Deconstructing Monetary Policy Surprises—The Role of Information Shocks

By Marek Jarociński and Peter Karadi

American Economic Journal: Macroeconomics, April 2020

Central bank announcements simultaneously convey information about monetary policy and the central bank's assessment of the economic outlook. This paper disentangles these two components and studies their effect on the economy using a structural vector au...

NSF has new Rapid Response Research funding program on COVID-19

[News]

May 08, 2020

The National Science Foundation (NSF) invites research proposals using the Rapid Response Research funding mechanism.

Tracking the evolution of work

[Page]

A new dataset on job tasks provides insight into how employment in the US changed.

Call for Papers: PSID User Conference 2020

[News]

March 13, 2020

The deadline for submissions is June 1, 2020.

Supplying a fiscal lifeline

[Research Highlight]

May 11, 2020

Valerie Ramey discusses what economists have learned about stimulus since the 2008 financial crisis.

Front Matter

AEA Papers and Proceedings, May 2020

Measuring the Impact of Free Goods on Real Household Consumption

By Erik Brynjolfsson, Avinash Collis, W. Erwin Diewert, Felix Eggers, and Kevin J. Fox

AEA Papers and Proceedings, May 2020

We suggest a methodology that allows statistical agencies to form approximations to the benefits that flow to households from new free goods. The present production-oriented GDP measures are not satisfactory for measuring real household consumption and wi...

Innovation α: What Do IP-Intensive Stock Price Indexes Tell Us about Innovation?

By Carol Corrado, David Martin, and Qianfan Wu

AEA Papers and Proceedings, May 2020

Patents and other intellectual property (IP) have grown in relative importance in investments and market capitalizations of public firms (e.g., Corrado and Hulten 2010). This paper illustrates the construction of IP-intensive stock price indexes, focusing...

Estimating Family Income from Administrative Banking Data: A Machine Learning Approach

By Diana Farrell, Fiona Greig, and Erica Deadman

AEA Papers and Proceedings, May 2020

The JPMorgan Chase Institute uses administrative banking data for research. In order to address representativeness in our data, we seek a reliable estimate of gross family income for population segmenting and reweighting purposes. JPMC Institute Income Es...

Technology and Big Data Are Changing Economics: Mining Text to Track Methods

By Janet Currie, Henrik Kleven, and Esmée Zwiers

AEA Papers and Proceedings, May 2020

The last 40 years have seen huge innovations in computing and in the availability of data. Data derived from millions of administrative records or by using (as we do) new methods of data generation such as text mining are now common. New data often requir...

Internalizing Externalities: Designing Effective Data Policies

By Ryan Hill, Carolyn Stein, and Heidi Williams

AEA Papers and Proceedings, May 2020

Many economics journals have recently invested in efforts to archive and curate research data and promote reproducible research. The economics profession has focused relatively less attention on what types of institutions and incentives might encourage an...

In Praise of Confidence Intervals

By David Romer

AEA Papers and Proceedings, May 2020

Most empirical papers in economics focus on two aspects of their results: whether the estimates are statistically significantly different from zero and the interpretation of the point estimates. This focus obscures important information about the implicat...

Research Transparency Is on the Rise in Economics

By Nicholas Swanson ⓡ Garret Christensen ⓡ Rebecca Littman ⓡ David Birke ⓡ Edward Miguel ⓡ Elizabeth Levy Paluck ⓡ Zenan Wang

AEA Papers and Proceedings, May 2020

This study provides a first assessment of awareness of, attitudes toward, perceived norms regarding, and adoption of open science practices within a broadly representative sample of active economics researchers. We observe a steep increase in adoption ove...

A Proposed Specification Check for p-Hacking

By Abel Brodeur, Nikolai Cook, and Anthony Heyes

AEA Papers and Proceedings, May 2020

We propose a specification check for p-hacking. More specifically, we advocate the reporting of t-curves and mu-curves—the t-statistics and estimated effect sizes derived from regressions using every possible combination of control variables from the re...

Do Pre-analysis Plans Hamper Publication?

By George K. Ofosu and Daniel N. Posner

AEA Papers and Proceedings, May 2020

Scholars assert that pre-analysis plans (PAPs) generate boring, lab-report style papers and thus hamper publication. We test this claim by comparing the publication rates of experimental NBER working papers with and without PAPs. We find that articles wit...

Forecasting the Results of Experiments: Piloting an Elicitation Strategy

By Stefano DellaVigna, Nicholas Otis, and Eva Vivalt

AEA Papers and Proceedings, May 2020

Forecasts of experimental results can clarify the interpretation of research results, mitigate publication bias, and improve experimental designs. We collect forecasts of the results of three Registered Reports preliminarily accepted to the Journal of Dev...

The Allocation of Decision Authority to Human and Artificial Intelligence

By Susan C. Athey, Kevin A. Bryan, and Joshua S. Gans

AEA Papers and Proceedings, May 2020

The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined. The principal trades off an AI's more aligned choice with the need to motivate the human agent to expend effort in learning choice ...

The Managerial Effects of Algorithmic Fairness Activism

By Bo Cowgill, Fabrizio Dell'Acqua, and Sandra Matz

AEA Papers and Proceedings, May 2020

How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by h...

An Economic Perspective on Algorithmic Fairness

By Ashesh Rambachan, Jon Kleinberg, Jens Ludwig, and Sendhil Mullainathan

AEA Papers and Proceedings, May 2020

There are widespread concerns that the growing use of machine learning algorithms in important decisions may reproduce and reinforce existing discrimination against legally protected groups. Most of the attention to date on issues of "algorithmic bias" or...

Algorithmic Social Engineering

By Bo Cowgill and Megan T. Stevenson

AEA Papers and Proceedings, May 2020

We examine the microeconomics of using algorithms to nudge decision-makers toward particular social outcomes. We refer to this as "algorithmic social engineering." In this article, we apply classic strategic communication models to this strategy. Manipula...