高校科研投入产出滞后性及效率研究

On Input-Output Lag Effect and Efficiency of Scientific Research in Universities

通过面板自回归分布滞后模型(PADL)探究高校科研投入的产出滞后性,选取山东省25所普通本科高校为研究样本,收集其2010—2020年的数据,分析得出高校科研产出的一般滞后期;基于投入产出滞后效应,通过DEA-BCC模型和DEA-Malmquist指数对样本高校的静态科研效率和动态科研效率进行测算。研究发现:高校科研产出的一般滞后期为1年;2020年多数样本高校为非DEA有效,整体科研效率水平较低;2011—2020年样本高校全要素生产率整体呈现上升趋势,但技术效率和技术进步效率的波动较大,技术效率、规模效率和纯技术效率水平均不高。未来应进一步提升高校科研效率评价的科学性,注重技术进步效率对整体科研效率的影响,同时避免盲目的规模扩张,强化监督与评估管理。

It is necessary to consider the input-output lag effect when measuring the efficiency of scientific research in universities.The panel autoregressive distributed lag(PADL) model is used to explore the output lag effect of scientific research input in universities. Twenty-five undergraduate universities in Shandong Province are selected as the research samples, and their data from 2010 to 2020 are collected. The regression analysis shows the general lag time of the output of scientific research in universities. This study, based on the input-output lag effect, calculates the static and dynamic research efficiency of sample universities by using DEA-BCC model and DEA-Malmquist index. The results show that the general lag time of the output of scientific research in universities is one year, and most of the sample universities in 2020 are not DEA effective, and their overall research efficiency is low. From 2011 to 2020, the total factor productivity of the sample universities shows an overall upward trend, but their technical efficiency and technological progress efficiency fluctuates significantly, and technical efficiency, scale efficiency and pure technical efficiency are not high. In the future, we should further improve the scientific evaluation of scientific research efficiency in colleges and universities and pay more attention to the impact of technological progress efficiency on the overall scientific research efficiency. Meanwhile, we are supposed to avoid blind expansion of scale, and strengthen supervision and evaluation management.