1. Quantitative Methods – Week 4: Application: Stephen Levitt’s “Where Have All the Criminals Gone?” Roman Studer Nuffield College roman.studer@nuffield.ox.ac.uk

2. “Where Have All the Criminals Gone?” List all the variables that Lewitt looks at in the course of the chapter  unemployment rate.  number of criminals sent to prison.  length of prison sentences.  number of executions.  number of police officers.  number of guns.  price of cocaine.  innovation in police techniques (dummy variable).  number of conceptions.  number of births.  number of abortions.  number of children living in poverty.  number of single parents.  average age of total population  number of crimes.  state.  year.  number of violent crimes.  number of financially motivated crimes.  number of homicides.  number of homicides involving a gun.  number of drug related homicides.  economic growth.  gender of criminals  ethnicity of criminals.  age of criminals.  What are the level of measurements, what are the basic units of observations (cases)? Are all ratio measurements?

3. “Where Have All the Criminals Gone?” Many factors (variables) are potentially associated with the drop in crime rates in the US. Where does he find correlations between a variable and the falling crime rate? Which variables are positively, which ones negatively correlated with the crime rate variable? Where does he find no correlation? Concept/Variable Measured Variable Correlation Strong economyUnemployment rate, growth rate No correlation Reliance on prisonsNumber of prisoners Negative [33%] Capital punishmentNumber of executions No correlation Number of policeNumber of policemen Negative (but…) [10%] Innovative policing strategies …….. No correlation

4. “Where Have All the Criminals Gone?” Concept/Variable Measured Variable Correlation Tougher gun lawsBrady act, … No correlation (in the present context) Changes in crack marketPrice of crack Negative [15%] Aging of population?? Legalizing abortionNumber of abortions Negative [The rest? About 50%] No correlation

5. “Where Have All the Criminals Gone?”  In which cases does Lewitt move from correlation to causation? How does he justify this change in language? Is it convincing?  Does this chapter potentially suffer from a omitted variable problem? Of what other factors can you think for explaining the fall in crime rates?  For each potential factor, he provides both the relevant data and some common sense explanation. What do you believe more – the data or the explanations? Why does he need the explanations at all?  Are you convinced by Lewitt’s overall argument? Has the fall in crime rate in the US herewith been explained once and for all?

6. “Where Have All the Criminals Gone?” Source: Bureau of Justice Statistics, http://www.ojp.usdoj.gov/bjs/glance.htm#Crimehttp://www.ojp.usdoj.gov/bjs/glance.htm#Crime

7. Again: Caution when Interpreting Correlation Results!! Correlation is NOT causation!!  Causation is very hard to ascertain in social sciences Beware of spurious or nonsense correlation! Examples:  Simultaneous decline of birth rates and of the number of storks in Sweden  Positive correlation between shoe size and income level  Omitted variables are one of the big problems in econometrics

8. Caution when Interpreting Correlation Results (II) Watch out for the influence of outliers on the correlation results!! Example: GDP per person and purchasing power around the world, 2007  Very strong association  Positive correlation  r = 0.95  Just 2 outliers  Weak association suggested  r = 0.34

9. Replicating Levitt’s Results with US Macroeconomic Data Computer Class:

10. US Macro Dataset We will no try to replicate some of Levitt’s results using national statistics from the US Sources: U.S. Department of Justice, Bureau of Justice Statistics; US Census Bureau Variables: Crime (total and rates), number of prisoners, abortion rate, education, unemployment, poverty Get dataset at http://www.nuff.ox.ac.uk/users/studer/teaching.htm http://www.nuff.ox.ac.uk/users/studer/teaching.htm

11. Exercises A.Data set and descriptive statistics Open dataset and convert the Excel dataset into a Stata dataset Look at each of the variables in turn Produce appropriate graphs: Get a first visualisation of the data; does it look normally distributed? Produce nice graphs including titles, etc. Compute the mean, median, standard deviation, coefficient of variation, kurtosis and skewness for every variable How do the different crime variables compare? Which one do you take for the correlation analysis? B.Correlation analysis Look at the association between crime and the other variables Make a scatter plot to get a first impression of their association: Do you think these variables are connected? Positively/negatively? Calculate the correlation coefficient; how would you explain the result? If you look at the scatter plot, are there any outliers?

12. Exercises (II) C.Save your data set and your results on the O: drive Save your new data set as a Stata file (.dta) Export and save your data set as an Excel file (.xls) Copy your results and save them in a word file (.doc)

13. Appendix: STATA Commands correlate varlistDisplays all the pairwise Pearson correlation coefficients between the variables listed after correlate pwcorr varlistLike correlate, but has some additional options like calculating the significance level spearman varlist Displays Spearman's rank correlation coefficients for all pairs of variables scatter varname1 varname2 Produces a scatter plot with variable 1 on the x-axis and variable 2 on the y-axis

14. Homework  Readings: Feinstein & Thomas, Ch. 4  Problem Set 3:  Finish the exercises from today’s computer class if you haven’t done so already. Include all the results and answers in the file you send me  Do exercise 3 from Feinstein & Thomas, p. 91 (using “Relief” dataset)