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Why Does Capital Flow from Equal to Unequal Countries?
Sergio de Ferra, University of Oxford
Kurt Mitman, Institute for International Economic Studies
Federica Romei, University of Oxford
Cristina Arellano, Federal Reserve Bank of Minneapolis

Capital flows from equal to unequal countries. de Ferra, Mitman, and Romei document this empirical regularity in a large sample of advanced economies. The capital flows are largely driven by private savings. de Ferra, Mitman, and Romei propose a theory that can rationalize these findings: more unequal countries endogenously develop deeper financial markets. Households in unequal counties, in turn, borrow more, driving the observed direction of capital flows.

Financial and Total Wealth Inequality with Declining Interest
Daniel Greenwald, Massachusetts Institute of Technology
Matteo Leombroni, Stanford University
Hanno Lustig, Stanford University and NBER
Stijn Van Nieuwerburgh, Columbia University and NBER
Atif R. Mian, Princeton University and NBER

Financial wealth inequality and long-term real interest rates track each other closely over the post-war period. Faced with unanticipated lower real rates, households which rely more on financial wealth must see large capital gains to afford the consumption that they planned before the decline in rates. Lower rates beget higher financial wealth inequality. Inequality in total wealth, the sum of financial and human wealth and the relevant concept for household welfare, rises much less than financial wealth inequality and even declines at the top of the wealth distribution. A standard incomplete markets model reproduces the observed increase in financial wealth inequality in response to a decline in real interest rates because high financial-wealth households have a financial portfolio with high duration.


This paper was distributed as Working Paper 28613, where an updated version may be available.

Fiscal Policy at the Zero Lower Bound without Rational Expectations
Martin S. Eichenbaum, Northwestern University and NBER
Joao Guerreiro, Northwestern University
Riccardo Bianchi Vimercati, Northwestern University
John V. Leahy, University of Michigan and NBER

Eichenbaum, Guerreiro, and Bianchi Vimercati address the question of how sensitive is the power of fiscal policy in the ZLB to the assumption of rational expectations. They do so through the lens of a standard NK model in which people are level-k thinkers. Their analysis weakens the case for using government spending to stabilize the economy when the ZLB binds. The less sophisticated people are, the smaller is the size of the government-spending multiplier. Their analysis strengthens the case for using tax policy to stabilize output when the ZLB is binding. The power of tax policy to stabilize the economy during the ZLB period is essentially undiminished when agents do not have rational expectations. Finally, Eichenbaum, Guerreiro, and Bianchi Vimercati show that the way in which tax policy is communicated is critical to its effectiveness.

Big Push in Distorted Economies
Francisco J. Buera, Washington University in St. Louis and NBER
Hugo Hopenhayn, University of California, Los Angeles and NBER
Yongseok Shin, Washington University in St. Louis and NBER
Nicholas Trachter, Federal Reserve Bank of Richmond
Charles I. Jones, Stanford University and NBER

Why don't poor countries adopt more productive technologies? Is there a role for policies that coordinate technology adoption? To answer these questions, Buera, Hopenhayn, Shin, and Trachter develop a quantitative model that features complementarity in firms' technology adoption decisions: The gains from adoption are larger when more firms adopt. When this complementarity is strong, multiple equilibria and hence coordination failures are possible. More important, even without equilibrium multiplicity, the model elements responsible for the complementarity can substantially amplify the effect of distortions and policies. In what Buera, Hopenhayn, Shin, and Trachter call the Big Push region, the impact of idiosyncratic distortions is over three times larger than in models without such complementarity, without coordination failures playing a role.


This paper was distributed as Working Paper 28561, where an updated version may be available.

Risk-Taking and Monetary Policy Transmission:
Evidence from Loans to SMEs and Large Firms
Cecilia R. Caglio, Federal Reserve Board
Matthew Darst, Federal Reserve Board
Ṣebnem Kalemli-Özcan, University of Maryland and NBER
Yueran Ma, University of Chicago and NBER

Using administrative firm-bank-loan level data from the US, Caglio, Darst, and Kalemli-Özcan document four new facts about the credit market. First, private firms’ (SMEs’) borrowing from banks comprises their entire balance sheet debt, compared to large publicly listed firms who can switch between bond markets and drawing from their credit lines. Second, SMEs borrow shorter maturity and pay higher interest rates relative to large listed firms. Third, SMEs mostly use their enterprise’s continuation value as collateral rather than fixed assets and real estate. Fourth, the relation between collateral and risk—where risk is measured by the loan spread—is positive for large listed firms but negative for SMEs. Based on these facts, Caglio, Darst, and Kalemli-Özcan show that monetary policy transmission and risk-taking differ across SMEs and large listed firms. When monetary policy is expansionary, credit demand of SMEs with high leverage increases more. SMEs’ borrowing capacity expands more given their frequent use of earnings and operations-based collateral. The researchers find no evidence of risk-taking by banks as they lend less to firms who defaulted before and likely to default in the future. Their results from the sample of all US firms mimic those of SMEs and imply that the aggregate effects of monetary policy might depend on the size distribution of firms and the type of collateral used. Since SMEs cover 99 percent of all firms and over 50 percent of US employment and output, their results also have important implications for aggregate boom-bust cycles.


This paper was distributed as Working Paper 28685, where an updated version may be available.

Tasks, Automation, and the Rise in US Wage Inequality
Daron Acemoglu, Massachusetts Institute of Technology and NBER
Pascual Restrepo, Boston University and NBER
Per Krusell, Stockholm University and NBER

Acemoglu and Restrepo document that between 50% and 70% of changes in the US wage structure over the last four decades are accounted for by the relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. They develop a conceptual framework where tasks across a number of industries are allocated to different types of labor and capital. Automation technologies expand the set of tasks performed by capital, displacing certain worker groups from employment opportunities for which they have comparative advantage. This framework yields a simple equation linking wage changes of a demographic group to the task displacement it experiences. Acemoglu and Restrepo report robust evidence in favor of this relationship and show that regression models incorporating task displacement explain much of the changes in education differentials between 1980 and 2016. Their task displacement variable captures the effects of automation technologies (and to a lesser degree offshoring) rather than those of rising market power, markups or deunionization, which themselves do not appear to play a major role in US wage inequality. Acemoglu and RestrepoAcemoglu and Restrepo also propose a methodology for evaluating the full general equilibrium effects of task displacement (which include induced changes in industry composition and ripple effects as tasks are reallocated across different groups). Their quantitative evaluation based on this methodology explains how major changes in wage inequality can go hand-in-hand with modest productivity gains.


This paper was distributed as Working Paper 28920, where an updated version may be available.


Ozge Akinci, Federal Reserve Bank of New York
Carlo Altavilla, European Central Bank
Juan Antolin-Diaz, London Business School
Keelan Beirne, Princeton University
Riccardo Bianchi Vimercati, Northwestern University
Jean-Felix Brouillette, Stanford University
Cristiano Cantore, Bank of England
Oren Danieli, Tel-Aviv University
Kevin Donovan, Yale University
Chiara Felli, LUISS
Filippo Ferroni, Federal Reserve Bank of Chicago
Joel P. Flynn, Massachusetts Institute of Technology
John V. Guria, Reserve Bank of India
Ana Maria. Herrera, University of Kentucky
Tom Holden, Deutsche Bundesbank
Zoltan Jakab, International Monetary Fund
Hannes Malmberg, University of Minnesota
Kristina Manysheva, Northwestern University
Christian Matthes, Indiana University
Elmar Mertens, Deutsche Bundesbank
Max Miller, University of Pennsylvania
Sara Moreira, Northwestern University
Alvaro Ortiz, BBVA Research
Marcelo Pedroni, University of Amsterdam
Ivan Petrella, Warwick University
Margit Reischer, Georgetown University
Tianyue Ruan, National University of Singapore
Hunter Wieman, Williams College
Eran Yashiv, Tel Aviv University

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