413047-Underground-Commercial-Sex-Economy

To account for this, we normalized all variables by the city-year with the most complete data: Miami in
2003 (for two of the seven proxies, we used Miami in 2004 instead). Thus, we define:
and
Then, we were able to estimate a simple linear model to estimate the time trend for the drug market
(based on time trends in each variable) and a relationship between the sizes of each city (based on the
relative level of all proxy variables), which accounts for missing data, imprecision in estimated proxy
values, and units that differ across different variables. Specifically, we estimated:
Each observation was weighted by
. Note that we allow for a different time trend for each city, and that
no term is necessary to adjust for different values of because they were all normalized by the Miami,
2003 value. Also, note that (other than discounting less accurately estimated proxies), we give each proxy
variable equal weight.
Based on these regression results, we can calculate
as:
The computed values of the drugs proxy
Table 3.8 Final Values of Proxy D*
are given in the table below.
Year Atlanta Dallas Denver Miami San Diego Seattle DC
2003 1.038 1.334 0.544 0.931 1.041 0.870 1.099
2007 1.153 1.879 0.630 0.944 0.949 0.862 1.007
1.2.3. Linear Proxy
We followed the exact same procedure to estimate with the exception of normalizing by Miami, 2007,
instead of Miami, 2003, because it had more complete data. Also, we had fewer weapons proxies, with
. The computed values of the guns proxy are given in the table below.
Table 3.9 Final Values of Proxy W*
Year Atlanta Dallas Denver Miami San Diego Seattle DC
2003 1.293 1.307 0.446 0.807 0.356 0.628 1.135
2007 1.123 1.317 0.362 0.913 0.368 0.459 1.221
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