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Replication Data for: Forecasting proportional representation elections from non-representative expectation surveys

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Forecasting proportional representation elections from non-representative expectation surveys
 
Identifier https://doi.org/10.7910/DVN/25XOBR
 
Creator Graefe, Andreas
 
Publisher Harvard Dataverse
 
Description This study tests non-representative expectation surveys as a method for forecasting elections. For dichotomous forecasts of the 2013 German election (e.g., who will be chancellor, which parties will enter parliament), two non-representative citizen samples performed equally well than a benchmark group of experts. For vote-share forecasts, the sample of more knowledgeable and interested citizens performed similar to experts and quantitative models, and outperformed the less informed citizens. Furthermore, both citizen samples outperformed prediction markets but provided less accurate forecasts than representative polls. The results suggest that non-representative surveys can provide a useful low-cost forecasting method, in particular for small-scale elections, where it may not be feasible or cost-effective to use established methods such as representative polls or prediction markets.
 
Subject Social Sciences
 
Contributor Graefe, Andreas