the effects of the tobacco products control ... - Wiley Online Library

South African Journal of Economics Vol. 74:1 March 2006

THE EFFECTS OF THE TOBACCO PRODUCTS CONTROL AMENDMENT ACT OF 1999 ON RESTAURANT REVENUES IN SOUTH AFRICA: A PANEL DATA APPROACH evan h. blecher* Abstract Prior to the implementation of this legislation the restaurant industry lobbied that a full-scale ban would severely hurt business. Their lobbying resulted in a restrictive restaurant smoking policy rather than a full-scale ban. Nevertheless the industry argued that this would still severely hurt business citing international evidence in support. The objective of this paper is to investigate the change in restaurant revenues after the implementation of a public smoking ban in South Africa. We use a fixed effects panel model to explore the response of restaurant revenues to the imposition of the ban. Provincial data is used over the period 1995 to 2003 and VAT receipts are used as a proxy of restaurant turnover. We conclude that restrictive restaurant smoking policies have not had a negative effect on restaurant revenue, indicating that claims of countrywide restaurant business declines under such a policy are unwarranted. JEL Classification: L66 Keywords: tobacco policy, restaurants, smoking, environmental tobacco smoke, panel data 1. INTRODUCTION

Before 1993 South Africa did not have a tobacco control policy. The Tobacco Products Control Act (Act 83 of 1993, promulgated 1995) banned smoking on public transport and introduced warning labels on cigarette packs and advertising material. The legislation was strongly opposed by the tobacco industry. The resulting debate increased public awareness of the dangers of smoking. Since 1992 consistent increases in the excise taxes levied on cigarettes (Van Walbeek, 2002) have augmented this legislation. The resultant effects have seen large declines in smoking indicators as seen in Table 1 below. Subsequently, the Tobacco Products Control Amendment Act (Act 12 of 1999, promulgated 2000) had far-reaching consequences. The main elements of the legislation were to: 1. prohibit smoking in workplaces and other public places; 2. prohibit all tobacco advertising and any promotion of tobacco products, including through sponsored events; * School of Economics, University of Cape Town. The author would like to thank Corné Van Walbeek, Paul Dunne, Andrew Mearman and participants at the Economic Society of South African bi-annual conference in Durban, September, 2005, for useful comments and suggestions and Lesley O’Connell at the SARS for providing the VAT data. The author would also like to acknowledge the financial support of Research for International Tobacco Control, the American Cancer Society and the Canadian Tobacco Control Research Initiative through the Small Grants Research Competition to support ratification, implementation and/or enforcement of the Framework Convention on Tobacco Control. All errors and omissions remain the author’s alone. © 2006 The Author. Journal compilation © 2006 Economic Society of South Africa. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. 123

124


Table 1. Percentage changes in smoking indicators (1993 to 2000) Real price per pack of cigarettes Aggregate consumption of cigarettes Per capita consumption (Population older than 15) Smoking prevalence Estimated number of smokers Average consumption per smoker

92.7 -26.0 -37.1 -16.9 -2.2 -24.2

Source: Van Walbeek (2002:18).

3. prohibit the sale of tobacco products to persons under the age of 16; 4. prohibit the free distribution of tobacco products; and 5. reduce the maximum yields on tar, nicotine and other constituents in tobacco. This Act caused intense public debate and again raised public awareness about the health impact of tobacco, and especially environmental tobacco smoke and passive smoking. The legislation gave the right of smoke-free air to non-smokers. Previously the right to clean air was disputed, dependent on the “courtesy” of smokers not to smoke in the presence of non-smokers. Now that property rights have been transferred, non-smokers have the right to demand clean air. A clean air policy is admittedly difficult to enforce in some settings (including restaurants). However, the measure of compliance is believed to be high in most restaurants. This has been achieved without police crackdowns, but mainly through public pressure. Initial claims by groups representing the hospitality industry of largescale noncompliance were unfounded. The Federated Hospitality Association of South Africa (FEDHASA) claimed, shortly after the introduction of the legislation, that 85% of its members did not obey the law and that sales were down 37% as a result (Saloojee and Ucko, 2001). Saloojee and Ucko (2001) indicate that this defies reason by asking the question: “How can a law that, according to them (FEDHASA), is being widely ignored result in a loss of more than a third of sales?” The legislation provided restaurants with two options: (1) to become an entirely smoke free environment, or (2) to create separate smoking and non-smoking areas, the former being properly ventilated by extractor fans; separated from the rest of the restaurant and not exceeding 25% of the restaurant’s area. This arrangement was a compromise offered by the industry to the Department of Health who originally proposed a blanket ban. The industry argued that the first option alone (the initial Department of Heath proposal) would result in lost business. The hospitality industry claimed that, should the initial proposal become law, they would lose a large proportion of their customers. According to the International Hotel & Restaurant Association, a survey among restaurant operators in Cape Town indicated that the proposed legislation would decrease their turnover by 32 per cent (Portfolio Committee and Health, 1999). They argued that the government should not be allowed to decide how hospitality establishments should run their businesses, and that this decision should be left to each individual operator. The antitobacco lobby argued that finances should not be an issue and that the medical evidence regarding environmental tobacco smoke was convincing enough. They also argued that the legislation could be financially beneficial to restaurants and presented international evidence to support this (Saloojee and Ucko, 2001). Five years later, it is now time to reflect on the effects of the legislation on the restaurant industry. © 2006 The Author. Journal compilation © Economic Society of South Africa 2006.


125

2. LITERATURE AND METHODOLOGY

Two separate sets of literature measuring the impact of clean air legislation on the hospitality industry, and more particularly restaurants, exist. There are those studies that have been published in peer reviewed academic journals and those commissioned by trade bodies or government authorities and published privately or in popular publications. Reviews of almost all studies investigating the effects of such legislation on the hospitality industry have been conducted by Scollo et al. (2003) and Scollo and Lal (2004). Scollo et al. (2003) examined the quality of 97 studies published in English prior to 31 August 2002 using Siegal’s (1992) criteria. Such criteria involve controlling for economic conditions, the use of independent funding sources, publication subject to peer review, and the measurement of actual events rather than predicted outcomes or assessments. Scollo et al. (2003) show that studies that do not meet these criteria generally find that legislation had negative impacts on the industry in terms of financial performance; customer satisfaction and employment, while studies that met these criteria show no negative and sometimes even positive impacts. The methodology followed in this paper has been specifically designed to meet the criteria suggested by Siegel (1992) and shown to be the least biased by Scollo et al. (2003). Scollo and Lal (2004) conduct a similar study including the literature up until February 2004 and draw the same conclusion as Scollo et al. (2003). Bartosch and Pope (1999 and 2002) investigate the impact of local smoke-free policies in Massachusetts using town-level tax receipts as a proxy for restaurant revenues. They failed to find a statistically significant effect of the policies on restaurant business although they do indicate that bans resulted in small positive effects. They use a fixed effect panel model, placing a fixed effect on each city to control for unobserved or immeasurable heterogeneity between cities. This methodology removes all city specific factors that do not vary significantly over time such as a greater number of tourists in one city or better infrastructure in another. They specify the model with quarterly data where per capita1 taxable meal receipts are specified as a function of town specific fixed effects, time since implementation of a smoke free policy, per capita income, proportion of border towns with a highly restrictive smoking policy surrounding the town, a time trend and quarter specific seasonal dummies (Bartosch and Pope, 2002). Bartosch and Pope (1999) use aggregate receipts and include population as an independent variable. They also exclude the border proximity variable and use a dummy to indicate the presence of a restaurant smoking policy that was not smoke-free. 3. DATA AND ESTIMATION

This study uses the LIMDEP econometrics package (version 7.0) in order to model the effect of clean air legislation on the restaurant industry in South Africa. We can specify the following function based on those used by Bartosch and Pope (1999 and 2002) where: Real Per Capita Revenue = f (Real Per Capita Income, Effect of the Legislation, Efficiency of Tax Collection) 1

The per capita receipts is interpreted as the receipts per person in each town rather than the receipts per customer. © 2006 The Author. Journal compilation © Economic Society of South Africa 2006.

126


This hypothesizes that real restaurant revenue is a function of real income, economic activity and smoke free policies. Consumption tax collections as a proportion of economic activity is used to control for improvements in the efficiency of tax collections (discussed in more detail later). A simple time series analysis of the data is not possible due to a lack of observations. Thus revenues are pooled and a panel analysis is undertaken using each of the nine South African provinces as cross sections. Firstly a pooled model is estimated and compared to a fixed effects model. In the fixed effects model a fixed effect is placed on each cross section (province), which removes all province specific factors that do not vary significantly over time. A random effects model is also considered. A two way effects model which places fixed effects on each time period to control for unobserved or immeasurable heterogeneity between periods in addition to the group fixed effects is not possible since the regressors are collinear. The sample is restricted to annual observations over the period 1995 to 2003 due to data availability. The revenue and income series are transformed using natural logarithms allowing interpretation of the coefficients as elasticities. (a) Dependent Variable Value Added Tax (VAT) is a consumption-based tax collected by the South African Revenue Service (SARS). SARS isolates the data by business type and has provided the author with the series “Restaurant/Tearoom Selling Food–Consumption Mainly on Premises”. This system requires all vendors to levy VAT at 14% on all sales but allows them to reclaim the VAT paid on inputs. Hence the state only collects 14% tax on any value added by a particular business. Thus in order to assess the revenue of the restaurant industry, the output2 VAT is used as a proxy from which actual revenue is calculated. The tax rate has remained constant since 1993 which will make handling of the data easier, only requiring adjustment for inflation consistent with Siegal’s methodological requirements (Siegal, 1992). The series made available by SARS indicated annual collections of 37 regional tax offices for the period 1995 to 2003. Each office was sorted according to province and aggregated to calculate each province’s restaurant revenue3. The series of nominal revenue is converted into real revenue using the Consumer Price Index (CPI) as published by Statistics South Africa (StatsSA). The series for each province was deflated using a region specific CPI. For eight of the nine provinces the region specific CPI was that of the major metropolitan area since a province wide index is not available for the entire period. Inspection of this index and comparison to the provincial index over the restricted period shows a very high correlation. For the ninth province, the North West, no series for the major metropolitan area is available. Instead a series of all metropolitan areas and cities is substituted although this is not complete over the entire period and values for 1995 and 1996 were approximated by assuming the inflation rate in the North West province was equal to the simple average of the other eight regions4. Real revenues were converted into per capita terms using the midyear provincial population estimates calculated by StatsSA (1998-2003). 2

The VAT collected by businesses on sales before the deduction of VAT paid on inputs is termed output VAT. 3 The location of each tax office and the province assigned is located in Appendix 1. 4 The details of each province specific CPI are shown in Appendix 2. © 2006 The Author. Journal compilation © Economic Society of South Africa 2006.


127

(b) Independent Variables The choice of a provincial approach rather than using the individual tax offices or cities as cross sections is due to the availability of data on economic activity. National Accounts data is collected on a national basis in South Africa and very little province specific data pertaining to economic activity is available. StatsSA has recently begun estimating provincial Gross Domestic Products (GDP). Not only does GDP act as a proxy of income it also controls for the level of economic activity as suggested by Siegel (1992). Although other measures of income like personal consumption expenditure or gross domestic expenditure may have been better proxies of income in this analysis due to restaurant meals being a non-durable and non-tradable good, GDP is the only provincial estimate available. In defense of GDP it does allow for the inclusion of free public sector services (Laugesen and Meads, 1993). Real GDP at constant 2000 prices is converted into per capita terms using the midyear provincial population estimates calculated by StatsSA (1998-2003)5. A dummy variable is included in the regression to control for the introduction of the ban. Normally a dummy variable is a binary variable taking the value of 0 in periods when no ban existed and 1 in those periods where a ban was in place. Since annual data is used in this analysis and the ban implemented halfway through the year (on 1 July 2001) a stepped dummy is used to specifically locate the ban in the analysis. The dummy variable takes on a value of 0 in all years prior to 2001 and 1 from 2002 onwards but uses a value of 0.5 in 2001. It is common knowledge that the SARS has become a significantly better collector of tax over the period in question. This has been explicitly indicated by the Minister of Finance, Trevor Manuel, in his 2005 budget speech (Manuel, 2005: 17 & 26). As such it is important to control for these improvements since the series of restaurant revenue is not only capturing the variation in restaurant revenue but also the improvements in collections by SARS. In order to do this a series of the ratio of total VAT collections as a proportion of final consumption expenditure by households would be appropriate since all such consumption expenditure (with few exceptions) is subject to VAT. Yet it is not possible to calculate this on a provincial basis and GDP is substituted for final consumption expenditure by households. Analysis of the growth rate of both these ratios at a national level over the period 1995 to 2003 indicates a high correlation and is thus included as an independent variable. 4. RESULTS

The results of the specified regressions are depicted in Table 2. Two regression were estimated, the first excluding the VAT collection ratio and the second including it. The two models are formally tested to justify the inclusion of the VAT collection ratio. From Table 2 it can be seen that the model, which excludes the VAT collections ratio, shows positive coefficients of per capita real income and the dummy. The coefficient of per capita real income of 0.659 indicates that a 10% increase in income will result in a 6.59% increase in per capita restaurant revenues although it is not statistically significant. The 5

The value for GDP in constant 2000 prices for Kwazulu Natal contained in StatsSA (2004, 76) is incorrect. The values for the nominal series have been shown instead. This has been corrected using the growth rate of real GDP and applying it to the nominal series in the base year and calculating all other years manually.

© 2006 The Author. Journal compilation © Economic Society of South Africa 2006.

128


Table 2. Regression Results Estimated Coefficients

Excluding Ratio

Including Ratio

Ln Real Per Capita GDP Ban Dummy VAT Collection Ratio Adjusted R2 Residual Sum of Squares

0.659 (1.359) 0.227 (5.917)***

0.789 (1.714)* 0.063 (0.962) 33.034 (3.051)*** 0.99 0.948

0.98 1.076

Note: Asymptotic t-statistics are shown in parenthesis. Dependent Variable: Ln Real Per Capita Restaurant Revenues. *Significant at the 10% level (p ⬍ 0.1); ***significant at the 1% level (p ⬍ 0.01).

coefficient of the dummy is 0.227 and is statistically significant at the 1% level. Its value of 0.227 indicates that the imposition of the ban results in a 22.7% increase in real per capita revenues. Furthermore, this fixed effects model is preferred to the pooled model (without the province specific fixed effects) since the Chi-squared value of 237.03, distributed X2(8), and the F-statistic of 154.51, distributed F(8,70), are both significant at the 1% level. A random effects model cannot be compared to the fixed effects model since no Hausman statistic can be computed due to singularity in the variance-covariance matrix. This is most likely a result of the use of the dummy variable. Although this model seems rigorous, the elasticity of the ban seems unrealistically high and is not consistent with the results of Bartosch and Pope (1999 and 2002). The model was subsequently estimated including the VAT collection ratio as an independent variable. Again all the estimated coefficients are positive and the coefficient of real per capita income of 0.789 indicates that a 10% increase in income will result in a 7.89% increase in restaurant revenues and is significant at the 10% level. The coefficient of the dummy is 0.063 and is not statistically significant. Furthermore, this fixed effects model is preferred to the pooled model (without the province specific fixed effects) since the Chi-squared statistic of 246.67, distributed X2(8), and the F-statistic of 172.63, distributed F(8,76), are both significant at the 1% level. Again the random effects model cannot be compared to the fixed effects model since no Hausman statistic can be computed. A standard F test was performed to test the inclusion of the VAT collection ratio where it was found that its inclusion was significant. The resulting F-statistic is 9.452, distributed F(1,70), and is significant at the 1% level. Of interest here are the coefficients of the dummy variables in the models. Including the VAT collection ratio the coefficient of the dummy variable is not significantly different from zero while it is when the VAT collection ratio is not included. A 90% confidence interval is calculated which for the model including the VAT collection ratio indicates that the true value lies between -0.046 and 0.171 (-4.60 and 17.1 per cent) while for the model excluding the VAT collection ratio the true value lies between 0.163 and 0.291 (16.3 and 29.1 per cent). 5. DISCUSSION

The results distinctively show that the restrictions placed on smoking in restaurants in 2001 have had at worst no significant effect on restaurant revenues, and at best a positive effect on revenues. Although the positive coefficients of the dummy variables correspond © 2006 The Author. Journal compilation © Economic Society of South Africa 2006.


129

with the evidence presented in the literature that meet Siegel’s (1992) methodological requirements, the magnitude of the coefficients are rather high when compared to Bartosch and Pope’s (1999 and 2002) who used a similar econometric technique and estimated the effect of the dummies to be positive and small (single figure percentages) and also insignificant. The discrepancy with the foreign literature can be explained by large scale improvements in the collection of all taxes, including VAT, although we have attempted to control for this. It is also difficult to control for other factors that may have influenced restaurant revenues upwards, including greater numbers of foreign tourists, since such data is not known on a provincial basis. The implications of the results of this paper are immense in that it suggests that the industry’s prior estimates of how the legislation would impact on their businesses were too pessimistic. This is consistent with the findings of Scollo et al. (2003) who show that studies that attempt to predict outcomes suggest that clean-air policies will negatively impact business while studies that investigate actual outcomes find otherwise. This paper does not investigate the effects on employment, profitability or even bankruptcy. Nor does it investigate self-reported intensions, expectations, predictions or perceptions of restaurant owners or managers who may be risk averse and fear change; but it does investigate the actual outcome. The results contained in this paper support the decision to implement clean-air regulations in restaurants since they have had a positive, albeit not a significant, impact on revenues. This conclusion may even support further proposals to completely ban smoking in restaurants and other hospitality establishments including casinos, bars and nightclubs. Furthermore it may also support such policies in other developing countries where they are not yet common practice. APPENDIX

Appendix 1. SARS Regional Tax Office Locations Office Location

Province

Office Location

Province

Alberton Beafort West Bellville Benoni Bloemfontein Boksburg Brakpan Cape Town Durban East London George Germiston Johannesburg Kimberley Klerksdorp Kroonstad Krugersdorp Mmabatho Nelspruit

Gauteng Western Cape Western Cape Gauteng Free State Gauteng Gauteng Western Cape Kwazulu Natal Eastern Cape Western Cape Gauteng Gauteng Northern Cape North West Free State Gauteng North West Mpumalanga

Nigel Paarl Pietermaritzburg Polokwane Port Elizabeth Pretoria Randfontein Roodepoort Rustenburg Sibaza Springs Standerton Uitenhage Umtata Vereeniging Welkom Witbank Worcester

Gauteng Western Cape Kwazulu Natal Limpopo Eastern Cape Gauteng Gauteng Gauteng North West Limpopo Gauteng Mpumalanga Eastern Cape Eastern Cape Gauteng Free State Mpumalanga Western Cape


130


Appendix 2. Regional Consumer Price Indexes Province

CPI Index

Eastern Cape Free State Gauteng Kwazulu-Natal Limpopo Mpumalanga North West Northern Cape Western Cape

Port Elizabeth/Uitenhage Bloemfontein Witwatersrand Durban/Pinetown Polokwane Nelspruit/Witbank Metro and Other Cities* Kimberley Cape Peninsula

Note: *1995 and 1996 were not available. The simple average of the indices used for the other eight provinces was substituted. REFERENCES BARTOSCH, W. J. and POPE, G. C. (1999). The Economic Effect of Smoke-Free Restaurant Policies on Restaurant Business in Massachusetts. Journal of Public Health Management and Practice, 5(1): 53-62. ——— (2002). Economic effect of restaurant smoking restrictions on restaurant business in Massachusetts, 1992 to 1998. Tobacco Control, 11: ii38-ii42. LAUGESEN, M. and MEADS, C. (1993). The author’s reply to ‘Tobacco consumption and advertising restrictions: a critique of Laugesen and Meads (1991)’ by M.J. Stewart. International Journal of Advertising, 12(1). MANUEL, T. (2005) Budget Speech 2005. National Treasury: Pretoria. 25 February 2005. Available online at: http://www.finance.gov.za/documents/budget/2005/speech/speech.pdf PORTFOLIO COMMITTEE ON HEALTH. (1999). Hearings on the Tobacco Control Amendments Bill (1999). Parliament of the Republic of South Africa: Cape Town. SALOOJEE, Y. and UCKO, P. (2001). Diners forced to eat arsenic and cyanide. In: Hotel and Restaurant. October 2001. Available online at: http://www.hotelandrestaurant.co.za/news/2001/october/smoking.asp SCOLLO, M. and LAL, A. (2004). Summary of studies assessing the economic impact on smoke-free policies in the hospitality industry – includes studies produced to 28 April 2004. VicHealth Centre for Tobacco Control: Melbourne. ———, HYLAND, A. and GLANTZ, S. (2003). Review of the quality of studies on the economic effects of smoke-free policies on the hospitality industry. Tobacco Control, 12: 13-20. SIEGEL, M. (1992). Economic impact of 100% smoke-free restaurant ordinances. In: Smoking and restaurants: a guide for policy makers. University of California at Berkeley and University of California at San Francisco Preventative Medicine Residency Programme; American Heart Association, California Affiliate; Alameda County Health Care Services Agency, Tobacco Control Programme: Berkeley. STATISTICS SOUTH AFFRICA. (STATSSA) (1998-2003). Mid-year estimates 1998. Statistics South Africa: Pretoria. Statistical Release P0302. ——— (2004). Gross Domestic Product (Annual estimates: 1993-2003, Annual estimates per region: 1995-2003, Third quarter: 2004). Statistics South Africa: Pretoria. Statistical Release P0441. 30 November 2004. VAN WALBEEK, C. P. (2002). The Economics of Tobacco Control in South Africa. Applied Fiscal Research Centre, School of Economics, University of Cape Town: Cape Town.
