Putting out fires: an examination of the determinants of state clean indoor-air laws.

• Author: Gallet, Craig A.

1. Introduction

   Few products have generated as much public scrutiny as tobacco. In light of the health consequences associated with smoking, the federal government took several steps in the 1960s (such as published health warnings and restrictions on cigarette advertising) to reduce the incidence of smoking. Beginning in the 1970s, state governments began to play an increasing role in the anti-smoking campaign by adopting clean indoor-air laws, which restricted smoking in particular locations, such as restaurants, bars, and work sites. (1)

   Various studies, including Wasserman et al. (1991), Chaloupka (1992), Chaloupka and Saffer (1992), and Yurekli and Zhang (2000), generally confirmed that clean indoor-air laws are effective at reducing cigarette demand. However, the nature of the smoking restriction appears to matter. In particular, Chaloupka (1992) and Chaloupka and Saffer (1992) found that restrictions on smoking in public places reduced cigarette demand, whereas restrictions at private work sites do not significantly reduce demand. (2)

   Due to variations in antismoking laws across states, only a few studies have addressed the demand for clean indoor-air laws. In a survey of individuals from San Luis Obispo, California, for example, Boyes and Marlow (1996) found nonsmokers and women to be more supportive of a smoking ban in restaurants and bars. Alternatively, Chaloupka and Saffer (1992) used state-level data and found that differences in smoking restrictions in public places and at private work sites depend on several factors, including cigarette prices, income, tobacco production, religious affiliation, and political activity. More recently, Hersch, Del Rossi, and Viscusi (2004) found that voting preferences of state residents and the political affiliation of lawmakers were the main factors that affect whether or not a restriction is adopted at a specific location (for example, restaurants, bars, malls, enclosed arenas, and hospitals). However, smoking habits could have an effect on voting preferences; therefore, these results could show the indirect effect of tobacco consumption on smoking restrictions.

   There are several limitations of the existing literature that lead to a need for further analysis. First, studies have ignored the potential role of cigarette taxation as a determinant of smoking bans. For example, if states adopt a general antismoking position, taxes could be used in conjunction with smoking bans to reduce tobacco consumption. Thus, depending on the argument, states may view taxation as a policy substitute or a policy complement to clean indoor-air laws. Second, the potential endogeneity of right-hand-side variables has not been addressed. For example, tobacco consumption is often included as a determinant of smoking bans; however, tobacco consumption may potentially be endogenous. In general, studies have found that restrictions on smoking in public places reduce cigarette demand, which implies that cigarette consumption is potentially endogenous in the reverse regression of smoking on restrictions. It is unfortunate that the literature has not addressed this possibility in a systematic manner. (3) Third, except for Hersch, Del Rossi, and Viscusi (2004), studies have failed to account for differences in the demand for clean indoor-air laws across different restricted locations.

   This study addresses these limitations by estimating the demand for clean indoor-air laws using a panel of state-level data. By using a panel data set, we were able to control for state-specific effects that made it more or less likely that a given state had a smoking ban in a particular area. Specifically, we accounted for the potential endogeneity of cigarette consumption and taxation within a random-effects Probit model of the adoption of smoking restrictions across six locations--public places, government buildings, private work sites, schools, health care facilities, and restaurants. Briefly, we found statistical differences across locations; however, the results revealed that the probability of a smoking ban being adopted tends to be lower in states with higher per capita cigarette consumption, more politically conservative lawmakers, higher metropolitan populations, lower per capita income, and higher tobacco production. However, the effect of cigarette taxes on the probability that a state will adopt a smoking ban depends on the handling of the endogeneity of cigarette consumption and cigarette taxes. For example, by treating cigarette consumption and cigarette taxes as exogenous, we found that taxes complement smoking restrictions. However, when endogeneity is accounted for, the role of cigarette taxes shifts toward being a policy substitute.

   Section 2 of this paper outlines the empirical procedures. Section 3 discusses our estimation results. Concluding remarks are provided in section 4.

2. Empirical Specification and Econometric Issues

   Empirical Specification

   Our model addressed the factors that affect the probability that a state will adopt a clean indoor-air law. We obtained State Legislated Actions on Tobacco Issues (SLATI) data from the American Lung Association, which provide information about when a state has adopted a smoking ban (i.e., equals one if smoking ban is adopted, zero if not) in six particular locations (public places, government buildings, private work sites, schools, health care facilities, and restaurants) for 1980 through 2000. (4) These dichotomous variables were then treated as dependent variables in a series of Probit regressions. (5)

   We hypothesized that the probability that a state will adopt a smoking ban will depend on several factors. First, we included per capita cigarette consumption as a regressor. Depending on the argument, however, cigarette consumption may increase or decrease the probability of a smoking ban being adopted. For example, it may be that higher smoking rates induce a state to adopt a smoking ban in order to reduce the incidence of smoking. Alternatively, higher smoking rates may signal greater political clout of smokers, who are less likely to favor smoking bans. (6)

   Second, state-level tax rates on cigarettes are included to control for possible substitutability or complementarity between taxes and smoking bans. It may be, for example, that if states adopt a general anti-smoking position, taxes could be used in conjunction with smoking bans to reduce tobacco consumption. In this case, higher tax rates will correlate with a greater probability of adopting a smoking ban. Alternatively, it may be that states are particularly keen on raising tax revenue and view taxes as competing with smoking bans. Therefore, if higher tax rates are adopted in an effort to raise tax revenue, then states will be less likely to adopt smoking bans, which reduce demand and tax revenue. In anticipation of the estimation methods, therefore, it seems reasonable to consider cigarette taxes and the chance of adopting smoking restrictions as being simultaneously affected by certain characteristics of states.

   Third, political pressure likely influences whether a state adopts a smoking ban. For example, Hersch, Del Rossi, and Viscusi (2004) found more politically conservative states are less likely to adopt a smoking ban, which they suggest may be tied to a lack of support for government involvement in markets. (7) In an effort to measure this, we included an index of conservatism in our model. This measure is an annual index of the voting pattern of every member of Congress regarding 20 key issues chosen by the American Conservative Union (ACU). Each member of Congress is awarded five points for each vote deemed "conservative" by the ACU; therefore scores can range from 0 to 100. A score of 100 would mean that this member of Congress had a perfect conservative voting record for that year. We derived a state-level annual score for a given year from the average of all members of Congress from that state.

   Fourth, we included the percentage of the state's population living in a metropolitan area. This variable accounted for several possible scenarios. For instance, if secondhand smoke is particularly a nuisance in highly populated areas, then a higher share of the population living in metropolitan areas may lead to greater pressure to adopt smoking bans. Alternatively, since metropolitan areas have more businesses (e.g., restaurants, bars, shopping centers) for which smoking bans could be detrimental, political pressure may be against the adoption of smoking bans. (8)

   Fifth, per capita income is included in our specification. Some studies (for example, those by Cremieux, Ouellette, and Pilon [1999] and Hitiris and Posnett [1992]), have found that income is positively correlated with health outcomes. Because of these findings, we expect states with higher per capita income to be more likely to take an anti-smoking position, and therefore have a higher probability of adopting a smoking ban.

   Lastly, Chaloupka and Saffer (1992) and Hersch, Del Rossi, and Viscusi (2004) included measures of the importance of tobacco production to states because states that are heavy producers of tobacco are expected to be less supportive of smoking bans. Consequently, we included the value of tobacco production as a percentage of...