Output, employment, and price effects of U.S. narrative tax changes: a factor-augmented vector autoregression approach

This paper examines the short- and medium-run effects of U.S. federal personal income and corporate income tax cuts on a wide array of economic policy variables in a data-rich environment. Using a panel of U.S. macroeconomic data set, made up of 132 quarterly macroeconomic series for 1959–2018, we estimate factor-augmented vector autoregression (FAVARs) models where an extended narrative tax changes dataset combined with unobserved factors. The narrative approach classifies if tax changes are exogenous or endogenous. This paper identifies narrative tax shocks in the vector autoregression model using the sign restrictions with the Uhlig's (J Monet Econ 52(2):381–419, 2005. https://doi.org/10.1016/j.jmoneco.2004.05.007 ) penalty function. Empirical findings show a significant expansionary effect of tax cuts on the macroeconomic variables. Cuts in personal and corporate income taxes cause a rise in output, investment, employment, and consumption; however, the effects of corporate tax cuts have relatively smaller effects on output and consumption but show immediate and higher effects on fixed investment and price levels. We validate the model's specification and the identification of tax shocks through a reliability test based on the Median-Target method. Additionally, sensitivity analysis employing the local projection vector autoregression model, number of iterations of the algorithm, and incorporating diverse factor specifications reaffirms tax cuts' persistent and expansionary effects. Our contribution to the narrative tax literature lies in providing empirical evidence that aligns with the notion that reductions in personal taxes demonstrate a higher efficacy as a fiscal policy tool when compared to reductions in corporate income taxes.