WHY UNDERSTANDING SMOKING BANS IS IMPORTANT FOR ESTIMATING THEIR EFFECTS: CALIFORNIA'S RESTAURANT SMOKING BANS AND RESTAURANT SALES

I. INTRODUCTION A voluminous literature has sought to quantify the effect of smoking bans, typically on the quantity of smoking, but also on the performance of affected businesses. (1) Various time periods have been explored, types of bans considered, locales examined, and specifications employed. Nonetheless, the majority of studies share two common failings. First, smoking bans of a given type (e.g., a restaurant ban) are treated as homogenous when, in fact, such bans have been enacted at various jurisdictional levels (municipality, county, or state), at various times, in various orders, and in various combinations (with other types of smoking bans and with similar bans at other jurisdictional levels). Second, empirical specifications do not adequately control for preexisting trends in the dependent variable. As a result, many studies arrive at fundamentally wrong conclusions. We illustrate these points in an investigation of how California's restaurant smoking bans have affected restaurant sales. We analyze the effect of smoking bans on restaurant sales for two reasons. First, the issue plays a role in the policy debate--more than 150 studies (many published in refereed journals and others in consulting reports undertaken for municipalities) have explored whether smoking bans help or hinder restaurants (Scollo and Lal 2005). Yet, few of these studies properly account for the effects of preexisting trends (or changes in trends), for the closely related question of ban endogeneity or for ban heterogeneity. Second, restaurant sales data are very rich: well measured, available by municipality, by quarter, and over an extended period. Therefore, we can identify effects that would be obscured by cruder data (e.g., periodic survey responses), enabling us to draw lessons applicable to studies of the effect of other types of smoking bans (e.g., workplace bans) in other dimensions (e.g., smoking prevalence). We investigate 267 California municipalities for which the California State Board of Equalization provides quarterly taxable restaurant sales. (2) We begin by employing "naive" specifications (which treat bans as homogenous and control simplistically for trends) representative of those typically estimated in the literature. The results of our naive specifications indicate that smoking bans have a large negative effect on restaurant revenues. However, when we examine municipal and state bans separately, we find a positive association between municipal bans and restaurant sales and a negative association between the state ban and restaurant sales. Finally, when we control properly for preexisting trends in the dependent variable, we find no substantial evidence of any causal relationship running from either municipal bans or the state ban to restaurant sales. Thus, we reach two main conclusions. The first is that California's municipal restaurant smoking bans are endogenous in a critical way--restaurant sales growth (or something correlated with restaurant sales growth) appears to cause restaurant bans, not vice versa. The second is that because communities are heterogenous, one must analyze the effect of a ban taking into account the level at which the ban was enacted. Pooling data and treating "smoking bans" as homogenous (as many studies do) is inappropriate--cities that impose bans on themselves are likely to differ systematically from those that have bans forced on them by a higher jurisdiction (county or state). Relatedly, analyzing and/or instrumenting for smoking bans using state-level data (again, as many studies do) may fail to capture the relevant variation when the bans have force at a lower level of jurisdiction (e.g., a municipal ban). Our analysis, although focused on restaurant bans and sales, has implications for the more extensive literature on how smoking bans influence smoking behavior. Most importantly, it demonstrates the crucial nature of identifying and controlling for trends in the dependent variable (whether restaurant revenue, as in our data set, or smoking rates). …