Yale Journal of Economics Spring 2013

minimum wage laws and also have earnings and employment data. Although these districts do not share certain characteristics with recognized states, their variation in legal tipped minimum wages is informative.

See Table 1 for a list of the variables I use and their summary statistics. The variable LOGTOTEMP is the log transformation of total number of waiters employed in a given state in a given year. I compile these data for waiters and waitresses by state by year from the Bureau of Labor Statistics’ “Occupation Employment Statistics.� I then take the log transformation for simple interpretation of my coefficients. Furthermore, the log transformation of employment results in a similar R-squared value, such that the log transformation fits the data.

The variable LOGHMEAN is the log transformation of the hourly earnings for the average waiter in a given state in a given year. This variable is likely misreported, since waiters and waitresses generally do not fully report cash tips. This in itself would not introduce an error in variable bias, unless this misreporting was related to time or state-varying characteristics.

One case where this might be true is if credit cards have become more prevalent over time. Since it is impossible to underreport credit card tips, this could increase observed hourly earnings.

As a result, LOGHMEAN might be higher in year 2009 due to increased credit card usage as opposed to shifting state tipped minimum wages. While this bias might indeed be present, it is unlikely that it shifts discontinuously with the FMWA, so I can still employ my instrumental variable to determine causal effects of an increase in the tipped minimum wage.

The variable MINTIPWAGE is the independent variable. I use the variable MINREGWAGE to link the FMWA exogenous shift to changes in tipped minimum wages as described in section 4.

To obtain data on both of these measures, I compile data from the Bureau of Labor Statistics to create a panel data set of regular and tipped minimum wages across states between the years 20042010. These data are based on legal cutoffs and therefore have no other bias except potential state factors that contribute to the setting of tipped minimum wages as well as state-specific waiter employment or earnings.

In order to control for certain variables, I compile demographic 58