Replication data for: Estimating the Probability of Events That have Never Occurred: When Is Your Vote Decisive? (doi:10.7910/DVN/0FT6ZL)

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Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Part 5: Other Study-Related Materials
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Replication data for: Estimating the Probability of Events That have Never Occurred: When Is Your Vote Decisive?

doi:10.7910/DVN/0FT6ZL

Harvard Dataverse

2007-12-09

4

Gelman, Andrew; King, Gary; Boscardin, John, 2007, "Replication data for: Estimating the Probability of Events That have Never Occurred: When Is Your Vote Decisive?", https://doi.org/10.7910/DVN/0FT6ZL, Harvard Dataverse, V4, UNF:3:ORDulVH6qEb4lsCyDn5W3A== [fileUNF]

Replication data for: Estimating the Probability of Events That have Never Occurred: When Is Your Vote Decisive?

doi:10.7910/DVN/0FT6ZL

Gelman, Andrew (Columbia University)

King, Gary (Harvard University)

Boscardin, John (University of California, Los Angeles)

1998

Harvard Dataverse

Harvard Dataverse

2006

1998

https://doi.org/10.7910/DVN/0FT6ZL

Social Sciences

Researchers sometimes argue that statisticians have little to contribute when few realizations of the process being estimated are observed. We show that this argument is incorrect even in the extreme situation of estimating the probabilities of events so rare that they have never occurred. We show how statistical forecasting models allow us to use empirical data to improve inferences about the probabilities of these events. Our application is estimating the probability that your vote will be decisive in a U.S. presidential election, a problem that has been studied by political scientists for more than two decades. The exact value of this probability is of only minor interest, but the number has important implications for understanding the optimal allocation of campaign resources, whether states and voter groups receive their f air share of attention from prospective presidents, and how formal "rational choice" models of voter behavior might be able to explain why people vote at all. We show how the probability of a decisive vote can be estimated empirically from state-level forecasts of the presidential election and illustrate with the example of 1992. Based on generalizations of standard political science forecasting models, we estimate the (prospective) probability of a single vote being decisive as about 1 in 10 million for close national elections such as 1992, varying by about a factor of 10 among states. Our results support the argument that subjective probabilities of many types are best obtained through empirically based statistical prediction models rather than solely through mathematical reasoning. We discuss the implications of our findings for the types of decision analyses used in public choice studies. <br /><br /> See also: <a href="http://gking.harvard.edu/category/research-interests/applications/presidency-research-voting-behavior" target="_blank">Presidency Research; Voting Behavior; Rare Events</a>

This dataset is made available without information on how it can be used. You should communicate with the Contact(s) specified before use.

Andrew Gelman, Gary King, John Boscardin. 1998. "Estimating the Probability of Events That have Never Occurred: When is Your Vote Decisive?," Journal of the American Statistical Association, Vol. 93, No. 441, Pp. 1-9: <a href="http://j.mp/mStKVJ" target="_blank">Link to article</a> and <a href="http://nrs.harvard.edu/urn-3:HUL.InstRepos:4266309" target="_blank">Link to DASH</a>

Andrew Gelman, Gary King, John Boscardin. 1998. "Estimating the Probability of Events That have Never Occurred: When is Your Vote Decisive?," Journal of the American Statistical Association, Vol. 93, No. 441, Pp. 1-9: <a href="http://j.mp/mStKVJ" target="_blank">Link to article</a> and <a href="http://nrs.harvard.edu/urn-3:HUL.InstRepos:4266309" target="_blank">Link to DASH</a>

• Number of cases: 594

• No. of variables per record: 23

• Type of File: text/tab-separated-values

UNF:3:ORDulVH6qEb4lsCyDn5W3A==

Data used in this study

• DVOTE - Democratic Vote
• YEAR - Year
• STATE - State
• CONSTANT - Constant term
• N1 - nationwide variables
• N2 - nationwide variables
• N3 - nationwide variables
• N4 - nationwide variables
• S1 - statewide variables
• S2 - statewide variables
• S3 - statewide variables
• S4 - statewide variables
• S5 - statewide variables
• S6 - statewide variables
• S7 - statewide variables
• S8 - statewide variables
• S9 - statewide variables
• R1 - South
• R2 - South in 1964
• R3 - regional variables
• R4 - regional variables
• R5 - regional variables
• R6 - regional variables

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Label:

Estimating Data

Text:

Program and data used in the study as well as Bayesian data analysis by Gelman and others, ASCII format

text/plain; charset=US-ASCII

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Estimating Probability of Events.pdf

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Article related to this study: Estimating the Probability of Events That have Never Occurred: When Is Your Vote Decisive?

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