Automated Author ProfileShonchoy, Abu S.
Florida International University and IDE-JETRO
Shonchoy, Abu S.
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: 1.4 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Rapid urbanization is reshaping economies and intensifying spatial inequalities. In Bangladesh, we experimentally introduced mobile banking to very poor rural households and family members who had migrated to the city, testing whether mobile technology can reduce inequality by modernizing traditional ways to transfer money. One year later, for active mobile banking users, urban-to-rural remittances increased by 26% of the baseline mean. Rural consumption increased by 7.5% and extreme poverty fell. Rural households borrowed less, saved more, sent additional migrants, and consumed more in the lean season. Urban migrants experienced less poverty and saved more, but bore costs, reporting worse health.
Authors
- Lee, Jean N. ;
- Morduch, Jonathan ;
- Ravindran, Saravana ;
- Shonchoy, Abu S. ;
- Zaman, Hassan
Rapid urbanization is reshaping economies and intensifying spatial inequalities. In Bangladesh, we experimentally introduced mobile banking to very poor rural households and family members who had migrated to the city, testing whether mobile technology can reduce inequality by modernizing traditional ways to transfer money. One year later, for active mobile banking users, urban-to-rural remittances increased by 26% of the baseline mean. Rural consumption increased by 7.5% and extreme poverty fell. Rural households borrowed less, saved more, sent additional migrants, and consumed more in the lean season. Urban migrants experienced less poverty and saved more, but bore costs, reporting worse health.
Authors
- Lee, Jean N. ;
- Morduch, Jonathan ;
- Ravindran, Saravana ;
- Shonchoy, Abu S. ;
- Zaman, Hassan