Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Economic agents react to signals about future tax policy changes. Consequently, estimating their macroeconomic effects requires identification of such signals. We propose a novel text analytic approach for transforming textual information into an economically meaningful time series. Using this method, we create a tax news measure from all publicly available postwar communications of US presidents. Our measure predicts the direction and size of future tax changes and contains signals not present in previously considered (narrative) measures of tax changes. We investigate the effects of tax news and find that, for long anticipation horizons, pre-implementation effects lead initially to contractions in output.