In November, 1996, California voters approved Proposition 215, which legalized the use of marijuana for medical purposes. Campaigns for and against the Proposition focused largely on the medical need for and effectiveness of marijuana. Although the debate emphasized cancer, AIDS, and spinal cord injuries, the Proposition’s language included virtually all chronic conditions, thereby effectively decriminalizing most marijuana use (Vitiello, 1998). Cultural values played a smaller role in the campaign. Opponents argued that legalization would “send the wrong message” to youth, for example, and this fear was realized to some extent. Public opinion surveys conducted before and after the election found increased acceptance of marijuana by Californians but no change in actual use (Khatapoush and Hallfors, 2004). The potential collateral consequences of legalization played no role in the public debate over Proposition 215. Would easier access to marijuana lead to an increase in driving under the influence, for example, and if so, would the higher prevalence of driving under the influence lead to an increase in traffic fatalities?
Public Safety Realignment
In May 2011, the Supreme Court affirmed a 9th Circuit Court ruling that ordered the state to reduce its prison population. Although California’s prison population fell dramatically post-AB109, critics argued that releasing prisoners would lead to an increase in property crime. To test this hypothesis, Lofstrom and Raphael (2013) compared post-AB109 crime in California with crime in a matched synthetic control group of states (Abadie, Diamond and Hainmueller, 2010). They found an effect in Part I Auto Theft but no other crime. In a replication of the Lofstrom-Raphael synthetic control group analysis, we find no effect on any crime. We attribute this difference to two factors. First, the pre-intervention matched synthetic control group used by Lofstrom and Raphael was less than ideal. Second, due to the passage of time, we were able to construct a longer, better match control group.
Computationally intensive permutation testing
Statistical tests used in Time-series intervention models assume a long time-series of white-noise observations. Since these assumptions are seldom warranted in Synthetic Control Group designs, conventional significance tests are not available. In light of this obstacle, the exact significance of the post-intervention difference between a treated time series and its synthetic control must be calculated from a permutation test model.
Continue Reading Computationally intensive permutation testing
The Department of State Hospitals
The Department of State Hospitals (DSH) manages the California state hospital system, which provides mental health services to patients admitted into DSH facilities. The department strives to provide effective treatment in a safe environment and in a fiscally responsible manner.
In an effort to help the department achieve their goals, ISML is working with DSH to develop and provide an effective organizational management tool that allows for the accurate prediction/estimation of hospital bed capacities based on patient length of stay times.
Los Angeles County
In 2014 we were contacted by the Los Angeles Sheriff’s Department (LASD) Custody Division, and asked to build a DES model that could project their jail population three years into the future. In April, 2015 we reported preliminary projections based on a March 1, 2015 starting date. From there, we worked with LASD to build a more sophisticated model and were able to update it in time in order to make better predictions based on an October 1, 2015 starting date. Not only did our projections improve with the availability of more information on how Proposition 47 affected the jail system, but they were also improved by knowledge we gained from LASD officials.