Replication Data for: Making Sense of Voting “Habits”: Applying the Process Model of Behavior Change to a Series of Large-Scale Get-Out-the-Vote Experiments (doi:10.7910/DVN/G07AO1)

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Replication Data for: Making Sense of Voting “Habits”: Applying the Process Model of Behavior Change to a Series of Large-Scale Get-Out-the-Vote Experiments

doi:10.7910/DVN/G07AO1

Harvard Dataverse

2023-04-05

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Ternovski, John, 2023, "Replication Data for: Making Sense of Voting “Habits”: Applying the Process Model of Behavior Change to a Series of Large-Scale Get-Out-the-Vote Experiments", https://doi.org/10.7910/DVN/G07AO1, Harvard Dataverse, V1

Replication Data for: Making Sense of Voting “Habits”: Applying the Process Model of Behavior Change to a Series of Large-Scale Get-Out-the-Vote Experiments

doi:10.7910/DVN/G07AO1

Ternovski, John (US Air Force Academy)

Harvard Dataverse

Ternovski, John

Ternovski, John

2023-04-05

https://doi.org/10.7910/DVN/G07AO1

Social Sciences, turnout persistence, experiment, habit

I apply a new theoretical framework to voting to more cohesively bridge the economic cost-benefit model of voting with the psychology-motivated voting-as-a-habit literature. This new theoretical frame gives greater clarity as to how a vote in one election might beget a vote in another election, while yielding testable predictions as to which circumstances are more favorable for developing turnout persistence. To test these predictions, I make use of a novel dataset consisting of nine large-N, door-to-door voter mobilization field experiments in various election contexts (with ~1.8 million voters in total). Consistent with prior empirical research, my analysis finds that being nudged to vote in one election leads to increased turnout four years later. But the main contribution of this paper is that the theoretical framework’s predictions and the corresponding empirical results make sense of turnout persistence heterogeneities that have been detected in certain prior empirical studies but not others.

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1_analysis.do

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Stata code for all analysis conducted in the paper.

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analysis_file.dta

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Data file with synthetic variables (generated as part of 1_analysis.do).

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downstream_turnout_full.dta

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Source data file (without synthetic variables).

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log.txt

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A file showing the full output of running 1_analysis.do in Stata.

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README.txt

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A readme file describing the replication files.

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