90% of the CLF, and they appear not to fluctuate as wildly as most of the other counties. The social welfare rate also fluctuates very little over the time period examined. The rates appear to mildly fluctuate about 10% of the CLF over the 84 month period examined herein. The general impression one gleans from Figure 5 is that there is a downward trend in the jail incarceration rates whereas there is little change in the employment or social welfare rates. In comparison to the other counties in the study Clinton county has a lower than average jail admission and social welfare rates, and a higher than average employment r a t e . Insert Figure 5 about here 123 The regression results for Clinton County will now be described. The jail rates are significantly predicted by a direct relationship with the employment rate, and a lag parameter for employment at one month. Both significant parameters are negative, thus this result is consistent with what was anticipated. More significant than t h i s , however, is that the variance in the jail rate which is explained by employment is 46% (the highest explained variance in the study). It appears that there is a direct response and an somewhat equal, though diminishing, response at a lag of one m o nth. (12) JAIL = .16 + (-.6727 EMP) + (-.5767 EMP lag 1)* (.0921) (.1185) *JAIL = Jail Admission R a t e . EMP = Employment Rate EMP lag = Employment Rate at a one month lag. I The employment rate explains 69% of the variation in the social welfare rates in Clinton County. As was the case for jail admission and employment, the employment rate is directly related to the social welfare rate and there is also a significant lag parameter at a 1 month lag. This relationship is quite consistent with the theory that social welfare is a state social response function. One would anticipate the social welfare rate to be inversely related to the employment rate, and that given the types of transfer payments involved, that a direct relationship is quite possible. 124 (13) SW = 3.34 + (-1.0195 EMP) + (-.9141 EMP lag 1) (.0778) (.1131) *SW = Social Welfare Rate. EMP = Employment Rate. EMP lag 1 = Employment Rate at a one month lag. In equation 14 (below), the relationship between employment and the state social response measures allows the explanation of 74% of the variation in employment. It is interesting to note that the although the individual models suggest that employment is a better predictor of both measures of social response, the model that presumes to predict employment from the jail and social welfare rates fits even better. There are direct negative relationships between the jail and Social Welfare rates, and as is true above social welfare is approximately two times the strength of the jail rate as a predictor. (14) EMP = 1.65+ (-.2627 JAIL) + (-.4780 SW) (.0887) ( .0846) + (-.9646 JAIL/SW lag 1) (.1134) *EMP = Employment R a t e . JAIL = Jail Admission Rate. SW = Social Welfare Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Eaton County. Eaton County is largely rural in its Southern and Western Townships, although it is a bit more urban in its Northeast section where it adjoins Ingham County, and the state capital in Lansing (See Map 1). 125 Although the principal economic base employers are somewhat diversified, they seem to be primarily manufacture in nature. The recent unemployment rate in Eaton County has been as low as 6% of the Civilian Labor Force. Table 1 demonstrates the relative employment by sector of the economy. It is apparent that the employment is divided, in descending order by service, retail trade, manufacture, government, farming, finance, construction, wholesale trade, forestry & fishing, and mining respectively. In addition, Eaton County ranks seventeenth in the sample in percent of civilian labor force as well as first in employment in the finance sector. Eaton County appears to be a Suburban Primary Industry County in that its population density is relatively low, and its rather small somewhat diversified employment market. The rates for jail admittance, employment, and social welfare for Eaton county are demonstrated in Figure 6. The jail admittance rates shown a pattern of wild fluctuation, but also indicate a downward trend over the 84 months . In 1980 the jail admittance rate is approximately 10% of the total jail population. This rate decreased through about 1984 when it seems to have stabilized about a 7% rate, although it seems to have increased in the latter part of 1986. The employment rate in Eaton county is not consistent with either the other counties (described to this point) or the jail admittance rate just described. There appears to be a strong decrease 126 in employment in 1980, 1982, and 1984. From a high in 1980 of 93% employed the rate dropped to a low of 87% (a 6 percent drop in one year). In 1982 the employment rate dropped from a high of 93% to a low of 85% or a decrease of 8 percent. In 1984 the rate total of 7% decrease). From dropped from 93% to 86% (a the end of 1984 the employment rate seems to have an increasing pattern. The social welfare rates for Eaton county from 1980 through 1986 appear to be somewhat of a mirror image of the employment rates. What is clear upon examination of the rates in Eaton county is that the jail admittance rate appears to respond little to the employment or social welfare rat e s , in fact, it appears to run counter to the theory that employment and/or social welfare patterns may help explain the patterns of incarceration. The relative standing of Eaton county on the three rates is that there is a lower than average jail admission and social welfare rates, and a higher than average employment rate. Insert Figure 6 about here The results of the application of the regression models for Eaton County shows that the prediction of jail rates indicates that only about 1% of the variation can be explained from the knowledge of the employment rate (See 127 Table 2). The beta coefficient indicates that at a one month lag there is a decrease in the jail admission rate. (15) JAIL = -1.84 + (-.2362 EMP lag 1)* (.1141) *JAIL = Jail Admission Rate. EMP lag 1= Employment Rate at a one month lag. The ability of the employment rate to predict the social welfare rate in Eaton county indicates that only 1% of the variation in that rate is explained. In addition there is a one month lag in the employment rate and the coefficient is negative and is nearly five times as large as the beta for the jail rate. It appears, therefore, that the model of the social welfare function is somewhat more cogent. (16) SW = 2.19 + (-1.1506 EMP lag 1) (.1146) *SW = Social Welfare R a t e . EMP lag 1 = Employment Rate at a one month lag. The results of the reciprocal model indicate that there is a significant lag parameter at one month predicting the employment rate. That i s , the combination of jail and social welfare rates as predictors indicates that for every unit increase in the social response rates there is a -.8750 decrease in the employment rate. 128 (17) EMP = .67 + (-.8750 JAIL/SW lag 1) (.1142) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effects of Jail Admission and Social Welfare Rates at a one month lag. Genesee County. Genesee County was founded in 1836 and takes its name from the Iroquoian word meaning "beautiful valley." It was given this name after a valley in Western New York from which many of the early settlers to the Flint area c a m e . It also "ranks in the second 20 percent of all counties in the United States for total value added by manufacture; only 35 counties in the entire nation are in this class" (Michigan Department of Commerce, 1986). The principal economic base employers in Genesee County are almost exclusively related to the automotive industry (See Appendix A ) . Given this concentration, it is not very difficult to understand why Genesee County has had unemployment rates in certain areas of the county (primarily the city of Flint) approaching 25% in the most recent recession. Table 1 demonstrates that the primary employment activities in Genesee County are in manufacture, followed by service, retail trade, government, wholesale trade, finance, construction, farming, forestry & fishing, and mining. Genesee County ranks fourth in the percentage of civilian labor force of the sample as well as first in the state in the percentage of manufacturing employment. is an Urban Factory County. Genesee County That is approximately 5% of the 129 state population resides in Genesee County, and the economy is nearly singularly based in the manufacture of automobiles and attendant manufactures. The patterns for Genesee county also seem rather unique in comparison to those in the counties described to this point, and those which will be examined. The Genesee county jail has been the subject of two federal court orders mandating decreases in their population. The jail admittance rate clearly demonstrates the compliance of the jail to these two occasions. In 1982 and 1985 the release rates (those released from jail) (not shown) were at a high of approximately 20% and 35% respectively. The great turnover in populations at this time would hardly be reflected in the economic indicators. Other than these two particularly volatile times the jail admittance rate appears to be quite stable about a quite moderate rate of 5 percent. The employment rate for Genesee county indicates downward cycles in 1980, 1982, and 1983. Following 1983, however the employment rate appears to rise to a high of nearly 90% in 1986. As is the case in many of the 24 counties examined in this study the social welfare rates in Genesee county mirror the employment rates, and they increase when the employment rate decreases and vice-versa. The overall impression one gains from an examination of Figure 7 is that the jail admittance rate does not seem to change in a similar pattern to the employment or social welfare rates. In comparison to 130 the other counties in this study Genesee county has a lower than average jail admittance rate, a lower than average employment rate, and a higher than average social welfare rate. Insert Figure 7 about here The results of the regression analyses for Genesee County will now be illustrated. In the model of the jail rate and employment rate, the employment rate explains approximately 2% of the variation in the jail rate. The model indicates that for every percentage change in the employment rate there is a -.5709 decrease in the jail admission rate. (18) JAIL = .45 + (-.5509 EMP lag 1) (.1167) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The model for the social welfare rates and the employment rate allows one to explain only .12% of the variation in the social welfare rates (See Table 3). Thus for every unit increase in the employment rates there is a .6648 decrease in the social welfare rate. (19) SW = .34 + (-.6648 EMP lag 1) (.1138) *SW = Social Welfare Rate. EMP lag 1 = Employment Rate at a one month lag. 131 The results for the model of employment as the dependent variable and the state social response variables as independent indicates that state social response explains only .44% of the variation in the employment rate. The model indicates that for every unit increase in state social response there is a -1.1649 decrease in the employment rate, at a one month lag. (20) EMP = .66 + (-1.1649 JAIL/SW lag 1) (.1161) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Ingham County. capital of Michigan. Ingham County is the site for the state As is true of many other state the state capitals it is not located in the largest city in the state but in the fourth largest (Lansing). In addition, the urban area within Ingham county is more-or-less concentrated in the Northwest corner of the county with the remainder of the county being rural in nature. The economic base of Ingham County indicates that there is a diversity of activities within the coun t y . The three primary areas are government, education (Michigan State University), and automotive manufacture. Although the state government is not considered an economic base activity (one which brings economic return) it employs 49,701 people in Ingham County, and is therefore a significant portion of the employment of that county. Table 1 indicates that the largest employment 132 sector is in fact the government sector, followed by service, manufacture, retail trade, finance, wholesale trade, construction, respectively. farming, forestry & fishing, and mining Last, Ingham County ranks fifth in the sample in percentage size of civilian labor force, as well as first in the state in the concentration of government employment in the state, and fifth in the proportion employed in the finance sector. County. Ingham County is an Urban Diversified The population density is relatively larger than the suburban type counties, and the economic activities in the county are quite diverse and relatively stable over time. Figure 8. The patterns for Ingham County are illustrated in There appears to be no readily discernable pattern in the jail admission rate in Ingham county. On the face it appears that the fluctuations are generally increasing, albeit at a very moderate rate. The employment rate in Ingham county, in general, appears to be increasing. There a r e , however, significant decreases in 1982 and 1984. The social welfare rate appears to be, again in a general sense, decreasing over the seven year study period. There are significant increases in the rates which coincide with the decreases in the employment rate in 1982 and 1984. The general impression one garners from an examination of these three graphs is that there are little similarities in the patterns of employment or social welfare and that of jail admission rates. In comparison with the other counties in 133 this study Ingham county has a lower than average jail admission rate, and a higher than average employment and social welfare rates. Insert Figure 8 about here The regression results for Ingham County illustrate that 4% of the variation in the jail rates can be explained by the employment rate. There are significant parameters at one and two month intervals. The lag at two months is slightly greater than half the magnitude of the lag at month. one For every unit increase in the employment rate there is a -.5007 decrease at one month and a -.2953 decrease at two months. (21) JAIL = a + (-.5007 EMP lag 1) + (-.2953 EMP lag 2) ( .1133) ( .1301) *JAIL = Jail Admission Rate. EMP lag l = Employment Rate at a EMP lag 2 = Employment Rate at a one two The results of the model for the for Ingham County does not fit the data. month lag. month lag. social welfare rates The reciprocal model of the employment rate indicates that 5% of the variation in the employment rate can be explained by the jail rate. There is a direct relationship, no lag, between the jail rate and the employment rate. That is for every unit increase in the jail rate there is a .1098 increase in the employment rate. 134 (22) EMP = a + (.1098 JAIL)* (.0554) *EMP = Employment Rate. JAIL = Jail Admission Rate. Ionia County. Ionia County was founded in 1837 and was named after a province in Greece. Ionia ranks seventh in the state for dairying, and other products of farm and orchard include apples, corn, hay soybeans, wheat, oats, h o g s , cattle, poultry, and dry b e a n s . In addition Ionia County has the highest concentration of prisons in the State of Michigan and perhaps the United States. The primary economic base employers for the county are the Michigan Department of Corrections as well as a number of automotive and non-automotive manufacturers (See Appendix A ) . Table 1 indicates that employment is concentrated in manufacture, followed by government, retail trade, service, farming, finance, wholesale t r a d e , and construction. Ionia County ranks twenty-first in the twenty four counties for size of civilian labor force, and is ranks sixth in proportion of farming and also ranks fourth in government employment across the sample. Ionia County is a type three county in that its population density is very low relatively to the other counties in the sample. In addition the orientation of employment is toward employment in the Michigan Department of Corrections and a few small manufacturers which does not approach the diverse 135 economies of the type two counties. In Figure 9 the three rates for Ionia county are shown. The jail admittance rate appears to follow the earlier described pattern wherein there are intra-year cycles or an increase which peak and then decreases through years end. very consistent in Ionia county. The general pattern is There appears to be a stable fluctuation about a 10% to 15% rate. There appear to be no significant trends, cycles, or seasonality in the overall pattern of the jail admittance rates. The employment rate in Ionia county seems somewhat inconsistent with the social response model of the jail admittance rate. A lead lag situation exists in certain months in Ionia county. For example in there is a decrease in 1980 (at years end) which is reflected in a increase in the jail admittance pattern. In general there appear to be some inverse relationships between employment and jail admittance. The social welfare rates appears to be generally increasing the across the seven years. There seems to be somewhat of a response in the social welfare rates to the employment rates, although it is clearly not direct. The jail admittance rate for Ionia county appear to correspond to the patterns in the employment rate in the expected, or theoretical way. That i s , there is an inverse relationship between incarceration and employment, and social welfare also corresponds to the anticipated conceptual relationships in that its patterns are similar to 136 those of the jail admittance rate. In comparison to the other counties in the study Ionia county has a greater than average jail admittance rate, and a lower than average employment and social welfare rate. Insert Figure 9 about here The results of the application of the for Ionia County are presented below. The regression models result of the model for the jail rate indicates that .39% of the variation in the jail rate can be explained by the employment rate. In addition, the model indicates that for every unit change in the employment rate there is a -. 4870 decrease in the jail rate. (23) JAIL = 1.79 + (-.4870 EMP lag 1) (.1143) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The model for the social welfare rate demonstrates that 2% of the variation in the social welfare rate can be explained from the knowledge of the employment rate. Moreover, for every percent increase in the employment rate there is a 1.7770 increase, at a one month lag, in the social welfare rate. 137 (24) SW = 6.46 + (1.7770 EMP lag 1) (.1135) *SW = Social Welfare Rate. EMP lag 1 = Employment Rate at a one month lag. The results of the reciprocal model indicate that 3% of the variation in the employment rate can be explained by the indicators of state social response. The model also indicates that at a one month lag for every percentage increase in the measures of social response there is a .5918 decrease in the employment rate. (25) EMP = .59 + (-.5918 JAIL/SW lag 1) (.1155) *EMP = Employment R a t e . JAIL/SW lag 1 = Interaction effects of Jail Admission and Social Welfare Rates at a one month lag. Jackson County. Jackson County is located Southeast of Lansing and is the home of the largest "walled" prison in the free world. The principal economic base employers are varied heavy and light manufacturing, although, as is true in many of the counties described so far, much of the main industries tend to be automotive, or service the automotive industry. Table 1 indicates that in Jackson County the major employment is in service followed by manufacture, retail trade, government, finance, wholesale trade, farming, construction, mining, and forestry & fishing respectively. Jackson County ranks first in the sample in the proportion of employment in mining employment, and fifth in wholesale trade. It also ranks thirteenth in the percentage size of 138 the civilian labor force across the sample. is a Suburban Primary Industry County. Jackson County Relative to the other counties in the sample the population density is rather low. Moreover, the economy is based on the Department of Corrections and some manufacture which is not characteristic of the diversification found in the Urban Diversified Counties. Figure 10 demonstrates the graphs for Jackson county. The jail admittance rate for Jackson county indicates that there is a rise from 1980 through 1981 and then a decrease to 1982 and a stable, but fluctuating, rater thereafter. The employment rate in Jackson county shows a general decrease through about 1982 and then an increase through 1986. The social welfare rate increases through 1983 and then decreases through 1986. The general impression one gains from these graphs is that the jail admission rate does not exhibit the anticipated pattern of an inverse relationship with employment nor a positive relationship with the social welfare rate. In reference to the other counties in the study Jackson county has a higher than average (for all the urban counties) social welfare rates. jail admittance and Conversely, the employment rate is lower than average. Insert Figure 10 about here 139 The results of the regression model for Jackson County show that 13% of the variation in the jail admission rates can be explained by the employment, rate. In addition, for every percentage increase in the employment rates there is a 1.8364 increase in the jail rates. (26) JAIL = 1.24 + (1.8364 EMP) (.5462) *JAIL = Jail Admission R a t e . EMP = Employment R a t e . The test of the model for the social welfare rates indicates that approximately 12% of the variation in the social welfare rates can be explained from the knowledge of the variation in the employment rates. In addition, there are significant parameters in the direct and lag periods. The direct parameter demonstrates that for every percentage increase in the employment rate there is 4.7783 increase in the social welfare rate. In the second mon t h , however, for every percentage increase in the employment there is a .3359 decrease in the social welfare rate. The reciprocal model for Jackson County was not statistically significant, or more conceptually, the model did not fit the data. (27) SW = 1.31 + (4.7783 EMP) + (-.3359 EMP lag 1) (1.5297) (.1157) *SW = Social Welfare Rate. EMP = Employment Rate EMP lag 1 = Employment Rate at a one month lag. 140 Kalamazoo County. Kalamazoo County was founded in 1830 and it derives its name from an Indian word, "Kikalamazoo,11 which is translated variously as the "mirage or reflecting river", "boiling w a t e r " , "beautiful wat e r " , "stones like otters", or "it smokes" 1986). (Michigan Department of Commerce, Additionally, Kalamazoo County ranks in the third 20 percent of all U.S. counties for value added by manufacture and only 87 counties in the entire country are in this class. An examination of the principal economic base employers in the county reveals that the employment in Kalamazoo County tends toward the service sector and the automotive industry. Table 1 indicates that the largest employment sector in the county is rnanufacturing, followed by service, retail tra d e , government, finance, wholesale trade, construction, and farming. Kalamazoo County ranks seventh in the sample in total civilian labor force, and also ranks second for all twenty-four counties in the proportion in the service sector of the economy. County is a Urban Diversified County. Kalamazoo It has a relatively high population density as well as a very diverse economy. Kalamazoo County's jail admittance, employment, and social welfare rates are demonstrated in Figure 11. The top graph indicates that the jail admittance rate is somewhat stable about a 5% rate over the eighty-four months examined. Although there appears to be some seasonal variations, the pattern is consistent throughout the study period. The 141 employment rate in Kalamazoo County decreases from 1980 through approximately 1983 and then increases through 1986. The social welfare rate seems to have a peak between 1981 and 1983 and is declining in the subsequent years in the study. Once again the general impression of these graphs is that there is little similarity in the pattern of the jail admittance and social welfare rates with that of employment and social welfare. In reference to the other counties in the study Kalamazoo County appears to have a lower than average jail admittance and social welfare rate, as well as a higher than average employment ra t e . Insert Figure 11 about here The results of the regression analysis for Kalamazoo County are described below. The results of the model of the jail admission rates was not significant for Kalamazoo County. That i s , the model did not fit the d a t a . The model of the social welfare rate indicates that approximately 20% of the variation in the social welfare rates can be explained by the employment rates. In addition, the test indicates that there is a significant direct parameter such that for every percentage increase in the employment rate there is a .1427 increase in the social welfare rate. There is also a significant lag parameter which indicates that for 142 every percentage increase in the employment rate there is a -.9588 decrease in the social welfare rate. (28) SW = .56 + (.1427 EMP) + (-.9588 EMP lag 1) (.0330) (.1145) *SW = Social Welfare Rate. EMP = Employment R a t e . EMP lag 1 = Employment Rate at a one month lag. The reciprocal model explains approximately 8% of the variation of the employment rate based on the variation in the jail and social welfare rates. The model also demonstrates that for every percentage increase in the social welfare rate there is a direct increase .2719 in the employment rate. In addition, there is a significant lag for the state social response function such that for every percentage increase in state social response there is a .2436 decrease in employment. (29) EMP = .45 + (.2719 SW) + (-.2436 JAIL/SW lag 1) (.1374) (.1145) *EMP = Employment R a t e . SW = Social Welfare Rate. JAIL/SW lag 1 = Interaction effects of Jail Admission and Social Welfare Rates at a one month lag. Kent County. Kent County was organized as a county in 1836 and was named after Chancellor James Kent, a celebrated New York jurist. It ranks in the third twenty percent of all counties in the country for total value added by manufacture. Additionally, Kent County has a the second largest urban area in the State of Michigan in Grand Rapids. 143 The principal economic base employers in Kent County are quite diverse. Kent County is a center for the manufacture of office furniture, although there are automotive manufacturing firms in the county (See Appendix A). Table l demonstrates that a major employment sector in Kent Count is manufacture followed by service, retail trade, government, wholesale trade, finance, construction, farming, forestry & fishing, and mining sectors respectively. One unique aspect about the types of manufacture in Kent County is that it appears to be much less sensitive to the economic shifts which are inevitable in the automotive industry employment counties. In the sample Kent County ranks third overall for proportion of civilian labor force, in addition to being first in wholesale trade , fifth in retail trade and third in service sector employment. Kent County is an Urban Factory County by virtue of its population density and its manufacturing orientation. Although this is the case it is also apparent that Kent County has a much more diverse economy than the other Urban Factory Counties found in the Detroit metropolitan region. Kent County's jail admittance, employment, and social welfare rates are demonstrated in Figure 12. The top graph indicates that the jail admittance rate is stable about a 4% rate over the eighty-four months examined. The employment rate in Kent county decreases from 1980 through approximately 1983 and then increases through 1986. The social welfare rate seems to have a peak between 144 1981 and 1983 and is somewhat stable in the other five years in the study. Once again the general impression of these graphs is that there is little similarity in the pattern of the jail admittance rate and that of employment and social welfare. In reference to the other counties in the study Kent County has a lower than average jail admittance and social welfare rates, as well as a higher than average employment rate. Insert Figure 12 about here The results of the application of the regression models for Kent County are shown below. The model for the jail rate allows one to explain approximately 2% of the variation in the jail admission ate based on the knowledge of the employment rate. There is one significant parameter such that for every percentage increase in the employment rate there is a -.2384 decrease in the jail admission rate. (30) JAIL = .34 + (-.2384 EMP lag 1) (.1129) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The employment model also fits the data for Kent County. The model allows for 16% of the variation in the employment rate to be explained by the measures of state social response. The significant parameter demonstrates 145 that at a one month lag, as the percentage of state social response increases the employment rate decreases -1.0004. (31) EMP = a + (-1.0004 JAIL/SW lag 1) (.3188) *EMP = Employment R a t e . JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Lapeer Countv. Lapeer County was organized in 1835 and takes its name from the French, as a derivation of "La Pierre", meaning "stone" or "flint." The principal agricultural activities are dairying and cattle raising as well as the production of hay, w h e a t , o a t s , and corn. Lapeer County's principal employers range from automotive parts to pickles to bathroom fixtures (See Appendix A ) . Additionally, Lapeer County has no concentration of urban settlements, it is classified as urban based on its proximity to Genesee County. Table 1 shows that the proportions of employment in the seven selected areas. Lapeer County appears to have a major portion of people employed in governmental activities, followed in proportion by manufacture, retail trade, service, farming, finance, construction, wholesale trade, forestry & fishing, and mining. Primary Industry County. Lapeer County is a Suburban There is a relatively low population density, and the economy is primarily based in government employment. The three graphs for Lapeer County are illustrated in Figure 13. One very striking feature about this graph is the extreme shifts in the monthly jail populations. Across the seven years involved in this study the jail admittance (and release which is not shown) approach 30% of the total jail population. The jail admission rate decreases through 1982 and then fluctuates through approximately 1983 and seems to increase through 1986. The employment rate in Lapeer County seems to wildly fluctuate through 1983 and increases through 1986 (at least back to its 1980 level). The employment rate seems to be a rough approximation of the jail admittance rate. The social welfare rate in Lapeer County increases through 1983 and then decreases through 1986. The overall impression one is left with from and examination of these graphs is that there is some similarity between the employment rate and the jail admittance rate, although it is inconsistent with what has been supposed (conceptually) for the employment and incarceration relationship. In addition, the social welfare rate seems to be quite dissimilar from the jail admittance ra t e . As is stated above Lapeer County has jail admittance rate which is higher than average (in fact it is the highest in the sample of counties). Moreover, both the employment and social welfare rates in Lapeer County are below the average of all other counties. Insert Figure 13 about here 147 The results of the regression model for Lapeer County are shown below. The result of the model testing the jail admission and employment rate indicates that approximately 3% of the variation in the jail rate can be explained from the knowledge of the employment rates. In addition, the model indicates that there is a significant parameter at a one month lag. That i s , as the employment rate increases the jail rate decreases -.6997 one month latter. (32) JAIL = 4.06 + (-.6997 EMP lag 1) (.1144) *JAIL = Jail Admission R a t e . EMP lag 1 = Employment Rate at a one month lag. The results of the application of the model to the employment model rates indicates that the state social response rates account for 4% of the variation. also a significant parameter at a one month lag. There is This model indicates that as the level of state social response increases the is a -.8645 decrease in employment. (33) EMP = -1.28 + (-.8645 JAIL/SW lag 1) (.1151) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Livingston County. Livingston County was organized in 1836 and its name was taken from the Honorable Edward Livingston of Louisiana, who was the Secretary of State under Andrew Jackson. Livingston County is one of the top 148 ten sheep counties in the State of Michigan and ranks in the upper third in milk production. One of the county's best known agricultural products is the Howell melon (canteloupe) (Michigan Department of Commerce, 1980). The principal economic base employers in 1986 were in the areas of automotive, insurance, health, metal working and fabrication, and packaging (See Appendix A for detailed listing). The employment in Livingston County is concentrated in retail t r a d e . In fact the employment in retail trade in Livingston County is the highest proportion across the sample of the 24 counties. That i s , the greatest percentage of employment is in the area of retail trade. The proportion employed in retail trade are followed (in magnitude) by service, government, manufacturing, finance, construction, farming and wholesale trade (See Table 1). In comparison to the other counties in the sample Livingston County, in addition to having the highest proportion of retail trade employment, is third in construction, second in financial services proportions of employment. Additionally, Livingston County ranks sixteenth in the twenty-four counties in the size of employed labor force. The general perception one can gain from this examination of the employment base is that the employment tends to be trade and service oriented with some supplemental automotive industry. below 10%. In addition the unemployment rate tends to be Livingston County is a Suburban Primary Industry 149 County. It has a relatively low population density in addition to being a retail center followed by service employment. Figure 14 demonstrates the graphs for Livingston County. The jail admittance rates indicate a intra-year cyclical pattern. That is, within each of the seven years there appears to be a rise to mid-year followed by a decrease through year's end. There also appears to be a decreasing trend from 1980 through 1986. The employment rate for Livingston County decreases through 1983 and increase through the remainder of the study period. One can suppose, albeit without assurance, that these two patterns (jail admittance and employment) are consistent with the theoretical supposition that employment should be inversely related to jail admission. The social welfare rate further supports this postulate in that the basic directions are somewhat similar to that of the jail admittance rate. The overall impression here is that there may be a consistency of the three graphs with that which is supposed in the theories which purport to explain the relationship between incarceration and employment. In addition, Livingston County has a higher than average jail admittance and employment rate, and a lower than average social welfare rate. Insert Figure 14 about here 150 The results of the application of the data to the hypothesized regression model are described below. The model of the jail admission rates and the employment rate indicates that only .01% of the variation in the jail rate can be accounted for by the variation in the employment rate. The significant parameter indicates that for every percentage increase in the employment rate there is a -.1997 decrease in the jail admission rat e . (34) JAIL = -.11 + (-.5997 (.1132) *JAIL = Jail EMP lag 1 = EM P lag 1) Admission Rate. Employment Rate at a one month lag. The results of the application of the observed data to the model of social welfare rate and employment did not fit the mod e l . The model of employment as dependent on state social response reveals that the measures of social response account for 5.5% of the variation in the employment rat e . In addition, there are significant parameters for the social welfare r a t e , at one month lag, and at a two month lag. That i s , there is a direct relationship between the social welfare rate and employment in Livingston County such that as social welfare increases the employment rate decreases at a rate of -.7221. The lag parameters indicate that there is a consistent, negative association, between the measures of social response and the employment rate after both one month and two month time intervals. 151 (35) EMP = -.09 + (-.7221 SW) + (-.5997 JAIL/SW lag 1) (.3760) (.1154) + (-.4003 JAIL/SW lag 2) (.1313) *EMP = Employment Rate. SW = Social Welfare Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. JAIL/SW lag 2 = Interaction effect of Jail Admission and Social Welfare Rates at a two month lag. Macomb County- Macomb County was organized in 1818 and was named after General Alexander Maco m b , and officer in the War of 1812. Macomb County also ranks in the second 20 percent of all United States counties for total value added by manufacture. Only 25 counties in the entire country are in this class (Michigan Department of Commerce, 1980). The principal economic base employers tend to be heavily concentrated in the automobile industry. Out of the top ten such industries every one is automotive or automotive related. Moreover, Macomb County appears to serve as the research base for the General Motors Corporation as fully 30,000 employees in Macomb County are employed at the General Motors Technical Center, in addition to 1,800 employed at the Modern Engineering in Warren. Clearly, the majority of the employment in Macomb County is in the area of manufacture and is concentrated in the automotive industry. The next highest proportions are in service, retail tra d e , government, finance, construction, wholesale trade, farming, forestry and fishing, and mining 152 (See Table 1). Macomb County ranks second in the sample for the proportion of employment, as well as being third in the sample in manufacture employment, and sixth in the sample in retail trade employment. County. Macomb County is an Urban Factory That is, there is a very high population density, and the primary economic activity is the manufacture of automobiles and attendant parts. The graphs for Macomb County are illustrated in Figure 15. The jail admittance rate indicates that there is a decrease from a 9% level in 1980 to a fluctuation of around 5% through 1986. The employment rate demonstrates that there is and general increasing trend throughout the study period. Conversely, the social welfare rate increases through about 1983 and then decreases through 1986. Overall it appears that these graphs demonstrate that Macomb County is not consistent with the theoretical postulation that employment and incarceration are similarly patterned. In addition Macomb County has a lower than average jail admission, employment and social welfare rates over the seven year period in comparison to the other 23 counties. Insert Figure 15 about here The results of the regression models indicate that the observed data for both the jail admission and social welfare rates and employment do not fit the data. That is there is 153 not a statistically significant model for either of these equations based on the sample relied on. The reciprocal model of the employment rate, however, indicates that approximately 6% of the employment rate can be explained by the variation in state social response (See Table 2). In addition, there are significant parameters at one and two month lags. After one month the for every percentage increase in the level of state social response there is a -.5775 decrease in employment, and at two months the decrease is -.3506. (36) EMP = 5.69 + (-.5775 JAIL/SW lag 1) (.1132) + (-.3506 JAIL/SW lag 2) (.1343) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. JAIL/SW lag 2 = Interaction effect of Jail Admission and Social Welfare Rates at a two month lag. Muskeaon County. Muskegon County acquired its name for the river traversing the a r e a . Muskegon is derived from a Chippewa word meaning "rive with marshes." The official name of Muskegon was adopted into law by the state legislature in 1859. Approximately 26% of Muskegon County's land use is devoted to agriculture and the principal products are tart and sweet cherries, blueberries, and apples with dairy products accounting for the major non-crop products (Michigan Department of Commerce, 1980). The top 154 ten principal economic base employers in Muskegon County are dominated by automotive industry support (specializing in the production of turbines). However, there are also firms which produce paper, furniture, ball bearings, communication systems, and bowling equipment. The highest proportion of employment in Muskegon County is in manufacture, followed by service, retail trade, government, construction, finance, wholesale trade, framing, forestry & fishing, and mining (See Table 1). Muskegon ranks eleventh in the twenty-four counties in employment, as well as fourth in employment in manufacture, and fourth in employment in service activities. The employment rate was about 10% in 1986. is a Suburban Primary Industry County. Muskegon County Although the economic activities would appear to qualify this county to be a Urban Diversified County, the population density is below 2% of the state total. Figure 16 shown the graphs for Muskegon County. The jail admittance rate shows somewhat of the intra-yearly cycles which have been described for the other counties, although it is not as clear as some of the aforementioned counties. In 1980 there is a great fluctuation in jail admission from a high of 7% to a low of 3%. In 1981, 1982, 1985, and 1986 there is a rise and fall over each of these years, albeit slight. In 1984 and 1985 there the intra- yearly cycles are most prominent for Muskegon County. employment rate drops through 1983 and then increases The 155 through 1986. Once again this is not consistent with the theory that jail admission is inversely related to the employment situation. The social welfare rate is also similar to the employment rate rather than what has been hypothesized. Insert Figure 16 about here The results of the regression models for Muskegon County indicate that the observed data for the jail admission rate and employment do not fit the data. That is, there is not a statistically significant model for this equation based on the sample relied on. The model of the social welfare rate and employment indicates that slightly over 1.5% of the variation in social welfare rate can be explained by the employment rate, and that at a one month lag, for every increase in the percentage employed there is a -1.4360 decrease in social welfare. (37) SW = 8.84 + (-1.4360 EMP lag 1) (.1147) *SW = Social Welfare Rate. EMP lag 1 = Employment Rate at a one month lag. The model of the employment rates indicates that approximately 2% of the employment rate can be explained by the measures of social response. There is also a significant parameter at a one month lag. This parameter 156 indicates that for every percentage increase in the measures of social response, there is a -.7889 decrease in employment. (38) EMP = -1.35 + (-.5997 JAIL/SW lag 1) ( .1132) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Oakland County. Oakland County is a one of the members of the Detroit Metropolitan area, and is the focus of much of the growth in that a r e a , as well as the State of Michigan. The principal economic base employers range from automotive assembly, communications industries, health care, retail, to cement products. As can be seen in Table 1 the majority of the employment in Oakland County is derived from the service sector, in fact, Oakland County ranks first in the sample for employment in the service sector. The service employment is followed by retail trade, manufacturing, government, finance, wholesale trade, construction, forestry & fishing, farming, and mining. Oakland County ranks first across the sample in the proportion employed in the sample, as well as second the proportions of wholesale and retail trade and third in financial services employment. Oakland County is an Urban Factory County. There is a very high population density (the greatest in the sample), and although the largest employment sector is the service 157 sector, it is apparent that there is a strong economic structure in this county. Oakland County appears to be more similar to Kent County rather than the other Detroit Metropolitan counties. In Oakland County the jail admittance rate decreased from a high in 1980 of 12% to a low of 2.5% in 1981 and then increases through 1986 (See Figure 17). The employment rate for Oakland county decreases through 1983 and then increases to 1986. Conversely, the social welfare rate increased through 1983 and decreases through 1986. The anticipated inverse pattern between employment and jail admittance rates seems to be plausible through 1981 for this county; however, after that time the patterns are not consistent with the employment incarceration hypothesis. The three rates in Oakland County compare to the other counties in the following w a y s : the jail admittance rate is lower than average across the 24 counties, the employment rate is higher than the average, and the social welfare rate is lower than the average. Insert Figure 17 about here The results for Oakland County and the regression models will now be described. The model of the jail admission rates indicates that approximately l .5% of the variation can be explained based on the knowledge of the employment rates. In addition, there is a significant 158 parameter at a one month lag. This parameter indicates that for every percentage increase in the employment rate there is a -.8147 decrease in the jail admission rate. (39) JAIL = -1.91 + (-.8147 EMP lag 1) * (.1128) JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The observed data for the social welfare rates in Oakland County do not fit the m o d e l . The employment model indicates that the knowledge of state social response levels allows one to explain 2.5% of the variation in the employment r a t e . In addition., there is a significant lag parameter at one month such that every increase in the level of measured state social response there is a -.7984 decrease in the employment rate. (40) EMP = -.11 + (-.5997 JAIL lag 1) (.1132) *EMP = Employment Rate. JAIL lag l = Jail Admission Rate at a one month lag. Oceana County. Oceana County is located on the East shore of Lake Michigan North of Muskegon County. It is primarily an rural county although it does conform to the Bureau of the Census definition of urban given its proximity to the city of Muskegon. The principal economic base employers are primarily in the food processing industries and in metal products. The highest proportion of employment is in the farming industry, 159 followed by government, retail trade, service, manufacturing, finance, construction, forestry & fishing, wholesale trade, and mining. Oceana County ranks last in the twenty-four counties in the sample in labor force. Although Oceana County is the smallest in terms of the proportion employed, it ranks first across all counties in those employed in farming and forestry & fishing as well as third in the proportion in construction employment, and fifth in the proportion in government employment. The unemployment rate, however, in Oceana County was 13.3% in 1986. Oceana County is clearly a Suburban Primary Industry County. There is a very low population density, and the primary economic activities are in food processing and agriculture rather than manufacture. Oceana County are shown in Figure 18. The results for The jail admittance rate appears to be somewhat stable about a rate of between four and 8 percent, with a notable exception in 1981. There does not appear to be significant trend or cyclical characteristics to this rate. The employment rate for Oceana County indicates that there is a intra-year cycle across all seven years similar to that found in many of the jail admission rates in other counties. employment rate seems rather stable. In addition the There does not appear to be a significant trend or cycle in employment since 1980. The social welfare rate appears to mirror (the relationship 160 is inverse) the employment rate in that there are cycles within each of the seven years in the study. The general impression from these graphs is that there is little similarity in the employment, social welfare rates and that of the jail admittance rates which has been anticipated. Oceana County has a lower than average jail admittance and employment rates, as well as a higher than average social welfare rate. Insert Figure 18 about here The results of the testing of the regression models for Oceana County will now be presented. The model of the jail admission rates indicates that only .45% of the variation in jail population can be accounted for by the variation present in the employment rate. There is also a significant parameter at one month lag which indicates that for every percentage increase in employment there is a -.3359 decrease in jail admissions. (41) JAIL = 2.89 + (-.3359 EMP lag 1) (.1133) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The model for the social welfare rates explains 2% of the variation in those rates based on the employment rates. Moreover, there is a significant lag parameter at one month 161 which indicates that for every percentage increase in the employment rate there is a -1.3352 decrease in the social welfare rate. The employment as dependent model for Oceana County is not significant. (42) SW = 12.63 + (-1.3352 EMP lag 1) (.1142) *SW = Social Welfare Rate. EMP lag 1 = Employment Rate at a one month lag. Ottawa County. Ottawa County was organized in 1837 and was named after the Indian Tribe that inhabited the territory, the contemporary county is a blend of the natural beauty of Western Michigan and a growing manufacturing (Michigan Department of Commerce, 1986). According base to the 1985 United States Department of Agriculture Statistics, the market value of all agricultural products sold by county farmers was $160.3 million - the second largest total sales figure of the 83 counties in Michigan. Ottawa County leads all other Michigan counties in the market value of agricultural products sold for nursery and greenhouse products and livestock, and poultry and their products. Important crops in Ottawa County include corn, hay, w h eat, celery, onions, and blueberries (Michigan Department of Commerce, 1986). The principal economic base employers in Ottawa County are quite diverse. There is automotive support industry, heating unit construction, 162 clock making, food processing, specialty glass, and food service equipment manufacture. In Ottawa County the highest proportion of the labor forces is employed in manufacture, followed by service, retail trade, government, finance, farming, construction, wholesale trade, forestry & fishing, and mining (See Table 1). Ottawa county ranks second in manufacture employment across the twenty-four counties in the sample, as well as fourth in forestry & fishing, and sixth in construction employment. County. Ottawa County is a Suburban Primary Industry Although Ottawa County meets the economic criteria of a diverse and relatively strong economy for a Urban Diverse County, the population density does n o t , so it is classified in the suburban category. results for Ottawa County. Figure 19 shows the The jail admittance rate indicates that there is a wild, somewhat stable (between 13% and 5%) pattern over the seven years of the study. The employment rate appears to be of a similar pattern although it does seem to be increasing from 1984 through 1986. The social welfare rate is inverse to that of the employment rate. The general impression of these graphs is that Ottawa County does not seem to conform to the theory that jail admittance and employment rates would be inversely related, or that social welfare rates would be similar to the jail admission rates. Relative to the other counties Ottawa 163 County has a lower than average jail admittance rate, and a higher than average employment and social welfare rates. Insert Figure 19 about here The results of the regression models for Ottawa County will now be presented. The model of jail admission and employment rates indicates that approximately 4% of the variation in the this measure of state social response can be explained through the knowledge of the employment rate. In addition, there is a significant parameter at a one month lag which demonstrates that as the employment rate increases the jail admission rate decreases at a rate of -.2567. (43) JAIL = .89 + (-.2567 EMP lag 1) (.1143) *JAIL = Jail Admission R a t e . EMP lag 1 = Employment Rate at a one month lag. The model of social welfare and employment was not significant. That is the data did not fit the m o d e l . The analysis of the reciprocal model reveals that the measures of state social response account for 5.6% of the variation in the employment rate. In addition, there is a significant lag parameter at one month such that for every percentage increase in state social response there is a -.7472 decrease in employment. 164 (44) EMP = 1.08 + (-.7472 JAIL/SW lag 1) (.1 2 0 0 ) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Saginaw County. Saginaw County was organized in 1835 and takes its name from the Indian "Sac-e-nong" (Sauk Town), referring to the tribe of Sauks that once lived at the mouth of the Saginaw River (Michigan Department of Commerce, 1986). The principal economic base employers in Saginaw County are concentrated in the automotive industry. 18,600 of the civilian labor force is so employed. Fully There are also a number of other major employers in the area of communications, health care, and the food and service industries. Saginaw County ranks eighth in the twenty-four counties in total labor civilian labor force. In addition, Saginaw County ranks fifth in manufacturing employment and fourth in retail trade employment in the sample. While Saginaw is an automobile manufacturing city, it is only a few miles to farmland whose output of sugarbeets places in the top four in the State of Michigan's production of the crop and is also one of the top five counties in Michigan for dry bea n s . The concentration of employment in the county is in manufacturing followed by service, retail trade, government, finance, construction, wholesale trade, farming, forestry and fishing, and mining. Saginaw County is a Urban Diversified County. Although the economic activity seems to be concentrated in the 165 manufacture of automobile, there are a number of other economic activities. In addition, the population of Saginaw County is 2.5% of the state total. Figure 20 shows the line graphs for Saginaw County. The jail admittance rate indicates there is some intra-year cycles across the seven years; however, the main pattern appears to be one of decreasing admission through 1983 and a moderate increase through 1986. The employment rate for Saginaw county shows a relatively high level of stability over the study period. Although there is a significant decrease in 1982, the employment rate seems to meander about a 89% rate over the study period. In addition there appears to be a slight increase from 1983 through 1986 which corresponds to a similar increase in the jail admission rate for that same period. It is of note to observe that the two significant decreases in employment are corresponded by increase in jail admission through 1984. The social welfare rate appears to increase through 1981 and again in 1983 followed by a decrease through 1986. These line graphs indicate that there is some similarities in the patterns with that anticipated by the employment and incarceration hypothesis. Relative to the other counties in the study Saginaw County has a higher than average jail admittance and social welfare ra t e , as well as a lower than average employment rate. Insert Figure 20 about here The results of the regression models for Saginaw County will now be presented. The model of jail admission rates and employment indicates that fully 13% of the variation in jail admission can be explained by employment. In addition, there are two significant parameters, one- which is direct and another at a one month lag. The direct parameter demonstrates that for every percentage increase in employment there is a -1.6794 decrease in the jail rate. The lag parameter shows that for every percentage increase in employment there is a -.3919 decrease in jail admission one month following the employment increase. (45) JAIL = 2.59 + (-1.6794 EMP) + (-.3919 EMP lag 1) (.5454) (.1198) *JAIL = Jail Admission Rate. EMP = Employment Rate. EMP lag 1 = Employment Rate at a one month lag. The results of the model of social welfare and employment demonstrates that 15% of the variation in social welfare rates can be explained by the employment rate. There are also significant direct and lag parameters in this model. The direct parameter indicates that for every percentage increase in the employment rate there is a 9.6761 decrease in the social welfare rate. The lagged parameter demonstrates that for every percentage increase in 167 employment there is a -.3849 decrease in social welfare one month latter. (46) SW = 20.35 + (-9.6761 EMP) + (-.3849 EMP lag 1) (2.7826) (.1198) *SW = Social Welfare Rate. EMP lag 1 = Employment Rate at a one month lag. The results of the employment model demonstrates that fully 32% of the employment rate can be explained by the variation in the jail and social welfare rates in Saginaw County. In addition, there is a significant parameter at a one month lag. That i s , for every percentage increase in measured state social response there is a -.9144 decrease in employment after one m o n t h . (47) EMP = -.81 + (-.9144 JAIL/SW lag 1) (.1149) *EMP = Employment R a t e . JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Shiawassee County. Shiawassee County was organized in 1837 and bears an Indian name meaning "river that twists about." Shiawassee County is one of Michigan's top four counties for soybeans, and it also produces oat s , wheat, corn, cattle and dairy products. The principal economic base employers are medium to heavy manufacture. They range from precision electric motors to small automotive parts, to plastic fabrication (See Appendix A ) . Employment in Shiawassee County is concentrated in manufacture, followed by service, retail trade , government, farming, finance, 168 construction, and wholesale trade (See Table 1). Shiawassee County ranks twentieth in the sample in size of civilian labor force. Additionally, Shiawassee County ranks sixth across all counties in the sample in proportion employed in the government sector. Shiawassee County is a Suburban Primary Industry County. The economic activities are primarily in the automotive industry and the population density of the county is relatively low. The line graphs for Shiawassee County are shown in Figure 21. The jail admittance rate demonstrates a somewhat stable pattern about 15% of the total jail population over the seven years studied. Although there is a large increase in 1983 (to approximately 21%) the rate appears to primarily meander about a 15% rate. The employment rate generally seems to exhibit a decreasing trend through 1983, followed by an increase through 1986. The social welfare rate, exhibits intra-year increases in 1980, and 1983 and then decreases through 1986. The general appearance the these three patterns indicates that the pattern in the jail admission over the 84 months seems to be antithetical to that of employment and social welfare. That i s , there is little correspondence between the pattern of the jail rates and what is anticipated if a relationship exists between these phenomenon. Shiawassee County has a higher than average jail admission and social welfare rate, as well as a lower than average employment rate. 169 Insert Figure 21 about here The results of the regression analyses for Shiawassee County will now be presented. The model for the jail admission rates and employment allows an explanation of only .08% of the variation in the former measure based on the latter. There is a significant lag parameter at one month such that for every percentage increase in employment there is a -.4045 decrease in jail admissions. (48) JAIL = 11.54 + (-.4045 EMP lag 1) (.1142) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The results of the model of social welfare and employment indicate that employment allows one to explain only 3% of the variation in the social welfare rate based on the observed values of employment. There is a significant parameter, as has been the case above, at a one month lag. This parameter indicates that for every percentage increase in employment there is a -1.0111 decrease in social welfare. (49) SW = 27.27 + (-1.0111 EMP lag 1) (.1142) *SW = Social Welfare Rate. EMP lag 1 = Employment Rate at a one month lag. 170 The employment model demonstrates that approximately 4% of the variation in the employment rate can be explained through the observed values of state social response. There is also a significant lag parameter, such that for every percentage increase in state social response there is a .9888 decrease in employment. (50) EMP = -1.54 + (-.9888 JAIL lag 1) (.1176) * EMP = Employment R a t e . JAIL = Jail Admission R a t e . St. Clair County. St. Clair County was named for General Arthur St. Clair, the first governor of the Northwest Territory. St. Clair ranks in the fourth 20 percent among United States counties for total value added by manufacture. In addition to its industrial output, the rural areas of the county are dotted with fruit orchards and w h e a t , oat, soybean, and corn production (Michigan Department of Commerce, 1986). The principal economic base employers are metal fabrication, food, paper, axles, automotive accessories, and sheet metal work. The proportion of employment in the ten selected sectors is largely in service employment followed by manufacture, retail trade, government, finance, construction, farming, wholesale tra d e , mining, and forestry & fishing. St. Clair County ranks fourteenth in the twenty-four counties for the size of civilian labor force. In addition, 171 it ranks first across all twenty-four counties in the sample in the proportion of the civilian labor force employed in construction, as well as fourth in mining employment, and sixth in finance employment. Primary Industry County. St. Clair County is a Suburban Like Muskegon and Ottawa Counties St. Clair County has a diverse collection of economic activities, yet its population density is relatively low. The line graphs for St. Clair County are shown in Figure 22. The jail admittance rate fluctuates about a 9% r a t e , with notable exceptions in 1980 and 1983. In addition there appears to be the intra-year pattern found above in 1980, 1981, 1982, 1983, and 1984 followed by a slight decrease through 1986. The employment rate for St. Clair County indicates a clear intra-year pattern as well as two significant trends. There appears to be a decrease through 1983 followed by an increase through 1986. This pattern is somewhat consistent with the employment incarceration relationship. In addition, although the social welfare rate is a mirror of the employment rate, it is somewhat consistent with the theoretical relationship. In comparison to the other counties in the study St. Clair County's jail admittance and employment rates are lower than average, and its social welfare rate is higher than average. Insert Figure 22 about here 172 The regression results for St. Clair County are presented below. The model of the jail rate and employment indicates that 4% of the variation in the jail rate can be explained by the employment r a t e . There is a significant parameter at a two month lag which indicates that for every percentage increase in the employment rate there is a -.2859 decrease in the jail admission rate two months latter. (51) JAIL = -1.88 + (-.2859 EMP lag 2) (.1117) *JAIL = Jail Admission Rate. EMP lag 2 = Employment Rate at a two month lag. The results of the model of social welfare and employment indicate that employment allows one to explain only .79% of the variation in the social welfare rate based on the observed values of employment. There is a significant parameter, at a one month lag. This parameter indicates that for every percentage increase in employment there is a -.3714 decrease in social welfare. (52) SW = 1.77 + (-.3714 EMP lag 1) (.1115) *SW = Social Welfare R a t e . EMP lag 1 = Employment Rate at a one month lag. The employment model demonstrates that approximately 1.5% of the variation in the employment rate can be explained through the observed values of state social response. There is also a significant lag parameter, such 173 that for every percentage increase in state social response there is a -.7125 decrease in employment. (53) EMP = .29 + (-.7125 JAIL lag 1) (.1146) *EMP = Employment Rate. JAIL = Jail Admission Rate at a one month lag. Van Buren County. Van Buren County was organized in 1837 and was named after Martin Van Buren, then Secretary of State and later President. One of the top three counties in Michigan for apple production, and one of the top two for grapes and blueberries, in addition to being one of the state's leading cherry growing counties. products are poultry, hogs and corn. Other agricultural The principal economic base employers in Van Buren County are largely in food processing, followed by metal products, rubber products, furniture, and paper products. Van Buren County ranks eighteenth in the twenty-four counties in the sample in size of civilian labor force. Moreover, the diversity of employment in the county are not evident elsewhere in the sample (See Table 1). The highest proportion are employed in manufacture followed by service, government, farming, retail trade, construction, finance, wholesale trade, forestry & fishing, and mining. Van Buren County is a Suburban Primary Industry County. The primary activity is the processing of food and the population density is relatively low. 174 Figure 23 shows the line graphs for Van Buren County. The jail admittance rate shows a very erratic, but stable, pattern over the study period. Although there appears to be a slight upward trend through 1983, there does not appear to be any significant pattern to the fluctuations. The employment rate in Van Buren County indicates somewhat of an intra-year pattern in addition to a decrease through 1983, and an increase through 1986. The social welfare rate shows an increase through 1983 and a decrease through 1986 while exhibiting the intra-year pattern, albeit less clearly than the employment rate. Overall the patterns exhibited indicate little consistency with what was anticipated above. Van Buren County has a higher than average jail admittance and social welfare rate, as well as a lower than average employment rate. Insert Figure 23 about here The regression results for Van Buren County will now be presented. The model of the jail rate and employment indicates that 2.9% of the variation in the jail rate can be explained by the employment rate. There is a significant parameter at a one month lag which indicates that for every percentage increase in the employment rate there is a -.4296 decrease in the jail admission rate one month latter. 175 (54) JAIL = 1.83 + (-.4296 EMP lag 1) (.1115) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The results of the model of social welfare and employment indicate that employment allows one to explain only 5% of the variation in the social welfare rate based on the observed values of employment. There are significant parameters, in a direct relationship and at a one month lag. The direct parameter indicates that for every percentage increase in employment there is an immediate decrease of .1067 in the social welfare rate. Moreover, the lag parameter indicates that for every percentage increase in employment there is a - l .3317 decrease in social welfare. (55) SW = 1.51 + (-.1067 EMP) + (-1.3317 EMP lag 1) (.0529) ( .1133) *SW = Social Welfare Rate. EMP = Employment Rate. EMP lag 1 = Employment Rate at a one month lag. The employment model demonstrates that approximately 13% of the variation in the employment rate can be explained through the observed values of state social response. There are significant parameters for the social welfare rate, and at a one month lag. The social welfare parameter indicates that for every percentage increase in social welfare there is a -.6273 decrease in employment. The significant lag parameter indicates that for every percentage increase in 176 state social response there is a -.9035 decrease in employment one month latter. (56) EMP = 1.37 + (-.6273 SW) + (-.9035 JAIL/SW lag 1) (.2120) (.1174) *EMP = Employment R a t e . SW = Social Welfare Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Washtenaw County. Washtenaw County was organized as a county in 1826 and derives its name from an Indian word, "Wash-ten-ong," meaning "at or on the river." Washtenaw County ranks in the third 20 percent for total value added by manufacture among all United States counties. At the same time the county is sufficiently rural to be Michigan's leading sheep producer and to rank third in the state for milk production. A p p l e s , w h e a t , o a t s , corn, and hay are its leading agricultural products (Michigan Department of Commerce, 1986). Although the principal economic base employers tend to be dominated by the automotive industry there are also the production of pharmaceuticals, ball bearings, and computers, as well as the University of Michigan and the Ypsilanti State Hospital. Washtenaw County's employment is in government, manufacture, service, retail trade, finance, construction, wholesale tra d e , and farming. Washtenaw County ranks sixth in the sample for the size of civilian labor force, as well as second in the sample in the 177 proportion employed in government. Washtenaw County is a Urban Factory County. The economic activities in Washtenaw County are quite diverse, albeit primarily automotive industry in nature. However, the diversity of activities and the density of population allow it to be classified as a type two county. The jail admittance rate for Washtenaw County indicates a decrease through 1982 followed by the intra-year cycles, and an increasing trend through 1986 (See Figure 24). The employment rate indicates a stability in employment through 1981 followed by a decrease in that year and a steady increase through 1986. The social welfare rate is stable in 1980 followed by and increase though 1983 and then a somewhat steady decrease through 1986. Overall there is little consistency of these patterns with that anticipated; there is little similarity between the social welfare rate and the jail admission rate, as well as with the employment ra t e . Relative to the other counties Washtenaw County has a lower than average jail admission and social welfare rat e , as well as a higher than average employment r a t e . Insert Figure 24 about here The regression results for Washtenaw County will now be presented. The model of the jail rate and employment indicates that 1% of the variation in the jail rate can be 178 explained by the employment rate. There is a significant parameter at a one month lag which indicates that for every percentage increase in the employment rate there is a -.4667 decrease in the jail admission rate one month latter. (57) JAIL = -2.22 + (-.4667 EMP lag 1) (.1128) *JAIL = Jail Admission Rate. EMP lag 1 = Employment Rate at a one month lag. The results of the model of social welfare and employment indicate that employment allows one to explain only 1.6% of the variation in the social welfare rate based on the observed values of employment. There are significant parameters at a one month and two month lags. These parameters indicate that for every percentage increase in employment there is a -.2507 and a -.2595 decrease insocial welfare at a lag on one and two months respectively. (58) SW = -2.13 + (-.2507 EMP lag 1) + (-.2595 EMP lag 2) (.1132) (.1137) *SW = Social Welfare Rate. EMP lag 1 = Employment Rate at a one month lag. EMP lag 2 = Employment Rate at a two month lag. The employment model demonstrates that approximately 1.2% of the variation in the employment rate can be explained through the observed values of state social response. There is also a significant lag parameter, such that for every percentage increase in state social response there is a -.9455 decrease in employment. 179 (59) EMP = 1.61 + (-.9455 JAIL/SW lag 1) (.1153) *EMP = Employment Rate. JAIL/SW lag 1 = Interaction effect of Jail Admission and Social Welfare Rates at a one month lag. Summary of the Individual Countv Results. The results of the application of the three models to the data are summarized in Tables 2, 3, and 4 (below). The summary of results of the jail admission rate and employment indicates that nearly all counties have a significant lag at one mon t h . That i s , for every percentage increase in the employment rate there is a approximately one half percentage decrease in the jail admission rates in the twenty-four counties in this study. In general, all counties, except Jackson, Kalamazoo, Maco m b , Muskegon, and Calhoun Counties, are consistent with the postulation that local correctional settings respond to changes in the local labor force participation rates. In Table 3 the results of the tests show that there is a general congruence with the hypothesized model which is more robust than the jail admission rates. As was found for the jail admission rates nearly all of the counties where there are significant models have a significant lag parameter at one mon t h . However the parameter are generally three time the magnitude of those found for jail admission rates. That i s , for every percentage increase in the employment rate there is approximately a one and one half 180 decrease in the social welfare rate. In addition, there are a number of direct relationships between employment and social welfare rates. The results of the application of the employment model are indicated in Table 4. Once again the most common significant parameter is at the one month lag, such that for every increase in the use of state social response there is approximately one percentage decrease in employment. cross-check of the first two models makes sense. This Taken individually the social welfare rate seems to allow a better explanation of social response than does the jail admission rate for more counties in the sample. The following section compares the counties by economic base. Insert Tables 2, 3, and 4 about here Comparisons of Counties by Economic Base The twenty-four counties in the sample were further classified in order to assess whether there were significant differences by population densities and industrial base (See Table 5). The result of the classification is a classification scheme with three basic categories. The assessment of the three general types of counties in the study reveal that there are sixteen counties which are of a Suburban Primary Industry, four which are Urban Diversified, and four are Urban Factory. The results of the regression 181 models for each type of county can be summarized from further consideration of the individual models for each of the counties to provide a more general perspective on the different types of counties in this stu d y . In addition, qualitative comparisons and results can be made explicit based on the earlier findings for the individual counties. Suburban Primary Industry Counties. The Suburban Primary Industry Counties are counties where there are one or two large employers or a number of small manufacturers and where the population density is relatively low. These counties are more appropriately labeled "suburban" counties given their generally low relative populations as well as the lack of a major urban area or city in the county. The modeling of the social response relationship for these counties revealed that the amount of the variance in the jail admission and social welfare rates which is explained by the employment rate is rather low. In addition, for both the jail admission, and social welfare rates the most common significant parameter is at a one month lag. Thus, for both measures of state response there is a decrease, one month latter, following an increase in employment. For the jail admission rate there are notable exceptions to this pattern in Calhoun, Clinton, and Jackson Counties. In Calhoun County there is a positive coefficient at a one month lag indicating that as employment rates increase the jail admission rate increases one month latter. 182 However, there is also a significant lag parameter at a four month lag indicating that as employment increases, admission decreases four months latter. jail In Clinton County there is a significant direct parameter such that as employment increases the jail admission rate directly decreases. By contrast, Jackson County there is a significant positive weight. This indicates that as employment increases there is a direct increase in the jail admission ra t e . It is particularly significant to note that the overall majority of these counties are consistent with the expected relationship between employment and incarceration and social welfare. Insert Table 5 about here It is apparent, based on both the coefficients of determination and the significant coefficients that there are factors outside this model which must be specified in order to more fully understand that nature of the relationship between the labor force and incarceration in these counties. The significant parameters for the social welfare rates are generally much higher (approximately two times) than those for the jail admission rat e s . There are also notable exceptions in Barry, Bay, Clinton, Jackson, and Ottawa Counties. In Barry County there is a significant negative 183 parameter which is direct. In Bay County the direct parameter is also significant yet positive such that as employment increases so does the social welfare rate. Yet at the one month lag there is a significant negative weight which is consistent with the other type three counties. in Clinton County there is a significant direct parameter which is negative. And in Jackson County there is a significant direct parameter which is very high and positive indicating that for every increase in employment there is a direct increase in the social welfare rate. Urban Diversified Counties. The Urban Diversified Counties are those where the population density is relatively high, where there is a major urban area, and there is a diversification in the economic base of employment in the county (Ingham, Kalamazoo, Saginaw, and Washtenaw Counties). For these type of counties there appears to be no apparent pattern. In Ingham County for example there are significant parameters at one and two month lags which are consistent with the conceptual model which holds that as employment increases jail admission rates decrease. The data for the model for social welfare and employment, however, is not significant. The results of the application of the model to the data for Kalamazoo County is not significant for the jail admission rates; however, there is a significant direct parameter for the social welfare rates, and a significant negative parameter 184 at a one month lag. For Saginaw County there are significant parameter in the model of the jail admission rates at one and two month lags, such that as employment increases jail admission decreases. In addition there is a significant, and negative, direct parameter for the social welfare rate and a significant lag parameter at one month. Last, for Washtenaw County there is a significant, and negative, lag parameter at two months. In the model of social welfare for that county there are significant parameters at one and two months. In summary the Urban Diversified counties appear to be consistent with the idea that the jail admission and social welfare rates are negatively associated with the changes in the labor force over time. Given the very wide differences in the four counties in this group; however, it is difficult to assert or discern a pattern. The individual differences in these counties probably leads to these less than clear results. Ingham County would seem to be must less sensitive to economic fluctuation than any of the other three in this group given its proportion of government employment. Similarly, Washtenaw County would also be somewhat less susceptible to fluctuations given its orientation in employment toward the University of Michigan and the medical field. In Kalamazoo on the other hand, given its manufacturing focus, may be more susceptible to economic "hard times." This has clearly been the case in 185 Saginaw county. Out of the four counties in this group the economic base of employment in Saginaw County is primarily in the automobile and support industries which are very sensitive to overall changes in the economy. In summary, three of the four counties demonstrate that the model that employment influences the jail admission rates is consistent with the theory. In addition, in both Saginaw and Washtenaw Counties employment also is consistent with the model of social welfare rates in that as employment increases social welfare rates decrease. Urban Factory Counties. The Urban Factory Counties are characterized as having high population densities, as well as an employment base which is somewhat diverse yet basically manufacturing oriented (Genesee, K e n t , Macomb, and Oakland Counties). In these type of counties there is somewhat of a pattern in the model for jail admission and no pattern in the social welfare rates. For the jail admission rates for all four counties there is a significant, negative, lag parameter at one month (although there is also a significant lag parameter at two months for Macomb County, the overall fit of the data to the model is not significant for that county). In the model of the social welfare rates the only significant model is for Genesee County and it is consistent with the hypothesized m o d e l . These counties are consistent with the mod e l , except for Macomb County, that as employment increases jail 186 admission decreases. Similar to the Urban Diversified Counties the model of the social welfare rates is not salient. Last, the jail admission and social welfare rates do allow a explanation of the employment rates which is consistent with the theory in all four counties. Summary of Findings. The results of the study of the qualitative aspects and quantitative modeling of the counties in this sample have been, in general, consistent with the conceptual m o d e l . The majority of modeled relationships demonstrate that at the local level, for urban counties, the employment rate is inversely associated with both jail admission and social welfare rates. The descriptive findings indicate that there is quite a variety of counties in the sample from Oceana County's farming orientation to Genesee County's focus on manufacturing . The overall results across these counties, however, is that there is manufacturing (primarily of automobiles or related industries) in all counties. In addition, it is apparent that even in the least dense counties in the sample there are adjacent counties which are highly urban and focused on manufacture alone. The descriptive findings also lead to the classification scheme which is based on population density and employment base. The individual county results indicate a somewhat wide range of differences across the sample. 187 Although the majority of counties conform to the conceptualization that employment is inversely related to both incarceration and social welfare, there are a number of individual differences across the sample. This categorization resulted in three different groups of counties. Although it is difficult to conclusively force the twenty four counties into three categories, they seem to fit in the typology which has been developed. largest group First, the of counties were those labeled "Suburban Primary Industry Counties." In general the employment based in these counties appears to be focused on a major employer and a few lessor employers. The second criteria is the population density and urban settlement pattern. In general this type of county has a low relative population density and lack a major urban area. The results of the modeling indicates that these suburban counties are most consistent (out of the three categories) with the conceptual m o d e l . The second category, "Urban Diversified Counties," indicates that these counties are most consistent with the model for incarceration (Ingham, Saginaw, and Washtenaw Counties), and social welfare (Saginaw and Washtenaw Counties). These results indicate that there are clear individual differences in this category of counties. Similar the Urban Factory Counties demonstrate a general consistency with the proposed model (Genesee, Kent, and Oakland Counties) for jail 188 admission rates, but social welfare rates do not conform to the model. Figure 1: Line Graphs for Barry County Rate Jail Adm ittance R ate 14 - 12 - 10 - 0 20 40 60 80 100 80 100 80 100 Time Em ployment R ate 100 - 80 - Rate 90 0 20 40 60 Time Social W elfare R ate 30 - 10 - Rate 20 0 20 40 60 Time 189 Figure 2: Line Graphs for Bay County Jail Adm ittance R ate - Rate 10 0 20 40 60 80 100 80 100 80 100 Time Em ployment R ate Rate 9088 - 86 - 84 - 82 - 80- 0 20 40 60 Time Social W elfare R ate 24- Rate 22 - 20- 18 - 16 - 0 20 40 60 Time 190 Figure 3: Line Graphs for Berrien County Jail Admittance R ate 8 7 Rate 6 5 4 3 0 20 40 60 80 100 80 100 80 100 Time Em ployment R ate 95 - 85 - Rate 90 0 20 40 60 Time Social W elfare R ate Rate 20- 10 - 0 20 40 60 Time 191 Figure 4: Line Graphs for Calhoun County Jail Admittance R ate 11 10 Rate 9 8 7 6 5 4 0 20 40 60 80 100 80 100 80 100 Time Employment R ate Rate 90- 80 0 20 40 60 Time Social W elfare R ate - 15 - Rate 20 0 20 40 60 Time 192 Figure 5: Line Graphs for Clinton County Jail Adm ittance R ate 30 - Rate 20 10- 0 20 40 60 80 100 80 100 Time Em ployment R ate 100 - 80 - Rate 90 70 0 20 40 60 Time Rate Social W elfare R ate 100 193 Figure 6: Line Graphs for Eaton County Jail Adm ittance R ate Rata 12 - 0 20 40 60 80 100 80 100 80 100 Tima Employment R ate Rata 96 94 - 92 - 90 - 88 - 86 - 0 20 40 60 Time Social W elfare R ate 16 - Rata 1210 - 0 20 40 60 Time 194 Figure 7: Line Graphs for G enesee County Jail Admittance R ate 30 - Rate 20 10- 0 20 40 60 80 100 80 100 80 100 Time Em ployment R ate 100 Rate 90- 80- 0 20 40 60 Time Social W elfare R ate 40 30- 0 20 40 60 "Time 195 Figure 8: Line Graphs for Ingham County Rate Jail Admittance R ate 4 - 0 20 60 40 80 100 80 100 80 100 Time Employment R ate 94 - 88 - 86 - Rate 92 84 0 20 40 60 Tima Social W elfare R ate 16 - Rate 141210 - 0 20 60 40 Time 196 Figure 9: Line Graphs for Ionia County Jail Admittance Rate Rate 25 20 - 15 - 10 - 0 20 60 40 80 100 80 100 80 100 Time Employment R ate 100 - 80 - Rate 90 0 20 40 60 Time Social W elfare R ate Rate 30 20- 0 20 60 40 Time 197 Figure 10: Line Graphs for Jackson County Jail Adm ittance R ate - 10 - Rate 12 o 20 40 60 80 100 80 100 80 100 Time Em ployment R ate Rate 100 90 - 80 0 20 40 60 Time Rata Social W elfare R ate 22 - 20 - 18 - 16 - 14 - 12 - 0 20 60 40 Time 198 Figure 11: Line Graphs for Kalamazoo County Jail Admittance R ate 7 Rata 6 S 4 3 0 20 40 60 80 100 80 100 80 100 Time Em ploym ent R ate Rata 96 94 - 92 - 90 - 88 0 20 40 60 Tima Rata Social W elfare R ate 14 - 12 - 10- 0 20 40 60 Time 199 Figure 12: Line Graphs for Kent County Jail Admittance R ate 7 Rate 6 5 4 3 0 20 40 60 80 100 80 100 80 100 Time Em ployment R ate 94 92- Rate 90 - 88 86 84 0 20 40 60 Time Social W elfare R ate 14 - Rate 1210 - 0 20 40 60 Time 200 Figure 13: Line Graphs for Lapeer County Jail Adm ittance R ate Rate 40 - 30- 20 - 0 20 40 60 80 100 80 100 80 100 Time Em ploym ent R ate 92 90 - Rate 88 86 84 - 8280 0 20 60 40 Time Social W elfare R ate Rate 20- 15 - 0 20 60 40 Time 201 Figure 14: Line Graphs for Livingston County Rate Jail A dm ittance R ate 18 - 16 - 14 - 12 10 - 0 20 40 60 80 100 80 100 80 100 Time Em ploym ent R ate Rate 100 90 - 80 0 20 40 60 Time Social W elfare R ate 30 Rate 20- 10 - 0 20 40 60 Time 202 Figure 15: Line Graphs for Macomb County Rats Jail A dm ittance R ate 6- 0 20 60 40 80 100 80 100 80 100 Time Em ploym ent R ate 100 - 80 - Rate 90 70 0 20 60 40 Time Social W elfare R ate 30 Rats 20- 10 - 0 20 60 40 Time 203 Figure 16: Line Graphs for Muskegon County Jail Adm ittance R ate 8 7 Rate 6 5 4 3 2 0 20 40 60 80 100 80 100 80 100 Time Employment R ate 90 - 88 Rate 86 84 - 8280 - 0 20 40 60 Time Rate Social W elfare R ate 20- 0 20 60 40 Time 204 Figure 17: Line Graph for Oceana County Jail Admittance R ate Rate 10 - 0 20 40 60 80 100 80 100 80 100 Time Employment R ate 100 - 80 - Rate 90 70 0 20 40 60 Time Social W elfare R ate 40 Rate 30- 20- 0 20 40 60 Time 205 Figure 18: Line Graphs for Oakland County Jail A dm ittance Rate Rate 10 - 0 20 40 60 80 100 80 100 80 100 Time Em ploym ent R ate Rate 100 95 - 90 - 85 - 80 0 20 40 60 Time Rate Social W elfare R ate 10 - 0 20 60 40 Time 206 Figure 19: Line Graphs for Ottawa County Jail Admittance R ate 12 - Rate 10 - 0 20 40 60 80 100 80 100 80 100 Time Employment R ate Rate 96 94 - 92 - 90 - 88 - 86 0 20 40 60 Time Rate Social W elfare R ate 14 - 12 - 10 - 0 20 40 60 Time 207 Figure 20: Line Graphs for Shiaw assee County Jail Admittance R ate 30 - Rate 20 10 - o 20 40 60 80 100 80 100 80 100 Time Em ploym ent R ate 100 - 80 - Rate 90 70 0 20 40 60 Time Social W elfare R ate Rate 30 20 - 0 20 40 60 Time 208 Figure 21: Line Graphs for Saginaw County Jail Adm ittance R ate 12 - 10 - 0 20 40 60 80 100 80 100 80 100 Tim® Em ployment R ate 100 90 - 80 - 0 20 40 60 Time Social W elfare R ate 2422 - 2018 - 16 - 14 - 0 20 40 60 Time 209 Figure 22: Line Graphs for St. Clair County Jail Adm ittance R ate - 10 - Rate 12 0 20 40 60 80 100 80 100 80 100 Time Employment R ate 92 90 - Rate 88- 86 848280 0 20 60 40 Time Social W elfare R ate 22 - Rate 20- 181614- 0 20 40 60 Time 210 Figure 23: Line Graphs for Van Buren County Jail Admittance R ate 10 - 0 20 60 40 80 100 80 100 80 100 Time Employment R ate 100 - 80 - Rate 90 0 20 60 40 Time Social W elfare R ate Rate 30 20- 0 20 40 60 Time 211 Figure 24: Line Graphs for Washtenaw County Jail Admittance R ate 10 9 Rate 8 7 6 5 4 3 0 20 40 60 80 100 80 100 80 100 Time Em ploym ent R ate Rate 98 96 - 94 - 92 - 90 - 88 - 0 20 40 60 Time Social W elfare R ate - 10 - Rata 12 0 20 60 40 Time 212 213 Table 1 Employment in the Twenty-Four Urban Counties: The Civilian Labor Force in Ten Sectors of the Economy County FA Bay Barry Berrien Calhoun Clinton Eaton Genesee Ingham Ionia Jackson Kalamazoo Kent Lapeer Livingston Macomb Muskegon Oakland Oceana Ottawa Saginaw Shiawassee St. Clair Van Buren Washtenaw 5 14 6 3 20 10 FO — — — 1 — _ _ — 1 13 4 2 1 13 5 — — — — .— — — — 22 3 1 4 3 11 5 16 1 MA 4 4 3 2 6 4 3 3 3 3 3 4 4 5 22 23 29 21 14 15 36 18 26 22 27 27 1.5 17 33 31 19 9 35 30 21 22 18 25 4 4 4 — 2 — CO — — — . 2 _ — 5 4 3 4 6 5 3 WT RT FI SE GO* 5 21 16 17 14 16 19 17 15 17 19 17 20 15 23 20 18 21 17 16 21 18 19 16 13 6 7 5 6 5 10 5 7 4 6 6 6 5 8 5 4 8 5 5 5 5 6 4 4 23 20 23 20 19 23 20 21 15 24 26 24 14 22 20 24 30 17 22 22 20 23 17 23 12 15 11 15 18 15 12 31 19 17 16 8 28 17 14 12 9 18 9 12 18 15 17 28 — 4 2 — 3 6 3 2 4 4 7 3 3 3 3 7 1 3 3 3 3 2 2 *A11 data are percentages. The employment sectors are: FA=Farming, FO=Forestry and Fishing, CO=Construction, MA=Manufacturing, WT=Wholesale Trade, RT=Retail Trade, FI=Finance, SE=Service, and GO=Government. Source: Michigan Department of Commerce, Economic Profiles of the Counties in the State of Michigan, 1986. 214 Table 2 Summary of the Regression Models with Standardized Coefficents for All Counties 1980-1986: Jail Admission as the Dependent Variable County Barry* Bay* Berrien Calhoun* Clinton* Eaton* Genesee* Ingham Ionia* Jackson* Kalamazoo* Kent Lapeer Livingston Macomb Muskegon* Oakland Oceana* Ottawa Saginaw* Shiawassee St. Clair* Van Buren* Washtenaw R .1377 .0679 .0159 .0203 .6923 .0125 .0012 .0301 .0211 .1172 .1980 .0001 .0047 .0309 .4746 .0168 .1013 .0202 .0308 .1566 .0271 .0079 .0508 .0164 TR EMP .9133 -.2683 .9540 .2034 .9295 .9567 .7611 -1.0195 .9003 .8911 .9354 .8360 .5037 4.7783 .9353 .1427 .9454 .9558 .8163 .9867 .0644 .9649 .9867 .1118 .8926 .9508 .2886 -9.6761 .9331 .5192 .8792 -.1067 .4051 LI -1.1545 -1.2052 -1.3002 -1.2933 -.9141 -1.1506 -.6648 -1.0921 1.7770 -.3359 -.9588 -1.3273 -1.4781 -.5587 -1.6083 -1.4360 -1.6220 -1.3352 -1.4778 -.3849 -1.0111 -.3714 -1.3317 -.2507 L2** .4991 -.3582 .6264 .6020 .4738 -.2595 *Significant models. The data fits the hypothesized model of the autoregressive process (p > .05). **R=r squared (error sum of squares), TR=total r squared (prediction power of structural part of the model), EMP=Employment Rate direct parameter, Ll=Employment Rate at a one month lag, and L2=Employment Rate at a two month lag. 215 Table 3 Summary of the Regression Models with Standardized Coefficents for All Counties 1980-1986: Social Welfare as the Dependent Variable County R TR Barry* .0071 .0002 .0003 .0044 .4629 .0085 .0193 .0435 .0039 .1328 .0079 .0175 .0278 .0001 .0301 .0117 .0161 .0045 .0421 .1338 .0008 .0414 .0294 .0119 .5604 .3099 .1579 .5891 .5302 .1265 .6466 .7929 .2895 .2061 .0613 .1894 .6792 .5690 .6321 .6217 .8304 .1733 .1926 .1044 .3822 .3866 .2606 .3292 Bay* * Berrien Calhoun* Clinton* Eaton* Genesee* Ingham* Ionia* Jackson* Kalamazoo Kent* Lapeer* Livingston* Macomb Muskegon Oakland* Oceana* Ottawa* Saginaw* Shiawassee* St. Clair* Van Buren* Washtenaw* EMP -.6727 LI L2 -.4841 -.4519 -.3378 .0874 -.5767 -.2362 -.5086 -.5007 -.4870 -.3023 L4** -.2242 -.2953 1.8364 -1.6794 -.2384 -.6997 -.5997 -.5011 -.5241 -.8147 -.3359 -.2567 -.3919 -.4045 -.3778 -.2859 -.4296 -.4667 *Significant models. The data fits the hypothesized model of the autoregressive process (p > .05). **R=r squared (error sum of squares), TR=total r squared (prediction power of structural part of the model), EMP=Employment Rate direct parameter, Ll=Employment Rate at a one month lag, L2=Employment Rate at a two month lag, and L4=Employment rate at a four month lag. 216 Table 4 Summary of the Regression Models with Standardized Coefficents for All Counties 1980-1986: Employment Rate as the Dependent Variable County Barry* Bay* Berrien Calhoun* Clinton* Eaton* Genesee* Ingham* Ionia* Jackson* Kalamazoo Kent* Lapeer* Livingston* Macomb Muskegon Oakland* Oceana* Ottawa* Saginaw* Shiawassee* St. Clair* Van Buren* Washtenaw* R TR .1781 .0432 .0297 .0049 .7440 .0077 .0044 .0514 .0329 .0002 .0779 .1609 .0392 .0550 .0591 .0234 .0258 .0039 .0564 .3269 .0393 .0152 .1310 .0122 .8936 .7095 .8719 .7886 .7171 .7178 .8592 .6966 .5005 .0129 .1864 .1205 .7112 .8689 .7354 .8323 .9242 .0158 .6850 .8234 .7945 .5367 .7942 .7934 JAIL -.2627 SW LI -.5759 -.9613 -.8072 -.8847 -.6448 -.9646 -.8750 -1.1649 -.4780 L2** .1098 -.5918 .2719 -1.0004 -.7221 -.2436 -.8645 -.5467 -.4003 -.5767 -.3506 -.7889 -.7984 -.6273 -.7472 -.9144 -.9888 -.7125 -.9035 -.9455 *Significant models. The data fits the hypothesized model of the autoregressive process (p > .05). **R=r squared (error sum of squares), TR=total r squared (prediction power of structural part of the model), EMP=Employment Rate direct parameter, Ll=Employment Rate at a one month lag, L2=Employment Rate at a two month lag 217 Table 5 Classification of Counties: Member Counties for Each Type Suburban Primary Industry Counties Barry Bay Berrien Calhoun Clinton Eaton Ionia Jackson Lapeer Livingston Muskegon Oceana Ottawa Shiawassee St. Clair Van Buren Urban Diversified Counties Urban Factory Counties Ingham Kalamazoo Saginaw Washtenaw Genesee Kent Macomb Oakland CHAPTER 5 SUMMARY AND CONCLUSIONS Summary. This analysis has examined the relationships between labor force participation rates, jail admission rates, and social welfare rates across twenty-four urban counties in the State of Michigan from 1980 through 1986. There has been an extensive history of scholarly research which has examined the potential for a relationship between economic factors and crime. The current "state-of-the-art" appears to be two opposing perspectives. On the one hand there are a number of theorists who maintain that there is a cogent, and empirically demonstrated, relationship between selected economic factors and criminality. These scholars maintain that the relationship between economic factors and criminality is inverse; as the economic situation of a community deteriorates the criminality will increase in a similar proportion. The other perspective maintains that there is no empirical relationship between economic measures and criminality. Although the latter group of theorists maintain there is no demonstrated relationship, they do acknowledge the conceptual possibility for such a relationship. In summary the relationship between labor 218 219 force participation and crime is unresolved. However, in the middle and late 1970's analyses of the relationship between economic indicators and incarceration rates appear to have produced consistent empirical results that there is an inverse relationship. That i s , as the economic situation in a geographic area decreases or is in recession, the use of institutional correctional environments would increase. These studies virtually all demonstrate a robust relationship between the economic situation (typically unemployment) and the incarceration rate (usually prison admission rates) across time. Although this approach is related to the assessment of the relationship between employment and crime, it does not address or attempt to resolve the argument that there is an empirical relationship. What this approach does allow; however, is the assessment of the response of the criminal justice system to changes in economic factors. In order to examine the interrelationship between the dimensions of employment, jail admission, and social welfare in the State of Michigan, this study has relied on both qualitative and quantitative techniques. In addition, this analysis has assessed the relationship between local labor force participation rates and the state response to changes in these rat e s . The local incarceration rate and the social welfare rates were used as measures of state response to the changing employment market at the local level. This was presumed because both the jail 220 and social welfare rates are logical and empirical measures of the use of government agencies and functions to address a decreasing labor market at the local level. In many of the past analyses the relationship between incarceration and employment has been relegated to the study of aggregate state prison population measures of incarceration. The local labor market and the state's response to changes in that market has never been assessed. Therefore, this analysis is a crucial assessment of the interrelationship between labor markets and local incarceration rates. The use of social welfare adds another significant dimension to the study of the relationship between economic factors and the response of the state. That i s , it is difficult to argue that citizens have only two choices in contemporary society; either participating in the local labor force or the commission of crime. Therefore, the factor of social welfare participation rates was included in this analysis. Although one can argue that there are other "labor markets" which are not considered here (i.e., criminal "employment," quasi-legal employment, e t c .), the two levels of state response employed in this analysis are presumed to be utilized in the majority of cases. In this analysis four general areas were assessed. First, an assessment was made of the relationship, over-time, between jail admission rates and labor force participation rates across urban counties. Second, there was an assessment of the relationship, over­ 221 time, between social welfare rates and labor force participation across urban counties. Third, there was an examination of the relationship between jail admission, social welfare, and labor force participation rates across the counties. Last, there was an analysis of the similarities or differences across three different types of counties in the sample based on a developed classification scheme. The results of the analyses indicate that there is consistent evidence for the claim that there is a response in the jail admission and social welfare rates to changes in the labor force participation rates over t i m e . For the jail admission rates it was found that for the majority of counties in the sample that one month after a decrease in the employment rate there was an approximately one-half percentage increase in the jail admission rate. A similar relationship was also found for the social welfare rates although it was not as consistent across the twenty-four counties. In general, for social welfare rates its was determined that for every percentage increase in the labor force participation rates there was approximately a one and one-half percentage decrease in the social welfare rates. There were; however, significant deviations in the social welfare models. That is, in Bay County, where the model was significant there was a direct positive relationship between social welfare and employment; for every increase in the 222 employment rate there was an increase in social welfare. In addition, in Jackson County there was an extremely high positive relationship between the social welfare rate and the labor force participation r a t e . Last, in Saginaw County there was an extremely high negative, and direct, relationship. The employment models indicated a consistency with the individual models for both the jail admission and social welfare rates in that there was typically a significant one month lag parameter between employment and jail admission and social welfare. The results of the classification scheme indicate that for the Suburban Primary Industry Counties (relatively low population density and a near primary employment base) are consistent with the model which holds that the reliance on local incarceration and social welfare are inverse to the labor force participation rates. As was found for the majority of the counties, the relationship with the jail admission rates is that for every percentage decrease in the employment rates there is a half of percentage increase in the jail incarceration rates. For social welfare for every percentage decrease in the employment rates there is a one and a half increase in the social welfare rates. In the Urban Diversified Counties where there are relatively high population densities, in addition to the presents of a moderate to large city area, and where the economy is more diverse that in the Suburban Primary 223 Industry Counties there is a mixed result in the relationships. Ingham, Kalamazoo, and Washtenaw Counties all have had very stable, somewhat prosperous counties over the study period and are somewhat similar in terms of population and economic viability. For these three counties the relationship between employment and jail admission is consistent with the Suburban Primary Industry Counties for Ingham and Washtenaw Counties and insignificant for Kalamazoo County. In Saginaw County there is a direct percentage increase in the jail admission rate relative to the employment rate. The models of the social welfare rates for the Urban Diversified Counties is also mixed. First, the model for Ingham County is not significant. Second, the models for Kalamazoo, Saginaw, and Washtenaw Counties are all dissimilar to each other. The model for Kalamazoo County indicates there is a direct increase in social welfare with an increase in employment and in the second month of an increase in employment there is a decrease in social welfare. In Saginaw County there is a very intense direct decrease in social welfare for every percentage increase in employment followed by a smaller decrease in the first m o n t h . In Washtenaw County there were increases in employment and mild decreases in the social welfare rates at one and two months subsequent to the increases in employment. 224 In the Urban Factory Counties; Genesee, Kent, Macomb, and Oakland Counties, there relationship between jail admissions and employment which is similar to that found in the Suburban Primary Industry Counties. The significant models for Genesee, Kent, and Oakland Counties indicated that for every percentage decrease in employment there was a half percentage increase in the jail admission rate at a one month lag. The only significant model for the social welfare rates in these counties was found in Genesee County. The model for that county indicates that for every percentage decrease in employment there is a half percentage decrease in social welfare at a one month lag. Conclusions. The results of this analysis appear to support the contention, in a very general and aggregate sense, that there is a relationship between labor force participation and jail admission and social welfare rates. This analysis does not, however, allow an explanation about the causes for these relationships. One can postulate that the changes in the jail admission rates may be due to changes in capacities (i.e., the construction of new facilities), or to changes in sentencing practice. It is similarly difficult to assert that the changes in the social welfare rates are the sole result of changes in employment rates rather than changes in allocation policies over the seven year study period. What this analysis does provide is an aggregate examination of the changes, all things being 225 equal, across time. There can be no assertion of any individual explanation of the relationship between criminality and unemployment gleaned through this analysis. One argument which can be postulated to explain the relationships found in the above analyses concerns the relationship between labor force utilization and the use of incarceration in contemporary society. One can argue that a focus on local incarceration in jails is appropriate in that it reflects the types and magnitude of crime which is indicated in a community at any given t i m e . Not only does the admission rate reflect the requirements of the local criminal justice system in terms of response to serious crimes and sentenced populations, but it also provides an indicator of the setting for the myriad of social and domestic problems that would result during changing economic situations. In addition, the local jail population would reflect the official response to increases or decreases in crime in the local context by the criminal justice function. The examination of this relationship demonstrates that there is evidence, albeit preliminary, that as employment decreases the incarceration increases. Based on the relationship which has been discovered it is plausible to argue that when employment rates are decreasing there would be a group of unemployed individuals in the community, a portion of which would filter into the local correctional setting. Therefore, it is possible to 226 argue that the portion of "marginal" citizens (e.g., those who are the last to be hired and the first to be laid-off) in the labor force filter into the jail setting in times of economic crises. One of the major characteristics of contemporary market economies in the 1980's, especially in the State of Michigan, and that of other industrial states is the existence of a number of so-called "marginal" persons in the community. Changes in the need in the labor market would necessitate the seeking of alternative means of survival for this group. These marginal individuals can range from those who are sporadically employed, part-time employed, migrant workers, to other forms of discouraged workers. A conclusion which can be offered is similar to the one stated by Jankovic in which he supposed that under conditions which make it advantageous to maintain a permanent oversupply of labor, imprisonment can be used to regulate the size of the surplus labor force (Jankovic, 1977). Although it is quite difficult to assert with any level of confidence that the local criminal justice system response is a planned balancing of the surplus labor market to economic hard times, the assessment of the overall rates over time clearly suggest that such a conceptualization is at least a possibility. This analysis demonstrates that the relationship between jail utilization and employment is apparent. In addition, it has been shown that the relationship is not similar across different "types"" of 227 counties. The conceptualization that employment or changes in the economic situation in the community influence has not focused on such aggregate overall differences. Gary Becker employs aggregate statistics to generalize about individual behaviors. The present analysis, however, focuses on aggregate conclusions based on aggregate data. It is quite significant to note that the employment and jail admission rate relationship is most significant in the less dense more economically sensitive counties (more sensitive to economic fluctuations). That i s , in the Suburban Primary Industry County group it was consistently determined that jail admissions were influenced by the employment r a t e . In the more dense and economically salient groups (i.e., both Urban Diversified and Urban Factory Counties) the relationship is more varied. These findings suggest the potential that the type of employment base should be considered in assessing the relationship. It is interesting that given the multitude of assessments of aggregate statistical analyses of employment and crime rates or incarceration rates that there has been absolutely no consideration of the possible influences of different bases of employment. For that matter there may be differential relationships based on the type of offender and geographic region. The argument for the relationship between the social welfare system and changes in the employment market is not unlike the one supposed for 228 the relationship with the jail admission rate. That is, the use of social welfare is used in greater proportion in times of economic crisis than otherwise. This has been supported, and somewhat more strongly, in the present analysis. Therefore, it can be asserted that the use of the social welfare system is a means of maintaining the marginal populations who exist in industrial society until such time as their labor is needed in production. Moreover, it seems quite likely that there is a significant overlap between the portion of the marginal population which filters into the local correctional setting and those who participate in the social welfare system. Although it is difficult to argue that the jail admission or social welfare rates contribute to a "class society," it is logical to purport that the interrelationships which have been uncovered in this analysis are indicative of the official response of the state to changes in labor force participation at the county level, and therefore, to the needs of specific segments of the population. Certain criminologists have claimed that "the capitalist class survives by appropriating the surplus labor of the working c l a s s , and the working class as an exploited class exits as long as surplus labor is required in the productive process: each depends on the other for its character and existence" (Quinney, 1968, p. 38). Although the semantics of this and similar theories are quite Marxist, one can argue that there is support for the 229 conceptual validity of the general proposal. That is, it was determined in this analysis that the response of the state, i.e., jail admission and social welfare rates, were negatively associated with change in the labor force participation rate, therefore, it appears to be possible that there is some level of "exploitation" and "dependence" in the manner in which jail commitment and social welfare are applied relative to the employment rate in the State of Michigan. Although this analysis clearly does not demonstrate that the state exists " . . . as a device for controlling the exploited class, the class that labors, for the benefit of the ruling class," it does demonstrate that, whether intentional or not, the reality of the situation is that the state adjusts to decreases in the labor force through supplying increased alternative social institutional functions (i.e., jail admission and social welfare). In summary, there appears to be a relationship between the level of employment and the local incarceration and social welfare rates in virtually every type of county. Although the relationships uncovered in this analysis are less than desired, they are consistent and demonstrate that monthly employment changes from 1980 through 1986 are generally inversely related to incarceration in local jails and the participation in social welfare programs. Recommendations. Further study in this area could be extremely valuable to further discern the interrelationships 230 between the economy and criminality. One area where this study should continue is increasing the length of time involved in the analysis. Further analysis of both a qualitative and quantitative nature about the trends in the incarceration, social welfare , and employment rates is advised in order to strengthen the scope of the conclusions about these interrelationships at the local level. In addition to increasing the time period examined, certain demographic factors should be included in the analysis. First, measures of the major correlates of crime such as race, gender, education, a g e , income should be included in the m o d e l . Given that the total variation in both jail admission and social welfare rates was relatively small, the addition of certain demographic factors could improve the power of the model. Second, some measure of criminality should also be included in the examination. Although the Uniform Crime Reports appear to be the logical choice, they should not be employed for the assessment of a relationship between the economy and criminality. In addition to the known reliability issues involved, official measures of crime, reported yearly, are inadequate measures of intra­ year fluctuation in criminality. The measure of criminality which would seem most amenable to the monthly reporting rates of employment, jail admission, and social welfare, would be police calls for service. Such a measure would also include more subtle increases in the demand for police 231 service which may occur as a result of the decrease in legitimate employment opportunities. Last, the addition of other sources of income or "urban labor markets" could also improve the strength of the m o d e l . Certain economists have examined the issues of human capital (i.e., the value added through personal training) and the concepts of the urban labor market. However, very few, if any, have examined the relationship of the relationship of these factors to criminality or incarceration rates. In addition to the reliance on legitimate employment and the social welfare system for income, there is also a reliance on illegitimate activities, and those who are investing in human capital through attending school and combinations of these areas. Finally, I believe that we need to know more about the interaction of the economy and criminality in advanced capitalist nations in order to more appropriately address the w a n t s , and needs of the citizens, not to mention the orientation of the criminal justice system toward those individuals who are in those groups which are subject control. FOOTNOTES 1This does not suggest that the concern for economic fluctuation and social conditions began at that time, only that the limitations of referencing systems, and reporting, does not allow an effective search prior to that time. 2Through analysis of both individual and aggregate data the Vera research project intended to address: 1) the theoretical assumptions that may or many not support a policy emphasis on employment initiative as part of a crime control strategy; 2) to identify the most efficient use of programs for influencing individual behavior; 3) to identify the most effective point in the life-cycle of individuals for employment assistance; 4) to identify the economic and social-psychological prcesses that the employment programs must work through; 5) description of work histories and the kinds of work valued in high crime areas; 6) describe how information of this kind can be used to shape the design, planning, conduct, and evaluation of employment programs in such communities (Thompson, et.al., 1981, p. 4-5). 3Social welfare can be defined as the collection of both monetary and trasfers that are given to indigent or indigent-like in the United States. In addition, social problems refer to the problematic aspects of local and/or 232 233 societal areas which are the result of changes in the economic situation or cycles. 4A s has been defined by a number of authors the official statistics (i.e., the Uniform Crime Reports and Labor Statistics of the Department of Labor) have a number of limitations (Newman, 1980). 5These costs can be dived into five categories: "the relationship between (1) the number of crimes, called 'offenses' in this essay, and the costs of offenses, (2) the number of offenses and the punishments meted out, (3) the number of offenses, arrests, and convictions and the public expenditures on police and courts, (4) the number of convictions and the costs of imprisonments or other kinds of punishments, and (5) the number of offenses and the private expenditures on protection and apprehension." 6There is clear evidence that institutationalization can effect personality changes, as well as increase competence in committing crimes. The former convict's social situation is also likely to have changed dramatically since being incarcerated. Stated simply, it is clear that although choosing to commit further criminal acts is one determinant of future criminality, there are collections of social, psychological and political factors which also influence that decision. 7Harrison states that there are "four kinds of labortime-consuming and remunerative activities in urban 234 economies which display remarkable similar characteristics.” He defines these as the secondary labor market, the training sector, the irregular illegal economy, the welfare sector, and the primary labor market. 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Washington, D.C.: National Academy of Sciences, 1978. Vanagunas, S. Crime and unemployment: Another look. The Journal of Behavioral Economics. 1984, 13(1), 101-112. Vipond, J. Intraurban unemployment differentials in Sydney, 1971. Urban Studies. 1980, 131-138. Void, G.B. The amount and nature of cri m e . American Journal of Sociology. 1935, 4 0 . 796-803. Wagner, A.c. Crime and economic change in Philadelphia, 1925-1934. Journal of Criminal Law and Criminology. 1936, 27, 483-490. Waldron, R.J. Relationship Between National Unemployment Rates and Court Commitments to the Bureau of Prisons. Washington, D . C . : United States Bureau of Prisons, 1975. _____________ , & Pospichal, T.J. The Relationship Between Unemployment Rates and Prison Inc_arcemtion Rat e s , Washington, D. C . : United States Department of Justice, 1979. 264 _________________________________, & Briggs, J.P. The Relationship Between Unemployment and Prison Incarceration R a t e s . United States Department of Justice, (unpublished manuscript), 1975. Wallace, D. The political economy of incarceration trends in late United States capitalism, 1971-1977. Insurgent Sociologist. 1980, 9, 59-65. Walsh, R.H. A deduction from the statistics of crime for the last ten yea r s . Journal of the Statistical Society of London. 1857, 20., 37-78. Wa rner, S.B. Crime Control and Criminal Statistics in Boston. Cambridge: Harvard University Press, 1934. Warren, E.H. The economic approach to crime. Candian Journal of Criminology. 1978, .20(4), 437-449. Webb, S.D. Crime and the division of labor: Testing a Durkheimian m o d e l . American Journal of Sociology. 1972, 78(3), 643-656. Welch, W. N. Crowding in jails and prisons: from behavioral sinks to behaviroral sewers. Unpulbished paper presented at the American Society of Criminology, Chicago, Illinois, 1988. W i e r s , P. Wartime increase in Michigan delinquency. American Sociological R e view . 1945, 1 0 . 515-523. Wilson, J.Q., & Cook, P.J. Unemployment and crime: What is the connection? The Public Interest. 1985, 79, 3-8. 265 Williams, K.R. Economic sources of homicide: Restating the effects of poverty and inequality. American Sociological R e v i e w . 1984, 19(2), 283-289. Winslow, E. Relationship between employment and crime fluctuations as shown by Massachusetts statistics. In National Commission on Law Observance and Enforcement. Report on the Causes of Crime (Vol. 1 ) , Washington, D.C.: United States Government Printing Office, 1931. Witte, A.D. Estimating the economic model of crime with individal data. Quarterly Journal of Economics r 1980, 2, 57-84. ___________, & Reid, P. An exploration of the determinants of labor market performance of prison releases. Journal of Urban Economics. 69-80, 8, 313-329. Wright, C.D. The relation of economic conditions to crime. The Annuals of the American Academy of Political and Social Science. 1893, 3, 764-784. Yeager, M.G. Unemployment and imprisonment. Journal of Criminal Law and Criminology. 1979, 70(4), 586-588. Zimring, F . , & Hawkins, G. Deterrence and marginal groups. Journal of Research in Crime and Delinquency. 1968, 5, 100-114. APPENDIX PROFILE OF INGHAM COUNTY MICHIGAN LOCATION SERVICES: Clinton-Eaton-Ingham Community Growth Alliance 510 West Washtenaw Lansing, MI 48901 Contact: Phone: Jim Jordan 517/487-6340 DISTANCE FROM: Miles 200 60 230 270 330 Chicago Detroit Cleveland Indianapolis Toronto POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 261039 275520 277663 296292 26.1 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 151625 140550 11050 7.3% 7.9% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm General Motors, Olds Div. Mich. State University GMC, Fisher Body Div. Motor Wheel Company etc. Federal Drop Forge Dart Container Corp. Wyeth Laboratories Dana Corp. Wohlert Corp. Employees 17000 8834 4500 1000 380 347 332 325 300 266 Product/Service Autos and parts Higher education Auto bodies Automotive wheels, Steel forging Styrofoam products Infant formula Rough steel forgings Flywheels, etc 267 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agr i c .,F o r .,Fi s h . Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 179572 173369 167428 12144 1427 10717 158840 14529 1360 13169 2124 177448 123803 550 377 6761 35488 2061 171307 118226 753 626 4570 30201 5094 5775 25935 10932 32891 4304 5274 24949 11436 36113 53645 2242 748 50655 53081 2605 775 49701 Zero values indicate confidential information or fewer than 10 employees. 268 PROFILE OF IONIA COUNTY MICHIGAN LOCATION SERVICES: Montcalm-Ionia Community Growth Alliance 227-1/2 West Main Street Ionia, MI 48846 Contact: Phone: Jamie Phillips 616/527-6252 DISTANCE FROM: Miles 200 130 270 270 320 40 Chicago Detroit Cleveland Indianapolis Toronto Lansing . POPULATION 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 45858 51815 52800 60800 27. years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 18200 15650 2525 13.9% 13.6% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1986) Firm Mich. Dept, of Corrections Belding Products Co. Diverstech General product Extruded Metals extrusion TRW American Bumper Brown Corporation Integral Engr. & Mfg. Corp. accessories Employees 1650 1165 550 Product/Service Corrections Air conditioners Custom-molded plastic 375 Aluminum and brass 350 185 175 105 Steering linkages Automotive press Automotive products Machine Tool 269 LuVan, Inc. Belco Industries, Inc. equipment 100 85 Specialty furniture Paint finishing EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 16853 16141 13152 3701 1633 2068 12234 3907 1554 2353 2081 14682 11324 0 0 583 4242 2005 14016 10937 0 0 458 4188 290 571 2687 687 2264 219 338 2715 657 2362 3358 113 114 3131 3079 108 116 2855 Zero values indicate confidential information or fewer than 10 employees. 270 PROFILE OF JACKSON COUNTY MICHIGAN LOCATION SERVICES: Jackson Alliance for Business Development 133 West Michigan Jackson, MI 49201 Contact: Phone: Wendell Mason or John O'Neill 517/788-4455 DISTANCE FROM: Miles 200 70 180 240 320 40 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 143274 152495 143700 150000 29.5 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment Rate: Unemployment Rate: 3-yr. moving average: 62400 56800 5600 9.% 10.7% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Aeroquip Corp. 1ines/f ittings/couplings ITT Hancock Eng. Prod. assemblies Spartan Corporation Mechanical Products devices Wolverine Technologies Wyman-Gordon Co. Kelsey-Hayes Co. Employees 1038 Product/Service Hose 435 Auto stampings & 420 400 Sonobuoys Circuit protection 350 340 329 Vinyl siding Induction Disc brakes 271 Camp International, Inc. ap p l . Aeroquip Corp. T-J Div. hydraulic/pneumatic Kent Moore Tool Group for trans. 260 Orthopedic supprt & 250 Ind. 246 Spec. serv. equip. EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric., For. Fish. Mining Construction Manufacturing Non-Durable Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 65635 58068 58331 7304 1455 5849 49680 8388 1385 7003 1979 63656 53759 180 303 2676 16759 1913 38544 29563 247 444 1666 11859 5371 1975 10934 2763 12798 0 2079 10281 2987 12798 9897 464 338 9095 8981 467 323 8191 Zero values indicate confidential information or fewer than 10 employees. 272 PROFILE OF KALAMAZOO COUNTY MICHIGAN LOCATION SERVICES: Kalamazoo County Economic Expansion Corporation 130 North Kalamazoo Mall Kalamazoo, MI 49007 Contact: Phone: John Bright 616/343-1137 DISTANCE FROM: Miles 150 140 270 270 380 70 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 201550 212378 215500 219700 27.8 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment R a t e : 3-yr. moving average: 110600 104000 6600 6.% 6.9% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Employees Upjohn Company General Motors Corp. (BOC) Western Michigan University Borgess Hospital Bronson Hospital James River Corporation National Waterlift Company Stryker Corporation Checker Motors Corporation Kalamazoo Reg. Psych. Hospital 8000 3682 2721 2650 2600 2406 1200 921 865 853 Product/Service Pharmaceuticals Body stamping Higher education Health care Health care Paper products Aircraft components Hospital equipment Automotive parts Health care 273 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish Mining Construction 3644 Manufacturing 28911 Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin.,Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 113198 111721 102786 10412 1157 9255 99475 12246 1101 11145 1803 110928 92161 0 0 5344 1751 109251 91768 0 0 30942 3660 3377 18766 5407 24665 2804 3843 18816 6058 27692 18767 1018 478 17271 17483 951 490 16042 Zero values indicate confidential information or fewer than 10 employees. 274 PROFILE OF LAPEER COUNTY MICHIGAN LOCATION SERVICES: Lapeer Development Corporation 449 McCormick Drive Lapeer, Michigan 48446 Contact: Phone: Patricia A. Crawford 313/667-0080 DISTANCE FROM: Miles 300 60 220 320 230 70 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 52361 70038 69300 88500 26.9 years LABOR FORCE (as of Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: ) 34850 31300 3550 10.2% 10.8% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Product/Service Durakon Ind. accessories Lapeer Fabricators Inc. plastic/imitat. leather Vlasic Food Products PSI Telecommunications Albar Industries, Inc. products Lapeer Metal Products stampings Employees 250 Auto Parts & 738 Auto 198 107 215 Pickles Plastics Plastic 163 Metal 275 Trayco, Inc. fixtures Growth & Opportunity Woodworking/bench working Hoover Universal/Johnson Controls automotive Howell Industries, Inc. stampings 80 Bathroom 225 157 Seats for 190 Metal EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm 1979 1984 20164 20038 15888 4276 1652 2624 15533 4505 1573 2932 Industry: Farm 2712 Nonfarm 17246 Private 11530 Agr i c .,F o r .,Fish. 0 Mining 0 Construction 1017 Manufacturing 3223 Non-Durable Goods --Durable Goods------------------------- --Transp. & Utilities 467 Wholesale Trade 400 Retail Trade 3112 Fin.,Ins., Real Est. 972 Services 2339 Government Federal, Civilian Military State and Local 5716 120 152 5444 2640 17398 11896 138 111 735 3042 510 591 3036 972 2761 5502 124 153 5225 Zero values indicate confidential information or fewer than 10 employees. 276 PROFILE OF KENT COUNTY MICHIGAN LOCATION SERVICES: Grand Rapids Area Chamber of Commerce 17 Fountain, N.W. Grand Rapids, MI 47503 Contact: Phone: Joseph D. Powers 616/459-7221 DISTANCE FROM: Miles 180 150 300 250 390 60 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 411044 444506 467800 511200 28.2 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 245900 227100 18800 7.6% 8 .6 % PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm General Motors (3 plants) Steelcase, Inc. Amway Corp. Lear Siegler, Inc. Wolverine World Wide Inc. products Keeler Brass Co. hardware American Seating Co. furniture Employees Product/Service 7000 8000 4500 2900 2000 Auto stampings, etc. Office furniture Home & personal care Precision instruments Footwear & leather 1800 Automatic & furniture 1000 Public seating/inst. 277 Lescoa, Inc. Westinghouse Furn. 950 800 Metal stampings Office furniture EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin.,Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 250425 260228 226303 24122 1907 22215 231028 29200 1815 27385 3547 245422 221899 0 0 12418 74546 3466 256762 235569 1262 640 10564 68568 10390 15131 43744 14262 51408 9757 18063 50248 15376 61091 23523 1752 996 20775 21193 1772 1049 18372 Zero values indicate confidential information or fewer than 10 employees. 278 PROFILE OF LIVINGSTON COUNTY MICHIGAN LOCATION SERVICES: Jim Thompson Livingston County Econ. Devel. Office, Inc. Howell, MI 48843 PH: 313/227-5299 or 517/546-0822 DISTANCE FROM: Miles 250 40 210 220 290 30 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980) 58976 100289 104600 182490 28.2 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 48675 45625 3050 6.3% 8.3% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Cars & Concepts, Inc. specialties Citizen's Insurance Tri-State Hospital Supply Western Wheel Kelsey-Hayes Co. Bent T u b e , Inc. Chem-Trend, Inc. Master Cast Co. casting Refrigeration Research Employees 1100 Product/Service Automotive Insurance Hospital supplies 800 300 270 150 125 125 120 Auto braking systems Fabricated tubing Die lubricants Zinc & aluminum die 115 Metal stamping 279 International Paper Co. containers 95 Corrugated shipping EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agri c.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 24538 25259 19533 5005 930 4075 19594 5665 885 4780 1219 23072 18407 0 0 1606 3918 1176 23861 19678 0 0 1312 4158 468 706 5381 2253 4075 455 629 5655 2096 5373 4665 141 213 4311 4183 157 222 3804 Zero values indicate confidential information or fewer than 10 employees. PROFILE OF MACOMB COUNTY MICHIGAN LOCATION SERVICES: Macomb County Community Growth Alliance 115 Grosbeck Highway Mt. Clemens, MI 48043 Contact: Phone: Ben Giampetroni or John Carroll 313/469-5285 DISTANCE FROM: Miles 280 10 290 250 90 Chicago Detroit Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 625309 694600 689700 743800 29.3 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 340750 311125 29625 8.7% 10.% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Employees General Motors Tech. Center development Chrysler Corp. Chrysler Corp. General Motrs-Hydramatic Div Ford Motr Co.-Axle Plant Chrysler Co r p .-Stamping Ford Motor-Trans/Chass. TRW, I n c .-Steering Sus. Modern Engineering & design General Electric 30000 Product/Service Research & 4600 4400 4000 4000 4000 2000 1900 1800 Stamping & assemblies Automobiles Wheels & transmission Axles Stamping & assemblies Rear axle assemblies Auto parts Automotive prototype 1400 Machine tools 281 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manuf acturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 270872 257353 243908 26964 1027 25937 224590 32763 978 31785 1764 269108 232822 1512 309 12669 98134 1720 255633 221147 1508 344 9406 83762 5530 9054 52763 11389 41462 5723 8077 49533 12456 50338 36286 9649 2413 24224 34486 10524 2545 21417 Zero values indicate confidential information or fewer than 10 employees. PROFILE OF MUSKEGON COUNTY MICHIGAN LOCATION SERVICES: Muskegon/Oceana Community Growth Alliance 349 West Webster Room 203 Muskegon, MI 49440 Contact: Phone: Phil Schultz 616/722-3751 DISTANCE FROM: Miles 180 170 320 250 420 100 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 157426 157589 156700 151600 29. years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 68200 60800 7400 10.9% 11.9% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Employees Howmet Turbine Compnents components Sealed Power Corp. SD Warren Co/Div. Scott Ppr . paper Johnson Technology Shaw Walker Co. paneling Kaydon Corp. Teledyne Continental Mtrs. General Telephone Product/Service 4000 Gas turbine 1800 1065 Automotive parts High grade printing 340 1300 Turbine hardware Office furniture, 550 500 956 Antifriction balls E n g . parts Communication systems 283 Brunswick Corporation Muskegon Piston Ring Co. castings 604 295 Bowling equipment Piston rings & EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 66739 62862 60452 6287 544 5743 55560 7302 517 6785 1075 65664 57487 167 179 3619 22409 1051 61811 54787 180 156 2700 18801 3303 2092 10518 2592 12608 2867 1984 10949 2541 14609 8177 426 428 7323 7024 390 413 6221 Zero values indicate confidential information or fewer than 10 employees. 284 PROFILE OF OAKLAND COUNTY MICHIGAN LOCATION SERVICES: Oakland County Community Growth Alliance Executive Office Building 1200 North Telegraph Pontiac, MI 48053 Contact: Phone: Jeff Kaczmarek 313/858-0732 DISTANCE FROM: Miles 290 20 190 300 260 70 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980) 907871 1011793 1014100 1133600 30.4 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 491925 455650 36300 7.4% 8.7% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm General Motors Corp. assembly Michigan Bell Telephone Providence Hospital William Beaumont Hospital K-Mart Corp. Headquarters Allen Industries Operations/Engineering Ford Tractor headquarters Employees Product/Service 28100 Autos, truck bodies, 3800 3800 3600 3000 2000 Communications Health care Retail merchandising Acoustical padding 1600 Automotive-world 285 Federal Mogul National Gypsum Co. 1275 1160 Communications Cement products EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private A g ric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 502896 537006 449319 53577 702 52875 469912 67094 668 66426 1203 501693 453495 2449 609 28532 109322 1172 535834 491148 3973 1085 18788 101795 17685 33639 100282 41832 119145 17840 33853 112137 43530 158147 48198 2839 2242 43117 44686 2754 2242 39690 Zero values indicate confidential information or fewer than 10 employees. 286 PROFILE OF OCEANA COUNTY MICHIGAN LOCATION SERVICES: Muskegon/Oceana Community Growth Alliance 349 West Webster, Room 203 Muskegon, MI 49440 Contact: Phil Schultz Ph 616/722-3751 Oceana County EDC/Resource Center P.O. Box 168 Hart, MI 49420722-3751 Contact: Lora Swenson PH 616/873-7141 DISTANCE FROM: Chicago Detroit Cleveland Indianapolis Toronto Lansing Miles 210 220 350 320 450 120 POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 17984 22002 22500 26700 30. years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 9200 7975 1225 13.3% 14.7% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Product/Service Employees Firm Canned fruits, 250 Oceana Canning Co. vegetables, etc Fish processor 250 Tempotech Industries, Inc. Canned fruit & 130 Stokely USA Inc. vegetables Fruit & vegetable 130 Sawyer Fruit & Vegetable processing Candied cherries 125 Gray & Co. Truck farming 38 J. Brandel Farms Grey iron castings 90 Kirdziel Iron Industries Wire Products 107 Pentwater Wire Prod. 287 Kysor of Cadillac devices New Era Canning Co. vegetables 50 200 Measuring/controlling Canned apples and EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 6643 6794 4856 2087 904 1183 4456 2338 860 1478 1537 5406 4024 162 49 297 851 1497 5297 4091 192 54 364 591 148 158 1115 281 963 134 81 1152 376 1147 1382 62 48 1272 1206 60 49 1097 Zero values indicate confidential information or fewer than 10 employees. 288 PROFILE OF OTTAWA COUNTY MICHIGAN LOCATION SERVICES: Allegan Ottawa Development Corporation 7 East 8th Street P.O. Box 912B Holland, MI 49423 Contact: Kenneth J. Rizzio Phone: 616/392-2389 DISTANCE FROM: Miles 150 170 300 230 420 90 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 128181 157174 167200 204000 27.6 y< LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 86825 81000 5850 6.7% 7.6% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Hart & Cooley Mfg. Co. Employees 659 H. Miller Clock Co. Herman Miller, Inc. 441 2100 Bil-Mar Foods, Inc. 1200 Prince Corporation 1450 J.S.J. Corporation 1300 Product/Service Registers, grills, diffusers Clocks Modular office space systems Turkeys/processed turkey Auto interiors and die cast Metal stampings, assemblies 289 Life Savers, Inc. Donnelly Mirrors, Inc. General Motors Corp. Bastian Blessing equipment 763 1200 950 460 Candy, mints and gum Specialty glass Fuel injection parts Comm, food serving EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 71213 78030 60396 10817 1914 66976 64901 13129 1823 74507 3602 66976 59846 0 0 5373 24862 3523 74507 67319 783 97 3426 26289 2179 2057 10807 3088 11480 2367 2352 12089 3587 16329 7130 291 443 6396 7188 293 459 6436 Zero values indicate confidential information or fewer than 10 employees. 290 PROFILE OF SAGINAW COUNTY MICHIGAN LOCATION SERVICES: Saginaw County Community Growth Alliance 301 E. Genesee Fourth Floor Saginaw, MI 48607 Contact: Jerry Breen Phone: 517/754-8222 DISTANCE FROM: Miles 320 100 270 340 270 70 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 219743 228059 216900 224400 27.9 y< LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 95775 87300 8450 8.8% 10.4% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm General Motors Michigan Bell Telephone Saginaw General Hospital St. Luke's Hospital Eaton Corporation Frankenmuth Bavarian Inn Baker Perkins, Inc. equipment Zehnders Vlasic Food dressings Employees 18600 1450 1200 990 770 521 500 420 400 Product/Service Autos and auto parts Communiciations Health care Health care Auto parts Restaurant Bakery & chemical Restaurant Pickles, sauces, 291 The Pillsbury Co. 375 Grain and dry beans EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private A gr i c .,F o r .,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est, Services Government Federal, Civilian Military State and Local 1979 1984 103728 93642 93488 10240 2416 7824 82362 11280 2300 8980 2807 100573 88833 0 0 4004 35796 2694 90948 80030 311 222 2823 26468 5033 3151 17580 5762 17507 4424 2718 18470 4880 19714 11740 1262 515 9963 10918 1338 497 9083 Zero values indicate confidential information or fewer than 10 employees. 292 PROFILE OF SHIAWASSEE COUNTY MICHIGAN LOCATION SERVICES: Economic Development Corporation of Shiawassee County 701 South Norton Street Corunna, MI 48817 Contact: Doug Hoover Phone: 517/743-3408 DISTANCE FROM: Miles 240 80 250 300 280 30 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 63075 71140 68500 78600 27.9 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 32300 28875 3425 10.6% 12.4% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Universal Electric Co. motors Midland-Ross Corporation brakes Johnson Controls batteries Toledo Commutater Div. collecter rings Motor Products-Owosso Corp. motors Electro-Wire Products, Inc. Employees Product/Service 1200 Precision electric 650 Vacuum & hydraulic 550 Automotive storage 280 Commutaters & 235 Permanent magnet 203 Wiring harnesses 293 F & E Manufacturing Co. parts Lee L. Woodard Sons, Inc. causal furn. MWA Company wheels/coated abrasive Ackco-SVCS, Inc. 200 Small automotive 175 Iron & aluminum 170 Grinding 144 Plastic fabrications EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 22212 20052 17553 4659 1733 2926 15127 4925 1650 3275 2183 19899 16240 0 0 846 5260 2103 17828 14435 0 0 708 4091 1312 699 3533 1020 3570 904 613 3398 948 3773 3659 153 164 3342 3393 149 157 3087 Zero values indicate confidential information or fewer than 10 employees. 294 PROFILE OF ST. CLAIR COUNTY MICHIGAN LOCATION SERVICES: Industrial Development Corporation 511 Fort Street Port Huron, MI 48060 Contact: Robert L. Patterson, Executive Director Phone: 313/982-9511 DISTANCE FROM: Miles 320 60 230 350 160 120 70 60 Chicago Detroit Cleveland Indianapolis Toronto Lansing Flint Pontiac POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980) 120175 138802 138600 159700 29. years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 69550 62550 7000 10.1% 10.8% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Mueller Brass Riverside Metal Prod. Diamond Crystal Salt Co. American Tape tape United Technologies Port Huron Paper Co. publishing Dunn Paper Co. papers U.S. Manufacturing Employees 3295 530 475 390 Product/Service C o m p . parts Metal fabrication Salt & condiments Pressure sensitve 367 331 Headliners Lightweight 287 Lightweight specialty 250 Axles 295 Wirtz Manufacturing Co. Chrysler Corporation 250 200 Battery equipment Sheet metal work EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 42942 43412 35525 7417 1706 5711 35019 8393 1623 6770 2033 40909 34575 150 332 2375 8841 1953 41459 35441 199 223 2303 8776 3805 1102 7800 2364 7806 3149 1083 7750 2490 9468 6334 328 426 5580 6018 326 396 5296 Zero values indicate confidential information or fewer than 10 employees. 296 PROFILE OF VAN BUREN COUNTY MICHIGAN LOCATION SERVICES: Southwest Michigan Community Growth Alliance 5060 St. Joseph Avenue Stevensville, MI 49127 Contact: Marsha Base Phone: 616/983-1529 DISTANCE FROM: Miles 130 160 280 180 400 90 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 56173 66814 66600 81300 29.7 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 28850 25750 3100 10.7% 1 2 .2 % PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Bohn Aluminum & Brass castings Coca Cola Foods Everett Piano Co. Duo-Tang Products Du Wei Metal Products Sherman Dairy, Inc. South Haven Rubber Co. products Burnette Farms Packing Employees . 671 Product/Service Aluminum piston 350 300 290 250 200 180 Canned fruits Pianos and benches Looseleaf binders Metal die castings Milk products Custom-molded rubber 175 Food processor 297 Controlled Rubber Prod, rubber goods Nursery Corp. 142 Molded mechanical 330 Nursery EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agr i c .,F o r .,Fi s h . Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est, Services Government Federal, Civilian Military State and Local 1979 1984 21365 20500 16298 5067 1952 3115 15116 5384 1859 3525 3349 17736 14469 0 0 1267 4843 3265 17235 13782 343 14 1017 3608 684 505 2872 868 3430 820 497 3142 874 3467 3267 135 145 2987 3453 146 148 3159 Zero values indicate confidential information or fewer than 10 employees. 298 PROFILE OF WASHTENAW COUNTY MICHIGAN LOCATION SERVICES: Washtenaw Development Council Waterworks Plaza Building Suite 300 3135 South State Street Contact: Harry Mial Phone: 313/761-9317 DISTANCE FROM: Miles 230 40 160 250 280 60 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): LABOR FORCE 234103 264748 262300 312900 26.6 years (asof 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 148300 141300 7000 4.7% 6.% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm General Motors (2 plants) University of Michigan Ford Motor Company components Ford Motor Company Ford Motor Company Ypsilanti State Hospital Chrysler Proving Grounds Parke Davis & Co. Hoover NSK Bearing Co. Northern Telecom, Inc. Employees 15600 14441 5675 2800 1019 929 850 772 550 500 Product/Service Auto assembly Higher education Auto & electronic Instrument panels Automotive plastics Health care Automotive testing Pharmaceuticals Steel ball bearings Com p . t e r m . 299 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,F o r . ,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 159241 161450 146737 12504 1579 10925 145872 15578 1502 14076 2051 156591 111157 0 0 4170 45572 1977 158556 114295 0 0 4310 39000 3865 2923 19119 5985 29523 3874 4031 20059 6698 36323 45434 2329 637 42468 44261 2309 649 41303 Zero values indicate confidential information or fewer than 10 employees. 300 PROFILE OF GENESEE COUNTY MICHIGAN LOCATION SERVICES: Flint Genesee Corporation 412 South Saginaw Street Suite 207 Flint, MI 48502 Contact: Anthony Schifano Phone: 313/238-7803 DISTANCE FROM: Miles 280 60 230 300 250 50 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980) 445589 450449 433900 445600 27.8 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment R a t e : 3-yr. moving average: 199200 177700 21500 10.8% 11.6% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm General Motors (7 plants) GMC-Metal Fab. Plant General Motors Nuvision, Inc. Country Fresh DuPont DeMours, El & Co. Creative Foam Corp. plastic Lear-Siegler Carpenter Enterprises accessories Employees 48000 4250 1000 322 312 280 250 200 200 Product/Service Automobiles Automotive Parts Optical goods Dairy products Paints, etc. Plastic foam and Metal stampings Auto parts and 301 Vemco Industries 200 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private A gr i c .,F o r .,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 206026 188975 191413 14613 1190 13423 172195 16780 1136 15644 1413 204083 179300 0 0 7358 80844 1357 187614 165356 410 396 5180 66185 6141 11259 31414 8911 33373 5052 11289 31431 8854 36559 24783 1370 1012 22401 22258 1373 976 19909 Zero values indicate confidential information or fewer than 10 employees. 302 PROFILE OF EATON COUNTY MICHIGAN LOCATION SERVICES: County of Eaton Economic Development 1045 Independence Blvd. Charlotte, MI 48813 Contact Person: Roger Clinard Phone: 517/487-6340 Growth Alliance Commerce DISTANCE FROM: Miles 200 90 210 220 330 20 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 68892 88337 89900 112200 28.4 y< LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 49150 46200 2950 6.% 8.% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Owens-Illinois Glass Co. Hoover Ball & Bearing Co. General Aluminum Prod. houses Eaton Stamping Co. Airway Mfg. Co. Green Bay Food Co. Michigan Packaging Co. Johnson Iron Industries counterweights Employees Product/Service 800 683 274 Glass containers Aluminum extrusions Portable screen 250 200 150 100 95 Small motors Hydraulic fittings Pickled food products Corrugated paper Grey iron 303 Maeward, Inc. mechanical tube Roberts Corp. equipment 95 Hydraulic & 78 Material-handling EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 18827 20712 13298 5529 1807 3722 14287 6425 1720 4705 2181 15898 12727 101 0 1019 3546 2096 18616 15601 87 18 845 3139 360 0 3172 1194 3335 318 530 3846 2087 4731 3171 130 192 2849 3015 134 199 2682 Zero values indicate confidential information or fewer than 10 employees. 304 PROFILE OF CALHOUN COUNTY MICHIGAN LOCATION SERVICES: Calhoun-Barry Growth Alliance c/o Calhoun-Barry Area Development Office 632 North Avenue/Battle Creek, MI 49017 Contact: Bob Quadrozzi Ph 616/965-3020 Battle Creek Unlimited, Inc. 4950 West Dickman Road/Battle Creek, MI 49015 Contact: James F. Hettinger 616/962-7526 DISTANCE FROM: Chicago Detroit Cleveland Indianapolis Toronto Lansing Miles 170 115 250 200 360 50 POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980) 141963 141557 136500 135800 30.5 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 63400 57800 5600 8 .8 % 9.9% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Product/Service Employees Firm Cereals 5000 Kellogg Company Federal supply 4500 Defense Logistics Serv. Ctr intelligence Cereals 2500 General Foods/B&B F Div. Health Care 1460 VA Hospital Higher education 800 Kellogg Comm. College Malleable iron 661 Hayes-Albion Power steering pumps 600 Eaton Corp. Regional headguarters 600 State Farm Insurance Co. Health care 500 Leila Hospital Health care 500 Community Hospital Automotive dashboards 835 American Fibrit 305 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private A gr i c .,F o r .,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 68191 59248 61389 6802 1665 5137 51760 7488 1584 5904 2189 64286 53800 228 0 2235 20664 2112 57136 46532 196 180 1691 13981 2597 0 10106 4722 13248 2373 1315 9273 4221 13302 10486 3555 355 6576 10604 4035 345 6224 Zero values indicate confidential information or fewer than 10 employees. 306 PROFILE OF CLINTON COUNTY MICHIGAN LOCATION SERVICES: Clinton Area New Development Organization 1003 S. Oakland St. Johns, MI 48879 Contact: Bill Dutton Phone: 517/224-6761 DISTANCE FROM: Miles 220 100 290 290 340 10 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 48492 55893 55300 62300 27.8 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 30625 28600 2025 6.6% 8.3% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Intercept Security systems Federal Mogul ITT Hancock John Henry Co. Sealed Power Michigan Milk Producers Franchino Mold Saylor Beal Mfg. Co. Schmelter Corp. Maco Tool & Engineering etc. Employees 1025 550 350 350 320 100 75 45 35 33 Product/Service Private security Ball bearings Auto seats Printing Piston rings Milk products Industrial molds Compressors Stampings Tools, dies, jigs, 307 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm 1979 1984 11616 12524 7676 3940 1970 1970 8774 3750 1875 1875 Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services 2332 8431 6483 0 0 666 1493 2239 9264 7278 156 0 631 1552 214 0 1970 597 1543 361 0 1820 617 2141 Government Federal, Civilian Military State and Local 1948 127 127 1694 1986 136 301 1549 Zero values indicate confidential information or fewer than 10 employees. 308 PROFILE OF BERRIEN COUNTY MICHIGAN LOCATION SERVICES: Southwest Michigan Community Growth Alliance Berrien County Area Development Office Dan Pelton 5060 St. Joseph Avenue Stevensville, MI 49127 Phone: 616/429-0611 DISTANCE FROM: Miles 90 180 300 160 430 125 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): 163940 170982 163800 163800 29.6 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 75200 68400 6800 9.% 1 0 .6 % PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Whirlpool Corporation Bendix Corporation Heath Co. National Standard specialty wire Simplicity Pattern Leco Corporation instruments Auto Specialties Mfg. Co. Employees Product/Service 2200 1450 1200 1050 Washers & dryers Auto parts Electronic equipment Reinforcing & 1045 525 Dress patterns Analytical 450 Malleable castings 309 Watervliet Paper Co. paper 450 Coated and specialty EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarra Industry: Farm Nonfarm Private A g ric.,F o r . ,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est. Services Government Federal, Civilian Military State and Local 1979 1984 78489 70455 67858 10631 2292 8339 58695 11760 2182 9578 3830 74659 65840 406 278 2943 25062 3730 66725 58978 548 256 2269 20020 2640 2774 12647 3411 15679 2757 2642 11546 3354 15586 8819 464 401 7954 7747 461 378 6908 Zero values indicate confidential information or fewer than 10 employees. 310 PROFILE OF BARRY COUNTY MICHIGAN LOCATION SERVICES: Calhoun-Barry Growth Alliance c/o Calhoun-Barry Area Development Office 632 North Avenue Battle Creek, MI 49017 Contact: Bob Quadrozzi Phone: 616/965-3020 DISTANCE FROM: Miles 150 130 230 240 380 40 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980): LABOR FORCE 38166 45781 46900 55500 30. years (asof 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 20900 19050 1850 8.9% 10.% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Hastings Mfg. Co. replacement parts Flex-Fab, Inc. ducts Viking Corporation Hastings Aluminum Prod. G & R Felpausch E. W. Bliss Company Hastings Fiberglass Prod J-Ad Graphics, Inc. commercial Employees Product/Service 675 Automotive 300 Flexible hose and 196 160 150 118 75 75 Car seals, sprinklers Building products Food items Presses Fiberglass products Newspapers, 311 Hastings Reinf. Plastics equipment ProLine Company 38 Corrosion-resistant 35 Archery products EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & Utilities Wholesale Trade Retail Trade Fin., Ins., Real Est, Services Government Federal, Civilian Military State and Local 1979 1984 11833 11377 8760 3073 1280 1793 8021 3356 1219 2137 1600 9993 8188 57 0 430 3149 1541 9623 7984 105 0 406 2532 274 0 1726 604 1948 223 0 1822 748 2148 1805 84 98 1623 1639 86 103 1450 Zero values indicate confidential information or fewer than 10 employees. 312 PROFILE OF BAY COUNTY MICHIGAN LOCATION SERVICES: Bay County Growth Alliance Area Development Office 301 Washington Avenue P.O. Box 369 Bay City, MI 48708 Contact: Mike Brandon Phone: 517/893-5596 DISTANCE FROM: Miles 300 100 280 360 280 80 Chicago Detroit Cleveland Indianapolis Toronto Lansing POPULATION: 1970 Census: 1980 Census: 1985 Estimate: 2000 Projection: Median Age: (as of 1980) 117339 119881 115300 115000 28.8 years LABOR FORCE (as of 1986) Total Labor Force: Employed: Total Unemployment: Unemployment Rate: 3-yr. moving average: 52675 46525 6175 11.7% 12.6% PRINCIPAL ECONOMIC BASE EMPLOYERS (as of 1987) Firm Bay City Chevrolet Bay Medical Center Dow Chemical Prestolite AP Parts/Northern Tube systems Wolverine Knitting Mills Newcor, Inc. R W C , Inc. Stalker Corp. Auburn Diecast/Walbro Employees 1900 1100 500 464 300 290 259 255 160 115 Product/Service Small auto parts Health services Plastic wrap Small auto parts Automotive exhaust Loungewear Welding machinery Welding machinery Machine Tools Custom die castings 313 EMPLOYMENT: TOTAL Components by Type: Wage and Salary Proprietors Farm Nonfarm Industry: Farm Nonfarm Private Agric.,For.,Fish. Mining Construction Manufacturing Non-Durable Goods Durable Goods Transp. & utilities Wholesale Trade Retail Trade Fin., Ins., Real Est, Services Government Federal, Civilian Military State and Local 1979 1984 42330 39453 36179 6151 1478 4673 32809 6644 1408 5236 1928 40402 35161 165 102 1790 10513 1861 37592 33086 208 272 1396 8457 1936 1824 8230 2251 8350 1670 2040 8104 2261 8678 5241 285 748 50655 4506 274 775 49701 Zero values indicate confidential information or fewer than 10 employees.