Settling the Inflation Targeting Debate: Lights from a Meta-Regression Analysis

Abstract

Inflation targeting (IT) has gained much traction over the past two decades, becoming a framework of reference for the conduct of monetary policy. However, the debate about its very merits and macroeconomic consequences remains inconclusive. This paper digs deeper into the issue through a meta-regression analysis (MRA) of the existing literature, making it the first application of a MRA to the macroeconomic effects of IT adoption. Building on 8,059 estimated coefficients from a very broad sample of 113 studies, the paper finds that the empirical literature suffers from two types of publication bias. First, authors, editors and reviewers prefer results featuring beneficial effects of IT adoption on inflation volatility, real GDP growth and fiscal performances; second, they promote results with estimated coefficients that are significantly different from zero. However, after filtering out the publication biases, we still find meaningful (genuine) effects of IT in reducing inflation and real GDP growth volatility, but no significant genuine effects on inflation volatility and the level of real GDP growth. Interestingly, the results indicate that the impact of IT varies systematically across studies, depending on the sample structure and composition, the time coverage, the estimation techniques, country-specific factors, IT implementation parameters, and publication characteristics.

IMF programs and economic growth: A meta-analysis

“…The problem of selective reporting has been found to be prominent in some fields in economics. For example, Card & Krueger (1995) and Doucouliagos & Stanley (2009) find evidence of publication bias in the literature studying the effect of minimum wage regulations on employment; Balima et al (2017) uncover publication bias in the literature estimating the macroeconomic effect of inflation targeting adoption; Havranek & Sokolova (2020) document strong publication bias for studies estimating shares of rule-of-thumb consumers with micro-level data due to the underreporting of negative estimates. The intuition behind testing for publication bias can be described as follows.…”

“…For recent examples seeHavránek (2015),Balima et al (2017),Card et al (2017),Doucouliagos et al (2018),Sokolova & Sorensen (2018),Havranek & Sokolova (2020).…”