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Meta-analysis of alcohol price and income elasticities - with corrections for publication bias.


ABSTRACT:

Background

This paper contributes to the evidence-base on prices and alcohol use by presenting meta-analytic summaries of price and income elasticities for alcohol beverages. The analysis improves on previous meta-analyses by correcting for outliers and publication bias.

Methods

Adjusting for outliers is important to avoid assigning too much weight to studies with very small standard errors or large effect sizes. Trimmed samples are used for this purpose. Correcting for publication bias is important to avoid giving too much weight to studies that reflect selection by investigators or others involved with publication processes. Cumulative meta-analysis is proposed as a method to avoid or reduce publication bias, resulting in more robust estimates. The literature search obtained 182 primary studies for aggregate alcohol consumption, which exceeds the database used in previous reviews and meta-analyses.

Results

For individual beverages, corrected price elasticities are smaller (less elastic) by 28-29 percent compared with consensus averages frequently used for alcohol beverages. The average price and income elasticities are: beer, -0.30 and 0.50; wine, -0.45 and 1.00; and spirits, -0.55 and 1.00. For total alcohol, the price elasticity is -0.50 and the income elasticity is 0.60.

Conclusions

These new results imply that attempts to reduce alcohol consumption through price or tax increases will be less effective or more costly than previously claimed.

SUBMITTER: Nelson JP 

PROVIDER: S-EPMC3722038 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

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Meta-analysis of alcohol price and income elasticities - with corrections for publication bias.

Nelson Jon P JP  

Health economics review 20130724 1


<h4>Background</h4>This paper contributes to the evidence-base on prices and alcohol use by presenting meta-analytic summaries of price and income elasticities for alcohol beverages. The analysis improves on previous meta-analyses by correcting for outliers and publication bias.<h4>Methods</h4>Adjusting for outliers is important to avoid assigning too much weight to studies with very small standard errors or large effect sizes. Trimmed samples are used for this purpose. Correcting for publicatio  ...[more]

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