| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w30293 |
| 来源ID | Working Paper 30293 |
| First Foot Forward: A Two-Step Econometric Method for Parsing and Estimating the Impacts of Multiple Identities | |
| Andrew S. Hanks; Kevin M. Kniffin; Xuechao Qian; Bo Wang; Bruce A. Weinberg | |
| 发表日期 | 2022-08-01 |
| 出版年 | 2022 |
| 语种 | 英语 |
| 摘要 | Marketing and strategy researchers have often studied how organizations navigate multiple identities in relation to category spanning but extant literature pays less attention to understanding how individuals do so. Moreover, current econometric approaches only scratch the surface with respect to addressing the impact of multiple identities in professional settings. As a model domain to study labor market returns when individuals have more than one identity, we focus on interdisciplinary dissertators in the United States since evidence shows clear uptrends in dissertators engaging multiple professional identities and unclear trends in their outcomes. Our novel estimation method leverages a two-step process to characterize salaries of interdisciplinary dissertators as functions of the identities (academic fields) they acquire as graduate students. We estimate a first-stage regression of log earnings for monodisciplinarians on field dummies and respondent characteristics. After capturing the estimated field coefficients, we then regress log earnings for interdisciplinarians on linear and non-linear functions of these coefficients. Our estimates robustly reject the hypothesis that interdisciplinarians receive a salary premium. We also find evidence that the academic market, but not other employment sectors, particularly compensates researchers based on their primary discipline, an outcome that challenges emphases on interdisciplinarity. While our findings for interdisciplinarians point to the primary identity holding predominant importance for doctoral graduates in the United States, our two-step method provides a framework for parsing and estimating the varied impacts of multiple identities across a wide range of contexts. |
| 主题 | Labor Economics ; Labor Supply and Demand ; Labor Compensation ; Development and Growth ; Innovation and R& ; D |
| URL | https://www.nber.org/papers/w30293 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/587966 |
| 推荐引用方式 GB/T 7714 | Andrew S. Hanks,Kevin M. Kniffin,Xuechao Qian,et al. First Foot Forward: A Two-Step Econometric Method for Parsing and Estimating the Impacts of Multiple Identities. 2022. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w30293.pdf（1041KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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