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Pre-evaluating efficiency gains from potential mergers and acquisitions based on the resampling DEA approach: Evidence from China's railway sector

机译:基于重采样DEA方法对潜在合并和收购带来的效率提升进行预评估:来自中国铁路部门的证据

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This study combines resampling DEA and the potential merger gains model to pre-evaluate the efficiency gains of three representative M&A schemes (i.e. regional M&A, megamerger, and a coalition between 'strong' and 'weak' railway bureaus) for China's railway sector over the period 2011-2015. The results reveal that geographically meaningful M&As are better than the other two types in creating efficiency gains due to the special characteristic of the railway sector - network economics. The timing of M&As and the roles and endowments of the railway bureaus must also be considered before any merger. A proper M&A can produce a so-called `stimulant' effect in the short run, but as the `stimulant's efficacy' becomes exhausted over time, the M&A's effect will gradually turn weak. At this time, it is particularly important for policy-makers to introduce a series of desirable institutions. Finally, our empirical findings also support the view that an M&A between two (efficient or not) DMUs does not ensure positive efficiency gains.
机译:这项研究结合了重采样DEA和潜在的合并收益模型,以预先评估中国铁路行业三个代表性的并购计划(即区域并购,大型合并以及“强”和“弱”铁路局之间的联盟)的效率收益。 2011-2015年。结果表明,由于铁路部门的特殊特征-网络经济学,具有地理意义的并购在提高效率方面要优于其他两种类型的并购。在任何合并之前,还必须考虑并购的时间以及铁路局的角色和and赋。适当的并购可以在短期内产生所谓的“刺激”效应,但是随着“刺激剂的功效”随着时间的流逝而逐渐枯竭,并购的效应将逐渐减弱。目前,对于政策制定者而言,引入一系列理想的机构尤为重要。最后,我们的经验发现也支持以下观点:两个(有效或无效)DMU之间的并购不能确保积极的效率提升。

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