Absolute Technical Efficiency Indices
绝对技术效率指数AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究技术效率、距离函数和 Tobit 估计，未明确提供结构估计或面板方法方面的证据。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
绝对技术效率指数
- 研究问题
- 如何构造可单独解释的技术效率指数，减少对最佳实践参照和误差分布限制的依赖，并分析航空公司的效率与生产率变化？
- 方法与思路
- 作者提出单步估计程序，利用资源利用系数的倒数估计距离函数，在 Tobit 框架中采用超越对数距离函数分析航空公司。
- 材料陈述的贡献
- 摘要称，所得指数可作绝对解释；十九家航空公司应用展示效率差异、危机期间下降及全要素生产率增长来源的变化。
- 阅读关注点
- 应核对绝对效率的定义和资源利用系数的测量，检查 Tobit 设定、距离函数约束及生产率分解如何支持个体解释。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未说明绝对解释的成立条件、误差限制及面板依赖处理，无法确认适用范围。
原文摘要
Technical efficiency indices (TEIs) can be estimated using the traditional stochastic frontier analysis approach, which yields relative indices that do not allow self-interpretations. In this paper, we introduce a single-step estimation procedure for TEIs that eliminates the need to identify best practices and avoids imposing restrictive hypotheses on the error term. The resulting indices are absolute and allow for individual interpretation. In our model, we estimate a distance function using the inverse coefficient of resource utilization, rather than treating it as unobservable. We employ a Tobit model with a translog distance function as our econometric framework. Applying this model to a sample of 19 airline companies from 2012 to 2021, we find that: (1) Absolute technical efficiency varies considerably between companies with medium-haul European airlines being technically the most efficient, while Asian airlines are the least efficient; (2) Our estimated TEIs are consistent with the observed data with a decline in efficiency especially during the Covid-19 crisis and Brexit period; (3) All airlines contained in our sample would be able to increase their average technical efficiency by 0.209% if they reduced their average kerosene consumption by 1%; (4) Total factor productivity (TFP) growth slowed between 2013 and 2019 due to a decrease in Disembodied Technical Change (DTC) and a small effect from Scale Economies (SE). Toward the end of our study period, TFP growth seemed increasingly driven by the SE effect, with a sharp decline in 2020 followed by an equally sharp recovery in 2021 for most airlines.