Measuring the size and dynamics of U.S. state-level shadow economies using a dynamic general equilibrium model with trends

We use a two-sector dynamic deterministic general equilibrium model that specifically accounts for trends among time-series variables to estimate the size of the shadow economy for the 50 U.S. states from 1999 to 2019, following Solis-Garcia and Xie (2018, 2022). This paper improves on existing measures of the state-level shadow economy (such as the multiple indicators, multiple causes (MIMIC) methodology by Wiseman (2013a)). In particular, this new measure is based on theoretical foundations, extends the previous measure to include the Great Recession, includes dollar value estimates of the shadow economy, and produces considerably more variation over time and across states. Furthermore, we explore determinants of this new shadow economy measure using a panel vector autoregressive model and find that, on average, states with higher levels of economic freedom, lower regulatory barriers, and larger real GDP have smaller shadow economies. States with bigger governments, on average, have larger shadow economies, and the effect of corruption on shadow economic activity is non-linear, with a positive initial and subsequent negative impact.