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The effects of the Proposed $2 tobacco tax on cigarettes

Institution: University of California, San Francisco
Investigator(s): James Milton Lightwood, Ph.D.
Award Cycle: 2015 (Cycle 24) Grant #: 24ST-0052 Award: $49,917
Subject Area: Public Health, Public Policy, and Economics
Award Type: Special Projects

Initial Award Abstract
In 2010-2011, the investigator developed several statistical relationships that predicted smoking behavior, cigarette tax receipts, and health care expenditures due to cigarette smoking in California. These relationships were used to forecast the effects of a proposed one dollar increase in the cigarette tax in California over the years 2012 to 2016. This research will update those forecasts in order to model the effect of a proposed two dollar increase in the California cigarette tax, to be imposed in 2017, on smoking behavior, cigarette tax receipts, and health care expenditures for the years 2017 to 2020. The statistical relationships that were estimated for the previous research still predict California smoking behavior and health care expenditures relatively well. Therefore, we will calculate the forecasts using the previously estimated statistical relationships. The method used to estimate smoking prevalence has changed since the previous research, though recent evidence indicates that this change in measurement method affects the level of the estimated prevalence, but not in the changes of prevalence over time. Therefore, the formal forecast will be for changes in prevalence due to the proposed increase in the cigarette tax. Also, the state specific measure of health care expenditure that is of most interest to policy analysts and required for the proposed forecasts, produced by the Center for Medicare and Medicaid (CMS) has not been updated since 2009, and therefore we cannot forecast that measure of health care expenditure. We can forecast a closely related measure produced by the Bureau of Economic Analysis (BEA). The CMS and BEA measures have long had a very strong and stable statistical relationship, therefore we will forecast the BEA measure of health care expenditure and use that to forecast the CMS measure. We will carefully analyze the changes in each time series in order to produce accurate forecasts of changes attributable to changes in the California cigarette tax. A methodologically sound approach is to adapt the model to forecast changes in the outcome variables (such as smoking behavior) associated with a tax increase, and do a careful statistical analysis of the data to arrive at the best forecast of the levels of the outcome variables.