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Structural effects of unemployment on juvenile delinquency and crime rates: a synchronic cross-sectional analysis
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Structural effects of unemployment on juvenile delinquency and crime rates: a synchronic cross-sectional analysis
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STRUCTURAL EFFECTS OF UNEMPLOYMENT ON JUVENILE DELINQUENCY AND CRIME RATES: A SYNCHRONIC CROSS-SECTIONAL ANALYSIS by WardeII Justin Payne A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (Sociology) September 1977 UMI Number: DP31785 All rights reserved INFORMATION TO ALL USERS The quality of this reproduction is dependent upon the quality of the copy submitted. In the unlikely event that the author did not send a complete manuscript and there are missing pages, these will be noted. Also, if material had to be removed, a note will indicate the deletion. Dls-sartâforii F W fe lis M n g UMI DP31785 Published by ProQuest LLC (2014). Copyright in the Dissertation held by the Author. Microform Edition © ProQuest LLC. All rights reserved. This work is protected against unauthorized copying under Title 17, United States Code ProQuest LLC. 789 East Eisenhower Parkway P.O. Box 1346 Ann Arbor, Ml 48106 = 1346 UNIVERSITY OF SOUTHERN CALIFORNIA T H E G R A D U A T E S C H O O L U N IV E R S IT Y P A R K LO S A N G E L E S , C A L IF O R N IA 9 0 0 0 7 PK.O. 5 o '18 P 34 t . T his dissertation, w ritten by WARDELL JUSTIN PAYNE under the direction of Dissertation C o m mittee, and approved by a ll its members, has been presented to and accepted by The Graduate School, in p artial fu lfillm e n t of requirements of the degree of D O D T O R O F P H I L O S O P H Y Dean Date. A u g u s t 2 9 , 1 97 7 DISSERTATIO N œ M M IT T E E .... Chairman ACKNOWLEDGMENTS I wish to express my gratitude to my committee members for their encouragement and constructive comments. I am especially indebted to Professor Glaser, from whose research this analysis germinates and from whom I have acquired an increased appreciation for the rigors of scientific research. A special thanks to H. Wayne Hansen, Research Asso ciate, Program for Data Research, University of Southern California, and to Edward Freudenberg, Research Director, Welfare Planning Council, Los Angeles Region, for their cooperation in programming and facilitating the acquisition of the data from the Los Angeles County Probation Depart ment. I am especially grateful to the Los Angeles County Probation Department for its cooperation in granting permission fo use their data files in this study. This work was supported by two research grants: (1) a National Service Research Award from the National Institute of Mental Health of the National Institutes of Health, United States Public Health Service, Rockville, Maryland 20852 (1 F31 MH05428-01); and (2) an Atlantic Richfield Foundation Fellowship in Population and Environ mental Studies, Atlantic Richfield Foundation, 515 South Flower Street, Los Angeles, California 90071. 11 TABLE OF CONTENTS Page ACKNOWLEDGMENTS...................................... ii LIST OF TABLES....................... v LIST OF FIGURES.............. ....................... . x Chapter I. THE THEORETICAL FRAMEWORK.............. 1 Introduction Statement of the Problem and Its Grounding in Prior Theory and Research Organization of the Dissertation II. DERIVATION OF HYPOTHESES................... . 15 Foundations of an Economic Delinquency Research Tradition Reconsiderations of Economic Determinants to Crime Discussion of the Implications of Prior Research Ecological Analysis Framework Hypotheses III. RESEARCH METHODOLOGY .............. 51 Method of Data Collection Operationalization of Key Concepts The Study Design The Research Design IV. ANALYSIS AND DISCUSSION ................... 78 Unemployment, Delinquency and Crime Income, Delinquency and Crime Youth Labor-Force Participation and Delinquency Chapter Page V. SUMMARY, IMPLICATIONS, LIMITATIONS, AND SUGGESTIONS FOR FUTURE RESEARCH ........ 171 Summary Implications Limitations and Suggestions for Future Research REFERENCES................... 186 APPENDICES................... 203 A. Referral Categories Aggregated into Juvenile and Adult Offense Types ........ 204 B. 1970 Census Tracts Included in Study Areas, with 1960-1970 Population . ............ 206 IV LIST OF TABLES Table Page 1 . Zero Ordered Correlation Coefficients, Means, and Standard Deviations for Independent Variables (Total) ......................... 79 2. Zero Ordered Correlation Coefficients, Means, and Standard Deviations for Iridependerit Variables (Anglo-White) ................... 80 3. Zero Ordered Correlation Coefficients, Means, and Standard Deviations for Independent Variables (Black) ......................... 81 4. Zero Ordered Correlation Coefficients, Means, and Standard Deviations for Independent Variables (Spanish-Surnamed) ........ . . 82 5 . Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Delinquency and Unemploy ment Rates for All R a c e s .......... .. 85 6. Unstandardized Regression Coefficients of Unemployment and Age-Offense Delinquency Rates for All Races................... 87 7. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Delinquency and Unemploy ment Rates for Anglo-Whites ........ 91 8 . Unstandardized Regression Coefficients of Unemployment and Age-Offense Delinquency Rates for Anglo-Whites ................... 93 9 . Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Delinquency and Unemploy ment Rates for Blacks ........ ...... 95 V Table Page 10. Unstandardized Regression Coefficients of Unemployment and Age-Offense Delinquency Rates for Blacks ............................ 97 11. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Delinquency and Unemploy ment Rates for Spanish-Surnamed ....... 98 12. Unstandardized Regression Coefficients of Unemployment and Age-Specific Delinquency Rates for Spanish-Surnamed........ .. 100 13. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Crime and Unemployment Rates for All Races . . . . . . . . . . . . . 104 14. Unstandardized Regression Coefficients of Unemployment and Age-Offense Crime Rates for All Races . . . . . . . .......... 106 15. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Crime and Unemployment Rates for Anglo-Whites . . . . . . . . . . . 108 16. Unstandardized Regression Coefficients of Unemployment and Age-Offense Crime Rates for Anglo-Whites . . . . . . . . . . . . . . 110 17. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Crime and Unemployment Rates for Blacks ........................... 111 18. Unstandardized Regression Coefficients of Unemployment and Age-Offense Crime Rates • f 3 ^ I B 1 - s ..... ...... ...... 113 19, Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations by Offense Type Age-Specific Crime and Unemployment Rates for Spanish-Surnamed . . . . . . . . . 114 vi Table Page 20. Unstandardized Regression Coefficients of Unemployment and Age-Offense Crime Rates for Spanish-Surnamed ........................ 116 21. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Total Median Family Income and Age- Specific Juvenile Delinquency Rates for All Races.......... ......................... 129 22. Unstandardized Regression Coefficients of Total Median Annual Family Income and Age- Of fense Juvenile Delinquency Rates for All Races . . . .............................. 131 23. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Anglo-White Median Annual Family Income and Age-Specific Juvenile Delinquency Rates for Anglo-Whites ..................... 132 24. Unstandardized Regression Coefficients of Anglo-White Median Annual Family Income and Age-Offense Juvenile Delinquency Rates for Anglo-Whites ........................... 134 25. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Black Median Annual Family Income and Age-Specific Juvenile Delinquency Rates for Blacks ................. . . . . . . . . 136 26. Unstandardized Regression Coefficients of Black Median Annual Family Income and Age-Offense Juvenile Delinquency Rates for Blacks ..... ....................... 138 27. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Spanish-Surnamed Median Annual Family Income and Age-Specific Juvenile Delinquency Rates for Spanish-Surnamed ................. 139 28. Unstandardized Regression Coefficients of Spanish-Surnamed Median Annual Family Income and Age-Offense Juvenile Delinquency Rates for ^Spanish-Surnamed........ .. 141 vii Table Page 29. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Total Median Annual Family Income and Age-Specific Crime Rates for All Races . . . 144 30, Unstandardized Regression Coefficients of Total Median Annual Family Income and Age- Offense Crime Rates for All Races . . . . . . 145 31. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Anglo-White Median Annual Family Income and Age-Specific Crime Rates for Anglo-Whites 146 32. Unstandardized Regression Coefficients of Anglo-White Median Annual Family Income and Age-Offense Crime Rates for Anglo-Whites . . 147 33. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Black Median Annual Family Income and Age-Specific Crime Rates for Blacks . . . . . 148 34. Unstandardized Regression Coefficients of Black Median Annual Family Income and Age-Offense Crime Rates for Blacks . . . . . . . . . . . 149 35. Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Spanish-Surnamed Median Annual Family Income and Age-Specific Crime Rates for Spanish-Surnamed . . . . . . . . . . . . . . 150 36. Unstandardized Regression Coefficients of Spanish-Surnamed Median Annual Family Income and Age-Offense Crime Rates for Spanish- 3 3 . T * T 3 d . . . . . . . . . . . . . . . . . . 151 37. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Youth Labor-Force Participation and Age-Specific Delinquency Rates by Offense Types for S . . . a . s . e . e o . o . o o . e 155 38. Unstandardized Regression Coefficients of Youth Labor-Force Participation and Age- Of fense Delinquency Rates for All Races . . . 157 viii Table Page 39. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Youth Labor-Force Participation and Age-Specific Delinquency Rates by Offense Types for Anglo-Whites ................................ 158 40. Unstandardized Regression Coefficients of Youth Labor-Force Participation and Age- Of fense Delinquency Rates for Anglo-Whites . 160 41. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Youth Làbor-Force Participation and Age-Specific Delinquency Rates by Offense Types for Blacks 161 42. Unstandardized Regression Coefficients of Youth Labor-Force Participation and Age- Of fense Delinquency Rates for Blacks .... 163 43. Zero Ordered Correlation and Standardized Regression Coefficients, Means, Multiple R Square and Standard Deviations of Youth Labor-Force Participation and Age-Specific Delinquency Rates by Offense Types for Spanish-Surnamed............................ 164 44. Unstandardized Regression Coefficients of Youth Labor-Force Participation and Age- Offense Delinquency Rates for Spanish- Sur named ..................... 166 45. Synopsis of Research Hypotheses ............... 173 IX LIST OF FIGURES Fage Figure Relations among Socioeconomic Factors, Demographic Characteristics, and Juvenile Delinquency at Three Levels of Conceptual ization .................................. CHAPTER I THE THEORETICAL FRAMEWORK Introduction Theories on the relationship of economic and social factors to juvenile delinquency and crime have generated considerable research. Some theorists maintain that economic conditions directly influence juvenile delinquency rates ; others that socioeconomic factors are only one dimension of myriad complexities underlying the comprehen sive issue of crime and juvenile delinquency. Two opposite postulations, relating economic factors to juvenile delin quency, orient theoretical and empirical approaches in the analysis of underlying social factors. They are : (1) that economic conditions relate inversely to rates of delin quency, and (2) conversely, that the relationship between economic conditions and juvenile delinquency rates are positive or direct (Void, 1958:174). Economic analysis of crime posits a model of man predicated upon maximization of utility functions (Fleisher, 1966; Becker, 1968, 1974; Ehrlich, 1973, 1975; Danziger & Wheeler, 1975). According to this perspective, individuals assess the returns from any possible activity-- both legal and illegal--prior to selecting their particular course of action. The determination of a course of activity is assumed to be directed towards the achievement of the most benefits at the lowest cost. When considering criminal behavior in this process, the maximization of utility functions involves determination of the relative ''costs" and "benefits" which may be derived from either engaging or not engaging in criminal or juvenile activity. As such, criminals or delinquents are responsive to criminal justice policies--e.g., increased surveillance by the police, or decreased conviction rates by the courts (Becker, 1968; Danziger & Wheeler, 1975). Similarly, it can be generalized that the officially recorded criminal activity level is also related to the range of legitimate pursuits, since these conditions can reduce the "reward structure" or relative "benefits" of criminal behavior. Sociological inquiry of the relationship between socioeconomic factors and crime and delinquency has incor porated many divergent perspectives. Research strategies have focused on both macro-level and micro-level explana tions of social behavior ; yet no general or exhaustive conclusions or paradigms, asserting a definitive relation between social factors and crime and delinquency, have evolved. The relationship between crime or delinquency and socioeconomic conditions can be characterized as fluid, with the determination of primary causal factors problem atic, especially when we attempt to relate specific incidences of offenses to socioeconomic and demographic factors. Exegeses of the relationship of socioeconomic conditions to juvenile delinquency, or crime, are further clouded when we focus on how basic population characteris tics pertain to rates of specific offenses. This problem can be clarified through nominal and empirical observa tions. For example, juvenile delinquency is statutorially defined in terms of an offender's age, thereby linking delinquency with age. This is an example of a nominal relationship. The modal age for auto theft arrests in the United States is 16 years old, the rate of arrest during the 15-19 age range is triple the rate of the next highest five-year age-span, i.e., 20-24 years old (Glaser, 1975: 33-46). This is an example of an empirical observation providing support for a nominal relationship between age and juvenile delinquency by focusing on a specific type of offense. It should be evident that the latter age span, 20-24 years old, refers to adult criminal behavior, whereas the 15-19 age refers to juvenile delinquency. These examples demonstrate some categorical distinctions between age and delinquency. The probability of being a delinquent increases with age (within the legally defined juvenile delinquent age range, generally held to be less than 18 years of age), and is at its maximum in the last two years prior to the statutory age limit. Race is an important variable in assessing involve ment in crime and delinquency. Certain racial groups (non-whites, blacks and Mexican Americans in particular) experience higher official rates of crime and juvenile delinquency than whites (see Wolfgang & Cohen, 1970:31-38). There is a high proportion of low income groups represented in the official rates of crime and juvenile delinquency. The assessment of the importance of socioeconomic condi tions to juvenile and criminal offense rates is problematic due to numerous intervening variables, specifying condi tions, and data limitations which obfuscate the relation ship. For this reason it has been especially difficult to determine the salience of socioeconomic conditions as they relate to juvenile delinquency or crime. During the 1970s the level of American youth unemployment has escalated and certain social structural problems, perhaps symbolic of a changing role of youth in contemporary society, may be correlated with this change in labor force characteristics. Youthful participation in society is limited, and most pronouncedly in terms of economic pursuits, Increases in social structural problems of youth may reflect their declining participation in society and increasing frustrations in response to this sytematic exclusion (cf. Gillis, 1974, and The Report of the Panel on Youth of the President's Science Advisory Committee, 1974). Current levels of unemployment in the United States have resulted in increased concern with the relationship between joblessness and substantive aspects of social disorganization for adults. However, these problems may be most directly related to the labor force characteristics of youth. The objective of this study is to reexamine the relationships between labor force participation and juvenile delinquency, focusing on the distinctions in age, race and offense types in a synchronic cross-sectional ecological analysis of juvenile delinquency and crime patterns in Los Angeles County for 1970. The data derive from the United States Census of Population (1970) and the Los Angeles County Probation Department Files (1970). The specific focus of this study is on the socioeconomic correlates of crime and juvenile delinquency, with delin quency viewed as a disorganizational consequence of blocked opportunities for youth to enter the job market. Most studies considering the relationship between crime and socioeconomic factors focus on adult criminal patterns, although age has been noted to be inversely correlated with crime (after the age of 16 years). This tendency to focus on adult criminality underscores the need for renewed interest in the age-specific correlates of crime. For example, in a study of robbery in the United States, 75% of all persons arrested were under 25 years of age, 56% were under 21, and 33% were under 18 (Sagalyn, 1974:12). The peak arrest rates for property crimes are at ages 16, 17 and 18, and tend to decrease after adolescence. There are certain qualifications which relate to the age- crime covariation: among the most important of these variables are differences by sex, time, place and offense (Sutherland & Cressey, 1970 :122-125). Gender appears to be a major variable since females are arrested for fewer crimes, with the bulk of them occurring later in their lives than is the pattern for males. Sutherland and Cressey (1970) show that 54% of the men arrested for larceny were under 18 years of age, compared to 32% for females, and note that this relation ship is reversed for specific crimes, e.g., 27% of the females arrested for forgery were under 18 years of age whereas only 12% of the males arrested for this crime were under this age. Crime appears to be predominantly male oriented. Sutherland and Cressey (1970:124) contend that sex status, or gender, is the most crucial variable in predicting the extent of criminal activity. We might speculate that different patterns of socialization and expected roles of participation for males and females play an important part in the underlying relationship between sex status and engagement in criminal activity. In particular this speculation may be extended by considering different aspects of labor force participation for males and females, and the corresponding legal and illegal opportunities and alternatives available to them. Unemployment is an economic factor generally considered an indicator of economic conditions. It has particular relevance to youth, since they have a unique problem in entering the labor force. Opportunities for youthful participation in the labor force are controlled by such factors as age, experience and education. Numerous theories have been formulated which discuss the association between adult and youth unemploy ment rates and their relationship to the crime rate. Since 1969, opportunities for youthful entry into the job market have diminished drastically (cf. U.S. Department of Labor, 1976 ; Phillips et al., 1972; Report of the Twentieth Century Task Force on Employment Problems of Black Youth, 1971:3-17), while there have been concomitant increases in the number of youths in the labor-force-entry age cohorts, and in the number and incidence of delinquent and criminal offenses committed by youths. The relationship of socio economic conditions to crime and juvenile delinquency merits further exploration; correlates of unemployment, labor force participation and socioeconomic conditions may be especially important in explanations of juvenile delin quency . If unemployment rates or poor labor force condi tions are positively correlated with delinquency, then the nuances of this relationship should have direct relevance in the formulation of social policy which focuses on youth in contemporary society. The problems associated with employment are especially salient for youths, who are generally dependent upon their parents for their income. In this regard, Fleisher contends : If, for instance, delinquency rates increase with unemployment, public policies which aim to eliminate excess aggregate labor supply will yield benefits over and above those usually claimed for them. Furthermore, we should like to know whether policies intended to ease the entrance of young persons into the labor force and policies which attempt to speed the geographic and industrial redistribution of structurally unemployed labor are likely to reduce delinquency. (1963:543) Phillips et al. (1972) considered some factors pertaining to socioeconomic conditions and juvenile delin quency, concluding that offenses committed by youths are related to economic conditions. In a retrospective analysis of male arrest rate patterns for economically oriented offenses, they demonstrate that arrest patterns varied by age ; this distinction being especially salient for males 14 to 24 years old. The post-World War II baby boom and the consequent shift in the age distribution toward a more youthful population were considered In explaining the increase in the incidence of youth-committed felonies. They concluded that there is a relationship 8 between youthful offenders and the crime rate, and that the shift in the age distribution towards a younger population does not totally explain the increase in the crime rate. Our study analyzes crimes by individual age groups in order to abstract from the effect of the secular shift in the age distribution of the population. It is interesting to observe that while crime rates were sky rocketing for youth, unemployment rates for eighteen- to-nineteen-year-old white males rose from a low of 7.0 percent in 1952 to a peak of 16.5 percent in 1958, and were still at 9.0 percent in 1967. For non-white males the situation was even worse. In 1952 their level of unemployment was 10.0 percent ; it rose to a high of 27.2 percent in 1959, and recovered to the level of 20.1 percent in the prosperity year of 1967. . . . In relating labor-market opportunities for youth to their arrest rates, it is necessary to take account of the fact that since youth have low participation rates, unemployment rates will have less weight because of the considerable fraction of youth outside the labor force. (1972:493)1 Phillips et al. (1972) note that the trend for a younger population accounts for some of the increase in the level of juvenile delinquency, yet the proportion of arrests for youths has risen substantially above the corresponding growth of the youth subset of the population. They contend that the residual proportion of the variance to be explained are social and economic conditions, rather Phillips et al. do not consider arrest rates by age and race, since they were using the FBI data on index crimes which did not provide detailed offense-specific data. Data by race have been available annually since 1962, but they have not been very specific in their cate gories, being limited to a dichotomous age classification (i.e., under 18 years of age, and 18 years old and over). Consequently, little research, stemming from official data sources, has ensued considering the relationship of age, offense and race. than demographic variables per se. They further contend that unemployment fluctuations apparently have less impact on whites than on non-whites, and propose that inquiry be made into the employment problems facing young people, since it may help to understand the rising juvenile offense rate. With the increasing levels of complexity in modern technological societies, social researchers are becoming more attentive to the dynamics of social change, especially as these social structural transitions pertain to sub groups of the population, e.g., age, sex and racial groups. As the social structure is further differentiated, or functionally specified, the role of youth in society becomes increasingly more complicated. The functional aspects of the various social institutions and socializing agents of the society reflect changes in the dynamics of the social structure. Highly mechanized and industrialized societies, for example, may not provide the functional integration of roles characterizing what Durkheim called mechanical solidarity. Shifts in the roles and functions of various social institutions result in anomie, according to Durkheim, and to social disorganization, according to later sociologists whose theories were offered as explana tions for various societal problems--i.e., alcoholism, crime, suicide and divorce. 10 Statement of the^Problem and Its Grounding to Prior Theory and Research This study investigates ecological correlates of juvenile and adult probation offense types with unemploy ment rates, considering the racial background of the offenders, their ages, and community demographic character istics. It further provides reformulation and retesting of hypotheses derived from the works of Bogen (1944), Glaser and Rice (1959), Fleisher (1966) and Gibbs (1966). Finally, it should be noted that these researchers tested their hypotheses primarily by diachronic data on total large cities or national populations, but that this study employs a synchronic analysis of ecological segments of a metropolitan county. The latter sections of this chapter summarize the theoretical and empirical approaches to be employed in this analysis. The theoretical model, methodo logical considerations and operationalization of key concepts will be discussed in the next two chapters. Organization of the Dissertation Throughout Chapter II, the theoretical, conceptual and substantive problems of the analysis are discussed, illustrating the rationale for the selection of the vari ables studied. The discussion explicates the theoretical relationship between the various concepts through an exercise in theory construction similar to that employed by 11 Kobrin (in Voss & Petersen, 1971:101-131). Three theoret ical elements are considered: (a) the volitional statement, which narratively asserts relationships among variables constituting the theory; (b) the structure of the theory, which conceptually relates the theoretical concepts to the empirical level; and (c) the formal-logical struc ture of the relationship among the concepts, which relates the empirical level of conceptualization to its operational system (cf. Kaplan, 1964; Zetterberg, 1965; Wilier, 1967 ; Kobrin, in Voss & Petersen, 1971:102; Mullins, 1971; Hage, 1972; and Burr, 1973) Relations among the variables are stated in the form of hypotheses which are interspersed throughout the text. The theoretical formulations of the study and its formal-logical properties are asserted through an analyt ical specification. The postulates are derived and the 2 . Kobrin (1971) distinguishes three elements which pertain to the structure of a theory : (1) vocabulary which consists of "primitive" conceptual terms, usually terms occurring in normal, nontechnical communication; (2) grammar which represents an asserted calculus of the relationships among terms ; and (3) a dictionary which defines the rules by which concepts are linked to observa tional items, i.e., the operational definitions employed. He further relates criteria for determining the adequacy of a theory, which are : (a) the logical adequacy which refers to the degree to which the relationships among the vari ables have been unambiguously explicated in a parsimonious and nonredundant manner ; (b) the operational adequacy which relates to the clarity of linkage between the conceptual and operational definitions of the variables ; and (c) the empirical adequacy which centers on the extent to which the "truth claims" of the theory are in fact supported by the available evidence (cf. Gould & Schrag, 1962:68-73). 12 epistemological correlates are presented. The conceptuali zation of the relationships is considered on three distinct levels: theoretical, empirical and operational (see Figure 1). Chapter III provides a discussion of the methodol ogy and the operationalization of concepts employed in assessing the relationships among the variables. The results of the empirical tests are presented and discussed in Chapter IV. Chapter V consists of the summary and conclusions of the analysis. 13 Level of Conceptualization Theoretical Relation among Variables Social Deviance Socioeconomic Factors Demographic Characteristics II. Empirical 4 (~) Age Race Juvenile Delinquency Income Labor Force Participation III. Operational Age and Race Specifics Unemployment Rate -----------^ Total Rate of Juvenile and Adult Offenses Percent Female Youth in Labor Force Rate of Juvenile and Status Offenses Percent Male Youth in Labor Force Rate of Juvenile and Adult Property Offenses Median Annual Family Income Rate of Juvenile and Adult Person Offenses Fig. 1. Relations among socioeconomic factors, demographic characteristics, and juvenile delin quency at three levels of conceptualization. 14 CHAPTER II DERIVATION OF HYPOTHESES Foundations of an Economic Delinquency Research Tradition Early research on crime and economic trends was primarily based on adult patterns of criminal behavior (Bonger, 1916; Thomas, 1927; Winslow, 1921). These studies generalize that crimes against property, such as theft, burglary and robbery, increased during periods of economic depression and high unemployment as indexed by the number of criminal prosecutions, convictions, or prison admissions (cf. Van Rleeck, 1931; Warner, 1934). The data excluded offenses committed by juveniles. It was assumed that juvenile delinquent activities were analogous to adult criminal behavior and increased in periods of depression, when, in actuality, the studies did not specifically consider juvenile delinquency (Bogen, 1944:179). Studies of juvenile delinquency and economic trend have not been as empirically substantiated as the adult criminal correspondence to economic trends. Two classic studies were conducted by Lunden (1938) and Carr (1941). Controversy remains over the direction and extent of the relationship between economic trend and juvenile delin quency . 15 Lunden presents data from several sources, but partic ularly from the Junvenile Court for Allegheny County, Pennsylvania, for the years 1924 to 1934, revealing a definite decline in delinquency during the depression years. Lunden also cites a study made in Wayne County, Michigan, which indicated a positive correlation between the incidence of juvenile delinquency and business prosperity. Carr reviews this material and adds further evidence from St. Clair County, Michigan, confirming the observation that delinquency tends to fluctuate in the same direction as the economic index, and not conversely as has been commonly supposed. Carr points out that the effect of economic trendsippon juvenile delinquency is evidently different from that which it exerts upon adult criminality, and discusses various factors associated with prosperity which may account for the increase in delinquency during periods of prosperity. (Bogen, 1944:179) Bogen (1944) further supports the postulation that juvenile delinquency increases in periods of prosperity and decreases in periods of widespread unemployment and economic distress in his empirical consideration of Los Angeles County Juvenile Court during 1935 to 1941. He plots four curves which pertain to business activity, the school population and juvenile court petitions. He con tends that the positive relationship between juvenile delinquency and economic prosperity suggests that economic conditions play a decisive role in the causation of delin quency, and maintains that the underlying socioeconomic characteristics of juvenile delinquency are not identical to adult criminality patterns. The disorganization of the family is one explana tion that Bogen offers, asserting that in times of prosperity parental supervision becomes relaxed, that 16 parents and children are away from home and each other for a greater duration, and that family ties, which seem to grow stronger in the face of adversity, are weakened; he posits that parental supervision is an important variable in curtailing delinquency, but that it is not a deterrent factor for adult law violation. Other factors which may contribute to the disorganization of the family are employ ment opportunities for women outside of the home, consump tion of alcohol and patronage of commercialized recreation facilities. Another plausible explanation for the increased level of juvenile delinquency during periods of prosperity may be related to the increased amount of carelessness with how people handle their property. That is, the combination of enforced idleness and financial need probably leads to increase in adult offenses against property in periods of economic depression, while the weakening of parental super vision leads to increased juvenile delinquency in periods of prosperity. (Bogen, 1944:183) Bogen contends that analyses of criminal trend should progress on an age-group basis. His rationale for this approach is centered on the concept of parental supervision. He postulates that there is no fixed age at which parental supervision gives way to adult responsibili ties in a universal sense, but to a large extent children of juvenile court age are governed by the habits and requirements of the parental home. Criminal court offenders, pjtobably include a younger age group who are 17 affected by the economic trends in the manner of juvenile delinquents, but these effects are masked by the effects of the economic cycle on crime in older age groups. Burgess (1952), researching the economic factors pertaining to juvenile delinquency, defined a delinquent as one who has been treated as such by society ; that is, there must be an official record. Accordingly, a delinquent must have completed one or more stages as outlined by Burgess. That is, the delinquent must have (1) been arrested by police; (2) appeared in juvenile court; (3) been detained in the juvenile detention home ; (4) been placed on proba tion; (5) been committed to an industrial school ; or (6) been released on parole. Official records of delinquency including arrest, appearance in Juvenile Court, probation, commitment to institutions and later parole and recidivism characterize many children from low income families. . . . From the practical standpoint those children that require official attention and supervision are the ones that are of interest to us in the study of the effect of low income upon juvenile delinquency. We do not need to concern ourselves with the delin quent acts of children from families in which parents have the financial resources and the ability to supervise and guide the behavior of their chil dren without the intervention of the juvenile court. (Burgess, 1952:29) Burgess contended that official records of juvenile delinquency and crime supply the best available evidence of who is a delinquent or a criminal. He postulated that low income and juvenile delinquency are correlated in the following ways ; 18 (1) Official juvenile delinquents are concentrated in certain areas of every city, called delinquency areas. (2) Juvenile delinquency areas are highly correlated with poverty and with low income. (3) Areas with high juvenile delinquency rates have high rates of other problems also. (4) Poverty is only one of a number of factors associ ated with juvenile delinquency. Some other factors are : (a) bad housing; (b) broken homes ; (c) work ing mothers; (d) parental negligence ; (e) over- severity; (f) over-leniency ; (g) rej ection; (h) gangs ; (i) delinquent and criminal traditions ; and (j) other neighborhood conditions. Burgess notes that the pattern of becoming a female delinquent is more specific and uniform than for males. The contention is that female delinquents are likely to be sexually promiscuous and almost invariably from low income families. Low income, however, is not the decisive factor but rather one that underlies many of the other factors. Low income is rather directly related to meager conditions of family life and insufficient provision in the home for wholesome recreation. In addition, there are almost always unfavorable interpersonal relations in the families. The home often is broken by death, desertion or divorce. (Burgess, 1952:36-37) Burgess (1952) builds his consideration of economic factors in juvenile delinquency on the appreciable 19 relationship between low income and juvenile delinquency. The contention of his argument is that the incidence of juvenile delinquency increases in low economic areas at a higher rate than in any other area. This is seen to be resultant of various aspects of social disorganization and social pathology which are accelerated in these communi ties . One empirical observation which should be further considered is the generalization that the rate of delin quency within a community is more salient for low socio economic communities and that the greater the delinquency within a community, the greater the extent of delinquency in the younger age groups. That is, the rate of juvenile delinquency is indirectly related to the age-specific delinquency rate for the community (Burgess, 1952:39). Reconsiderations of Economic Determinants to Crime Glaser and Rice (1959) were concerned with the relationship between crime and economic conditions, especially as it reflected countervailing influences on juvenile behavior, positing a negative relationship between offenses committed by juveniles and unemployment rates, and a positive relationship for adult property crimes and unemployment rates. Their study supported the thesis that the lower age juvenile delinquency rates decreased with unemployment while adult crime rates increased. Due to 20 unavailability of sufficient categorical breakdowns of juvenile arrest data by offense types, age, sex and race, this thesis was not fully analyzed by them and was not replicated by Fleisher (1966). Glaser and Rice (1959) infer that the relationship between juvenile delinquency and unemployment rates is inversely related, since youth are dependent upon their parents for their welfare and care. They reasoned that as parents are unemployed, the quality of parental care increases. This relationship was not postulated to be constant for all youth age groups, since youthful activity varies considerably by age. Glaser and Rice argue that adult unemployment, or the general economic conditions, would be most salient for younger aged youths, since younger aged youths are mote likely to be intensively involved within the familial unit, and thus their behavior is more likely to be restrained by the increased parental availability which adult unemployment may produce. This is an indication that age groups are especially important in differentiating social behavior. Similarly, we should also expect that there would be a difference in the behavior exhibited by males and females, with males and older youths being more likely to respond to factors outside the home (e.g., peer pressure, economic conditions or opportunities for crime). 21 As noted previously. Void (1958) concludes that the correlations between economic variables and crime rates vary so much in both direction and magnitude that no definite conclusions are warranted. Glaser and Rice (1959), however, provide a basis for reconsidering the relationship between economic factors, crime, and delinquency rates. Their study demonstrated, through a diachronic analysis of large cities across the United States from 1932 to 1950, that the relationship between arrest rates for property offenses and adult unemployment rates varied from one age group to the next (Glaser & Rice, 1959:681-684). Gibbs (1966) discusses the merits of these findings and discerns that Glaser and Rice's results are of substantive impor tance in facilitating the reanalysis of crime and its relation to economy. Gibbs states that ". . . the relation between unemployment and relative crime rates (arrests in an age group as a proportion of total arrest) changes from inverse to direct and then from direct to inverse with increasing age" (Gibbs, 1966, in Rushing, 1975:96). Gibbs further comments that "this pattern indicates that the consequences of unemployment are, at least as far as crime is concerned, relative to social status (in this case, age)" (Rushing, 1975:96). Block and Zimring (1973) illustrate the dramatic increase in robbery killings by considering rates of criminal homicide in Chicago from 1965 to 1970. Their 22 study revealed that the patterns of homicide during the 1965 to 1970 period increased over the aggregate homicide rate level. In particular the findings indicated that robbery killings, killings involving younger victims and offenders, group-related killings, and gun killings all increased. The homicide offense rates for black males aged 15 through 24 almost tripled during the six-year period, while victimization among the same group more than tripled. Their findings further indicate that young and very young offenders accounted for half the general increase and are rapidly bringing the age distribution of known homicide offenders in Chicago closer to age distributions noted in other cities. The analysis is descriptive and does not consider social and economic factors. However, it does provide credence for further consideration of the basis of the growing criminal activity of younger juveniles. In considering economic correlates to delinquency, Fleisher concludes that the economic factor in delinquency is more a function of mean income than of rates of unem ployment, especially in areas of high delinquency. He suggests that the proclivity for crime may be resultant of a general "taste" for conforming behavior, speaking in terms of the relative costs of deciding either to engage or not engage in criminally related activities (Fleisher, 1966:19-37). According to Glaser and Rice (1959), Fleisher 23 (1966) and Bogen (1944), during periods of high unemploy ment parents may have more time to supervise their children, despite difficulties encountered in providing them with the accustomed amount of goods and services. This increased contact across the generations may offset the expected tendency for crime to increase during periods of high unemployment, especially crimes committed by young people who have not entered the job market. Glaser and Rice found slightly negative correlations between crime and employment for the youngest age groups, but strongly positive relationships for adults. We can, therefore, hypothesize that: IA. The age-specific delinquency rate for , juveniles less than 16 years old varies inversely with the adult unemp 1oyment rate. IB. The age-specific delinquency rate for juveniles 16 years old and over varies directly with the adult unemployment rate. IC. The age-specific crime rate for young adults varies directly with the adult unemployment rate. (+) Adult Unemp loyment Rate Adult Unemployment Rate Younger Juvenile Age-Specific Delinquency Rate Older Juvenile Age^ Specific Delinquency and Young Adult Crime Rates 24 The above propositions derive from Glaser and Rice (1959), who assert that there is a curvilinear relationship between age-specific delinquency rates and adult employment Their rationale for this relationship is based on the assumption that adult unemployment results in increased time for parents to supervise their children, and that parental supervision deters younger rather than older juveniles from participating in delinquent activities (see also Fleisher, 1966). Older aged juveniles rely less on their parents for provisions of their own welfare than do younger juveniles, and they are more likely to be involved in the labor force, which reduces the influences of parental supervision on their behavior. This relationship is schematically represented above. The age-specific crime rate for young adults is similarly derived from an extension of the above rationale. Changes in economic conditions provided differential avenues of accessibility to legitimate and illegitimate opportunities. Under conditions where unemployment rates are high, the legitimate opportunity structure for young adults is restricted. Some examples of their legitimate recourses are to enter the military or to seek further education. When the economic conditions are not as restricted, young adults are more likely to be engaged in the labor force or other "worthwhile" pursuits. The third hypothesis in this set (proposition 1C) considers the 25 influence of adult unemployment on young adult criminal behavior, and argues that there should be greater preva lence of crime under conditions of high unemployment. The converse of this proposition is that there should be lower criminal behavior under conditions of full employment. This is interpreted in part to be due to the increased availability of legitimate alternatives to crime and the relative rewards that ensue from an economic system which provides greater participation of all segments of the labor force. In cities where the average wage or salary income was relatively low, the labor force per 1,000 of working population was relatively high. Among urban areas . . . inverse associations were definite for persons 14 and older, were rather more so for females than for males over 24, and were especially distinct for boys and girls of high school age and for women living with their husbands (or the economic equivalent). Also fairly evident was the inverse association for men and women 65 and older. (Dornbusch, 1956:340) Dornbusch (1956) analyzed the relationship between income and labor-force participation by age, sex, and color for 50 cities with a population over 100,000, which provided data for whites and non-whites for 1940. His study indicated that the negative correlations which were reflected in previous studies (Douglas, 1934; Long, 1950) were influenced by the universal negative relationship of the non-white labor force component. Dornbusch found negative correlations between income and labor-force participation for non-white males and females for all age 26 categories. His findings for white persons differed : negative correlations were observed for youths and the aged, but a slightly positive correlation was found for white males aged 20 to 65. His most salient finding indicated that there was a positive correlation between income and labor-force participation for white females 18 to 19 and 20 to 24. Dornbusch concludes that white women’s labor-force participation patterns have changed to the extent that white females are participating in the labor market at a more substantial level than ever before. This increase in female labor-force participation is of interest to both labor economists and sociologists. The increasing opportunities for women in the labor market may indicate that as women enter the labor force, their roles as house wives have changed- The restructuring of the household may result in increased problems in home management and may be especially reflected in the rate of juvenile delinquency. 2A. The age-specific juvenile delinquency rate for all juvenile age levels is inversely related to the median annual family income. 2B. The age-specific rate of criminal activity for all adult age levels is inversely related to the median annual family income. These hypotheses reconsider the importance of income as a "taste" variable in the relationship to crime and delinquency (Fleisher, 1966 :59-62). The independent 27 variable, median annual family income, is assumed to be an indicator of family resources which is viewed as a legiti mate alternative to illicit or illegal behavior. The hypotheses suggest that the relationship between median annual family income and crime and delinquency differs due to the type of alternatives available. Youth can seek the financial resources of their parents, which reduces their delinquency rates, while adults do not have this alterna tive. Therefore, the relationship of the independent variable, median annual family income, to the dependent variable, crime and delinquency rates, varies;..con#iderably, with less delinquency and crime in areas having high incomes than in areas having low levels of income. That is, offense rates decrease as the income level increases. The relationships are outlined below in schematic form. Median Annual Family Income Age-Specific Juvenile Delinquency and Crime Rates for All Ages Phillips et al. (1972), in a longitudinal analysis for the years 1953 to 1967, utilized an economic age- specific model to analyze juvenile delinquency offense types and youth labor-force characteristics. Their model partitioned the population by classifications of labor- market status and related the various age groups to property crimes. They extended Fleisher’s (1966) inquiry 28 by considering the relationship of economically oriented offenses to labor-force participation observing how economic motives explain several criminal offenses: larceny, robbery and auto theft. Their conclusions supported Fleisher*s contention that economic opportunity is a key factor in determining youthful participation in crime, and methodologically demonstrate that, when labor- force participation rates are properly weighted, a more efficient indicator of economic opportunity is derived. This indicator is in contradistinction to simple unemploy ment rates. Gibbs (1966) considers the relationship of crime and unemployment as it pertains to status integration and notes that Glaser and Rice (1959) clearly break the trend of research previously employed in the classical economic deterministic mode. He argues that the problem with their analysis, however, is that there is an inadequate rationale for their results (cf. Glaser & Rice, 1959:685-686 ; and Gibbs, 1966, in Rushing, 1975:96-100). The concept of anomie as utilized by Merton (195 7) is used to interpret changes in the relationship between criminal activity . levels and unemployment rates from one age group to another, but Gibbs maintains that the interpretation is ex post facto and not empirically supported. He urges further research along these lines, linking the findings of Glaser and Rice to concepts that are empirically demonstrable. 29 Gibbs (1966) uses the same data employed by Glaser and Rice (1959) and reports that the magnitude and direc tion of the relationship between rates of unemployment and crime vary over time according to age. His findings indicated that this relationship is negative for both the young (persons under 19 years old) and old population (persons over 35 years old), but positive for middle-age categories (especially persons between 20 and 34 years old). His hypothesis was that the crime rate for a specific age group is not only a function of its unemploy ment rate, but also is influenced by the cultural employ ment expectations for members of that age group (Rushing, 1975:95). To illustrate this, we may note that persons under 16 years of age are not expected to be employed, and that most of them are not in the employed labor force; for this group, then, age and employment statuses are inte grated under their unemployment conditions. As a result there is no basis for an expected positive correlation between unemployment and crime, since unemployment is not a barrier to the achievement of the cultural goals that most members of the age cohort are likely to pursue. Gibbs contends that the sociological theory of status integration explains Glaser and Rice’s findings and provides a more abstract generalization. Status integra tion refers to the degree to which status occupancy conforms to the modal population pattern. The theory 30 maintains that in a population where status integration is at maximum, knowledge of the deviance of one’s position from that of most others in the population explains involvement in deviant activity. In the analysis of unemployment this can be seen more readily, according to Gibbs (1966): When the proportion in a given age group who are not employed is high, an increase in unemployment actually increases integration of age with the labour force status, i.e., results in an increase in the proportion not in the labour force, including unemployed. (Rushing, 1975:98) Focusing on the observations of anomie by Merton (1968: 175-260) and the theory of status integration by Gibbs and Martin (1964), Gibbs makes the following generalization about the relationship between the proportion unemployed or not in the labor force and its correlation to unemployment and arrest rates : If the influence of unemployment on the crime rate depends on how it changes status integration, then we should find that an increase in unemployment is associated with an absolute or relative decline in the crime rate of those age groups where a large proportion of the persons are not employed. Stated in the way of an empirical proposition : Unemp1oyment in an age group varies inversely over time with the property crime rate to the ext en t^ha t members of the age group are not employed. (Rushing, 1975 :98) 3A. The age-specific juvenile delinquency rate for 14-15-year-olds is inversely related to the percent of males 16 to 21 years old in the labor force. 31 3B. The age-specific 14-15-year-old juvenile delinquency rate is inversely related to the percent of females 16 to 21 years old in the labor force. Male 16-21-Year-Old Labor-Force Participation Rate Female 16-21-Year-Old Labor-Force Participation Rate 14-15-Year-Old Age Specific Juvenile I Delinquency Rate These hypotheses provide a test of Gibbs's (1966) propositions which indicate that status consistency is an important factor in explaining involvement in juvenile delinquency. This argument contends that nonemployment is a characteristic feature of certain age groups and that the more consistent a person is with the modal patterns of their respective age-sex group, the less strain or anomie results. Youth in the 14-15-year-old group are not likely to be in the labor force and their unemployment rates should be low. It follows that youths in this age group have fewer opportunities for employment; therefore their legitimate alternatives are more limited than those of adults. According to Stinchcombe (1964), if youth perceive opportunities for employment, then delinquency rates decrease. This proposition tests whether youth labor-force participation, a crude indicator of anticipatory employment 32 opportunities for 14- and 15-year-old juveniles, provides an explanation for the type of delinquent acts in which 14-to-15-year-old youths engage. Stinchcombe contends that youth who anticipate employment have a greater stake in conforming to social norms, remain in high school, and show less tendency to be juvenile delinquents than youth who perceive that their opportunities are limited. The contention of the above hypotheses asserts that 14- and 15-year-old youth look to the opportunity structure of older youth in formulating the basis for their commitment to legitimate alternatives. If 14- and 15-year-old youths can perceive some expected value in their adherence to the social structure, their delin quency rates should be inversely related to the opportuni ties of older youth. The more youth 16 to 21 years old in the labor force indicates the legitimate alternatives for employment expectations of youth 14 to 15 years old, and should result in reduced delinquency rates for youth under 16 years old. Propositions 3A and 3B assert an inverse relation ship between unemployment of youth 16 to 21 years old and juvenile delinquency rates. We can also posit that the legitimate opportunities for males and females differ substantially. Males experience more legitimate opportu nities than females. Male youth labor-force participants . will exhibit higher correlates to delinquency than females. 33 Discussion of the Implications ~ o iof Research Prior research, focusing on socioeconomic determi nants and consequences of crime and delinquency, indicates that social structural factors and opportunity structures provide heuristic theoretical potential for the comprehen sion of the interaction of social and economic conditions with crime and delinquency (Hirschi & Selvin, 1967:15-33). This discussion reviews some of the empirical analyses leading to this conclusion and elaborates on the social structural and opportunity themes. The discussion overlaps with the preceding review of economic determinants to crime, but extends the conceptualization of the relation ships previously discussed. Youthful activity is primarily determined by social structural elements. It is becoming increasingly apparent that the opportunity structures of adolescents and youths diverge substantially from those of adults. Formal recognition of differential aspects of social participation based on age and sex characterizes technological society. Research has demonstrated that child labor laws, for example, were especially problematic for the poor, who were accustomed to the supplementary income earned by youths for the economic viability of the household (Platt , 1969 ; Report of the Panel on Youth of the President's Science Advisory Committee, 1974 : 35-41, 64-75 ; Gillis, 1974: 37-93). 34 Opportunity structure theories assume that persons resort to crime and delinquency as consequences of blocked opportunities to achieve goals through legitimate mecha nisms (Cohen & Short, 1971:141-142). The consequences of these restrictions are most pronounced for the economically disadvantaged. Certain social factors are also important in relation to opportunities for greater inclusion into the social structure through legitimately defined activities. Economic and social conditions provide a basic typology of relative opportunities to function legitimately within the social system. We have reviewed aspects of participation in crime and delinquency based on variations of age, sex, race and ethnicity, social class, and ecological factors (cf. Cohen & Short, 1971:104-113), observing that the relationship between crime and delinquency rates and various demographic concepts are not unambiguously asserted, We can generalize that older adolescents and young adults have higher crime rates than other age groups (Cohen & Short, 1971:107-108); that the rates for blacks are higher than what would be expected on the basis of the proportion of blacks in the total population, which is also the case for Puerto Ricans, Mexicans, and American Indians (Cohen & Short, 1971:108-110); and that lower-class persons are more likely to be defined as criminal or delinquent (Cohen & Short, 1971:110-113). All of these factors interrelate, interacting with and influencing one another, such that the 35 salience of legitimate opportunity structures and other socioeconomic factors provide different implications for the causes and consequences of involvement in crime and delinquency. 4. Hypotheses 1 through 3, stated previ ously, are more pronounced for ethnic minorities than for Anglo-whites. This set of propositions analyzes the importance of race in specifying the various relationships to crime and delinquency. This distinction is warranted by prior research which has indicated that minorities are involved in criminal and juvenile court records at higher rates than whites (Fleisher, 1966; Glaser, 1975; Cressey & Sutherland, 1970). Cloward (1959) reviews the literature on the theory of anomie, cultural transmission, and differential associa tion as they focus on theories of deviant behavior in criminology. He particularly explicates the differences in Durkheim’s and Merton's theoretical approaches to the study of social deviance. Durkheim explains deviant behavior by focusing on social conditions as external regulating forces to the social order which result in "over-weaning ambition" or unlimited aspirations when societal regulatory norms break down. Merton systematized and extended this perspec tive by directing his theoretical paradigm to patterns of disjunction between culturally prescribed goals and 36 socially organized access to them by "legitimate" means. Cloward outlines a third phase in the conceptualization of anomie theory. His synthesis incorporates the concept of differentials in access to success goals by illegitimate means. "Illegitimate means" are those proscribed by the mores of the society. In reference to the concept of illegitimate means, Cloward contends : Apart from both socially patterned pressures, which give rise to deviance, and from values, which deter mine choices of adaptations, a further variable should be taken into account: namely, differentials in availability of illegitimate means. . . . The availability of illegitimate means, then, is con trolled by various criteria in the same manner that has long been ascribed to conventional means. Both systems of opportunity are (1) limited, rather than infinitely available, and (2) differentially avail able depending on the location of persons in the social structure. (Cloward, 1959:167 and 168) Thus Cloward argues that socially structured differentials in access to illegitimate learning experi ences and roles are equally important as legitimate means for understanding deviant behavior. He points out the utility of studying the social dynamics of opportunity structures for both legitimate and illegitimate roles. He comments in reference to studying illegitimate means : If differentials exist and can be identified, we may then inquire about their consequences for the behavior of persons in different parts of the social structure. (Cloward, 1959:169) Merton (1959) further discusses the merits of Cloward's synthesis, noting that Cloward conceptually 37 refines, extends and develops the analysis of social distribution of structural pressures for deviant behavior. Economists have similarly derived a model of opportunity factors which differentiate legitimate and illegitimate opportunity structures (Fleisher, 1966; Ehrlich, 1971, in Juster, 1975:313-337; Ehrlich, 1973). We can hypothesize from Cloward's theory that ethnic groups that have been economically handicapped, and/or impeded by housing discrimination from moving out of high crime rate neighborhoods, will have greater reliance on illegitimate means than other groups, even those similar in economic status. The distinctions among age groups become increas ingly more important as the opportunities for youthful activities decrease. Present manpower needs do not require extensive male or female youthful labor-force participa tion, which further reduces the legitimate opportunity structures for the young. The skills that youth bring to the labor force are minimally applicable to present-day demands in an increasingly technological society. Social structural changes, manifested in the changes in appropriate sex-role behavior, have resulted in different types of opportunity structures. Males have been typically involved in the labor force to the general exclusion of women. The typical role of women has been to bear and raise children, while the male earns the income to sustain the family. As 38 the labor-force opportunities for men reduce, and the corresponding opportunities for females increase, various aspects of social organization are altered. De-emphasis on expanding youth entry into the job market can have some negative consequences. Youth may deem that their time and activities are not important, and that their lives have little, if any, purpose. Exclusion of youth from early entrance into the labor force may produce disillusioned or unmotivated youths who have more leisure time, but little or no financial means of pursuing leisure goals without the assistance of a parent or guardian. This in turn would further emphasize the importance of part-time employment for youths. As youths are marginal participants in the dominant society, expendable commodities on today's labor market, their delinquent pursuits may increase. As the legitimate opportunity structure closes, an anomic condition is created within the social system, according to Merton, and youth have to innovate new patterns of achieving success. This anomie may differ for various social groups or elements within the social system. White middle-class youths may not react in a fashion analogous to that of black middle- or working-class youth, and similarly Chicano, or Mexican American youths, may not respond similarly to black or white youths. Males may react differently from females and various age groups may react or perceive their situations differentially. As adults 39 increasingly isolate themselves from youth, or as age- grouping increases within the social structure, there will be further deviations. That is, as the functional specificity of age increases, the role of youth in society becomes more problematic. If youth are denied access to legitimate participation in the labor market, but are encouraged to invest their time and money in education when these activities are not rewarding, there should be an increase in juvenile delinquent activities. In general we expect the data to demonstrate that the more youth have to rely upon themselves as their primary economic agents, the more likely their involvement in juvenile delinquency and crime, when the opportunities for economic participation decrease, As a corollary, we expect the results to indicate that the offense type of involvement of juveniles from communities which have few economic opportunities for youth progresses from statutory violation to property crimes as the youth population age distribution increases. 5. Propositions 1 through 3 are more pronounced for property offenses than for person offense types. Gibbs (1966) notes that there is no empirical basis which would lead us to expect that the incidence of all types of crimes is affected uniformly by conditions of unemployment. That is, there is no justification for generalizing that the incidence of property crimes and personal crimes is similarly affected by changes in 40 economic conditions. This study empirically tests the linkages between these offense types. To some extent we might speculate that the types of crimes are interrelated, but this remains an empirical issue. This caution is made because social scientists tend to generalize their findings beyond their data when they consider the research implica tions of their findings. A strict economic perspective, for instance, could lead to the generalization that property crimes would be rampant during periods of high unemployment and virtually nonexistent during periods of low unemployment. This is far from the case. In general, most research conducted in this area has focused on property crimes, and should not be generalized to other criminal or delinquent offense types, Gibbs points out these problems and urges that more research be conducted exploring the relationship between crime rates and labor-force participation by age, illus trating the need for further analysis of the convergence between various offenses correlated to crime and delin quency considering economic and ecological factors. The previously stated propositions are further analyzed by offense type. This reconsideration of the specified relationship is warranted by previous research findings (Glaser, 1959; Fleisher, 1966; Votey & Phillips, 1969) . These studies note that the salience of economic conditions as an influencing variable on engagement or 41 involvement in crime and delinquency is reflected more readily in offenses which have an economically oriented basis. Consequently a comparative analysis of offense types should indicate that crimes which have an underlying economic factor should be more highly correlated with economic variables than crimes which have a social deter minant . It should be noted, however, that research by Wolfgang and associates (1972) found youth under 18 unspecialized in their offenses, with the most recidivistic committing crimes against both property and persons. Specialization in property crime appears to develop later. Therefore, one would expect the above propositions to be most valid for the older age group. 6. Proposition lA is more pronounced for status offense types. These hypotheses are made in contradistinction to the purpose of the set of propositions enumerated in proposition 6 above. This set of propositions also differentiates offense types. The distinction is predi cated on the differential impact of social factors as they pertain to delinquency. Family disorganization, female labor-force participation and female-headed households have traditionally been considered to be indicative of high rates of crime and delinquency. This set of proposi tions focuses on the social elements as they pertain to ______________ 42 participation in crime and delinquency. This inquiry progresses from the works of Nye and Hoffman (1963), Burr (1974) and Fleisher (1966), who considered the importance of familial structure to delinquency. This study expli cates on the salience of familial structure by arguing that its ramifications are more pronounced in juvenile status offenses, since it reflects the extent to which youth are generally considered to be incorrigible. Such behavior should be higher in single-parent families than in two- parent families. Most sociologists emphasize different factors as being crucial in the causation of crime ; however, there are some similarities in their theoretical perspectives. The causes are perceived to be social factors which are inde pendent of individual personalities and motives (Rushing, 1975:94-95). Rushing notes in a discussion of the antece dents and determinants of criminal behavior: Thus an increase in the rate of property crime, if in fact there has been an increase, is due to changes in the structure of society, not in the character of individual personalities and basic motivational processes. Even when motivational processes are implied, as in Merton’s framework of "Social Structure and Anomie," which is the most well-known sociological theory of economic crime, motives are viewed as deriv ing from the social and cultural order. (Rushing, 1975:95) Glaser and Rice (1959) observe that the relationship between the unemployment rate, an index of blocked mobility, and the crime rate varied over time according to age ; it 43 was negative for young and old populations, but positive for middle-aged groups. Ecological. Analysis Framework The tradition of ecological analysis of crime has enriched the development of empirical social deviance inquiry (Elmer, 1933; Lindesmith & Levin, 1937 ; Morris, 1957; Voss & Petersen, 1971). The utility of an ecological approach in the study of juvenile delinquency has long been debated (Shaw et al,, 1929; Shaw & McKay, 1931, 1942, 1969; Jonassen, 1949; Lander, 1954, 1968; Gordon, 1967 ; Slatin, 1969 ; Wilks, 1967) . Shaw and McKay pioneered in establish ing a theoretical and empirically based analysis of juvenile delinquency within the United States (Kobrin, 1971) . They made the ecological mode of analysis the major mechanism for assessing causal relationships in juvenile delinquency in both theoretical and practical aspects (Shaw, McKay et al., 1929, 1942, 1969). Their research provided basic formulae for ecological factors as they pertain to and determine delinquency. The Shaw and McKay approach developed from the ecological and social psychological traditions of the Chicago School in the early 1920s. The works of Robert E, Park and W, I. Thomas illustrate the major leaders of this orientation to sociological inquiry. According to Kobrin (in Voss & Petersen, 1971:101-131), these two theorists 44 provided the basis of the theoretical framework adopted by Shaw and McKay. The theoretical task undertaken by them was to explain the variations in juvenile delinquency rates in urban areas as being functionally related to variations in local subcultures which imparted to children and youth a life organization in which violation of the law was defined as a valued group goal. Shaw and McKay merged the social ecological perspective of Park with the social psycholog ical orientation of Thomas in the analysis of juvenile delinquency. Jonassen (1949) addresses the works of Shaw and McKay in regard to contradictory findings indicated in two of their works which concentrated on the comparative influences of nationality or ethnic background and "inherent" community elements or factors, that they deduced as being important in juvenile delinquency (Shaw & McKay, 1931, 1942). Jonassen examined the practical and theoret ical implications, as well as the logical and empirical basis supporting Shaw and McKay’s unequivocally stated proposition relating ethnicity to community patterns of delinquency. The proposition asserted: It appears to be established . . . that diverse racial, nativity, and national groups possess relatively similar rates of delinquents in similar social areas ; and that each of these groups displays the effect of disproportionate concentration in its respective areas at a given time. In the face of these facts it is difficult to sustain the contention that, by them selves, the factors of race, nativity, and nationality are vitally related to the problem of juvenile 45 delinquency. It seems necessary to conclude, rather, that the significantly higher rates of delinquents found among the children of Negroes, the foreign born, and more recent immigrants are closely related to existing differences in their respective patterns of geographic distribution within the city. (Shaw & McKay, 1942:156) Shaw and McKay contend that nationality is not saliently related to juvenile delinquency. Their rationale for this postulate is based on empirical analysis of Chicago juvenile delinquency patterns, where they observed that the juvenile court records indicated that the racial and nationality composition of the popula tions in the areas of high rates of delinquents changed almost completely between 1900 and 1920, while the relative rates of delinquents in these areas remained practically unchanged. (Shaw & McKay, 1931: 388) Shaw and McKay (1931:51) further alleged "that an area is likely to maintain a high rate of delinquency over a long period of time, irrespective of the different nationalities that occupy it." They later contended that as certain nationality groups move out of an area, their delinquency rates reduce (Shaw & McKay, 1942:151). From these observa tions they deduced that factors of race, nativity and nationality are not crucially related to juvenile delin quency (Shaw 6e McKay, 1942:156), and that "the delinquency producing factors are inherent in the community" (Shaw & McKay, 1942:435). According to Jonassen (1949), factors that affect delinquency rates may be divided into two major categories: 46 factors which indirectly influence rates but not delinquent behavior, and factors which directly affect delinquent behavior. Factors which indirectly affect juvenile delin quency are : 1. Differences in laws as to what constitutes delin quent behavior. 2. Differences in police policy of enforcement. 3. Differences in administrative policy of the court. Delinquency rates are tricky data and wide varia tions from place to place and time to time may be differences of administrative policy rather than of delinquent behavior. For example, alleged delinquency in Manhattan declined from 1939 to 1940, but further study reveals that most of the decrease resulted from a change of administrative and referral policies of the court. 4. Differences in liabilities of different groups to arrest at different times. 5. Existence or absence of extra-legal and non-court procedure for handling delinquents. (Jonassen, 1949:136-137) Factors which directly affect juvenile delinquency rates are : 1. General economic and social forces of the times. 2. Social conditions in local areas at different times 3. Ecological characteristics of the areas at different times. 4. Socioeconomic status of delinquents’ environment. 5. Demographic characteristics of the population, age and sex composition of the populations under consideration. 6. Cultural orientation of the areas. Are the groups native white, or colored, or are they of foreign nationality, and what nationality? (Jonassen, 1949:137) 47 Hypotheses The research questions guiding this analysis are grouped into six sets of propositions. The most general propositions are stated initially (propositions 1 through 3), and are followed by hypotheses which lead to more specific inquiry of the underlying relationships that have been posited. Propositions 4 through 6 assert relation ships which consider ethnicity and offense-specific crime and delinquency rates as they pertain to the relationships specified in propositions 1 through 4. Following each set of propositions (excluding propositions 4 through 6) are: (1) an illustration of the conceptualized relationship between the variables ; and (2) an identification of prior research which has either tested the postulated relation ship or from which these specific propositions are derived. In addition to these hypotheses, proposition 4 will consider the differential impact of race. Specific scrutiny will be directed to the relationship between delinquency and labor-force participation of black, Anglo- white and Spanish-surnamed (Mexican American) youths. This further consideration is necessary due to the tremendous variation that has been observed in youth unemp1oyment rates for various racial groups. This study will also focus on various juvenile offense types (propositions 5 and 6). Previous research 48 has considered the offense of property crimes. This offense type will be considered in order to compare the findings of our study to previous works. The study will also focus on other types of juvenile offenses in order to broaden the empirical and theoretical knowledge of differ ent aspects of juvenile delinquency. It is expected that the data will indicate that due to the lack of employment opportunities, crime is dispro portionately associated with youth ; males and non-whites experience higher youth and adult rates of unemployment, and are more disadvantaged in our society, when we focus on juvenile delinquent behavior, labor-force participation and unemployment. This research will hopefully shed some light on the development of social policy which pertains to and affects youth. These findings will have specific relevance to the formulation of social policy on such projects as the Job Corps and the Neighborhood Youth Corp. This study contrib utes to the literature on the extent of juvenile delin quency and labor-force participation, and may demonstrate that there is further need for programs which provide youth with productive jobs in their communities. The findings may indicate that when youth are productively employed, the extent of crime may decline, which would support the contention that there may be some value in subsidizing community programs which facilitate youth employment, 49 The next chapter provides a description of the methodological considerations of this analysis. It contains a specific operationalization of the concepts referred to in this chapter and expands upon the statis tical analysis to be employed. 50 CHAPTER III RESEARCH METHODOLOGY This chapter describes the procedures used in assessing the relationships among the variables discussed in the previous chapter. It is divided into three sec tions : the first consists of a report on the methods of collecting the data ; the next is on the operationalization of the variables ; and the final part presents the design of the study. This research centers on an analysis of labor-force participation factors as they relate to juvenile delin quency. Research in juvenile delinquency has questioned how law enforcement agencies and other mechanisms of social control operate and define social deviance. Such analyses have challenged the use of official statistics, both criminal and delinquent, as indicators of crime or delin quency . Method of Data Collection Many researchers note that it is impossible for social scientists to measure fully the extent of criminal or delinquent behavior on any level (cf. Glaser, 196 7; Fleisher, 1966). Kitsuse and Cicourel (1963) contend, however, that official statistics reflect the variety of 51 organizational contingencies evoked in the process of differentiation of deviants from non-deviants, at least as nominally defined. Their consideration of the usage of officially aggregated data focuses primarily on the theo retical framework of the analysis of deviance and social control processes. The utility of official statistics depends upon both the theoretical and methodological orientations of the analysis. They argue that criminal justice statistics generally reflect the application of specific statutes to conduct through the interpretations, decisions, actions and interactions of the various units of the criminal justice system. Critical appraisal of official statistics, aggre gated by various law enforcement agencies, has resulted in numerous controversial issues with respect to the objective utility of these data as measures of the amount and nature of the incidence of crime and delinquency (Wolfgang, 1963; Sellin & Wolfgang, 1964 ; Shulman, 1966 ; Glaser, 1967 ; Hirschi & SeIvin, 1967). This debate continues and can be observed in the analysis of the relationship between socio economic factors and social deviance. Glaser (1967) contends that the optimum procedures for assessment of the extent of crime vary, by offense type. Although he views all crimes as violations of the criminal law, the differentiation in the measurement of crime is predicated upon whether the crime generates a 52 death, a complaining victim, a satisfied customer, an annoyed audience, or a dangerous condition. He develops a typology of crimes, based on a historical review of types of criminal laws, distinguishing them as: (1) predatory crimes, which are acts having a definite and intended victim; (2) illegal service crimes, which are acts having no clearly delineated victim (unless one maintains that society is the victim) since these acts are mutually engaged in by the "criminal" and his customer ; generally in such acts both parties have a vested interest in maintain ing a covert relationship and rarely report the occurrence of their relationship ; (3) public disorder crimes , which are acts that are dealt with as criminal only when performed before an audience that is offended; and (4) crimes of negligence, which are acts usually involving an unintended victim. Each type of offense poses different problems of measurement ; the illegal service crimes are especially difficult to measure with official data. Fleisher (1966) points out problems associated with gathering information about delinquency among the young. He concludes that there are no measurements of direct criminal behavior. Focusing on the official sources, he notes that they may be affected by the tendency of the public to report offenses to the police on a differential basis ; that the frequency with which these reports appear officially varies; and that the success of the police in 53 apprehending offenders is also questionable. Generally, he notes that the further a particular measure of criminality is removed from the actual commission of the offense, the more new possibilities of error and bias are introduced. As he focuses on the errors of measurement of what ever constitutes the true delinquency level represented in the official sources of delinquency data, Fleisher notes: 1. Practices of reporting offenses and the efficiency of apprehending offenders and of reporting their apprehension may vary over time. 2. Such practices and such efficiency may also vary among police and court agencies at any given time. 3. Even within police and court jurisdictions, the treatment of individuals may vary not only with their degree of criminality but also with factors that have nothing explicitly to do with criminal behavior, such as political influence. (Fleisher, 1966:38-39) Sellin and Wolfgang (1964) similarly present a historical review of the issues relevant to the measurement of criminal activity. Their analysis focuses specifically on juvenile delinquency. The problems of measurement are similar to those which pertain to adult criminality. The basic issues which are relevant to criminal indicators are the quality and adequacy of the official data. Sellin and Wolfgang conclude that the social researcher needs to be critical of both the quantitative and qualitative aspects of the measurement of crime. They especially note that the qualitative dimension needs to be further developed. Sutherland and Cressey (1970) further criticize the adequacy of indices of delinquency and crime, noting that 54 it is virtually impossible to determine the actual amount and extent of crime in any area at any particular time. They argue that n^ sample can be taken as an indicator of the extent of criminality within an area, since the whole cannot be estimated. Their position is that "both the true rate and the relationship between the true rate and any index of this rate are capricious 'dark figures' which vary with changes in police policies, court policies, and public policy" (Sutherland & Cressey, 1970:25). Since it is virtually impossible to measure the full extent of criminal behavior, their position is to make the best of a bad situation, prefacing oneself with the knowledge that the indicators in the criminal justice system are incomplete initially and become more crude as one progresses through the system. Thus, arrest data reflect aspects of crime and delinquency different from those of probation data. In summary, we shall be forced to deal only with official data on crime and delinquency, and will attempt to assess the implications of the deficiencies of the data when discussing our findings. Because we shall be dealing with offense-specific rates, this assessment can be much more precise and rigorous than it would be were we dealing with total delinquency, or crime rates. Hirschi and Selvin (1967:106-110) review methodo logical problems having direct application to juvenile delinquency research. They distinguish four terms which 55 may characterize the relational structure between variables. They are: conditional relations, interaction, specifica tion and non-additivity (Hirschi & Selvin, 1967:73-113). As an illustration of the interaction effect, Hirschi and Selvin (1967:107-108) refer to the elaboration of a zero-order relationship between unemployment and delinquency which was considered by Glaser and Rice (1959). They note that although there is a basis for expecting a direct relationship between unemployment and crime rates, prior studies indicate that either there is no relationship, or that this relationship is weak and inconsistent. Glaser and Rice controlled the criminal’s age and type of crime in order to ascertain the relationship between unemployment and crime. Their findings indicated that unemployment and crime, especially when considering property offenses, varied with the offender’s age. Hirschi and Selvin demon strate that Fleisher’s (1966) analysis investigated the effects of income on delinquency through a stepwise regres sion procedure, a statistical design which considers independent variables and how they account for amounts of variance on the dependent variable--juvenile delinquency rates--by adding the variables in decreasing order of the proportion of the explained variance until all of the independent variables are included. Unfortunately, Fleisher’s analysis did not consider the individual effects of entering the independent variables into the regression equation. Studies which focus on the effect of unemployment on delinquency generally operationalize the dependent variable as being the number of arrests or court appear ances per thousand persons in the appropriate population, according to age (Fleisher, 1966:45). The independent variables include a measure of unemployment, usually the civilian male unemployment rate for persons whose ages correspond to the dependent variable age range, and are expressed as the number of unemployed persons in the appropriate population (i.e., community or census tract) divided by the civilian labor-force population of the community. Fleisher (1966) and Glaser and Rice (1959) used aggregate adult unemployment rates to test their hypotheses regarding parental supervision, unemployment and delin quency for the younger age groups, Fleisher contends : It may be argued that in order to test properly the Glaser and Rice hypothesis regarding parental super vision, unemployment, and delinquency for the younger age groups, the aggregate or adult unemployment rate should be used. Actually the statistical results differ only slightly, depending upon which measure of unemployment is used, Male age-specific unemployment rates seem to be especially appropriate because of the nature of juvenile crimed and because they provide information useful in public policy. That is, by far the largest proportion of property crime is committed by males, and many forms of public policy deal with attempts to reduce youth unemployment. Hence it is desirable to have information about the probable effect of labor-market policies on delinquency rates. (Fleisher, 1966:45) Fleisher’s study considers both the longitudinal and cross-sectional relationships between crime and 57 delinquency and economic factors. The longitudinal aspects of the study focus on a time-series analysis of the effect of unemployment on delinquency in three cities and for the United States. He cautions that the results are tentative due to inherent problems in processing juveniles. Prior to World War II, the FBI based its Uniform Crime Reports on fingerprint records, which kept the delinquency rates at a relatively lower level, since there was some reluctance to adhere to this practice in the local police departments. Between 1951 and 1952 the FBI changed its operating proce dures, and based the Uniform Crime Rates on reports from individual police agencies. This resulted in a marked increase in juvenile delinquency rates, since juveniles did not have to be fingerprinted and treated like adults in order to be considered as part of the measure of crime and delinquency (Fleisher, 1966:49-53). Fleisher discusses the merits of cross-sectional analysis. He specifically considers the advantages that synchronic cross-sectional analysis provides in analyzing effects of income on delinquency. The primary justification for a synchronic design is that it provides greater variation in income by geographical areas than would be obtained from longitudinal studies (Fleisher, 1966 : 53-67). The Juvenile Court was created in Los Angeles County in 1903 by enactment of the Juvenile Court Act. This Act curtailed the extent of criminal proceedings 58 against minors, giving original jurisdiction to the juvenile court when a minor, a person under the age of 18 years, committed a criminal act, and discretionary juris diction for juveniles between the ages of 18 and 21 years (Kamm et al., 1968:10). Adult criminal court and juvenile court can be distinguished philosophically and proceduraily. Kamm et al. (1968) note: In the adult criminal court, the defendant is accused of the commission of a specific crime, a complaint is filed, and a warrant issued. A trial is held for the purpose of presenting sufficient evidence to prove the commission of a crime. The prevailing philosophy is personal and punitive in nature, and the trial is characterized by contentiousness between two adver saries, often in bitter conflict. Conversely, in the juvenile court, the ward is alleged to have committed an offense and a petition is filed in his behalf. The purpose of the hearing is to ascertain if the minor performed as alleged, and, if so, what caused the minor to commit the offense. This is usually accomplished through social-scientific methods of investigation. Should custody, care, treat ment, correction or protection be necessary, then the state, acting as parens patriae, assumes this respon sibility. Every child is regarded as a potential asset to the community if properly guided and directed. (Kamm et al., 1968:10-11) As can be inferred from the above, juvenile court is characterized by informal procedures, while adult Criminal court is more formal. The major distinction to be made in this study is the difference in probation proceedings. For adults, probation is not considered until after the trial, but for juveniles, cases are initially referred to probation and it is the probation officer's function to 59 investigate the case. This means that adult probationers are qualitatively different from juvenile probationers. Operationalization of Key Concepts All population variables are taken from the 1970 United States Census of Population; income sources pertain to the year prior to the 1970 Census, i.e., 1969; and all juvenile delinquency and crime data are derived from the 1970 Los Angeles County Probation Department Juvenile and Adult Files. Delinquency and Crime Indicators The dependent variable in this study, the rate of delinquency or crime, is derived from the officially recorded probation offense. Delinquency is measured by the officially registered charge against the juvenile as indi cated by the probation department intake personnel upon initial referral of the case to juvenile court. Crime is indexed by the subsequent action of the criminal court on officially adjudicated charges which were referred to the criminal court. The probation offense-specific charge is classified by the type of offense. The following typology of criminal and delinquent acts is applied, grouping juvenile offenses into : (a) the total juvenile delinquency rate ; (b) delinquent offenses against persons ; 60 (c) delinquent offenses against property; and (d) delinquent acts which are statutorially defined to constitute an offense, only because juve niles have a special status under the law. The latter acts, called "juvenile status offenses," would not be considered delinquent, were it not for the minority age of the juvenile. For adults, the criminal offense- specific probation records are aggregated into the below- indicated offense types: (a) the total crime rate; (b) crimes against persons ; and (c) crimes against property. For a detailed listing of the specific offenses grouped within each offense type, please refer to Appendix A. Age probation offense type charge is the probation department's offense-specific charge classified by the offender’s age and type of offense (cf. Appendix A and offense types which were specified above) at the time of the alleged offense, and can be further refined by considering the ethnicity and sex of the adult or juvenile. In such instances the refined measure that is obtained is referred to as the age, sex and/or race-specific probation offense type charge. For this study the dependent variable, delinquency and criminal probation offense charges, is categorized by the following racial groups : Anglo-white, black and Spanish- surnamed (or Mexican American). Juvenile probation offense 61 charges are expressed in the following age group categories (a) 10-13 years old; (b) 14-15 years old; (c) 16 years old; (d) 17 years old; (e) 18 years old; (f) 10-18 years old. For adults, the criminal probation offense age group categories are : (a) 19-20 years old (b) 21-22 years old (c) 23-24 years old (d) 25-29 years old (e) 30-49 years old (f) 19-49 years old. The age and race juvenile and adult probation offense charges are generally expressed in rates, which are calculated by taking the number of probation offense type charges with appropriate qualifying demographic variables and offense types over the base population, per 1,000 or 10,000 persons. The operationalization of the dependent variable in this study is; (1) Age, race offense type juvenile delinquency rate. This is calculated as: # juvenile referrals by appropriate age, race and offense type Total population of youth in the X 1,000 community within the appropriate age and racial group category. (2) Age, race offense type crime rate. This rate is calculated as follows : # adult probationers by appropriate age, race and offense type ______. Total adult population in the X 1,000 community with the appropriate age and racial group. Economic Factors All the independent variables to be considered derive from the 1970 United States Census of Population, fourth count records. The income measures pertain to 1969 annual earnings reported in the 1970 United States Census. Income. In this study income is operationalized as the median annual income of families and unrelated indi viduals . Fleisher (1966:59-62) operationalizes income by considering the net effect of income on delinquency holding taste variables constant. The only data source in his analysis was based on the United States Census Bureau's measurement. He considered family income as an index of both the legitimate earning prospects of potential delin quents , and the young persons' legitimate command of their desired resources. He notes that the selection of the most appropriate indicator of income remains an empirical issue, e.g., family income can be distinguished from family earning power. In order to differentiate the costs of engaging in illegitimate activities from the benefits or 63 returns of participating in such activities, Fleisher incorporates two measures of income in his delinquency model. One measure referred to the median family income above the poverty level, and the other to the median family income below the poverty level. Labor force participation is a measure of the economic status of a population subgroup. This variable measures the proportion of the group who are either employed or actively searching for work. Votey and Phillips (1974) note that participation rates for youth generally fall below those of adults for two reasons. Youth may be entering school in increasing numbers or they may have become so discouraged at the possibilities for enter ing employment that they are dropping out of the labor force, that is, they no longer actively seek employment (Votey & Phillips, 1974:1063). Votey and Phillips (1974: 1066) postulate that the labor-force status of youth is a representation, in the aggregate, of economic opportunities for youth. They further contend that since youth have low participation rates, unemployment rates will have less weight because of the considerable fraction of youths outside the labor force. Converse to labor-force partici pation is the concept not in labor force, which refers to that segment of the population which is not actively seeking employment. 64 The labor force is further distinguished by also considering two other segments which comprise the bulk of work experiences. These sub-segments are the institutional population and the armed services sub-population. Those persons who do not fit into either of these categories are generally considered as the non-institutionalized civilian populat ion. The work experiences of the non-ins titutional- ized civilian population can be considered as being either within the labor force or not depending on the economic experiences of the respective individual. If one is employed one is defined to be participating in the labor force. Unemployment measures are based on the number of persons out of work within the population. They generally refer to the number of non-institutionalized civilian personnel in the population who are currently without work, but who are actively looking for work and who can accept work immediately should it be offered to them. As a converse to unemployment, there is the concept of employed which indicates that this sub-segment of the civilian non-institutionalized population currently has work experience. Current concepts of labor market characteristics, unemployment, employment and labor-force participation, were initially developed and utilized at the close of the 1930s. The Works Progress Administration initially 65 conducted national unemployment registration surveys which were followed by monthly household surveys and were first published in 1940. The Census Bureau later refined the sampling technique considering persons 14 years of age and over in the non-institutionalized population as either employed, unemployed, or not in the labor force. Employ ment and unemployment constituted the labor-force popula tion. The remainder of the population was defined as "not in the labor force." Critics of labor-market definitions were quick to point out problems of adequacy in measurement and conceptu alization. As a consequence the Bureau of Labor Statistics implemented some substantive changes. The household survey was increased, the concept of unemployment was modified to make it more compatible with active job searching, and new series of statistical data were developed in order to illuminate other aspects of the labor market (Levitan & Taggart, 1974:4-11). Levitan and Taggart (1974:8) note that one major development in the labor market was the increase in secondary workers. Female labor-force participation incremented drastically in World War II and continued to reflect this trend during the 1950s. Levitan and Taggart further distinguish such struc tural problems as the increasing trend for youth labor- force participation and differential racial patterns. 66 Though the products of the post-war baby boom had not entered the labor force by 1950, structural changes were occurring, intensifying the relative unemployment problems of teenagers. The average unemployment rate of youth aged 16 to 19 years rose from 2.3 times the overall rate in 1950 to 2.7 times as high in 1960. . . . Another structural problem was the increasing disparity between the unemployment rates of whites and blacks. In 1948, the unemployment rate for non-whites was 1.7 times that for whites. It rose to 2.0 times as high in 1954, and 2.2 times by 1959. The major factor in this increase was the exodus of rural and frequently underemployed blacks to the cities, where they became more visible as unemployed and uneven competition with whites for available jobs. (Levitan & Taggart, 1974:8,9) The changes adopted by the Bureau of Labor Statis tics in 1967 were: 1. The unemployment definition was tightened to include only individuals who had taken specific steps within the previous four weeks to look for a job and who were currently available for work. Inactive job seekers who have quit looking because of the lack of opportunities, and students seeking summer jobs before the end of the school year, were no longer counted as unemployed. 2. The labor force was redefined to include only individuals age 16 years of age and over, excluding persons 14 to 15 years of age from regular employ ment and unemployment figures. 3. Persons holding a job but not at work who were looking for other employment were classified as employed. 4. Most of the labor market statistics were sub- classified by household status permitting a differentiation between primary and secondary workers. 5. A number of questions were added to determine the activities and behavior of nonlabor force partici pants . They were asked their intentions to seek work in the next year, whether they wanted a job, if so why they were looking, , and when and why they had left their past position. (Levitan & Taggart, 1974:11-12; cf. Stein, 1967:1-7) 67 Even with the refinements in the measurement of labor-force characteristics, the statistics have not been reduced. In 1960 the teenage labor force grew by 35%, more than three times the adult labor-force rate. This incre ment, in part due to the post-war baby boom generation entering the labor force, intensified the unemployment problems (Levitan & Taggart, 1974:12). The labor-force variables are operationalized as follows : A. Civiliah male adult unemployment rate : # of Males 21-64 years old in the labor_ force unemployed Male population 21-64 years X 100 old in the non-institutionalized civilian labor force. B. Civilian female adult unemployment rate : # of Females 21-64 years old in the labor force unemployed Female population 21-64 years ~ X 100 old in the non-institutionalized civilian labor force. C. Percent 16-21 years old males in the labor force : # of Males 16-21 years old in the labor force Total male population 16-21 X 100 years old in the community. D. Percent 16-21 years old females in the labor force : ~ # of Females 16-21 years old in the_labor force Total female population 16-21 X 100 years old in the community. 68 Demographic Factors The basic unit of analysis for this study disaggre gates Los Angeles County into 133 Community Study Areas. The boundaries of the Community Study Areas correspond to the 133 Community Study Areas that were developed and utilized by the Los Angeles County Welfare Planning Council for 1970 (cf. Meeker, 1964; Kimball & Freudenberg, 1971). Census Tract numbers are geo-coded on both the census and probation data files. The probation data are recorded with 1960 geo-codes and the census data are geo coded to 1970 Census Tracts. In order to construct the 133 Community Study Areas Data File, the 1970 Census Tracts were converted to their corresponding 1960 tracts. The data were subsequently aggregated into the 133 Community Study Areas. Synchronic ecological analysis as used here is based on a comparison of socioeconomic and criminal measures to these contiguous geographical units, with the basic geographical unit being Los Angeles County. The Community Study Areas, constituting the basic unit of analysis, attempt to delineate Los Angeles County as homogeneously as possible, with its boundaries following existing political and natural barriers (Meeker, 1964:1-4). Meeker notes that the first attempt to divide Los Angeles County into Study Areas was conducted by Frank (1946), who distinguished 65 Study Areas. 69 The 133 Study Areas for this analysis each consist of groups of Census Tracts. No Study Area cuts across Census Tract boundaries. A Census Tract is a small geographical unit which the Bureau of the Census uses for reporting characteristics of the population in metropolitan areas. An average Census Tract generally comprises a population between 4,000 and 5,000 persons, and exhibits considerable homogeneity with respect to population charac teristics (Meeker, 1964:2). In 1960, Los Angeles County was divided into 1,297 Census Tracts. The criteria for determining Study Area boundaries were, in order of impor tance as listëd by Meeker (1964:2): (1) homogeneity; (2) municipal and physical boundaries ; (3) comparability with geographical units recog nized by other agencies in Los Angeles County. Meeker further observes that since number of persons within a Study Area was not one of the criteria, Study Areas show considerable variation with respect to total population. The four characteristics which were used to assess homogeneity of the Study Areas were : (1) average rent ; (2) racial composition; (3) marital status ; (4) age composition of the population. 70 Please refer to Appendix B for a listing of the 1970 Census Tracts included in the Study Areas, with 1960 and 1970 populations. Race is the classification of racial background, or ethnicity, which for this analysis is categorized as being ; Anglo-white, black or Spanish surname. Fleisher (1966:63) relates that these variables reflect "attitudes toward the established moral codes of the community and commitments to a legitimate way of life." He contends that family structure should directly influence criminal behavior of the very young, especially through income, since one-parent families are generally female headed households whose incomes are approximately 50% lower than two-parent families. He further explicates that ■ family structure may affect delinquent behavior through parental supervision and legitimate opportunity structures. His latter contention is based on the fact that the major ity of delinquent offenses are committed by young males, who are socialized to participate in adult society through adult male role models. The presence of a female head of household implies that the adult male role model may be weak or lacking. In this regard Fleisher (1966:63) as serts: To the extent that legitimate endeavor requires a clearer, longer range view of relatively distant pay offs than does illegitimate behavior, the absence of a father probably encourages delinquency. Moreover, the father probably also helps to reduce delinquency by sharing the mother's supervisory role. 71 Fleisher (1967:64) also considered variables which reflected the social organization of the community. These variables were related to his "taste" factors for involve ment in delinquent activities. It was his contention that these variables reflect social rootlessness, or "anomie." Home ownership, population density, and residential mobil ity have been some of the variables that have been incor porated into this perspective (cf. Lander, 1954; Bordua, 1958; Chilton, 1964; Fleisher, 1966:32). The Study Design Subjects of the Study The crime and delinquency data are obtained from the Los Angeles County Probation Department. Age-specific data on initial referrals to juvenile court for considera tion is based on juveniles from age 10 through 17. Data on convicted adults referred to the Probation Department for presentencing investigation is also included. The social and economic characteristics of the Los Angeles County Area are obtained from published and unpublished United States Census Bureau Reports on Population and Housing, 1970. The population to which the hypotheses apply are juvenile offenders of the Los Angeles County Area. The entire juvenile case file for the year 1970 is considered in the analysis. In calendar year 1970, there were 35,304 juveniles and 36,582 adults.referred to the Los Angeles 72 County Probation Department. The rationale for using the juvenile population for this given year is based on its being a census enumeration year, so that demographic data and court data are for an identical period. The entire delinquency and crime file is used because the types of comparisons to be made, relating census data based on economic characteristics of the community to the level of juvenile delinquent activity, involves large numbers of cross-tabulations. To have a sufficient sample size to test completely the empirical model employed in the analy sis, the total population of juvenile delinquent offenders is utilized. It should be noted that the juvenile data cover all persons referred to the intake officers of the juvenile court, operated by the County Probation Department, for possible adjudication. Many, however, would be dealt with informally by the probation staff and not referred to court. The adult data, however, represent a- more screened population, since they are referred to the Probation Department for presentence investigation only after being convicted of a crime. Data Collection The data that are used in this study derive from 1970 officially collected and independent records : the U.S. Census of the Population and the Los Angeles County 73 Department of Probation. This study is a secondary analy sis of these data sources. An advantage in using these data is that both sources are geo-coded by census tract areas for the Los Angeles County Area, making it possible to aggregate them on comparable grids of community composi tion . Since this study is a secondary data analysis, there is no actual data collection phase in the research design. However, the problem with secondary analysis is that the researcher has no control over the quality of the data that are considered. It is believed that this is not a major factor in this analysis because of the high quality of both the census data processing staff and the Los Angeles County Probation Department. The latter has completed more than a decade of research involvement, so that its information system has become better than most juvenile justice agencies. The specific steps which will have to be made in doing this research are distinguishable in terms of preliminary and secondary stages of data processing. The preliminary stage represents the phase in which the researcher obtains the data and processes it to the point at which it can be further used to test research hypothe ses. The secondary stage of the analysis centers on the specific aspects of conducting a statistical analysis. Since this analysis primarily involves the use of a 74 computer, the major consideration in this regard is the necessary preliminary steps which must be conducted to proceed with the analysis. Since the Los Angeles County Probation Department Files are geo-coded to the I960 census tract grids, the 1970 Census data are transcribed to the 1970 census tracts which serve as the standard tract grid for the composition of the communities. This step involves the transcription of the 1970 census data to appropriate geo-coded census tracts for 1960. The geo-coded grid is based on the 1960 census tracts for the communities comprising the 1960 community profiles adopted by the Welfare Planning Council for Los Angeles County as their standard community index. The Research Design The research design used in this analysis is a multivariate analysis. It is a secondary analysis which uses multiple regression. This is because (a) the research question focuses on the extent of the relationship of various independent variables on the dependent variable, the extent of juvenile delinquency; and (b) the research question directs the researcher to consider the interacting relationship among the various independent variables. Multiple regression analysis provides a parsimonious design for this purpose, by using a linear model. Although this is considered preferable to tabular analysis, some of the analysis will be cross-tabular. The regression model can be expressed simply in the form of a linear equation: Y = a + bX^ + 0 X2 + u where Y = delinquency rate X^ = "economic" variable X 2 = social variable u = that part of Y not explained by economic variable X-. and social variable % 2 . Some examples of various independent variables which can be considered in the regression model are: (a) median annual family income ; (b) male civilian unemploy ment; (c) female civilian unemployment ; (d) percent males 16 to 2 1 years old in the labor force ; and (e) percent females 16 to 2 1 years old in the labor force. Multiple regression analysis requires that the variables are measured on interval or ratio scales, and that the relationship among the variables is linear and addi tive. Nominal variables can be incorporated through the use of dummy, non-linear and non-additive variable trans formations, or through the introduction of product-terms (Nie et al., 1975:320-321). Multiple regression analysis is a general statis tical technique through which one can analyze the relation ship between a dependent or criterion variable and a set of independent or predictor variables. It can be either descriptive or inferential. Descriptive analysis 76 summarizes and decomposes linear dependence of one variable on other variables. Inferential analysis involves evaluat ing a sample, and generalizing the relationships observed from the sample to the population from which the sample is derived. This analysis progresses from the descriptive features of multiple regression, which emphasizes the evaluation and measurement of the overall dependence of a variable on a set of other variables. This implies that the statistical inference aspects of multiple regression analysis will not be of primary importance in this study. Since Los Angeles County comprises the population for this analysis, no particular level of significance is required; rather, the highest level of statistical significance that is observed will be reported. This study considers the influences of age, race, and offense-specific juvenile delinquent behavior and economic conditions through a regression analysis. It also considers the relationship of labor-force participation to juvenile delinquency, testing hypotheses derived from Glaser and Rice (1959), Fleisher (1966) and Gibbs (1966) . The next chapter presents an analysis and discussion of the data. 77 CHAPTER IV ANALYSIS AND DISCUSSION This chapter presents the results of the regression analysis outlined in the preceding chapters and further discusses the relevance of the findings in relation to other studies. It is organized into three sub-sections which correspond to the hypotheses posited in Chapter II. They are : (a) unemployment, delinquency and crime ; (b) income, delinquency and crime ; and (c) youth labor-force participation and delinquency. Tables 1 through 4 present the simple correlation matrix between the independent variables. They are based on the 133 Study Areas which comprise Los Angeles County (cf. Appendix B).^ The tables are organized by race and Since Los Angeles County is residentially segre gated by race, the zero-order correlations presented in Tables 2 through 4 are not calculated on all of the 133 Study Areas. This was necessary in order to avoid having correlation coefficients based on communities with too few ethnic groups. Table 2, the independent variable intercor relation matrix for Anglo-whites, is based on 122 communi ties. Table 3, the matrix for blacks, is calculated on 49 Study Areas, and Table 4, the matrix for Spanish-surnamed, is based on 129 communities. This consideration by areas was necessitated in order to obtain correlation coeffi cients by race which would correspond to the ecological units for the dependent variable. Any areas having less than 2 0 delinquent, or criminal, probation referrals in the respective race-age-and-offense groups were defined as missing and no rates were calculated on them for the dependent variable. This accounts for the reduction in the number of communities included in the ethnicity analysis. 78 TABLE 1 ZERO ORDERED CORRELATION COEFFICIENTS, MEANS, AND STANDARD DEVIATIONS FOR INDEPENDENT VARIABLES (Total) ^ 1 X2 X3 ^4 ^5 Male Civilian Adult Unemployment Rate (X^) 1 . 0 0 0 0 .6491 -.5595 -.2628 -.2665 Female Civilian Adult Unemployment Rate (X^) 1 . 0 0 0 0 -.3673 -.2856 -.4839 Median Annual Family Income (X^) 1 . 0 0 0 0 -.2324 .0524 % Males 16-21 Years Old in Labor Force (X^) 1 . 0 0 0 0 .4913 % Females 16-21 Years Old in Labor: Force (X5 ) 1 . 0 0 0 0 Mean 6.237 6.557 11491.73 55.727 44.539 S.D. 2.251 2.034 4165.80 8.850 6.955 N=133 79 TABLE 2 ZERO ORDERED CORRELATION COEFFICIENTS, MEANS, AND STANDARD DEVIATIONS FOR INDEPENDENT VARIABLES (Anglo-White) Xi X^ X3 ^4 Male Civilian Adult Unemployment Rate (X^) 1.0000 .3994 -.4089 .2976 .1057 Female Civilian Adult Unemployment Rate (X^) 1 . 0 0 0 0 -.0996 -.1229 -.3474 Median Annual Family Income (X^) 1 . 0 0 0 0 -.5351 - . 2 0 2 2 % Males 16-21 Years Old in Labor Force (X^) 1 . 0 0 0 0 .1917 % Females 16-21 Years Old in Labor Force (Xg) 1 . 0 0 0 0 Mean 5.679 5.976 12313.28 58.743 47.324 S.D. 1.836 1.941 4084.97 10.144 9.611 N=122 80 TABLE 3 ZERO ORDERED CORRELATION COEFFICIENTS, MEANS, AND STANDARD DEVIATIONS FOR INDEPENDENT VARIABLES (Black) ^ 2 ^4 %5 Male Civilian Adult Unemployment Rate (X^) 1 . 0 0 0 0 .3038 -.4836 .0043 .0537 Female Civilian Adult Unemployment Rate (X^) 1 . 0 0 0 0 -.3606 .1379 .1655 Median Annual Family Income (X^) 1 . 0 0 0 0 .0116 -.1787 % Males 16-21 Years Old in Labor Force (X^) 1 . 0 0 0 0 .1381 % Females 16-21 Years Old in Labor Force (X3 ) 1 . 0 0 0 0 Mean 11.149 8.626 8305.92 41.813 37.781 S.D. 8.605 5.375 2963.51 17.132 21.515 N=49 - 81 TABLE 4 ZERO ORDERED CORRELATION COEFFICIENTS, MEANS, AND STANDARD DEVIATIONS FOR INDEPENDENT VARIABLES (Spanish-Surnamed) ^ 2 ^3 ^4 ^5 Male Civilian Adult Unemployment Rate (X^) 1.0000 .4226 -.3427 .1048 -.0910 Female Civilian Adult Unemployment Rate (X^) 1.0000 -.4162 .2608 -.2600 Median Annual Family Income (X^) 1 . 0 0 0 0 -.1901 .2900 % Males 16-21 Years Old in Labor Force (X^) 1 . 0 0 0 0 -.1529 % Females 16-21 Years Old in Labor Force 1 . 0 0 0 0 Mean 6.257 7.482 10091.07 55.497 41.488 S.D. 2.619 3.343 3095.93 13.005 11.083 N=129 82 correspond to the order in which they are presented in the study. This was done in order to facilitate interpretation of the remaining tables. We shall initially focus on the relationships among the independent variables (Tables 1 through 4) and the dependent variables for the entire population, and later consider the nuances of these relationships by racial groups. A discussion on the find ings follows each of the sub-sections. In order to make comparisons about the direction and relative importance of the variables and the strength of the relationship between different age and ethnic groups, two types of analysis are presented. The tables which present these results are grouped in two categories : standardized and unstandardized. Unemployment, Delinquency and Crime Focusing on the unemployment variables, male and female civilian adult unemployment rates, we note from Table 1 that they are positively related with a rather large measure of association. Tables 2 through 4 indicate that this relationship is attenuated by race, although the direction remains positive. The highest rate of male and female unemployment is noted for blacks. White males and females have the lowest level of civilian unemployment in Los Angeles County for 1970. 83 In considering the problem of multicollinearity, it should be noted that the gender-specific indicators of civilian unemployment differ considerably in magnitude. Table 1 shows a fairly close relationship between the independent variables, but when we further consider this association by racial subgroups, there is less support for the contention that either variable can be used to explain the other. Conceptually, we have further support to use these two indicators of unemployment even with a moderate degree of multicollinearity. In the first place, taken together we obtain an indicator of the total civilian unemployment rate ; second, since we are employing a step-wise multiple regression analysis with forward inclusion, we obtain an indicator of explained variance which refers to the total regression equation; and third, since no causal model is postulated for this analysis, the inclusion of interdepen dent variables indicates the combined adult unemployment effect. Table 5 presents the results of the analysis of the male and female civilian adult unemployment rate for all races by offense types. It demonstrates that age-specific delinquency rates are positively correlated with male and female unemployment. The regression coefficients support this observation, with the exception of the regressions for female unemployment and 10-to-13-year-old total and juvenile status offenses. 0 ^ CO vO CO o CO vO CO o LA C M m CM CO CM CO CO o CO O C M VO 00 O vO CO LO o CM CO LO lO CM CO CM tH m CM 00 o 00 o M 4 - 1 -I r4 CO r4 <1- CO C3^ VO O 00 C T \ CO LO Ml" tH CM o m 00 CO vo CO Ml" oo CT\ o cr -I ^ o vo CO o <y M UO r-4 CO VO Mf VO O Ml- CT\ VO r-4 CO CO 00 LD LO 00 vo Ml" o m CT\ CM o o lO o M vo CO M O CO un N M 5 - 1 "O C t J r— i C U O Ml- CM CO CO Ml- CM VO CM un CT\ VO VO Ml" CT\ « - 4 O CM 00 CO CO C O 1 —1 5 - 1 'O 1 C d O CU o vo CTi CO vo in un o mt O M O H rH 4 - 1 > fS O pc! CU 4-1 CD W O o 4J CD 4-1 M -1 1—I ■ u a, 4-1 D h M 85 CO \o CO o C T > UO C M CM CM (Tv O VO CO UO 4J CM CM vO UO O iH ( T v CO O O 0 0 CO •H O ( U o UO O (Tv -T O O UO UO •vj" v o iH m CO UO UO o o M T ) C t J I—I CU O 00 4-1 (Tv UO iH O O C M i - H cr* vo (Tv 0 0 C M CO vO O CU V pH pH 0 3 JO C M CO 00 vO C M 0 0 UO vO vO CM CO CO O -vT 0 0 CO O vO UO S « 3 CU o UO 0 0 C M vo C M CO UO m C M (Tv mt O 00 v o CO 0 0 CO C M UO cd. I—I CU o iH O 00 CO CM (Tv CO C M I-H 0 0 CO (Tv O CO o o CO CO -vT 00 M H M H CJ PÜ CU 4-1 C J U 4J 4- 1 CO 86 M TABLE 6 UNSTANDARDIZED REGRESSION COEFFICIENTS OF UNEMPLOYMENT AND ACE-OFFENSE DELINQUENCY RATES FOR ALL RACES (N=132) Age-Specific Delinquency Rate Independent Variable 10-13 14-15 16 17 18 10-18 Years Years Years Years Years Years Old Old Old Old Old Old Total Offense Type: Adult Male Civilian Unemployment Rate Adult Female Civilian Unemployment Rate Person Offense Type: Adult Male Civilian Unemployment Rate Adult Female Civilian Unemployment Rate Property Offense Type; Adult Male Civilian Unemployment Rate Adult Female Civilian Unemployment Rate Juvenile Status Offense Type: Adult Male Civilian Unemployment Rate Adult Female Civilian Unemployment Rate .2427 -.0185 8093 1.0474 .6308 .0387 .4519 1879 .0781 .5648 .1621 .1095 0353 .1586 .1577 .1607 .0143 .0861 0033 .0212 .1019 .0649 .0260 .0246 0799 .2722 .3344 .2230 .0250 .1535 0184 .1036 .1702 .2417 .0630 .0827 0964 .2054 .1889 .0520 .0026 .1016 -.0472 .0069 -.0237 .0738 .0038 -.0041 87 Table 5 provides a consideration of the following hypotheses which were proposed in Chapter II: IA. The age-specific delinquency rate for juveniles less than 16 years old varies inversely with the adult unemployment rate. IB. The age-specific delinquency rate for juveniles 16 years old and over varies directly with the adult unemployment rate. As can be noted from Table 5, the standardized regression coefficients, indicators of the direction of the relationship between the independent variables (male and female civilian adult unemployment rates) and the dependent variable (age- and offense-specific delinquency rates), are positive, except for four situations. These exceptions are all for the regressions between female unemployment and: (1) 10-to-13-year-old total offenses ; (2 ) 10-to-13-year-old juvenile status offenses ; (3) 10-to-18-year-old juvenile status offenses ; and (4) 16-year-old juvenile status offenses. In Table 5 we can discern a marked decrease in the amount of variance in delinquency rates explained by male adult unemployment with each successive older age group for all offenses. This is opposite of what hypothesis lA predicts for those under 16 and what hypothesis IB predicts for those over this age. This trend is reversed when we focus on female unemployment, which has insignificant trends supportive of hypothesis lA and definitely supports 88 hypothesis IB. The patterns and amount of variance explained for the 10-to-18-year-old age group differs only slightly for person and property offenses, but differs considerably for female unemployment and juvenile status offenses. Here there is a marked negative relationship for the 10-to-13-year-old group, suggesting that their delinquency rate increases in areas where the rate of female unemployment is greatest. Table 5 also indicates a decrease in the strength of the association between male adult unemployment and age. The direction of the relationship is positive. The converse is true when we consider the strength of the association between female adult unemployment and delin quency. That is, an increase in the association between female unemployment and age is observed. The direction of the relationship remains positive, as was the case for male unemployment. Hypothesis IB is strongly supported by the results of the analysis for all races presented in Table 5. The age-specific delinquency rate of juveniles 16 years old and over is positively related to the adult male and female unemployment rate for all offense types. The only instance where this generalization is not supported is for the 16-year-old juvenile status offense. In this instance the standardized regression coefficient for male unemployment is in the hypothesized direction, while female unemployment 89 is inversely related. The zero-order correlation coeffi cients in this comparison lend credence to hypothesis IB, in that there is a positive relationship between areas with high unemployment and delinquency in the older age groups. The deviation in explained variance in Table 5 indicates that the association between areas with high unemployment and delinquency is greatest for property offenses and least for juvenile status offenses ; person offenses are closely related to the trend observed for property offenses. Table 7 shows the results of a similar regression analysis on Anglo-whites. Considerable differences between the findings for the total population and those for Anglo- whites are observed, but the data do not support hypotheses lA and IB. The amount of variance explained varies consid erably by age group and offense type, with the greatest amount accounted for by female unemployment and the person offenses for delinquents aged 10 to 13. In explaining the variance of Anglo-white 10-to-18-year-old delinquency, person offenses provide the least explanation and property offenses provide the most. Adult female unemployment relates significantly to delinquency for juveniles under 16 regardless of offense type. The Anglo-white adult male unemployment rate appears to be more important in explain ing delinquency for older juveniles. There is some varia tion by offense type in this generalization; the property 90 w I CJ VO r - . vO o r - . o r - . 00 - d " 00 O r - . r - . r - . CO 1 —1 1 —1 t —1 rH rH 1 —1 I I n- - d " r - . :s O r - . O r - . f l c\ m Ov m c t i m < O T m or 0 ) ) E 3 m m m m rP / - X -O c t i 00 CO <f VO -cf in -d" CO VO o or o - d " 1 —1m O 1 —1 u ’ T O <N O 1 —1 VO m - d " m - d " o r- vo q vo OJ 1 c O1 —1 CO 1 —1 r- rH m - d "O r - . CO CM o vo CM vo o 0 )o CM CO O CM 1 —1t —11 —1 t —1CM rH CM rH CM O 4 J1 —1 CO CM o j ' 2 - C O c\ CO m 00 o cO o - d " O -d- or r - . CO > 1 CO Î 4TO c\ - d - o C 7\ 00 m or m CO CM 1 —1r - . CO c O1 —1 \0 uo CM vO CM 00 CM CO CM r- -d" vo CM r - . a 0 )o 1 —1 1 —1 o o - d "or o 1 —1CM tH CM O rH o >4 f l rH O J P 3 rP ' C C J c t i c t i % - s 0 3 CO CO <1 - or 1 —1 CO 00 o CO - d " tH VO O O r - . Î 4TO cM 0\ vo CM o C 7\ CM - d " ov vO O 00 PÎ c OI —1 CO vO vO r -. rH VO vO - d " m rH iH m vo OJ o CM CM O O CO VO o CM t —1 CM tH - d "r - .o •H 1 VO - d " 1 1 1 —1 OJ Q CJ ^ Ü c t i c t i o CO rH <1 - -d- O o - d "m CM OT r-. CM O r - .- d " u TO ( 7 \ 00 I —1 GO r - .r - .or r - .o r - . vo O -d" 00 c O tH CO 00 o VO O 1 —1 t —1CO CM CO vo vo -d" O 0 )o CO CO O tH or r - .1 — 1 CM CO CM CO CO vO t —1 •H VO m 44 •H CJ O O m CO CM C 7\ - d - 00 -d" O 1 —1 r - . -d" CM or CM 00 O 1 —1 u TO CM 1 —1 - d " CM or CO O o CM tH -d" m m OJ 1 c OI —1 00 o CO CM t —100 VO 00 1 —1 00 - d - • < r CO CM 0 )o CO - C l O CM o m 1 —1 o o rH 1 —1 CM - d "o p .1 —1>* m CO 1 1 1 C O < u O % - s o Cj %-v o CO CO in o m r- 00 O CO m o -d" CM CM m vO bO1 — 1 Î 4TO tH CO C 7\ 00 o CM VO vO OT tH CO CM 00 m 1 C C J I — 1 \0 - d - O r- CO O -d" m o CO o r-. t —1m <3 o 0 ) o O rH -d- CO o O rH o CM m m o -d" CM 1 —1pH 1 1 —1CM 1 •H Î - I g I a, 0) 'a M < U PU g O J g 4-1 44 o I O J o P J O J T3 f l 4 - 1 <3 o j c t i •H Pi O t —I t H - H - P M > f l <3 o e o I —I Î P 3 3 t —( *H bO > Ü -H ( U ex 1 p 1 Po Pn rH EH 0 ) o P o 1 —1 rH B r —1 0 ) P p4 P § 2 § Ph 8 0 ) p p P P 44 1 —1 5 1 — 1 PJ OJ P • 44 P P P P u P Q O T3 p P TO P P 0 3 0) 'CM <3 p P <3 P P Pi S CO p3 P •H Pi •H Pi o o rH o rH P CO 1 — 1 •H P 1 —1 •H P P Î4 bO > P M > P OJ OJ P •H P P •H P 6 PH <3 CJ B <3 O B f l • c t i Q . 0) »CN I S cn M 91 vO o « 00 C O 1 — 1 I— 1 1 — 1 1 — 1 II Z C 3 o c O (d\ in C D m C d \ ÏS! • m in Xi C J 00 C O c r > C M 00 1 — 1 vo 00 1 — 1 5 4 Td 00 vO vo C d v C D 1 c d 1 — 1 (d\ < f o m 1 — 1 C M o 0) o C M C O 1 — 1 C M 1 — 1 u 1 — 1 > H C Ü c d C O m m C O C M o o > , 5 4 Td 00 o vo m 1 — 1 vO C d 1 — 1 C O m m 1 — 1 C O < f Ü C D o C M rH rH C D C O m o p H a 1 1 0) 0 cr • C O CN 00 00 vo < f 1 — 1 5 4 Td 1 — 1 C O o 1 — 1 T ) a c d 1 — 1 (d\ <N in m o m C D C U o 1 — 1 C M o 1 — 1 C M 1 -1 o 3 •H p H a 1 — 1 1 — 1 •H 1 — 1 •u a C D o o O rX p 1 C O C O 1 — 1 vo 00 < f C M 1 5 4 Td o C O m 00 C J V 00 c d 1 — 1 C M (d\ m o m <J- 00 Ü C U o C O C M o 1 — 1 < f O w pH •H 1 1 — 1 1 — 1 9 44 E H •H O C J Ü m C O o C M m 00 cjv C O 1 — 1 5 4 Td (d\ C O 00 C M o vo in C U 1 c d I— 1 oo o o C J V I — 1 o < f C U o C O 1 — 1 C O 1 — 1 C J V C M C 4 1 — 1 pH 1 — 1 C / 3 C D 43 P C O C O C O (d\ m C M C M C M P Û C 1 — 1 5 4 Td C O m m CM o C J V C M 1 c d 1 — 1 C O 00 O 1 — 1 C T v < o C U o o 1 — 1 <N C O C M C O o 1 — 1 tH C U 0) P h 1 — 1 IH /o EH 1 C U 1 c d tH 1 — 1 tH •H C U âi o 5 2 O 5 4 C O 1 — 1 1 — 1 B 1 — 1 c d fl c d p , 0) P > C U B P m G 44 C U C U 44 44 44 (3 44 a a O rH P 1 — 1 p < u P C U 3 C U a Td Td fd 44 Td Id 44 c d « a 44 <1 c d Cd <d c d Cd •CM C D 54 •H Pd •H Pd CO M (X C U O 1 — 1 o rH C D a 1 — 1 •H 44 1 — 1 •H 44 o 00 > fd o o > Id a 54 0 •H C U a •H M p 4 < O e < O B P o 00 C U 1 — 1 1 — 1 N •H 'C d 5 4 c d o td C J V m c d m C d \ u C O m in b O 5 d • r - l '2 c d p Id C O 1 — 1 00 p 00 00 o o C J V 1 — 1 P C d v p o C O m C M o C O C M C O 1 — ! C M o Ov 1 — 1 C U 5 4 1 — 1 5 4 o C J 5 4 . c d V •H 1 — 1 C M m C O 00 m c j v C U o O C M in m C M < f o P o P m C M C J V C O ■ o - P O o C M p o P o V 1 o 1 — 1 p C U p I I 'T d C J 00 00 1 — 1 1 C O C U C d v O C J V C M p 1 — 1 C J V C M C M Id 1 — 1 1 — 1 1 — 1 o o C O C M o C U o C O 1 ^ 1 — 1 1 — 1 C U u V P p C U P P p I I C O 1 — 1 p p 1 — 1 -sf c d O 00 o p C M C M O C O m C O 00 C O C M C M o C O m o C O C U m 1 1 C M C M C O o C U P p V td P C J C U p C J V C O O C O p C O C O u p C M 1 — 1 'vl; C J V C M C O c d I I o 00 m o C O 1 — 1 V O p C O C O p C O p Ov 1 — 1 c d Id C M 1 — 1 p C U C O p p o Ü P i C J Id 1 2 5 00 1 — 1 P m m C U C J V 1 — 1 P CM m C O •H 1 1 — 1 m in 00 CM o C J CM P CM •H P P td 1 — 1 P C U CD I CU 0 cd 0 3 CO 1 —11 —1 S 1 —1 1 —1 3 3 cd p CU P Q ) 0 4 1 S 0 p B 54 •H cd CU CU CU 54 CO P P p Id p .3. 0 3 CO tH 1 —1p 1 —1 P CJ 3 P 3 CU 3 ! CU 3 • P CU 'Td t i P '3 •3 P 3 P 3 00 I —1 CU p 00 > t d 0 0 > 3 3 p td •H 2 3 •H 2 0 < 0 B <3 0 B 92 o o Ü ‘ H 4 - 1 a 44 TABLE 8 UNSTANDARDIZED REGRESSION COEFFICIENTS OF UNEMPLOYMENT AND AGE-OFFENSE DELINQUENCY RATES. FOR ANGUOr^WHITES (N=117) Age-Specific Delinquency Rate Independent Variable 10-13 Years Old 14-15 Years Old 16 Years Old 17 Years Old 18 Years Old 10-18 Years Old Total Offense Type: Anglo Adult Male Civilian Unemployment Rate -.0708 .7983 1.2429 .7214 .0964 .3481 Anglo Adult Female Civilian Unemployment Rate .4586 .0630 -.2598 -.1803 -.0135 .0845 Person Offense Type: Anglo Adult Male Civilian Unemployment Rate -.0136 . 0 2 0 2 .0829 .1116 . 0 1 0 2 . 0 2 2 2 Anglo Adult Female Civilian Unemployment Rate .1244 -.0444 .0847 -.1066 .0124 .0148 Property Offense Type: Anglo Adult Male Civilian Unemployment Rate .0061 .2049 .2667 .1236 .0728 .0807 Anglo Adult Female Civilian Unemployment Rate .0510 .0557 -.0441 .0333 -.0524 .0285 JuvenileuStatus Offense Type: Anglo Adult Male Civilian Unemployment Rate .3371 .4728 .1268 .0170 .1280 Anglo Adult Female Civilian Unemployment Rate .2840 .1615 -.2848 -.0482 .0047 .0714 93 offense type delinquency rate correlation shows more significance in all age-specific groups, except for 1 0 -to- 13-year-olds, where the female civilian unemployment rate provides a greater contribution to the explanation of the relationship between unemployment and delinquency. The black civilian adult male and female unemploy ment rate is higher than that of any of the other racial groups, Yet, Table 9 shows few significant relationships between black unemployment rates and age-specific total delinquency rates. There is a curvilinear pattern in the amount of variance explained by age for all offense types except juvenile status offenses, which is substantially different from any of the other ethnic group comparisons between unemployment and age-specific offense types. This indicates that the salience of the two unemployment indica tors to the regression equation is not as great as it was for the other racial subgroups. We conclude that hypoth eses lA and IB are not supported by the data. Table 11 shows that in the regression analysis for the Spanish-surnamed population, the male unemployment rate is significantly and positively related to total delin quency, except in the oldest age group, refuting hypothesis lA. The relationship of the salience of female unemploy ment to delinquency is not as uniform, but consideration of male unemployment clearly contradicts hypotheses lA and IB for person and property offenses, but provides some weak 94 m \o o co o m rH rH rH rH m L A m m o C O m C O o m C T i m m o C O o rH O rH o> CO 00 CO I —I 00 CO C M 00 o C M 'd" o (U o C O M rH w 00 (U O <1 - en VA C M en m C M o 'd- en r» o rH 00 en rH M O en en <r \o o CM O* M O 00 O VO CM m en <1 - 00 vo c r > 00 m rH o en 1 — I 00 vO o < 1- 00 CO o W M C M M O w in vo en m 00 < 1- o 00 vO vo rH rH LA C M LA C M LA en en en rH CM rH OV T 1 1—I CJV o rH (U o en bO O < U O rH tH o CM rH M M O A O ex •H -U CM rH 95 M vo O CO O m I—I L O uo U O LO CO CO CO CO •u CO o o o> 6 0 uo CO LO CO vo OV T-4 CO 00 00 00 00 LO uo CO CO vt 00 00 vO T-4 oo 00 r -l CM CO O rH vO vO CN 00 OV 00 CO <f 00 CO rH U Xl I c d I— I O 0) o ‘ H O CU O CO CN CN LO C7\ CN O vo O V CO o o CO 00 CO <f m vo CN vj- cr Ov CO a o o v o o o CU V A fx •u o CO CN C 3 V O o OV OV CO CO o o CO CO rH OV CO rH rH CO CO LO C U o vw CN vj- CO C3V CO rH O LO Vt 1^ m C 3 V 00 CO CN 00 o vw M4 I—I ^ cd o B rH CU A rH rH M4 M4 + J r —i + J 60 C d O •CN •H Pd ^ rH CU -H 4J r H ■H +J XI rH TABLE 10 UNSTANDARDIZED REGRESSION COEFFICIENTS OF UNEMPLOYMENT AND AGE-OFFENSE DELINQUENCY RATEg FOR,BLACKS (N=35) Age-Specific Delinquency Rate Independent Variable 10-13 Years Old 14-15 Years Old 16 Years Old 17 Years Old 18 Years Old 10-18 Years Old Total Offense Type: Black Adult ;:Male Civilian Unemployment Rate .0487 -.0400 .0762 .2039 -.0268 .0757 Black Adult Female Civilian Unemployment Rate .0239 .4302 .6904 .3442 .1224 .1853 Person Offense Type: Black Adult Male Civilian Unemployment Rate .0182 .0339 .0309 -.0725 .0191 .0219 Black Adult Female Civilian Unemployment Rate .0073 .0875 .1091 .1183 .0060 .0368 Property Offense Type Black Adult Male Civilian Unemployment Rate .0325 -.0817 -.0565 -.0880 Black Adult Female Civilian Unemployment Rate .0042 .2041 .1785 .1167 .1009 .0872 Juvenile Status Offense Type: Black Adult Male Civilian Unemployment Rate -.0090 -.0270 .0515 .1160 .0170 Black Adult Female Civilian Unemployment Rate .0274 .1077 .1542 .0979 .0490 97 c s i \o oo CO CO CO CO m C O CO CO C O O' C O vo <f o C S I rH OV O I - H <f C S I CH rH M C S I C O W O <f vo CN CO m o o rH C S I CO < f . m vo o o CN O OO O w o o o cr M O ë II 00 CO CN CO < f CN O O rH 0 0 cr m CN rH CN CN cr CN rH OO 00 vO o vO . _ cr CN vO < f CN oo oo rH CO c r CO C _ ) hH M O 1 - 4 M m w rH M TO ( cd . rH NT CU O O O < f m v o Ov rH CO N f CO CO < f C O CO W . 00 vo in m vo 00 O 0 0 CN m m CN VÛ vO m CN oo v o rH CN o I — I — CO v o o o 1-4 o . • i H rH -W TO t> -M •tH cd d) t H C d > pd e o pd •rH c d O -M (Ü C U 4 -J 4 - J VH M H C O C O O W •CN 4 J A N a A"o a p CO < j to CO to Oi no a and p C O to C O < j t3 98 oo CN Q C O vO C O c r C O < r CN C N 1-4 1-4 II O O c r d C O o c d C O Q J m o ! 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S 3 o 4 3 ft O C N Q J d C O iH C O H ! S 3 C O ft f t Q J •H u ft •H 44 ft Q J ft, d 1 - 4 B d H B T3 O c d d Q J c d d Q J d d ftno d ft no d M ft C O <3 5 C O <3 3 = » O O C N C O vO C O c r CN CN O O c r C O o CO LO o vO CO c d C d oo C O LO c r -d" LO oo o C N C O CN -d" LO CN i H vO CN C N o i H LO -d" O LO LO C N CN i H C N C N C N i H i H C O C O 1 O O 00 O o O o i H C O O • • • • c d C d i H vo C O -d" i H CN C O -d" o o -d" LO vO vO C O C O LO vO vO O i H CN CN O S O -d" c r O S -d" C O C O O c r vo C O C N os CN LO LO vO CN -d" C N 00 O o o O O C N -d" O i H 1 —1 c d c d C O vO o ' O O vo LO vO i H CO vO C O o CN LO C N c r vO LO CN CN O i H CN CN o '— / i H 1 —1 C J r - C N C O 00 O vO iH C O c r LO LO C O CN H O i d c d d •H cd 1-4 •H Q J •H no 1-4 44 no > Q J •H C d Q J •H 6 > ft B O c d •H c d rO C s J CM CO Nt- vo oo m o 1 - 4 CN I—I CO 1 - 4 •H d ( U ( U 0 Q ) d c d ( U A 4 - 1 ^ d iH e cd d QJ A'd d C O <5 to d d - - 7 § K rd P4 O CO t-4 •H 4J f t d r4 6 C d d ( U ft'd d C O <J t3 d • cd Q Q J #CN C O pd 'S N •H T3 Î 3 "d cd t x O d •H no d O f t CO Q J U U o CJ u iH •H O Q J O 43 44 V 3 o f t iH Q J II 43 CJ no Q J 44 H d O Q J CO Q J V u f t f t Q J II u cd 43 CO LO Q J o CO Q J rd V 44 d ft Q J u II cd ft cd d •H Q J 44 CO O 44 f t d Q J •H CJ CO •H 44 L W d M4 Q J Q J •H O CJ CJ •H LM d M4 O Q J •H O 44 CJ Cd H d Q J o n J 4 CO O CO CJ Q J U Q J t x O 43 Q J E4 U ■ K 99 TABLE 12 UNSTANDARDIZED REGRESSION COEFFICIENTS OF UNEMPLOYMENT AND AGE-OFFENSE DELINQUENCY:-RATES FOR SPANISH-SURNAMED (N=114) Age-Specific Delinquency Rate Independent Variable 10-13 14-15 16 17 18 10-18 YearG Ycare Years Yearc Yearc Years Old Old Old Old Old Old Total Offense Type: Spanish-Surnamed Adult Male Civilian Unemployment Rate Spanish-Surnamed Adult Female Civilian Unemployment Rate Person Offense Type: Spanish-Surnamed Adult Male Civilian Unemployment Rate Spanish-Surnamed Adult Female Civilian Unemployment Rate Property Offense Type; Spanish-Surnamed Adult Male Civilian Unemployment Rate Spanish-Surnamed Adult Female Civilian Unemployment Rate Juvenile Status Offense Type: Spanish-Surnamed Adult Male Civilian Unemployment Rate Spanish-Surnamed Adult Female Civilian Unemployment Rate____ 1249 .4690 .5769 .3506 -.0709 .2296 -.0173 .1389 .3328 .0219 .0435 0100 .0884 .0668 .0199 .0037 .0316 0059 .0117 -.0098 .0351 .0043 .0063 0515 .1165 .2646 .0732 -.0364 .0768 -.0053 .0877 .0092 .0466 .0028 .0162 0339 .1126 .0377 .0263 -.0054 .0402 -.0124 .0242 .0120 .0759 .0081 100 support for the relationship on juvenile status offenses. The data from Tables 5, 7, 9 and 11 present an interesting pattern in the explained variance attributed to the regressions of adult male and female unemployment and age-specific offenses by ethnicity. The basic observation is that there is a difference between the regression for all races (the total population) and the sub-analysis of the three ethnic groups. For example, in the case of the regressions for all races (Table 5) we find tremendous statistical significance, which holds up when further distinguished by offense types. In fact for all races and offense types, 57.6 % of the variance is explained (Table 5). The corresponding explained variance for Anglo-whites is 10.5% (Table 7); for blacks, 15.3% (Table 9); and for Spanish-surnamed, 18.1% (Table 11). The only statistically nonsignificant regression in the above-mentioned set of comparisons is the black sub group . These are communities having a higher concentration of blacks. The data for blacks indicate that adult unem ployment rates do not explain much of the variance in delinquency rates of 10-to-18-year-old black youths. This finding might be explained by the increasing number of blacks who have given up hope in finding work, and are therefore outside the labor force. Another plausible aspect of this explanation which might account for the observation may be that underemployment, low wages and lack 101 of job skills have lowered the reward structure, or work incentive, for blacks relative to the other ethnic groups. This decrease in perceived reward, or potential benefits of working, can then result in a decline in the desire to secure employment, in which case unemployment rates under- reflect the extent of the problem, since the proportion of the population unemployed may be smaller than the propor tion of the population not in the labor force. Tables 6 , 8 , 10 and 12 present the unstandardized regressions of unemployment on age- and offense-specific delinquency rates by ethnicity. The unstandardized regres sion coefficients indicate the magnitude of the relation ships between adult unemployment and age-offense delin quency rates. Table 6 demonstrates a curvilinear pattern by age for the delinquency and adult male unemployment relationships, showing that the strength of this relation ship for total offenses is greatest for 16-year-old probation referrals and least for 18-year-olds. This curvilinear pattern is similarly noted in the analysis between male civilian unemployment and delinquency and other offenses, regardless of ethnicity (Tables 8 , 10 and 12). Consideration of the female unemployment rates, on the Other hand, does not totally reject this pattern. In fact, for two offense types Xtotal and juvenile status) we observe a weaker relationship than that noted for male 102 unemployment. Our analysis by ethnicity indicates that the relationship between 10-to-18-year-old delinquents and male unemployment is greatest for Anglo-whites and least for blacks. The relationship for female unemployment and delinquency, however, results in the converse conclusion. Female unemployment is more related to delinquency refer rals for blacks and least for Spanish-surnamed. A one-tailed sign test on black and Anglo-white differences in the direction of the relationship for the three offense types and five juvenile age groups reveals that the relationship of Anglo adult male unemployment rates to delinquency rates is not quite significantly greater than that observed for blacks (p = .06). Even less significance was found for ethnicity differences of black and white female unemployment and its relationship to juvenile delinquency age and offense types (p - .31). Tables 13, 15, 17 and 19 provide a consideration of the unemployment correlation to crime. Focusing on the explained variance we observe a positive relationship between the independent and dependent variables. This further supports Glaser and Rice’s (1959) hypothesis, which was stated in Chapter IT: 1C, The age-specific crime rate for young adults varies directly with the adult un emp 1 o y men t rate . Table 13 considers this relationship for all racial groups and offense types. Both male and female adult 103 en en en o en o CM CM CM en C O CO C en M M e j ) CO en CM 00 vO m m CO CO CM w o M en EH M ^ H CO CM C"» T — 1 CO CM I— I T — 1 iH CO vO LO O. CO vO I—I CO o. - < j - LO CM 00 CM LO vO O O 0" \ vO LO vO I —' M M CO CO CM CO CM CO CO lO CO I —I I —I LO LO - < j - - d " o. CO 0"\ CO CM V - l TO I c O . I— I 10 0 ) 0 CM IX e _ ) M LO CM -d" vO O O M H e j ) CM M CM 1 —! M 00 LO CO M rH O CM CM CO O CO CO CM O -d" ON • d - lO O CO CM V - l dO I c d I — I O N 0) o en CO o ON o PQ H ex O + - * -M M M - l M - l 44 44 Cd Q •CM en pü 4 J ex 4 J e x 104 H ON CO o O uo CO Td CM o e u en N CM CM •H Td CO X 4 CO c d I — 1 Td II d g c d CO 0 0 4 - 1 C i VO C O c d CO UO ( U CM UO 0 0 S • d VO vO Td d o e x C O C J C J O e u C d N C O <f M ON vo CM O CO Xj <f > 4•O vO I —1 ON r - ~ CO uo 1 — 1 X 4 1 c d I — 1 VD VO ( 3 Nvo CO CO o ON e u O I / O CM vo CO CMvo u e u I — 1 >4 X I 1 — 1 4 - 1 •HO e u O C d 43 4 - 1 p ci V e J \ C J !2 ON e o ON CO CM 1 — 1 uo CO uo o e x -d" J 4 •d o VO 1-4 uo vo uo 1 — 1 (U 1 c d t H o CO UO 1-4 uo CM<f e u II O e u o r - ~ 0- . 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P3 105 TABLE 14 UNSTANDARDIZED REGRESSION COEFFICIENTS AGE-OFFENSE CRIME RATES FOR (N=133) OF UNEMPLOYMENT ALL RACES AND Age-Specific Crime Rate Independent Variable 19-20 Years Old 21-24 Years Old 25-29 Years Old 30-49 Years Old 19-49 Years Old Total Offense Type: Adult Male Civilian Unemployment Rate .2404 .2486 .2542 .1335 .1996 Adult Female Civilian Unemployment Rate .3941 .3726 .1223 .0339 .1216 Person Offense Type: Adult Male Civilian Unemployment Rate .0314 .0556 .0319 .0187 .0301 Adult Female Civilian Unemployment Rate .0678 .0653 .0278 .0084 .0238 Property Offense Type : Adult Male Civilian Unemployment Rate .1183 .0957 .0931 .0393 .0692 Adult Female Civilian Unemployment Rate .1613 .1290 .0380 .0035 .0406 106 unemployment rates are significant in explaining the vari ance accounted for by the regression coefficients. Tables 15, 17 and 19 present a further explication of this relationship by ethnicity: Anglo-white, black and Spanish- surnamed. The subgroup having the largest amount of variance explained is the Spanish-surnamed population, while blacks have the least. We also should note that the amount of variance explained differs by offense type, with the property offense type having a greater association than the person offense type. Hypothesis 1C is confirmed by the strong signifi cant positive correlations and beta-weights for the regression analysis of unemployment and crime when all races are considered (Table 13). As much as 63% of the variance in the 19-to-49-year-old property offense rate is explained by unemployment. The ethnicity comparison leads to the same conclusion, with the exception of the insignif icant relationship observed for blacks (Table 17). The strength of the relationship between the variables and the explained variance is reduced considerably. For Anglo- whites, 8.87o of the 19-to-49-year-old property offense irate variance is explained by unemployment (Table 15), 15.6% for Spanish-surnamed (Table 19), and as little as 1.5% for blacks (Table 17). The amount of variance explained by male and female unemployment rates decreases as age increases. That is, 107 LO w g S Q C / 3 I 00 C M ON CO 'd- u T3 1 C l J 1 —1 ON QJ O 1 —1 ON CO Ht M T3 1 cQ 1 —1 o QJ O CO ON C O CM M TO U 1 cQ 1—1 lO QJ o •H CM >4 lh •H O QJ Ht C O CM M TO (X 1 cQ 1 — 1 1 — 1 QJ o CO CM QJ 00 CO M TO c Q . 1 —I QJ O 00 1 —I LO vo 00 vO Ht 00 C M 00 LO LO 00 LO 00 O' < t C M LO vo 00 O' LO vO < t 00 1 —I C M 00 LO LO oo LO o c Q ON C M 0 0 0 0 ON LO 1 —1 0 0 o H t CO o H t o 0 0 LO vO CO C M H t O ' CO LO 0 0 LO o O ' CO LO C M 0 0 1 —1 1 —1 1 —1 LO vO H t H t 1 —1 o 1 —1 C M C M C M O O 1 —1 o O 1 —1 1 —1 o o 1 —1 1 —1 o N _/ 1 1 —1 1 —1 \_/ 1 a 4 3 /-N a a ü CO 0 0 0 0 LO O' o Ht o o LO LO CM C3N C3N C Si 1 —1 vo LO CO O' Ht O' CM 0 0 CO CM vo CM oo 0 0 O' CM 0 0 LO H t 1 —1 o CO CN vo oo vo CO CM CN rH LO vo 04 Ht Ht o 1 —1 o o 1 —1 1 1 1 3 /-N 4 3 C Q X-X c Q VO Ht C 3 N 0 0 vO CO O ON 1 —1 CM 0 0 O ' Ht C M CN LO LO O ' iH O ' CO O vO C 3 N vO CM O ' LO C 3 N LO ON 1 —1 OO OO O ' O ' LO 1 -1 Ht ON iH vO CM C M O o CN CO o O iH C M C M O iH O 1 1 —! 1 —1 '■'X' O ' H t H t CO 0 0 O 1 —1 LO O ' O ' ON C M 1 —1 vD vO 1 —1 C 3 N LO H t CO O ' oo LO oo ON o oo CO vO o OO iH vD Ht 1 —1 Ht C M LO CO C M oo O O 1 —1 O iH C M CO o O o o o C M CO O CN C M 1 1 C Q X - H 4 3 / X — X C Q y — X Ht O O Ht 1 — 1 CO ON LO ON vO CO vD vD Ht O CO O ' 1 —1 O ' CO o O O ON O ' ON 1 —1 O ' O ' O ' Ht 1 — 1 ON Ht vD O O ' iH LO ON ON O ' O ' CO 1 — 1 C M iH C M ON CO o O O 1 — 1 I—1 1 —1 C M o C M C M 1 +J H +J +J QJ S3 (3 c c 1 — 1 QJ QJ QJ QJ Si 43 QJ B QJ B ex i QJ 1 C l J ex 1 — ! > N 1 —1 •H >1 QJ O cQ o H QJ o S 3 O XI H 1 — 1 1 —1 B 1 —1 1 -11 —1 B 1 —1 C t J cQ fX QJ ex QJ C l J ex QJ A > QJ S B B CO s B CO QJ QJ ü QJ (3 M (3 +J 3! QJ +J (3 +J (3 (3 QJ T —1 33 1 — 1 33 LM 1 — 1 33 1-4 33 QJ LM 3» 3» (3 LM 3J 3J Tj lh tO (3 Tj (3 C l J Q O Tü (3 T j (3 (3 O < cQ <î cQ QJ •CM <1 cQ +J 00 > +J Xi 60 > H 33 > ■M ( 3 O S 3 •H C l J ( 3 •H cQ QJ î3 •H cQ T3 •H c Q M E-i < O Pi <3 CJ Pi P4 < CJ Pi < CJ Pi o • c Q Q Q J «CM 2 CO p£| 108 T3 e u g •H ■ U (3 0 CJ 1 I m M i Q en t3 I ON C O Xi T3 1 e u 1 —1 ON < u o 1 —1 tH ON C O XI T3 1 c d 1 —1 o e u O CO PH 00 1^ Ht CN m v£) 00 1^ uo o o Oh Oh ON ON CN CN O x-N On uo - H t vo CO ON • CN vo < r 00 CN 00 uo UO 00 uo uo 00 I r — I ON 1 —I O H f CO O rH O I ^ O uo vO CN uo r H 00 CN CN 0 0 CO CO O 43 CO vO UO 1 ^ CN CN CO O 1 —I CN 1 —I Q J V ON C O u o C 3 0 CN CN CN CN o Xi CN XI T d CO rH 1 ^ UO CO 1 —1 CN ex ex O 1 cd 1 — 1 CN HT < d N O 1 — 1 CN UO Q J o rH 1 —1 o o H t UO O Q J II •H CN pH XI cd 4 3 « 4 4 •H C O UO Q J O O C O Q J Q J H t C O 1 ^ uo H t O vO 43 V CN Xi T d ON 'd ' vO O 0 0 XJ ex 1 cd 1 —1 UO 0 0 vo CN 1 —1 oo CO 33 ex rH Q J O 1 —1 1 —1 o 1 —1 vO uo O Q J en CN pH X 4 II cd 1 ex cd Q J 33 •H tuO Q J C d cd O XJ <3 O C O 1 —1 CO UO O CN C O O CN Xi T d CO 1 ^ vo ON UO 0 0 XJ îz ; 1 cd 1 — 1 1 —1 ON rH ON 1 ^ H t CN 33 ON Q J O CN CN CN CN 1 ^ oo 1 —1 Q J r 4 pH • •H H_X Ü C O •H XJ « X - l 3 3 « X - l Q J QJ •H O O O •H Q J XJ XJ « 4 4 Q) ex e 3 e 3 3 3 « X - l 1 — 1 P o Q J gj q Q J 4 3 54 B Q J g •H o c d 1 —1 4 - 1 o •H Q J Q J O C d o C d Xi çg 1 — 1 1 — 1 B rH rH 3 3 c d 1 3 C d ex Q J ex Q J O > Q J S - B F X S U •H «H Q J Q J Xi C O 4 - 1 « 4 4 XJ e 3 XJ C 3 o C O t3 O rH 33 1 — 1 33 o Q J Q J e J 3J (3 XI T d P o T U 1 3 Td (3 c d eo Q J bû t3 U <3 cd < î c d m CN 4 3 Q J Q J Xi •H •H 2 en Pi EH Xi ex Q J o rH o 1 —1 ■K Q J ex rH •H Q J 1 —1 •H Q J T d o 6 0 > -XI 00 > XJ e 3 XI g •H C d g •H c d (H ex <3 CJ e K 3 < CJ Pi - 3 N •H T3 M c U § 4-1 C O G •H "B O ex C O (U XI X i o o X i •H (U ■s g 'S 4 3 TU 0) 4-1 S C O ex rH O 109 TABLE 16 UNSTANDARDIZED REGRESSION COEFFICIENTS OF UNEMPLOYMENT AGE-OFFENSE CRIME RATES FOR ANGLO-WHITES (N=128) AND Age-Specific Crime Rate Independent Variable 19-20 Years Old 21-24 Years Old 25-29 Years Old 30-49 Years Old 19-49 Years Old Total Offense Type: Angle Adult Male Civilian Unemployment Rate .1536 .0727 .1890 .1191 .1223 Anglo Adult Female Civilian Unemployment Rate .2016 .0959 -.0609 .0832 -.0403 Person Offense Type: Anglo Adult Male Civilian Unemployment Rate -.0026 .0088 .0039 .0169 .0090 Anglo Adult Female Civilian Unemployment Rate .0253 -.0104 .0118 .0015 -.0026 Property Offense Type: Anglo Adult Male Civilian Unemployment Rate .0841 .0437 .0294 .0376 .0445 Anglo Adult Female Civilian Unemployment Rate .0841 .0173 . 0 1 1 0 .0139 - 1 1 0 fH. T —I w i H HI C O n C O o m , 1 1 % • K C O 0 • C d C J <u> 4 C J M M H Px M •H C J W O P - , C O <u> 4 pq 1 P m eu E H Oj O pq C O < O C O 525 C M X l T d pq 1 c d 1 — 1 Px C3N Q J o Px rH ^ O k H pq C O S O M Q J E H 1 — 1 < 43 M c d > •H pq U Q c d !> X J C Q J T d c Q J 3X Q J T d C M 00 I —I I —I m m Ht 00 00 0 - 1 fH. CO m v o vo v o CO v o CO I —I I —I m m CO 00 ON CO m vO vO vO 00 vO ON cd 00 o vO f H . CO CO r-. CM CM CO 1 — 1 O 00 CdN 00 CO Hf Hf CO CdN Hf CO f H . un CO m ON Ht f H . ON CN VO ON f H . vo o CO o o ON CdN o 1 — 1 CM CM m 1—1 o o 1 — 1 CO CO vo CO O CO CM 1 Cd cd cd CM o O vo Hf f H . m 00 00 CdN CdN l-H. vO CM Hf Hf Hf m ON 1—1 1 — 1 m 1 — 1 f H . CM m 1 CO vo VO CM m vo m o ON CM 1 — 1 CdN o T—1 CN CN CN 1 — 1 o o CM CM CO CO Ht 1 — i CM CM CdN O ON vo o f H . 00 m vO vo vo m CdN VO 00 Hf m o f H . CM CM ON ON CdN m O <dN o f H . CM O f H . CM 1 CM CM CM Ht vo o O o 1 — 1 1—1 un Hf o o O o VO vo o 1 1 Ht Hf y 1 1 Cd 1 — 1 CO CM H f O H f o O T— 1 o vD 1 — 1 CO CO m CM CM H f H f 00 CdN vo Ht Ht ON 00 vo Ht CO H f m CO 00 m T — 1 vo CM 00 CM ON q o O 1 —1 1 —1 o CO O 1 —1 O CO CM CM CM 1 —i 1 fH. un 1 1 —1 1 —1 Ht Ht CO CM CO un !—! CdN rH 00 ON CM vo ON fH. CO 00 CdN fH. CM ON VO O f-4 VO CM r> . H f CM H f T — 1 H f vo O CO ON rH 00 CM CdN H f O O O O 1 —1 fH. O 1—1 1—1 I —1 1 — 1 en vo O 1 1 fH. CO 1 t 1 1 1 — 1 I— 1 XJ XJ XJ 0 0 0 QJ 0 0 0 QJ B QJ B 04 K A 1 — 1 >N >N QJ O 0 O 54 0 O EH 1 —1 rH G rH 1 —! 1 —1 Cd ex 0 04 0 0 04 QJ S Px R 0 S B W 0 0 0 C XJ 0 XJ 0 0 XJ 0 QJ t —1 u 1 —1 Pd MH T —j 33 UH 0 0 0 • MH 0 UH Td 0 Td 0 0 o o Td 0 O < 0 < 0 0 *CM < 0 •H •H s CO p c î 0 •H t —1 4 * 51 — 1 4 * 51 — 1 o 4 * 5 rH c d O •H QJ O •H 0 m O •H XJ c d > XJ 0 > XJ XI 0 > o 1 — 1 •H Cd 1 — 1 •H Cd 0 1 — 1 •rH H p q CJ Pi p q CJ Pi PX p q O 0 » I o § ft g § •H rH •H 5 0 9 4 * 5 U c d c d pq o pd g • c d o (U «CM 2 CO p c î 111 00 m m o m 4 -1 00 o o < ! • C M C M OO O m o ( T ) W > 4 'U I ( 0 I —I o> < u o 4 - J CO 0 0 VD C M OO CO iH O CO m CO O uo UO iH O Ov C O ^ } - 4 T3 I c O 1 —I o e u O CO >4 ü o CJ UO C M \D O UO O O O uo 00 rH C M i H 0 0 iH O 4 - J CJ 0 , 4 0 rH < 4-1 C O UO Q J O C M C M 00 00 O O I —I iH C M H "Tj I 0 rH iH 0 O C M >4 O QJ 4 -J •H C M C M •H 4 -1 0 *H C J -H < + - l C M-l 4 -J 4 - J I —I rO 0 O S rH < + - l d 00 4 - J 4 -J 112 M TABLE 18 UNSTANDARDIZED REGRESSION COEFFICIENTS AGE-OFFENSE CRIME RATES FOR (N=50) OF UNEMPLOYMENT BLACKS AND Age-Specific Crime Rate Independent Variable 19-20 Years Old 21-24 Years Old 25-29 Years Old 30-49 Years Old 19-49 Years Old Total Offense Type: Black Adult Male Civilian Unemployment Rate .0338 -.0341 .0787 - .0315 Black Adult Female Civilian Unemployment Rate -.0350 .1753 .1683 .1223 .1062 Person Offense Type: Black Adult Male Civilian Unemployment Rate -.0429 -.0390 — .0046 -.0046 Black Adult Female Civilian Unemployment Rate -.0403 .1042 .0042 .0313 .0288 Property Offense Type: Black Adult Male Civilian Unemployment Rate -.0302 - .0174 .0330 - Black Adult Female Civilian Unemployment Rate .0311 -.0639 .0406 .0514 .0263 113 w i p r - l o c / 3 s I < u bO C O u s >4 <T\ C O M I c c j O 0 ) CO >4 C0\ CO CM Pi o 1 c c J 1 — I LO 0) o •H CM 4-1 •H O ( U CO CM P i (X 1 c c J1 - 4 I — 1 0) o CO CM (U 1 —I •H cti > § I A 0) "H oo CM vO CM CM m vO CM VO VO Ov OO CO CO CM CM C T v CO 00 1 —I C M vO C M CM uO vO CM vO vO C T v 00 CO CO CM CM C0\ CO o c c J o y » * * ^ o y ^ o VO VO m CO CTi C M CO CO O C0\ C0\ O m r~- CTi m 1 —I m C T v < O v 0 0 < t vO CO C M C T v o C M O CO O m C T v 0 0 0 0 vO C M CO CM CO 0 0 1 —I r 4 CM CO CO O o 1 —1 • c c J y « — X I y ^ y ^ aj CO VO CO i-H CO C T v m VO CM 1 —I m 0 0 CO 1 — 1 vO o vO CTv 0 0 0 0 C0\ vO vO CM CTv VO o CM CO 0 0 CO I —1 O m o r~- vO CM CM o 1 — 1 CO o rH CM 1 —I CM o o O c c J X < t I—1 m VO CM CM 1 —I fH 0 0 0 0 1 —I C0\ 1 —1 CM r~- 0 0 r~- vO vO vO vO vO o vO CM O o 0 0 o \ C0\ C0\ CM C M C M I—1 C M C0\ O rH O o o O 1 —I O 1 1 3 O y ^ X y ^ O m CO 1 —I 1 —1 C0\ 1 —I C M CO CM vO 1 —I C T v CO C0\ 1 ^ 0 0 O vO o UO CO vO C T v CO OV CO 0 0 C M I—1 C M UO CM CO vd- CM UO UO CM CM CO I— 1 C M CM H C M C M CO 1 —I CM 1— 1 1 —I 1 —I ; V-/ c d c d y » * * ^ c d O 1 — CTv O vO OV CO O O CO C T v O vO CO o C M CO UO 1 — 1 C T v vO ON CO O t-4 vO 1 0 0 o o O UO O 1— I 1— I CM t-4 vO o : o CM C M CM CO O C M rH 44 44 44 1 —I 1-4 tH PJ PJ P3 T3 no OJ no 0) < < Pm < cx OJ 0) Po pO T3 44 no 44 EH no H 0) Cd 0) Pi Cd 0) B Pi B Cd Pi 0) B 0) cd PS cd •H W cd PS 03 PS Id 44 C3 rH 44 P a cd PI Pi •H PS Pi •H PS 0) u •H 0) d I —1 PJ > OJ 44 P3 rH 4-1 C / 3 •H CO •H B Pi 44 CO •H 44 1 > 1 O cd O O 1 > O X! •H o X o 03 . CM X •H 03 O 1 —I 03 0) 1 —I S CO Pi P 03 O rH •H CP •H 1 — I CP O •H C t J pS OJ B PS cd B 03 PS 0) 44 cd 1 — I cd 6 0) PI Cd 1 —1 O CP cd P S pp 0) pi OJ pp cd H C / 3 S p4 c / 3 P4 X pH c / 3 S r P S 4 - > r—I 3 1 n j -H c / 3 tH I o rC C O 0) iH •H iH g I § pin 0) Pi CO fX4 |0 Pi a o cti Q (U 'CM S CO Pi 114 CX3 vO 1 — 1 CJv O cN 00 'Td vO CO (U c/3 N CS| CO •H 'Td rH u CO cd 1 —1 '73 II q Ï3 cd CS| CN 4J C m CN CO c c J vO CTv (U CS| CO 00 S q vD r - v . •H t3 13 O A CO 43 o (U CO O oo m o VO m u <r m 1 — 1 o VO C T \ 1 — 1 m u 1 03 I —1 (T, VO 00 C?v m m o (T, (U o CN CO 1 —1 CN rH 1 —1 1 — 1 o Q) 1 —1 >-« v_> u 1 —1 U •H O cu O c c J 43 4J p4 V 15 (T, CO CN m OO 1 — 1 CO O CL. <f H 'Td CO CTv CTv DO in CTv 1 —1 (U 1 c c J 1 — 1 cN 1 — 1 o CO C?v O 1 — 1 (U II O (U o 1 —1 1 —1 O o o 1 — 1 o 43 6 CO >-« • o 1 Td •H (U 'Td 4J 1 — 1 Q) } - i 13 o D CU C O CO •H a a (U V ■U (T, CO VO CO CO 1 — 1 m 1 - 1 C CS| H CO CS| CO m CTv <f oo Cu Cu O o 1 c c J I —1 VO CTv 1 —1 in CO m o m (U o CO CO o CN CN CO 1 —1 cu II 1 •H CS| >-« u 1 cd 43 CTi « 4 - 1 1 —1 •H CO m M (U o i-J O CO PQ cd 40 (U < (U CO <f <f m CTv OO 1 —1 43 V E-t CS| u 'Td VO CTv O CTv o O 4J A 1 c c J iH CO CN CO CTv CN CJv o 13 Cu rH <u>4 1 - 1 II cd > cu cd Q) a •H 00 cu 4J <3 O CO m VO <f OO CN CO CN CO o CS| u 'Td CO VO CO o 00 CJv 4J iz: 1 c c J 1 —1 1 —1 M. CJv 00 CN CO c Q) o o 1 —1 r-i 1 — 1 in IN O cu 1 — 1 >4 • •H v_V a CO •H 4J «H 13 «H CU cu •H o a 4J 4J a •H (U 1 — 1 1 — 1 «H (U PJ q «44 1 — 1 H T3 'Td o cu E-4 <3 <3 •H o c c J CD" Q) 4J o ♦H 0) T) 4 -> 4J cd U CO gj Cd Q) (3 cd rH 13 c c J c B pci B cd M cu o > (U c c J q cd •H 11 •H «44 (3 Cd 4 -) C rH 4 - 1 u CO 4 -) « 4 - 1 U •H c U •H q o CO d O g 1 — 1 g > m a cu (U CO •H 6 CO •H e (3 1 - 1 1 > >> 1 o ^ cd Q cu 00 a 4J 43 •H o 43 o CN 43 cu Q) u CO O 1 — 1 CO d) 1 — 1 S CO pa E-i 1 - 1 a <u for="" method="" type="" high="" challenge="" low=""> O M Pm O 0 co M @ <3 M E- 4 O c/3 g M 3- 13 M M p c 3 M Q [3 CD* W co hJ p c î H 3 1 —1 s 1 CN fH M M M E-4 CJ hJ M g 5 B4 O W CO P4 g CO <3 1 M M S co <3 Q ë M O U IH 3 M HH O CJ >4 d s co co M o (H Q g co c/3 g s 00 0) ■ M (3 a S g. •S Q (U rH 'H g § M Ü 'H M - t *H 0 0 ) ÇU en 1 < 00 vO co H T3 ( 7 ) i —I < U O >4 CO u s >4 no m ro co H no cd . i— I < u o >4 CO J-t n o c d . 1 — I < u o >4 co H n O c d ' i —I 0 ) o >4 CO H n o c d . 1 — I < u o >4 CN "d" O 00 CN vO O O m C N "d" O 00 c - j v £ 3 O O m O o o O C 3 3 C N •d" C 3 > •d" 00 o O r ~ . >0" en C N en <n en 1 — 1 m 00 m en en LO o en vO o lO •d" lO CN en C N 1 en rH 1 o o o o m 1 — 1 en VO en • d " en VO m >0" o en o • d " o. C N vO VO o en C N en LO o en -d" C T k 1 —1 en o 1 —1 rH 1 1— 1 1 o o o O o 1 —1 o N N. LO o. en t H 1 —1 lO •d" •d" o •d" •d" v £ 3 VO rH rH 00 o LO en LO •d" •d" en •d" VO O' CN 1 vO en 1 O o o o en C 3 > VO C N vO o. O' o Ni- en en lO o o. en 1 — 1 o 1 ^ LO vO C N 00 <n VO oo VO en lO LO VO CN 1 VO en r o o o O o IN vO IN o \ LO C N en o N C T V lO o 00 rH rH VO en •d" en 00 en <d" en vO C 3 3 1 ^ •d" • d " en LO CN t ~d" C N 1 O O o O < 3 N vO vO VO o o Ni- o rH lO o 00 VO 00 1 — 1 00 00 VO o en lO |N C N o lO o o 1 ^ en •d" o 1 —1 C N (U e # 1 —1 1 — 1 0) 1 — 1 0) c d C H c d Cd & d J to 3 ! > v ( d E H fd U EH f d q cd < 3 0) < 3 > e u f l ) CO ou CO f d s f d C B + J C d c d o ( U c d o ( d ou •H o L 4 H •H o 0) L W n d a LW T3 q Td L W < u M f d O < u M fd o s cd Q s 0 ) >v d ) < N q l>h A I — 1 rH rH S c/3 P d o 1 —1rH 0) cd cd •H co cd •H Td 4J 4 - J B 5 - 1 4J B q O o cd 0 ) o q M H EH Pm CH EH P4 § Q c/3 C N pc! 129 CN . O Q 00 1 — 1 C/3 <f CN cn t-4 II CN g VO q q o q O 3S3 m 1 — 1 1 — 1 o o 00 W CO t-4 1 — 1 00 1 — 1 V4 X) IN 00 UO 1 — 1 1 q 1 — 1 VO CN o q O UO 00 VO CO 1 —1 >4 1 o o q w 00 o UO 4J X 00 UO CO CO q q 1 — 1 IN vO 00 Pd q o CO CO CO 1 — 1 >4 IN 1 o q q q c/4 o o q w CO 00 N- CN x) •H X cn UO 03 o q t-4 q 1 —1 VO CO o CN q q q o Nf UO CO CN q Q >4 •H 1 1 — 1 1 —1 4J q q 1 — 1 o •r4 o q 1 q o O 1 > w 00 VO CN O i - H q X CO 00 o UO CN * - ) q 1 —1 CO 00 CO OO q o UO VO CN M o >4 •H 1 1 — 1 1 — 1 pq «44 •r4 EH o q q. o o c/3 m w UO CN CN CO 1 1—1 q X cn 00 vO NT q 1 q 1 — 1 VO CO 1 — 1 CN oo q o UO Nf t-4 CO <3 1 — 1 >4 1 1 — 1 1 — 1 o O CO w CO 1 — 1 00 CO 1—1 X UD, 03 o UO 1 q 1 — 1 O o o UO o q o UO CO CO CN 1 — 1 >4 1 * .. q q Cu 1 — 1 ÎN 1 — 1 rO P4 q q q •H q q w q q q <3 > q «44 q B 44 «44 q o q O •H o q X q q Td ÎN q M q o q 44 ;s3 q CN q U ÎN a c/3 Pd fH q 1 —1 r—1 q a q -H X) o 44 e q u o q 1-4 P 4 EH PH CN O 00 CN VO C D O m o m IN VO o CN IN I 1 —1 q q q q q q <3 44 q q q q B 44 cu q o C/3 !N •r4 o H X q q q M 1 —1 q ; s 3 •H w (N q q 1 — 11 — 1 q q q •r4 > «44 44 B q «44 o q X O EH PH m cn IN 00 o IN o o m VO m VO 1 — 1 IN 0 3 O vO VO IN 00 o o o 1 — 1 o 00 03 IN IN CN 1 — 1 1 — 1 CO 1 — 1 o 03 CN 1 — 1 1 1 — 1 IN VO 00 <n CN VO O o <n CN CN VO CN 03 00 UO o 03 UO VO 00 CN VO 00 1 1 — 1 1 — 1 1 — 1 o 03 o 00 00 CO 00 CN CN 03 UO CO CN CN q • • • rC g a Q c/3 CN P d O ) s h J o o O) ■5 4 J q 4 J q q o •H L W •H a •H C/3 II O q xi A - > 4 -1 q 4 -» g o •H M H •H & •H C/3 q S t - 3 m 4 -1 q 4J q q u • H « 4 4 •H q 0 0 •H C /3 II q 130 TABLE 22 UNSTANDARDIZED REGRESSION COEFFICIENTS OF TOTAT. MF.DTAN ANNUAL FAMILY INCOME AND ACE-OFFENSE JUVENILE DELINQUENCY RATES for: ALL%RACES (N=132) Age-Specific Juvenile Delinquency Rate Independent Variable 10-13 Years Old 14-15 Years Old 16 Years Old 17 Years Old 18 Years Old 10x18 Years Old Total Offense Type: Total Median Annual Family Income Person Offense Type: Total Median Annual Family Income Property Offense Type: Total Median Annual Family Income Juvenile Status Offense Type: Total Median Annual Family Income -.0001 -.0004 -.0005 -.0005 -.0001 -.0002 -.0000 -.0001 -.0001 -.0001 -.0000 -.0000 -.0000 -.00 02 -.00 02 -.0 0 0 2 -.0000 -.0001 -.0000 -.0001 .0001 -.0001 -.0000 -.0001 131 <r» CN M i B FH co m X H X I q . 1 —I o q o 1 —I î > 4 en CO o m m o CN 1 —1 co m O co CN o O o 1 1 —1 CN m Q CN CN 00 CO co O O |N rH 1 —1 i l a\ <03 S co co q q 00 oo q N S CN CN 1 —1 X O o o ü c o q O X N CN 1 —1 X 1 —1 1 —1 u X en VO X X vO O X 1 q 1 —1 N X CN CN vO CN o o q o 1 —1 1 —1 CN X 1 —1 CN 1 —1 1 —1>4 1 X CN 1 4P 4P q q q q m co O CO o (03 N CT3 44 c o H X 03 co <03 o 1 —1 N CO q q 1 —1 CN co X N vO CN CN P d q o CN Nt (03 o 1 —1 O 1 —1 O >4 !N 1 1 —1 1 O q q Ü O q q q q CN X co X o O O o •H H X VO <03 CN 1 —1 o vO O o r —1 q X N N 1 —1 o 1 —1 X q q o X X VO 1 —1 CN o Q >4 1 vO Nt 1 q 1 —1 •H q q O ü Ü ü > q o O X O N CN q u X oo N N co X o X X q 1 —1 c r\ vO O X o vO vO <03 q o X <03 X X X vO O ü >4 • i H 1 VO X 1 «44 •H O q a, O o q q co m q N o N co CN CO 1 —1 1 1 —1 u X N CN <03 N N X <03 q 1 q 1 —1 cr\ t H co Nt <Oi Nt X X 50 Nt q o O X CN 1 —1 CN Nt o < 3 1 —1> 4 1 X X 1 VO CN X X CN co vO CN 1 —1 1 —1 1 —1 o O q q q 1 — 1 B q B 4P q G O & G o q o. q ü Fn q ü •H •H G X •H G U X Td X Td X q q q q > q >1 >3 q S q 1 —1 G 1 —1 44 G q -H q q -H Ü q 44 B X 44 B q «44 •H q G X •H q 'S «H O q q ® CN o jG X q 1 X % co Pd G 1 X A 1 —1 o q O o q q q 1 — 1 G q X G 'Td X 50 G u 50 G a O G G q G G X X <3 <3 X <3 <3 q I Q CO C N I Pd 132 X Q CN 0 0 co o 1 —1 X I I 0 3 z : oo G q oo q S CN 1 —1 ü o 0 0 q p '' 3 0 X U Td 0 0 p '' 3 0 0 0 1 q 1 —1 0 3 1 —1 P '' <p>4 1 q q q q O 1 —1 44 G Td X 1 —1 X q q 1 —1 X X X X Pd q O CN X X o >4 >3 1 o G q G cr* o o G q oo 3 0 < t X nd •H G Td CN </p><p>4 •H 1 1 —1 1 —1 44 q G 1 —1 o •H O G 1 q u o 1 > q X O O oo X G G Td X oo 0 3 CN X X q 1 —1 o X < p 3 0 q O 'CP 1 —1 w o >4 X •H 1 1 —1 1 —1 P Q X < •H H U q X o o co X q 1 —1 0 3 3 0 1 1 —1 G Td X o 3 0 p^ q 1 q 1 —1 X 0 3 1 —1 oo 50 q O 1 —1 0 3 (—1 <3 1 —1 >4 1 1 —1 4P 4P X q CN CN 1 —1 1 —1 G Td 3 0 o < 0 3 oo 1 q 1 —1 0 3 p ^ 1 —1 oo o q O CN CN X o 1 —1 >4 1 q q X q 1 —1 !> 3 s 4P H G O q q o •H q *H G U q Td X q G q > q >3 X X X X q X G O 44 B q •H q G nd X X q o G X q CN q u I 1 —1 S co Pd X q o q q X X G nd o 50 G G X G G X X <3 <3 q G 44 q q 44 (X co >3 H q X q •H q G G q q > X G X X o X 00 o 0 3 00 oo p ' ' 30 00 P ' ' < 0 3 X 30 O 30 p ' ' CN CN </p><p>E3 >3 q 1 —1 •H X B •H q FH 1 X o q 1 —1 G 50 G G G <3 <3 I X o ° CN CO Pd V X V X I I 4 P X O V X I I q 133 TABLE 24 UNSTANDARDIZED REGRESSION COEFFICIENTS OF ANGLO-WHITE MEDIAN ANNUAL FAMILY INCOME AND AGE-OFFENSE JUVENILE DELINQUENCY RATES FOR ANGLO-WHITES (N=117) Age-Specific Juvenile Delinquency Rate Independent Variable 10-13 Years Old 14-15 Years Old 16 Years Old 17 Years Old 18 Years Old 10-18 Years Old Total Offense Type: Anglo-White Median Annual Family Income Person Offense Type: Anglo-White Median Annual Family Income Property Offense Type: Anglo-White Median Annual Family Income Juvenile Status Offense Type: Anglo-White Median Annual Family Income -.0001 -.0004 -.0006 -.0004 -.0001 -.0003 -.0000 -.0002 -.0001 -.0000 -.0000 -.0000 -.0000 -.0001 -.0001 -.0001 -.0000 -.0001 -.0001 -.0002 -.0002 -.0001 -.0000 -.0001 134 races (Table 21). For blacks, the largest amount of variance (25.4%,) is due to person type offenses and the least (16.5%) is explained for juvenile status offenses (Table 23). Spanish-surnamed 10-to-18-year-old juveniles (Table 25) show little difference by offenses in the per cent of explained variance (31.7%) for property offenses ; 30.2% for person offenses and 28% for status offenses. All of the above comparisons were statistically significant. Fleisher regards tastes for delinquency as impor tant determinants of legitimate versus illegitimate behav ior and maintains that economic factors are salient in explaining the rate of delinquency. To be more explicit we can note some of Fleisher's comments on the relationship between economic factors and juvenile delinquency. The principal theoretical reason for believing that low income increases the tendency to commit crime is that it raises the relative costs of engaging in legitimate activity. In the first place, youngsters probably view their families' income as an index of their own long-run legitimate earning possibilities. Thus, so long as there is not a substantial positive covariation between the expected returns to legitimate and illegitimate activity, individuals with low incomes (or whose parents have low incomes) probably expect relatively large payoffs for committing delin quent acts. To such individuals, the probable cost of getting caught is relatively low, since because they view their legitimate lifetime earning prospects dismally they may expect to lose relatively little earning potential by acquiring criminal records ; furthermore if legitimate earnings are low, the oppor tunity costs of time actually spent in delinquent activity, or in jail, is also low. (Fleisher, 1966, in McPheters & Stronge, 1976 :232-233) 135 X CN w I c / 3 § g q X fS >3 G q I 1 —4 q 0 q u G 1 X •H O q (H c/3 i . < 00 c4 vD X CN ~ d " CO CO CO o CO CO CN 30 X CN CO CO CO o CO x> JO JO JO CO q X X o 30 03 X 03 X u T3 03 X 03 03 X X 1 q X CO o NT X o CN X X o q O X 30 CN X o X CN X > 4 1 30 CN 1 X q X 30 30 X X 00 03 CO u TO o 03 X 30 X X 00 30 q X X 30 X 03 03 X Nf X q o X X 30 o X NT o >4 1 X CN 1 q CO o X 03 CN CN 03 u TO 03 30 X X X CN q X X X X X 00 o q o X 00 o o N T X o >4 1 X X 1 CN X X rO JO q CO o X X 30 X u TO o 03 03 < r X 03 o q X 03 o 03 00 o 03 00 q o X X CN CN X o >4 1 X 30 1 CN 1 —1 X q q JO JO X q CO o X 00 o X 30 o X G TO X X CN X X X 00 X 1 q X X 03 X CN o o 00 X q o X 03 o X X 30 X CN X >4 1 03 X 1 X 1 —1 JO q q X q X X X CO X X 30 X X u TO X X X 03 CN 00 1 q X < r X X 03 00 o q o X X X X X X X X >4 1 CN X 1 q # # X X q X JO q q ex q -q a G >3 G •H k*3 G X G u X G G q < q <d > q q q q G G G m 44 G q O q q o G q •H O X •H o q X TD G G X t3 G TD X q X q O o q X G O S q • CN s q >3 S C / 3 pa G k*3 fH X X O . i d t —1 q q o •H q O •H TD u q B G q B G o X q q X q X H PQ fH (H PQ pH o CN M 136 0 0 0 0 o CN CN 3 0 3 0 c/3 X X CN CN X X II g G 0 0 oo q q 0 0 0 0 ;s! oo 0 0 O o 0 0 0 0 q q X JO 0 0 q 0 3 4d" 0 0 O ' X O ' O ' r-4 rH G Td X 3 0 1 —1 NT r-4 C 3 3 3 0 CN 1 q r-4 o 0 0 1 —1 3 0 O ' CN 1 —1 CN o q o X O 1 —1 X X CN 1 —1 >4 1 CN 1 —1 1 1 —1 q q 0 0 O ' O '. 0 3 O 1 —1 X 1 —1 4 -1 0 0 G Td 3 0 X X o 4d" 0 0 q q 1 —1 CN X o C 3 3 X O ' o Pd q o O X 30 o O 1 —1 CN o >4 >3 1 1 —1 1 —1 1 a G q G c r q . q G q 0 0 X oo o 1 —1 O ' 0 0 T3 •H G Td CN 3 0 X 1 —1 X o X q i-H q r-4 0 0 o 0 3 X X X o 0 0 G q q o 1 —1 3 0 0 3 o C 3 3 3 0 1 — 4 G Q >4 •H 4d" CN 1 1 —1 1 —1 4 -1 q G 1 —1 O •H O G 1 q 1 > q 3 0 X O ' CN O ' 1 —1 CN X G G Td 0 0 O ' CN X I—1 X 1 —1 0 0 CN X q . 1 —1 o O ' X C 3 3 o O ' X 1 —i q o X X O CN O ' C 3 3 o o M ü >4 ■ ■ 9 o X i •H 1 CN CN 1 CN t—1 FQ X < •H V X Ü q PU a . c/3 X q o 0 0 1 —1 3 0 O X X CN I I 1 1 —1 G Td 3 0 X 3 0 O ' X C 3 3 O ' C 3 3 q 1 q 1 —1 3 0 0 3 CN CN 1 —1 O ' CN 0 3 o 50 < r q o 1 —1 0 0 1 —1 O X X CN O < 1 —1 >4 1 X CN 1 CN 1 —1 1 —1 o V a Ü X q X CN X 0 0 oo O X O ' PU 1 —1 G Td 0 0 O ' C 3 3 C 3 3 3 0 0 0 o 0 0 1 q 1 —1 X o C 3 3 0 0 C 3 3 o X X II o q o X o 3 0 CN I — 1 3 0 o 1 —1 >4 r û 1 1 —1 1 X o q q ex 1 —1 >3 1 —1 1 —1 V jo X q q q G G pu •H q G G u q G q G I I q G <1 G < ! > q X q q X G ë q q G B +-I X q o X P H q o G o •H a c/3 >3 •H a q Td G G E -4 Td G G q Td >3 q M q Q q q M q n ; X G X S q CN 1 —1 q S q CN ^ O q G > 3 S C /3 Pd • r 4 q s en Pd : g a q rid r-4 G G rid 1 — 4 q Pu Ü -H q q Ü 'r4 V o q B > «X q B G G 1 —1 q G «X 1 — 4 q M Pm FQ fH X o FQ Pu 137 TABLE 26 UNSTANDARDIZED REGRESSION COEFFICIENTS OF BLACK MEDIAN ANNUAL FAMILY INCOME AND AGE-OFFENSE JUVENILE DELINQUENCY RATES FOR BLACKS (N=35) Age-Specific Juvenile Delinquency Rate Independent Variable 10-13 Years Old 14-15 Years Old 16 Years Old 17 Years Old 18 Years Old 10-18 Years Old Total Offense Type: Black Median Annual Family Income Person Offense Type: Black Median Annual Family Income Property Offense Type Black Median Annual Family Income Juvenile Status Offense Type: Black Median Annual Family Income .0003 -iOOOS .0012 -.0003 -.0003 -.0006 -.0001 -.0003 -.0001 -.0000 -.0001 -.0001 -.0002 ,0002 -.0003 -.0002 -.0001 -.0002 -.0000 -.0002 -.0002 -.0003 -.0000 -.0001 Q r-~ r * ' a \o p O I—1 rH p p P CT\ H t M rH c/3 O M 1 M M II c/3 ^ P VO vO Z S 0 P P C O pH C/3 (U r-~ r * ' CO 1 S p p t d H t 'O ' P m c/3 a\ o g CO ^ u u Ü u % PM P C O p CM O rH r * ' VO H t CM o CO M P h T 3 m p P l~H CM rH P M 1 c O 1 —1 H t r-~ P rH 'O ' O rH Q EH C£j O (U o vO I—1 P H t p P CM p < O M M P m 1 CM I—1 1 P £ 1 CO O M EH Q rO rO S « O J C O m P t H r * ' o CT\ CM 'O ' < 4-1 P U T 3 o CM p CM rH rH p Q > -i (0 cO < —1 m P 'C f p vO P H t r * ' S O P C D O M O P O P O CM o ^ IS EH M 1 I—1 I—1 1 CO P3 u O ' C (U P 3 < t-4 c r* u u Ü u P3 f i C O r-~ P p p H t P P CM W Q •H P4 T 3 m CT\ o ct> 'O ' P 'O ' P p ^ t H C O P c n 'C f p p CM rH p o < p q O J C D O H t CM CM rH P P o rH P 3 1-3 Q O M 1 P 'C f 1 I—1 CO a (U CN M I—1 P d > •H W P i H :| H M O J u U U PQ i- J > C O p P rH O u P CT\ P < P h O P 3 P4 T 3 p r-~ rH C 3 > I—1 CM EH M M C O t *H 'C f P 'C f O p P P rH EH P m C D o H t P P P p 'O ' P rH m 3 M Ü p B o •H 1 'C f p S Pd P 1 P •H A CO U CO 1 (U g M p U U Ü U ^ O p LT) C O H t P rH O 'O ' 1 P P h "O M rH p P l~H P P P s O J 1 cO I—1 r-~ CM r-~ CM o O CM VO Q Ü 0 'C f O J o 'C f CT\ 'O ' CM 'O ' P P rH < M > -' CO ^ 1 P H t 1 EH a w P d s M o o o a C J o U M IS P C O P P P 'O ' 'O ' P P P M M P4 TO p P p 'O ' 'O ' vO H t P P 1 C O r - \ p O p 'O ' p H t a > P P >-< O O J O p P P p p o o rH O P P > -l ^ g 1 1 I s ^ c O P cO C O M •H •H CO p (U •TO C D TO (D CO I—1 ^ s C D ^ s M P ,13 C D a o S o P S cO P u u O 2 •H T ) 0 EH TO 0 P < U H C D HH C D M P cO S (D P > C D C O rn T) « < 3 C O d rH 0 rH P M + J f i P4 *H (D P4 *H N Q f i (D d e MH 3 p M p O J MH P cO P! MH P cO C J T 3 1 4 - 1 1 p C O O O 1 P m cO Q PJ O , d CM r d CM (U C O rH p P h P 3 C O rH p c d a P •H cO O • iH cO 0 ) C O f i p ) C O C 3 d < "O 4 J cO C P I C O f i EH c O P C (D CM C 3 P M EH p , Q S 4 3 H S o c O o • H C L ) T O d C l C O d ) I H C O d S > d ) C O P h. < 4 4 d t H 4 4 < 4 4 C 4 ' H d o d B 0 ) P C O T O p H , 1 P m d 4 4 4 d d ) C l C O r H C M d ) • H C O C L ) a d d o ( 0 d d C l C M d M P m p c § g < s , p cd rn. VO p p p IH . VO ' O ' o\ o CM C O V C M p I C O V o r ' ' P o o p C M o P p VO 4 3 Ht 1 — 1 p p O p P ' O ' p p C M r ' ' O O V O r ' ' H - P o C M 1 — 1 fH. c o \ o C M 1 1 — 1 1 — 1 • Ht P P o\ 1 —1 c t \ P CM P CM P P 1 —1 1 —1 CM Ht CO o o 1 1 —1 I —1 p V O p 1 — 1 P H - v O I I p o 1 —1 p P V O P p p c r . Ht t H < 0 \ V O C M U p 1 — 1 r ' ' 1 — 1 ' O ' C M C M C M 1 1 —1 1 — 1 1 1 — 1 1 —1 t —1 VO C M C M P r ' ' t'. Ht r ' ' p P P p Ht i H C M C M 1 d p c O o “ H T O Ç D d B V S o o C M T O d C O C DM I I d B 44 c O t H C O ( 0 ( U d H 44 C M C l" H P t H d B BH p ( 0 d C D C L ) I4 , ( 0 Q 44 1 —1C D 4 3 d .CM O • H ( 0 C O1 —1 s p p c i g d d • H ( 0 C L )( D d d > < 4 4 c O d d < 4 H C Md p O P < V C M V C M I I 43 140 TABLE 28 UNSTANDARDIZED REGRESSION COEFFICIENTS OF SPANISH-SURNAMED MEDIAN ANNUAL FAMILY INCOME AND ACE-OFFENSE JUVENILE DELINQUENCY RATES FOR SPANISH-SURNAMED: „ ; (N=114) Age-Speclfic Juvenile Delinquency Rate Independent Variable 10-13 Years Old 14-15 Years Old 16 Years Old 17 Years Old 18 Years Old 10-18 Years Old Total Offense Type: Spanish-Surnamed Median Annual Family Income -.0002 Person Offense Type; .0011 ,0009 -.0010 .0001 .0005 Spanish-Surnamed Median Annual Family Income -.0000 Property Offense Type: .0001 -.0001 -.0002 -.0000 .0001 Spanish-Surnamed Median Annual Family Income Juvenile Status .0001 0004 -.0004 -.0002 ,0000 .0002 Offense Type: Spanish-Surnamed Median Annual Family Income .0001 0003 -.0001 0001 0000 -.0001 141 In this respect Fleisher contends that it appears that poverty causes high delinquency rates. The association between areas of low income, high unemployment and high rates of delinquency in urban areas have been well sup ported by the classic delinquency studies of Shaw and McKay (1942). In the interpretation of crime data, one salient demographic characteristic is the high correlation between youth and crimes against property. In 1961, for instance, 93 percent of the serious arrests of people aged less than 25 were for the crimes of robbery, burglary, larceny, and auto theft. Although this age group constituted only 43 percent of the 1960 population of the United States in 1961 it accounted for almost 90 percent of the arrests for auto theft and over 60 percent of arrests for the remaining property crimes. (Fleisher, 1963:544) Although it makes more prima facie sense to look at the association between economic factors such as income and property offenses, the economic approach is equally tenable for crimes of violence. That is, crimes of violence are also negatively correlated with income (Fleisher, 1966, in McPheters & Stronge, 1976:233). Our data further support Fleisher's observation on the relevance of income to other delinquency and crime offense types. Tables 22, 24, 26 and 28 present the unstandardized regression coefficients for our analysis of ethnicity, age and offense type. Focusing on total offenses for lO-to-18- year-olds, we notice that the strength of this 142 relationship is strongest for blacks, very similar for Spanish-surnamed, and least for Anglo-whites. This finding supports the thesis that income is inversely related to delinquency, and further illustrates the differential impact that income exhibits on various ethnic groups. We can infer that an increase in income will be a greater deterrent for blacks than what would be the case for Anglo- whites. This seems to be due to the greater variance in income for Anglo-whites. The economic diversification of the ethnic groups provides a crude indicator of the relative opportunity structure for those ethnic groups, which supports the argument that the greater the legitimate or meaningful alternatives to crime, the less the likeli hood of engaging in illegitimate pursuits. Tables 29, 31, 33 and 35 present the results of the regression analysis between median annual income and age- specific crime rates by offense types and race. Blacks have the highest crime rate (Table 33). The Spanish- surname population has the lowest rate of crime (Table 35). There is a clear trend which shows that the age-specific crime rate decreases with age, regardless of race or offense type. These tables provide empirical support to hypothesis 2B: 2B. The age-specific rate of criminal activity, for all adult age levels, is inversely related to the median annual family income. 143 o 00 o oo m \o m o m o CO oo oo oo oo oo o o M E - t CO o\ o C N J o v j o O oo m oo m oo o v j oo iH cN oo c r > o ■ v j - cN oo o m fH fH oo CO M TO I c ü 1 —I < T > ( U o vO Ovj vo o v j oQ c r > O m c r > < j - o\ m o o CM o m o o oo oo o CM fH iH CM CM CM O m m ' d - oo CM O oo H oo fH m m fH CM fH CM O O CM CM CM M O O CM oo oo UO < T > fH m OO fH oo < J - CM oo CM m OO OO fH o ' C f CM OO \0 fH ( T f CM O O m o oo m o M P h P h EH m m o m MH m MH O M M M 144 M TABLE 30 UNSTANDARDIZED REGRESSION COEFFICIENTS OF TOTAL MEDIAN ANNUAL FAMILY INCOME AND ACE-OFFENSE CRIME RATES FOR ALL RACES (N=133) Age-Specific Crime Rate Independent Variable 19-20 Years Old 21-24 Years Old 25-29 Years Old 30-49 Years Old 19-49 Years Old Total Offense Type; Total Median Annual Family Income Person Offense Type: Total Median Annual Family Income Property Offense Type: Total Median Annual Family Income -.0002 -.0002 -.0001 -.0001 -.0001 -.0 0 0 0 -.0 0 0 0 -.0 0 0 0 -.0 0 0 0 -.0 0 0 0 -.0001 -.0001 -.0000 -.0000 -.0000 145 o o S g M EH O C O § ( U 0 ) 60 C CTi C O u T 3 1 td I — 1 CTi e u O I —1 k H c r i C O - < r ^ 4 Tf I c d I— I o e u o CO >4 <Ti CO CN U T 3 O 1 cd I — 1 in e u O •H CN kH MH •H O e u CO CN M T3 A 1 cd I — 1 rH eu O CO CN kH vO o m o C T i o vO O og o r- 00 C T i I u m 00 o m I o 00 C T i m •vf 1 CO CO 00 VO CO o C T i m cg <r CO I —I I —I ^ T - l o (N U O vO CO CO 00 m in LO vO CN o vO CO <f T — I I --1 00 00 rH rg CO CN o 0 0 o vO Ht CO rH vO rH CN CO rH CN CN o CO < T i o CO O CN VO O <f C T i CO rH CN CN VO VO Ht <t O O m m 1— 1 I—1 - H t o O CTi CTi O O vO vO O CN 8 I —1 1— 1 O o O O I —1 o <t CO r- m VO tH CO o r- m O m I — 1o m o I — 1o CTi CN CN Ht Ht I — 1 I —1 CN <t CO CO CN 1 1 u U Ü U CN CTi r- I —1 CO vO cri vO CN VO CO CN r- CN cri I — 1 vO OO r- I — 1 r- CO VO <t O O I —1 Ht I — 1 CN I —1 1 1 O O O o VO r- <t r- <t CN CN r- <t CN r- 0 0 CO CO I —1<t <t cri I —1 I —1 CTi o I —1 m CO o I —1 rH CO <t m I —1 1 1 I — 1 o o HO HO O o m CN I —1 m tH <t o CO V VO o 0 0 vO 0 0 o 0 0 m tH CN 0 0 r- I — 1 I — 1 0 0 r- Ph CN CN CO O <t vO in I — 1 • • • • • • II 1 ' o I — 1 O HO rO o O tH VO -d" O m O tH CO m tH tHvO Ht m tH m V tH tH tH tH CN tH -d" o CN I -1 CN O CO tH 0 0 I — 1 A eu eu eu I — 1 B eu B HO eu td o çx td o cd a cd o cd o •H •H td H •H td U H t d M t d M cd eu eu eu > eu Cft 2 C O td S 4 J td eu •H eu eu *d td eu 4 - 1 B M-l 4-1 B eu V H •H cd td M-l •H cd 'td MH cd O O td o eu •C N eu 1 I— 1 S CO Pi td 1 rH (X I— 1 o cd o O C d eu cd rH OJ C O I — 1 o s t d 4H 0 0 td J-l 0 0 td td O td a eu td td t-H H c <1 P h < d < d a C t j o ( U • s CO CN e u ç x > 1 B H t d o c d o e u •H t d C O td M t d e u Qi ^ > 1 M-l I —1 MH e u -H O 4J B •H c d rC PM 4H M 1 I —1 e u o c d O. rH 05 o 0 0 t d j - i t d ü P h <î <3 g â Q • CN CO pd m o V a, II cd 146 TABLE 32 UNSTANDARDIZED REGRESSION COEFFICIENTS OF ANGLO-WHITE MEDIAN ANNUAL FAMILY INCOME AND AGE-OFFENSE CRIME RATES FOR ANGLO-WHITES (N=128) Age-Specific Crime Rate Independent Variable 19-20 Years Old 21-24 Years Old 25-29 Years Old 30-49 Years Old 19-49 Years Old Total Offense Type: Anglo-White Median Annual Family Income Person Offense Type: Anglo-White Median Annual Family Income Property Offense Type: Anglo-White Median Annual Family Income -.0002 -.0002 -.0002 -.0001 -.0001 -.0000 -.0000 -.0000 -.0000 -.0000 -.0001 -.0001 -.0001 -.0000 -.0000 147 m 1 —I m m 1 —I rH m m C O m o o m en m ro m VO oo VO oo oo VO 00 r-« . 00 M & - j 00 C O M O O O 0 0 iH rH m OV 0 0 < j- o C 3 V m CH vO CO o CO o 00 00 m VO m 00 CH rH CH (O V O rH CO CO CO CH CO CO Ov CH ( o v CH LO O CO + J CO CO CO rH -vj- <J- VD CO rH LO 0 0 rH I—I 1 —I O 00 00 CH CH OV C O <f > H T3 I c d 1 —I o e u o CO t > - l LO 0 0 LO o rH o O' C L ) CO M CH CH M CD CD CH CO 0 0 < f LO HT rH VO OV CO rH LO VO o 00 LO CO CH 0 0 o CH < J- o vD 1 —I CD O» <J" CH LO O 1 —I 1 —I LW C O o o v < J - ov LO o LO o CH > H T3 I c d rH rH ( U O CH >H 00 VO o CD VO HT O O LO CD LO OV CN CO CO vO CN <J- LO CN OV VO CH CO VO CD LO O OV -vj- CO OV rH LO o v C N o o o v 00 o LO CO LO rH o \ rH -vj- vD < r CH rH o CD CH CH H CO CO a 1 —I N M MH L 4 H +J LW LW e u M 0) M eu M CO M rH Ü *H 148 M PQ P h TABLE 34 UNSTANDARDIZED REGRESSION COEFFICIENTS OF BLACK MEDIAN ANNUAL FAMILY INCOME AND AGE-OFFENSE CRIME RATES FOR BLACKS (N=50) Age-Specific Crime Rate Independent Variable 19-20 Years Old 21-24 Years Old 25-29 Years Old 30-49 Years Old 19-49 Years Old Total Offense Type: Black Median Annual Family Income Person Offense Type: -.0001 -.0001 -.0002 -.0000 -.0001 Black Median Annual Family Income Property Offense Type: -.0001 -.0000 -.0000 -.0000 -.0000 Black Median Annual Family Income -.0000 -.0000 -.0001 -.0000 -.0000 149 f X 4 O CO o CO s I e u bd C O s ( U !x C D I c c J iH O ( U O en > 4 CO CN U T3 a 1 C C S 1 — 1 en e u O •H CN m •H o 0 )<f CO CN TO Pi 1 C C S rH rH e u O CO CN C O * eu M vO CNJ CN 00 CN en m o- cJ en CN o o o o ( T \ CN vo rH m m o 0 0 o < 1 - 00 < 1 - CN r o ( T \ en C C S oo o ( T \ rH O 00 ( J \ CN 00 00 < ! ■ en .H < ! ■ <f en iH C C S CN CN < 1 - < f 00 vO o o en C T i o vO CN CN 00 CN en m < ! ■ CN en CN c3 3 CN ( T \ o - â CN en CN 0 0 vO 0 0 0 0 vO CN O o O O 0 0 en < ! ■ ( T \ vO o CN < ! ■ < ! ■ O O O O O VO CN CN 00 CN en m - d " CN en CN o o O 'd" VO r-~ CN vO ers 1—1 en en ers LO 1—1 en 1—j 1—1 1—1 CCS ecS 00 1—1en O m C J N o o C T i O < !■ CN O tH O (T\ 1 — 1 (T\ 00 rH (T\ 00 vO vO o (T\ o CN C3\ (T\ o CN LO en o O O rH o O CN en o o O rP rP O o 1 —1 CTi O en CN m o vO m LO vO LO CN (T \ CN en e u P œ P >y p c ü % C O P i —H P P rH MH P rH +J p U •H p J H •H MH S h •H P e u P B MH P B O P B e u MH CP P P MH CP P P CP P P nO MH 1 P P O O 1 P h P Q ! > . 1 P p P O rP P •CN rP p • CN u rP p e u C O 1 —1 S cp p c S p CO I —1 S CP p c S j H C O rH : s 3 P 1 —1 •H P o •H P P •H C d e u p P P p P P P P p nO 4 - > P P u P P O P p P O P P p P P u P p M EH CP < ■ p CP <3 p CP < o O V A LO O V P i : II C C S ê 150 TABLE 36 UNSTANDARDIZED REGRESSION COEFFICIENTS OF SPANISH-SURNAMED MEDIAN ANNUAL FAMILY INCOME AND AGE-OFFENSE CRIME RATES FOR SPANISH-SURNAMED (N=131) Age-Specific Crime Rate Independent Variable 19-20 Years Old 21-24 Years Old 25-29 Years Old 30-49 Years Old 19-49 Years Old Total Offense Type; Spanish-Surnamed Median Annual Family Income Person Offense Type: Spanish-Surnamed Median Annual Family Income Property Offense Type: Spanish-Surnamed Median Annual Family Income -.0002 -.0001 -.0000 -.0000 -.0001 -.00 00 -.0000 -.0000 -.0000 -.0000 -.0001 -.0000 -.0000 -.0000 -.0000 151 Consideration of the standardized regression f coefficients, which are identical to the zero-order corre lation coefficients in this analysis, clearly supports this proposition. Table 29, for example, shows a consistently inverse relationship between income and crime. This negative relationship becomes more apparent with the increasing age-specific crime rate. The pattern is noted for all offense types. There is some variability in the amount of explained variance. The explained variance for the total offense type is greater than that of the other two offense types, although there is no apparent difference in the level of significance with which the independent variable (median annual family income) contributes to explaining the inverse association with age-specific crime rates. In fact. Table 29 indicates that income is highly significant in all of the regression equations with strong negative coefficients. This is taken as further substan tiation of proposition 2B. The trends observed for the total population are reflected in Table 31, which presents the relationship between income and crime for Anglo-whites by offense type and age, but it shows that the amount of variance explained is not as great as we noticed for the total population. The explained variance is significantly different in all of the comparisons. Person offenses are the only offense types not as consistently high in their significance 152 pattern; however, even here the independent variable (income) is still significant. In Table 13, which shows the association between income and crime rates for blacks, the relationship is inverse, but with no statistical significance. None of the equations here demonstrate that income offers much explana tion for black age-specific rates, neither collectively nor by offense type, as the greatest percent of variance explained is approximately 6.4% and in most of the equa tions it is less than 1%. Table 35 completes our analysis of income and age- specific crime rates by demonstrating for the Spanish- surnamed population, a pattern similar to that for Anglo- whites (Table 31). Income contributes significantly to the regression equations, although the variance was not as con sistently high as was the case for Anglo-whites but was greater than the explained variance found for the black population. The only exception to this generalization is that for person offenses, in which the explained variance is very low, paralleling that found for blacks. Our analysis of the relationship of income and crime by ethnicity is further clarified by data presented in Tables 30, 32, 34 and 36. These tables show that there is no major difference in the strength of the relationship between the total crime rate for 19-to-49-year-olds and income when further considered by ethnicity. There is 153 evidence that the relationship is stronger for younger aged adults than for older adults. Spanish-surnamed is the only ethnic group which does not reflect this pattern ; the 25- to-29-year-old total offense rate relationship to income is inverse, but is noticeably weak. Youth Labor-Force Participation and Delinquency The relationship between youth labor-force partici pation and age-specific delinquency rates by offense types is presented in Tables 37, 39, 41 and 43. They provide an empirical examination of two hypotheses developed in Chapter II: 3A. The 14- and 15-year-old juvenile delin quency rate is inversely related to the percent of males 16 to 21 years old in the labor force. 3B. The 14- and 15-year-old juvenile delin quency rate is inversely related to the percent of females 16 to 21 years old in the labor force. For all offenses except juvenile status, the zero- order correlation coefficients in Table 37 and the standardized regression coefficients support hypothesis 3A, that male youth labor-force participation is negatively related to 14- and 15-year-old delinquency rates. An even stronger inverse relationship is indicated for the female youth labor-force participation (hypothesis 3B) with respect to all age-specific delinquency rates and offense types, especially juvenile status offenses. The correlates 154 ro M O O g g W O o g O ) S 00 I —1 C O u < u 1 cd I —1 O < uO 4J I—1 >4 M Po u e u 3 u" Pi •H ! -1 O ) O CJ •H MH •H U O ) ex CO I O ) 00 <3 C O u tO cd iH e uO >4 O O O o CO CN r~. 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LO lO VO en CN LO o CO LO CN Os LO ro 00 VO e n LO CO ! -1 VO Ht 00 1 —1o Ht Ht 00 vO ro Ht ro Ht 1-1 Ht vO en Ht VO O 1 —1 ro ro en r~. I—1 CN Ht ro Ht ro LO CN t l vi Ht CN r 1 1 1 .Q 4P 43 c d /— V U CN o o LO VO VO 00 VO CN vO Os O st st O LO Os I—1 LO o CN O Os en VO 00 I—1 rH LO 00 Os O vO o en Ht LO ro LO Os CN Os o I—1 CN CO 00 Os o CN CO CN ro o I —1 1 — 1 1 r 1 1 ‘‘ v i 1 1 v_/ C O C O e u f 4 u r H C O C d ( U C O c d 4 3 e u u e u e u e u P X H e u e u e u c d e x c d o > 4 ü > > C d u f > 4 C J N e u i- i f 4 E - 4 e u H S 4 u H > 4 o I — 1 O > 4 o I — 1 O c d P h C N P h e u P h C N P h > ( U r H 1 C O rH i C O C N U V O U P i C N U V O U 4 - 1 P i 1 O I — 1 O e u 1 O I — 1 O P J e u V O 4 3 4 3 M - 4 V O 4 3 4 3 e u M - 4 I — 1 c d C O c d P i ' M - 4 ! -1 c d C O c d n O M - 4 | 4 | e u c d Q O rJ ( U rJ P i O C O r H e u - C N C O I — 1 e u e u P i ç g P i S e n P £ | P i e u P i P i fX 1 — 1 1 — 1 •H • H o I — 1 ‘H • H e u C d c d e u C O c d e u n O 4 J S ^ p H n d U S n d P h n d P i O 1 — 1 1 — 1 e u I — 1 1 — 1 M H O O P 4 O O Pi • c d o e u • CN S CO p c i 155 ■s § •H 4 - 1 d 0 o 1 I r~- CO s en d I G ) U C Ü Pi iH o d (U d cd d •H 1 —I (U Q O •H M - l •H 0 0 1 CM en I e u (30 00 VO m CO 'd •H d C Ü > 4 -1 5 "d (U 6 'd 00 C O f - i u Td 1 C Ü1 —1 o ( UO 1 —1>4 C O s >4 C O u C Ü (U >4 C O d Td C Ü > —I (U o >4 ' ü j ' ü j (U CM B a i I M - 4 M - 4 O m C M o u CM VO CN 0 0 03 0 0 VO VO VO m - Ü " m - Ü " o o iH iH 1—1 LO 03 O en CM 0 0 LO iH en <n 03 iH <r o C s | en- ü " 0 0 VO 1—1 • • * • • • • 1 o O iH en en o VO o o m en C N J 1 r ~ - m m VO 0 0 CM 1—1 en en en eniH • • • • • • o O 03 0 0 1 —1 iH 0 0 VO 0 0 M . 0 0 1—1 m 03en o 03 0 0 - Ü " en O o 1—1 C s | en• < r m en CM 1 1 1 1 1—1 1—1 O o 1 —11 —1 en C M VO C M VO CJ3 VO lO CJ3 0 0 O en < 1 -lO C M -Ü" 0 0 eno o C M -Ü" -Ü" VO -ü" C M 1 1 1 1 —11 —1 VO C M 00 CJ3 00 VO r~- VO VO < r lO -ü" lO -ü" o o o o -ü" CJ3 lO o en -ü" < r CJ3 iH CJ3 VO 00 O o en 1 —1 -ü" C M 00 -ü" C M 1 1 o C M O iH 1 —1 CJ3 lO CJ3 LO VO VO VO 00 vO -ü" O 00 -ü" O o O O o O 1 —1O 1 1 o C J 0 3 en 1 —1 < r 'Ü" CJ3 O 1 —1 en C M 0 0 C M 1—1 1—1 r ~ - -ü" < r lO 1—1 O lO 1 —1o -ü" en C M r-s 1—1 1 1 1 1 —1 o C J C J C M r ~ - r ~ - -Ü" CJ3 CJ3 C M o lO o C M lO VO en r ~ - lO VO VO C M -Ü" C M -ü" C M 0 0 o C M 1 1 1—1 1 —1 C J C J JP C J C J en C M lO lO C M C M r ~ - C M m VO -ü" C M CJ3 C M o C M -ü" o 0 0 VO C M r ~ - 1 —1 1—1 VO -ü" o VO CJ3 0 0 lO iH en iH o m 0 0 m VO 0 0 T — 1 o O en en 1 —1 1—1 C M o en C M 0 0 1-1 1 1 1 1 —1 1—1 1 1 iH C Ü JP C J C Ü dO o VO lO 1 — 1 0 0 r ~ - m m en en I —1 o CJ3 CJ3 lO O r ~ - CJ3 o C M o m VO o 0 0 en CJ3 en 0 0 r ~ - o o C M o en 0 0 C M CJ3 0 0 1 —1 en C M en enen 1 —1 en C M C M o en C M o 1 1 1 1 1 1 C O C O u u C O C Ü C O C Ü U (U (U (U u O J O J O J C Ü C J >4 C J C Ü C J >4 u ni u M (U u u >4 o iH o C O >4 o iH o P m C M p-i d P = 4 C M iH 1 u i - H 1 C M u VO u C Ü Q ) C M d VO d 1 o 1—1 O 4 -1 Ç U 1 O 1 —1 o VO rO C O VO -û JP 1—1 C Ü C O C Ü d H 1 — 1 C Ü C O C Ü d hM (U hd C Ü Q (U hd O J hd C Ü Q C O 1 — 1 (U C M 1 —1 (U C O 1 — 1 m •CM (U d C Ü d s C O M •H C O (U d çü d s C O M iH •H & •H d d 1 — 1 •H 6 •H C Ü (U (U O J C Ü O J S t O P = 4 Td > Lt-t s t O p-i % ) I — 1 1 —1 d < 4 - 4 1 —1 1 —1 o o M o B -S O B< o no O J N •H no u C Ü no d C Ü 4 -1 C O d •H no d o d4 C O e u u u o ü d iH •H O (U O rC 4 -1 V 1 3 O A 1 —1 (U II rÛ C J nO (U 4 -1 1 —1 d o (U C O (U V d O. A (U II V 4 C Ü dO .«1 C O m . (U o C O (U rd V 4 -1 d du e u u II C Ü o . C Ü d •H (U 4 -1 C O O 4 -1 13 d (U •H C J C O •H 4 -1 Lj-j d LW e u e u•H O C J C J •H M - l d LW o e u •H O 4 -4 a (ü iH d (U o w •H u C O o C O C J (U u (U (30 rC (U H u ■ K 156 TABLE 38 UNSTANDARDIZED REGRESSION COEFFICIENTS OF YOUTH LABOR-FORCE PARTICIPATION AND ACE-OFFENSE DELINQUENCY RATES FOR ALL RACES (N=132) Age-Specific Delinquency Rate Independent Variable 10-13 14-15 16 17 18 10-18 Years Years Years Years Years Years Old Old Old Old Old Old Total Offense Type: % Males 16-21 Years 2 Old in Labor Force % Females 16-21 Years Old in Labor Force Person Offense Type: % Males 16-21 Years Old in Labor Force % Females 16-21 Years Old in Labor Force Property Offense Type % Males 16-21 Years Old in Labor Force % Females 16-21 Years Old in Labor Forc^ Juvenile Status Offense Type: % Males 16-21 Years Old in Labor Force % Females 16-21 Years Old in Labor Force -.0040 -.0281 ,0141 0926 .0098 .0145 1546 -.2458 -.2102 -.0518 -.0965 -.0033 -.0149 -.0122 -.0026 -.0008 -.0059 -.0042 -.0271 -.0376 -.0383 -.0051 -.0164 -.0068 .0114 -.0079 -.0161 -.0029 -.0122 -.0572 -.0880 -.0735 -.0194 -.0367 ,0106 .0256 .0555 .0136 ,0015 .0182 -.0100 -.0486 -.0731 -.0449 -.0010 -.0281 157 03 m M i Pi M O Pd en M M P d O >4 Ü 0) 4 J C Ü Pi t n Ü i d 0) d o" P 3 •H 1 - 4 (U Pd O •H M4 •H 0 0) PX| en 1 0) ù O | < 1 00 VO m d C Ü 00 C O iH X) I C Ü I —I o e u o M >4 C O } - < no C Ü 1—I e u o >4 o 43 u O CO 1—1 CM < Ü " CO OO rs 1—1 o o CO d X 03 O o lO 03 CM CO CM VO o CM CÜ 1—1 1 - 4O r - ' 1—1 t H 03 LO 03 vo LO 1— 1 LO 00 eu O CO CO o o CO VO O CO CO 1—1 CM ■ < a - 1— 1 >4 VO ■ < a - C O s e u >4 XJ C O M X C Ü r - 4 0) o >4 C O d X C Ü 1—I e u o >4 en CO oo r-H VO r-H en CÜ r - ~ 03 m CM CM o CM c v j m en CM CM CO oo m CO - ü " 00 ■ < a - CO CO 00 pN. VO LO CO 00 LO CO ■ < a - 1 —1 ■ < a - CO 0 3 CO 0 3 CM LO 1 —1LO O ■ < a - vo LO r - H 0 3 CO LO CO vo o ■ < a - CM LO ■ < a - r > » 0 0 1 —1vD VO CM CO o 1 —1 1 —1 o 1 —1 1 —1 o O 1 —1CM o 1 CO CM CÜ o oo O LO X OO CM CM 0 3 1 —1 0 0 0 3 oo LO CM 0 0 1 —1CO 1 —1r - H 0 0 CO CM 0 0 1-4 o 0 0 LO O VO CM CO o ■ < a - 0 3 O CM 1 - 4 O O O 1 —1o iH 1 o VO ■ < a - r-H 03 o o o 03 VO CO o l<- 03 CO CO 03 o r-H lo CO CO LO 1 -4 o ■ < a - 03 03 LO o- vO o oo LO X CO VO VO 1 — 1 CM CM 1 — 1 O 03 o o o 1 —1 1 —1 CO VO o 1 I VO LO 1 1 43 43 C Ü x-x 43 o r-' C v j CM < Ü - o LO VO CM CO vO CM 00 lO CO lO <ü " r - ' CM 03 LO 1 - 4 <j - r - i CO ■ < a - LO CO o 0 0 vO 1 —1 1 - 4 00 oo CO VO CM 00 CO oo CO CM o o 0*0 0 CM CM 1 —1 1 —1 CM o LO CO 'w' CM LO CO lO 0 0 o CO LO -Ü" X — s CJ3 LO CM LO CÜ X CM VO o 00 o CM LO CO 0 0 0 3 VO CM 0 0 C73 VO 0 3 CM CO O CO r - o CM r-H r-H 1 — 1 LO 1 - 4 1 —1 O X o o o X CM 1 —1 1 —1 O ■ < a - O 1 1 1 1 1 —1 CM 1 1 1 C O C O ( U e u e u 1 —1 C O 1 — 1 e u C O 1 —1 4 3 e u e u d C Ü d e x e u d C Ü d C Ü e x X • H B X > 3 1 — 1 X B X •r 4 > , C Ü ( U H C Ü e u d EH S X p L | X s X p L | X C Ü 1 — 1 1 — i e u 1 — i 1 — 1 > e u e n S Q ( U X o C O d ai X o 0 ) X o X d • H C O e u X C O e u e u X C O e u X C O e u d e u d O d ej X d u d o e u X C Ü d C Ü d d • X C Ü d C Ü d X X 1 e u O 1 e u O C Ü ed O 1 e u o 1 e u o d O O >4 p L H o > 4 k e u «CM o > 4 p L H o > 4 p L H e u 1 — 1 1 — 1 S e n p c S d X 1 — 1 ex 1 — 1 00 X d 0 0 X d o 0 0 X d 0 0 X d e u C Ü d C M O d CM O C O d CM O d CM O X X C 1 lO C 1 4 3 u <d I 4 3 < 1 l O d o vO C Ü vO C Ü e u vo C Ü vO C Ü M H X X 1 — 1 X Pd 1 —1 X B ' S 1 — 1 X g • C Ü Q 0 ) «CM S C / 3 p p ; 158 ro oo oo oo CO 00 m oo + J m m oo o oo < d - 0 0 -d - VO m i - i m uo iH CN CN ex 4 - 1 Ü o < u o o m vO 0\ vo o o oo C T i <f oo ON O O O I —I vO O OO 00 O o 4 - 1 O CN m uo C T v uo o o oo oo 00 CN <f CN cr <f uo O o oo iH O m 00 CN <f 00 oo o o VO CN G o 4 - 1 O CN m vo m o 00 CN CN CN Xi V 4 - 1 m cT v m CTv -d" 4 - 1 OO OO rH }-) T J I C O . I — I O e u O O (U *H O -H cw h -H (U O 4 - 1 O t u o + J 4 - 1 4 - 1 C W < 4 - 1 4 - 1 4 - 1 (U 0 0 O >H fH O >H fH I —I 00 rH H 00 iH H O >H fH rH . . 00 iH H 00 rH H 4 - 1 159 TABLE 40 UNSTANDARDIZED REGRESSION COEFFICIENTS OF YOUTH LABOR-FORCE PARTICIPATION AND AGE-OFFENSE DELINQUENCY RATES FOR ANGLO-WHITES (N=117) Age-Specific Delinquency Rate Independent Variable 10-13 14=13 16 17 18 10-18 Years Years Years Years Years Years Old Old Old Old Old Old Total Offense Type: % Anglo-White Males 16-21 Years Old in Labor Force % Anglo-White Females 16-21 Years Old in Labor Force Person Offense Type: % Anglo-White Males 16-21 Years Old in Labor Force % Anglo-White Females 16-21 Years Old in Labor Force Property Offense Type: % Anglo-White Males 16-21 Years Old in Labor Force % Anglo-White Females 16-21 Years Old in Labor Force Juvenile Status Offense Type: % Anglo-White Males 16-21 Years Old in Labor Force % Anglo-White Females 16-21 Years Old in Labor Force ,0342 .1306 .2013 .1768 .0148 .0582 -.0207 -.0264 -.0979 -.0372 .0208 -.0087 .0121 .0042 .0318 .0031 .0048 -.0062 .0061 -.0111 .0140 -.0008 .0004 .0550 .0134 .0236 .0024 .0144 0021 -.0212 -.0342 -.0105 .0098 -.0073 -.0238 .0320 .1266 .0219 .0012 .0204 ,0119 -.0419 -.0422 -.0134 -.0004 -.0156 160 M p e i . f H O f H o Q ro A A A c / 3 1 — 1 ( T f 1 — 1 e n 1 — 1 1 — 1 A ro I I iz v D ' d " v o ' d " G C N I A C N I A G G ro A A A A ' d " A C X ) 0 3 f H CO A A f H o A e n C N C N A A f H H T ) A e n A < ! • f H e n A o C N C N A 0 ) 1 G f H C N I o C T f O ' d ‘ A 1 A A A C N A C N O G O o A A A A v O 1 — 1 1 — 1 1 — 1 1 — 1 O A O 4 -1 f H M V O C N I f H C d Pi G f H C N I v D f H V O V O ' d " v O O O A e n A 00 H T ) f H A ' d ‘ A e n A C N I V O 1 — 1 1 — 1 A A A G 1 — 1 A A A C N i v O 1 — 1 A 1 A e n e n A ' d " A o G O C N I A 1 — 1 A A v O 1 — 1 f H f H 1 — 1 < ! • O > 4 G A C N I G G cr G v D C T f A O O 1 — 1 e n A A A C N C N A U T) f H C 3 \ v O 1 — 1 v O A A A 1 — 1 e n A < 3 - A O G G f H f H C 3 \ 1 — 1 O ' d " A ' d " ' d " CNI CN O A ' d " O G O 1 — 1 1 — 1 f H C N J ' d- A O o O O O ■< r A O • H > 4 A A 1 1 1 CN 1 — 1 f H 1 — 1 G Q G G G O 1 — 1 A A O A CNI v O ' d- 1 — 1 A ' d- A v D U n O A A 1 — 1 v O e n e n o 1 — 1 O < ! • e n A G f— 1 A A f H 0 0 e n o A o A o CN O e n O O G O O A A < ! • A ' d " CNI 1 — 1 O f H O CN 1 — 1 O > 4 • H 1 A v o 1 1 CN 1 — 1 1 — 1 M - l • H O L A G f H A A A O f H A A O A A A A A 1 — 1 U A V O O 1 — 1 CNI A A A A CN A A A G 1 G 1 — 1 C N J ( T, ' d' e n f H' 1 — 1 1 — 1 C N J 1 — 1 CN O A A ' d " G O O CNI A A e n O 1 — 1 A CN f H f H A 1 — 1 O Gf 1 — 1 W • • 1 e n A 1 1 — 1 f H C/3 G G ro G ' d " 00 00 A 1 — 1 A A e n A A f H A O bù 1 — 1 U T3 A A A A ' d " e n A 1 — 1 O e n CN A 1 G 1 — 1 1 CNI a\ en A A A e n A A A CN < O G O CNI CNI CNi A A o 1 —1f H ' d " A A A f H f H > 4 CNI f H 1 1 — 1 A G CN CN 1 — 1 1 — 1 1 G 1 — 1 1 A G CN A P h CN A G P h 1 1 — 1 >N 1 1 — 1 •H fo A M A J - l E h fH G 1 — 1 G G G G G > G G 1 — 1 G G A G G G G G G G G G G 4 -1 G 1 — 1•H G B •H G G 1 — 1 •H G B •H G G G G O G G M H G O G G G M - l a T) U [ h T) M G • M H a •A U Ph T) U t 3 MH rH O 1 —1 O G O o A O 1 —1 O G O Xi O Ph Xi O Ph G "CN Xi O Ph Xi O Ph G o O S A peî G U O Ph fH G G J h G G J h o G G J h G G J h G G fH jH O A jH O G 1 — 1 jH O 1 —1 jH O T) 4 -1 A G A A G A U A G A A G A G O G G G G G G G G G M H 5^ >4 A X >4 A PH 5^ >4 A 6^ pH A g • n ) O 0 ) • CNI c / ^ p ! i 161 T3 0 > § 'H ■ U Ü 0 o 1 \ M ; o I 0 > 4-1 C d p i ! tH O d o > d cr d 'H 1 —I eu Q O 'H "4-t •H 0 0 > O h | CO 1 0 > ùû "d H CO C O I —1u T3 1 d I —1 o e u O 1 — 1 >* T3 C O g o> o C O u c d eu > - * T3 C O > 4 T3 c d I— I eu o > - < LO C O I—1 u dJ 1 d 1 —1 <d" eu O 1 —1 > - * C O > 4 Td c d 1 — I e u o e u O j g e u g C4-t C4-t O !H 4-1 g CM o > - ( p4 I— 1 O 1 — 1 O CO CO CO CO d ) e u I — 1 cr» I — 1 CT, N I — 1 I — 1 'H d > u d ' d MO -d" MO d CM CO CM CO d 4-1 CO LO CO LO C O CO CO PO d • H ' d d d /— \ d d CO LO LO -d" CO v O o CM M . 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O •H C O C U ® C M •H , — 1 o •H C O <u> CO CO o OV CN uo C S I 00 O C S I <1- O ■vf OV ov vo uo vO o o O rH 00 uo rH "vf o 00 vO OO CSJ CSI o C S I uo rH CO uo OJ o o rH rH pq < + - l 00 vo 00 vo CO CTi vO uo C S I C S I o o o o •vf rs ■<f oo CO vo O rH uo u o C O rH M T3 I cd rH •< f 0) o rH >4 O rH CSJ CSJ uo oo rH rH C S J C S J CO r ~ v -vf vo 00 CO o rH C S I rH CO r~v oo CO 00 vo rH C S I CO C O rH M T3 I cd rH o c u o rH >4 o O rH vO 00 o o vo rH uo uo rH O M - l 0 ) cu -H H -H < + - l MH œ rH o œ c d rH *H cd c/ 3 S Td cu rH MH MH cu rH 165 TABLE 44 UNSTANDARDIZED REGRESSION COEFFICIENTS OF YOUTH LABOR-FORCE PARTICIPATION AND ACE-OFFENSE DELINQUENCY RATES FOR SPANISH SURNAMED (N=114) Age-Specifle Delinquency Rate Independent Variable 10-13 14-15 16 17 18 10-18 Years Years Years Years Years Years Old Old Old Old Old Old Total Offense Type: % Spanish-Surnamed Males 16-21 Years Old in Labor Force % Spanish-Surnamed Females 16-21 Years Old in Labor Force Person Offense Type: % Spanish-Surnamed Males 16-21 Years Old in Labor Force % Spanish-Surnamed Females 16-21 Years Old in Labor Force Property Offense Type; % Spanish-Surnamed Males 16-21 Years Old in Labor Force % Spanish-Surnamed Females 16-21 Years Old in Labor Force Juvenile Status Offense Type; .1249 -.0173 .0100 .0059 .0515 -.0053 % Spanish-Surnamed Males 16-21 Years Old in Labor Force % Spanish-Surnamed Females 16-21 Years Old in Labor Force ,0339 -.0124 4690 .5769 .3506 -.0709 .1389 .3328 .0219 0884 .0668 .0199 .0037 0117 -.0098 .0351 .0043 ,1165 .2646 .0732 -.0364 0877 .0092 .0466 .0028 ,1126 .0377 .0263 -.0054 0242 .0120 .0759 2296 ,0435 .0316 .0063 .0768 .0162 .0402 .0081 166 for 10-through-13-year-old delinquents to youth labor force are significantly inverse and further support the hypothesis. In considering hypothesis 3A, we observe that the zero-order correlations indicate that the 14- and 15-year- old delinquency rate is inversely related to the percent of males 16 to 21 years old in the labor force. This pattern is observed for both person and property offenses, but is inversely significant only for person offenses. Consider ation of the percent of females 16 to 21 years old in the labor force and its association to delinquency types clearly reveals that there is a significant inverse relationship between these two variables, regardless of offense type. For the 14- and 15-year-old delinquency rate (Table 37), the amount of explained variance differs by offense type, with 26.1% accounted for by person offense rates and 10.3% by property offenses. In the total juvenile popula tion (i.e., the 10-to^18-year-old age group, rather than the age-specific subgroups), labor-force participation explains most variance for person offense rates (23.3%) and less in decreasing order: for juvenile status (20,1%), property (17.2%) and total offenses (15.7%). In contrast, the relationship between youth labor-force participation and delinquency for Anglo-whites, analyzed in Table 39, shows male youth labor-force 167 participation (i.e., the percentage of males 16 to 21 years old in the labor force) not inversely related to the 14- and 15-year-old delinquency rate for any of the offense types. The relationship is positive for this age group for all offense types. Female youth labor-force participation, however, is inversely related to the 14- and 15-year-old delinquency rate for all offenses except person offenses, for which there is a positive relationship. Thus these data do not support hypothesis 3A, but lend credence to hypothesis 3B. We should also note that for Anglo-whites, the amount of explained variance (Table 39) for 14- and 15- year-olds is greatest for property offenses (23.6%) and least for juvenile status offenses (4,2%). In areas where more females enter the youth labor force, there is a significant decline in the juvenile status offense rate. Table 41 shows that the percentage.of male youth labor-force participation is not significantly related to any age-specific delinquency rates for blacks. Female youth labor-force participation, however, is directly related to some of the younger group rates, especially for the property and juvenile status offenses. The overall indication is that the explained variance in delinquency rates accountable to black youth labor-force participation is greatest for the 14- and 15-year-old and 16-year categories, regardless of offense type. 168 The correlations for the Spanish-surnamed popula tion in Table 43 show that the male youth labor-force participation rate is significant in explaining most age- specific delinquency rates. This relationship generally is positive, especially for the younger age categories. Therefore, hypotheses 3A and 3B are not supported for the Spanish-surnamed population, since the relationship is in the opposite direction to that predicted. This means that the delinquency rate of 14- and 15-year-olds increases in areas with higher percentages of males and females 16 to 21 years old in the labor force. In comparison with other racial or ethnic subgroups, more explained variance in delinquency rates can be accounted for by youth labor-force participation in this population subgroup than Anglo-whites or blacks. Tables 38, 40, 42 and 44 present unstandardized regression coefficients of youth labor-force participation and age-offense delinquency rates by ethnicity. We notice that the 10-to-18-year-old total delinquency offense rate is most directly associated with the percent of male Spanish-surnamed youth in the labor force and least for Anglo-whites and blacks. For female youth labor-force participation, we observe that this relation is strongest for blacks and indirect for Anglo-whites. This suggests that the opportunity structure varies considerably by ethnicity and sex. 169 One aspect of this relationship which bears further consideration is the relatively strong relationship of young black female labor-force participation to delinquency. We might speculate whether the increase in black juvenile delinquency rates may be attributed to the greater competi tion for jobs typically held by male youths. That is, does the increased labor-force participation of black female youth in the job market result in a reduction of labor- force participation opportunities for young black males? If this is the case, then we might contend that labor force policy-makers consider the alternative impact of youth labor-force participation on the problems of youth in society. In particular, we seem to obtain some support for more consideration of the changes effected by labor-force policy as it relates to delinquency. We should note that this analysis of youth labor- force participation does not fully consider whether the youth in the labor force are employed. There is room for further exploration of the substantive significance of youth employment correlates to delinquency types. 170 CHAPTER V SUMMARY, IMPLICATIONS, LIMITATIONS, AND SUGGESTIONS FOR FUTURE RESEARCH Summary The analytical task of this study was: (1) to describe the relationship between age, crime and delin quency considering the impact of unemployment, youth labor- force participation, ethnicity and age ; and (2) to test a set of hypotheses which stem from a reconsideration of crime, age and unemployment originally posited by Glaser and Rice (1959) and later elaborated by Fleisher (1966). Los Angeles County probation records for 1970 served as the indicator of crime and delinquency. During the 1970 calendar year there were 35,304 juvenile and 36,582 adult referrals to probation. All the records were used in this analysis. Offenses were grouped into a threefold category for adults : person, property and total offenses ; and a fourfold typology for juveniles : person, property, juvenile status and total offenses (cf. Appendix A for a more specific description of the crimes and how they were grouped). In order to protect the identity of the subjects, the probation department records were aggregated into geo-coded areas which conformed to census tract areas 171 in which the individual resided at the time of initial referral to the probation department. These census tract areas were then further aggregated into 133 Study Areas, or communities. The community boundaries were coterminous with the Planning Districts for Los Angeles County (see Appendix B for a listing of the communities and their respective census tract areas). The communities serve as the unit of analysis in this ecological study on delin quency and crime. The study builds on the tradition of ecological analysis of delinquency and crime. The results are based on the analysis of community characteristics and should not be generalized to an individual unit of analysis. This point is emphasized in order to remind the reader of the tendency for ecological fallacies to be made in this mode of research. Several hypotheses were presented and tested. Table 45 provides a synopsis of the conclusions. The hypotheses are stated on the left. On the right the research conclusions are indicated for each ethnic group: all races (total), Anglo-whites, blacks, and Spanish- surnamed. The data indicated that the age-specific delin quency rate for juveniles under 16 years old did not vary inversely with adult unemployment, which was the hypothe sized relation for all ethnic groups. The converse results were found. That is, areas having more younger aged 172 TABLE 45 SYNOPSIS OF RESEARCH HYPOTHESES Hypothesis Population Total Anglo- White Black Spanish- Surname IA. The age-specific delin quency rate for juveniles <16 varies inversely with adult unemployment IB. The age-specific delin quency rate for juveniles 16+ varies directly with adult unemployment IC. The age-specific crime rate for young adults varies directly with adult unemployment 2A. The age-specific juvenile delinquency rate, for all juvenilejage levels, is inversely related to the median annual family income 2B. The age-specific rate of criminal activity, for all adult age levels, is inversely related to the median annual family income 3A. The 14-to-15-year old juvenile delinquency rate is inversely related to the percent of males 16 to 21 years old in the labor force 3B. The 14-to-15-year-old juvenile delinquency rate is inversely related to the percent of females 16 to 21 years old in the labor force rej ected Table 5 accepted Table 5 rej ected Table 7 rej ected Table 7 rejected rejected Table 9 Table 11 rej ected Table 9 rej ected Table 11 accepted accepted rejected accepted Table 13 Table 15 Table 17 Table 19 accepted accepted accepted accepted Table 21 Table 23 Table 25 Table 27 accepted accepted accepted**accepted Table 29 Table 31 Table 33 Table 35 accepted rejected accepted rejected Table 37 Table 39 Table 41 Table 43 accepted accepted rejected rejected Table 37 Table 39 Table 41 Table 43 *Not significant **Direction is inverse but not statistically significant. 173 delinquents were more likely to have higher adult unemploy ment rates. The analysis also indicated that the age- specific delinquency rate for juveniles 16 years old and over was directly related to unemployment only for the total population. When ethnicity was considered, the relationship was attenuated considerably. The analysis of age-specific crime rates indicated that crime was directly related to the rate of adult unemployment for all races, except blacks. These data contradict the earlier analysis of delinquency and unemployment as formulated by Glaser and Rice (1959). However, support was obtained for Glaser and Rice's hypothesis on adult crime and its direct relation ship to unemployment. Glaser and Rice did not analyze the distinctions of race in their study. We find that when such a consideration is made, their findings are supported for all race categories, except blacks. We interpret this finding to indicate that unemployment does not explain much of black involvement in crime. Primarily because blacks are more residentially segregated,and havelessistake in conformity and greater levels of unskilled labor and under employment, an argument is made that blacks have a higher rate of labor-force dropout than other ethnic groups. Since we are employing an ecological analysis in areas characterized by relatively high levels of racial segrega tion (especially for blacks), there is a significant 174 reduction in the number of communities with black residents. We should, however, expect a stronger relationship for blacks, since they have the highest conviction rates and probation referral rates. We find further support for the contention that income is inversely related to delinquency and crime. This finding is true regardless of ethnicity, although it was not statistically significant for the analysis of black income and crime rates. As noted by Wolfgang et al. (1972), this finding is not especially surprising. Most ecolog ical and individual analyses of crime and delinquency have supported this conclusion, but few studies have considered the amount of variance explained for income by offense type, age and ethnicity. As indicated in Table 45, the analysis of male youth labor-force participation (i.e., the percent of males 16 to 21 years old in the labor force) and 14- and 15-year- old delinquency rates produced evidence supporting an inverse relationship only for the total population (i.e., all racial groups) and blacks. No inverse relationship for Anglo-whites or Spanish-surnamed groups was observed. The analysis of female youth labor-force participation for all races indicated an inverse relationship between female youths 16 to 21 years old in the labor force and delin quency rates of 14- and 16-year-olds. The ethnicity comparison indicated that this finding was observed only 175 for Anglo-whites ; blacks and Spanish-surnamed did not show evidence to support this contention. Implications Sociological Perspectives This research explored economic, social and demo graphic factors as they contributed to the explanation of crime and delinquency. A structural analysis was employed to consider the nuances of these factors. The social structural approach in sociological literature implied a functional specification of social reality, with the implicit assumption that social life can be characterized in some static mechanism. In this case we have presented selected independent variables and posited a functional specification, or relationship, for them. Although the structural/functional paradigm has wide appeal to sociolo gists, there is criticism in sociology over its usage (Merton, 1968; Etzioni, 1976:1-32). Two of the most salient competing sociological perspectives relating to crime and delinquency are: Marxist (or conflict) and symbolic interactionist perspec tives . The Marxist theories hold that crime is symptomatic of the inherent problems of distributing wealth, power and privilege in modern industrial society. The social organi zation in industrialized society results in anomie or alienation which leads to the event--crime. Marxists 176 contend that criminal behavior is a necessary by-product of capitalistic society and that eradication of the problem of crime is only possible by a revolution in the social organization of society. Marxist theory can be regarded as a form of functionalist theory which predicts an especially strong relationship between crime and unemployment. The symbolic interactionist perspective takes a critical view of the functional analysis of social behavior The basic contention of this paradigm is that the factors which influence an individual's decision to engage in criminal or delinquent behavior are unique to the individ ual ' s perception of his social reality and society's response to him. Such a perspective challenges the feasi bility of a macro-level analysis of delinquency or crime, since these problems are viewed from a micro-level, which makes it impossible to consider an aggregated or collective methodological approach. Both of these perspectives have merit, and the limited size of our correlation coefficients may reflect, in part, the social and individual psycholog ical factors in crime. Economic Perspectives Economists have also been concerned with the relationship of socioeconomic conditions to delinquency and crime. Considerable debate has been raised by economists on the pragmatic adequacy of sociological models in the 177 analysis of crime and delinquency (Fleisher, 1966; Ehrlich, 1973; Becker, 1974). In particular, the debate centers on the specification of "taste" variables, according to economists, and "opportunity" variables, according to structural functionalists. More explicitly, economic theories formulate causal models which posit a rationale for the engagement in crime and are predicated upon the maximization of an individual's resources (Danziger & Wheeler, 1975). Economists also favor models of crime and delin quency which include deterrent variables (Tullock, 1969, 1974 ; McPheters & Stronge, 1976 :5-137). Fleisher (1966) noted that economists attempt to estimate the net causal relationship between variables by considering two forces : supply and demand. Demand refers to a causal relationship between economic desires and other characteristics of persons, on the one hand, and their tendencies to commit acquisitive acts, on the other (Fleisher, 1966, in McPheters & Stronge, 1976:231). Supply depends on the economic and social opportunity characteristics of the environment. Fleisher contends that the interaction taste for alternative means of gratifying demand, including the propensity to be delinquent, and the alternative acquisi tion opportunities available (supply) determine the number of delinquent acts actually committed. 178 Fleisher is aware of the importance of non-economic factors conceptualized as the "taste for delinquency" in his model. He observes that taste for delinquency and Income levels will be rather highly correlated. He further distinguishes the role of sociologists and economists as they investigate juvenile delinquency, especially noticing the variance in these disciplines as they conceptualize models of delinquency. It is perhaps risky for someone trained in economics to try to explain what has been going on in another discipline, such as sociology. However, it is desir able to attempt to relate the ’’economist's view" of delinquency to the "sociologist view." In my opinion, the concept of taste is closely related to sociological concepts such as "anomie." When sociologists argue the relative merits of "commitment to the existing social structure" vs. "opportunities" or "material well-being" as variables explaining delinquency, they refer to questions which I hope to be able to answer at least partially with a model taking into explicit considera tion both tastes and economic variables. The problem that criminologists and sociologists have not solved, and for which it will become apparent that I have not provided the solution, is the identification of those observable phenomena which are the operational equiva lents of conceptual tastes for delinquency. In statis tical jargon, we are not reasonably certain how to specify properly a model of delinquency. Consequently all interpretations of the results of empirical inves tigations are weakened by our not knowing whether we are observing the effects of the variable we conceive as relating to delinquency or whether we are observing the effects of the model of delinquency. (Fleisher, 1966, in McPheters & Stronge, 1976:232) Policy Imp 1icat ions The results of this study indicate that unemploy ment is directly related to delinquency. This implies that the higher a community’s rate of unemployment, the greater 179 its crime and delinquency rates. Support was obtained for the encouragement of social and economic programs that serve to increase labor-force involvement. Our findings indicate that delinquency of 14- and 15-year-olds was inversely related to the labor-force participation of youth 16 to 21 years old. This implies that if youth can anticipate meaningful productive work alternatives, they y will have a higher stake in conforming to social norms and delinquency rates should decrease. These findings support the rationale for such programs as the Job Corps and the Neighborhood Youth Corps, which were legislated during the 1960s. It maintains that programs which provide youth with job skills and training can also serve another function. This function is to provide youth with an increased commitment to exhibiting social conforming behavior, and it should result in a reduction in delinquency. An empirical issue which develops from a consideration on the impact of extending the current work alternatives for youth is centered on its spill-over consequences. As more women are entering the labor force, researchers need to study whether their entrance leads to higher delinquency due to the changing environment in the home and the reduced opportunities for youth participation in the labor force. That is, are the jobs that are occupied by women more likely to be jobs which would characteristically be held by youth in the 180 labor force? If this is the case, then our work policies for women and youth put these two groups into competition with each other. It would seem that such a policy would not serve the needs of our society. An empirical question which remains to be explored should the above argument be true is: What are the social consequences of labor-market policies which pit mothers against children in their compe tition for part-time and unskilled labor? Limitations and Suggestions ' £br~Future Research"" ' The methodology employed in this study consisted of a cross-sectional analysis on delinquency and crime. Glaser and Rice (1959) and Fleisher (1966) conducted a longitudinal study. Our findings did not fully support their results. This may be due to an artifact attributable to cross-sectional analysis. The conditions in our economy are changing drastically. The environment in which youth are socialized is likewise experiencing great levels of change. For instance, more women today are in the labor force than ever before, and the rate of divorced and single parent family heads is increasing at a very rapid rate; also, the social restrictions against young mothers being employed have dropped as the social norms have changed toward both working mothers and fathers. In short, the social structure of the American family has been altered. Unfortunately this study does not consider the consequences _____________________________________________________ iai_ of this disorganization in traditional family structure and its impact on youth. A longitudinal analysis would have the benefit of measuring the impact of such changes in familial structure and its spill-over effects on delin quency and crime. Another limitation of the research, previously discussed, is that it is an analysis of official rates of crime and delinquency. Numerous studies on victimization and self-reported deviance have indicated that official data sources do not provide a full picture of crime or delinquency. Rather, they provide a crude indicator of crime. Research on this topic might consider age-specific victimization and self-reported delinquency and crime rates as a dependent variable in a model studying the relation ship between unemployment and labor-force participation factors. Another data limitation, which is pertinent in our consideration of bias in official statistics, is the unreliability of unemployment reports. Unemployment statistics do not fully reflect the number of potentially employable persons outside the labor force ; they now include only those individuals who are currently out of work and who would be able to take a job immediately, if one were offered to them. This definition is biased against certain ethnic and socioeconomic groups, since they may experience relatively little incentive to look for work 182 due to their low potential for finding work which would provide them with a secure wage or work motivation. It also does not provide an indicator of underemployment, which may lead to labor-force dropout. These limitations may have a serious impact on the ethnic aspects of the relationship of unemployment to crime and delinquency, since (1) blacks typically experience more underenumeration than Anglo-whites, and (2) blacks are more likely to drop out of the labor force. Another attenuation factor that might account for high delinquency rates in areas having a larger number of blacks may be the reduced opportunities that individuals in these communities may experience after they have an arrest record. The consequent reduction in legitimate opportuni ties may result in a decrease in their stake in conformity, which may produce even less incentive to enter the labor force. This raises the issue of: (1) whether delinquency is the cause of the reduction in youth and adult labor- force employment or vice versa ; and (2) whether an individ ual or aggregate analysis would be most pertinent in describing the dynamics of the relationship between the two variables. More critically, the model of this analysis should be expanded to incorporate deterrent factors as well as taste and opportunity variables (Chapman, 1976). Since this study was a reformulation of Glaser and Rice’s (1959) 183 analysis of age, crime and unemployment, the research design was not oriented toward building a causal model on delinquency and crime.that includes deterrence. Thus rates of clearance by arrest and by conviction could be added to a multicausal matrix. Such variables should result in an increase in the explained variance, due to distinctions made in the treatment of delinquents and criminals by socioeconomic status. Glaser and Rice (1959) did not consider deterrent factors in their analysis of arrest data. This reconsideration of their study uses probation data. In order to research the implications of deterrent factors to crime and delinquency, we should further study the impact of variations in police and court activity. Unfortunately, this was not available for the ecological units covered in this study. A review of the literature on age, crime, race, unemployment, youth labor-force participation, and familial structure indicates that such an exercise has heuristic potential, especially in formulating a sociological model which incorporates the dynamics of a changing economic system. In order to conduct a causal order analysis, there needs to be further synthesis of both empirical and theo retical knowledge pertaining to the intricate relationships characterizing social and economic variables. In ecological analysis researchers are bound to the unit of analysis employed in their studies. In this case 184 it is the 133 Los Angeles communities. The decision to aggregate individual probation records into census tracts was necessitated as a precondition to obtaining the data from the Los Angeles County Probation Department. The further compilation of the data to Los Angeles County Welfare Planning Districts was made in order to reduce the tremendous number, of cases (there were approximately 15,000 census tracts). Since we were concerned with age-and-race- specific crime and delinquency rates, this resulted in another methodological problem. Since residential areas in the County are highly stratified by race, the calculation of the dependent variable on the community level meant that there would be a substantial reduction in the sample size based on the specific dependent variables considered. On the Other hand, had we maintained census tracts as our ecological unit of analysis, there would have been many areas for which the dependent variable could not have been calculated due to th.eir skewed race and age distribution. 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Totowa, N.J.: The Bedminster Press 202 203 APPENDIX A REFERRAL CATEGORIES AGGREGATED INTO JUVENILE AND ADULT OFFENSE TYPES 204 REFERRAL CATEGORIES AGGREGATED INTO JUVENILE AND ADULT OFFENSE TYPES JUVENILE OFFENSE TYPE Crimes against Persons: Assaultive nonweapon Assaultive weapon Robbery Crimes against Property: Nonviolent property Burglary Theft, petty Juvenile Status Offense: Juvenile status Total Juvenile Delinquency Rate: Drugs Drunk and disorderly Other Malicious mischief Trespassing Traffic Crimes against persons Crimes against property Juvenile status offenses ADULT OFFENSE TYPE Crimes against Persons; Assaultive nonweapon Assaultive weapon Robbery Crimes against Property: Nonviolent property Burglary Petty theft with prior conviction Petty theft Juvénile Status Offense: None Total Crime Rate; Drugs Drunk and disorderly Other Malicious mischief Trespassing Unemployment insurance act and miscellaneous felonies Nonsupport Traffic Crimes against persons Crimes against property 205 APPENDIX B 1970 CENSUS TRACTS INCLUDED IN STUDY AREAS, WITH 1960-1970 POPULATION 206 1970 CENSUS TRACTS INCLUDED IN STUDY AREAS, WITH 1960-1970 POPULATION STUDY AREA 1. Sylmar 2. San Fernando 3. Pacoima 4. Sunland 5. Tujunga 6 . Sun Valley 7. Panorama City 8 . Mission Hills 9. Granada Hills 10. Chatsworth 11. Northridge 12, Sepulveda 13, North Hollywood 1970 POPULATION 56,114 16,571 48,967 32,780 20,850 35,643 50,730 30,170 39,379 43,938 29,251 34,751 107,908 1960 POPULATION 31,703 16,093 39,522 29,356 16,277 34,164 46,145 31,883 24,002 22,367 16,678 23,910 97,955 14 i Van Nuys 15, Reseda 110,721 74,076 94,105 70,307 1970 TRACTS 1061.01-1068.00 3201.00-3203.00 1041.01-1048.00, 1095.00 1021.01-1021.02, 1031.01-1034.00, 1211.00 1011.00-1014.00 1212.00-1222.00 1191.00-1204.00 1091.00-1094.00, 1096.01-1098.02 1111.01-1114.02 1131.00-1134.02 1151.01-1154.02 1171.00-1176.00 1223.00-1224.00, 1231.01-1233.02, 1237.00-1244.00, 1247.00-1256.00, 1431.00-1432.00, 1434.01-1434.02 1234.00-1236.02, 1245.00-1246.00, 1271.01-1289.00 1311.00-1331.02 207 1970 1960 1970 STUDY AREA POPULATION POPULATION TRACTS 16. Canoga Park 85,622 56,134 1341.01-1352.03 17. Woodland Hills 45,712 23,759 1371.01-1376.00 18. Encino-Tarzana 50,966 32,065 1391.00-1398.02 19. Sherman Oaks 34,741 28,334 1411.00-1417.00 2 0 . Studio City 30,595 25,677 1433.00, 1435.00-1439.02, 3200.00 2 1 . Malibu-Calabasas 30,644 12,386 8001.00-8005.00 2 2 . Pacific Palisades 23,055 21,174 2625.00-2628.00 23. Brentwood-Bel Air 32,288 28,191 2621.00-2624.00, 2640.01-2642.00, 2651.00 24. Beverly Hills 22,427 18,487 1943.00, 2611.01-2612.00, 7006.00-7007.00 25. Beverly-La Cienega 23,285 21,358 7008.00-7010.00 26. Westwood-Sawtelle 26,164 29,297 2652.00-2654.02, 2656.00-2657.00 27. West Los Angeles 53,877 21,358 2643.01-2643.02, 2655.00, 2671.00-2679.00 28. North Santa Monica 39,478 35,315 7012.01-7016.02 29. South Santa Monica 48,811 47,934 7017.01-7023.00 30. Venice Del Rey 63,590 62,546 2731.00-2742.00, 2751.00-2756.00 31. Mar Vista 58,814 54,529 2711.01-2723.02 32. Palms 46,762 43,681 2165.00, 2691.00-2702.00 33. Culver City 31,525 32,698 7024.00-7028.03 208 1970 1960 1970 STUDY AREA POPULATION POPULATION TRACTS 34. Baldwin Hills 50,220 45,200 2 2 0 1 .0 0 -2 2 0 2 .0 0 , 2361.00-2364.00, 7030.00-7032.00 35. Westchester 54,163 56,480 2761.00-2774.00, 2781.00 36. Inglewood 74,448 64,729 6005.01-6014.02 37. East Inglewood 41,311 34,023 2384.00-2386.00, 6001.00-6004.00 38. Lennox 35,634 31,295 6015.00-6020.02 39. Hawthorne 72,482 65,141 6021.01-6025.00, 6027.00, 6037.01-6037.02 40. Lawndale 24,825 22,240 6038.00-f6041.00 41. El Segundo 16,789 15,116 6200.00-6202.00 42. Manhattan Beach 35,352 33,934 6203.01-6204.00, 6208.00-6209.00 43. Hermosa Beach 17,412 16,115 6210.01-6211.00 44. Redondo Beach 59,188 48,717 6205.01-6207.02, 6212.00-6214.00 45. West Torrance 104,678 83,325 6500.01-6507.02 46. East Torrance 71,577 48,021 2921.00, 2931.00-2933.00, 6508.00-6511.00, 6700.01-6701,00 47. Palos Verdes 67,554 31,814 2974.00, 6702.01-6707.02 48. San Pedro 36,446 34,254 2964.00, 2967.00-2969.00, 2972.00-2973.on, 2975.00-2976.00 49. Wilmington 63,970 56,136 2941.00-2951.00, 2962.00-2963.00, 2965.00-2966.00, 2971.00 209 1970 1960 1970 STUDY AREA POPULATION POPULATION TRACTS 50. Dominguez 92,092 50,927 5433.01-5440.00 51. Gardena 76,934 6 / i ,4 / | / | 2911.00-2913.00, 6026.00, 6028.00-6036.00 52. South-Vermont, Green Meadows 100,485 87,103 23/1.00-2383.00, 2402.00-2405.00, 2411.00-2414.00 53. Watts 64,618 72,203 2399.00-2401.00, 2406.00-2409.00, 2415.00-2416.00, 2421.00-2431.00, 5352.00, 5354.00, 5404.00 54. Florence-Gr aham 68,230 68,548 2391.00-2398.00, 5327.00-5333.00, 5349.00-5351.02, 5353.00 55. Avalon 57,853 65,484 2281.00-2294.00, 2311.00, 2318.00-2319.00, 2328.00 56. Exposition Park 58,189 57,490 2312.00-2317.00, 2321.00-2327.00 57. Leimert 48,883 43,289 2341.00-2352.02 58. West Adams 77,251 76,920 2128.00-2129.00, 2181.00-2199.00, 2703.00 59. Santa Barbara 81,353 75,505 2131.00-2134.00, 2211.00-2227.00 60. University 21,450 20,775 2241.00-2247.00 61. Central 16,296 23,367 2261.00-2267.00 62. Wholesale 18,261 16,023 2034.00, 2045.01-2045.02, 2061.00, 2065.00, 2071.00 2 1 0 1970 1960 1970 STUDY AREA POPULATION POPULATION TRACTS 63. Downtown 13,349 23,831 2062.00-2064.00, 2072.00-2079.00 64. Westlake 73,183 69,377 1957.00-1958.00, 2081.00-2098.00 65. Wilshire 54,973 50,104 2111.00-2115.00, 2117.00-2126.00 6 6 . Wilshire-Pico 45,185 44,863 2111.00-2164.00, 2166.00-2172.00 67. West Wilshire 50,073 51,518 1923.00, 1945.00, 2116.00, 2127.00, 2141.00-2153.00 6 8 . West Hollywood 61,520 55,041 1898.00-1901.00, 1941.00-1942.00, 1944.00, 7001.00-7005.00 69. Hollywood 93,656 83,838 1905.00-1922.00, 1924.00-1927.00, 1959.00 70. Hollywood Hills, Griffith Park 45,839 43,098 1882.01-1882.02, 1891.00-1897.02, 1902.00-1904.00 71. Silver Lake 25,729 24,650 1951.00-1956.00 72. Elysian Park 26,258 24,459 1971.00-1977.00 73. Mount Washington 17,635 15,147 1851.00-1853.00 74. Eagle Rock 20,106 20,249 1811.00-1816.00 75. Atwater, Glassell 38,588 36,042 1861.00-1864.00, 1871.00-1873.00, 1881.00, 1883.00 76. Burbank 88,871 90,155 3101.00-3118.00 77. North Glendale 85,502 75,912 3001.00-3014.00 78. South Glendale 66,782 61,112 3015,00-3025.00 2 1 1 1970: 1960 1970 STUDY AREA POPULATION POPULATION TRACTS 79. La Canada, Flintridge 20,714 18,838 4605.01-4607.00 80. Altadena 37,239 35,643 4601.00-4603.00, 4611.00-4613.00 81. Central Pasadena 25,342 31,350 4604.00, 4609.00-4610.00, 4616.00-4618.00, 4620.00, 4637.00 82. Southwest Pasadena 27,277 24,948 4608.00, 4635.00-4636.00, 4638.00-4640.00 83. East Pasadena 75,193 75,889 4600.00, 4614.00-4615.00, 4619.00, 4621.00-4634.00 84. El Sereno 36,671 29,477 2011.00-2017,00 85. Lincoln Heights 29,634 31,396 1991.00-1999.00 8 6 . Boyle Heights 73,311 75,065 2031.00-2033.00, 2035.00-2044.00, 2046.00-2051.00 87. East Los Angeles 104,688 105,464 5303.00-5319.00 8 8 . Highland Park 39,329 33,716 1813.01-1838.00 89. Alhambra 62,125 54,807 4803.00-4804.00, 4808.01-4810.00, 4815.00-4816.02, 4818.00-4819.02 90. Monterey Park 49,308 37,821 4817.01-4817.02, 4820.01-4822.00, 4826.00-4828.00 91. South San Gabriel 36,328 26,313 4823.01-4825.02, 4336.01-4336.02 92. San Gabriel 24,950 22,524 4802.00, 4811.00, 4814.00 93. South Pasadena 22,979 19,706 4805.00-4807.02 2 1 2 1970 1960 1970 STUDY AREA POPULATION POPULATION TRACTS 94. San Marino 14,177 13,658 4641.00-4642.00 95. Temple City 49,037 41,652 4318.00-4321.02, 4800.01-4801.02, 4812.01-4812.02 96. Rosemead 14,151 15,808 4322.00, 4329.00, 4813.00 97. El Monte 83,605 62,987 4323.00-4324.00, 4326.00-4328.00, 4331.00-4335.00, 4337.00-4340.00 98. Arcadia 73,002 67,045 4304.00-4308.03, 4313.00-4317.00, 4325.00 99. Monrovia 52,065 47,866 4300.01-4303.00, 4309.00-4312.00 1 0 0 . Baldwin Park 48,543 36,749 4046.00-4052.00 1 0 1 . Azusa 37,679 33,919 4006.00, 4040.00-4045.00 1 0 2 . Covina 126,339 102,812 4037.01-4038.00, 4053.00-4062.00, 4065.00-4069.00, 4074.00-4075.00, 4079.00 103. Covina Highlands 47,401 29,599 4024.04, 4035.00-4036.00, 4063.00-4064.02, 4078.00, 4080.00-4981.03 104. Glendora, San Dimas 63,166 38,779 4003.00-4005.00, 4008.00-4013.02, 4039.01-4039.02 105. Claremont, La Verne 39,112 21,678 4002.00, 4015.00-4016.00, 4018.00-4020.00 213 1970 1960 1970 STUDY AREA POPULATION POPULATION TRACTS 106. Pomona 113,317 76,222 4017.00, 4021.01-4024.03, 4025.01-4030.00, 4032.00-4034.00, 4088.00 107, La Puente 46,037 39,445 4070.00-4073.00, 4076.00-4077.00 108. Industry 69,126 26,771 4082.01-4807.02 109. Whittier 99,401 91,021 5001.00-5003.00, 5010.00, 5012.00-5022.00, 5033.01-5034.02 1 1 0 . Pico Rivera 55,076 49,150 5004.01-5009.00, 5024.00-5026.02 1 1 1 . La Mirada, Santa Fe Springs 114,831 94,583 5023.00, 5027.00-5032.02, 5035.01-5041.02 1 1 2 . Montebello 43,010 32,097 5300.01-5302.00, 5302.00-5322.00 113. Bell, Bell Gardens 78,678 66,721 5323.01-5323.02, 5336.00, 5338.01-5344.02 114. Huntington Park, Maywood 60,154 54,454 5324.00-5326.00, 5331.00-5335.00, 5337.00, 5345.00, 5347.00-5348.00 115. South Gate 56,909 53,807 5355.00-5362.00 116. Lynwood 37,028 34,513 5400.00-5403.00, 5405.00, 5417.00 117. Compton, Willowbrook 67,250 71,725 5406.00-5414.00, 5427.00-5431,00 118. East Compton 69,037 61,644 5415.00-5416.02, 5418.00, 5420.00-5422.00, 5424.01-5426.00, 5432.00 214 STUDY AREA 119. Downey 120, Norwalk 122. Bellflower 123. Paramount 124. Lakewood 125. Hawaiian Garden 126. East Long Beach 127. Ocean Front 128. Central Long Beach 129. Terminal Island 130. West Long Beach 131. Bixby Knoll, Signal Hill 1970 POPULATION 88,445 92,047 121. Artesia, Dairy Valley 30,613 51,454 34,734 68,440 32,689 75,183 59,359 52,522 2,971 24,643 79,652 1960 POPULATION 82,505 88,939 13,501 44,860 27,249 70,298 15,835 76,234 51,548 54,325 2,934 24,518 75,735 1970 TRACTS 5504.00-5518.00, 5534.00 5500.00-5503.00, 5519.00-5524.00, 5526.00-5530.00, 5546.00-5547.00 5545.01-5545.02, 5548.00-5549.00 5531.00-5533.00, 5540.00-5544,02 5535.00-5539.00 5700.01-5701.00, 5707.01-5711.02, 5713.00-5714.00 5550.00-5552.02, 5739.01-5739.02 5712.00, 5736.00-5738.00, 5740.00-5741.00, 5743.00-5759.02 5765.00-5768.00, 5771.00-5776.03 5732.01-5733.00, 5752.00-5754.00, 5758.00-5764.00 2961.00, 5755.00-5757.00 5723.00-5729.00 5715.01- 5718.00- 5730.00- 5734.00- 5742.01- 5750.01- 5769.00- 5715.02, 5722.02, 5731.00, 5735.00, 5742.02, 5751.00, 5770.00 215 STUDY AREA 132. North Long Beach 133. North County 1970 POPULATION 58,017 133,673 1960 POPULATION 50,815 83,157 1970 TRACTS 5702.01-5706.00, 5716.00-5717.00 1081.00-1082.00, 9001.00-9012.02, 9100.00-9110.00, 9200.01-9203.03, 9300.00-9302.00 216</u></u></p></u></u></u></u>
Abstract (if available)
Abstract
Theories on the relationship of economic and social factors to juvenile delinquency and crime have generated considerable research. Some theorists maintain that economic conditions directly influence juvenile delinquency rates; others that socioeconomic factors are only one dimension of myriad complexities underlying the comprehensive issue of crime and juvenile delinquency. Two opposite postulations, relating economic factors to juvenile delinquency, orient theoretical and empirical approaches in the analysis of underlying social factors. They are: (1) that economic conditions relate inversely to rates of delinquency, and (2) conversely, that the relationship between economic conditions and juvenile delinquency rates are positive or direct.
The objective of this study is to reexamine the relationships between labor force participation and juvenile delinquency, focusing on the distinctions in age, race and offense types in a synchronic cross-sectional ecological analysis of juvenile delinquency and crime patterns in Los Angeles County for 1970. The data derive from the United States Census of Population (1970) and the Los Angeles County Probation Department Files (1970). The specific focus of this study is on the socioeconomic correlates of crime and juvenile delinquency, with delinquency viewed as a disorganizational consequence of blocked opportunities for youth to enter the job market.
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Asset Metadata
Creator
Payne, Justin Payne
(author)
Core Title
Structural effects of unemployment on juvenile delinquency and crime rates: a synchronic cross-sectional analysis
School
Graduate School
Degree
Doctor of Philosophy
Degree Program
Sociology
Degree Conferral Date
1977-09
Tag
OAI-PMH Harvest
Advisor
Glaser, Daniel (
committee chair
), Chapman, Jeffrey I. (
committee member
), Heer, David M. (
committee member
)
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-oUC11256297
Unique identifier
UC11256297
Legacy Identifier
DP31785
Document Type
Dissertation