除了创新补贴的平均效果之外:通过匹配来选择案例,以打破为决策者提供有用建议的局
Maciej Koniewski1, Seweryn Krupnik1, Paulina Skórska1
1Institute of Sociology, Jagiellonian University, Poland.
Evaluation and program planning
|April 7, 2024
概括
这项研究结合了反事实和基于理论的程序评估方法. 该方法揭示了影响创新补贴成功或失败的因素,超出了平均影响估计.
科学领域:
- 项目评价 项目评价
- 创新经济学的创新经济学
- 因果推理的原因推理.
背景情况:
- 专家们主张在项目评估中整合反事实和基于理论的方法.
- 结合这些方法可以提供对干预有效性的更深入的见解.
- 以前的评估通常依赖于单一的方法,这可能会限制理解.
研究的目的:
- 在单个程序评估中展示反事实和基于理论的方法的联合应用.
- 通过识别导致成功或失败的因素来评估创新补贴的影响.
- 超越平均效果估计,揭示干预结果的细微原因.
主要方法:
- 使用"通过匹配选择案例"来识别可比的公司进行分析.
- 进行了跟进深度采访,与选定的公司进行了深度采访.
- 采访了对比的两对公司 (成功与不成功) 在关键共变量上进行了匹配.
主要成果:
- 综合方法揭示了传统数据分析可能遗漏的因素.
- 采访中的定性见解为定量发现提供了背景.
- 确定了创新补贴计划中成功和失败的具体驱动因素.
结论:
- 综合方法提高了对干预机制和结果的理解.
- 这种方法提供了比单纯的平均效应估计更丰富的方案效应分析.
- 它为改善创新支持政策的设计和实施提供了有价值的见解.
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