Automated Author ProfileSzerman, Christiane
Princeton University
Szerman, Christiane
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
This paper studies an increasingly popular anti-corruption policy --- corporate debarment or blacklisting --- to understand how both disclosing illicit corporate practices and the sanctions for these practices affect firm and worker outcomes. I exploit a unique policy change in Brazil that imposed stricter penalties for corrupt firms. I combine the universe of firms that were publicly debarred and excluded from public procurement with detailed matched employer-employee administrative data. Using a matched difference-in-differences approach, I find that debarment is associated with a sizable decline in employment and an increase in the probability of exiting the formal sector. I also document that workers' annual earnings fall by about 22 percent after debarment. The impacts are driven by lost revenues from government contracts. Workers who have previously worked in debarred firms also experience earnings losses. The results shed light on the costs to workers when their employers are debarred in weighing the consequences of corruption crackdown.
Authors
- Szerman, Christiane
This paper studies an increasingly popular anti-corruption policy --- corporate debarment or blacklisting --- to understand how both disclosing illicit corporate practices and the sanctions for these practices affect firm and worker outcomes. I exploit a unique policy change in Brazil that imposed stricter penalties for corrupt firms. I combine the universe of firms that were publicly debarred and excluded from public procurement with detailed matched employer-employee administrative data. Using a matched difference-in-differences approach, I find that debarment is associated with a sizable decline in employment and an increase in the probability of exiting the formal sector. I also document that workers' annual earnings fall by about 22 percent after debarment. The impacts are driven by lost revenues from government contracts. Workers who have previously worked in debarred firms also experience earnings losses. The results shed light on the costs to workers when their employers are debarred in weighing the consequences of corruption crackdown.
Authors
- Szerman, Christiane