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Econometric Analysis of Alcohol and Cigarettes Consumption in the UK

Econometric Analysis of Alcohol and Cigarettes Consumption in the UK. Indirect Taxes - Excise Team Magda Piec, Andrew Grayson, Surjinder Johal, James Collis. 2011 International Conference on Taxation Analysis and Research 2 nd December 2011. Outline. Context:

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Econometric Analysis of Alcohol and Cigarettes Consumption in the UK

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  1. Econometric Analysis of Alcohol and Cigarettes Consumption in the UK Indirect Taxes - Excise Team Magda Piec, Andrew Grayson, Surjinder Johal, James Collis. 2011 International Conference on Taxation Analysis and Research 2nd December 2011

  2. Outline • Context: - Revenue from alcohol and tobacco duties - Elasticities • Cigarettes Elasticity - Market - Data and method - Results and Impacts • Alcohol Elasticities - Market - Data and method - Results and Impacts • Summary and questions

  3. Alcohol and Tobacco Revenue • Alcohol and Tobacco taxes raised just under £20bn in 2010-11 • This was over 3% of total government tax revenue.

  4. Elasticities • Assuming demand stays the same, an increase in the duty rate increases revenue. • However an increase in duty leads to an increase in price. This results in a behavioural effect, as consumers reduce their demand. This has an offsetting effect on revenue. • Price elasticity measures how large this behavioural effect is. Tax rate = x Revenue Demand

  5. Cigarettes Elasticity

  6. Tobacco Clearances

  7. Cigarettes Elasticities – choice of approach • Previous methodology • The Chambers (1999) model used an Almost Ideal Demand System (AIDS) specification. • The Cullum and Pissarides (2004) used similar model specification, but extended the analysis to various levels of consumer choice. • Current approach • Cointegration modelling • Establishing the long term relationship between prices and consumption

  8. Tobacco Data ONS Consumer Trends • Quarterly time series of UK expenditure on cigarettes: 1982 – 2009 • Cigarettes make up over 80% of the tobacco consumption Other data sources • Price data from Tobacco Manufacturers • HMRC and Tobacco Manufacturers’ data on illicit tobacco market • ONS data on travel and consumption expenditure • Limitations • Non UK duty paid consumption

  9. Tobacco Data: Non-stationarity and Non-Duty Paid

  10. Cointegration modelling • Standard regressions • Non-stationary data and spurious results • Solutions: - modelling differences vs. long term properties of the relationship - modelling cointegration • Cointegration • Establishing the long run relationship that is stable over time • Two-Step Engle-Granger Approach • Johansen Procedure as an auxiliary check

  11. Final Specifications • Consumption of duty paid cigarettes depends on: • - Own-price • - Price of hand rolled tobacco • - Household income • - Travel numbers/ Exchange rate • - Smoking ban • - EU Single Market • - Dummy variables • - Constant • Results are stable over time and across functional forms

  12. Results • Elasticities: • New estimate: -1.05 less elastic than previously (-1.26) • Ready reckoner: • Policies: • Rebalancing of cigarette duties at Budget 11

  13. Alcohol Elasticities

  14. Alcohol Consumption Trends • Increase in wine; decline in beer; cider increasing but small

  15. Alcohol Consumption Trends: On-Off Trade • Decline in the on trade; increase in the off trade

  16. Alcohol Elasticities – choice of approach • Previous estimates • Time series approach • Chambers (1999) model used an Almost Ideal Demand System (AIDS) specification. • HMRC (2003) - Single Equation Error Correction Model • Why use Cross Section ahead of Time Series data? • Cross section dataset allows more alcohol types to be considered • - 10 alcohol types; previously we had only 4 • Attempts at estimating more alcohol types, split into on and off, with time series data produced less robust results.

  17. Alcohol Data – Expenditure and Food Survey (EFS) • Expenditure and Food Survey (EFS) = National Food Survey (NFS) + Family Expenditure Survey (FES). Combined in April 2001. • Annual survey of 7,000 households in UK. From 2001/02 to 2006. N=40,000. • 2 weeks Diary Data: quantity of goods consumed, expenditure, and where from. • Numerous alcohol types grouped into 10 categories: beer, wine, cider, spirits, RTDs all split between on trade (bars/restaurants) and off trade (supermarkets/off licenses). • Other socio-economic characteristics e.g. occupation, region, household size, income, etc. • Limitations • Low response rate (around 55%) • Potential under-reporting or mis-reporting of alcohol consumption

  18. Data – zero consumption of alcohols • Zero Shares/Expenditures: • There are many that only drink certain types, which results in lots of zero shares/expenditures. • Of those that purchased alcohol:

  19. Tobit Model • Standard regressions • Standard regression procedures are biased by the presence of all the zeros: • - Ordinary Least Squares (OLS) would predict negative consumption shares • - OLS slope and intercept would be biased • Taking a sub-sample would massively reduce the dataset and entail a selection bias. • Tobit • Estimation procedure designed to deal with corner solutions and bounded datasets. • Hybrid of a binary choice model (using MLE) and a standard regression: • - binary element estimates probability of positive consumption • - second element estimates our regression based on those predicted to have • positive consumption

  20. Final Model • Consumption share of a given alcohol (out of total consumption) depends on: • - own-price • - prices of other alcohols • - household income • - drink prevalence: number of alcohol types consumed • - year and quarter dummies • - regional control dummies • - occupation dummies e.g. student, managerial • - constant

  21. Elasticities • Note: differences do not suggest a change over time since the last estimations; but reflect the use of a different dataset and different methodology.

  22. Impacts • Ready Reckoner • Policies • 2010 Alcohol Review • New duty categories for beers of different strengths • Cross-Whitehall review of Alcohol Pricing • - ban of selling alcohol below duty plus VAT

  23. Summary • We have presented econometric analysis of cigarettes and alcohol consumption in the UK – both models represent improvements on previous HMRC work. • In the case of tobacco, we have used a time-series approach, focussing on the main source of duty, the duty-paid cigarettes market. • The use of a cointegration method overcame the non-stationarity of the data; whilst proxies were used due to shortfalls in data on the illicit market. • However for alcohol we required elasticities for a vast range of products. A cross-sectional approach was used to give the required detail. • Using a Tobit model prevented zero observations from having a biasing effect. • These estimates have improved the Government’s models and fed into several policies

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