Three theories

Collective efficacy

the linkage of cohesion and mutual trust with shared expectations for intervening in support of neighborhood social control1

 

Rooted in:

  1. Social capital theory, e.g., Coleman (1990)
  2. Kornhauser’s (1978) unidimensional control theory of social disorganization

The Code of the Street

The inclination to violence springs from the circumstances of life… The code of the street is actually a cultural adaptation to a profound lack of faith in the police and the judicial system

 

An oppositional culture emerging from alienation from institutions

Two threads

Matsueda (2006)

 

Social organization can be against or for crime

  • A return to pre-Kornhauser multidimensional social disorganization
  • Integrates collective action theory and symbolic interaction
  • Building consensus is important for:
    • Acting collectively
    • Establishing norms
    • Producing predictable social interactions

Differential social organization

 

Brunton-Smith et al. (2018)

Lower consensus in collective efficacy → higher crime

Demographic heterogeneity → lower consensus

Predictions

 

Levels of crime under differential social organization

Against
High Low
For
High Moderate High
Low Low Moderate

 

Matsueda (2006) and Brunton-Smith (2018) implies consensus magnifies organization

Questions

 

  • Does organization both for and against crime predict crime?
    • Are they correlated?
    • Are they additive or multiplicative?
  • How is consensus related to differential organization?
    • Does consensus predict crime rates?
    • What predicts consensus?

Design

Data

Project in Human Development in Chicago Neighborhoods (1995)

  • 8,782 respondents in 343 neighborhood clusters (NCs)
  • Collective Efficacy and Legal Cynicism
  • Police-recorded homicide, gun assaults, robbery, and burglary

Seattle Neighborhoods and Crime Study (2002-2003)

  • 4,904 respondents in 123 census tracts
  • Collective Efficacy and Code of the Street
  • Police-recorded aggravated assault, robbery, and burglary1

Analytical model

 

Measurement estimator

A multivariate three-level location scale measurement model:

\[ \begin{array}{cc} \begin{aligned} \color{red}{\eta_{Ak}} = & \ \gamma_{A} + \sum_{m=1}^{M} \kappa_{Am} Z_{mk} + u_{Ak} \\ \color{red}{\eta_{Bk}} = & \ \gamma_{B} + \sum_{m=1}^{M} \kappa_{Bm} Z_{mk} + u_{Bk} \\ log(\color{blue}{\tau_{Ak}}) = & \ \zeta_A + \sum_{m=1}^{M} \upsilon_{Am} Z_{mk} + w_{Ak} \\ log(\color{blue}{\tau_{Bk}}) = & \ \zeta_B + \sum_{m=1}^{M} \upsilon_{Bm} Z_{mk} + w_{Bk} \\ \end{aligned} & \begin{aligned} \begin{pmatrix} u_{Ak} \\ u_{Bk} \\ w_{Ak} \\ w_{Bk} \end{pmatrix} \sim & \ MVN \left\{ \begin{pmatrix} 0 \\ 0 \\ 0 \\ 0 \end{pmatrix}, \begin{pmatrix} \xi^{2}_{A} & & & \\ \xi_{AB} & \xi^{2}_{B} & & \\ \omega_{AA} & \omega_{BA} & \phi^{2}_{A} & \\ \omega_{AB} & \omega_{BB} & \phi_{AB} & \phi^{2}_{B} \\ \end{pmatrix} \right\} \end{aligned} \\ \hline \begin{aligned} \pi_{Ajk} = & \ \color{red}{\eta_{Ak}} + \sum_{q=1}^{Q} \delta_{Aq} X_{qjk} + r_{Ajk} \\ \pi_{Bjk} = & \ \color{red}{\eta_{Bk}} + \sum_{q=1}^{Q} \delta_{Bq} X_{qjk} + r_{Bjk} \end{aligned} & \begin{aligned} \begin{pmatrix} r_{Ajk} \\ r_{Bjk} \end{pmatrix} \sim & \ MVN \left\{ \begin{pmatrix} 0 \\ 0 \end{pmatrix}, \begin{pmatrix} \color{blue}{\tau^{2}_{Ak}} & \\ \tau_{ABk} & \color{blue}{\tau^{2}_{Bk}} \end{pmatrix} \right\} \end{aligned} \\ \hline \begin{aligned} Y_{ijk} = & \ a_{ijk} (\pi_{Ajk} ) + b_{ijk}(\pi_{Bjk}) + \sum_{p=1}^{P} \alpha_p D_{pijk} + e_{ijk} \end{aligned} & \begin{aligned} e_{ijk} \sim & \ N(0,\sigma^{2}_{Y}) \end{aligned} \end{array} \]

Crime estimator

 

Conventional Poisson-lognormal regression:

\[ C_{k} \sim Pois(\lambda_k) \\ log(\lambda_k) = \beta_{0} + \sum_{m=1}^{M} \beta_{m} Z_{mk} + \theta_1 \color{red}{\hat{\eta_{Ak}}} + \theta_2 \color{red}{\hat{\eta_{Ak}}} + \theta_3 \color{red}{\hat{\eta_{Ak}} \hat{\eta_{Bk}}} + \theta_4 \color{blue}{\hat{\tau_{Ak}}} + \theta_4 \color{blue}{\hat{\tau_{Ak}}} + \psi_k \\ \psi_k \sim N(0,\sigma^2_C) \] Empirical Bayes estimates as covariates:

  • Differential social organization (e.g., \(\color{red}{\hat{\eta_{Ak}}}\)) with interaction
  • Consensus in perceptions (e.g., \(\color{blue}{\hat{\tau_{Ak}}}\))

Preliminary results

Chicago

Seattle

Takeaways

Next steps

  • Get

Appendix

PHDCN

 

  • Collective efficacy:
    • Cohesion and trust:
      • “This is a close-knit neighborhood.”
      • “People around here are willing to help their neighbors.”
      • “People in this neighborhood generally don’t get along with each other”
      • “People in this neighborhood don’t share the same values”
      • “People in this neighborhood can be trusted
    • Social control:
      • If a group of neighborhood children were skipping school and hanging out on a street corner, how likely is it that your neighbors would do something about it?
      • If some children were spray-painting graffiti on a local building, how likely is it that your neighbors would do something about it?
      • If a child was showing disrespect to an adult, how likely is it that people in your neighborhood would scold that child?
      • If there was a fight in front of your house and someone was being beaten or threatened, how likely is it that your neighbors would break it up?
      • Suppose that because of budget cuts the fire station closest to your home was going to be closed down by the city. How likely is it that neighborhood residents would organize to try to do something to keep the fire station open?
  • Legal cynicism
    • “Laws were made to be broken.”
    • “It’s okay to do anything you want as long as you don’t hurt anyone.”
    • “To make money, there are no right and wrong ways anymore, only easy ways and hard ways.”
    • “Fighting between friends or within families is nobody else’s business”
    • “Nowadays a person has to live pretty much for today and let tomorrow take care of itself”

SNCS Measures

  • Collective efficacy: “If [item], how likely is it that your neighbors would do something about it?”
    • “a group of neighborhood children were skipping school and hanging out on a street corner”
    • “some children were spray-painting graffiti on a local building”
    • “a child was showing disrespect to an adult”
    • “children were fighting out in the street”
    • [w/o lead] “People around here are willing to help their neighbors”
    • [w/o lead] “People in this neighborhood can be trusted.”
  • Code of the street: “In this neighborhood…”
    • “for young people to gain respect among their peers, they sometimes have to be willing to fight.”
    • “if a loved one is disrespected, people retaliate even if it means resorting to violence.”
    • “young men who own guns are often looked up to and respected.”
    • “residents believe that sometimes you have to resort to crime to get ahead.”
    • “parents teach their kids to fight back if they are insulted or threatened.”
    • “young men often project a tough or violent image to avoid being threatened with violence.”

Shaw & McKay’s transmission model

 

Kornhauser’s control model