scholarly journals Does the party in power affect FDI? First causal evidence from narrow margin US state elections

2021 ◽  
pp. 135406882110301
Author(s):  
Kunyu Wang ◽  
Anthony Heyes
Keyword(s):  
Causal Inference ◽  
Foreign Investment ◽  
Regression Discontinuity ◽  
Gubernatorial Elections ◽  
Government Influence

Does the party of government influence the amount and type of inward foreign investment? Correlational studies provide inconsistent evidence. Moreover no existing study, for any level of government or any jurisdiction, uses methods that allow for causal inference. We apply regression discontinuity methods to a set of narrow margin US gubernatorial elections. Over the course of a 4-year term the election of a Republican governor causes a 17% boost in the growth of manufacturing-oriented FDI stock, compared to a Democrat. However, the same approach provides no evidence that partisanship matters for the overall level of FDI.

scholarly journals distcomp: Comparing distributions

2019 ◽  
Vol 19 (4) ◽  
pp. 832-848 ◽  
Author(s):  
David M. Kaplan
Keyword(s):  
Experimental Data ◽  
Causal Inference ◽  
False Positive ◽  
Regression Discontinuity ◽  
Regression Discontinuity Design ◽  
Finite Samples ◽  
Comparing Distributions

In this article, I introduce the distcomp command, which assesses whether two distributions differ at each possible value while controlling the probability of any false positive, even in finite samples. I discuss syntax and the underlying methodology (from Goldman and Kaplan [2018, Journal of Econometrics 206: 143–166]). Multiple examples illustrate the distcomp command, including revisiting the experimental data of Gneezy and List (2006, Econometrica 74: 1365–1384) and the regression discontinuity design of Cattaneo, Frandsen, and Titiunik (2015, Journal of Causal Inference 3: 1–24).

Splitting the Difference? Causal Inference and Theories of Split-party Delegations

2006 ◽  
Vol 14 (4) ◽  
pp. 439-455 ◽  
Author(s):  
Daniel M. Butler ◽  
Matthew J. Butler
Keyword(s):  
Causal Inference ◽  
Strong Evidence ◽  
Regression Discontinuity ◽  
The Other ◽  
Regression Discontinuity Design ◽  
Senate Elections ◽  
The Difference ◽  
Using Data

We provide an introduction to the regression discontinuity design (RDD) and use the technique to evaluate models of sequential Senate elections predicting that the winning party for one Senate seat will receive fewer votes in the next election for the other seat. Using data on U.S. Senate elections from 1946 to 2004, we find strong evidence that the outcomes of the elections for the two Senate seats are independent.

Three Approaches to Causal Inference in Regression Discontinuity Designs

Epidemiology ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. e28-e30 ◽  
Author(s):  
Jacob Bor ◽  
Ellen Moscoe ◽  
Till Bärnighausen
Keyword(s):  
Causal Inference ◽  
Regression Discontinuity ◽  
Regression Discontinuity Designs

scholarly journals Much Ado About Nothing: RDD and the Incumbency Advantage

2017 ◽  
Vol 25 (2) ◽  
pp. 269-275 ◽  
Author(s):  
Robert S. Erikson ◽  
Kelly Rader
Keyword(s):  
Causal Inference ◽  
Regression Discontinuity ◽  
Incumbency Advantage ◽  
Much Ado About Nothing ◽  
Influential Paper ◽  
Incumbent Party ◽  
As If

An influential paper by Caughey and Sekhon (2011a) suggests that the outcomes of very close US House elections in the postwar era may not be as-if random, thus calling into question this application of regression discontinuity for causal inference. We show that while incumbent party candidates are more likely to win close House elections, those who win are no different on observable characteristics from those who lose. Further, all differences in observable characteristics between barely winning Democrats and barely winning Republicans vanish conditional on which party is the incumbent. Any source of a special incumbent party advantage in close elections must be due to variables that cannot be observed. This finding supports the conclusion of Eggers et al. (2015) that Caughey and Sekhon’s discovery of lopsided wins by incumbents in close races is a mere statistical fluke.

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