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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/1910.02170 |
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| _version_ | 1866913939236847616 |
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| author | Rosenman, Evan Rajkumar, Karthik Gauriot, Romain Slonim, Robert |
| author_facet | Rosenman, Evan Rajkumar, Karthik Gauriot, Romain Slonim, Robert |
| contents | Volunteer labor can temporarily yield lower benefits to charities than its costs. In such instances, organizations may wish to defer volunteer donations to a later date. Exploiting a discontinuity in blood donations' eligibility criteria, we show that deferring donors reduces their future volunteerism. In our setting, medical staff manipulates donors' reported hemoglobin levels over a threshold to facilitate donation. Such manipulation invalidates standard regression discontinuity design. To circumvent this issue, we propose a procedure for obtaining partial identification bounds where manipulation is present. Our procedure is applicable in various regression discontinuity settings where the running variable is manipulated. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1910_02170 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Donor's Deferral and Return Behavior: Partial Identification from a Regression Discontinuity Design with Manipulation Rosenman, Evan Rajkumar, Karthik Gauriot, Romain Slonim, Robert Methodology Applications Machine Learning Volunteer labor can temporarily yield lower benefits to charities than its costs. In such instances, organizations may wish to defer volunteer donations to a later date. Exploiting a discontinuity in blood donations' eligibility criteria, we show that deferring donors reduces their future volunteerism. In our setting, medical staff manipulates donors' reported hemoglobin levels over a threshold to facilitate donation. Such manipulation invalidates standard regression discontinuity design. To circumvent this issue, we propose a procedure for obtaining partial identification bounds where manipulation is present. Our procedure is applicable in various regression discontinuity settings where the running variable is manipulated. |
| title | Donor's Deferral and Return Behavior: Partial Identification from a Regression Discontinuity Design with Manipulation |
| topic | Methodology Applications Machine Learning |
| url | https://arxiv.org/abs/1910.02170 |