scholarly journals But Economics Is Not an Experimental Science

2010 ◽  
Vol 24 (2) ◽  
pp. 59-68 ◽  
Author(s):  
Christopher A Sims
Keyword(s):  
Instrumental Variables ◽  
Regression Discontinuity ◽  
Single Equation ◽  
Applied Microeconomics ◽  
Computational Experiments ◽  
Regression Discontinuity Design ◽  
Natural Experiments ◽  
Experimental Science ◽  
Rhetorical Devices ◽  
The Core

The fact is, economics is not an experimental science and cannot be. “Natural” experiments and “quasi” experiments are not in fact experiments. They are rhetorical devices that are often invoked to avoid having to confront real econometric difficulties. Natural, quasi-, and computational experiments, as well as regression discontinuity design, can all, when well applied, be useful, but none are panaceas. The essay by Angrist and Pischke, in its enthusiasm for some real accomplishments in certain subfields of economics, makes overbroad claims for its favored methodologies. What the essay says about macroeconomics is mainly nonsense. Consequently, I devote the central part of my comment to describing the main developments that have helped take some of the con out of macroeconomics. Recent enthusiasm for single-equation, linear, instrumental variables approaches in applied microeconomics has led many in these fields to avoid undertaking research that would require them to think formally and carefully about the central issues of nonexperimental inference—what I see and many see as the core of econometrics. Providing empirically grounded policy advice necessarily involves confronting these difficult central issues.

scholarly journals Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments

2001 ◽  
Vol 15 (4) ◽  
pp. 69-85 ◽  
Author(s):  
Joshua D Angrist ◽  
Alan B Krueger
Keyword(s):  
Measurement Error ◽  
Instrumental Variables ◽  
Supply And Demand ◽  
Single Equation ◽  
Causal Relationships ◽  
Natural Experiments ◽  
Omitted Variables ◽  
Demand Elasticities ◽  
Key Variables

Instrumental variables was first used in the 1920s to estimate supply and demand elasticities and later to correct for measurement error in single equation models. Recently, instrumental variables have been widely used to reduce bias from omitted variables in estimates of causal relationships. Intuitively, instrumental variables methods use only a portion of the variability in key variables to estimate the relationships of interest; if the instruments are valid, that portion is unrelated to the omitted variables. We discuss the mechanics of instrumental variables and the qualities that make for a good instrument, devoting particular attention to instruments derived from “natural experiments.”

scholarly journals Geographic Boundaries as Regression Discontinuities

2015 ◽  
Vol 23 (1) ◽  
pp. 127-155 ◽  
Author(s):  
Luke J. Keele ◽  
Rocío Titiunik
Keyword(s):  
Voter Turnout ◽  
Regression Discontinuity ◽  
Regression Discontinuity Design ◽  
Natural Experiments ◽  
Causal Inferences ◽  
Political Advertisements ◽  
Geographic Boundaries ◽  
Random Fashion ◽  
And Control ◽  
Administrative Boundary

Political scientists often turn to natural experiments to draw causal inferences with observational data. Recently, the regression discontinuity design (RD) has become a popular type of natural experiment due to its relatively weak assumptions. We study a special type of regression discontinuity design where the discontinuity in treatment assignment is geographic. In this design, which we call the Geographic Regression Discontinuity (GRD) design, a geographic or administrative boundary splits units into treated and control areas, and analysts make the case that the division into treated and control areas occurs in an as-if random fashion. We show how this design is equivalent to a standard RD with two running variables, but we also clarify several methodological differences that arise in geographical contexts. We also offer a method for estimation of geographically located treatment effects that can also be used to validate the identification assumptions using observable pretreatment characteristics. We illustrate our methodological framework with a re-examination of the effects of political advertisements on voter turnout during a presidential campaign, exploiting the exogenous variation in the volume of presidential ads that is created by media market boundaries.

Methods Matter: p-Hacking and Publication Bias in Causal Analysis in Economics

2020 ◽  
Vol 110 (11) ◽  
pp. 3634-3660 ◽  
Author(s):  
Abel Brodeur ◽  
Nikolai Cook ◽  
Anthony Heyes
Keyword(s):  
Publication Bias ◽  
Instrumental Variables ◽  
Regression Discontinuity ◽  
Causal Analysis ◽  
Randomized Control Trials ◽  
Regression Discontinuity Design ◽  
Hypothesis Tests ◽  
Difference In Differences ◽  
Randomized Control ◽  
Economics Journals

The credibility revolution in economics has promoted causal identification using randomized control trials (RCT), difference-in-differences (DID), instrumental variables (IV) and regression discontinuity design (RDD). Applying multiple approaches to over 21,000 hypothesis tests published in 25 leading economics journals, we find that the extent of p-hacking and publication bias varies greatly by method. IV (and to a lesser extent DID) are particularly problematic. We find no evidence that (i) papers published in the Top 5 journals are different to others; (ii) the journal “revise and resubmit” process mitigates the problem; (iii) things are improving through time. (JEL A14, C12, C52)

scholarly journals Effectiveness of a Power Factor Correction Policy in Improving the Energy Efficiency of Large-Scale Electricity Users in Ghana

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2582 ◽  
Author(s):  
Samuel Lotsu ◽  
Yuichiro Yoshida ◽  
Katsufumi Fukuda ◽  
Bing He
Keyword(s):  
Energy Efficiency ◽  
Power Factor ◽  
Large Scale ◽  
Regression Discontinuity ◽  
Power Factor Correction ◽  
Energy Crisis ◽  
Regression Discontinuity Design ◽  
The Government ◽  
Power Factor Improvement ◽  
The Impact

Confronting an energy crisis, the government of Ghana enacted a power factor correction policy in 1995. The policy imposes a penalty on large-scale electricity users, namely, special load tariff (SLT) customers of the Electricity Company of Ghana (ECG), whose power factor is below 90%. This paper investigates the impact of this policy on these firms’ power factor improvement by using panel data from 183 SLT customers from 1994 to 1997 and from 2012. To avoid potential endogeneity, this paper adopts a regression discontinuity design (RDD) with the power factor of the firms in the previous year as a running variable, with its cutoff set at the penalty threshold. The result shows that these large-scale electricity users who face the penalty because their power factor falls just short of the threshold are more likely to improve their power factor in the subsequent year, implying that the power factor correction policy implemented by Ghana’s government is effective.

How Much of the Incumbency Advantage is Due to Scare-Off?

2015 ◽  
Vol 3 (3) ◽  
pp. 493-514 ◽  
Author(s):  
Andrew B. Hall ◽  
James M. Snyder
Keyword(s):  
Regression Discontinuity ◽  
Regression Discontinuity Design ◽  
Incumbency Advantage ◽  
Previous Election ◽  
Us Senate ◽  
State Legislative

This paper uses a regression discontinuity design to estimate the degree to which incumbents scare off challengers with previous officeholder experience. The estimates indicate a surprisingly small amount of scare-off, at least in cases where the previous election was nearly tied. As Lee and others have shown (and as we confirm for our samples) the estimated party incumbency advantage in these same cases is quite large—in fact, it is about as large as the average incumbency advantage for all races found using other approaches. Drawing from previous estimates of the electoral value of officeholder experience, we thus calculate that scare-off in these cases accounts for only about 5–7 percent of the party incumbency advantage. We show that these patterns are similar in elections for US House seats, statewide offices and US senate seats, and state legislative seats.

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