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Neighborhoods, Perceived Inequality, and Preferences for Redistribution :Evidence from Barcelona. (2022). Arumi, Gerard Domenech.
In: Working Papers ECARES.
RePEc:eca:wpaper:2013/341785.

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  1. Housing, Neighborhoods and Inequality. (2023). Domènech-Arumí, Gerard ; Gobbi, Paula Eugenia ; Domenech-Arumi, Gerard.
    In: Working Papers ECARES.
    RePEc:eca:wpaper:2013/356803.

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  10. (a) Year 2012 (b) Year 2015 Figure 6: Example of an “apartment building shock” Notes: Example of a new apartment building shock. In 2012, the Local Neighborhood Gini (LNG) (r = 100) associated with the building in the top panel was 0.02. In 2015, the LNG increased to 0.23 after the construction of a new apartment building on a former parking lot. Building details: C/ Aiguablava 3, 08042 Barcelona (Cadastre code 1690916DF3819B).
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  11. 13A result consistent with previous research (e.g., Cruces et al. 2013, Sands 2017). demand for redistribution between 6 and 9% of a SD. Effects are also slightly stronger among left-wingers and older individuals, but differences are not significant.
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  12. 2Evidence suggests that interactions at the local level matter. For example, Wellman (1996), in the context of Toronto, shows that close neighbors account for a significant share of contacts. Bayer et al. (2008), in the context of Boston, shows that interactions at the city-block level can have positive effects in the marketplace, for example, in terms of job referrals.
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  13. 3Several studies suggest that random forests typically overperform standard hedonic price regressions and other machine learning methods such as LASSO (Čeh et al. 2018, Fan et al. 2006, Mullainathan and Spiess 2017). 4I implemented the algorithm using hyperparameter tunning (sample split, variables per split, nodes). The final prediction grew 500 trees, nine nodes, an 80% sample split, 42 variables to split in each node, and allowing the algorithm to decide on each variable’s importance based on the reduction of node impurity after each split.
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  14. 9Information treatments are sometimes unable to shift beliefs. Research suggests that these types of experiments are more effective when they are less informational and have a strong visual or emotional component (Engelhardt and Wagener 2018, Kuziemko et al. 2015). of a SD (1.3 to 2.5% of the mean). These shifts are slightly larger and more significant at the top (percentiles 90 and 99) and in the middle (percentiles 50 and 30). In other words, the treatment did not significantly affect beliefs about inequality but made participants think there was more income to redistribute. That explains the positive but not significant effect on Perceived Gini, and it might partly explain the small effect on Preferences for Redistribution documented in Table D2.
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  15. • Household income question: do not ask about current income. Ask about income earned during 2019 instead. • Unemployment question: add COVID as a reason for unemployment. • On several questions (e.g., commuting and social interactions): explicitly ask before and after COVID. Make sure the question is well-adapted to the pandemic (e.g., add “work from home” as an option in the commuting question).
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  21. All continuous variables are standardized. Preferences for Redistribution measures demand for redistribution in a scale from 0 to 10, with 10 representing the highest demand for redistribution. New Building Treatment is an indicator taking the value of 1 if the individual resides within 350 meters of a new construction (built in 2017-19).
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  30. Citywide value/space inequality is above the mean LNG. It is helpful to go back to the toy example in Figure A1 to interpret this result. Both cities have a City Gini of 0.167, but they substantially differ in their mean LNG. The mean LNG in City 2 is 0.161. It is 0.091 in City 1 (about 43% smaller). The large discrepancy is due to the differential spatial distribution of dwellings within the city or, in other words, due to differences in residential segregation. As Glaeser et al. (2009) articulated, local inequality and segregation are essentially two sides of the same coin. Hence, even if not formally defined in this paper, the gap between city Gini and mean LNG is informative about the level of housing segregation in the city.
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  31. Columns 3-6 further restrict the sample to individuals who have lived in the same dwelling since at least 2010 or 2005. Columns 7-10 further restrict the sample to include only either renters or homeowners. Individual controls include age, log household income, household size, and indicators for female, foreign, university, marital status, religiosity, left-wing ideology, rental status, and employment status. Neighborhood controls (at the census tract level in 2015) include population density, median apartment size (log square meters), quality, year of construction, share of foreign population, left-wing parties’ vote share in the 2015 national elections, and value LNG (r = 350). All regressions include city-district fixed effects. Robust standard errors clustered at the city-neighborhood level in parenthesis.
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  43. Figure B3: Screenshot of the alternative question eliciting inequality perceptions, borrowed from the ISSP (2009) Notes: This figure is a screenshot of the (translated) “pyramid question”, first introduced in a survey by the Social Survey Programme (ISSP) in 2009. It serves as an alternative to the question illustrated in Figure 1 to elicit respondents’ perceived inequality. It confronts participants with five diagrams representing hypothetical societies and asks them to choose the one that best represents Spain in their view. Table B1: Sample distribution across the (10) districts and (73) neighborhoods of Barcelona, compared to actual population District Neighborhood Sample (%) Pop.
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  54. Individual controls include age, log household income, household size, and indicators for female, foreign, university, marital status, religiosity, leftwing ideology, rental status, and employment status. Neighborhood controls (at the census tract level in 2015) include population density, median apartment size (log square meters), quality, year of construction, share of foreign population, left-wing parties’ vote share in the 2015 national elections, and value LNG (r = 350). All regressions include city-district fixed effects. Robust standard errors clustered at the city-neighborhood level in parenthesis.
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  55. Individual controls include age, log household income, household size, and indicators for female, foreign, university, marital status, religiosity, leftwing ideology, rental status, and employment status. Neighborhood controls (at the census tract level in 2015) include population density, median apartment size (log square meters), quality, year of construction, share of foreign population, left-wing parties’ vote share in the 2015 national elections, and value LNG (r = 350). All regressions include city-district fixed effects. Robust standard errors clustered at the city-neighborhood level in parenthesis.
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  69. New Building Treatment is an indicator taking the value of 1 if the individual resides within 200 (Columns 1-2), 350 (Columns 3-4), or 500 (Columns 5-6) meters of a new apartment building (constructed in 2017-19). The sample is restricted to individuals who have resided in the same dwelling since at least 2015. Individual controls include age, log household income, household size, and indicators for female, foreign, university, marital status, religiosity, leftwing ideology, rental status, and employment status. Neighborhood controls (at the census tract level in 2015) include population density, median apartment size (log square meters), quality, year of construction, share of foreign population, left-wing parties’ vote share in the 2015 national elections, and value LNG (r = 350). All regressions include city-district fixed effects. Robust standard errors clustered at the city-neighborhood level in parenthesis.
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  70. New Building Treatment is an indicator taking the value of 1 if the individual resides within 350 meters of a new construction (built within the past one (Panel C), two (Panel B) or three (Panel A) years). Sample is restricted to individuals residing in the same dwelling from at least 2015. No Previous Exposure (Columns 2, 4, and 6) further restricts the sample to individuals not having been exposed to a new building treatment before the time window considered.
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  73. Note: Black markers represent the actual income at the percentile .125 .25 .5 1 2 4 8 16 16 32 64 128 256 Perceived Monthly Income (1000s EUR) P10 P30 P50 P70 P90 P99 Figure 3: Perceived national income distribution among respondents Notes: Boxplot of respondents’ perceived monthly income at different percentiles. The figure excludes outliers. The y-axis is log-scaled. The median values for the percentiles 10, 30, 50, 70, 90, and 99 were 500, 1000, 1400, 2000, 4000, and 8000, respectively. The actual monthly incomes in these percentiles were 446, 790, 1144, 1678, 2795, and 5791, respectively (ECV, 2018). The black markers in the figure represent these values.
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  79. Pre-tax income inequality is high in these cities’ regions, and always above Value Gini (in Barcelona) and Space Gini. At least three reasons could explain this. First, income is likely to exhibit higher variance than dwelling sizes (and therefore possibly dwelling values too). Second, space is scarce in cities, even when it is possible to increase density (e.g., by building taller buildings). Third, preferences over housing consumption are likely to be non-homothetic. Those at the top might be more prone to invest in assets other than real estate once a certain amount of dwelling consumption is attained (Albouy et al. 2016, Couture et al. 2019, Yang 2009).
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  84. The sample is restricted to individuals who have resided in the same dwelling since at least 2015. Individual controls include age, log household income, household size, and indicators for female, foreign, university, marital status, religiosity, left-wing ideology, rental status, and employment status. Neighborhood controls (at the census tract level in 2015) include population density, median apartment size (log square meters), quality, year of construction, share of foreign population, left-wing parties’ vote share in the 2015 national elections, and value LNG (r = 350). All regressions include city-district fixed effects. Robust standard errors clustered at the city-neighborhood level in parenthesis.
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  85. This implies excluding individuals exposed to treatment in 2017 (Panel B) or 2017 and 2018 (Panel C). Individual controls include age, log household income, household size, and indicators for female, foreign, university, marital status, religiosity, left-wing ideology, rental status, and employment status. Neighborhood controls (at the census tract level in 2015) include population density, median apartment size (log square meters), quality, year of construction, share of foreign population, left-wing parties’ vote share in the 2015 national elections, and value LNG (r = 350). All regressions include city-district fixed effects. Robust standard errors clustered at the city-neighborhood level in parenthesis.
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  88. Xu, Ping and James C Garand, “Economic context and Americans’ perceptions of income inequality,” Social Science Quarterly, 2010, 91 (5), 1220–1241.
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    RePEc:eee:jeborg:v:179:y:2020:i:c:p:523-543.

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  18. Settlement location shapes the integration of forced migrants: Evidence from post-war Germany⁎. (2020). Dwenger, Nadja ; Braun, Sebastian.
    In: Explorations in Economic History.
    RePEc:eee:exehis:v:77:y:2020:i:c:s0014498320300164.

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  19. Skills, signals, and employability: An experimental investigation. (2020). Woessmann, Ludger ; Simon, Lisa ; Schwerdt, Guido ; Piopiunik, Marc.
    In: European Economic Review.
    RePEc:eee:eecrev:v:123:y:2020:i:c:s0014292120300064.

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  20. Turning Opposition into Support to Immigration: The Role of Narratives. (2020). Grieco, Daniela ; Cattaneo, Cristina.
    In: CReAM Discussion Paper Series.
    RePEc:crm:wpaper:2021.

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  21. Designing Information Provision Experiments. (2020). Wohlfart, Johannes ; Roth, Christopher ; Haaland, Ingar.
    In: CAGE Online Working Paper Series.
    RePEc:cge:wacage:484.

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  22. COVID-19, Trust and Solidarity in the EU. (2020). Dolls, Mathias ; Cabrales, Antonio ; Lisa, Windsteiger ; Giray, Aksoy Cevat.
    In: EconPol Policy Reports.
    RePEc:ces:econpr:_27.

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  23. The Role of Information Provision for Attitudes Towards Immigration: An Experimental Investigation.. (2020). Uebelmesser, Silke ; Bareinz, Patrick.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8635.

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  24. Between Fear Mongers and Samaritans: Does Information Provision Affect Attitudes towards the Right of Asylum in Germany?. (2020). Neumeier, Florian ; Hayo, Bernd.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8420.

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  25. Designing Information Provision Experiments. (2020). Wohlfart, Johannes ; Roth, Christopher ; Haaland, Ingar K.
    In: CESifo Working Paper Series.
    RePEc:ces:ceswps:_8406.

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  26. THE DETERMINANTS OF ECONOMIC FREEDOM: A SURVEY. (2020). Murphy, Ryan ; Lawson, Robert A ; Powell, Benjamin.
    In: Contemporary Economic Policy.
    RePEc:bla:coecpo:v:38:y:2020:i:4:p:622-642.

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  27. Cultural Transmission with Incomplete Information: Parental Perceived Efficacy and Group Misrepresentation. (2019). Panebianco, Fabrizio ; della Lena, Sebastiano.
    In: Working Papers.
    RePEc:ven:wpaper:2019:11.

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  28. Perceived Immigration And Voting Behavior.. (2019). Conzo, Pierluigi ; Zotti, Roberto ; Bellucci, Davide.
    In: Department of Economics and Statistics Cognetti de Martiis. Working Papers.
    RePEc:uto:dipeco:201915.

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  29. Internal migration and public policy. (2019). Giuranno, Michele ; Rongili, Biswas.
    In: MPRA Paper.
    RePEc:pra:mprapa:94217.

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  30. Taxation and Migration: Evidence and Policy Implications. (2019). Stantcheva, Stefanie ; landais, camille ; Muoz, Mathilde ; Kleven, Henrik.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:25740.

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  31. Conflicting Identities: Cosmopolitan or Anxious? Appreciating Concerns of Host Country Population Improves Attitudes Towards Immigrants. (2019). Stöhr, Tobias ; Wichardt, Philipp ; Stohr, Tobias.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp12630.

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  32. Preferences for redistribution and exposure to tax-benefit schemes in Europe. (2019). Olivera, Javier ; Andreoli, Francesco.
    In: Working Papers.
    RePEc:inq:inqwps:ecineq2019-508.

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  33. Immigration and Right-Wing Populism: Evidence from a Natural Experiment. (2019). Mehic, Adrian.
    In: Working Papers.
    RePEc:hhs:lunewp:2019_005.

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  34. Immigration and voting for redistribution: Evidence from European elections. (2019). Turati, Riccardo ; Peri, Giovanni ; Moriconi, Simone.
    In: Labour Economics.
    RePEc:eee:labeco:v:61:y:2019:i:c:s0927537119300910.

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  35. Immigration and electoral support for the far-left and the far-right. (2019). Poutvaara, Panu ; Giesing, Yvonne ; Oztunc, Jonathan ; Edo, Anthony.
    In: European Economic Review.
    RePEc:eee:eecrev:v:115:y:2019:i:c:p:99-143.

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  36. Taxation and Migration: Evidence and Policy Implications. (2019). Kleven, Henrik ; Stantcheva, Stefanie ; Munoz, Mathilde ; Landais, Camille.
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:13649.

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  37. Revealing Stereotypes: Evidence from Immigrants in Schools. (2019). Pinotti, Paolo ; la Ferrara, Eliana ; Laferrara, Eliana ; Carlana, Michela ; Alesina, Alberto F.
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:13555.

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  38. Immigrant Voters, Taxation and the Size of the Welfare State. (2018). Elsner, Benjamin ; Chevalier, Arnaud ; Pestel, Nico ; Lichter, Andreas.
    In: Working Papers.
    RePEc:ucn:wpaper:201814.

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  39. Immigrant Voters, Taxation and the Size of the Welfare State. (2018). Elsner, Benjamin ; Chevalier, Arnaud ; Pestel, Nico ; Lichter, Andreas.
    In: Working Papers.
    RePEc:ucd:wpaper:201820.

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  40. Facts, Alternative Facts, and Fact Checking in Times of Post-Truth Politics. (2018). Zhuravskaya, Ekaterina ; Henry, Emeric ; Guriev, Sergei ; Barrera, Oscar .
    In: Sciences Po publications.
    RePEc:spo:wpmain:info:hdl:2441/1dhd1b1s319fbai85khk40fudc.

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  41. Facts, Alternative Facts, and Fact Checking in Times of Post-Truth Politics. (2018). Zhuravskaya, Ekaterina ; Henry, Emeric ; Guriev, Sergei ; Barrera, Oscar .
    In: Sciences Po Economics Discussion Papers.
    RePEc:spo:wpecon:info:hdl:2441/1dhd1b1s319fbai85khk40fudc.

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  42. Talking Behind Your Back: Asymmetric Communication in a Three-person Dilemma. (2018). Huang, Lingbo ; Abbink, Klaus ; Hugang, Lingbo ; Dong, LU.
    In: Discussion Papers.
    RePEc:not:notcdx:2018-11.

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  43. Revealing Stereotypes: Evidence from Immigrants in Schools. (2018). pinotti, paolo ; La Ferrara, Eliana ; Carlana, Michela ; Alesina, Alberto ; Laferrara, Eliana .
    In: NBER Working Papers.
    RePEc:nbr:nberwo:25333.

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  44. Skill of the Immigrants and Vote of the Natives: Immigration and Nationalism in European Elections 2007-2016. (2018). Turati, Riccardo ; Peri, Giovanni ; Moriconi, Simone.
    In: NBER Working Papers.
    RePEc:nbr:nberwo:25077.

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  45. Revealing Stereotypes: Evidence from Immigrants in Schools. (2018). Carlana, Michela ; Pinotti, Paolo ; la Ferrara, Eliana ; Laferrara, Eliana ; Alesina, Alberto.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp11981.

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  46. Immigrant Voters, Taxation and the Size of the Welfare State. (2018). Elsner, Benjamin ; Chevalier, Arnaud ; Pestel, Nico ; Lichter, Andreas.
    In: IZA Discussion Papers.
    RePEc:iza:izadps:dp11725.

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  47. Favoritism and Fairness in Teams. (2018). Huang, Lingbo ; Dong, LU.
    In: Games.
    RePEc:gam:jgames:v:9:y:2018:i:3:p:65-:d:168048.

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  48. Immigrant Voters, Taxation and the Size of the Welfare State. (2018). Elsner, Benjamin ; Pestel, Nico ; Lichter, Andreas ; Chevalier, Arnaud.
    In: SOEPpapers on Multidisciplinary Panel Data Research.
    RePEc:diw:diwsop:diw_sp994.

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  49. Skill of the Immigrants and Vote of the Natives: Immigration and Nationalism in European Elections 2007-2016. (2018). Turati, Riccardo ; Peri, Giovanni ; Moriconi, Simone.
    In: Discussion Papers (IRES - Institut de Recherches Economiques et Sociales).
    RePEc:ctl:louvir:2018013.

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  50. Facts, Alternative Facts, and Fact Checking in Times of Post-Truth Politics. (2017). Zhuravskaya, Ekaterina ; Henry, Emeric ; Guriev, Sergei ; Barrera, Oscar .
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:12220.

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