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Assessing the Monotonicity Assumption in IV and fuzzy RD designs

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  • Fiorini, Mario
  • Katrien Stevens

Abstract

Whenever treatment e_ects are heterogeneous and there is sorting into treatment based on the gain, monotonicity is a condition that both Instrumental Variable and fuzzy Regression Discontinuity designs have to satisfy for their estimate to be interpretable as a LATE. However, applied economic work rarely discusses this important assumption. This is in stark contrast to the lengthy discussions dedicated to the other IV and fuzzy RD conditions. We show that monotonicity can and should be investigated using a mix of economic insights, data patterns and formal tests. This is just an extra step to validate the results. We provide examples in a variety of settings as a guide to practice.

Suggested Citation

  • Fiorini, Mario & Katrien Stevens, 2014. "Assessing the Monotonicity Assumption in IV and fuzzy RD designs," Working Papers 2014-13, University of Sydney, School of Economics.
    • Handle: RePEc:syd:wpaper:2014-13
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    5. Lionel Page & Dipanwita Sarkar & Juliana S. Goncalves, 2015. "The older the bolder? Does relative age among peers influence children's confidence and risk attitudes?," QuBE Working Papers 029, QUT Business School.
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    7. Anna Godøy & Venke Furre Haaland & Ingrid Huitfeldt & Mark Votruba, 2019. "Impacts of hospital wait time on patient health and labor supply," Discussion Papers 919, Statistics Norway, Research Department.
    8. Claudia Noack, 2021. "Sensitivity of LATE Estimates to Violations of the Monotonicity Assumption," Papers 2106.06421, arXiv.org.

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    Keywords

    essential heterogeneity; monotonicity assumption; LATE; instrumental variable; regression discontinuity;
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