Replication Data for: The Effectiveness of a Neighbor-to-Neighbor Get-Out-the-Vote Program: Evidence from the 2017 Virginia State Elections
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'We analyze the results of a neighbor-to-neighbor, grassroots get-out-the-vote (GOTV) drive in Virginia, in which unpaid volunteers were encouraged to contact at least three nearby registered voters who were likely co-partisans yet relatively unlikely to vote in the 2017 state election. To measure the campaign’s effectiveness, we used a pairwise randomization design whereby each volunteer was assigned to one randomly-selected member of the most geographically proximate pair of voters. Because some volunteers unexpectedly signed up to participate outside their home districts, we analyze the volunteers that adhered to the original hyper-local program design separately from those that did not. We find that the volunteers in the original program design drove a statistically significant 2.3% increase in turnout, which was concentrated in the ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Elections","Voter turnout"]} <|eot_id|>
 Here is the Description of a dataset. Description:'We analyze the results of a neighbor-to-neighbor, grassroots get-out-the-vote (GOTV) drive in Virginia, in which unpaid volunteers were encouraged to contact at least three nearby registered voters who were likely co-partisans yet relatively unlikely to vote in the 2017 state election. To measure the campaign’s effectiveness, we used a pairwise randomization design whereby each volunteer was assigned to one randomly-selected member of the most geographically proximate pair of voters. Because some volunteers unexpectedly signed up to participate outside their home districts, we analyze the volunteers that adhered to the original hyper-local program design separately from those that did not. We find that the volunteers in the original program design drove a statistically significant 2.3% increase in turnout, which was concentrated in the ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
We analyze the results of a neighbor-to-neighbor, grassroots get-out-the-vote (GOTV) drive in Virginia, in which unpaid volunteers were encouraged to contact at least three nearby registered voters who were likely co-partisans yet relatively unlikely to vote in the 2017 state election. To measure the campaign’s effectiveness, we used a pairwise randomization design whereby each volunteer was assigned to one randomly-selected member of the most geographically proximate pair of voters. Because some volunteers unexpectedly signed up to participate outside their home districts, we analyze the volunteers that adhered to the original hyper-local program design separately from those that did not. We find that the volunteers in the original program design drove a statistically significant 2.3% increase in turnout, which was concentrated in the 
Elections; Voter turnout