Automated Author ProfileMartins, Theo Cotrim
Martins, Theo Cotrim
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: 3.5 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
ABSTRACT Context: natural experiments or quasi-experiments have become quite popular in management research. The differences-in-differences (DiD) estimator is possibly the workhorse of these techniques. Objective: the goal of this paper is to provide a tutorial that serves as practical guide for researchers considering using natural experiments to make causal inferences. Methods: we discuss the DiD advantages, concerns, and tests of validity. We also provide an application of the technique, in which we discuss the effect of government guarantees on banks’ degree of risk, using the 2008 financial crisis as a natural experiment. The database used, as well as the Stata and the R scripts containing the analyses, are available as online appendices. Conclusion: DiD may be used to tackle endogeneity concerns when treatment assignment is random.
Authors
- Schiozer, Rafael Felipe ;
- Mourad, Frederico Abou ;
- Martins, Theo Cotrim
ABSTRACT Context: natural experiments or quasi-experiments have become quite popular in management research. The differences-in-differences (DiD) estimator is possibly the workhorse of these techniques. Objective: the goal of this paper is to provide a tutorial that serves as practical guide for researchers considering using natural experiments to make causal inferences. Methods: we discuss the DiD advantages, concerns, and tests of validity. We also provide an application of the technique, in which we discuss the effect of government guarantees on banks’ degree of risk, using the 2008 financial crisis as a natural experiment. The database used, as well as the Stata and the R scripts containing the analyses, are available as online appendices. Conclusion: DiD may be used to tackle endogeneity concerns when treatment assignment is random.
Authors
- Schiozer, Rafael Felipe ;
- Mourad, Frederico Abou ;
- Martins, Theo Cotrim