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Ludger Woessmann (University of Munich) Can Patience Account for Subnational Differences in Student Achievement? Regional Analysis with Facebook Interests (with Eric A. Hanushek, Lavinia Kinne, & Pietro Sancassani) Abstract: Decisions to invest in human capital depend on people’s time preferences. We show that differences in patience are closely related to substantial subnational differences in educational achievement, leading to new perspectives on longstanding within-country disparities. We use social-media data – Facebook interests – to construct novel regional measures of patience within Italy and the United States. Patience is strongly positively associated with student achievement in both countries, accounting for two-thirds of the achievement variation across Italian regions and one-third across U.S. states. Results also hold for six other countries with more limited regional achievement data.
Qiwei He (Cornell) Estimating Matching Games Without Individual-level Data: Multidimensional Sorting in Government Recruitment Abstract: This paper investigates how various matching mechanisms influence government recruitment, taking the National Civil Service Exam (NCSE) — a primary method for recruiting entry-level government officials in China — as the empirical context. I conceptualized the NCSE as a Non-transferable Utility (NTU) matching mechanism where each candidate applies to one position and subsequently takes a meritocratic exam determining admission. An econometric challenge arises due to the absence of individual-level data in the NCSE dataset I collect. To overcome this challenge, I build a NTU matching model with two-sided heterogeneity and demonstrate its non-parametric identification using only position-level data, given instrument availability. Applying this model to the NCSE, I assume that candidates differ in terms of their ability and civic-mindedness, with the latter being undetectable in the exam. Upon estimating the empirical model, I explore the sorting pattern induced by the NCSE. In the counterfactual analysis, I introduce a strategy-proof mechanism as an alternative. In this mechanism, following the meritocratic exam, each candidate chooses one position according to their informed ranking, with higher-ranked candidates choosing first. Simulations show that the counterfactual mechanism more frequently matches candidates with similar abilities and civic-mindedness together in the same positions compared to the NCSE. This is because the exam uncertainty in a "first apply, then exam" system like the NCSE diminishes such sorting. Finally, I explore the potential impact of matching mechanisms on government performance by highlighting the pros and cons of adopting a "first apply, then exam" system (NCSE) or the counterfactual mechanism in the context of multi-dimensional sorting, emphasizing the crucial influence of civic-mindedness.
AI and Data Science in the Workplace
The role of AI and data science on the future of work is a growing emphasis for ILR faculty research and teaching.
New research by Professor Virginia Doellgast is an example of how ILR faculty are studying the effects of new technology on the workplace. In her latest paper, Doellgast explores how workers can be protected from algorithmic management and AI abuses.
With a professional mission of challenging assumptions about limits around disability, Wendy Strobel Gower has a long to-do list and a long list of accomplishments.
the Future of Work.
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