Automated Author ProfileWolpin, Kenneth I.
Wolpin, Kenneth I.
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: 3.2 (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 uses data from a randomized social experiment in Mexico to estimate and validate a dynamic behavioral model of parental decisions about fertility and child schooling, to evaluate the effects of the PROGRESA school subsidy program, and to perform a variety of counterfactual experiments of policy alternatives. Our method of validation estimates the model without using post-program data and then compares the models predictions about program impacts to the experimental impact estimates. The results show that the models predicted program impacts track the experimental results. Our analysis of counterfactual policies reveals an alternative subsidy schedule that would induce a greater impact on average school attainment at similar cost to the existing program.
Authors
- Todd, Petra E. ;
- Wolpin, Kenneth I.
This paper uses data from a randomized social experiment in Mexico to estimate and validate a dynamic behavioral model of parental decisions about fertility and child schooling, to evaluate the effects of the PROGRESA school subsidy program, and to perform a variety of counterfactual experiments of policy alternatives. Our method of validation estimates the model without using post-program data and then compares the models predictions about program impacts to the experimental impact estimates. The results show that the models predicted program impacts track the experimental results. Our analysis of counterfactual policies reveals an alternative subsidy schedule that would induce a greater impact on average school attainment at similar cost to the existing program.
Authors
- Todd, Petra E. ;
- Wolpin, Kenneth I.