Supply, Demand and Estimation

The Nevada Gaming Control Board in its Gaming Revenue Reports provides monthly hold data for slot machines (http://gaming.nv.gov/index.aspx?page=149). Hold data is the aggregate of what is payed out to Players divided by what they pay to play. From this, the long run trends in holds can be established.  For example consider Dr. David G Schwartz’ claim that holds have tightened by 13.54% between 2004 and 2014 in the following report: http://gaming.unlv.edu/reports/nv_slot_hold.pdf.  

In the above chart I have placed aggregate monthly data versus a linear trend beginning in January of 2004.  It illustrates Schwartz’ claims, but how useful is this data for evaluating hypotheses like a “great contraction” corresponding to the 2007-2010 economic turmoil?  It turns out it is not so obvious how to interpret this kind of data, and the reason is that each month we are observing the intersection of both an aggregate demand and an aggregate supply function.

A rise in hold from one period to the next could for example mean that on average Players have chosen to play tighter games, or could be that other things equal, the Casino has tightened the average return of its machines.  The reality, we should all realize, is we are seeing layers of strategy from both parties.

So what is it that we need?  First steps might involve analyzing the data by region, which reveals that the Casinos and Players differ in their strategies, region to region.  For example, Elko seems to have tightened during the recession, whereas Mesquite seems to have tinkered less than they did prior to the recession.  One single model is unlikely to explain all the data, and each region can be explained by multiple models.

I bet Players, Casino operators and salesmen in the regions could tell me a lot about what happened during this time.  But given the difficulty of finding all of the relevant information, we have to settle for models which, using what information we have, prescribe behaviors to the Players and Casinos.  Given these assumptions, we then see what the data has to say.  Fortunately we will have lots of models to choose from, and each model chosen should teach us something new.

As a start, I would want to know the change in employment (both aggregate and by region) for each month.  We could use it as a predictor for the change in hold, and this would probably be the best way to gauge the effect of a typical Player’s income.  However, in the regions of interest, the Casinos are also major suppliers of jobs, so the employment data will also give partial information about the financials of the Casinos. It stands to reason that a Casino's financial situation determines in part their slot floor strategy. It is up to the researcher then to make assumptions when interpreting the results (in this example determining the effect of employment on hold through supply and demand channels).

Cleaning the data using time series methods is another step I would likely take.  This would filter out noise from seasonal patterns like Casino purchase timings or the Christmas season, and also long run trends.  But even though time series methods help us get a clearer picture, they implicitly make assumptions about the behavior of Players and Casinos.  Really there isn't a way to do the empirical work without making economic assumptions.  To me this means that we must use our training, experience and imaginations to come up with models that have meaningful predictions, and then test them against the data.