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Instrumental Variables with Unordered Treatments: Theory and Evidence from Returns to Fields of Study

Author

Listed:
  • Eskil Heinesen
  • Christian Hvid
  • Lars Johannessen Kirkebøen
  • Edwin Leuven
  • Magne Mogstad
Abstract
We revisit the identification argument of Kirkeboen et al. (2016) who showed how one may combine instruments for multiple unordered treatments with information about individuals’ ranking of these treatments to achieve identification while allowing for both observed and unobserved heterogeneity in treatment effects. We show that the key assumptions underlying their identification argument have testable implications. We also provide a new characterization of the bias that may arise if these assumptions are violated. Taken together, these results allow researchers not only to test the underlying assumptions, but also to argue whether the bias from violation of these assumptions are likely to be economically meaningful. Guided and motivated by these results, we estimate and compare the earnings payoffs to post-secondary fields of study in Norway and Denmark. In each country, we apply the identification argument of Kirkeboen et al. (2016) to data on individuals' ranking of fields of study and field-specific instruments from discontinuities in the admission systems. We empirically examine whether and why the payoffs to fields of study differ across the two countries. We find strong cross-country correlation in the payoffs to fields of study, especially after removing fields with violations of the assumptions underlying the identification argument.

Suggested Citation

  • Eskil Heinesen & Christian Hvid & Lars Johannessen Kirkebøen & Edwin Leuven & Magne Mogstad, 2022. "Instrumental Variables with Unordered Treatments: Theory and Evidence from Returns to Fields of Study," NBER Working Papers 30574, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30574
    Note: ED LS TWP
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    References listed on IDEAS

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    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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