413047-Underground-Commercial-Sex-Economy

in column 3 of the table below. The coefficient obtained for coordinate 9 is the value
represents the estimated distance decay exponent of the attraction force.
Table 3.6 Proxy for Pimp Population Size
, which
City
Atlanta 6.254 1.192
Dallas 6.079 1.108
Denver 3.379 0.156
Miami 8.049 1.785
San Diego 5.811 0.975
Seattle 5.463 0.805
DC 5.861 1.000
Kansas City N/A N/A
By including the distance between a UCSE participant’s home city and other candidate cities in the form
of the “force of attraction,” the Gravity Model yields a proxy for pimp population size which deftly
sidesteps potential biases that may arise from assuming that the UCSE participants are blind to distance
(as was indeed the case for the initial proxy ). In what follows, we use this as our proxy for pimp
population size, and then combine with the mean weekly gross cash intake to produce
. The computed final values of the sex proxy are given in the table
below. Note that these proxy values are without units and have no inherent interpretation.
Table 3.7 Final Values of Proxy
Year Atlanta Dallas Denver Miami San Diego Seattle DC
2003 30,835 12,877 6,117 39,141 16,090 5,433 16,700
2007 39,128 13,320 4,852 31,666 10,852 14,489 11,588
1.2.2. Linear Proxy
Originally, we envisioned using a single variable as a proxy for cross-city and over-time drug market
differences. In reading the relevant literature, however, we developed a list of potential proxies. Rather
than choosing one, we used regressions to aggregate across different variables.
Let be the value of the proxy variable for drug activity, measured in city at time . For this
analysis, we used, , , and . That is, Kansas City is excluded.
Let be the standard error of the estimate of the proxy for city at time . To aggregate across the 7
different drug proxy variables, we relied on a simple linear regression. While this imposes a lot of
structure on the proxy information, we believe that it eliminates some of the concerns about noisy
measurement. Our plan was to use a single regression for each city, with all proxies modeled as a linear
function of time. The problem is that the different proxy variables are in different units, so a linear
regression cannot properly combine them.
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