I worked a bit to develop a regression model for annual violent crime
data in DC. I sought to create a core model that estimated and
accounted for the basic variations in annual violent crime. Once this
was done, I added ME variables for the intervention year, 1993, plus
lagged ME variables to test if there is a continuing effect of the ME
in subsequent years.
For the core model I tested if the non-violent aka personal crimes
(PC) were significant in explaining variations in violent crimes.
Both in the current year, and also lagged variables, to see if
personal crimes affected violent crimes in subsequent years.
I hypothesized that these PCs could be correlated to strong control
variables such as weather, police on the street, LE funding -- but
which I have not yet aquired. This correlation hypothesis makes sense
in that PC variables should also go up in hot weather and down with
increased police and LE $. (And in regression, a variable that is
correlated to a "good" control (independent) variable can be an
effective replacement for the actual control variable.)
And I pulled down 30 or so national economic variables -- not ideal,
but the best I have at the moment -- no DC specific control data yet.
This national economic variables should correlate, to a degree, with
specific DC economic data, so it is a reasonable first step.
I found a five variable model with a fairly good fit (1964-2003) with
an adjusted R^2 of .89. That means the model explains 89% of the
variation in violent crime. And each variable was statistically
significant -- the t-values for each var was >2, meaning the is less
than a 5% chance that the variable's contribution is just a fluke, a
random chance effect.
Several other diagnostics were run: the durbin-watson stat was good,
showing that the model did not have undue levels of autocorrelation
(variables were not correlated to their previous values -- t-1, t-2,
etc.) And there was low correlation between independent variables,
called non-collinearity -- an important characteristic for models to
have. Another diagnostic, hetroscadasticity was mild.
So generally, for a quick model, it was fairly strong.
In the below table, for years 1990-2003 you can see the actual annual
changes in violent crime in DC in the first column, and the estimated
or predicted series from the model in the second column. As you can
see, it generally rises and falls in synch with the actual crime data.
Having a good core model as a baseline, I then added some ME
variables. First 3 variables -- one for the intervention year, and two
for subsequent years, to see if there were continuing effects. And
then another model with 5 ME variables -- for the test year and 4
subsequent years.
The ME effects were interesting. Their effect in the model was to show
about a 4% increase in violent crime from the ME in the test year, and
then a continuing decreasing crime impact of about 5% in the next 2-4
years.
The hypothesis could be that in the intervention ME year, things are
stirred up in the "collective consciousness" -- social unstressing so
to speak, and then good effects emerge in the subsequent years.
However, the significance of the ME variables was weak. There is a
20-50% chance that they are having no more impact than random chance.
It could jsut be the abortion effect we have discussed (per Levitt) or
other untested factors. Better DC specific data, more economic and
demographic and weather data, and the acquistion of monthly data
should shed light on this and determine better if there is a
significant ME effect that can be demonstrated by this type of analysis.
For now, its an interesting and thought-provoking result. Haiglin and
all may have been looking at the wrong thing -- current ME effects,
instead of where the action may really be --- future effects. That is,
ME may have its effect via long-term structural changes in collective
consciousness, not immediate ones -- which actually may be negative
(washing machine effect, perhaps).
Actual ----- Predicted ----
No ME ME 3 ME 5
1990 14.8% 12.3% 12.0% 7.8%
1991 -0.2% 1.6% 1.4% 4.4%
1992 15.5% 4.5% 4.5% 9.6%
1993 3.1% -2.0% 3.1% 4.9%
1994 -8.9% -6.2% -8.9% -10.6%
1995 0.0% 6.3% 0.0% 0.0%
1996 -7.2% -2.6% -2.6% -0.7%
1997 -18.0% -15.7% -16.0% -11.1%
1998 -15.1% -12.6% -12.6% -7.4%
1999 -5.3% -8.2% -8.1% -6.6%
2000 -7.4% -5.6% -5.6% -6.1%
2001 15.2% 8.2% 8.1% 8.0%
2002 -5.7% -0.5% -0.6% 8.2%
2003 -1.8% 0.0% -0.1% -7.5%
------
Estimated Independent Variables
I Vars Beta Std. Error t-value Sig
LAR 0.146493072 0.086854908 1.68664126 0.101712298
ROB 0.540438633 0.054472023 9.92139825 3.86814E-11
MT-2 0.118127037 0.05135057 2.300403597 0.028316925
UNEI 0.029748611 0.020410475 1.457516846 0.155030356
ME 0.041842476 0.049411284 0.846820246 0.40358555
ME-1 -0.035634855 0.050277557 -0.708762667 0.483768091
ME-2 -0.067917481 0.050595442 -1.342363634 0.189226664
ME3 -0.0532541 0.049249815 -1.081305573 0.287901536
ME4 -0.038371741 0.053936408 -0.711425589 0.48213999
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