Balancing the health workforce: breaking down overall technical change into factor technical change for labour-an empirical application to the Dutch hospital industry
Balancing the health workforce: breaking down overall technical change into factor technical change for labour-an empirical application to the Dutch hospital industry
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DOI：
10.1186/s12960-017-0184-5
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发表时间：
2017-02-17
影响因子：
4.5
通讯作者：
van Hulst, Bart L.
中科院分区：
文献类型：
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作者：
Blank, Jos L. T.;van Hulst, Bart L.
背景：训练有素、分布良好和富有成效的卫生工作者对于获得高质量、具有成本效益的医疗保健至关重要。由于既不希望出现卫生工作者短缺，也不希望出现卫生工作者过剩，因此决策者利用劳动力规划模型来获取有关未来劳动力市场的信息，并相应调整政策。劳动力规划模式的一个被忽视的主题是生产力增长，这对未来的劳动力需求有影响。然而，计算特定类型投入的生产率增长并不像看起来那么简单。本研究展示了如何计算要素技术变化（FTC）为特定类型的inputs.Methods：本文首先从理论上推导出FTC从技术变化在一个一致的方式。FTC不同于产出和投入的比例，因为它涉及卫生部门生产过程的多投入、多产出特点。此外，它还考虑了不同投入之间的替代效应。FTCs的计算的应用程序给出了2003-2011年期间的荷兰医院行业。一个translog成本函数估计和用于计算技术变革和FTC为个人的投入，特别是特定的劳动inputs.Results：结果表明，技术变革每年增加2.8%，在荷兰医院在2003-2011年。FTC在不同的输入中有所不同。护理人员的FTC每年增加3.2%，这意味着需要更少的护士才能满足劳动力市场的需求。敏感性分析显示一致的结果为FTC的nurses.Conclusions：生产力的增长，特别是个人的产出，是一个被忽视的主题，在劳动力规划模型。FTC是一种与技术变革相一致的生产率衡量标准，并考虑了替代效应。对荷兰医院行业的应用表明，护理人员的公平贸易委员会在2003-2011年期间超过了技术变革。最佳投入组合发生了变化，导致需要更少的护士来满足劳动力市场的需求。决策者在预测未来对卫生工作者的需求时，应考虑使用关于技术变革性质的更详细和具体的数据。
Background: Well-trained, well-distributed and productive health workers are crucial for access to high-quality, cost-effective healthcare. Because neither a shortage nor a surplus of health workers is wanted, policymakers use workforce planning models to get information on future labour markets and adjust policies accordingly. A neglected topic of workforce planning models is productivity growth, which has an effect on future demand for labour. However, calculating productivity growth for specific types of input is not as straightforward as it seems. This study shows how to calculate factor technical change (FTC) for specific types of input.Methods: The paper first theoretically derives FTCs from technical change in a consistent manner. FTC differs from a ratio of output and input, in that it deals with the multi-input, multi-output character of the production process in the health sector. Furthermore, it takes into account substitution effects between different inputs. An application of the calculation of FTCs is given for the Dutch hospital industry for the period 2003-2011. A translog cost function is estimated and used to calculate technical change and FTC for individual inputs, especially specific labour inputs.Results: The results show that technical change increased by 2.8% per year in Dutch hospitals during 2003-2011. FTC differs amongst the various inputs. The FTC of nursing personnel increased by 3.2% per year, implying that fewer nurses were needed to let demand meet supply on the labour market. Sensitivity analyses show consistent results for the FTC of nurses.Conclusions: Productivity growth, especially of individual outputs, is a neglected topic in workforce planning models. FTC is a productivity measure that is consistent with technical change and accounts for substitution effects. An application to the Dutch hospital industry shows that the FTC of nursing personnel outpaced technical change during 2003-2011. The optimal input mix changed, resulting in fewer nurses being needed to let demand meet supply on the labour market. Policymakers should consider using more detailed and specific data on the nature of technical change when forecasting the future demand for health workers.