市长经验还是市政能力？秘鲁市政预算执行的负结果证据
Mayoral Experience or Municipal Capacity? Negative-Outcome Evidence on Municipal Budget Execution in Peru
AI总结：
本研究以秘鲁1642个区市政2015-2022年数据，区分市长经验与市政能力对预算执行的影响，发现经验与预算执行相关但无纯粹因果，展示负结果控制可优化领导者效应识别。
AI中文摘要：
当有经验的市长治理绩效更好的市政时，人们很容易将功劳归于这位领导者。然而，行政能力更强的市政也可能更有可能吸引并选举有经验的市长，从而产生对既有市政能力的选择效应。我们利用2015年至2022年期间1642个秘鲁区市政的平衡面板数据来研究这一识别问题。市政内估计量和面板双重机器学习方法发现， prior公共管理经验与投资预算执行之间存在正相关关系，而正规教育的影响则明显弱得多。负结果控制揭示了一个重要区别：在各市政之间，市长的人力资本可预测市长就职前的GDP和人类发展指数（HDI），为横截面比较中的选择效应提供了证据。相比之下，与市政内设计一致的一阶差分负控制显示，市长经验的变化无法预测预先确定的区GDP，但仍与预算执行相关。不过，敏感性分析和部分识别界限并不支持这种市政内关联的纯粹因果解释。因此，研究结果区分了政治领导力研究中经常混淆的两种变异来源，并展示了负结果控制如何能够更清晰地识别领导者效应。
英文摘要：
When experienced mayors govern better-performing municipalities, it is tempting to credit the leader. Yet municipalities with stronger administrative capacity may also be more likely to attract and elect experienced mayors, generating selection on pre-existing municipal capacity. We examine this identification problem using a balanced panel of 1,642 Peruvian district municipalities from 2015 to 2022. Within-municipality estimators and panel double machine learning recover a positive association between prior public-management experience and investment-budget execution, while formal education is substantially weaker. Negative-outcome controls reveal an important distinction. Across municipalities, mayoral human capital predicts GDP and HDI measured before the mayor took office, providing evidence of selection in cross-sectional comparisons. By contrast, the first-difference negative control aligned with the within-municipality design shows that changes in mayoral experience do not predict pre-determined district GDP, while they remain associated with budget execution. Sensitivity analysis and partial-identification bounds nevertheless do not support a clean causal interpretation of that within association. The results therefore separate two sources of variation often conflated in studies of political leadership and show how negative-outcome controls can sharpen the identification of leader effects.
