4
Empirical Strategy
In order to estimate the effects of higher tipped minimum wages on my two outcome variables (LOGTOTEMP and LOGHMEAN), I break up my analysis in three phases, each technique yielding progressively more causal results. First, I first use a standard OLS regression. This approach can be modeled using the following equation: Yi = α + β 1 Xi + ui
(1)
Second, I capitalize on the massive variation of legal wage structures. To this end, I use a state and time fixed effects model. I employ this specific model in my analysis across 32 states and 2 districts across the years 2004-2010. The fixed effects equation is modeled as follows: Yi = αi + λt + ρDit + ε it
(2)
where • Yit = (1) LOGTOTEMP, (2) LOGHMEAN; • Dit = instrumental variables; dummy variable indicating whether a state was affected • ρ = causal effect of interest • αi = state fixed effect • λt = year fixed effect Third, I employ an instrumental variables (IV) approach to determine causality. The passing of the FMWA in 2007 served as an exogenous shock to certain states, which previously had minimum wages below the new federal levels. This federal shift increased regular minimum wages at varying levels for states across the country. In order to relate this to the tipped minimum wage, I run a first-stage regression using a fixed effects model to establish a positive correlation between an increase in the regular minimum wage and an increase in the tipped minimum wage. This is likely, since as the legal wage is shifted up, states tend to 61