What Deters Crimes?: Dealing with Arbitrary Forms of Serial Correlation and Heteroskedasticity

Crime is imposes several different cost of society.  Besides the untold human cost of changed lives and property damages, the resources used to deter crimes and enforce laws can add up to a substantial cost to society.  Understanding what factors reduce crime can channel societies resources into their most effective use in reducing crime.  This post will analyze the impact that the probability of arrest, probability of conviction,  probability of receiving a prison sentence, the average prison sentence and the number of police per capita have on crime rates.  All of these variables account for the expected cost to criminals of engaging in their activities and as a society, we try and increase these cost while still holding on to a sense of just conviction and punishment.  So, which one of these factors has a greater impact at reducing crime?

Using panel data and differencing with more than two time periods this post calculates a regression model that is robust to serial correlation and arbitrary forms of heteroskedasticity.  The data comes from an introductory econometrics text book and is a real data set that was used by researchers. The data contains data from over 90 counties with data from over 7 years, from 1981 to 1987, containing several variables useful for a statistical analysis on crime deterrence.

Differencing with More Than Two Periods:  STATA based econometric analysis

1)  Specify panel data with STATA by specifying the individual and time variables

2) Estimate the elasticity of crime rates relative to the deterrence variables including dummy variables with the base year as 1982

Description of Variables

  • prbarr = probability of arrest
  • prbconv = probability of conviction
  • prbpris = probability of prison sentence
  • avse = average prison sentence in days
  • polpc = number of police per capita

First Difference Model as OLS Regression

An “l” in front of the variables above means that the variable has been transformed by taking the natural logarithm.  Placing a “d.” in front of a variable tells STATA to take the first difference of this variable.


Notice that a 10% increase in the probability of arrest decrease the per capita crime rate by 32.7%.  The order in which deterrence variables reducing crime in descending order are the probability of arrest, probability of conviction, probability of receiving a prison sentence, and the length of the average sentence.  Notice the positive coefficient on “lpolpc” is positive, there is a potential for reverse causation that is causing this perplexing results.  The result is perplexing is because the calculations seems to say that an increase in the police force would have an increase crime rates, but one would suspect that the causality runs the other way,  higher crime rates prompts a response from the community for more police on the streets.  Another problem that could be influencing the calculations is serial correlation because this data set deals with panel data with more than two time periods.

Test for Serial Correlation in the Error Term

Testing for serial correlation is important when dealing with panel data with more than two time periods.

The statistical significance of the coefficient on the lagged residuals is high.  This makes a strong case for serial correlations in the OLS estimate.  In order to correct for this serial correlation and heteroskedasticity is to use clustered standard errors.  These errors are clustered by individual, in this case by county.


Using Clustered Standard Errors to Eliminate Serial Correlation and Heterskedasticity

The estimates using clustered standard errors to control for serial correlation and heteroskedasticity have the same sign as the OLS estimates, but the coefficients have slightly changed as well as the standard errors.

According to the estimates above, the probability of arrest seems to have the greatest impact on reducing crime.  Surprisingly, the length of the average prison sentence is not statistically significant as a deterrent for crimes per capita, as expected, the probability of conviction and of getting a prison sentence reduces the crime rate per capita.  The final estimates are that increasing the probability of arrest by  by 10% is expected to reduce the crime rate by 3.2%.  A extension of this analysis is to account for the cost of these deterrence variables, and choose the level of deterrence where the marginal cost of the deterrence equals the marginal benefit of reducing the per capita crime rate.









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