Thomas Piketty
Malka Guillot
Jonathan Goupille-Lebret
Bertrand Garbinti
Antoine Bozio
Hakki Yazici
Slavík Ctirad
Kina Özlem
Tilman Graff
Tilman Graff
Yuri Ostrovsky
Martin Munk
Anton Heil
Maitreesh Ghatak
Robin Burgess
Oriana Bandiera
Claire Balboni
Jonna Olsson
Richard Foltyn
Minjie Deng
Iiyana Kuziemko
Elisa Jácome
Juan Pablo Rud
Bridget Hofmann
Sumaiya Rahman
Martin Nybom
Stephen Machin
Hans van Kippersluis
Anne C. Gielen
Espen Bratberg
Jo Blanden
Adrian Adermon
Maximilian Hell
Robert Manduca
Robert Manduca
Marta Morazzoni
Aadesh Gupta
David Wengrow
Damian Phelan
Amanda Dahlstrand
Andrea Guariso
Erika Deserranno
Lukas Hensel
Stefano Caria
Vrinda Mittal
Ararat Gocmen
Clara Martínez-Toledano
Yves Steinebach
Breno Sampaio
Joana Naritomi
Diogo Britto
François Gerard
Filippo Pallotti
Heather Sarsons
Kristóf Madarász
Anna Becker
Lucas Conwell
Michela Carlana
Katja Seim
Joao Granja
Jason Sockin
Todd Schoellman
Paolo Martellini
UCL Policy Lab
Natalia Ramondo
Javier Cravino
Vanessa Alviarez
Hugo Reis
Pedro Carneiro
Raul Santaeulalia-Llopis
Diego Restuccia
Chaoran Chen
Brad J. Hershbein
Claudia Macaluso
Chen Yeh
Xuan Tam
Xin Tang
Marina M. Tavares
Adrian Peralta-Alva
Carlos Carillo-Tudela
Felix Koenig
Joze Sambt
Ronald Lee
James Sefton
David McCarthy
Bledi Taska
Carter Braxton
Alp Simsek
Plamen T. Nenov
Gabriel Chodorow-Reich
Virgiliu Midrigan
Corina Boar
Sauro Mocetti
Guglielmo Barone
Steven J. Davis
Nicholas Bloom
José María Barrero
Thomas Sampson
Adrien Matray
Natalie Bau
Suraj Sridhar
Attila Lindner
Arindrajit Dube
Pascual Restrepo
Łukasz Rachel
Benjamin Moll
Kirill Borusyak
Michael McMahon
Frederic Malherbe
Gabor Pinter
Angus Foulis
Saleem Bahaj
Stone Centre at UCL
Phil Thornton
James Baggaley
Xavier Jaravel
Richard Blundell
Parama Chaudhury
Dani Rodrik
Alan Olivi
Vincent Sterk
Davide Melcangi
Enrico Miglino
Fabian Kosse
Daniel Wilhelm
Azeem M. Shaikh
Joseph Romano
Magne Mogstad
Suresh Naidu
Ilyana Kuziemko
Daniel Herbst
Henry Farber
Lisa Windsteiger
Ruben Durante
Mathias Dolls
Cevat Giray Aksoy
Angel Sánchez
Penélope Hernández
Antonio Cabrales
Wendy Carlin
Suphanit Piyapromdee
Garud Iyengar
Willemien Kets
Rajiv Sethi
Ralph Luetticke
Benjamin Born
Amy Bogaard
Mattia Fochesato
Samuel Bowles
Guanyi Wang
CORE Econ
David Cai
Toru Kitagawa
Michela Tincani
Christian Bayer
Arun Advani
Elliott Ash
Imran Rasul

Shocks, frictions, and inequality in US business cycles

What is this research about and why did you do it?

Inequality in income and wealth in the US has substantially increased since the 1980s and is frequently the subject of public debate. Potential policy responses depend on our understanding of the underlying drivers of inequality. We assess to what extent business cycles, and the policy responses to them, are an important contributor to inequality dynamics in the short and long run. This business cycle perspective expands the study of inequality, which has typically focused on permanent changes such as the rising skill premium or changes in the tax and transfer system.

How did you answer this question?

A new generation of monetary business cycle models that feature heterogeneous agents and incomplete markets, known as HANK models, allow us to study inequality through the lens of the business cycle. We use a full-information Bayesian approach that has become the standard practice in macroeconomics, extending this technique to the analysis of HANK models. We first estimate the model on aggregate time series only, covering the period from 1954 to 2015. We then re-estimate the model with two additional observables for the shares of wealth and income held by the top 10% of households in each of these dimensions.

What did you find?

We find that business cycle shocks explain a substantial fraction of movements in inequality because they generate very persistent movements in wealth and income inequality, shown by the black line in the figure below. These movements are consistent with the U-shaped evolution of US inequality during the period from 1954 to 2015. In the HANK model, transitory shocks persistently redistribute across households because wealth accumulates past shocks and has a long memory. 


The black line shows the evolution of the top 10% wealth share (measured by log deviations) and the contribution of ten sources of shocks implied by the model. Shaded areas correspond to NBER-dated recessions.

Our analysis suggests that increasing price mark-ups, high equity returns (indicated in the figure by investment-specific technology shocks), and low tax progressivity are key drivers of the increase in wealth inequality since the 1980s, while monetary shocks do not play an important role.


What implications does this have for the research on wealth concentration or economic inequality?

Future research on inequality should take business cycles into account, and the study of optimal business cycle policy should take inequality into account. A framework that allows for permanent and transitory shocks is required to study the interaction between trends like skill premia and recessions. Our findings further suggest exploring the implications for inequality of shocks that affect household insurance and portfolios for the business cycle.

What are the next steps in your agenda?

Understanding differences in portfolios is key to understanding wealth inequality. We will further explore:

  • the heterogeneity in household portfolios, 
  • the liquidity of asset markets,
  • the importance of fluctuations in the liquidity of an asset for portfolio choices and the business cycles.

  

Citation and related resources

This paper can be cited as follows: Bayer, C., Born, B., Luetticke, R. (2020). 'Shocks, Frictions, and Inequality in US Business Cycles.' CEPR Discussion Paper 14364. Available at:

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About the authors