Automated Author ProfileConlin, Michael
Conlin, Michael
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 2.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This paper explores a group rule–utilitarian approach to understanding voter turnout, inspired by the theoretical work of John C. Harsanyi (1980) and Timothy J. Feddersen and Alvaro Sandroni (2002). It develops a model based on this approach and studies its performance in explaining turnout in Texas liquor referenda. The results are encouraging: the comparative static predictions of the model are broadly consistent with the data, and a structurally estimated version of the model yields reasonable coefficient estimates and fits the data well. The structurally estimated model also outperforms a simple expressive voting model.
Authors
- Coate, Stephen ;
- Conlin, Michael
This paper explores a group rule–utilitarian approach to understanding voter turnout, inspired by the theoretical work of John C. Harsanyi (1980) and Timothy J. Feddersen and Alvaro Sandroni (2002). It develops a model based on this approach and studies its performance in explaining turnout in Texas liquor referenda. The results are encouraging: the comparative static predictions of the model are broadly consistent with the data, and a structurally estimated version of the model yields reasonable coefficient estimates and fits the data well. The structurally estimated model also outperforms a simple expressive voting model.
Authors
- Coate, Stephen ;
- Conlin, Michael