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Empirical Practice in Economics: Challenges and Opportunities

Paper Session

Sunday, Jan. 5, 2020 8:00 AM - 10:00 AM (PDT)

Marriott Marquis, San Diego Ballroom A
Hosted By: American Economic Association
  • Chair: Guido Imbens, Stanford University

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

Janet Currie
,
Princeton University
Henrik Kleven
,
Princeton University
Esmée Zwiers
,
Princeton University

Abstract

This paper presents a plea for methodological tolerance. There is some irony in the fact that economists do not view themselves as ideological, and yet often have extremely strong views about methods. There is seldom one and only one way to address a question and that it is unfortunate that economists are increasingly divided into methodological rather than ideological camps. Some examples are divides between experimental vs. non experimental methods (and field vs. lab experiments); structural vs. non-structural methods; and in many applied fields, the “identification police” who make it impossible to publish work that is “merely” descriptive even if it uncovers interesting new facts about the world.

In Praise of Confidence Intervals

David Romer
,
University of California-Berkeley

Abstract

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 implications of the results for economically interesting hypotheses about values of the parameters other than zero, and in some cases, about the strength of the evidence against values of zero. This limitation can be overcome by reporting confidence intervals for papers’ main estimates and discussing the economic interpretation of parameter values within the confidence intervals.

Internalizing Externalities: Designing Effective Data Policies

Ryan Hill
,
Massachusetts Institute of Technology
Carolyn Stein
,
Massachusetts Institute of Technology
Heidi Williams
,
Stanford University

Abstract

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 and subsidize the creation and sharing of datasets that are likely to encourage novel follow-on research of high social value. This paper describes some examples from other scientific fields of institutions and incentives designed to promote subsequent research, and speculates on some potential reforms that could be undertaken within economics to encourage the type of data sharing that is most likely to encourage socially valuable follow-on research.
Discussant(s)
Lawrence Katz
,
Harvard University
Guido Imbens
,
Stanford University
Benjamin F. Jones
,
Northwestern University
JEL Classifications
  • C1 - Econometric and Statistical Methods and Methodology: General