Automated Author ProfileChugh, Sanjay
Ohio State University
Chugh, Sanjay
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: 5.3 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
We build a model to study how the countercyclicality of temporary
layoffs in U.S. data affects unemployment, firm entry and exit dynamics,
and macroeconomic fluctuations. The model can quantitatively generate
the rich cyclical dynamics of temporary layoffs, unemployment, and
firms in the data. We show that the countercyclicality of temporary
layoffs plays a key role in limiting the contraction in job creation
and the rise in unemployment during recessions. Moreover, amid factual
wage dynamics, the countercyclical buffer from temporary layoffs extends
beyond the labor market and limits the depth of contractions in the
number of firms and GDP, with the interaction between temporary layoffs
and endogenous firm exit being particularly important in shaping the
magnitude of the buffer effect. Finally, despite limiting the magnitude
of employment and output contractions during recessions, countercyclical
temporary layoffs appear to have little influence on the pace of recoveries.
Authors
- Chugh, Sanjay ;
- Finkelstein Shapiro, Alan
We build a model to study how the countercyclicality of temporary
layoffs in U.S. data affects unemployment, firm entry and exit dynamics,
and macroeconomic fluctuations. The model can quantitatively generate
the rich cyclical dynamics of temporary layoffs, unemployment, and
firms in the data. We show that the countercyclicality of temporary
layoffs plays a key role in limiting the contraction in job creation
and the rise in unemployment during recessions. Moreover, amid factual
wage dynamics, the countercyclical buffer from temporary layoffs extends
beyond the labor market and limits the depth of contractions in the
number of firms and GDP, with the interaction between temporary layoffs
and endogenous firm exit being particularly important in shaping the
magnitude of the buffer effect. Finally, despite limiting the magnitude
of employment and output contractions during recessions, countercyclical
temporary layoffs appear to have little influence on the pace of recoveries.
Authors
- Chugh, Sanjay ;
- Finkelstein Shapiro, Alan
We build a model to study how the countercyclicality of temporary
layoffs in U.S. data affects unemployment, firm entry and exit dynamics,
and macroeconomic fluctuations. The model can quantitatively generate
the rich cyclical dynamics of temporary layoffs, unemployment, and
firms in the data. We show that the countercyclicality of temporary
layoffs plays a key role in limiting the contraction in job creation
and the rise in unemployment during recessions. Moreover, amid factual
wage dynamics, the countercyclical buffer from temporary layoffs extends
beyond the labor market and limits the depth of contractions in the
number of firms and GDP, with the interaction between temporary layoffs
and endogenous firm exit being particularly important in shaping the
magnitude of the buffer effect. Finally, despite limiting the magnitude
of employment and output contractions during recessions, countercyclical
temporary layoffs appear to have little influence on the pace of recoveries.
Authors
- Chugh, Sanjay ;
- Finkelstein Shapiro, Alan