社区、感知不平等与再分配偏好:来自巴塞罗那的证据
Neighborhoods, perceived Inequality, and preferences for Redistribution: Evidence from Barcelona
Journal of Public Economics · 2024 · Gerard Domènech-Arumí
中文摘要
• 局部社区基尼系数(LNG)是一种不受行政边界限制的新型局部不平等衡量指标。
• 新的调查证据表明,局部不平等暴露与对全国层面不平等的感知呈正相关。
• 准随机暴露于住宅附近新建公寓楼的个体感知到的不平等程度更高。
• 如果社区会影响再分配需求,其影响也很小。
• 个体在形成全国层面的认知时,会根据其所处的局部环境进行外推(至少在不平等方面如此)。
本文研究社区层面的不平等对全国层面不平等感知和再分配偏好的影响。我利用具有地理位置标记的住房数据构建了一种新的局部不平等衡量指标,并通过在巴塞罗那开展的原创调查获取个体的认知与偏好。局部不平等与不平等感知呈正相关,但与再分配偏好无关。本文利用对新建公寓楼暴露程度的准随机变异来处理内生性分选问题;这种暴露使不平等感知提高了7%,并对再分配需求产生正向但统计不显著的影响。这些效应源于对收入分布顶端收入水平更高的感知。局部不平等塑造了个体对全国层面不平等的认知,但即使它会影响再分配需求,其影响也很小。本研究表明,个体在形成认知时会根据其所处的局部环境进行外推,并凸显了研究社区效应时数据粒度的重要性。
Abstract
• The Local Neighborhood Gini (LNG) is a novel measure of local inequality independent of administrative boundaries. • New survey evidence reveals a positive relation between local inequality exposure and perceived national-level inequality. • Individuals quasi-randomly exposed to a new apartment building close to their homes perceive more inequality. • If neighborhoods influence demand for redistribution, the effect is small. • Individuals extrapolate from their local environments when forming national-level perceptions (at least about inequality). I study the effects of neighborhood-level inequality on perceived national-level inequality and preferences for redistribution. I construct a novel measure of local inequality using geolocated housing data and elicit perceptions and preferences from an original survey conducted in Barcelona. Local inequality is positively associated with perceived inequality but not with preferences for redistribution. I address endogenous sorting by exploiting quasi-random variation in exposure to new apartment buildings; this increases perceived inequality by 7% and has a positive but not statistically significant effect on demand for redistribution. Effects come from higher perceived income at the top. Local inequality shapes national-level inequality perceptions, but if it influences demand for redistribution, the effect is small. This work suggests that individuals extrapolate from their local environments when forming beliefs and highlights the importance of data granularity when studying neighborhood effects.
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