The Cost of the Death Penalty in Maryland - Urban Institute

sample are predictable, but have not yet occurred. These costs are costs associated with
incarceration, and are described in the next section.
Cost of Prison
Many individuals in the sample were in prison at the time data were collected for this study.
Thus, individual prison costs include a retrospective component – how much time has already been
spent in prison (and how much of that time has been spent on death row) – and a forecasted
component – an estimate of how much time any individual will spend in prison until they either
complete their sentence or die. Because we cannot observe when inmates died, when they will die,
or when they will be released from prison, we estimate an expected date of exit from prison for each
inmate predicted by individual attributes (including sentence length). Past prison costs were
estimated in constant 2007 dollars. Future costs were estimated using forecasted rates of increases in
spending and were discounted at a rate of 5% per year. The prison cost estimates are based on
observed costs of prison in Maryland for both general prison populations and death row inmates.
Prison cost estimates are adjusted to account for prison and health care inflation. Methods used to
estimate lifetime prison costs for each individual in our sample can be found in Appendix A.
METHODS
Multivariate models were used to estimate the lifetime costs of cases as a function of capital
punishment as well as case characteristics. The analysis proceeded in three stages.
• In the first stage, we account for the fact that we were unable to observe data in our sample
for every case in our population of interest. In order for the cases with data to be
comparable to the cases where data are missing, we generated weights so the sample data
resemble the population of all death penalty cases between 1978 and 1999. A logistic
regression was specified in order to generate sampling weights. These weights are used in
all analyses. The explanatory power of the model (R 2 = 0.44) was high, indicating that the
econometric model is able to accurately predict whether or not cases were complete. We
found no difference in the probability that cases with a death notice had complete data.
These weights were used in all subsequent analyses.
• In the second stage, we accounted for potential differences between cases where a death
notice was filed and cases where no death notice was filed. By modeling the prosecutor’s
decision to file a death notice, we account for the possibility that cases that received a death
notice might have been more costly even if there had been no death statute. A second
logistic regression model was utilized to model the prosecutor’s decision to file a death
notice. These models yielded a propensity score – the probability that a case received a
death notice conditional on that case’s attributes – for each case in our sample. The
propensity scores were then used in outcome models to reduce any potential bias resulting
from differences in death notice and no death notice cases. Variables included in the
The Cost of the Death Penalty in Maryland
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