<p>This study evaluates Taiwan’s national competitive research funding scheme in higher education from both effectiveness and equity perspectives. Drawing on a 20-year scholar–year panel of 941 Industrial Engineering and Management scholars (2004–2023), we link administrative data from the National Science and Technology Council Research Talent Database to annual measures of research funding and publication output. Academic productivity is captured by the number of journal articles indexed in the Science Citation Index Expanded and Social Sciences Citation Index, as well as an impact-factor-weighted publication count. Scholar- and year-fixed-effects logistic and linear regression models are used to estimate associations between prior publications and subsequent funding success and amounts, and between prior funding and later publication incidence and volume, under varying accumulation windows. Results show strong positive associations across all accumulation windows, with larger coefficients in shorter windows. Descriptive comparisons further reveal disparities in both productivity and funding across institutional sectors and, to a lesser extent, across recorded gender categories, raising concerns about cumulative advantage and stratification within the system. The study demonstrates how large-scale administrative data can support longitudinal evaluation of performance-based research funding in a non-Western, high-R&amp;D-intensity context and informs debates about the design of competitive grant schemes.</p>

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Evaluating a Competitive Research Funding Scheme in Higher Education: A 20-Year Panel Study of Industrial Engineering and Management Scholars in Taiwan

  • To-Cheng Wang

摘要

This study evaluates Taiwan’s national competitive research funding scheme in higher education from both effectiveness and equity perspectives. Drawing on a 20-year scholar–year panel of 941 Industrial Engineering and Management scholars (2004–2023), we link administrative data from the National Science and Technology Council Research Talent Database to annual measures of research funding and publication output. Academic productivity is captured by the number of journal articles indexed in the Science Citation Index Expanded and Social Sciences Citation Index, as well as an impact-factor-weighted publication count. Scholar- and year-fixed-effects logistic and linear regression models are used to estimate associations between prior publications and subsequent funding success and amounts, and between prior funding and later publication incidence and volume, under varying accumulation windows. Results show strong positive associations across all accumulation windows, with larger coefficients in shorter windows. Descriptive comparisons further reveal disparities in both productivity and funding across institutional sectors and, to a lesser extent, across recorded gender categories, raising concerns about cumulative advantage and stratification within the system. The study demonstrates how large-scale administrative data can support longitudinal evaluation of performance-based research funding in a non-Western, high-R&D-intensity context and informs debates about the design of competitive grant schemes.