The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

7 LIS participants who were transferred back to standard supervision as a result <strong>of</strong> a violation were
analyzed as randomly assigned.
8 <strong>The</strong>re was a transitional period after random assignment during which some treatment group participants
were still attending appointments that were scheduled before random assignment. In addition, one <strong>of</strong> the
low-intensity probation <strong>of</strong>ficers was somewhat resistant to the idea <strong>of</strong> reducing supervision and continued
to schedule monthly visits. This was discovered about two months into the experiment, and with further
training the <strong>of</strong>ficer began to schedule visits according to the protocol.
9 <strong>The</strong> ‘power few,’ as described by popular author Malcolm Gladwell (see Sherman, 2007) is a
phenomenon found throughout social research. It is the small fraction <strong>of</strong> a population to which a
disproportionate amount <strong>of</strong> a certain resource or condition may be attributed. In criminological research it
is <strong>of</strong>ten noted that a small proportion <strong>of</strong> <strong>of</strong>fenders or places produce a substantial amount <strong>of</strong> the total crime
(e.g., Sherman, Gartin, & Buerger, 1989; Weisburd et al, 2004). Within a probation agency, a small
proportion <strong>of</strong> probationers are at the greatest risk for committing most <strong>of</strong> the serious <strong>of</strong>fending among the
agency’s clients (see Fig. 2.1).
10 Regular Poisson or negative binomial models may underpredict zeros and overpredict larger numbers,
which is problematic when the majority <strong>of</strong> the data are zeros.
11 Problems in the coding <strong>of</strong> the race variable in our dataset forced us to use this dichotomy rather than a
more detailed categorical variable for race. <strong>The</strong> race indicator variable was populated with data from two
different sources, with one source selected as the default. However, serious discrepancies arose because
the categories <strong>of</strong> race in the two original sources were substantially different.
12 Information about SES at the individual <strong>of</strong>fender level was not available. However, 2000 Census data
were obtained for each <strong>of</strong>fender’s recorded zip code. We used the Census measure <strong>of</strong> average household
income for the <strong>of</strong>fender’s zip code as an estimate <strong>of</strong> SES. This was coded as a categorical variable with
four levels: less than $20,000 (used as the reference category in our models); $20,000-$29,999; $30,000-
$39,999; and $40,000 or more.
13 We recognize that the jail time variables included in our models may be endogenous; that is, the effect <strong>of</strong>
jail time on the odds <strong>of</strong> recidivism may in fact represent a causal effect <strong>of</strong> the recidivism outcome on the
jail variable. We are unable to separate post-random assignment jail time resulting from pre- and post-RA
<strong>of</strong>fending. Thus, while we present the models with jail time controls included, we also ran each model
without those variables and include the results in Appendix F. Appendix F shows that the inclusion <strong>of</strong> the
terms did not substantially bias our findings.
14 Our only data on the timing <strong>of</strong> jail stays are contained in monthly dummies for whether or not the
<strong>of</strong>fender was in jail in that month. A further limitation is that these variables are only available for the first
year post-random assignment. Thus, while we control for post-RA time at risk as far as possible, it is
important to remember in the analysis that the second year <strong>of</strong> follow-up data is analyzed as if none <strong>of</strong> the
sample spent time in jail. While this does not greatly affect the participation-based outcome measures, it
does mean that our post-random assignment <strong>of</strong>fending frequency estimates may be overstated and the
survival analysis models overstate the number <strong>of</strong> person-days at risk (some <strong>of</strong>fenders who would have been
incarcerated in the second year are treated as if they had a nonzero probability <strong>of</strong> <strong>of</strong>fending for the entire
year).
15 Where these <strong>of</strong>fense-specific outcomes are used, the covariate for monthly pre-RA <strong>of</strong>fending rate used in
our models is also based on these specific <strong>of</strong>fense types, rather than all <strong>of</strong>fending.
16 On the other hand, given the possibility <strong>of</strong> endogeneity, it could also suggest that <strong>of</strong>fenders who commit
more than one <strong>of</strong>fense post-random assignment spend more time in jail.
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