电晕民粹主义的影响：来自奥地利的经验证据和理论
The Impact of Corona Populism: Empirical Evidence from Austria and Theory
摘要：
我通过调查新冠肺炎疫情期间奥地利一个主要右翼政党（FPOE）的政策转变，研究了危机情况下公众舆论和政党政策之间的共同演变。我的分析表明，存在两个i）“唐氏”效应，这导致选民调整他们的政党偏好的基础上的政策一致性和ii）“政党识别”的效果，这导致党派重新调整他们的政策偏好的基础上，“他们”党的平台。具体来说，我使用个人层面的面板数据来表明，i）在2019年新冠肺炎前的选举中没有投票给FPOE的“电晕怀疑”选民，在该党接受“电晕民粹主义”后更有可能投票给该党，和ii）在2019年宣布投票支持FPOE的受访者的信念与其他人口在四分之三的健康方面存在分歧-只有在转弯后才能看到维度，导致他们低估了与其他人群相比COVID-19带来的威胁。使用总体水平的面板数据，我研究是否转向产生了显着的行为差异，可以观察到的报告病例和人均死亡。奇怪的是，在回合结束后，FPOE投票份额与人均死亡人数显著正相关，但与报告的感染人数无关。我假设这是由于测试中的自我选择偏差造成的，这导致了“电晕怀疑论者”的数量和转向后未报告病例的比例之间的相关性。我在一项Covid-19流行率研究的个人层面数据中找到了对这一假设的实证支持，该研究涉及参与者真实与报告感染状况的信息。最后，我研究了一个简单的异质混合流行病学模型，并表明，测试偏差确实可以解释明显的悖论，增加死亡而不增加报告的病例。
英文摘要：
I study the co-evolution between public opinion and party policy in situations of crises by investigating a policy U-turn of a major Austrian right-wing party (FPOE) during the Covid-19 pandemic. My analysis suggests the existence of both i) a "Downsian" effect, which causes voters to adapt their party preferences based on policy congruence and ii) a "party identification" effect, which causes partisans to realign their policy preferences based on "their" party's platform. Specifically, I use individual-level panel data to show that i) "corona skeptical" voters who did not vote for the FPOE in the pre-Covid-19 elections of 2019 were more likely to vote for the party after it embraced "corona populism", and ii) beliefs of respondents who declared that they voted for the FPOE in 2019 diverged from the rest of the population in three out of four health-dimensions only after the turn, causing them to underestimate the threat posed by Covid-19 compared to the rest of the population. Using aggregate-level panel data, I study whether the turn has produced significant behavioral differences which could be observed in terms of reported cases and deaths per capita. Paradoxically, after the turn the FPOE vote share is significantly positively correlated with deaths per capita, but not with the reported number of infections. I hypothesize that this due to a self-selection bias in testing, which causes a correlation between the number of "corona skeptics" and the share of unreported cases after the turn. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study that involves information about participants' true vs. reported infection status. I finally study a simple heterogeneous mixing epidemiological model and show that a testing bias can indeed explain the apparent paradox of an increase in deaths without an increase in reported cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
