Ccr whitworth inequality & crime

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<ul><li> 1. Inequality, crime and the role ofgeographical scale Dr Adam Whitworth,Dept of Geography</li></ul> <p> 2. Background Oxford Crime domain of Indices of Multiple Deprivation NDC crime displacement &amp; costs of crime analyses Sheffield Jan 2010- Inequality &amp; crime A multiple fraud: neither acriminologist nor a geographer! 3. Inequality &amp; Crime: Two strands Inequalities of crime Spatial distributions, concentrations, persistence Inequality and crime Focus on issue of scale, sub-national relevance &amp;variation in inequality-crime links 4. Inequality and Crime Much recent attention on inequalityas a driver of social ills 3 main theories linking inequality andcrime Economic theory (Becker, 1968) Strain theory (Merton, 1938) Social disorganisation theory (Shaw &amp; McKay, 1942) 5. Inequality and Crime Much empirical work Tends to find positive links Range of data &amp; methods Range of scales, though tend to be large &amp; USfocussed But no sub-national England analyses 6. Step 1: Inequality &amp; crime across Englands local authorities Outcomes: crime rates (per at risk population) Burglary &amp; criminal damage rate (households) Vehicle crime rate (vehicle owning hholds) Violence and robbery rate (resident population) Hierarchical data: multilevel models 7 Years of data: 2002/03-2008/09 (Level 1) in 352 local authorities (Level 2) in 39 Police Force Areas (Level 3) All variables log transformed % change in outcome variable for each one % change in the explanatory 7. Explanatory variablesTotal populationPopulation controlsYouth population ratePopulation densitySocial DisorganisationPopulation turnoverthesis (Shaw &amp; McKay) Non-white percentageUnemployment rate% Not achieving 5 GCSE A*-CStrain theory (Merton)Teenage conception rate(per1000)Mean house pricePFA OfficersEconomic Theory (Becker)PFA detection rateInequality (weighted Gini ofMain interest MSOA income 2004/05) 8. Burglary Robbery Veh Cri Violence Crim DInequality 0.20* 0.28* 0.27* 0.10*0.07+Pop Density0.11* 0.37* 0.22* 0.10*0.08*Turnover 0.23* 0.35*0.05-0.03 -0.37*Total Pop0.07+ 0.22* 0.10+ -0.06+-0.04Youth Pop -0.19+-0.29 -0.180.42*0.36*% Unem 0.06+ 0.35* 0.10* 0.12* 0Av house price-0.41* -0.49*-0.21*-0.18* -0.18*% 5 GCSEs A*-C 0.03 0.08-0.06-0.15* -0.18* % Non-white0.060.33* 0.06 0.010.10*Youth conceptions00.080.05 0.17*0.12*PFA Detection rate 0.02-0.27* -0.11-0.10*-0.05PFA Officers 00.04-0.02 0 -0.12*2003/04-0.020-0.06*0.19*0.11*2004/05 -0.12* -0.09*-0.20*0.32*0.12*2005/06 -0.16* -0.09*-0.24*0.31*0.09*2006/07 -0.19* -0.09*-0.29*0.31*0.10*2007/08 -0.25* -0.18*-0.41*0.25* -0.012008/09 -0.26* -0.27*-0.53*0.15*-0.15*Constant 7.84* 3.28* 6.74* 4.95*7.52*Between-PFA0.04 0.01variance (L3)(% explained)56.20% 89.30%76.80%50.90% 62.80%Within-PFA btw-0.04 0.02CDRP (L2)(% explained)51.00% 84.60%65.50%78.40% 75.80%Within-CDRP btw0.02 0.01years (L1)(% explained)62.80% 17.20%71.30%27.20% 53.60% 9. Same models, alternative inequality measures 10. Implications Consistent relationship between inequality &amp; crime Greater support for sociological theories Risk of individualised focus in policy: need greaterrecognition of the structural socio-economicinequalities which provide context &amp; drivers for crime 11. Step 2: experimenting with varying theunderstanding of local in the local inequalitymeasure 12. Local inequality &amp; crime: the conceptualstarting point Given findings at LA level, why explore relevance of more localinequality?(Wilkinson &amp; Pickett, 2006) 13. Step 2: Exploring local inequality and crime Alternative plausible explanation for Wilkinson andPickett findings Local inequality matters but only in certain contexts: local findings less consistent National results may be the weighted aggregation of local findings: more consistent But processes are locally (not nationally) driven Plausibility in relation to crime specifically Theoretical: esp Beckers economic theory Intuitive: wouldnt we expect potential offenders to notice local inequality as well as/rather than inequality at larger scales Some empirical evidence to support its relevance 14. London &amp; South Yorkshire IMD2010quartiles Rationale for Met &amp; South Yorkscase study areas Data: 2 years per force 15. Varying the scale of the local inequality measure 16. Gini (left) &amp; Gini-Robbery correlation (right)across the 10 layers1 12 23 34 4Met 5 56 67 78 89 9 10101 12 23 34 4 South5 5 Yorks6 67 78 89 9 10100 2 4 68 10 12-.3 -.2 -.1 0 .1 .2 .3 .4Gini Ineq-Crime Correlation 17. Explanatory variables in the modelling Youth population ratePopulation controlsPopulation densitySocial Disorganisation Population turnoverthesis (Shaw &amp; McKay)Non-white percentage Unemployment rate % Adults only basic educationStrain theory (Merton) Teenage conception rate (per1000) Mean house priceEconomic Theory (Becker) Richest area dummy Year dummy CDRP dummiesSpatial autocorrelationSpatially lagged crime rate Inequality (weighted Gini ofMain interestMSOA income 2007/08) 18. Modelling approach Outcomes: Crime counts at MSOA level Burglary, vehicle crime, robbery, violence Morans I shows spatial autocorrelation in crimes and sospatially lagged outcome added to RHS Overdispersed Poisson distribution: negative binomial models Total population added to RHS as exposure variable:interpretation shifts from counts to rates per capita Centres on one: ie 1.04 for a continuous X relates to a 4% point increase in outcome for 1% increase in X 19. Modelled inequality coefficients across all 10 contiguous layersMetSouth Yorks1 12 23 34 45 56 67 78 89 91010 .951 .7.8.9 1 1.1Inequality coefficient Inequality coefficient Burglary Vehicle Crime Burglary Vehicle Crime RobberyViolenceRobberyViolence 20. Implications Local inequality can have relevance, but context looks to matter Findings line up with prior hypotheses across the two case studyareas London: coherent socio-spatial system South Yorks: tendency for lower crime levels and fractured socio-spatial system across the contiguity layers Processes linking inequality &amp; crime : is this a substantive finding(ie local inequality really does matter but only in certaincontexts) or an artefact of the numbers/method? 21. Next steps More systematic analyses around Whether consistent story builds up around local inequality&amp; crime Whether any such variation in finds can be lik to profiles oflocal contexts How? Geographically weighted regression models And map findings onto geodemographic profiles of local contexts Quali work with offenders? How does inequality fit in as a factor, if at all? And does this vary by local context?</p>