Antecedents and Near-Term Consequences for Interdisciplinary Dissertators
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Given the complexity of questions studied by academicians, institutions are increasingly encouraging interdisciplinary research to tackle these problems; however, neither the individual-level pathways leading to the pursuit of interdisciplinary research nor the resulting market outcomes have been closely examined. In this study, we focus attention on the individuals who complete interdisciplinary dissertations to ask “who are they and how do they fare after earning the PhD?” Since interdisciplinary research is known to be relatively risky among academics, we examine demographic variables that are known to be associated in other contexts with risk-taking before considering whether interdisciplinarians’ outcomes are different upon graduating. First among our three main findings, students whose fathers earned a college degree demonstrated a 1.2% higher probability of pursuing interdisciplinary research. Second, the probability that non-citizens pursue interdisciplinary dissertation work is 4.7% higher when compared with US citizens. Third, individuals who complete an interdisciplinary dissertation tend to earn approximately 2% less in the year after graduation; however, mediation analyses show that the decision to become a postdoctoral researcher accounts for the apparent salary penalty. Our findings shed light on the antecedents and near-term consequences for individuals who complete interdisciplinary dissertations and contribute to broader policy debates concerning supports for academic career paths. DOI: 10.1007/s11192-017-2317-y Keywords: Interdisciplinary Research; Wages; Risk; Immigrants *** Information on requesting the 2010 SED Data is online at . *** Sending an initial email to is among the first steps that is required.
Citations (2)
Cited on 01 January 2026
Weight: 1.00
Cited on 27 March 2017
Weight: 1.00
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Publication Details
Subfield
Artificial Intelligence
Field
Computer Science
Domain
Physical Sciences
Confidence Score
63%
Source
Scholar Data Model