Determinants of state tobacco-control expenditures

Abstract

While Centers for Disease Control and Prevention believe that most state governments under-fund tobacco-control programs, little is known about why large variation in spending exists between state governments. This study explores reasons for spending variation through an econometric model of per capita spending on tobacco-control programs that explores the effects of smoking prevalence while holding constant tobacco settlement funds, state budget deficits, and other factors that might also be expected to influence spending variation. Empirical evidence indicates no support for the hypothesis that states with high smoking prevalence spend more on tobacco-control than other states. This finding may be quite surprising to those working in areas of public health and clearly leads to many important policy questions regarding why the data indicate that funding does not appear to bear any relation to perceived public health problems as would be predicted if policymakers were following a ‘rational needs’ approach to funding.

Acknowledgement

William Orzechowski, Alden Shiers, Takis Papakyriazis and an anonymous referee provided many helpful comments.

Notes

¹ CDC (1999). Arizona and Massachusetts did not provide data for 2002 because their budgets had not been finalized at the time the CDC's publication went to press.

² Best Practices spending is estimated based on nine program elements: community programs to reduce tobacco use; chronic disease programs to reduce the burden of tobacco-related diseases; school programs; enforcement; statewide programs; counter-marketing; cessation programs; surveillance and evaluation; and administration and management.

³ See, for example, Hu et al. (1995a, b, Farrelly et al. (2003) and Tauras et al. (2005).

⁴ CDC (2001) and CDC (2002).

⁵ GAO (2004).

⁶ See e.g. Gross et al. (2002), Johnson (2004) and Sloan et al. (2005).

⁷ CDC (2002).

⁸ Orzechowski and Walker (2004).

⁹ Marlow and Shiers (1999) discuss this issue when they examine whether higher public funding of crime-related programs leads to changes in public education funding.

¹⁰ Funding also comes from federal and private sources. Federal funding comes from CDC's Office on Smoking and Health that manages the National Tobacco Control Program and the Health and Human Service's (HHS) Substance Abuser and Mental Health Services Administration (SAMHSA); see CDC (2002).

¹¹ CDC (2002) notes limitations to its data collection. Reported amounts exclude appropriations for multiple purposes that included an unspecified amount of funding for tobacco control. State spending data are based on appropriations, rather than expenditures, and expenditures may differ from appropriated amounts, because of delays in implementation, program cuts, or establishment of trusts or endowments.

¹² As Pindyck and Rubinfeld (1991, p. 88) state: ‘While stepwise regression can be useful in looking at data when there are a large number of possible explanatory variables, it is of little value when one is attempting to analyze a model statistically. The reason is that t and F-tests consider the test of a null hypothesis under the assumption that the model is correctly specified.’

¹³ An economically efficient allocation does not necessarily mean that states with high prevalence should spend more on control if, for instance, programs are not equally effective or when states have heterogeneous policy objectives. Differences in demographics across states might also lead to spending differences across states under a ‘rational needs’ approach. For example, Yen (2005) finds that smoking falls with education, but older smokers consume more cigarettes than younger smokers, in the USA. Goel and Nelson (2005) find significant differences in tobacco consumption across age and gender as well in the USA. Following the Bask and Melkersson (2004) argument that alcohol and cigarette consumption are simultaneous decisions, tobacco-control policy should perhaps also be influenced by alcohol consumption in states. Such differences might suggest, for instance, that state spending should differ according to age, gender and other characteristics of the population, even when states have similar overall smoking prevalence.

¹⁴ See Hu et al.(1995a, b), Farrelly et al. (2003) and Tauras et al. (2005).

¹⁵ CDC (2001) and CDC (2002).

¹⁶ See CDC (2004).