A grouped data regression approach to estimating economic and social influences on individual drinking behaviour

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Abstract
General Household Survey (GHS) data sets, covering the period 1978‐1990, are pooled to investigate the relationship between the riskiness of individuals' self‐reported drinking behaviour and a wide range of personal characteristics and economic factors. A grouped data regression approach is used to reduce problems with the inaccuracy of self‐reports of alcohol consumption and clustering of observations in the consumption data. Results for males aged 18 to 24 years are presented, and possible methods for interpreting the results of grouped data regression are illustrated. Controlling for other factors, current smokers are estimated to be at a 75% higher risk of drinking over recommended levels than non‐smokers. Particular attention is paid to the interactions between the price of alcohol, income and heavy drinking. At average levels of income, a 5% increase in the real price of alcohol is predicted to reduce the probability of 'at‐risk' drinking by 1.5%. At lower initial levels of income, drinking patterns are found to be more responsive to both price and income changes. Grouped data regression is proposed as a way of focusing policy analysis on individual risks of alcohol‐related health and social problems.