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The “Drivers” of Crime. Steven Deller Department of Agricultural and Applied Economics University of Wisconsin – Madison/Extension. The “Drivers” of Crime. The “Drivers” of Crime. Top Ten State Expenditures. The “Drivers” of Crime. The “Drivers” of Crime. The “Drivers” of Crime. - PowerPoint PPT Presentation
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Steven DellerDepartment of Agricultural and Applied
Economics
University of Wisconsin – Madison/Extension
The “Drivers” of Crime
The “Drivers” of Crime
The “Drivers” of Crime
Top Ten State Expenditures
The “Drivers” of Crime
The “Drivers” of Crime
The “Drivers” of Crime
The “Drivers” of Crime
The “Drivers” of Crime
The “Drivers” of Crime
The “Drivers” of Crime
Barron 5.1 Vilas 55.1Buffalo 7.2 Langlade 56.5Wood 9.4 Sauk 56.6Walworth 9.9 Jefferson 56.6Lafayette 12.3 Jackson 57.1Shawano 16.1 Grant 59.4Portage 18.8 Rusk 66.4Trempealeau 19.0 Richland 69.1Green Lake 19.2 Adams 72.0Lincoln 21.0 Pepin 72.8Green 26.2 Dunn 77.0Monroe 27.1 Marquette 83.2Marinette 28.9 Juneau 91.9Door 29.6 Burnett 93.0Vernon 34.3 Washburn 98.0Oneida 37.2 Ashland 107.0Manitowoc 39.5 Florence 114.7Dodge 44.6 Polk 125.0Waushara 45.5 Iron 143.5Clark 48.9 Sawyer 149.3Waupaca 50.0 Price 162.2Taylor 52.7 Bayfield 167.2Crawford 52.8 Forest 208.8
Violent Crime Rate: Average 2005-2007
The “Drivers” of Crime
Manitowoc 299.5 Iron 840.8Dodge 305.3 Crawford 869.5Wood 317.0 Clark 871.1Pepin 332.2 Taylor 873.9Ashland 363.4 Lafayette 883.5Walworth 369.0 Waupaca 895.9Grant 378.2 Adams 911.6Trempealeau 411.9 Rusk 912.3Green 457.7 Vilas 938.4Buffalo 501.3 Juneau 949.5Green Lake 515.3 Marquette 996.0Vernon 523.0 Marinette 1016.0Price 542.2 Sauk 1040.7Portage 542.8 Shawano 1103.8Jefferson 555.7 Langlade 1255.4Barron 561.5 Waushara 1293.7Monroe 564.7 Washburn 1347.4Dunn 594.8 Jackson 1371.7Lincoln 665.5 Bayfield 1496.4Oneida 755.0 Forest 1569.2Richland 761.5 Sawyer 1642.3Door 769.5 Burnett 1928.4Polk 839.6 Florence 2529.9
Property Crime Rate: Average 2005-2007
The “Drivers” of Crime
The study of crime is composed of two parts:
The first is criminology which seeks to address the question what influence society has on crime. This can be social/cultural and economic.
The second is criminal justice which focuses on public institutions and political elements.
Criminology focuses on how to prevent crime, criminal justice focuses on how society responses to crime.
The “Drivers” of Crime
Laws,Penalties
PrisonInterests
Social &Demographic
Conditions
Crime
PoliticalProcesses
PoliceEnforcement
CorrectionsOutcomes
Feedback from Imprisonment to Social Conditions
Social Control, Deterrence
JudicialProcesses
Arrests
Anomie/Strain Theory
Social Disorganization Theory
Rational Economic Theory
Emphasizes social, economic and political forces at the macro or community level.
Social norms weak or breakdown.
Conflict/tension within the community.
Emphasizes individual choices and maintains crime can be rational.
Compares benefits of the crime vs costs.
High benefits to the crime vs low risks of capture and incarceration.
Emphasizes individuals and conflicts between goals and means to achieve those goals.
Envy effects between rich and poor.
Frustration with personal economic/social position.
Poverty
Inequality
Social Capital
Social Norms
Figure 2. Triangulation of the Core Theories of Criminology
The “Drivers” of Crime
The “Drivers” of Crime
The “Drivers” of Crime
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Population 1990
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-541.5759 - .0021X R2 = .1410 F=11.33 (3.37)
Figure 3b: Population on Change in Wisconsin County Property Crime Rates
The “Drivers” of Crime
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15000 20000 25000 30000 35000 40000 45000 50000
Median Household Income 1990
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1990
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-5.5818 - .0261X R2 = .0421 F=3.03 (1.74)
Figure 4b: Median Household Income on Change in Wisconsin County
Property Crime Rates
The “Drivers” of Crime
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0
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Poverty Rate 1990
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1990
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-792.7073 + 9.3672X R2 = .0026 F=.18 (0.42)
Figure 5b: Poverty Rate on Change in Wisconsin County Property Crime Rates
The “Drivers” of Crime
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Youth Poverty Rate 1990
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1990
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-668.3695 – 1.1992X R2 = .0001 F=.01 (0.08)
Figure 6b: Youth Poverty Rate on Change in Wisconsin County Property Crime Rates
The “Drivers” of Crime
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0.35 0.4 0.45 0.5
Gini Coefficient 1990
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1990
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234.3506 – 3157.6599X R2 = .0464 F=3.36 (1.83)
Figure 7b: Gini Coefficient on Change in Wisconsin County Property Crime Rates
The “Drivers” of Crime
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30000 40000 50000 60000 70000 80000 90000 100000
Median House Value 1990
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1990
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70.7115 – .0145X R2 = .0756 F=5.64 (2.38)
Figure 10b: Median House Value on Change in Wisconsin County Property Crime Rates
The “Drivers” of Crime
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Unemployment Rate
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-1541.1034 + 165.9381X R2 = .1040 F=8.01 (2.83)
Figure 11b: Unemployment Rate on Change in Wisconsin County Property Crime Rates
The “Drivers” of Crime
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Percent Age 25+ with at Least a Bachelar's Degree 1990
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1990-2
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-86.6283 – 44.3736X R2 = .1016 F=7.80 (2.79)
Figure 13b: Percent of the Population Age 25+ with at Least a Bachelor's Degree on Change in Wisconsin County Property Crime Rates
The “Drivers” of Crime
One area of work that is gaining more attention is the role of social capital
One can argue that higher levels of social capital can increase the moral threshold of the potential criminal which in turn reduces the likelihood of committing crime.
1: Risk of capture2: Pressure to not commit crime
The “Drivers” of Crime
Following the work of Coleman (1988), Flora and Flora (1993) Putnam (1993 1995), and Turner (1999), Shaffer, Deller and Marcouiller (2004) offer the following definition of social capital:
Social capital refers to features of social organization such as networks, norms, and social trust that facilitate coordination and cooperation for mutual benefit. Networks of civic engagement foster norms of general reciprocity and encourage the emergence of social trust. Social capital consists of the social networks in a community, the level of trust between community members, and local norms. These networks, norms and trusts help local people work together for their mutual benefit. (p203-204).
The “Drivers” of Crime
How does one measure social capital?
Many empirical studies that have specifically modeled the relationship between crime and social capital have tended to rely on survey data of individuals where questions aimed at measuring social capital can be more easily crafted (e.g., Kennedy et al. 1998; Rosenfeld, Messner and Baumer 2001; Messner, Baumer and Rosenfeld 2004)
The “Drivers” of Crime
How does one measure social capital?
For this study we follow an approach outlined in Rupasingha, Goetz and Freshwater (2006), Rupasingha and Goetz (2007) and Goetz and Rupasingha (2006) and use a range of secondary data sources to build a set of proxy measures of social capital.
In his 2006 Johan Skytte Prize Lecture Putnam (2007) commends the work of Rupasingha and his
colleagues in their approach and resulting measures.
The “Drivers” of Crime
How does one measure social capital?
We offer up four blocks of variables designed to capture social capital:
1)Businesses2)Cooperatives3)Religious Congregations4)Non-profits
The “Drivers” of Crime
How does one measure social capital?
Businesses (CBP)
Artisans and cultural per 1K population Youth and family services per 1K population Sports and recreational per 1K population Business, professional and labor (union) associations per 1K population Food banks per 1K population Temporary shelters per 1K population Civic and social organization per 1K population Barber and beauty shops per 1K population
The “Drivers” of Crime
How does one measure social capital?Cooperatives
(UWCC) Number of arts cooperatives per 1K population Number of child care cooperatives per 1K population
Number of educational cooperatives per 1K population
Number of grocery store cooperatives per 1K population
The “Drivers” of Crime
How does one measure social capital?Church
Congregations Number of Evangelical church congregations per 1K population Number of Catholic church congregations per 1K population
Number of Jewish synagogues per 1K population
Number of other congregations per 1K population
The “Drivers” of Crime
How does one measure social capital?Non-profits (IRS 990)
Number of arts, culture, and humanities nonprofits per 1K population Number of educational nonprofits per 1K population Number of environmental nonprofits per 1K population Number of human services nonprofits per 1K population Number of public and social benefit nonprofits per 1K population Number of crime and legal nonprofits per 1K population Number of sports and recreation nonprofits per 1K population Number of youth development nonprofits per 1K population Number of civil rights, social action & advocacy nonprofits per 1K population Number of community improvement & capacity building nonprofits per 1K population
The “Drivers” of Crime
Control Variables
Three blocks:
Social-demographic
Economic (wealth-poverty)
Change
The “Drivers” of Crime
Table 2: F Tests for Social Capital
Violient Crime Rate
Robbery Rate
Assault Rate
Property Crime Rate
Burglary Rate
Larceny Rate
Motor Vehicle Crime
Social Capital by Business Concentration 1.993 0.600 2.027 4.055 3.539 3.848 3.046
(0.06) (0.88) (0.04) (0.01) (0.01) (0.01) (0.01)
Social Capital by Church Concentration 0.000 5.066 0.212 1.560 3.947 0.715 1.341
(0.99) (0.01) (0.93) (0.18) (0.01) (0.68) (0.25)
Social Capital by Cooperative Concentration 0.042 0.147 0.079 0.220 0.129 0.715 2.045
(0.99) (0.96) (0.99) (0.93) (0.97) (0.68) (0.09)
Social Capital by Nonprofit Concentration 0.864 0.248 0.912 3.483 2.715 4.631 0.707
(0.77) (0.99) (0.52) (0.01) (0.01) (0.01) (0.72)
Marginal significance in parantheses (p values).
The “Drivers” of Crime
Violient Crime Rate
Robbery Rate
Assault Rate
Property Crime Rate
Burglary Rate
Larceny Rate
Motor Vehicle Crime
Artisans and cultural per 1K population 0.20554 0.01960 0.21428 3.80446 0.70252 2.88988 0.28942
(0.30) (0.38) (0.21) (0.00) (0.06) (0.00) (0.00)
Youth and family services per 1K populaiton 0.02786 0.01452 -0.00037 -0.99218 -0.28970 -0.67131 -0.04145
(0.84) (0.36) (1.00) (0.21) (0.27) (0.22) (0.55)
Sports and recreational per 1K populaiton -0.02751 0.01420 0.01578 -0.64980 -0.35297 -0.30000 -0.01311
(0.86) (0.41) (0.90) (0.45) (0.21) (0.61) (0.86)Business, professional and labor (union) associations per 1K pop -0.13602 -0.01965 -0.11809 -2.40977 -0.37893 -1.87274 -0.18362
(0.44) (0.34) (0.45) (0.02) (0.26) (0.01) (0.04)
Food banks per 1K population -0.08318 -0.06026 0.04483 -3.07126 -0.60053 -2.33295 -0.09353
(0.87) (0.29) (0.92) (0.27) (0.52) (0.23) (0.71)
Temporary shelters per 1K population -0.47571 -0.11036 -0.38020 -3.78533 -0.33677 -3.06323 -0.16409
(0.59) (0.27) (0.62) (0.44) (0.84) (0.37) (0.71)
Civic and sociol organization per 1K population -0.30720 0.01356 -0.23922 -0.74707 -0.64248 -0.18004 0.07435
(0.10) (0.48) (0.10) (0.43) (0.04) (0.78) (0.38)
Barber and beauty shops per 1K population -0.37964 -0.00219 -0.34244 -1.27223 -0.40791 -0.68057 -0.21152
(0.02) (0.91) (0.02) (0.18) (0.20) (0.30) (0.01)
Marginal significance in parantheses (p values).
The “Drivers” of Crime
Violient Crime Rate
Robbery Rate
Assault Rate
Property Crime Rate
Burglary Rate
Larceny Rate
Motor Vehicle Crime
Number of Evangelical church congregations per 1K pop -0.03450 -0.00821 -0.03319 0.26971 0.10315 0.18637 -0.01595
(0.37) (0.07) (0.33) (0.22) (0.16) (0.23) (0.42)
Number of Catholic church congregations per 1K pop -0.01714 0.00792 -0.03086 0.21703 -0.11597 0.29163 -0.02581
(0.85) (0.46) (0.70) (0.68) (0.51) (0.43) (0.58)
Number of Jewish synagagues per 1K pop -0.28851 -0.39603 0.21662 -10.16647 -5.23309 -5.52640 -0.58405
(0.83) (0.01) (0.86) (0.20) (0.05) (0.32) (0.41)
Number of other congregations per 1K pop 0.00548 0.00818 -0.00564 0.41175 0.20537 0.20983 -0.00585
(0.92) (0.19) (0.91) (0.18) (0.05) (0.33) (0.83)
Marginal significance in parantheses (p values).
The “Drivers” of Crime
Violient
Crime RateRobbery
RateAssault
RateProperty
Crime RateBurglary
RateLarceny
Rate
Motor Vehicle Crime
Number of arts, culture, and humanities nonprofits per 1K pop 0.08109 0.01491 0.13149 2.80693 0.39214 2.36074 0.13621 (0.58) (0.37) (0.30) (0.00) (0.15) (0.00) (0.06)Number of educational nonprofits per 1K pop 0.05708 0.00402 0.03797 -0.92723 0.10605 -0.91746 -0.06365
(0.72) (0.82) (0.77) (0.28) (0.71) (0.12) (0.41)Number of environmental nonprofits per 1K pop 0.61888 0.00984 0.57342 2.99028 1.03234 1.95190 0.02994 (0.00) (0.68) (0.00) (0.01) (0.01) (0.02) (0.78)Number of human services nonprofits per 1K pop -0.00182 0.00237 0.00105 0.28450 0.09851 0.11130 0.02594 (0.98) (0.83) (0.99) (0.58) (0.57) (0.76) (0.58)
Number of public and social benefit nonprofits per 1K pop -0.22947 -0.00948 -0.20024 -2.11472 -0.58248 -1.54747 -0.05101 (0.03) (0.45) (0.03) (0.00) (0.00) (0.00) (0.35)Number of crime and legal nonprofits per 1K pop -0.28870 -0.02248 -0.26931 -1.61985 0.21635 -1.89470 -0.09632 (0.59) (0.72) (0.56) (0.59) (0.83) (0.37) (0.72)
Number of sports and recreation nonprofits per 1K pop 0.11988 0.01100 0.03059 -0.13725 -0.55116 0.52224 -0.05798 (0.53) (0.62) (0.85) (0.90) (0.13) (0.48) (0.55)Number of youth development nonprofits per 1K pop -1.01884 -0.04832 -0.77946 -5.85342 -1.16583 -4.00016 -0.45249
(0.04) (0.40) (0.07) (0.03) (0.21) (0.04) (0.07)Number of civil rights, social action & advocacy nonprofits per 1K pop -1.08820 0.01124 -1.13428 -7.94649 -1.77954 -5.13766 -0.84018 (0.28) (0.92) (0.19) (0.16) (0.35) (0.19) (0.10)Number of community improvement & capacity building nonprofits per 1K pop 0.23463 0.01151 0.19646 1.76364 0.35418 1.40404 0.05387 (0.19) (0.58) (0.21) (0.08) (0.30) (0.04) (0.55)Marginal significance in parantheses (p values).
The “Drivers” of Crime
So, what are the overall conclusions……
Well….It Depends…..
There is a lot of contradictory evidence….
It depends on the type of crime that one is talking about…..
It is much more than simply poverty….
For rural there is evidence that as a county grows there will be upward pressure placed on crime rates….
There is evidence that certain types of social capital matter, but not always in the way predicted by theory…..
The “Drivers” of Crime
So, what are the overall conclusions……
“While the research finds that social capital matters both theoretically and empirically, policy options are much more subtle. There is strong evidence that a higher concentration of organizations that allow for networking, such as professional, business, and labor organizations (e.g., chambers of commerce and labor unions), as well as civic, social, and community benefit-focused organizations (e.g., community foundations, fraternal organizations, and alumni associations), is associated with lower rural crime rates.”
The “Drivers” of Crime
So, what are the overall conclusions……
The results presented here support Reisig and Cancino (2004), who argue that social capital is too broad of a concept with respect to crime and should be more focused on notions of collective value or social norms. Highly organized and effective criminal enterprises could be described as having high levels of social capital.
Wojan, McGranajan, and Lambert (2009) make a distinction between civic capacity and social capital.
The “Drivers” of Crime
So, what are the overall conclusions……
What does social capital mean as a deterrent to crime? Does it mean networking opportunities and density of acquaintance, or does it involve notions of civic capacity and civic engagement? Simply having a chamber of commerce or fraternal organizations or religious based social organizations is not sufficient to deter rural crime. In essence, social capital is necessary but not sufficient to deter crime. By examining why social capital is not sufficient we are beginning to remove the layers of a very complex problem.
The “Drivers” of Crime
Thanks!