Tuesday, 28 June 2022

Social disorganisation and crime over two centuries

Social disorganisation theory is the idea that differences (or changes) in family structures and community stability are a key contributor to differences (or changes) in crime rates between different places (or times). It suggests that neighbourhoods that are more unstable (higher social disorganisation) will have higher crime rates than neighbourhoods that are less unstable (lower social disorganisation), ceteris paribus (holding everything else constant). It also suggests that if neighbourhoods become more unstable over time, crime rates will increase, ceteris paribus. I've mostly encountered social disorganisation theory in relation to the effect of alcohol outlets on crime. In that context, things become tricky, because alcohol outlets tend to locate in more unstable (higher social deprivation) neighbourhoods, which may also have higher crime because of social disorganisation. So, disentangling the effects of alcohol outlets from the effects of social disorganisation more generally is difficult.

The broader literature on social disorganisation theory faces issues as well, because of potential reverse causation and endogeneity. If you want to test the effect of neighbourhood instability on crime, you have to recognise that not only can instability cause crime, but crime can cause instability. Finding ways of dealing with these issues is important.

So, I was interested to read this recent article by Zeresh Errol (Monash University), Jakob Madsen (University of Western Australia), and Solmaz Moslehi (Monash University), published in the Journal of Economic Behavior and Organization (sorry, I don't see an ungated version online). They use annual data covering the period 1840 to 2018 for 16 countries: Australia, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, Norway, Sweden, Switzerland, the U.K. and the U.S. Their data includes crime rates (per 100,000 population), family structure (proxied by the divorce rate and the share of out-of-wedlock births), community structure (proxied by the urbanisation rate), GDP per capita, and the proportion of the population aged 15-29 years. The crime rates are disaggregated into property crime, violent crime, homicide, robbery and assault. Errol et al. then look at the relationship between the family structure and community structure variables and crime rates (controlling for the other variables, along with country and time fixed effects).

However, remember that endogeneity is a problem here for the family structure variables, so any simple regression analysis is not going to tell us about the causal relationship between family structure and crime. Errol et al. solve this problem using instrumental variables analysis. Essentially, this involves finding an instrument that affects the endogenous variable (divorce or out-of-wedlock birth rate), but has no direct effect on the outcome variable (crime rates). In a unique twist to this paper, Errol et al. use the weighted average of the family structure variables in all other countries as instruments for the family structure variables in country i. For example, for Australia's divorce rate, they use as an instrument the weighted average of the divorce rates in all other countries. The weighted average of other countries' rates is a valid instrument because divorce rates should be related across countries, but the divorce rate in Australia should not exert any effect on the crime rate in Belgium (or any other country). You can tell a similar story for out-of-wedlock birth rates.

Now, rather than using the straight average of all other countries, Errol et al. use a weighted average, where the weights are based on the linguistic distance between the countries. So, data from countries where the main language is more similar will have a greater weight in the calculation of the average. This is quite an exciting aspect of the article to me, because it relates closely to some ongoing work I have been doing on using cultural distance measures in new and exciting ways (more on that in future posts).

Looking at the IV regression results, Errol et al. run separate regressions for the five crime rates, and separately using the divorce rate and the out-of-wedlock birth rate as their proxy for family structure. Across these various models, they find that:

...the coefficients of Div and Owed are significantly positive in seven of the ten cases, where two of the insignificant coefficients pertain to homicide.

In other words, higher divorce rates and out-of-wedlock birth rates cause an increase in crime rates. The size of the coefficients is a little difficult to interpret, given that what Errol et al. report is a coefficient that sums several lagged values. However, they do seem to be meaningful in size, given that Errol et al. note for the ordinary least squares (not IV) regression, that:

Based on the coefficients of the 10-year first difference estimates of Div (Owed), a one standard deviation increase in Div (Owed), is associated with a 15.4(1.8) and 174.4(153.3) percentage point increase in the rates of violent crime and property crime, respectively...

So, this paper demonstrates the important of social disorganisation in understanding differences in crime rates over an extremely long time period. However, it also illustrates a potentially fruitful way of constructing instruments for instrumental variables analyses, using cultural (or, in their case, linguistic) distance weighted averages. Expect to see more work in the future employing this approach.

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