After my debacle (which is still ongoing) with getting the prison data, I was blown away to find this paper that had data on prostitute “transactions” and “customers” and charges and profits and such.
That right there is a rockstar dataset! How did they get it? By being adventurous, taking some risks, and venturing into brothels to interview prostitutes and pimps and get accurate data on these “tricks.” For the life of me, I can’t imagine how these people agreed to have their transactions recorded and analyzed, but the authors managed to do it and I’m blown away by the data they collected.
Some very interesting points in the paper. A prostitute is more likely to have a “transaction” with a police officer than to be arrested by him. Prostitutes rarely use condoms (25% of the time) and when they do, the price paid is not significantly less.
For me the most interesting part of the paper was estimating the “elasticity” of prostitution services. How would you go about simulating a supply or a demand shock? If I were to think about this without knowing how the paper did it, I would have guessed the enactment of some sort of law that upped penalties for prostitutes, or a sudden increase in the number of police officers on the streets. (I originally might have also guessed an STD outbreak, but this wouldn’t be just a simple supply shock – this would also affect demand and a whole host of other variables.) The method that the paper used is even beautifully more simple than these ideas – a simple spontaneous increase in the number of people around Chicago for the 4th of July.
The authors estimated that the 4th of July demand shock correlated with a 30% increase in prices, and that the supply of prostitutes was fairly elastic, as current prostitutes put in more “hours”, women who did not normally serve as prostitutes temporarily entered the business for the weekend, and prostitutes from outside the area came to Chicago to grab their share of the extra business.
What an awesome paper – cool data, innovative ways of simulating demand shocks, and interesting implications for the “business” in Chicago.