413047-Underground-Commercial-Sex-Economy

We also looked to estimates of the illegal weapons economy, another significant underground market, to
develop methods. Estimates of the illegal weapons trade, such as the Small Arms Survey, are often surveydriven,
a technique demonstrated to be invalid for our purposes. Illegal arms estimates are also derived
from identification of trading partners. This is sometimes done through weapons tracing (Bevan 2004).
Other researchers have used changes in stock prices of weapons manufacturers in certain circumstances
to identify which companies are likely to be trading illegally (DellaVigna and La Ferrara 2007). Neither of
these methods has an applicable counterpart for an investigation of the illegal sex economy.
Researchers estimating the prevalence of gun violence in Europe have employed police records (e.g.,
INTERPOL data), hospital data regarding weapons injuries, and victimization surveys. These methods are
unfortunately not applicable to sex trafficking. Law enforcement has only recently been required to report
cases of human trafficking to crime reporting databases such as UCR, and record-keeping and
understanding about what constitutes human trafficking varies across jurisdictions. Hospitalizations are
not adequate to identify all incidents that occur in conjunction with illegal sexual activity, nor would all
illegal sex acts necessarily lead to hospitalizations. National victimization surveys would miss difficult-toreach
populations that are particularly important in the underground commercial sex economy. Thus,
past estimations of illegal weapons markets present few repeatable methodologies for the purposes of our
study.
These techniques rely on bottom-up data (e.g., prevalence estimates, arrest data, surveys of law
enforcement, victim service providers, offenders, and victims) that either does not exist or are
methodologically unsound and unreliable for estimating the illegal sex economy as a whole. As a result,
we turn our attention to studies that have estimated the extent of the underground economy through a
top-down approach. Several studies have econometrically estimated the extent of unmeasured activity
(see Fleming, Roman, and Farrell 2000) by comparing, for instance, actual electricity usage with that
which would be expected given official economic data (Portes, Blitzer, and Curtis 1986). Other studies
have compared actual demand for cash with the amount to be expected in the absence of unmeasured
activity to determine the amount of cash which is devoted to unmeasured activity (e.g., Tanzi 1999). These
approaches, referred to as discrepancy approaches, serve as the foundation for our proposed
methodology.
However, though these methods can identify the extent of unmeasured economic activity, they do not
allow us to attribute various portions of this underground economy to different types of unmeasured
activity (e.g., the drug market, the illegal sex market, or even household labor). Thus, they cannot usefully
estimate the size of any specific component, such as the illicit sex economy. Our methodology extends
these techniques to overcome this limitation by using known variations in economic activity over time and
across space to triangulate estimates of sub-economies.
In this study, we combine elements of top-down and bottom-up approaches. Bottom-up approaches offer
important detail about underground activity. Unfortunately, to generate an estimate of the total financial
size of an activity, this detailed information (e.g., revenue, profits, etc.) must be combined with estimates
of prevalence (e.g., the total number of victims). In the absence of prevalence estimates, it is impossible to
estimate the size of the illicit sex economy from bottom-up information alone. Top-down approaches do
not rely on prevalence estimates. However, they are generally unable to separate underground activity
into sub-components. In the following sections, we describe our approach to use detailed information (as
in bottom-up studies) to help disentangle top-down estimates of underground activity.
Process for Deriving Estimates (Overview)
In section 1.1, we suggest a fundamental “Conservation law” governing the expenditure of cash within
dynamically stable systems over finite time intervals. In the context of our study, this conservation law
implies a set of 16 simultaneous linear equations relating the values of 80 variables, of which 16 relate to
cash-based UCSE in eight cities across two time intervals; the remaining 64 refer to metrics for cashbased
economies of drugs, guns, and “other” 37 (non UCSE, drugs, or gun related) expenditures. Because
37 Other illicit economies that the estimate could include are selling of fake goods, money laundering, and organ sales.
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