The fiscal response to revenue shocks

TL;DR

This paper investigates how local governments in Zurich respond to sudden changes in property tax revenues, revealing tendencies to smooth shocks and patterns of fiscal conservatism through causal machine learning methods.

Contribution

It introduces a causal machine learning approach to analyze the effects of revenue shocks on local fiscal policy, focusing on property gains tax in Zurich.

Findings

Local policymakers tend to smooth revenue shocks.

Positive shocks are smoothed, negative shocks are mitigated by spending cuts.

Patterns of fiscal conservatism are observed.

Abstract

We study the impact of fiscal revenue shocks on local fiscal policy. We focus on the very volatile revenues from the immovable property gains tax in the canton of Zurich, Switzerland, and analyze fiscal behavior following large and rare positive and negative revenue shocks. We apply causal machine learning strategies and implement the post-double-selection LASSO estimator to identify the causal effect of revenue shocks on public finances. We show that local policymakers overall predominantly smooth fiscal shocks. However, we also find some patterns consistent with fiscal conservatism, where positive shocks are smoothed, while negative ones are mitigated by spending cuts.