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Can conflict affect individuals’ preferences for income redistribution?

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Abstract

This study examines the effect of conflict on individuals’ preferences for income redistribution. To this end, I compare individuals’ preferences before and after a war between Israel and a Lebanese terror organization in 2006. Using information from both panel and repeated cross-sectional datasets, I find that residing in war-affected regions increases individuals’ support for income redistribution. An examination of several mechanisms that may elicit this finding reveals that conflict increases the importance of luck in individuals’ perceptions and rules out other channels such as changes in individuals’ risk preferences or beliefs. Placebo analyses using the years preceding the war and individuals’ preferences unrelated to violence (e.g., attitudes about the environment) reinforce my main findings.

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Data availability

The data that support the findings of this study are available from the International Social Survey Programme (http://www.issp.org/data-download/by-topic/), from the European Social Survey (https://www.europeansocialsurvey.org/), and from the Survey of Health, Aging and Retirement in Europe (http://www.share-project.org/home0.html). Restrictions apply to the availability of these data, which were used under license for this study.

Notes

  1. For example, the terror attacks on the USA on September 11, 2001, and the ongoing Russian–Ukrainian conflict and Syrian civil war.

  2. Namely, they are less likely to believe in a just world (Bénabou and Tirole 2006).

  3. This study also relates to the literature that explores how experiencing income inequality (Kuziemko et al. 2015; Bechtel et al. 2018; Roth and Wohlfart 2018; Pellicer et al. 2019), recessions (Fisman et al. 2015), and living under a communist regime (Alesina and Fuchs-Schündeln 2007) affect individuals’ preferences for redistribution. In addition, this study relates to the literature that explores the effect of culture (Eugster et al. 2012), perception about intergenerational mobility (Alesina et al. 2018), and ethnic diversity (Dahlberg et al. 2012) on support for redistribution.

  4. This study also relates to the literature that examines the effects of violence on risk preferences (Kim and Lee 2014; Callen et al. 2014; Wang and Young 2020; Jakiela and Ozier 2019), time preferences (Voors et al. 2012; Shai 2022b), and pro-social behavior (e.g., Blattman 2009; Bellows and Miguel 2009; Gneezy and Fessler 2011; Grosjean 2014; Bauer et al. 2016; Lupu and Peisakhin 2017).

  5. Known as the Second Lebanon War (2006). For more information, please visit: https://www.gov.il/en/Departments/General/the-second-lebanon-war.

  6. Individuals therefore did not leave the northern region, as there had been no anticipation of the war.

  7. The last military confrontation between Israel and Hezbollah occurred in 1996. In 2000, Hezbollah abducted three Israeli soldiers. That incident, however, was not followed by violence large-scale military response.

  8. For more information on the Israeli National Health Insurance Law, visit: http://www.health.gov.il/English/Topics/RightsInsured/Pages/default.aspx.

  9. For more information on the National Insurance Institute, visit: https://www.btl.gov.il/English%20Homepage/Benefits/Pages/default.aspx.

  10. For more information, visit: http://www.oecd.org/economy/surveys/Israel-2018-OECD-economic-survey-overview.pdf.

  11. I estimate Eq. (1) using linear probability model (LPM).

  12. For more detailed information on the data and the constructed variables, see the data in the Appendix.

  13. European Social Survey Round 1 Data (2002) and European Social Survey Round 4 Data (2008). For more information, visit: www.europeansocialsurvey.org/.

  14. The ESS was conducted in Israel in four additional years (2010, 2012, 2014, 2016). However, in order to capture a short time window before and after the war, I use only the years 2002 and 2008.

  15. Answers were given using the following scale: strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree.

  16. In Israel, political orientation is usually associated with attitudes regarding the peace process between Israel and the Arabs, and it is less associated with economic perceptions. I therefore did not use measures of political orientation.

  17. ISSP Research Group (199520032019). For more information, visit: www.issp.org/menu-top/home/.

  18. Importantly, the upcoming analysis examines individuals’ preferences before and after the war. Due to missing information, I did not use other ISSP modules.

  19. Other studies (e.g., Corneo and Grüner 2002; Bernasconi 2006; Breznau and Hommerich 2019) also use individuals’ preferences for redistribution, taken from the ISSP.

  20. Over the years, Israel had several military clashes with Palestinian forces in the Gaza Strip, located near Israel’s southern region. Moreover, individuals who reside in the Judea–Samaria region are more likely to be exposed to terror attacks. Therefore, throughout the analysis, I exclude Israelis who reside in these areas from the control group. Using the ISSP dataset, I exclude individuals who reside in the southern region. The Judea–Samaria and Gaza Strip region categories are not mentioned in this dataset.

  21. Following the ESS guidelines, in Fig. 2, I use post-stratification weights (PSPWGHT) throughout the calculations, adjusting for sampling error and non-response bias. For more information, visit: https://www.europeansocialsurvey.org/docs/methodology/ESS_weighting_data_1.pdf.

  22. One should note that the ISSP and the ESS datasets include different years.

  23. Unfortunately, neither the ESS nor the ISSP contains information on preferences for redistribution toward out-groups (e.g., Arabs).

  24. Specifically, the Palestinian uprising against Israel (known as the “Second Intifada”) began in 2000 and was characterized by terror attacks against Israelis. One could argue that these terror attacks affected the control group because they occurred in certain and central places such as Jerusalem. Excluding Israelis who reside in Tel Aviv and Jerusalem (Israel’s largest cities) and the central district from the control group addresses this concern (panel A of online Appendix Table 2).

  25. The results remain the same when controlling for region fixed effects, instead of an indicator for whether individuals reside in the northern region or not (panel B of online Appendix Table 2).

  26. To reinforce the results, I conducted both propensity score matching (nearest-neighbor matching with a caliper of 0.01 and replacement) and coarsened exact matching. Specifically, I used data from the post-war period (year 2008) and matched individuals who reside in the war-affected region with individuals who reside in the regions not affected by war, based on the following similar characteristics: employment status, age, age squared, education, whether or not individuals’ children live at home, household income, and marital status. As can be seen in columns 1 and 2 in panel C of online Appendix Table 2, individuals who reside in the war-affected region are more likely to support redistribution, than individuals who reside in regions that were not affected by the war.

  27. To mitigate the concern that individuals who reside in the war-affected area differ in their characteristics from those who reside in regions not affected by war, I conducted entropy balancing (Hainmueller 2012). Specifically, using the ESS, I balanced treated individuals (those who reside in the war-affected region) with untreated individuals (those who reside in unaffected regions) on the following exogenous variables: age, age squared, education, whether or not individuals’ children live at home, and gender. By weighting the observations, I then estimated the difference-in-differences model, using Eq. (1). As can be seen in panel D of online Appendix Table 2, the results show that residing in war-affected regions increases support for income redistribution.

  28. Specifically, gaining information during war, such as the extent of property damage, may affect individuals’ support for redistribution. I therefore use individuals’ current news consumption as a proxy for news consumption during the war.

  29. Panels A and B of online Appendix Table 3 present the estimates for all the covariates using Eq. (1).

  30. To reinforce the results, I use data from the post-war period (year 2008), and as additional proxies for individuals’ preferences for redistribution, I utilize responses to the following two questions: “For a society to be fair, differences in people’s standard of living should be small,” and “Large differences in people’s incomes are acceptable to properly reward differences in talents and efforts” (these questions were not available in the pre-war survey). I then compare the preferences of individuals who reside in the northern region, against a control group of individuals who reside in other regions that were less exposed to the war. Using these measures, my findings reveal that after the war (year 2008), those who reside in the conflict area are more likely to support redistribution than those who reside in other regions (the difference between these two groups is statistically significant).

  31. The results remain the same when clustering the standard errors by individuals’ age (panel B of online Appendix Table 2).

  32. I also use the original statement: “It is the responsibility of the government to reduce the differences in income between people with high incomes and those with low incomes” and construct an outcome variable scaled between 1 (“strongly agree”) and 5 (“strongly disagree”). I then estimate Eq. (1), using this outcome, and find that the qualitative results remain the same. That is, residing in a war-affected area increases support for redistribution.

  33. To reinforce the results, I conducted both propensity score matching (nearest-neighbor matching with a caliper of 0.01 and replacement) and coarsened exact matching. Specifically, I used data from the post-war period (year 2010) and matched individuals who reside in the war-affected region with individuals who reside in the regions not affected by war, based on the following similar characteristics: employment status, age, age squared, marital status, income, gender, and education. As can be seen in columns 3 and 4 in panel C of online Appendix Table 2, individuals who reside in the war-affected region are more likely to support redistribution, than individuals who reside in regions that were not affected by the war.

  34. To mitigate the concern that individuals who reside in the war-affected area differ in their characteristics from those who reside in regions not affected by war, I conducted entropy balancing (Hainmueller 2012). Specifically, using the ISSP, I balanced treated individuals (those who reside in the war-affected region) with untreated individuals (those who reside in unaffected regions) on the following exogenous variables: age, age squared, education, and gender. By weighting the observations, I then estimated the difference-in-differences model, using Eq. (1). As can be seen in panel D of online Appendix Table 2, the results show that residing in war-affected regions increases support for income redistribution.

  35. Panel C of online Appendix Table 3 presents the estimates for all the covariates using Eq. (1).

  36. This paper uses data from SHARE Waves 1 and 5 (DOIs: 10.6103/SHARE.w1.710, 10.6103/SHARE.w2.710, 10.6103/SHARE.w3.710, 10.6103/SHARE.w4.710, 10.6103/SHARE.w5.710, 10.6103/SHARE.w6.710, 10.6103/SHARE.w7.711, 10.6103/SHARE.w8.100, 10.6103/SHARE.w8ca.100); see Börsch-Supan et al. (2013) for methodological details. (1) The SHARE data collection has been funded by the European Commission, DG RTD, through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA No. 211909, SHARE-LEAP: GA No. 227822, SHARE M4: GA No. 261982, DASISH: GA No. 283646), and Horizon 2020 (SHARE-DEV3: GA No. 676536, SHARE-COHESION: GA No. 870628, SERISS: GA No. 654221, SSHOC: GA No. 823782) and by DG Employment, Social Affairs & Inclusion through VS 2015/0195, VS 2016/0135, VS 2018/0285, VS 2019/0332, and VS 2020/0313. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, and the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C, RAG052527A) and from various national funding sources is gratefully acknowledged (see www.share-project.org). The project development and data collection in Israel were supported by the National Institutes of Health (NIH) of the USA, National Insurance Institute of Israel, German-Israeli Foundation for Scientific Research and Development (GIF), European Commission through the 7th framework program, Ministry of Science and Technology, and Ministry of Senior Citizens. The data was collected by the Israeli Gerontological Data Center at the Hebrew University in Jerusalem. Wave 3 data collection in Israel was funded by the NIH (R01-AG031729) and the Ministry for Senior Citizens. The SHARE-Israel Project is administered by the Israel Gerontological Data Center at the Hebrew University in Jerusalem, Israel. The data were collected by the B. I. and Lucille Cohen Institute for Public Opinion Research.

  37. SHARE-Israel was conducted in 2005–2006 (Wave 1), 2009–2010 (Wave 2), 2013 (Wave 5), 2015 (Wave 6), and 2017 (Wave 7). Israel did not participate in the third and fourth waves of the SHARE survey.

  38. Although this question refers to violence originating from the Lebanon war or other terrorist actions (missile attacks from the Gaza Strip and hostile attacks), it allows me to investigate the mechanisms behind the increase in support for redistribution following violence.

  39. I did not use SHARE Wave 2 in my analysis because following the same individuals over several time periods may exacerbate attrition problems.

  40. Individuals who were interviewed in SHARE Wave 1 during or after the Second Lebanon War (i.e., after June 2006) were dropped from the analysis.

  41. I did not construct a general index of self-exposure to violence (including injuries/property damage) because suffering from injuries/property damage induces damage claims and, consequently, may increase support for redistribution, whereas the goal of this study is to examine whether violence exposure (but not necessarily property damage or injuries) alters individuals’ attitudes about the importance of luck and redistribution.

  42. For more information, visit: https://www.cbs.gov.il/EN/Pages/search/yearly.aspx.

  43. The SHARE data used in this study do not contain information on the individuals’ place of residence and locality or on the region/district in which they reside.

  44. To the first two questions, responses were coded into the following categories: (1) Often, (2) Sometimes, (3) Rarely, and (4) Never, and to the last question as follows: (1) Obvious excessive guilt or self-blame, (2) No such feelings, and (3) Mentions guilt or self-blame, but it is unclear if these constitute obvious or excessive guilt or self-blame.

  45. Specifically, I constructed a responsibility index scaled between 0 and 3 that counts whether individuals report that they rarely/never feel that what happens to them is out of their control and often/sometimes feel that they can do the things they want to do or tend to blame themselves or feel obvious excessive guilt.

  46. To reinforce the results, I conducted both propensity score matching (nearest-neighbor matching with a caliper of 0.01 and replacement) and coarsened exact matching. Specifically, I used data from the post-war period (year 2013) and matched individuals who were exposed to the war with individuals who were not exposed to the war, based on the following similar characteristics: income, age, age squared, marital status, education, and gender. As can be seen in columns 5 and 6 in panel C of online Appendix Table 2, individuals who were exposed to violence are less likely to feel responsible for their fate than those who were not exposed to violence.

  47. Online Appendix Table 4 presents the estimates for all the covariates using Eq. (2).

  48. The economics literature provides three possible explanations, which are not mutually exclusive, for increased pro-social behavior following violence: an economic channel, a change in social preferences, and a psychological effect (Bauer et al. 2016). Investigating these channels, however, is beyond the scope of this paper.

  49. Similarly, following Ajzen’s (1991) seminal paper, individuals may change their support for redistribution because their requisite opportunities and resources were harmed due to the war, which, in turn, decreased their perceived behavioral control.

  50. For more information, visit: https://www.cbs.gov.il/EN/Pages/search/yearly.aspx.

  51. Specifically, only 0.26% of the population left the northern region in the years before the war (2002–2005).

  52. Specifically, there is only one pre-war year in the ESS, and there are only two pre-war years in the ISSP.

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Acknowledgements

Shai thanks editors Milena Nikolova and Madeline Zavodny, the three anonymous reviewers, and Todd Kaplan for their helpful comments.

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  1. Department of Economics, University of Haifa, 199 Aba Khoushy Ave, Mount Carmel, Haifa, Israel

    Ori Shai

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Shai, O. Can conflict affect individuals’ preferences for income redistribution?. J Popul Econ 36, 3071–3096 (2023). https://doi.org/10.1007/s00148-023-00963-z

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