413047-Underground-Commercial-Sex-Economy

Table 2.2 Proxy Variables
Dataset
Variables
Illicit Drugs
NSDUH D1: Illicit drug use in the past month among population age 12+
D2: Illicit drug use (other than marijuana) in past month among population age 12+
D3: Cocaine use in past year among population age 12+
YRBSS
D4: Percent of high school students offered, sold, or given drugs at school
multiplied by total MSA population
DAWN
D5: Emergency room visits attributed to drug use
D6: Emergency room visits at which drug use was mentioned
ADAM
D7: Percent of property crime arrestees who tested positive for any drug, scaled by
MSA population
Weapons
YRBSS
W1: Percent of high school students who carried a gun multiplied by total MSA
population
ATF
W2: Number of weapons seized by the ATF
NVSS
W3: Fraction of suicides committed with firearms, multiplied by MSA population
Other
N/A
O1: Percent of employment in private-sector service industry, scaled by MSA
population
O2: Percent of employment in construction, scaled by MSA population
O3: Percent of employment in food services and drinking establishments, scaled by
MSA population
Limitations of the Study
As with all research, this study is subject to methodological limitations. As discussed previously, our
initial objective was to adopt methods that had previously been used to estimate the size of illicit
economic activity. After reviewing some of these techniques, we determined that none were suitable for
our purposes, and sought to develop a new method that could overcome these challenges. As a result,
there are a number of limitations to the method developed. Primarily, these limitations center on the data
used for the approach. The theory in itself (building a system of equations and solving the system using
ratios implied by proxy variables) is complete, concise, and accurate. Given our definitions of variables,
the equality is a tautology and, given the right input data, the method is guaranteed to produce accurate
answers. The challenge is finding the right data. Identifying proxy variables is not an exact science, and
there is often little or no past literature on which to base our choices. Assumptions and approximations
must be made to estimate such ratios, and variable selection, imprecise measurement, and data
availability all presented significant challenges.
We believe that this method holds promise for future research seeking to estimate illicit economic
activities in multiple geographic entities. However, such research must further explore what proxies
should be used, their reliability, and necessary adjustments. Here, we offer a foundation for such
considerations based on our own analyses and assumptions, but other researchers might have different
preferences and assumptions.
Similarly, we were surprised by the difficulty involved in estimating currency in circulation at the city
level. While this quantity is the focus of significant attention at the national level, almost no work seeks to
estimate it locally. In the absence of a literature upon which to draw, our estimates are based on what we
believed were relatively conservative assumptions. We sought to employ the simplest possible approach,
acknowledging that estimating currency is of central importance and erring in favor of transparency over
sophistication.
In addition to the limitations with the proxy data used to estimate the UCSE in the eight study sites, there
were limitations to how the offender sample was chosen. We were only able to document the business
practices, networks and technology use of traffickers and child pornographers who were incarcerated at
the time of data collection. Thus, we were limited to speaking to those individuals that were convicted of
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