413047-Underground-Commercial-Sex-Economy

This approach requires us to develop a strategy to estimate
, the mean weekly gross cash intake
per pimp (by city and time) and , a linear proxy for pimp population size (by city). These matters
are addressed in sections 1.2.1.1. Estimating the Mean Weekly Gross Cash Intake per Pimp and 1.2.1.2.
Estimating the Relative Sizes of Pimp Populations, respectively.
1.2.1.1. Estimating the Mean Weekly Gross Cash Intake per Pimp
To estimate the mean weekly gross cash intake per pimp (by city and time), we conducted interviews with
convicted pimps and sex workers (see chapter 2 for more details). Of the many outcomes of this process,
the one that is most relevant to this analysis is a corpus of n = 109 subject (pimps and sex workers)
interviews, each quantifying daily and weekly income, customer volume, and years their business
operated.
The process of determining weekly pimp revenue in each city was carried out as follows. Following the
survey instrument, all pimps were asked to directly estimate their weekly revenue. As a secondary
validation of this direct estimate, a bottom-up estimate was also determined. Toward the latter, pimps
were asked to estimate the number of sex workers they employed at any given time, as well as the average
weekly revenue brought in by an average sex worker while in their employment. In cases where the pimp
reported a range of revenues for “average” versus “exceptional” sex workers, the lower figure was used. In
cases where the pimp reported separate weekday versus weekend revenue figures, the two were combined
to produce a weekly revenue estimate using the formula:
(3* weekday revenue+ 2* weekend daily revenue) * number of sex workers employed
If the direct estimate was found to diverge from the bottom-up estimate by more than 25 percent, the
lower of the two figures was used. For pimps who were able to provide sufficient data for only one form of
estimation, that value (direct or bottom-up) was used. Pimps who could provide data for neither form of
estimate were excluded from the process of pimp revenue calculation.
A. Splitting Time and the Selection of
Each interviewed subject was questioned about the interval of calendar year(s) in which they were active.
In this part of the analysis, we aggregated this interval to the unique median year within it. So, for
example, if a pimp or sex worker said they were active from 1990–1996, we assigned the pimp or sex
worker the year 1993. In addition, of course, each pimp was asked to identify which of the cities
they operated from.
In applying the interview data to the construction of cross-time proxy ratio constraints, our strategy is to
decide on a splitting year T, and then take and , for suitable choice of . Note
that each potential choice of splitting year T partitions the set of interviewed subjects (pimps) into two
sets: (i) subjects whose median year of operation lies on or before T, and (ii) subjects whose median year
lies after T. The “evenness” of this split (with consideration to both number of subjects and cities of
operation represented) is of great importance, because it determines the number of cross-city and crosstime
sex proxy ( ) ratios that can be generated from the interview data. Clearly we want to choose a
splitting year T that will maximize the evenness of this partitioning, thus maximizing the number of
computable ratios. The table below provides the number of sex proxy ratios generated for each candidate
choice of splitting year.
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