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Efficiency in rail transport: Evaluation of the main drivers through meta-analysis with resampling

机译:铁路运输效率:通过荟萃分析和重采样对主要驱动因素进行评估

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摘要

Meta-analysis is a statistical method used to make a systematic review of the literature to integrate the results of a series of studies. It is increasingly adopted in social sciences but according to our best knowledge used for the first time to aggregate and contrast findings on rail transport efficiency. The experiment adopted a permutation test to evaluate the influence of variables discussed in the literature in the mean efficiency scores. The results suggest that railways located in Japan and in the US have characteristics that push them toward increasing efficiency. The passenger rail systems reached significantly higher estimates than conventional cargo systems. Estimates from parametric and nonparametric models showed significant difference, while from nonparametric models including Data Envelopment Analysis (DEA) and from Network DEA did not. The number of variables and the ratio between the number of decision making units and the number of variables employed significantly influenced the scores. Unexpectedly, different data structures did not. Validation methods are presented. Public policies based on the empirical results are commented.
机译:荟萃分析是一种统计方法,用于对文献进行系统的综述,以整合一系列研究的结果。它在社会科学中越来越多地被采用,但是根据我们首次用于我们的最佳知识,来汇总和对比有关铁路运输效率的发现。实验采用了置换检验来评估文献中讨论的变量对平均效率得分的影响。结果表明,位于日本和美国的铁路具有推动其提高效率的特性。与传统的货运系统相比,客运铁路系统的估算值要高得多。来自参数和非参数模型的估计值显示出显着差异,而来自包括数据包络分析(DEA)和网络DEA的非参数模型的估计值则没有。变量的数量以及决策单位数量与使用的变量数量之间的比率显着影响得分。出乎意料的是,不同的数据结构却没有。提出了验证方法。评论基于经验结果的公共政策。

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