st: analysing experimental panel data

[Matthew Sunderland <matthews@unsw.edu.au>]

Hi All

I am seeking advice on how best to analyse data arising from an experiment. We surveyed 2,000 people asking them to hypothetically purchase and consume alcohol for an imaginary Saturday night. 

We collected data for three imaginary nights - First we presented participants with a set of alcohol prices reflecting current prices (baseline). We presented participants with two mores set of prices in a randomized order reflecting  price increase resulting from i) the establishment of a minimum price and ii) an increase in the rate of tax. Participants comprise six quotas, differentiated by gender and recent cannabis and ecstasy use.  Alcohol consumption is measured by the number of standard drinks, calculated by us from participant reports of how many items of alcohol they would consume eg glasses of wine, stubbies of beer etc. About 30% of the participants did not drink at baseline. 

We'd like to know: Do the two reforms have different impacts? Do people in different quotas respond differently to the reforms?  Do people with different levels of base-line drinking  respond differently to the reforms? 

One option we've thought of is for us to run two sets of fixed effects analysis (washes unobserved heterogeneity relating to alcohol consumption and quota membership)- using panel data for drinking at baseline and one of the reforms. Another option is for us to simply control for baseline consumption. We're thinking of running the analysis in two steps - a logit for whether or not someone drinks and an OLS regression for drinkers  -  log of standard drinks consumed, controlling for the predicted values coming from the logit. 

Thanks,

Dr Matthew Sunderland
Drug Policy Modelling Program,  National Drug and Alcohol Research Centre 
The University of New South Wales
Sydney NSW AUSTRALIA 2052


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