Romer and Romer (2004) propose a simple method to estimate monetary policy shocks using forecasts and real-time data. However, such data is not always (publicly) available, especially in a historical context. We explore the consequences of using revised data instead of the original forecast and real-time data when estimating policy shocks using the Romer and Romer framework. To this end, we estimate policy shocks for the same period as Romer and Romer. We find that using revised data has little impact on actual shock estimates, and the estimated effects of monetary policy shocks are similar.
There is a growing body of literature investigating if and how monetary policy impacts income inequality. Labor unions are generally found to mitigate income inequality and recent literature highlights that changing labor market structures, such as de-unionization, may be important for monetary policy. This paper tests whether labor unions influence the impact of monetary shocks on income inequality in the United States over the period 1970-2008, and the channels this effect runs through. This is the first paper to identify variations in unionization rates as a moderator of the impact of monetary policy on income inequality. I measure income inequality and unionization at the state level and can therefore exploit that unionization rates vary both within and across states while monetary shocks are common to all states. The main finding is that contractionary monetary shocks increase income inequality, but the impact is weaker with a higher union density. A one percentage point monetary shock increases the Gini coefficient by 5.4 % when union density is 5 %, while it increases the Gini coefficient by 1.7 % when union density is 15 %. I find evidence that both wages and employment are two channels explaining how unions mitigate the monetary policy and income inequality relationship. These findings suggest that unions make adjustments to monetary shocks more even across workers, rather than mitigating the aggregate effect of the shocks.
This paper estimates monetary policy shocks for Sweden between 1996-2019. I employ the Romer and Romer (2004) (R&R) approach and use annual forecasts of output growth and inflation to estimate monetary policy shocks. I complement the analysis with shocks from a recursive VAR including output, prices, and the repo rate, as well as a set of high-frequency shocks. A comparison of the three sets of shocks shows that the R&R and VAR shocks are similar, while the high-frequency shocks are fewer and smaller in size. Local projections show expected impulse responses on most economic variables, regardless of data frequency, but responses to the recursive VAR shocks are more in line with textbook findings compared to responses to the R&R and high-frequency shocks. Overall, results are robust to alternative model specifications and lag lengths in local projections.
This paper explores the relationship between monetary policy and individual wealth using Swedish administrative register data for the years 2000-2007. Specifically, I study how monetary shocks impact wealth and wealth inequality, taking into account that shocks can impact the amount of savings, and the value and composition of assets an individual holds. The data covers balance sheets and socioeconomic information on the entire Swedish population. This allows for a comprehensive study of heterogeneous responses to monetary shocks on wealth across the distribution of wealth, age, education, and income, as some examples. Results suggest that contractionary monetary policy shocks decrease the market value of all assets, while there are weaker effects on wealth inequality. Findings indicate distributional effects of monetary shocks when individuals are ranked by net wealth, age, income, and the size of the city they live in. Results point towards lower wealth inequality when monetary shocks are contractionary. Wealthier individuals reduce their quantity of riskier financial assets in response to a policy tightening while less wealthy individuals increase their quantity of real assets.
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