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Assessing the performance of Spanish secondary education institutions: Distinguishing between transient and persistent inefficiency, separated from heterogeneity

机译：评估西班牙二级教育机构的表现：区分瞬态和持续低效率，与异质性分开

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This paper evaluated the performance of Spanish secondary schools whose 15-year-old students were assessed in mathematical competencies by the OECD (PISA program) in 2003 and 2012. The technique employed was the stochastic frontier analysis for panel data using a sample of schools which participated simultaneously in both waves. First, the parametric measurement of time-varying technical inefficiency was done in this paper using three standard models. Second, we used the four random component stochastic frontier model proposed by Kumbhakar, Lien, and Hardaker [2014.Journal of Productivity Analysis, 41(2), 321-337] that distinguishes between residual or transient technical inefficiency and persistent technical inefficiency, separated from heterogeneity. Persistent (time invariant) inefficiency was a larger problem than residual (time varying) inefficiency when evaluating the educational performance of Spanish secondary schools over time. Finally, we introduced the recent model recommended by Badunenko and Kumbhakar [2017.European Journal of Operational Research, 260(2), 789-803] to accommodate heteroscedasticity associated with both heterogeneity and the noise terms, incorporating at the same time determinants of both persistent and time-varying inefficiency. School inefficiency was presumably not caused by something unexpected within each year such as greater difficulty in hiring teachers, but rather by persistent factors such as classroom management-schools with better disciplinary climate tend to be less inefficient in educational production. In addition, we identified the motivation of the students of each school (interest in and enjoyment of mathematics) as the effect of heterogeneity on learning outcomes.
机译：本文评估了2003年和2012年由经合组织（PISA计划）在数学竞争力评估的西班牙中学的表现，由经合组织（比萨计划）于2003年和2012年进行了评估。采用的技术是使用学校样本的面板数据的随机前沿分析在两个波浪中同时参加。首先，使用三种标准模型在本文中完成了时变技术低效率的参数测量。其次，我们使用了Kumbhakar，Lien和Hardaker提出的四个随机组件随机前沿模型[2014.生产力分析学报，41（2），321-337]，区分剩余或瞬态技术效率低，持续技术低效率，分开来自异质性。持久性（时间不变）效率低于残余（时变）低效，当时评估西班牙中学的教育表现随着时间的推移。最后，我们介绍了Badunenko和Kumbhakar推荐的最新模型[2017.European运行研究，260（2），789-803]以适应与异质性和噪声术语相关的异源性，同时包含两者的决定簇持续和时变低效率。学校的低效率可能不是由每年内意外的东西造成的，例如雇用教师的更大困难，而是通过课堂管理学校等持续因素，具有更好的纪律气候往往越来越低，教育生产效率低。此外，我们确定了每所学校的学生（数学兴趣和享受的兴趣）作为异质性对学习成果的影响。

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