Smoking Bans and Employment

Transcription

1 Smoking Bans and Employment Statewide Analysis Jakob Peterson and Michael Shea ECO 490B Economics of Vice Metropolitan State College of Denver 4/25/2012 The legislation of smoking bans is ever increasing. With more knowledge and information about the health risks and unintended consequences associated with smoking, more states and counties are quick to pass these bans. In our research, we used employment state data of five states to determine the effect of smoking bans on employment. We find that the effect of smoking bans remains unclear, but income and population are significant factors in employment patterns in the restaurant industry. Further speculation into a larger portion of state data may show more variation of employment upon the implementation of a statewide smoking ban. JEL Classification: I18, J20, J81, K32 Keywords: employment, state government regulation, smoking bans

2 I. Introduction Smoking cigarettes has the negative externality of second hand smoke, which can affect all people. By banning smoking in the food and drink industries, governments can eliminate that externality in bars and restaurants causing an improvement in public health. The working conditions may be more desirable for employees to be in, as a result of the smoking ban. In our paper, we examine the effect smoking bans have on employment in the restaurant and bar industry. It is easy to assume that after the initiation of a smoking ban, restaurant and bar revenues will be affected. In the presence of imperfect information or externalities, the implementation of regulations may lead to even greater profits for firms (Adams and Cotti 2007). Many studies have been completed on the effect of restaurant and bar revenue from smoking bans. Using data for 5 states we look at the effect of these smoking bans on monthly employment in the restaurant and bar industries. We do not find these significant for the effect on smoking bans, but find that population growth and income has a large impact in the restaurant industry. We find a negative trend in employment over time in the bar industry. Our analysis brings doubt to other studies that prove that smoking bans affect employment in restaurants and bars. The structure of our paper is as follows. Section II reviews the literature and provides the economic background. Section III provides the empirical strategy and model for regression analysis with fixed effects. Section IV describes the employment data. Section V shows evidence of our findings and provides analysis. The final section VI concludes. 1

3 II. Literature Review Empirical rational addiction is the emphasis in Becker, Grossman, and Murphy (1990), that indicates smoking is addictive. They develop a theoretical model that past consumption of some goods influences their current consumption by affecting the marginal utility of current and future consumption (Becker, Grossman and Murphy 1990). The effect of addiction on the response of cigarette consumption to a change in cigarette prices is tested in the Becker-Murphy model through a bimodal distribution of consumption, quitting cold turkey, a negative or complementarity cross effect between the price of the good at one time and its consumption at another time, larger long-run than short-run elasticity s of demand, larger responses to anticipated than unanticipated price changes, and larger responses to permanent than temporary price changes (Becker, Grossman and Murphy 1990). The cigarette industry maintains a monopoly power controlling the pricing policy that affects the addictive behavior on aggregate cigarette consumption (Becker, Grossman and Murphy 1990). The study of smoking bans conducted on Germany s restaurant and bar industry by Silke, Kvasnicka, and Siedler (2011) suggest that smoking bans can be an effective tobacco control policy and they can provide important health benefits beyond reducing the exposure of non-smokers to second-hand smoke, which is the immediate and prime objective. Increasing taxes on cigarettes reduces average exposure to cigarette smoke of nonsmokers (Adda and Cornaglia 2010). However, it is possible for smoking regulations to have perverse effects on nonsmokers with the implementation of smoking bans across time and different US states affecting young children and those 2

4 living with smokers (Adda and Cornaglia 2010). The timing of the state smoking bans may be related to specific state characteristics that control for state fixed effects (Anger, Kvasnicka and Siedler 2011). The unique aspect of Silke, Kvasnicka, and Siedler s (2011) paper is the use of detailed information on individual propensities to go to bars and restaurants to study the existence and magnitude of such potential heterogeneous effects. Evidence of short-term effects for individuals who tend to go out to bars, restaurants, and cafes more frequently, such as the young, unmarried, and city dwellers can be explained by the greater exposure of these individuals, to the constraints of public smoking bans (Anger, Kvasnicka and Siedler 2011). Public smoking bans/restrictions limit smokers opportunities to smoke, which raises the costs of smoking, such as going outside to smoke, which can reduce the perceived benefits of this behavior as social camaraderie (Albers, et al. 2007). Marlow (2010) addresses policy implication that underestimates the harm done to establishments as a result of not complying with smoking bans. Ohio s smoking ban was in effect May 2007 and at the end of 2009, over 21,000 citations for violating the smoking ban were issued to 4,422 restaurants and bars, and another 11,000 citations were issued to 1,190 veterans organizations, fraternal organization, and private clubs (Marlow 2010). Bars suffer more harm from bans than restaurants because bars provide a more social atmosphere where customers enjoy mingling with one another (Marlow 2010). Actual and perceived costs were formulated through surveys in Quebec and determined to have little to no effect on restaurants and firms (Pierre-Yves and Ouellette 2001). Firms will experience no additional direct costs either because they 3

5 already comply with the proposed legislation, or they simply intend to forbid smoking, thus avoiding any construction costs for smoking areas (Pierre-Yves and Ouellette 2001). From , Collins et al. (2010) looks at the taxable revenue in bars and restaurants for ten Minnesota cities. Minnesota became the first state to adopt clean indoor air legislation affecting private workplaces, restricting smoking in order to protect public health, comfort, and the environment (Collins, et al. 2010). It was one of the first studies to examine the economic effects of different levels of clean indoor air ordinances through the use of pooled time-series analysis and shows that enactment of a clean indoor air ordinance appears to improve the profit margin of bars and restaurants overall rather than detract from it (Collins, et al. 2010). Fleck and Hanssen (2008) discuss causes of a smoking ban via restaurant sales growth by directly reducing the incentive of restaurant owners to lobby against bans. Human capital may be under looked as it may affect the passing of a ban and the frequency of dining out (Fleck and Hanssen 2008). Cowling and Bond (2005) take on the argument that smoking bans will reduce restaurant and bar profits, which stems from an assumption that smokers will substitute away from these firms and find alternative forms of leisure. Their statewide and county level analyses show an increase in restaurant and bar revenues after the bans had taken place, as wells as patrons spending more money and now smoke-free establishments (Cowling and Bond 2005). Adams and Cotti (2007) aid in the economic impact of smoking bans by performing the first national difference-in-difference study of the impact of the smoking ban laws and find that smoking bans reduce employment in bars, yet have a neutral 4

6 effect or even a mildly positive effect of restaurant employment (Adams and Cotti 2007). Adams and Cotti (2007) state that there will be little to no wage impacts to go along with the employment impacts caused by shifts in labor demand because of highly elastic labor supply curves faced by bars and restaurants. III. Empirical Strategy While the previous literature has focused on restaurant or bar revenues as a measure of gains or losses, our study will focus on employment. New York City restaurant employment is studied and found to have a positive change over time. This study fails to account for the change in population and they acknowledge a possible increase in business as the crime rate dropped dramatically (Hyland and Cummings 1999). Using data from Erie County in New York, Hyland, Vena, et al. (2000) use a multivariate linear regression model to show that there is no effect of the smoking ban on employment of restaurants. These two studies do shift from using revenues and use employment as a marker of the industry, but their correlations between smoking bans and employment are weak. The sample sizes are very small and just compare one county to the rest of the state (Hyland, Vena, et al. 2000) or a group of counties to the rest of the state (Hyland and Cummings 1999). Increasing the sample size will lead to more meaningful results. Adams and Cotti (2007) use a larger sample of countywide data from across the United States with quarterly data. They separate the bars and restaurants as different entities. They compare the effect on employment between areas with a high and low prevalence towards smoking and cold versus warmer states. Adams and Cotti (2007) 5

7 find a 4.5 percent decrease in bar employment at the county level. Phelps (2007, 59-80) uses a similar county-wide study with fixed effects finds a reduction in bar employment of percent. Our model is similar to (Phelps 2007, 64), but we are using monthly statewide data so do not have percent of the county effected variable. Monthly data is used since the variation in employment may not be found in a yearly data set. ( ) ( ) ( ) ( ) ( ) Our dependent variable is the natural logarithm of employment; the dependent variables are the smoking ban, natural logarithm of population, income per capita, and fixed state effects to account for other variations between the states. IV. Data The data used is from the Bureau of Labor Statistics (BLS) there are 5 states in our sample California, Illinois, Nebraska, New Jersey, and Wisconsin. We have monthly data for employment in the whole food and drink industry, full restaurant, limited restaurant, and drinking places. We also have monthly data for population, unemployment, and labor force from the BLS. The BLS data covers employment and population per month from January 1990 to February The Bureau of Economic Analysis (BEA) provides quarterly income data per state from 1990 to The BEA 6

8 data is quarterly from 1990 to The data for the effective dates of smoking bans comes from Descriptive statistics of our data from stata output are below. The foodanddrink variable is monthly employment of all food and drinking establishments (in thousands), the fullrest variable is the monthly employment in full service restaurants (in thousands), the emplbars variable is the monthly employment of drinking places (in thousands), the pop variable is statewide monthly population, the inccap variable is the quarterly personal income per capita, and the Scainccap variable is the scaled income per capita (divided by 10000). Variable Obs Mean Std. Dev. Min Max foodanddrink fullrest emplbars pop e+07 inccap scainccap Ideally the data used would be monthly for all aspects and cover all fifty states and include at least 2 years before and after the smoking ban effective date. Data differentiating employment in the bar industry and restaurant industry for more states would provide a better sample to use in our model of statewide employment. V. Results Our results initially find that there is a significant effect of a 5.9% decrease in employment in bars, a 4.64% decrease in employment in restaurants, and a 2.92% 7

9 decrease in employment in the food service industry after the smoking bans go into effect. The variables in the table below are Smoking Ban for the smoking ban dummy variable, Logpop for the natural log of the population, and Scainccap for the income per capita scaled. Fixed Effects Model Bars Restaurant Food Service Smoking Ban *** (0.012) *** (0.0077) *** (0.0059) Logpop 0.362** (0.147) 0.565*** (0.0983) 0.386*** (0.071) Scainccap *** (0.0087) 0.122*** ( ) 0.104*** (0.0042) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. These results appear to show a negative effect of the smoking ban, but when using robust standard errors the statistical significance of the smoking ban disappears. The population and income variables do show significant results for the full service restaurants and food service industry as a whole. Fixed Effects Model with Robust Standard Errors Bars Restaurant Food Service Smoking Ban (0.0312) (0.0255) (0.0189) Logpop (0.308) 0.565** (0.186) 0.386*** (0.0584) Scainccap (0.035) 0.122*** (0.0117) 0.104*** (0.0047) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. The full service restaurants have a 5.6% increase in employment for every 10% increase in population. They have a 12.2% increase in employment for every $10,000 increase in per capita income. In the food service industry there is a 3.86% 8

10 increase in employment for every 10% increase in population. They have a 10.4% increase in employment for every $10,000 increase in per capita income. To explain why the model for bars did not have any significant values and had a negative value for our income variable. We look at the employment data over time. Both the food service industry as a whole and the full service restaurant industry show an increase in employment over time across our states. The recession beginning in 2008 we think explains the dip in employment for some states. The bar employment though, has a general downward trend from 1990 to the present. This explanation is not due to any of our variables as the trend was in place long before the smoking bans went into effect. Food Services and Drinking Places Employment (All employees, in thousands) California Illinois Nebraska New Jersey Wisconsin Full Service Restaurant Employment (All employees, in thousands) California Illinois Nebraska New Jersey Wisconsin

11 Bar Employment (All employees, in thousands) California Illinois Nebraska New Jersey Wisconsin Also, by looking at the lead and lagged variables as Adams and Cotti (2007), we find that the dummy variables are all significant in the fixed effects model in Table 3. But in Table 4 they no longer significant with robust standard errors. Looking at the values there is little difference between the lead and lagged variables as they all have a similar trend. We cannot show that there is an effect of a smoking ban using this model. With the significance of the smoking ban dummy variable in question, there is inconclusive evidence that smoking bans decrease employment. Our results do not show that there is any evidence that smoking bans decrease employment for the food service industry. Lagged Variables with Robust Standard Errors Bars Restaurant Food Service 12 month lead Smoking Ban (0.216) (0.022) (0.017) 6 month lead Smoking Ban * (0.025) (0.0247) (0.0196) 2 month lead Smoking Ban (0.0316) (0.0263) (0.0199) Effective Month Smoking Ban (0.0312) (0.0255) (0.0189) 2 month lag Smoking Ban (0.031) (0.0229) (0.0176) 6 month lag Smoking Ban (0.0295) (0.021) (0.016) 12 month lag Smoking Ban (0.0311) (0.0181) (0.0148) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. 10

12 We do show in Table 4 that the population and income per capita of a state do have a significant effect on bar employment regardless of the lead or lagged variables. Fixed Effects Model with Robust Standard Errors Restaurant Food Service Logpop 0.556* (0.233) 0.383*** (0.0427) Scainccap 0.110*** (0.0139) *** ( ) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. By taking out the dummy variable for a smoking ban we can now see the effect of population and income on employment. For full service restaurants a 10% increase in population leads to a 5.56% increase in employment. They have an 11.0% increase in employment for every $10,000 increase in per capita income. In the food service industry there is a 3.83% increase in employment for every 10% increase in population. They have a 9.6% increase in employment for every $10,000 increase in per capita income. VI. Conclusion While other studies (Adams and Cotti 2007, Phelps 2007) find a negative impact of smoking bans on bar employment, we are unable to verify these claims at the statewide level. By using the lead and lagged dummy variables we find the effect of smoking bans is unclear and brings doubt to some other studies. The population and income of a state 11

13 significantly affect the employment of the food service industry and the full service restaurants. Population and income do not affect bar employment. There are other factors that explain bar employment as the general trend is negative over time and there is not the increase in bar employment as population goes up. The possibility is that the demand for bar employees is inelastic as bars do not employ more employees over time. Full service restaurants employment goes up as population increases. A different model or method is needed to see the true effect of smoking bans on employment. Ours does not work to find the change in employment after a smoking ban goes into effect. Further research may be concluded to investigate the effects of smoking bans. Adams and Cotti (2008) show the negative externality of smoking bans by an increase of drunk driving fatalities. Their research shows that cigarettes and alcohol are highly complementary; when a ban takes effect, consumers will drive greater distances and across state lines to consume cigarettes and alcohol (Adams and Cotti 2008). The risk and cost of drunk driving increase the further away a patron goes to achieve satisfaction. Advanced studies on the unintended consequences of smoking bans may be explored on the effect it has on children. When a smoking ban is initiated, consumers that smoke will substitute away from these locales and increase their consumption of smoking in private places such as their home and vehicles where children and non-smokers will be subject to second-hand smoke (Adda and Cornaglia 2010). In the end, an important element to the smoking ban is that the health benefits outweigh the health costs. 12

14 Bibliography Adams, Scott, and Chad Cotti. "Drunk Driving after the Passage of Smoking Bans in Bars." Journal of Public Economics (Elsevier), no. 92 (January 2008): Adams, Scott, and Chad D. Cotti. "The Effect of Smoking Bans on Bars and Restaurants: An Analysis of Changes in Employment." The B.E. Journal of Economic Analysis & Policy (The Berkeley Electronic Press) 7, no. 1 (2007): Adda, Jerome, and Francesca Cornaglia. "The Effect of Bans and Taxes on Passive Smoking." American Economic Journal 2 (2010): Alamar, Benjamin C., and Stanton A. Glantz. "Smoke-Free Ordinances Increase Restaurant Profit and Value." Contemporary Economic Policy (Western Economic Association International) 22, no. 4 (October 2004): Albers, Alison B., Michael Siegel, Debbie M Cheng, Lois Biener, and Nancy A. Rigotti. "Effect of Smoking Regulations in Local Restaurants on Smokers' Anti-smoking Attitudes and Quitting Behaviours." Tobacco Control (BMJ Publishing Group) 16, no. 2 (April 2007): Anger, Silke, Michael Kvasnicka, and Thomas Siedler. "One Last Puff? Public Smoking Bans and Smoking Behavior." Journal of Health Economics (Elsevier B.V.) 30, no (2011): Becker, S. Gary, Michael Grossman, and M. Kevin Murphy. "An Empirical Analysis of Cigarette Addiction." National Bureau of Economic Research, April Collins, Natalie M., Qun Shi, Jean L. Forster, Darin J. Erickson, and Traci L. Toomey. "Effects of Clean Indoor Air Laws on Bar and Restaurant Revenue in Minnesota Cities." American Journal of Preventive Medicine (Elsevier Inc.) 39, no. 6S1 (2010): S10-S15. Cowling, David W., and Philip Bond. "Smoke-free Laws and Bar Revenues in California - The Last Call." Health Economics (John Wiley & Sons, Ltd.), no. 14 (April 2005): Fleck, Robert K., and Andrew F. Hanssen. "Why Understanding Smoking Bans is Important for Estimating their Effects: California's Restaurant Smoking Ban and Restaurant Sales." Western Economic Association International 46, no. No. 1 (January 2008): Hyland, Andrew, and K. Michael Cummings. "Restaurant employment before and after the New York City Smoke-Free Air Act." Journal of Public Health Management and Practice 5, no. 1 (1999): Hyland, Andrew, Christopher Vena, K. Michael Cummings, and Arnold Lubin. "The effect of the clean air act of Erie County, New York on restaurant employment." Journal of Public Health Management and Practice 6, no. 6 (2000): Marlow, Michael L. "The Economic Losers from Smoking Bans." Regulation Summer , no. No. 2 (June 2010): Phelps, Ryan Todd. "The Causes and Effects of Comprehensive Private Sector Smoking Bans." University of Kentucky, Pierre-Yves, Cremieux, and Pierre Ouellette. "Actual and Perceived Impacts of Tobacco Regulation on Restaurants and Firms." Tobacco Control (BMJ Publishing Group) 10, no. No. 1 (March 2001):

15 Table 1 Fixed Effects Model Bars Restaurant Food Service Smoking Ban *** (0.012) *** (0.0077) *** (0.0059) Logpop 0.362** (0.147) 0.565*** (0.0983) 0.386*** (0.071) Scainccap *** (0.0087) 0.122*** ( ) 0.104*** (0.0042) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. Fixed Effects Model with Robust Standard Errors Bars Restaurant Food Service Smoking Ban (0.0312) (0.0255) (0.0189) Logpop (0.308) 0.565** (0.186) 0.386*** (0.0584) Scainccap (0.035) 0.122*** (0.0117) 0.104*** (0.0047) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. 14

16 Table 2 Food Services and Drinking Places Employment (All employees, in thousands) California Illinois Nebraska New Jersey Wisconsin Full Service Restaurant Employment (All employees, in thousands) California Illinois Nebraska New Jersey Wisconsin Bar Employment (All employees, in thousands) California Illinois Nebraska New Jersey Wisconsin

17 Table 3 Lead and Lagged Variables Fixed Effects Bars Restaurant Food Service 12 month lead Smoking Ban *** (0.0117) *** (0.0072) *** (0.0056) Logpop (0.152) 0.513*** (0.0999) 0.366*** (0.0732) Scainccap *** (0.0092) 0.122*** (0.0062) 0.102*** (0.0044) 6 month lead Smoking Ban *** (0.012) *** (0.0074) *** (0.0058) Logpop 0.291** (0.148) 0.527*** (0.098) 0.365*** (0.072) Scainccap *** (0.0089) 0.124*** (0.0060) 0.104*** (0.0043) 2 month lead Smoking Ban *** (0.0122) *** (0.0076) *** (0.0059) Logpop 0.341** (0.147) 0.548*** (0.097) 0.375*** (0.0707) Scainccap *** (0.0088) 0.124*** (0.0059) 0.105*** (0.0042) Dummy variable Smoking Ban *** (0.012) *** (0.0077) *** (0.0059) Logpop 0.362** (0.147) 0.565*** (0.0983) 0.386*** (0.071) Scainccap *** (0.0087) 0.122*** ( ) 0.104*** (0.0042) 2 month lag Smoking Ban *** (0.011) *** (0.0072) *** (0.0054) Logpop 0.375** (0.148) 0.576*** (0.0978) 0.391*** (0.0699) Scainccap *** (0.0085) 0.121*** ( ) 0.103*** (0.0041) 6 month lag Smoking Ban *** (0.011) *** (0.0070) *** (0.0052) Logpop 0.388*** (0.148) 0.581*** (0.099) 0.397*** (0.070) Scainccap *** (0.0083) 0.117*** (0.0058) 0.102*** (0.0040) 12 month lag Smoking Ban *** (0.010) *** (0.0068) *** (0.0048) Logpop 0.390*** (0.0150) 0.575*** (0.102) 0.395*** (0.07 Scainccap *** (0.0083) 0.113*** ( ) *** ( ) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. 16

18 Table 4 Lead and Lagged Variables Fixed Effects with Robust Standard Errors Bars Restaurant Food Service 12 month lead Smoking Ban (0.216) (0.022) (0.017) Logpop (0.270) 0.513* (0.216) 0.366*** (0.0396) Scainccap (0.032) 0.122*** (0.015) 0.102*** ( ) 6 month lead Smoking Ban * (0.025) (0.0247) (0.0196) Logpop (0.295) 0.527* (0.198) 0.365*** (0.0382) Scainccap (0.0339) 0.124*** (0.0148) 0.104*** (0.0044) 2 month lead Smoking Ban (0.0316) (0.0263) (0.0199) Logpop (0.300) 0.548** (0.186) 0.375*** (0.0543) Scainccap (0.0342) 0.124*** (0.0123) 0.105*** (0.0049) Dummy variable Smoking Ban (0.0312) (0.0255) (0.0189) Logpop (0.308) 0.565** (0.186) 0.386*** (0.0584) Scainccap (0.035) 0.122*** (0.0117) 0.104*** (0.0047) 2 month lag Smoking Ban (0.031) (0.0229) (0.0176) Logpop (0.299) 0.576** (0.183) 0.391*** (0.0659) Scainccap (0.0337) 0.121*** (0.0115) 0.103*** (0.0045) 6 month lag Smoking Ban (0.0295) (0.021) (0.016) Logpop (0.305) 0.581** (0.187) 0.397*** (0.073) Scainccap (0.0337) 0.117*** (0.0112) 0.102*** (0.0048) 12 month lag Smoking Ban (0.0311) (0.0181) (0.0148) Logpop (0.267) 0.575* (0.209) 0.395*** (0.066) Scainccap * (0.0317) 0.113*** (0.0118) *** ( ) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. 17

19 Table 5 Fixed Effects Model with Robust Standard Errors Restaurant Food Service Logpop 0.556* (0.233) 0.383*** (0.0427) Scainccap 0.110*** (0.0139) *** ( ) Note: Statistical significance is indicated at the 1%(***), 5% (**), and 10% (*) levels of significance. Scainccap is the income per capita divided by Logpop is the natural logarithm of population. 18