## C.4 Exploratory Factor Analysis

To construct a latent variable of support for global redistribution, I proceed in three steps. First, I standardize each variable of support by converting them into z-scores, by subtracting the sample average and then dividing the result by the standard error. Both the mean and the standard error are computed using survey weights on the global sample. Second, I run an exploratory factor analysis (EFA) with one factor, to obtain the loadings, i.e. the weight of each variable in the latent factor (reported in Table S3). Third, I average all z-scores, weighted by the loadings.

<table><caption>Table S3: Loadings from the Exploratory Factor Analysis</caption><thead><tr><th>Variable name</th><th>Loading</th></tr></thead><tbody><tr><td>share_solidarity_diff</td><td>0.991</td></tr><tr><td>share_solidarity_ratio</td><td>0.926</td></tr><tr><td>share_pl_supported</td><td>0.901</td></tr><tr><td>share_solidarity_opposed</td><td>-0.852</td></tr><tr><td>pl_support_loss damage</td><td>0.800</td></tr><tr><td>pl_support_ncqg_300bn</td><td>0.792</td></tr><tr><td>pl_support_foreign aid</td><td>0.767</td></tr><tr><td>pl_support courting_levy</td><td>0.759</td></tr><tr><td>pl_support_bridgetown</td><td>0.736</td></tr><tr><td>pl_support_debt_relief</td><td>0.724</td></tr><tr><td>pl_support_un_reform</td><td>0.684</td></tr><tr><td>pl_support.aviation_levy</td><td>0.675</td></tr><tr><td>pl_support_billionaire_tax</td><td>0.670</td></tr><tr><td>pl_support_corporate_tax</td><td>0.669</td></tr><tr><td>top_tax_support</td><td>0.564</td></tr><tr><td>my_tax_global_nation</td><td>0.536</td></tr><tr><td>vote_intl_coalition</td><td>0.533</td></tr><tr><td>ics_support</td><td>0.522</td></tr><tr><td>global_movement_no</td><td>-0.522</td></tr><tr><td>wealth_tax_support</td><td>0.489</td></tr><tr><td>gcs_support</td><td>0.483</td></tr><tr><td>help_lic_none</td><td>-0.464</td></tr></tbody></table>

<table><tr><td>convergence_support</td><td>0.456</td></tr><tr><td>reparations_support</td><td>0.431</td></tr><tr><td>how_agencies</td><td>0.428</td></tr><tr><td>how_govt_conditional</td><td>0.402</td></tr><tr><td>sustainable_future</td><td>0.395</td></tr><tr><td>how_ngo</td><td>0.388</td></tr><tr><td>ncqg</td><td>0.385</td></tr><tr><td>global_movement_spread</td><td>0.357</td></tr><tr><td>how social protection</td><td>0.323</td></tr><tr><td>universalist</td><td>0.306</td></tr><tr><td>help_lic_duty</td><td>0.298</td></tr><tr><td>ncs_support</td><td>0.268</td></tr><tr><td>help_lic_responsibility</td><td>0.267</td></tr><tr><td>global_movement_donate</td><td>0.258</td></tr><tr><td>help_lic_interest</td><td>0.257</td></tr><tr><td>ncqg_fusion</td><td>0.249</td></tr><tr><td>global_movement_demonstrate</td><td>0.239</td></tr><tr><td>how_local_authories</td><td>0.230</td></tr><tr><td>how_govt_unconditional</td><td>0.226</td></tr><tr><td>how cash unconditional</td><td>0.224</td></tr><tr><td>nationalist</td><td>-0.219</td></tr><tr><td>revenue_splitFew_global</td><td>0.200</td></tr><tr><td>global_movement_strike</td><td>0.158</td></tr><tr><td>individualist</td><td>-0.153</td></tr><tr><td>humanist</td><td>0.140</td></tr></table>

Note: Some variable names have been shortened: I shortened occurrences of help_lic into why_hic_help_lic, solidarity_support into pl_support, and expanding security_council into un_reform.
The loading of a variable is similar to the average absolute correlation with the other support variables. Interestingly, the average absolute correlation of the latent indicator, at .54, is only marginally greater than that of share_solidarity_diff (the difference between the shares of plausible policies supported and opposed), at .49, or that of the share of plausible policies supported, at .45. In other words, simple indicators based on the support or opposition to plausible policies capture attitudes towards global redistribution almost as well as a sophisticated latent variable.

I constructed another latent variable of support, using a full-information item factor analysis, based on multidimensional item response theory (MIRT). Unlike the EFA, MIRT does not assume that levels in a Likert-scale are spaced one unit apart; instead, it estimates parameters encoding the Likert scale that best account for latent support. The correlation between the two latent variables is extremely high (0.98). Therefore, although MIRT is a more accurate method, I use EFA because its results are more transparent to describe.
(Back to Section 6.1.)
# D Representativeness of the Surveys

<table><caption>Table S4: Sample representativeness overall, in Europe, and in the European Union.</caption><thead><tr><th rowspan="2"></th><th colspan="3">All</th><th colspan="3">Eu</th><th colspan="3">EU</th></tr><tr><th>Pop.</th><th>Sample</th><th>Weighted sample</th><th>Pop.</th><th>Sample</th><th>Weighted sample</th><th>Pop.</th><th>Sample</th><th>Weighted sample</th></tr></thead><tbody><tr><td>Sample size</td><td></td><td>12,001</td><td>12,001</td><td></td><td>5,000</td><td>5,000</td><td></td><td>3,705</td><td>3,705</td></tr><tr><td>Gender: Woman</td><td>.51</td><td>.50</td><td>.51</td><td>.51</td><td>.51</td><td>.51</td><td>.52</td><td>.51</td><td>.52</td></tr><tr><td>Gender: Man</td><td>.49</td><td>.49</td><td>.49</td><td>.49</td><td>.49</td><td>.49</td><td>.48</td><td>.49</td><td>.48</td></tr><tr><td>Income_quartile: Q1</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.27</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td></tr><tr><td>Income_quartile: Q2</td><td>.25</td><td>.24</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td></tr><tr><td>Income_quartile: Q3</td><td>.25</td><td>.24</td><td>.25</td><td>.25</td><td>.21</td><td>.25</td><td>.25</td><td>.22</td><td>.25</td></tr><tr><td>Income_quartile: Q4</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td></tr><tr><td>Age: 18-24</td><td>.10</td><td>.10</td><td>.10</td><td>.09</td><td>.10</td><td>.09</td><td>.09</td><td>.10</td><td>.09</td></tr><tr><td>Age: 25-34</td><td>.16</td><td>.17</td><td>.16</td><td>.15</td><td>.15</td><td>.15</td><td>.14</td><td>.15</td><td>.14</td></tr><tr><td>Age: 35-49</td><td>.26</td><td>.26</td><td>.26</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td></tr><tr><td>Age: 50-64</td><td>.24</td><td>.23</td><td>.24</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td></tr><tr><td>Age: 65+</td><td>.25</td><td>.24</td><td>.25</td><td>.26</td><td>.25</td><td>.26</td><td>.27</td><td>.25</td><td>.27</td></tr><tr><td>Diploma_25-64: Below upper secondary</td><td>.09</td><td>.08</td><td>.09</td><td>.13</td><td>.12</td><td>.13</td><td>.14</td><td>.13</td><td>.14</td></tr><tr><td>Diploma_25-64: Upper secondary</td><td>.31</td><td>.29</td><td>.31</td><td>.26</td><td>.26</td><td>.26</td><td>.28</td><td>.27</td><td>.28</td></tr><tr><td>Diploma_25-64: Post secondary</td><td>.29</td><td>.32</td><td>.29</td><td>.25</td><td>.28</td><td>.25</td><td>.22</td><td>.25</td><td>.22</td></tr><tr><td>Urbanity: Cities</td><td>.63</td><td>.52</td><td>.52</td><td>.41</td><td>.41</td><td>.41</td><td>.42</td><td>.44</td><td>.42</td></tr><tr><td>Urbanity: Towns and suburbs</td><td>.15</td><td>.17</td><td>.15</td><td>.36</td><td>.38</td><td>.36</td><td>.35</td><td>.34</td><td>.34</td></tr><tr><td>Urbanity: Rural</td><td>.20</td><td>.14</td><td>.16</td><td>.22</td><td>.21</td><td>.22</td><td>.23</td><td>.22</td><td>.23</td></tr><tr><td>Country: FR</td><td>.07</td><td>.07</td><td>.07</td><td>.18</td><td>.16</td><td>.18</td><td>.22</td><td>.22</td><td>.22</td></tr><tr><td>Country: DE</td><td>.08</td><td>.09</td><td>.08</td><td>.23</td><td>.21</td><td>.23</td><td>.28</td><td>.28</td><td>.28</td></tr><tr><td>Country: IT</td><td>.06</td><td>.06</td><td>.06</td><td>.16</td><td>.15</td><td>.16</td><td>.20</td><td>.20</td><td>.20</td></tr><tr><td>Country: PL</td><td>.04</td><td>.04</td><td>.04</td><td>.10</td><td>.10</td><td>.10</td><td>.13</td><td>.13</td><td>.13</td></tr><tr><td>Country: ES</td><td>.05</td><td>.05</td><td>.05</td><td>.13</td><td>.12</td><td>.13</td><td>.16</td><td>.16</td><td>.16</td></tr><tr><td>Country: GB</td><td>.07</td><td>.07</td><td>.07</td><td>.18</td><td>.17</td><td>.18</td><td></td><td></td><td></td></tr><tr><td>Country: CH</td><td>.01</td><td>.04</td><td>.01</td><td>.02</td><td>.09</td><td>.02</td><td></td><td></td><td></td></tr><tr><td>Country: JP</td><td>.13</td><td>.17</td><td>.13</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Country: RU</td><td>.14</td><td>.08</td><td>.14</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Country: SA</td><td>.03</td><td>.08</td><td>.03</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Country: US</td><td>.33</td><td>.25</td><td>.33</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: This table displays summary statistics of the samples alongside actual population frequencies. Bold cells denote frequencies beyond ±20% of population frequencies. Detailed sources for each variable and country population frequencies, as well as the definitions of regions, diploma, urbanity, employment, and vote are available in this spreadsheet. (Back to Section 2.)
<table><caption>Table S5: Sample representativeness in France, Germany, Italy.</caption><thead><tr><th rowspan="2"></th><th colspan="3">France</th><th colspan="3">Germany</th><th colspan="3">Italy</th></tr><tr><th>Pop.</th><th>Sample</th><th>Weighted sample</th><th>Pop.</th><th>Sample</th><th>Weighted sample</th><th>Pop.</th><th>Sample</th><th>Weighted sample</th></tr></thead><tbody><tr><td>Sample size</td><td></td><td>798</td><td>798</td><td></td><td>1,048</td><td>1,048</td><td></td><td>756</td><td>756</td></tr><tr><td>Gender: Woman</td><td>.52</td><td>.52</td><td>.52</td><td>.51</td><td>.49</td><td>.51</td><td>.52</td><td>.52</td><td>.51</td></tr><tr><td>Gender: Man</td><td>.48</td><td>.48</td><td>.48</td><td>.49</td><td>.51</td><td>.49</td><td>.48</td><td>.48</td><td>.49</td></tr><tr><td>Income quartile: Q1</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.27</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td></tr><tr><td>Income quartile: Q2</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.27</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td></tr><tr><td>Income quartile: Q3</td><td>.25</td><td>.23</td><td>.25</td><td>.25</td><td>.20</td><td>.25</td><td>.25</td><td>.22</td><td>.25</td></tr><tr><td>Income quartile: Q4</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td></tr><tr><td>Age: 18-24</td><td>.10</td><td>.11</td><td>.10</td><td>.09</td><td>.10</td><td>.09</td><td>.08</td><td>.08</td><td>.08</td></tr><tr><td>Age: 25-34</td><td>.15</td><td>.15</td><td>.15</td><td>.15</td><td>.16</td><td>.15</td><td>.12</td><td>.12</td><td>.12</td></tr><tr><td>Age: 35-49</td><td>.23</td><td>.23</td><td>.23</td><td>.23</td><td>.25</td><td>.23</td><td>.23</td><td>.23</td><td>.23</td></tr><tr><td>Age: 50-64</td><td>.24</td><td>.24</td><td>.24</td><td>.27</td><td>.27</td><td>.27</td><td>.28</td><td>.29</td><td>.28</td></tr><tr><td>Age: 65+</td><td>.27</td><td>.27</td><td>.27</td><td>.27</td><td>.22</td><td>.27</td><td>.29</td><td>.28</td><td>.29</td></tr><tr><td>Diploma 25-64: Below upper secondary</td><td>.10</td><td>.09</td><td>.10</td><td>.11</td><td>.11</td><td>.11</td><td>.22</td><td>.19</td><td>.22</td></tr><tr><td>Diploma 25-64: Upper secondary</td><td>.26</td><td>.26</td><td>.26</td><td>.32</td><td>.32</td><td>.32</td><td>.28</td><td>.28</td><td>.28</td></tr><tr><td>Diploma 25-64: Post secondary</td><td>.26</td><td>.27</td><td>.26</td><td>.22</td><td>.24</td><td>.21</td><td>.14</td><td>.17</td><td>.14</td></tr><tr><td>Urbanity: Cities</td><td>.47</td><td>.47</td><td>.46</td><td>.39</td><td>.42</td><td>.39</td><td>.36</td><td>.37</td><td>.36</td></tr><tr><td>Urbanity: Towns and suburbs</td><td>.19</td><td>.19</td><td>.19</td><td>.42</td><td>.42</td><td>.42</td><td>.46</td><td>.47</td><td>.46</td></tr><tr><td>Urbanity: Rural</td><td>.34</td><td>.33</td><td>.34</td><td>.19</td><td>.17</td><td>.19</td><td>.18</td><td>.16</td><td>.18</td></tr><tr><td>Region: 1</td><td>.18</td><td>.19</td><td>.18</td><td>.17</td><td>.19</td><td>.17</td><td>.66</td><td>.70</td><td>.65</td></tr><tr><td>Region: 2</td><td>.22</td><td>.23</td><td>.22</td><td>.29</td><td>.32</td><td>.29</td><td>.34</td><td>.29</td><td>.34</td></tr><tr><td>Region: 3</td><td>.11</td><td>.11</td><td>.11</td><td>.54</td><td>.48</td><td>.54</td><td></td><td></td><td></td></tr><tr><td>Region: 4</td><td>.21</td><td>.22</td><td>.21</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Region: 5</td><td>.28</td><td>.26</td><td>.28</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: This table displays summary statistics of the samples alongside actual population frequencies. Bold cells denote frequencies beyond ±20% of population frequencies. Detailed sources for each variable and country population frequencies, as well as the definitions of regions, diploma, urbanity, employment, and vote are available in this spreadsheet. (Back to Section 2.)
<table><caption>Table S6: Sample representativeness in Poland, Spain, the UK, Switzerland.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Poland</th><th colspan="3">Spain</th><th colspan="3">United Kingdom</th><th colspan="3">Switzerland</th></tr><tr><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th></tr></thead><tbody><tr><td>Sample size</td><td></td><td>500</td><td>500</td><td></td><td>603</td><td>603</td><td></td><td>826</td><td>826</td><td></td><td>469</td><td>469</td></tr><tr><td>Gender: Woman</td><td>.52</td><td>.53</td><td>.52</td><td>.51</td><td>.51</td><td>.51</td><td>.51</td><td>.50</td><td>.51</td><td>.50</td><td>.48</td><td>.50</td></tr><tr><td>Gender: Man</td><td>.48</td><td>.46</td><td>.47</td><td>.49</td><td>.49</td><td>.49</td><td>.49</td><td>.50</td><td>.49</td><td>.50</td><td>.52</td><td>.50</td></tr><tr><td>Income_quartile: Q1</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.28</td><td>.25</td><td>.25</td><td>.28</td><td>.25</td><td>.25</td><td><strong>.30</strong></td><td>.26</td></tr><tr><td>Income_quartile: Q2</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td>.27</td><td>.25</td><td>.25</td><td>.23</td><td>.25</td><td>.25</td><td>.28</td><td>.25</td></tr><tr><td>Income_quartile: Q3</td><td>.25</td><td>.23</td><td>.25</td><td>.25</td><td>.21</td><td>.25</td><td>.25</td><td>.21</td><td>.25</td><td>.25</td><td><strong>.17</strong></td><td>.25</td></tr><tr><td>Income_quartile: Q4</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td>.25</td><td>.24</td><td>.25</td><td>.27</td><td>.25</td><td>.25</td><td>.25</td><td>.24</td></tr><tr><td>Age: 18-24</td><td>.08</td><td>.09</td><td>.08</td><td>.10</td><td>.11</td><td>.09</td><td>.11</td><td>.10</td><td>.11</td><td>.09</td><td>.10</td><td>.09</td></tr><tr><td>Age: 25-34</td><td>.15</td><td>.16</td><td>.15</td><td>.15</td><td>.14</td><td>.14</td><td>.17</td><td>.17</td><td>.17</td><td>.16</td><td>.18</td><td>.17</td></tr><tr><td>Age: 35-49</td><td>.30</td><td>.29</td><td>.30</td><td>.30</td><td>.27</td><td>.31</td><td>.24</td><td>.25</td><td>.25</td><td>.26</td><td>.27</td><td>.25</td></tr><tr><td>Age: 50-64</td><td>.23</td><td>.21</td><td>.23</td><td>.19</td><td>.22</td><td>.19</td><td>.25</td><td>.25</td><td>.24</td><td>.26</td><td>.24</td><td>.26</td></tr><tr><td>Age: 65+</td><td>.24</td><td>.24</td><td>.24</td><td>.26</td><td>.26</td><td>.26</td><td>.24</td><td>.24</td><td>.23</td><td>.23</td><td>.22</td><td>.24</td></tr><tr><td>Diploma_25-64: Below upper secondary</td><td>.04</td><td><strong>.05</strong></td><td>.04</td><td>.23</td><td><strong>.18</strong></td><td>.23</td><td>.12</td><td>.11</td><td>.12</td><td>.09</td><td><strong>.06</strong></td><td>.09</td></tr><tr><td>Diploma_25-64: Upper secondary</td><td>.38</td><td>.34</td><td>.38</td><td>.15</td><td>.15</td><td>.15</td><td>.19</td><td>.17</td><td>.19</td><td>.27</td><td>.29</td><td>.27</td></tr><tr><td>Diploma_25-64: Post secondary</td><td>.26</td><td>.28</td><td>.26</td><td>.27</td><td>.29</td><td>.26</td><td>.35</td><td>.38</td><td>.35</td><td>.31</td><td>.33</td><td>.31</td></tr><tr><td>Urbanity: Cities</td><td>.35</td><td>.37</td><td>.35</td><td>.54</td><td>.58</td><td>.54</td><td>.40</td><td>.36</td><td>.39</td><td>.30</td><td>.32</td><td>.30</td></tr><tr><td>Urbanity: Towns and suburbs</td><td>.28</td><td>.29</td><td>.28</td><td>.32</td><td>.30</td><td>.33</td><td>.42</td><td>.45</td><td>.43</td><td>.53</td><td>.54</td><td>.53</td></tr><tr><td>Urbanity: Rural</td><td>.37</td><td>.34</td><td>.37</td><td>.13</td><td>.12</td><td>.13</td><td>.18</td><td>.19</td><td>.18</td><td>.17</td><td>.14</td><td>.17</td></tr><tr><td>Region: 1</td><td>.47</td><td>.41</td><td>.47</td><td>.15</td><td>.16</td><td>.15</td><td>.13</td><td>.14</td><td>.13</td><td>.70</td><td>.70</td><td>.70</td></tr><tr><td>Region: 2</td><td>.53</td><td>.59</td><td>.53</td><td>.28</td><td>.25</td><td>.28</td><td>.31</td><td>.33</td><td>.31</td><td>.26</td><td>.26</td><td>.26</td></tr><tr><td>Region: 3</td><td></td><td></td><td></td><td>.14</td><td>.16</td><td>.14</td><td>.21</td><td>.17</td><td>.21</td><td>.04</td><td>.04</td><td>.04</td></tr><tr><td>Region: 4</td><td></td><td></td><td></td><td>.18</td><td>.19</td><td>.18</td><td>.24</td><td>.25</td><td>.24</td><td></td><td></td><td></td></tr><tr><td>Region: 5</td><td></td><td></td><td></td><td>.25</td><td>.24</td><td>.25</td><td>.11</td><td>.10</td><td>.11</td><td></td><td></td><td></td></tr></tbody></table>

Note: This table displays summary statistics of the samples alongside actual population frequencies. Bold cells denote frequencies beyond ±20% of population frequencies. Detailed sources for each variable and country population frequencies, as well as the definitions of regions, diploma, urbanity, employment, and vote are available in this spreadsheet.

(Back to Section 2.)
<table><caption>Table S7: Sample representativeness in non-European countries.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Japan</th><th colspan="3">Russia</th><th colspan="3">Saudi Arabia</th><th colspan="3">USA</th></tr><tr><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th><th>Pop.</th><th>Sam.</th><th>Wght. sam.</th></tr></thead><tbody><tr><td>Sample size</td><td></td><td>2,000</td><td>2,000</td><td></td><td>1,001</td><td>1,001</td><td></td><td>1,000</td><td>1,000</td><td></td><td>3,000</td><td>3,000</td></tr><tr><td>Gender: Woman</td><td>.51</td><td>.50</td><td>.51</td><td>.54</td><td>.52</td><td>.54</td><td></td><td></td><td></td><td>.50</td><td>.52</td><td>.50</td></tr><tr><td>Gender: Man</td><td>.49</td><td>.50</td><td>.49</td><td>.46</td><td>.48</td><td>.46</td><td></td><td></td><td></td><td>.50</td><td>.48</td><td>.50</td></tr><tr><td>Income_quartile: Q1</td><td>.25</td><td>.26</td><td>.25</td><td>.25</td><td><strong>.19</strong></td><td>.24</td><td>.25</td><td><strong>.32</strong></td><td>.26</td><td>.25</td><td>.23</td><td>.25</td></tr><tr><td>Income_quartile: Q2</td><td>.25</td><td>.24</td><td>.25</td><td>.25</td><td><strong>.18</strong></td><td>.24</td><td>.25</td><td>.23</td><td>.25</td><td>.25</td><td>.24</td><td>.25</td></tr><tr><td>Income_quartile: Q3</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td>.27</td><td>.24</td><td>.25</td><td>.22</td><td>.24</td><td>.25</td><td>.27</td><td>.25</td></tr><tr><td>Income_quartile: Q4</td><td>.25</td><td>.25</td><td>.25</td><td>.25</td><td><strong>.32</strong></td><td>.24</td><td>.25</td><td>.23</td><td>.24</td><td>.25</td><td>.26</td><td>.25</td></tr><tr><td>Age: 18-24</td><td>.08</td><td>.08</td><td>.08</td><td>.09</td><td>.09</td><td>.09</td><td>.15</td><td>.16</td><td>.16</td><td>.12</td><td>.10</td><td>.12</td></tr><tr><td>Age: 25-34</td><td>.12</td><td>.12</td><td>.12</td><td>.16</td><td>.15</td><td>.16</td><td>.32</td><td>.35</td><td>.32</td><td>.17</td><td>.18</td><td>.17</td></tr><tr><td>Age: 35-49</td><td>.22</td><td>.23</td><td>.22</td><td>.30</td><td>.30</td><td>.30</td><td>.36</td><td>.37</td><td>.37</td><td>.25</td><td>.24</td><td>.25</td></tr><tr><td>Age: 50-64</td><td>.24</td><td>.24</td><td>.24</td><td>.25</td><td>.25</td><td>.25</td><td>.13</td><td>.11</td><td>.13</td><td>.24</td><td>.24</td><td>.24</td></tr><tr><td>Age: 65+</td><td>.34</td><td>.34</td><td>.34</td><td>.21</td><td>.20</td><td>.21</td><td>.04</td><td><strong>.00</strong></td><td><strong>.02</strong></td><td>.23</td><td>.24</td><td>.23</td></tr><tr><td>Diploma_25-64: Upper secondary</td><td>.26</td><td>.25</td><td>.26</td><td>.62</td><td>.62</td><td>.62</td><td>.15</td><td><strong>.23</strong></td><td>.16</td><td>.27</td><td>.27</td><td>.27</td></tr><tr><td>Diploma_25-64: Post secondary</td><td>.32</td><td>.33</td><td>.32</td><td>.28</td><td>.32</td><td>.28</td><td>.35</td><td><strong>.50</strong></td><td>.39</td><td>.33</td><td>.34</td><td>.33</td></tr><tr><td>Diploma_25-64: Below upper secondary</td><td></td><td></td><td></td><td>.10</td><td><strong>.06</strong></td><td>.10</td><td>.31</td><td><strong>.11</strong></td><td>.27</td><td>.05</td><td>.05</td><td>.05</td></tr><tr><td>Urbanity: Cities</td><td>.92</td><td>.92</td><td>.92</td><td></td><td></td><td></td><td></td><td></td><td></td><td>.76</td><td>.78</td><td>.76</td></tr><tr><td>Urbanity: Towns and suburbs</td><td>.08</td><td>.08</td><td>.08</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urbanity: Rural</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.24</td><td>.22</td><td>.24</td></tr><tr><td>Region: 1</td><td>.17</td><td>.17</td><td>.17</td><td></td><td></td><td></td><td>.14</td><td><strong>.06</strong></td><td>.12</td><td>.17</td><td>.18</td><td>.17</td></tr><tr><td>Region: 2</td><td>.17</td><td>.18</td><td>.17</td><td></td><td></td><td></td><td>.34</td><td><strong>.45</strong></td><td>.35</td><td>.21</td><td>.21</td><td>.21</td></tr><tr><td>Region: 3</td><td>.34</td><td>.35</td><td>.34</td><td></td><td></td><td></td><td>.36</td><td>.36</td><td>.36</td><td>.38</td><td>.40</td><td>.38</td></tr><tr><td>Region: 4</td><td>.11</td><td>.11</td><td>.11</td><td></td><td></td><td></td><td>.16</td><td><strong>.12</strong></td><td>.16</td><td>.24</td><td>.21</td><td>.24</td></tr><tr><td>Region: 5</td><td>.20</td><td>.19</td><td>.20</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Gender_nationality: Woman, Saudi</td><td></td><td></td><td></td><td></td><td></td><td></td><td>.24</td><td><strong>.31</strong></td><td>.25</td><td></td><td></td><td></td></tr><tr><td>Gender_nationality: Woman, non-Saudi</td><td></td><td></td><td></td><td></td><td></td><td></td><td>.10</td><td>.12</td><td>.11</td><td></td><td></td><td></td></tr><tr><td>Gender_nationality: Man, Saudi</td><td></td><td></td><td></td><td></td><td></td><td></td><td>.24</td><td><strong>.33</strong></td><td>.27</td><td></td><td></td><td></td></tr><tr><td>Gender_nationality: Man, non-Saudi</td><td></td><td></td><td></td><td></td><td></td><td></td><td>.41</td><td><strong>.24</strong></td><td>.37</td><td></td><td></td><td></td></tr><tr><td>Race: White only</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.58</td><td>.56</td><td>.58</td></tr><tr><td>Race: Hispanic</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.20</td><td>.21</td><td>.19</td></tr><tr><td>Race: Black</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.14</td><td>.15</td><td>.14</td></tr><tr><td>Race: Other</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>.09</td><td>.07</td><td>.09</td></tr></tbody></table>

Note: This table displays summary statistics of the samples alongside actual population frequencies. Bold cells denote frequencies beyond ±20% of population frequencies. Detailed sources for each variable and country population frequencies, as well as the definitions of regions, diploma, urbanity, employment, and vote are available in this spreadsheet. (Back to Section 2.)
E Determinants of Support

<caption>Figure S68: Variance decomposition: share of the variance explained by each covariate ("Group defended when voting" is present only in bottom subfigures.).</caption>

<figure><img src="image_80.png" /><figcaption>(b) Share of plausible global policies supported (15% of the variable's variance is explained by this linear model). (Question 38)</figcaption></figure>

<figure><img src="image_81.png" /><figcaption>(c) Support for the Global Climate Scheme (14% of the variable's variance is explained by this linear model). (Question 28)</figcaption></figure>

<figure><img src="image_82.png" /><figcaption>(d) Share of plausible global policies supported (20% of the variable's variance is explained by this linear model). (Question 38)</figcaption></figure>

<figure><img src="image_83.png" /></figure>

(Back to Section 2.)
<table><caption>Table S8: Correlates of support for global redistribution (multivariate OLS regressions).</caption><thead><tr><th></th><th>Share of plausible policies supported</th><th>Supports the Global Climate Scheme</th><th>Universalist (Group defended: Humans or Sentient beings)</th><th>More likely to vote for party in global coalition</th><th>Endorses convergence of all countries' GDP p.c. by 2100</th><th>Supports an international wealth tax funding LICs</th><th>Prefers a sustainable future</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th></tr></thead><tbody><tr><td>Mean</td><td>0.508</td><td>0.554</td><td>0.454</td><td>0.365</td><td>0.61</td><td>0.704</td><td>0.681</td></tr><tr><td>Vote: Center-right or Right</td><td>0.015<br>(0.010)</td><td>0.008<br>(0.015)</td><td>-0.083***<br>(0.014)</td><td>0.029**<br>(0.013)</td><td>0.041***<br>(0.014)</td><td>-0.026*<br>(0.014)</td><td>-0.061***<br>(0.014)</td></tr><tr><td>Vote: Far right</td><td>-0.090***<br>(0.013)</td><td>-0.143***<br>(0.020)</td><td>-0.225***<br>(0.019)</td><td>-0.063***<br>(0.018)</td><td>-0.065***<br>(0.020)</td><td>-0.140***<br>(0.019)</td><td>-0.169***<br>(0.020)</td></tr><tr><td>Vote: Left</td><td>0.211***<br>(0.010)</td><td>0.170***<br>(0.014)</td><td>0.150***<br>(0.015)</td><td>0.257***<br>(0.014)</td><td>0.190***<br>(0.014)</td><td>0.184***<br>(0.013)</td><td>0.147***<br>(0.014)</td></tr><tr><td>Gender: Man</td><td>0.016**<br>(0.007)</td><td>0.018*<br>(0.010)</td><td>-0.044***<br>(0.010)</td><td>0.029***<br>(0.010)</td><td>0.009<br>(0.010)</td><td>-0.007<br>(0.009)</td><td>-0.043***<br>(0.009)</td></tr><tr><td>Age: 18-24</td><td>0.012<br>(0.014)</td><td>0.175***<br>(0.020)</td><td>0.104***<br>(0.021)</td><td>0.108***<br>(0.022)</td><td>0.109***<br>(0.020)</td><td>0.101***<br>(0.018)</td><td>0.062***<br>(0.019)</td></tr><tr><td>Age: 25-34</td><td>0.020*<br>(0.011)</td><td>0.094***<br>(0.015)</td><td>0.075***<br>(0.016)</td><td>0.102***<br>(0.016)</td><td>0.046***<br>(0.015)</td><td>0.046***<br>(0.014)</td><td>0.027*<br>(0.015)</td></tr><tr><td>Age: 50-64</td><td>-0.002<br>(0.010)</td><td>-0.036**<br>(0.014)</td><td>-0.034**<br>(0.014)</td><td>-0.033**<br>(0.014)</td><td>-0.025*<br>(0.013)</td><td>-0.021<br>(0.013)</td><td>-0.020<br>(0.013)</td></tr><tr><td>Age: 65+</td><td>0.041***<br>(0.012)</td><td>-0.020<br>(0.018)</td><td>-0.010<br>(0.018)</td><td>0.002<br>(0.017)</td><td>-0.021<br>(0.017)</td><td>-0.018<br>(0.016)</td><td>0.016<br>(0.016)</td></tr><tr><td>Income quartile: Q2</td><td>0.018*<br>(0.010)</td><td>0.004<br>(0.015)</td><td>-0.025*<br>(0.015)</td><td>0.016<br>(0.015)</td><td>-0.014<br>(0.014)</td><td>0.013<br>(0.013)</td><td>0.010<br>(0.014)</td></tr><tr><td>Income quartile: Q3</td><td>0.007<br>(0.010)</td><td>-0.010<br>(0.015)</td><td>0.019<br>(0.015)</td><td>-0.009<br>(0.015)</td><td>-0.024*<br>(0.014)</td><td>-0.018<br>(0.014)</td><td>0.002<br>(0.014)</td></tr><tr><td>Income quartile: Q4</td><td>-0.010<br>(0.011)</td><td>-0.042***<br>(0.016)</td><td>-0.004<br>(0.016)</td><td>-0.032*<br>(0.017)</td><td>-0.075***<br>(0.015)</td><td>-0.078***<br>(0.015)</td><td>0.007<br>(0.015)</td></tr><tr><td>Diploma: Upper secondary</td><td>0.042***<br>(0.011)</td><td>0.001<br>(0.016)</td><td>0.018<br>(0.016)</td><td>0.036**<br>(0.015)</td><td>0.029*<br>(0.015)</td><td>0.022<br>(0.014)</td><td>0.022<br>(0.015)</td></tr><tr><td>Diploma: Above upper secondary</td><td>0.085***<br>(0.011)</td><td>0.026<br>(0.016)</td><td>0.025<br>(0.016)</td><td>0.079***<br>(0.015)</td><td>0.015<br>(0.015)</td><td>0.015<br>(0.015)</td><td>0.039**<br>(0.016)</td></tr><tr><td>Urbanicity: Rural</td><td>-0.012<br>(0.010)</td><td>-0.054***<br>(0.015)</td><td>0.016<br>(0.015)</td><td>-0.006<br>(0.014)</td><td>-0.015<br>(0.015)</td><td>-0.021<br>(0.014)</td><td>-0.020<br>(0.015)</td></tr><tr><td>Urbanicity: Towns and suburbs</td><td>-0.014<br>(0.010)</td><td>-0.039**<br>(0.015)</td><td>-0.022<br>(0.015)</td><td>-0.023<br>(0.015)</td><td>-0.016<br>(0.015)</td><td>-0.024*<br>(0.014)</td><td>0.026*<br>(0.014)</td></tr><tr><td>Will become millionaire: Likely</td><td>0.036***<br>(0.008)</td><td>0.070***<br>(0.012)</td><td>-0.001<br>(0.012)</td><td>0.039***<br>(0.013)</td><td>0.055***<br>(0.012)</td><td>-0.019*<br>(0.011)</td><td>-0.019<br>(0.012)</td></tr><tr><td>Will become millionaire: Already</td><td>-0.020<br>(0.017)</td><td>-0.019<br>(0.023)</td><td>0.008<br>(0.024)</td><td>-0.058**<br>(0.023)</td><td>-0.042*<br>(0.023)</td><td>-0.236***<br>(0.023)</td><td>-0.047**<br>(0.022)</td></tr><tr><td>Foreign born</td><td>0.065***<br>(0.014)</td><td>0.083***<br>(0.020)</td><td>0.088***<br>(0.021)</td><td>0.051**<br>(0.022)</td><td>0.037*<br>(0.020)</td><td>0.040**<br>(0.018)</td><td>0.030<br>(0.019)</td></tr><tr><td>Observations</td><td>12,001</td><td>12,001</td><td>12,001</td><td>10,000</td><td>12,001</td><td>12,001</td><td>12,001</td></tr><tr><td>R<sup>2</sup></td><td>0.141</td><td>0.104</td><td>0.100</td><td>0.115</td><td>0.105</td><td>0.091</td><td>0.069</td></tr></tbody></table>

Note: Robust standard errors are reported in parentheses. Covariates omitted in the Table: Country; Employment; Couple; Region; Constant. Omitted variables are: Vote: Non-voter, PNR or Other; Gender: Woman; Age: 35-49; Income quartile: Q1; Diploma: Below upper secondary; Urbanicity: City. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.

(Back to Section 2.)
<table><caption>Table S9: Correlates of answers on custom redistribution (multivariate OLS regressions).</caption><thead><tr><th></th><th colspan="3">Custom transfer<br>(in % of world GDP)</th><th>Loses<br>from custom<br>redistribution</th><th>Satisfied with<br>own custom<br>redistr.</th><th>Has not<br>talented the<br>sliders</th><th>Touched the<br>sliders and<br>satisfied</th></tr></thead><tbody><tr><td>Mean</td><td>5.138</td><td>5.443</td><td>5.809</td><td>45.596</td><td>47.417</td><td>55.945</td><td>40.609</td><td>39.818</td></tr><tr><td>Vote: Center-right or Right</td><td>-0.098<br>(0.140)</td><td>0.046<br>(0.224)</td><td>0.073<br>(0.309)</td><td>-0.188<br>(1.221)</td><td>-4.590**<br>(2.137)</td><td>5.296***<br>(1.363)</td><td>-0.663<br>(1.389)</td><td>3.201**<br>(1.337)</td></tr><tr><td>Vote: Far right</td><td>-0.532***<br>(0.198)</td><td>-0.571*<br>(0.298)</td><td>-0.816**<br>(0.408)</td><td>-1.514<br>(1.699)</td><td>-5.659**<br>(2.819)</td><td>5.180***<br>(1.882)</td><td>-0.442<br>(1.874)</td><td>3.801**<br>(1.876)</td></tr><tr><td>Vote: Left</td><td>0.838***<br>(0.151)</td><td>1.027***<br>(0.228)</td><td>1.465***<br>(0.315)</td><td>3.837***<br>(1.235)</td><td>3.604*<br>(2.118)</td><td>10.655***<br>(1.369)</td><td>-2.586*<br>(1.393)</td><td>6.785***<br>(1.363)</td></tr><tr><td>Gender: Man</td><td>0.130<br>(0.103)</td><td>0.021<br>(0.154)</td><td>-0.066<br>(0.216)</td><td>0.594<br>(0.856)</td><td>1.305<br>(1.437)</td><td>14.488***<br>(0.944)</td><td>-9.003***<br>(0.960)</td><td>12.468***<br>(0.953)</td></tr><tr><td>Age: 18-24</td><td>0.440*<br>(0.230)</td><td>0.381<br>(0.311)</td><td>0.356<br>(0.420)</td><td>5.203***<br>(1.774)</td><td>4.328<br>(2.703)</td><td>6.111***<br>(1.869)</td><td>-4.468**<br>(1.920)</td><td>7.171***<br>(1.969)</td></tr><tr><td>Age: 25-34</td><td>0.084<br>(0.157)</td><td>0.115<br>(0.217)</td><td>0.109<br>(0.299)</td><td>-0.205<br>(1.304)</td><td>0.241<br>(2.051)</td><td>1.342<br>(1.403)</td><td>-1.653<br>(1.439)</td><td>2.230<br>(1.475)</td></tr><tr><td>Age: 50-64</td><td>-0.297**<br>(0.142)</td><td>-0.500**<br>(0.206)</td><td>-0.652**<br>(0.286)</td><td>-1.761<br>(1.238)</td><td>-5.753***<br>(2.034)</td><td>-8.345***<br>(1.356)</td><td>5.846***<br>(1.370)</td><td>-6.881***<br>(1.363)</td></tr><tr><td>Age: 65+</td><td>-0.066<br>(0.179)</td><td>0.142<br>(0.296)</td><td>0.291<br>(0.424)</td><td>-1.220<br>(1.512)</td><td>-4.200<br>(2.745)</td><td>-12.927***<br>(1.713)</td><td>11.619***<br>(1.764)</td><td>-11.794***<br>(1.677)</td></tr><tr><td>Income quartile: Q2</td><td>-0.294**<br>(0.148)</td><td>-0.351<br>(0.226)</td><td>-0.536*<br>(0.318)</td><td>24.103***<br>(1.142)</td><td>25.065***<br>(1.966)</td><td>1.227<br>(1.333)</td><td>-0.627<br>(1.357)</td><td>1.091<br>(1.327)</td></tr><tr><td>Income quartile: Q3</td><td>-0.400**<br>(0.157)</td><td>-0.559**<br>(0.235)</td><td>-0.838**<br>(0.330)</td><td>41.546***<br>(1.235)</td><td>36.022***<br>(2.098)</td><td>-1.066<br>(1.427)</td><td>-0.675<br>(1.444)</td><td>0.202<br>(1.417)</td></tr><tr><td>Income quartile: Q4</td><td>-0.910***<br>(0.168)</td><td>-1.030***<br>(0.252)</td><td>-1.506***<br>(0.356)</td><td>55.784***<br>(1.370)</td><td>47.659***<br>(2.306)</td><td>0.829<br>(1.560)</td><td>0.492<br>(1.598)</td><td>1.589<br>(1.572)</td></tr><tr><td>Diploma: Upper secondary</td><td>-0.034<br>(0.154)</td><td>-0.002<br>(0.244)</td><td>-0.032<br>(0.354)</td><td>0.042<br>(1.225)</td><td>-2.482<br>(2.214)</td><td>4.167***<br>(1.468)</td><td>-0.750<br>(1.495)</td><td>3.213**<br>(1.416)</td></tr><tr><td>Diploma: Above upper secondary</td><td>0.074<br>(0.162)</td><td>0.077<br>(0.253)</td><td>0.053<br>(0.367)</td><td>2.772**<br>(1.315)</td><td>1.238<br>(2.382)</td><td>4.277***<br>(1.537)</td><td>-3.900**<br>(1.567)</td><td>5.666***<br>(1.486)</td></tr><tr><td>Urbanicity: Rural</td><td>-0.277*<br>(0.161)</td><td>-0.214<br>(0.245)</td><td>-0.251<br>(0.338)</td><td>0.933<br>(1.293)</td><td>1.297<br>(2.154)</td><td>-2.899**<br>(1.459)</td><td>-1.949<br>(1.458)</td><td>-2.878**<br>(1.456)</td></tr><tr><td>Urbanicity: Towns and suburbs</td><td>-0.199<br>(0.171)</td><td>-0.097<br>(0.255)</td><td>-0.085<br>(0.358)</td><td>0.990<br>(1.324)</td><td>0.148<br>(2.199)</td><td>-0.537<br>(1.488)</td><td>0.163<br>(1.495)</td><td>-1.293<br>(1.482)</td></tr><tr><td>Will become millionaire: Likely</td><td>0.235*<br>(0.130)</td><td>0.392**<br>(0.186)</td><td>0.677***<br>(0.260)</td><td>2.006*<br>(1.081)</td><td>1.359<br>(1.720)</td><td>6.105***<br>(1.172)</td><td>-0.230<br>(1.195)</td><td>1.806<br>(1.207)</td></tr><tr><td>Will become millionaire: Already</td><td>0.400<br>(0.257)</td><td>0.342<br>(0.391)</td><td>0.550<br>(0.564)</td><td>4.912**<br>(2.043)</td><td>-2.519<br>(3.520)</td><td>-0.547<br>(2.243)</td><td>5.065**<br>(2.319)</td><td>-3.604<br>(2.249)</td></tr><tr><td>Subsample: Satisfied</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Subsample: Touched &amp; Satisfied</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Foreign born</td><td>-0.119<br>(0.197)</td><td>-0.372<br>(0.250)</td><td>-0.472<br>(0.369)</td><td>-2.265<br>(1.614)</td><td>-1.128<br>(2.608)</td><td>3.051*<br>(1.810)</td><td>3.123*<br>(1.867)</td><td>-0.312<br>(1.863)</td></tr><tr><td>Observations</td><td>10,990</td><td>6,148</td><td>4,374</td><td>10,990</td><td>4,374</td><td>11,000</td><td>11,000</td><td>11,000</td></tr><tr><td>R<sup>2</sup></td><td>0.023</td><td>0.030</td><td>0.042</td><td>0.266</td><td>0.195</td><td>0.092</td><td>0.038</td><td>0.059</td></tr></tbody></table>

Note: Robust standard errors are reported in parentheses. Covariates omitted in the Table: Country; Employment; Couple; Region; Constant. Omitted variables are: Vote: Non-voter, PNR or Other; Gender: Woman; Age: 35-49; Income quartile: Q1; Diploma: Below upper secondary; Urbanicity: City. *p<0.1; **p<0.05; ***p<0.01.

(Back to Section 2.)
# F The Determination of a Custom Redistribution

In Question 55, respondents are asked for their preferred redistribution of the world's post-tax income. This custom redistribution is determined by modifying the current distribution using the respondent's three input parameters:<fnref n="54" />

* The *proportion of winners*, i.e. the share of people (at the bottom of the distribution) advantaged by the custom redistribution;

* The *proportion of losers*, i.e. the share of people (at the top) disadvantaged by the redistribution;

* The *degree of redistribution*, ranging from 0/10 (no redistribution) to 10/10 (maximal redistribution).

The determination of the custom distribution given these parameters relies on the algorithm *Dis/adv* introduced by Fabre (2022). In that paper, Fabre (2022) surveyed two representative samples of French respondents. The first survey uncovered the median preferred parameters for a national income redistribution.<fnref n="55" /> The second survey showed that 52% supported the income redistribution defined using these parameters while only 26% opposed it. Furthermore, a majority among the French respondents who expressed an opinion agreed that it is a good idea to “determine the citizens’ preferred tax schedule from a survey and then submit the proposal that would emerge from the survey to a referendum.” Therefore, the algorithm *Dis/adv* applied to median preferred parameters has been validated both through the support for the resulting redistribution and through the support for such democratic method of preference aggregation to determine an income redistribution. Nonetheless, the algorithm *Dis/adv* is just a first attempt to adjust the tax schedule by aggregating citizens’ preferences, and more appropriate methods may be proposed. Although Fabre (2022) finds that the algorithm *Dis/adv* fares better than another algorithm tested, the method still suffers from some limitations. In particular, the current method is difficult to understand for the users, and it only allows for

<footnote n="54">⁵⁴Overall, 35% of the respondents did not move the sliders from their original position. Excluding the 39% of them who still state that they are satisfied with the redistribution does not change the results qualitatively. Indeed, the average responses are similar between satisfied respondents who moved the sliders and all satisfied respondents: the shares of winners or losers, the implied minimum income or transfer all differ by less than 8%. Therefore, I keep all satisfied respondents in the analysis.</footnote>

<footnote n="55">⁵⁵The median preferred proportions of winners and losers were 50% and 10%, respectively. The median preferred degree of redistribution was defined indirectly, using the median preferred demogrant: €800/month. The resulting redistribution entailed 12% of GDP redistributed from the top 10% to the bottom 50%.</footnote>
redistribution from the rich to the poor (it would thus be inappropriate if the level of in-
equality were considered too low). Below, I describe the algorithm *Dis/adv*, available for
use at bit.ly/custom_global_redistr, and implemented in the R function algo_dis_av at
github.com/bixiou/robustness_global_redistr/raw/main/code_robustness/2_prepare.R.

**Algorithm Dis/Adv** It is worth recalling that over a range of income (concerning people who are neither winner nor loser from the reform), both the current and custom distributions coincide. The algorithm proceeds as follows:

1. Define the *extreme distribution* as the current distribution bounded by the income thresholds of winners and losers. In other words, draw two horizontal lines at each end of the distribution, by setting the incomes of winners to the income of the richest winner, and those of losers to the income of the poorest loser.

2. Compute what can be redistributed on either side as the area between the extreme and current distributions: what can be given to winners on the left side (L) or taken on the right side (R). If and only if what can be given is lower than what can be taken (L < R, as in Figure S69), the left side is binding, and it determines the *amount transferred* from the rich to the poor: $\min\{L; R\} \cdot \text{degree}$.

3. On the binding side, define the custom distribution as a linear mixture between the current and extreme distributions, with the mixture parameter set by the *degree of*

<figure><img src="image_84.png" /><figcaption>Figure S69: Algorithm for the custom redistribution, with parameters *winners*: 60%, *losers*: 20%, *degree*: 4/10.</figcaption></figure>
*redistribution*. In other words, starting from the current distribution, narrow the gap with the extreme distribution by a factor *degree*.

4. Adjust the non-binding side by narrowing the gap with the extreme distribution, using the unique mixture parameter that preserves aggregate income (so that the amount transferred is the same on both sides).<fnref n="56" />

5. [Optional step, used in the survey.] To increase the demogrant (i.e. the lowest income) and make the reform more progressive on the left side, try to replace the left side with a straight line. In other words, find the demogrant and the straight line between the demogrant and the threshold of winners that respects the amount transferred. If this straight line crosses the current distribution or if it implies a regressive redistribution (in that some incomes would increase less than higher incomes), abandon the straight line and keep the custom redistribution as is.

Once the custom redistribution has been determined, it is straightforward to compute the additional tax schedule required to attain it.<fnref n="57" /> Figure S70 presents the tax schedule required to attain the average custom redistribution (weighted over all respondents). Figure S70 shows that (on the losers’ side) this redistribution can be approximated by additional marginal tax rates of 7% above $25,000 per year, and 16% above $40,000. Figure S70 also compares this tax schedule with those associated with the radical income redistribution tested in Questions 45-46: While the average custom redistribution features a much larger tax base than the radical tax targeting the top 3% (as it taxes the top 28%), its top tax rate is three times lower, so that the two redistributions entail similar transfers from the rich to the poor, at around 5% of the world’s income.

(Back to Section 6.4.)

<footnote n="56">⁵⁶While we do not account for behavioral responses, one can adjust the algorithm to account for them. It suffices to define a post-reform aggregate income, which can be lower than the pre-reform one if the reform disincentivizes economic activity.</footnote>

<footnote n="57">⁵⁷For a sophisticated calculation of the required tax schedule, which allows for behavioral responses and a gradual implementation, see Appendix IX of Fabre (2022).</footnote>
<figure><img src="image_85.png" /><figcaption>Figure S70: Additional tax schedule associated with the radical and custom redistributions. (Questions 45-46, 55).</figcaption></figure>

(Back to Section 6.4.)
# G Attrition Analysis

<table><caption>Table S10: sociodemographic determinants of attrition and exclusion. (Back to Section 2.)</caption><thead><tr><th></th><th>Dropped out<br>(1)</th><th>Dropped out<br>after socio-eco<br>(2)</th><th>Failed<br>attention test<br>(3)</th><th>Duration<br>(in min)<br>(4)</th><th>Duration<br>below 6 min<br>(5)</th></tr></thead><tbody><tr><td>Mean</td><td>0.166</td><td>0.102</td><td>0.088</td><td>53.896</td><td>0.087</td></tr><tr><td>Vote: Center-right or Right</td><td>-0.046***<br>(0.008)</td><td>-0.040***<br>(0.008)</td><td>-0.007<br>(0.007)</td><td>-3.723<br>(10.677)</td><td>-0.027***<br>(0.008)</td></tr><tr><td>Vote: Far right</td><td>-0.055***<br>(0.010)</td><td>-0.049***<br>(0.010)</td><td>-0.011<br>(0.009)</td><td>-16.327<br>(15.607)</td><td>-0.022**<br>(0.009)</td></tr><tr><td>Vote: Left</td><td>-0.032***<br>(0.008)</td><td>-0.026***<br>(0.008)</td><td>-0.013*<br>(0.007)</td><td>-11.710<br>(10.647)</td><td>-0.043***<br>(0.008)</td></tr><tr><td>Gender: Man</td><td>-0.041***<br>(0.005)</td><td>-0.040***<br>(0.005)</td><td>0.024***<br>(0.005)</td><td>-17.884**<br>(8.557)</td><td>0.005<br>(0.005)</td></tr><tr><td>Age: 18-24</td><td>-0.029***<br>(0.009)</td><td>-0.028***<br>(0.009)</td><td>0.027**<br>(0.011)</td><td>-10.346<br>(11.025)</td><td>0.092***<br>(0.013)</td></tr><tr><td>Age: 25-34</td><td>-0.029***<br>(0.007)</td><td>-0.030***<br>(0.007)</td><td>0.021***<br>(0.008)</td><td>9.105<br>(13.496)</td><td>0.051***<br>(0.009)</td></tr><tr><td>Age: 50-64</td><td>0.011<br>(0.007)</td><td>0.010<br>(0.007)</td><td>-0.032***<br>(0.006)</td><td>13.629<br>(13.104)</td><td>-0.054***<br>(0.006)</td></tr><tr><td>Age: 65+</td><td>0.038***<br>(0.010)</td><td>0.037***<br>(0.010)</td><td>-0.055***<br>(0.007)</td><td>28.381*<br>(14.773)</td><td>-0.099***<br>(0.008)</td></tr><tr><td>Income quartile: Q2</td><td>-0.026***<br>(0.007)</td><td>-0.027***<br>(0.007)</td><td>-0.040***<br>(0.007)</td><td>-3.204<br>(8.024)</td><td>-0.012<br>(0.007)</td></tr><tr><td>Income quartile: Q3</td><td>-0.025***<br>(0.008)</td><td>-0.026***<br>(0.008)</td><td>-0.057***<br>(0.007)</td><td>-6.466<br>(8.615)</td><td>-0.014*<br>(0.008)</td></tr><tr><td>Income quartile: Q4</td><td>-0.026***<br>(0.008)</td><td>-0.027***<br>(0.008)</td><td>-0.060***<br>(0.008)</td><td>28.244*<br>(15.506)</td><td>-0.033***<br>(0.008)</td></tr><tr><td>Diploma: Upper secondary</td><td>-0.019**<br>(0.008)</td><td>-0.018**<br>(0.008)</td><td>-0.050***<br>(0.008)</td><td>12.642<br>(10.115)</td><td>-0.010<br>(0.008)</td></tr><tr><td>Diploma: Above upper secondary</td><td>-0.045***<br>(0.009)</td><td>-0.045***<br>(0.009)</td><td>-0.061***<br>(0.008)</td><td>-8.224<br>(11.372)</td><td>-0.016*<br>(0.008)</td></tr><tr><td>Urbanicity: Rural</td><td>-0.003<br>(0.008)</td><td>-0.004<br>(0.008)</td><td>-0.008<br>(0.007)</td><td>-2.606<br>(8.635)</td><td>-0.004<br>(0.007)</td></tr><tr><td>Urbanicity: Towns and suburbs</td><td>0.008<br>(0.008)</td><td>0.008<br>(0.008)</td><td>-0.015**<br>(0.007)</td><td>6.228<br>(16.432)</td><td>0.001<br>(0.007)</td></tr><tr><td>Foreign born</td><td>0.006<br>(0.010)</td><td>0.006<br>(0.010)</td><td>0.017<br>(0.011)</td><td>44.695*<br>(24.418)</td><td>-0.029***<br>(0.008)</td></tr><tr><td>Country: Germany</td><td>-0.364<br>(0.262)</td><td>-0.362<br>(0.263)</td><td>-0.691**<br>(0.332)</td><td>-19.841<br>(36.683)</td><td>-0.193***<br>(0.025)</td></tr><tr><td>Country: Italy</td><td>-0.138<br>(0.318)</td><td>-0.134<br>(0.318)</td><td>-0.684**<br>(0.332)</td><td>640.179<br>(602.441)</td><td>-0.193***<br>(0.032)</td></tr><tr><td>Country: Japan</td><td>-0.334<br>(0.262)</td><td>-0.329<br>(0.263)</td><td>-0.716**<br>(0.332)</td><td>-34.599<br>(30.589)</td><td>0.219<br>(0.195)</td></tr><tr><td>Country: Poland</td><td>-0.279<br>(0.261)</td><td>-0.276<br>(0.262)</td><td>-0.739**<br>(0.332)</td><td>-96.647***<br>(36.916)</td><td>0.784***<br>(0.026)</td></tr><tr><td>Country: Russia</td><td>-0.355<br>(0.261)</td><td>-0.347<br>(0.262)</td><td>-0.664**<br>(0.332)</td><td>117.725<br>(136.213)</td><td>-0.097**<br>(0.043)</td></tr><tr><td>Country: Saudi Arabia</td><td>-0.093<br>(0.280)</td><td>-0.084<br>(0.281)</td><td>-0.271<br>(0.356)</td><td>-44.669<br>(41.881)</td><td>-0.299***<br>(0.036)</td></tr><tr><td>Country: Spain</td><td>-0.313<br>(0.261)</td><td>-0.311<br>(0.262)</td><td>-0.715**<br>(0.332)</td><td>26.355<br>(34.327)</td><td>-0.304***<br>(0.027)</td></tr><tr><td>Country: Switzerland</td><td>-0.002<br>(0.025)</td><td>-0.001<br>(0.025)</td><td>-0.002<br>(0.020)</td><td>-3.527<br>(14.853)</td><td>-0.001<br>(0.020)</td></tr><tr><td>Country: United Kingdom</td><td>-0.278<br>(0.261)</td><td>-0.278<br>(0.262)</td><td>-0.841**<br>(0.332)</td><td>9.534<br>(33.235)</td><td>-0.373***<br>(0.030)</td></tr><tr><td>Country: USA</td><td>-0.038<br>(0.025)</td><td>-0.032<br>(0.025)</td><td>-0.147***<br>(0.022)</td><td>-47.279*<br>(25.547)</td><td>-0.130***<br>(0.021)</td></tr><tr><td>Observations</td><td>16,066</td><td>16,066</td><td>14,301</td><td>13,040</td><td>13,040</td></tr><tr><td>R<sup>2</sup></td><td>0.044</td><td>0.044</td><td>0.080</td><td>0.011</td><td>0.087</td></tr></tbody></table>

Note: Robust standard errors are reported in parentheses. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.
<caption>Table S11: Treatment effects on attrition.</caption>

(Back to Section 2.)

<table><thead><tr><th></th><th colspan="8">Random branch:</th></tr><tr><th></th><th>Wealth tax coverage: Global (1)</th><th>Wealth tax coverage: Int'l (2)</th><th>Int'l CS coverage: Low (3)</th><th>Int'l CS coverage: High (4)</th><th>Int'l CS coverage: High color (5)</th><th>National CS asked (6)</th><th>Warm glow substitute: Control (7)</th><th>Warm glow realism: Info treatment (8)</th></tr></thead><tbody><tr><th>Mean</th><td>0.332</td><td>0.331</td><td>0.25</td><td>0.255</td><td>0.251</td><td>0.36</td><td>0.36</td><td>0.49</td></tr><tr><th>Dropped out</th><td>-0.038 (0.026)</td><td>-0.014 (0.027)</td><td>-0.024 (0.017)</td><td>-0.004 (0.017)</td><td>0.021 (0.017)</td><td>-0.004 (0.010)</td><td>0.003 (0.010)</td><td>0.006 (0.010)</td></tr><tr><th>Observations</th><td>14,609</td><td>14,609</td><td>14,968</td><td>14,968</td><td>14,968</td><td>17,150</td><td>17,150</td><td>17,150</td></tr><tr><th>R<sup>2</sup></th><td>0.0001</td><td>0.00002</td><td>0.0001</td><td>0.00000</td><td>0.0001</td><td>0.00001</td><td>0.00000</td><td>0.00002</td></tr></tbody></table>

Note: Robust standard errors are reported in parentheses. *p<0.1; **p<0.05; ***p<0.01.

<caption>Table S12: Treatment effects on attrition (continued).</caption>

(Back to Section 2.)

<table><thead><tr><th></th><th colspan="9">Random branch:</th></tr><tr><th></th><th>Field: Concerns (1)</th><th>Field: Injustice (2)</th><th>Field: Issue (3)</th><th>Field: Wish (4)</th><th>Budget split: Few (5)</th><th>GCS belief: Own (6)</th><th>NCQG: Full (7)</th><th>Sustainable Future: A (8)</th><th>Income tax: top 1% (9)</th><th>Custom sliders: Diffuse (10)</th></tr></thead><tbody><tr><th>Mean</th><td>0.246</td><td>0.251</td><td>0.248</td><td>0.256</td><td>0.508</td><td>0.503</td><td>0.496</td><td>0.506</td><td>0.494</td><td>0.499</td></tr><tr><th>Dropped out</th><td>0.009 (0.011)</td><td>0.006 (0.011)</td><td>0.001 (0.011)</td><td>-0.017 (0.011)</td><td>-0.006 (0.010)</td><td>0.009 (0.010)</td><td>-0.007 (0.010)</td><td>-0.050 (0.031)</td><td>-0.010 (0.010)</td><td>0.002 (0.010)</td></tr><tr><th>Observations</th><td>15,876</td><td>15,876</td><td>15,876</td><td>15,876</td><td>16,112</td><td>16,112</td><td>16,112</td><td>14,564</td><td>17,150</td><td>16,112</td></tr><tr><th>R<sup>2</sup></th><td>0.00005</td><td>0.00002</td><td>0.00000</td><td>0.0002</td><td>0.00002</td><td>0.00005</td><td>0.00003</td><td>0.0002</td><td>0.0001</td><td>0.00000</td></tr></tbody></table>

Note: Robust standard errors are reported in parentheses. *p<0.1; **p<0.05; ***p<0.01.
# H Balance Analysis

<table><caption>Table S13: Balance analysis.</caption><thead><tr><th rowspan="2"></th><th colspan="8">Random branch:</th></tr><tr><th>Wealth tax coverage: Global (1)</th><th>Wealth tax coverage: Int'l (2)</th><th>Int'l CS coverage: Low (3)</th><th>Int'l CS coverage: High (4)</th><th>Int'l CS coverage: High color (5)</th><th>National CS asked (6)</th><th>Warm glow substitute: Control (7)</th><th>Warm glow realism: Info treatment (8)</th></tr></thead><tbody><tr><td>Mean</td><td>0.332</td><td>0.334</td><td>0.25</td><td>0.256</td><td>0.252</td><td>0.36</td><td>0.358</td><td>0.489</td></tr><tr><td>Vote: Center-right or Right</td><td>0.001 (0.013)</td><td>0.018 (0.013)</td><td>-0.011 (0.013)</td><td>0.001 (0.012)</td><td>0.011 (0.012)</td><td>-0.013 (0.014)</td><td>0.003 (0.013)</td><td>-0.013 (0.014)</td></tr><tr><td>Vote: Far right</td><td>-0.026 (0.018)</td><td>0.016 (0.018)</td><td>-0.015 (0.017)</td><td>0.025 (0.017)</td><td>0.019 (0.017)</td><td>-0.008 (0.018)</td><td>0.019 (0.019)</td><td>-0.010 (0.020)</td></tr><tr><td>Vote: Left</td><td>-0.004 (0.014)</td><td>0.001 (0.014)</td><td>-0.004 (0.013)</td><td>-0.007 (0.013)</td><td>0.002 (0.012)</td><td>-0.003 (0.014)</td><td>-0.005 (0.013)</td><td>-0.014 (0.014)</td></tr><tr><td>Gender: Man</td><td>-0.00003 (0.009)</td><td>-0.015* (0.009)</td><td>-0.013 (0.008)</td><td>0.005 (0.008)</td><td>-0.00005 (0.008)</td><td>0.001 (0.009)</td><td>-0.007 (0.009)</td><td>0.011 (0.010)</td></tr><tr><td>Age: 18-24</td><td>-0.009 (0.018)</td><td>0.007 (0.018)</td><td>-0.013 (0.017)</td><td>-0.001 (0.017)</td><td>0.007 (0.017)</td><td>0.009 (0.019)</td><td>-0.020 (0.018)</td><td>0.001 (0.020)</td></tr><tr><td>Age: 25-34</td><td>-0.024* (0.014)</td><td>0.025* (0.014)</td><td>-0.008 (0.012)</td><td>0.018 (0.013)</td><td>-0.004 (0.013)</td><td>-0.003 (0.014)</td><td>-0.001 (0.014)</td><td>-0.011 (0.015)</td></tr><tr><td>Age: 50-64</td><td>-0.009 (0.013)</td><td>0.008 (0.013)</td><td>0.006 (0.012)</td><td>0.016 (0.012)</td><td>-0.003 (0.012)</td><td>0.008 (0.013)</td><td>0.003 (0.013)</td><td>-0.009 (0.013)</td></tr><tr><td>Age: 65+</td><td>-0.013 (0.016)</td><td>0.031* (0.016)</td><td>0.011 (0.015)</td><td>0.030** (0.015)</td><td>-0.023 (0.015)</td><td>-0.014 (0.016)</td><td>0.020 (0.016)</td><td>-0.0004 (0.017)</td></tr><tr><td>Income quartile: Q2</td><td>-0.006 (0.013)</td><td>0.001 (0.013)</td><td>0.00001 (0.012)</td><td>-0.018 (0.012)</td><td>-0.008 (0.012)</td><td>-0.003 (0.013)</td><td>-0.010 (0.013)</td><td>-0.012 (0.014)</td></tr><tr><td>Income quartile: Q3</td><td>0.002 (0.013)</td><td>0.0003 (0.013)</td><td>0.005 (0.012)</td><td>-0.014 (0.012)</td><td>0.0001 (0.012)</td><td>-0.010 (0.014)</td><td>-0.013 (0.014)</td><td>0.004 (0.014)</td></tr><tr><td>Income quartile: Q4</td><td>-0.015 (0.015)</td><td>0.012 (0.015)</td><td>-0.006 (0.013)</td><td>-0.001 (0.014)</td><td>0.010 (0.013)</td><td>-0.012 (0.015)</td><td>0.001 (0.015)</td><td>-0.006 (0.016)</td></tr><tr><td>Diploma: Upper secondary</td><td>0.013 (0.014)</td><td>0.003 (0.014)</td><td>-0.0003 (0.013)</td><td>-0.010 (0.013)</td><td>0.012 (0.013)</td><td>-0.013 (0.014)</td><td>0.018 (0.014)</td><td>-0.021 (0.015)</td></tr><tr><td>Diploma: Above upper secondary</td><td>0.030** (0.015)</td><td>-0.008 (0.014)</td><td>0.002 (0.013)</td><td>-0.015 (0.014)</td><td>0.020 (0.013)</td><td>-0.006 (0.015)</td><td>0.003 (0.015)</td><td>-0.004 (0.015)</td></tr><tr><td>Urbanicity: Rural</td><td>0.010 (0.014)</td><td>0.012 (0.014)</td><td>0.009 (0.013)</td><td>-0.013 (0.013)</td><td>-0.005 (0.013)</td><td>-0.003 (0.014)</td><td>0.012 (0.014)</td><td>-0.006 (0.015)</td></tr><tr><td>Urbanicity: Towns and suburbs</td><td>0.021 (0.015)</td><td>-0.015 (0.015)</td><td>0.008 (0.014)</td><td>-0.004 (0.013)</td><td>0.003 (0.013)</td><td>0.011 (0.015)</td><td>-0.015 (0.015)</td><td>0.004 (0.016)</td></tr><tr><td>Will become millionaire: Likely</td><td>0.016 (0.011)</td><td>-0.015 (0.011)</td><td>-0.004 (0.010)</td><td>0.014 (0.010)</td><td>-0.013 (0.010)</td><td>0.005 (0.011)</td><td>0.0002 (0.011)</td><td>0.006 (0.012)</td></tr><tr><td>Will become millionaire: Already</td><td>0.006 (0.022)</td><td>-0.010 (0.022)</td><td>-0.008 (0.020)</td><td>-0.001 (0.020)</td><td>-0.014 (0.020)</td><td>0.029 (0.022)</td><td>-0.005 (0.022)</td><td>-0.035 (0.023)</td></tr><tr><td>Foreign born</td><td>-0.010 (0.017)</td><td>0.017 (0.017)</td><td>-0.014 (0.016)</td><td>0.035** (0.017)</td><td>-0.012 (0.016)</td><td>0.014 (0.018)</td><td>-0.006 (0.018)</td><td>-0.014 (0.018)</td></tr><tr><td>Observations</td><td>12,001</td><td>12,001</td><td>11,993</td><td>11,993</td><td>11,993</td><td>12,001</td><td>12,001</td><td>12,001</td></tr><tr><td>R<sup>2</sup></td><td>0.006</td><td>0.006</td><td>0.005</td><td>0.005</td><td>0.005</td><td>0.021</td><td>0.025</td><td>0.006</td></tr></tbody></table>

Note: Robust standard errors are in parentheses. CS: Climate Scheme. *p<0.1; **p<0.05; ***p<0.01.
# I Placebo Tests

<table><caption>Table S14: Placebo tests of treatments on unrelated outcomes (simple OLS regressions).</caption><thead><tr><th></th><th>Supports<br>the Global<br>Climate Scheme<br>(1)</th><th>Supports<br>the Int'l<br>Clim. Sch.<br>(2)</th><th>Supports<br>the Int'l<br>Clim. Sch.<br>(3)</th><th>Share of<br>policies<br>supported<br>(4)</th><th>Supports<br>the int'l<br>wealth tax<br>(5)</th><th>Supports<br>the int'l<br>wealth tax<br>(6)</th></tr></thead><tbody><tr><td>Open-ended field variant: Injustice</td><td>0.001<br>(0.013)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Open-ended field variant: Issue</td><td>0.018<br>(0.013)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Open-ended field variant: Wish</td><td>0.031**<br>(0.013)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Revenue split variant: Many</td><td></td><td>0.006<br>(0.009)</td><td></td><td></td><td></td><td></td></tr><tr><td>GCS belief variant: U.S.</td><td></td><td></td><td>0.003<br>(0.009)</td><td></td><td></td><td></td></tr><tr><td>Warm glow variant: National CS</td><td></td><td></td><td></td><td>0.009<br>(0.007)</td><td></td><td></td></tr><tr><td>Warm glow variant: Donation</td><td></td><td></td><td></td><td>0.007<br>(0.008)</td><td></td><td></td></tr><tr><td>Int'l CS variant: High color</td><td></td><td></td><td></td><td></td><td>-0.019**<br>(0.009)</td><td>-0.010<br>(0.012)</td></tr><tr><td>Int'l CS variant: Low</td><td></td><td></td><td></td><td></td><td>0.007<br>(0.009)</td><td>0.004<br>(0.012)</td></tr><tr><td>Int'l CS variant: Mid</td><td></td><td></td><td></td><td></td><td>0.008<br>(0.009)</td><td>0.019<br>(0.012)</td></tr><tr><td>(Intercept)</td><td>0.543***<br>(0.009)</td><td>0.561***<br>(0.007)</td><td>0.665***<br>(0.006)</td><td>0.503***<br>(0.005)</td><td>0.509***<br>(0.006)</td><td>0.701***<br>(0.008)</td></tr><tr><td>Observations</td><td>11,839</td><td>11,000</td><td>11,000</td><td>12,001</td><td>11,993</td><td>11,993</td></tr><tr><td>R<sup>2</sup></td><td>0.001</td><td>0.00004</td><td>0.00001</td><td>0.0001</td><td>0.001</td><td>0.001</td></tr></tbody></table>

Note:

*p<0.1; **p<0.05; ***p<0.01

Table S14 shows that in two cases (out of thirteen), treatments are significantly correlated with unrelated outcomes later in the survey, with an effect size up to 3 p.p. While these framing effects are undesirable, their magnitude is limited. Indeed, the average value of affected outcomes always remain within ±2 p.p. of the value it is estimated to have had if any treatment had been generalized. (Back to Section 2.)
# J Main Results on Selected Demographics, Including Vote

Figures S71 and S72 shows that polarization between left- and right-wing voters is comparably high in Europe and the U.S. but almost non-existent in Japan, with Japanese support comparable to the support among non-voters in Western countries. Interestingly, although Fabre et al. (2025) exhibited much higher polarization in the U.S. compared to Western Europe on similar questions (about the GCS, the globally redistributive millionaire tax, and universalism), in the current survey polarization has converged in the two regions. More specifically, compared to early 2023, in Western Europe (France, Germany, Spain and the UK), support has declined across the political spectrum, but more so among right-wing voters. Meanwhile, in the U.S., support has increased among Republican voters and decreased among Democrat voters.

<figure><img src="image_86.png" /><figcaption>Figure S71: [On selected demographics] Support for global redistribution action/policies.</figcaption></figure>

(Back to Section 2.)
<figure><img src="image_87.png" /><figcaption>Figure S73: [On selected demographics] Average synthetic indicators of support for global redistribution. (Question 38). (Back to Section 6.1.)</figcaption></figure>

<figure><img src="image_88.png" /></figure>

(Back to Section 2.)
# K Main Results Weighted by Vote

<figure><img src="image_89.png" /><figcaption>Figure S75: [Weighted by vote] Support for an international wealth tax with 30% of revenue funding LICs, depending on the country coverage (*Yes/No* question). (Questions 41-43).</figcaption></figure>

<figure><img src="image_90.png" /></figure>

(Back to Section 2.)
Figure S76: [Weighted by vote] Effect on the likelihood that a political program is preferred of containing the following policy (compared to no foreign policy in the program). (Question 23)

<figure><img src="image_91.png" /></figure>

Figure S77: [Weighted by vote] Testing warm glow (negative effects would indicate the presence of warm glow).

(a) Effect of a *Donation lottery* treatment on support for the Global Climate Scheme. (Questions 27-28)

<figure><img src="image_92.png" /></figure>

(b) Effect of information about ongoing global redistribution initiatives on the share of plausible global policies supported. (Questions 36-38)

<figure><img src="image_93.png" /></figure>
<figure><img src="image_94.png" /><figcaption>Figure S79: [Weighted by vote] Support for broad action or radical proposals of global redistribution. (Questions 44-46, 49-51, 53, 61).</figcaption></figure>

<figure><img src="image_95.png" /></figure>

(Back to Section 2.)
# L Main Results on the Extended Sample

<figure><img src="image_96.png" /><figcaption>Figure S81: [Extended sample] Support for an international wealth tax with 30% of revenue funding LICs, depending on the country coverage (*Yes/No* question). (Questions 41-43).</figcaption></figure>

<figure><img src="image_97.png" /></figure>

(Back to Section 2.)
Figure S82: [Extended sample] Effect on the likelihood that a political program is preferred of containing the following policy (compared to no foreign policy in the program). (Question 23)

<figure><img src="image_98.png" /></figure>

Figure S83: [Extended sample] Testing warm glow (negative effects would indicate the presence of warm glow).

(a) Effect of a *Donation lottery* treatment on support for the Global Climate Scheme. (Questions 27-28)

<figure><img src="image_99.png" /></figure>

(b) Effect of information about ongoing global redistribution initiatives on the share of plausible global policies supported. (Questions 36-38)

<figure><img src="image_100.png" /></figure>
<figure><img src="image_101.png" /><figcaption>Figure S85: [Extended sample] Support for broad action or radical proposals of global redistribution. (Questions 44-46, 49-51, 53, 61).</figcaption></figure>

<figure><img src="image_102.png" /></figure>

(Back to Section 2.)
# M Influence of the Item Order on Answers

<caption>Table S15: Influence of the item order on answers.</caption>

(Back to Section 2.)

<table><thead><tr><th></th><th>Prefers<br>Sustain.<br>future<br>(1)</th><th>Finds<br>Uncond.<br>cash<br>transfers<br>Right<br>(2)</th><th>Agrees it<br>is HIC's<br>duty to<br>help LICs<br>(3)</th><th>Understood<br>Global<br>Clim. Sch.<br>(4)</th><th>Preferred<br>NCQG<br>≥ $100 bn<br>(5)</th><th>Pref. NCQG<br>≥ $100 bn<br>(variant<br>Short)<br>(6)</th><th>Supports<br>a<br>plausible<br>policy<br>(7)</th><th>Allocates<br>≥ 15% to<br>spending<br>item<br>(8)</th></tr></thead><tbody><tr><td>Scenario A = Sustainable<br>Cash transfers first item</td><td>0.036***<br>(0.009)</td><td>-0.140***<br>(0.009)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Duty last item</td><td></td><td></td><td>-0.049***<br>(0.010)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Correct answer first item</td><td></td><td></td><td></td><td>0.050***<br>(0.008)</td><td></td><td></td><td></td><td></td></tr><tr><td>Variant: Short</td><td></td><td></td><td></td><td></td><td>-0.076***<br>(0.009)</td><td></td><td></td><td></td></tr><tr><td>Items in increasing order</td><td></td><td></td><td></td><td></td><td></td><td>-0.092***<br>(0.013)</td><td></td><td></td></tr><tr><td>That item is the first one</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.024***<br>(0.005)</td><td></td></tr><tr><td>Order of the item: 2</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.020**<br>(0.008)</td></tr><tr><td>Order of the item: 3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.040***<br>(0.008)</td></tr><tr><td>Order of the item: 4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.064***<br>(0.008)</td></tr><tr><td>Order of the item: 5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.071***<br>(0.008)</td></tr><tr><td>Constant</td><td>0.662***<br>(0.006)</td><td>0.396***<br>(0.006)</td><td>0.554***<br>(0.006)</td><td>0.713***<br>(0.006)</td><td>0.638***<br>(0.007)</td><td>0.608***<br>(0.009)</td><td>0.511***<br>(0.002)</td><td>0.592***<br>(0.006)</td></tr><tr><td>Observations</td><td>12,001</td><td>12,001</td><td>11,000</td><td>12,001</td><td>11,000</td><td>5,476</td><td>110,000</td><td>37,088</td></tr><tr><td>R<sup>2</sup></td><td>0.001</td><td>0.022</td><td>0.002</td><td>0.003</td><td>0.006</td><td>0.009</td><td>0.0002</td><td>0.003</td></tr></tbody></table>

Note:

*p<0.1; **p<0.05; ***p<0.01
# N Supplementary Tables

<table><caption>Table S16: Support for an International Climate Scheme depending on country coverage. (Back to Section 4.1.)</caption><thead><tr><th rowspan="2"></th><th colspan="14">Supports the International Climate Scheme</th></tr><tr><th>All</th><th>Europe</th><th>FR</th><th>DE</th><th>IT</th><th>PL</th><th>ES</th><th>GB</th><th>CH</th><th>JP</th><th>RU</th><th>SA</th><th>US</th></tr></thead><tbody><tr><th>Variant: High Color</th><td>-6.610<sup>***</sup><br>(1.344)</td><td>-5.372<sup>***</sup><br>(1.917)</td><td>-14.437<sup>***</sup><br>(4.634)</td><td>-9.561<sup>**</sup><br>(3.946)</td><td>0.328<br>(4.368)</td><td>-13.170<sup>**</sup><br>(6.072)</td><td>3.570<br>(5.253)</td><td>2.012<br>(4.859)</td><td>-11.681<sup>*</sup><br>(6.130)</td><td>-7.254<sup>**</sup><br>(2.948)</td><td>-6.131<br>(4.626)</td><td>-3.512<br>(4.130)</td><td>-8.161<sup>***</sup><br>(2.481)</td></tr><tr><th>Variant: Low</th><td>-3.789<sup>***</sup><br>(1.333)</td><td>-4.520<sup>**</sup><br>(1.877)</td><td>0.221<br>(4.392)</td><td>-14.023<sup>***</sup><br>(4.000)</td><td>4.404<br>(4.073)</td><td>-10.900<sup>*</sup><br>(5.788)</td><td>-3.060<br>(5.271)</td><td>-1.939<br>(4.766)</td><td>-13.109<sup>**</sup><br>(6.178)</td><td>-4.559<br>(2.944)</td><td>-2.570<br>(4.774)</td><td>-3.028<br>(3.775)</td><td>-4.275<sup>*</sup><br>(2.503)</td></tr><tr><th>Variant: Mid</th><td>-1.354<br>(1.333)</td><td>-0.185<br>(1.871)</td><td>1.200<br>(4.385)</td><td>-5.431<br>(3.993)</td><td>3.302<br>(4.028)</td><td>-17.458<sup>***</sup><br>(6.354)</td><td>3.772<br>(5.216)</td><td>5.861<br>(4.726)</td><td>-10.552<sup>*</sup><br>(6.368)</td><td>-3.660<br>(2.944)</td><td>4.728<br>(4.447)</td><td>-3.323<br>(3.749)</td><td>-5.145<sup>**</sup><br>(2.561)</td></tr><tr><th>Observations</th><td>11,993</td><td>4,996</td><td>798</td><td>1,047</td><td>756</td><td>500</td><td>602</td><td>826</td><td>467</td><td>1,997</td><td>1,001</td><td>999</td><td>3,000</td></tr><tr><th>R<sup>2</sup></th><td>0.003</td><td>0.003</td><td>0.021</td><td>0.013</td><td>0.002</td><td>0.017</td><td>0.004</td><td>0.004</td><td>0.012</td><td>0.003</td><td>0.007</td><td>0.002</td><td>0.004</td></tr></tbody></table>

Note: Robust standard errors (HC1) are reported in parentheses.

*p<0.1; **p<0.05; ***p<0.01.

<table><caption>Table S17: Support for an international wealth tax depending on country coverage. (Back to Section 4.2.)</caption><thead><tr><th rowspan="2"></th><th colspan="14">Supports the International Wealth Tax</th></tr><tr><th>All</th><th>Europe</th><th>FR</th><th>DE</th><th>IT</th><th>PL</th><th>ES</th><th>GB</th><th>CH</th><th>JP</th><th>RU</th><th>SA</th><th>US</th></tr></thead><tbody><tr><th>Variant: Global</th><td>0.046<sup>***</sup><br>(0.011)</td><td>0.063<sup>***</sup><br>(0.016)</td><td>0.090<sup>**</sup><br>(0.036)</td><td>0.045<br>(0.033)</td><td>0.050<br>(0.036)</td><td>0.132<sup>***</sup><br>(0.049)</td><td>0.066<br>(0.046)</td><td>0.007<br>(0.040)</td><td>0.137<sup>**</sup><br>(0.057)</td><td>0.071<sup>***</sup><br>(0.025)</td><td>0.078<sup>**</sup><br>(0.035)</td><td>0.011<br>(0.037)</td><td>0.005<br>(0.021)</td></tr><tr><th>Variant: Int'l</th><td>-0.010<br>(0.011)</td><td>0.001<br>(0.016)</td><td>0.001<br>(0.039)</td><td>-0.035<br>(0.035)</td><td>0.008<br>(0.036)</td><td>-0.098<sup>*</sup><br>(0.054)</td><td>0.082<sup>*</sup><br>(0.047)</td><td>0.008<br>(0.040)</td><td>0.045<br>(0.057)</td><td>-0.051<sup>*</sup><br>(0.027)</td><td>0.042<br>(0.037)</td><td>-0.008<br>(0.036)</td><td>-0.025<br>(0.021)</td></tr><tr><th>Observations</th><td>12,001</td><td>5,000</td><td>798</td><td>1,048</td><td>756</td><td>500</td><td>603</td><td>826</td><td>469</td><td>2,000</td><td>1,001</td><td>1,000</td><td>3,000</td></tr><tr><th>R<sup>2</sup></th><td>0.003</td><td>0.004</td><td>0.010</td><td>0.005</td><td>0.003</td><td>0.040</td><td>0.006</td><td>0.0001</td><td>0.014</td><td>0.011</td><td>0.005</td><td>0.0004</td><td>0.001</td></tr></tbody></table>

Note: Robust standard errors (HC1) are reported in parentheses.

*p<0.1; **p<0.05; ***p<0.01.
<table><caption>Table S18: Effect on the likelihood that a political program is preferred of containing the following policies (compared to no foreign policy in the program). (Back to Section 5.1.)</caption><thead><tr><th rowspan="2"></th><th colspan="11">Program is preferred</th></tr><tr><th>All</th><th>Europe</th><th>FR</th><th>DE</th><th>IT</th><th>PL</th><th>ES</th><th>GB</th><th>CH</th><th>JP</th><th>US</th></tr></thead><tbody><tr><th>Cut aid</th><td>-0.029***<br>(0.010)</td><td>-0.030**<br>(0.014)</td><td>0.035<br>(0.034)</td><td>-0.035<br>(0.030)</td><td>-0.087**<br>(0.035)</td><td>-0.059<br>(0.043)</td><td>-0.060<br>(0.038)</td><td>-0.002<br>(0.034)</td><td>0.028<br>(0.045)</td><td>0.015<br>(0.021)</td><td>-0.046***<br>(0.017)</td></tr><tr><th>Int'l tax</th><td>0.051***<br>(0.010)</td><td>0.050***<br>(0.014)</td><td>0.038<br>(0.033)</td><td>0.053*<br>(0.031)</td><td>0.067*<br>(0.037)</td><td>0.046<br>(0.043)</td><td>0.085**<br>(0.042)</td><td>0.036<br>(0.035)</td><td>-0.049<br>(0.048)</td><td>0.052**<br>(0.022)</td><td>0.052***<br>(0.018)</td></tr><tr><th>Foreign3</th><td>0.015<br>(0.010)</td><td>0.006<br>(0.014)</td><td>0.047<br>(0.033)</td><td>-0.007<br>(0.029)</td><td>0.045<br>(0.038)</td><td>-0.009<br>(0.044)</td><td>0.012<br>(0.039)</td><td>-0.021<br>(0.034)</td><td>-0.130***<br>(0.043)</td><td>0.037*<br>(0.021)</td><td>0.017<br>(0.018)</td></tr><tr><th>Observations</th><td>20,000</td><td>10,000</td><td>1,596</td><td>2,096</td><td>1,512</td><td>1,000</td><td>1,206</td><td>1,652</td><td>938</td><td>4,000</td><td>6,000</td></tr><tr><th>R<sup>2</sup></th><td>0.004</td><td>0.003</td><td>0.001</td><td>0.004</td><td>0.015</td><td>0.006</td><td>0.011</td><td>0.002</td><td>0.015</td><td>0.002</td><td>0.006</td></tr></tbody></table>

Note: Robust standard errors (HC1) clustered at the individual levels are reported in parentheses. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01.

<table><caption>Table S19: Effect of a *Donation lottery* treatment on support for the Global Climate Scheme. (Questions 27-28) (Back to Section 5.2.)</caption><thead><tr><th rowspan="2"></th><th colspan="11">Supports the Global Climate Scheme</th></tr><tr><th>All</th><th>Europe</th><th>FR</th><th>DE</th><th>IT</th><th>PL</th><th>ES</th><th>GB</th><th>CH</th><th>JP</th><th>US</th></tr></thead><tbody><tr><th>Variant: Donation</th><td>0.028**<br>(0.012)</td><td>0.054***<br>(0.017)</td><td>0.048<br>(0.041)</td><td>0.087**<br>(0.037)</td><td>0.017<br>(0.039)</td><td>0.100*<br>(0.055)</td><td>0.028<br>(0.048)</td><td>0.053<br>(0.040)</td><td>0.054<br>(0.055)</td><td>0.008<br>(0.027)</td><td>0.001<br>(0.021)</td></tr><tr><th>Constant</th><td>0.55</td><td>0.551</td><td>0.484</td><td>0.471</td><td>0.628</td><td>0.396</td><td>0.766</td><td>0.595</td><td>0.907</td><td>0.408</td><td>0.381</td></tr><tr><th>Observations</th><td>6,665</td><td>3,307</td><td>525</td><td>687</td><td>484</td><td>349</td><td>392</td><td>544</td><td>326</td><td>1,350</td><td>2,008</td></tr><tr><th>R<sup>2</sup></th><td>0.110</td><td>0.118</td><td>0.126</td><td>0.150</td><td>0.081</td><td>0.097</td><td>0.136</td><td>0.250</td><td>0.137</td><td>0.041</td><td>0.140</td></tr></tbody></table>

Note: Robust standard errors (HC1) are reported in parentheses. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01. Control variables (omitted in the table) are: vote, gender, age, income, education, urbanity, likelihood of becoming millionaire, living with partner, employment status, foreign born, country region.

<table><caption>Table S20: Effect of information about ongoing global redistribution initiatives on the share of plausible global policies supported. (Questions 36-38). (Back to Section 5.2.)</caption><thead><tr><th rowspan="2"></th><th colspan="11">Share of plausible policies supported</th></tr><tr><th>All</th><th>Europe</th><th>FR</th><th>DE</th><th>IT</th><th>PL</th><th>ES</th><th>GB</th><th>CH</th><th>JP</th><th>RU</th><th>SA</th><th>US</th></tr></thead><tbody><tr><th>Info Treatment</th><td>0.013**<br>(0.007)</td><td>0.018*<br>(0.009)</td><td>0.003<br>(0.023)</td><td>0.039**<br>(0.019)</td><td>0.027<br>(0.023)</td><td>0.005<br>(0.029)</td><td>0.058**<br>(0.029)</td><td>-0.002<br>(0.023)</td><td>-0.011<br>(0.030)</td><td>0.013<br>(0.013)</td><td>-0.014<br>(0.022)</td><td>-0.024<br>(0.025)</td><td>0.020<br>(0.012)</td></tr><tr><th>Constant</th><td>0.23</td><td>0.411</td><td>0.517</td><td>0.338</td><td>0.517</td><td>0.359</td><td>0.469</td><td>0.444</td><td>0.579</td><td>0.181</td><td>0.215</td><td>0.328</td><td>0.314</td></tr><tr><th>Observations</th><td>12,001</td><td>5,000</td><td>798</td><td>1,048</td><td>756</td><td>500</td><td>603</td><td>826</td><td>469</td><td>2,000</td><td>1,001</td><td>1,000</td><td>3,000</td></tr><tr><th>R<sup>2</sup></th><td>0.141</td><td>0.152</td><td>0.139</td><td>0.234</td><td>0.136</td><td>0.153</td><td>0.180</td><td>0.215</td><td>0.190</td><td>0.096</td><td>0.062</td><td>0.180</td><td>0.181</td></tr></tbody></table>

Note: Robust standard errors (HC1) are reported in parentheses. \*p<0.1; \*\*p<0.05; \*\*\*p<0.01. Control variables (omitted in the table) are: vote, gender, age, income, education, urbanity, likelihood of becoming millionaire, living with partner, employment status, foreign born, country region.
<table><caption>Table S21: Effect on support for global redistribution of believing that it is likely, without control (Back to Section 5.2.)</caption><thead><tr><th rowspan="2"></th><th>Believes global<br>redistr. likely</th><th colspan="3">Share of plausible global policies supported</th></tr><tr><th>IV 1st Stage<br>(1)</th><th>IV 2nd Stage<br>(2)</th><th>OLS<br>(3)</th><th>Direct Effect<br>(4)</th></tr></thead><tbody><tr><td>Information treatment</td><td>0.077***<br>(0.010)</td><td></td><td></td><td>0.011<br>(0.007)</td></tr><tr><td>Believes global redistr. likely</td><td></td><td>0.141<br>(0.088)</td><td>0.150***<br>(0.007)</td><td></td></tr><tr><td>(Intercept)</td><td>0.332***<br>(0.007)</td><td>0.456***<br>(0.033)</td><td>0.453***<br>(0.004)</td><td>0.503***<br>(0.005)</td></tr><tr><td>Effective F-statistic</td><td>65.04</td><td></td><td></td><td></td></tr><tr><td>Observations</td><td>12,001</td><td>12,001</td><td>12,001</td><td>12,001</td></tr><tr><td>R<sup>2</sup></td><td>0.006</td><td>0.043</td><td>0.043</td><td>0.0002</td></tr></tbody></table>

Note: Robust standard errors (HC1) are reported in parentheses.

*p<0.1; **p<0.05; ***p<0.01.
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## List of Tables

<table><tr><td>1</td><td>Effect on support for global redistribution of believing that it is likely</td><td>30</td></tr><tr><td>S1</td><td>Sources for plausible global policies</td><td>121</td></tr><tr><td>S2</td><td>Country-specific features</td><td>124</td></tr><tr><td>S3</td><td>Loadings from the Exploratory Factor Analysis</td><td>130</td></tr><tr><td>S4</td><td>Sample representativeness in All, Eu, EU</td><td>132</td></tr><tr><td>S5</td><td>Sample representativeness in FR, DE, IT</td><td>133</td></tr><tr><td>S6</td><td>Sample representativeness in PL, ES, GB, CH</td><td>134</td></tr><tr><td>S7</td><td>Sample representativeness in JP, RU, SA, US</td><td>135</td></tr><tr><td>S8</td><td>Correlates of support for global redistribution</td><td>137</td></tr><tr><td>S9</td><td>Correlates of answers on custom redistribution</td><td>138</td></tr><tr><td>S10</td><td>Determinants of attrition</td><td>143</td></tr><tr><td>S11</td><td>Treatment effects on attrition (1)</td><td>144</td></tr><tr><td>S12</td><td>Treatment effects on attrition (2)</td><td>144</td></tr><tr><td>S13</td><td>Balance analysis</td><td>145</td></tr><tr><td>S14</td><td>Placebo tests of treatments on unrelated outcomes (simple OLS regressions).</td><td>146</td></tr><tr><td>S15</td><td>Influence of the item order on answers</td><td>155</td></tr><tr><td>S16</td><td>Effect of country coverage on support for the ICS</td><td>156</td></tr><tr><td>S17</td><td>Effect of country coverage on support for the wealth tax</td><td>156</td></tr><tr><td>S18</td><td>Conjoint analysis: effect of development aid and millionaire tax</td><td>157</td></tr><tr><td>S19</td><td>Warm glow: substitute</td><td>157</td></tr><tr><td>S20</td><td>Warm glow: realism</td><td>157</td></tr><tr><td>S21</td><td>Warm glow: IV without control</td><td>158</td></tr></table>

## List of Figures

<table><tr><td>1</td><td>Country coverage</td><td>10</td></tr><tr><td>2</td><td>Survey flow</td><td>13</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>3</td>
      <td>Word cloud of open-ended field, per variant</td>
      <td>16</td>
    </tr>
    <tr>
      <td>4</td>
      <td>Preferred split of revenue of a global wealth tax</td>
      <td>19</td>
    </tr>
    <tr>
      <td>5</td>
      <td>Example maps of the International Climate Scheme</td>
      <td>22</td>
    </tr>
    <tr>
      <td>6</td>
      <td>Support for the NCS, GCS, ICS</td>
      <td>23</td>
    </tr>
    <tr>
      <td>7</td>
      <td>Support for an international wealth tax depending on country coverage</td>
      <td>25</td>
    </tr>
    <tr>
      <td>8</td>
      <td>Conjoint analysis: effect of development aid and millionaire tax</td>
      <td>27</td>
    </tr>
    <tr>
      <td>9</td>
      <td>Testing warm glow</td>
      <td>29</td>
    </tr>
    <tr>
      <td>10</td>
      <td>Acceptance of plausible global redistribution policies</td>
      <td>32</td>
    </tr>
    <tr>
      <td>11</td>
      <td>Acceptance of broad or radical global redistribution</td>
      <td>36</td>
    </tr>
    <tr>
      <td>12</td>
      <td>Moral circle</td>
      <td>37</td>
    </tr>
    <tr>
      <td>13</td>
      <td>Right or Best way to transfer resources to LICs (global average)</td>
      <td>38</td>
    </tr>
    <tr>
      <td>14</td>
      <td>Custom redistribution task screenshot</td>
      <td>39</td>
    </tr>
    <tr>
      <td>S1</td>
      <td>Average synthetic indicators of support for global redistribution</td>
      <td>50</td>
    </tr>
    <tr>
      <td>S2</td>
      <td>Keyword classification of open-ended fields</td>
      <td>51</td>
    </tr>
    <tr>
      <td>S3</td>
      <td>AI classification of open-ended fields</td>
      <td>52</td>
    </tr>
    <tr>
      <td>S4</td>
      <td>Manual classification of open-ended fields</td>
      <td>53</td>
    </tr>
    <tr>
      <td>S5</td>
      <td>Manual classification of concerns fields</td>
      <td>54</td>
    </tr>
    <tr>
      <td>S6</td>
      <td>Manual classification of wish fields</td>
      <td>55</td>
    </tr>
    <tr>
      <td>S7</td>
      <td>Manual classification of injustice fields</td>
      <td>56</td>
    </tr>
    <tr>
      <td>S8</td>
      <td>Manual classification of issue fields</td>
      <td>57</td>
    </tr>
    <tr>
      <td>S9</td>
      <td>Conjoint analysis: effect of development aid and millionaire tax by vote</td>
      <td>58</td>
    </tr>
    <tr>
      <td>S10</td>
      <td>Conjoint analysis in France</td>
      <td>59</td>
    </tr>
    <tr>
      <td>S11</td>
      <td>Conjoint analysis in Germany</td>
      <td>60</td>
    </tr>
    <tr>
      <td>S12</td>
      <td>Conjoint analysis in Italy</td>
      <td>61</td>
    </tr>
    <tr>
      <td>S13</td>
      <td>Conjoint analysis in Poland</td>
      <td>62</td>
    </tr>
    <tr>
      <td>S14</td>
      <td>Conjoint analysis in Spain</td>
      <td>63</td>
    </tr>
    <tr>
      <td>S15</td>
      <td>Conjoint analysis in the UK</td>
      <td>64</td>
    </tr>
    <tr>
      <td>S16</td>
      <td>Conjoint analysis in Switzerland</td>
      <td>65</td>
    </tr>
    <tr>
      <td>S17</td>
      <td>Conjoint analysis in Japan</td>
      <td>66</td>
    </tr>
    <tr>
      <td>S18</td>
      <td>Conjoint analysis in the U.S.</td>
      <td>67</td>
    </tr>
    <tr>
      <td>S19</td>
      <td>Conjoint analysis in France (French)</td>
      <td>68</td>
    </tr>
    <tr>
      <td>S20</td>
      <td>Conjoint analysis in Germany (German)</td>
      <td>69</td>
    </tr>
    <tr>
      <td>S21</td>
      <td>Conjoint analysis in Italy (Italian)</td>
      <td>70</td>
    </tr>
    <tr>
      <td>S22</td>
      <td>Conjoint analysis in Poland (Polish)</td>
      <td>71</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>S23</td>
      <td>Conjoint analysis in Spain (Spanish)</td>
      <td>72</td>
    </tr>
    <tr>
      <td>S24</td>
      <td>Conjoint analysis in Japan (Japanese)</td>
      <td>73</td>
    </tr>
    <tr>
      <td>S25</td>
      <td>Average preferred revenue split (few)</td>
      <td>74</td>
    </tr>
    <tr>
      <td>S26</td>
      <td>Decomposition of preferred spending in revenue split, variant Few</td>
      <td>75</td>
    </tr>
    <tr>
      <td>S27</td>
      <td>Decomposition of preferred spending in revenue split, variant Many</td>
      <td>76</td>
    </tr>
    <tr>
      <td>S28</td>
      <td>Average preferred revenue split (many)</td>
      <td>76</td>
    </tr>
    <tr>
      <td>S29</td>
      <td>Amounts donated to plant trees.</td>
      <td>77</td>
    </tr>
    <tr>
      <td>S30</td>
      <td>Support for the NCS, GCS, ICS, and belief of support for GCS</td>
      <td>77</td>
    </tr>
    <tr>
      <td>S31</td>
      <td>Belief about the likelihood of global redistribution</td>
      <td>78</td>
    </tr>
    <tr>
      <td>S32</td>
      <td>Absolute support for plausible global redistribution policies</td>
      <td>79</td>
    </tr>
    <tr>
      <td>S33</td>
      <td>Share of plausible global policies supported</td>
      <td>79</td>
    </tr>
    <tr>
      <td>S34</td>
      <td>Share of plausible global policies opposed</td>
      <td>80</td>
    </tr>
    <tr>
      <td>S35</td>
      <td>Preferred NCQG, variant Short</td>
      <td>80</td>
    </tr>
    <tr>
      <td>S36</td>
      <td>Preferred NCQG, variant Full</td>
      <td>81</td>
    </tr>
    <tr>
      <td>S37</td>
      <td>Support for an international wealth depending on country coverage</td>
      <td>81</td>
    </tr>
    <tr>
      <td>S38</td>
      <td>Prefers a sustainable future</td>
      <td>82</td>
    </tr>
    <tr>
      <td>S39</td>
      <td>Acceptance of a global income tax on the richest to fund LICs</td>
      <td>82</td>
    </tr>
    <tr>
      <td>S40</td>
      <td>Absolute support for an income tax on top 1% to fund LICs</td>
      <td>83</td>
    </tr>
    <tr>
      <td>S41</td>
      <td>Acceptance of a global income tax among affected respondents</td>
      <td>83</td>
    </tr>
    <tr>
      <td>S42</td>
      <td>Best way to transfer resources to LICs</td>
      <td>83</td>
    </tr>
    <tr>
      <td>S43</td>
      <td>Wrong way to transfer resources to LICs</td>
      <td>84</td>
    </tr>
    <tr>
      <td>S44</td>
      <td>Support for making all countries' GDP p.c. converge by 2100</td>
      <td>84</td>
    </tr>
    <tr>
      <td>S45</td>
      <td>Willingness to participate in a global movement for sustainable development</td>
      <td>85</td>
    </tr>
    <tr>
      <td>S46</td>
      <td>Would vote for a party in a global coalition for sustainable development</td>
      <td>86</td>
    </tr>
    <tr>
      <td>S47</td>
      <td>Agreement with rationales for global redistribution</td>
      <td>86</td>
    </tr>
    <tr>
      <td>S48</td>
      <td>Support for reparations for colonization and slavery</td>
      <td>87</td>
    </tr>
    <tr>
      <td>S49</td>
      <td>Median custom redistribution</td>
      <td>88</td>
    </tr>
    <tr>
      <td>S50</td>
      <td>Average custom redistribution</td>
      <td>89</td>
    </tr>
    <tr>
      <td>S51</td>
      <td>Average answers to custom redistribution</td>
      <td>90</td>
    </tr>
    <tr>
      <td>S52</td>
      <td>Average answers to custom redistribution among satisfied</td>
      <td>90</td>
    </tr>
    <tr>
      <td>S53</td>
      <td>Median answers to custom redistribution among satisfied</td>
      <td>91</td>
    </tr>
    <tr>
      <td>S54</td>
      <td>Preferred share of winners in the custom redistributions</td>
      <td>91</td>
    </tr>
    <tr>
      <td>S55</td>
      <td>Preferred share of losers in the custom redistributions</td>
      <td>91</td>
    </tr>
    <tr>
      <td>S56</td>
      <td>Minimum income implied by custom redistributions</td>
      <td>91</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>S57</td>
      <td>Transfer implied by custom redistributions</td>
      <td>92</td>
    </tr>
    <tr>
      <td>S58</td>
      <td>Comprehension question on GCS</td>
      <td>92</td>
    </tr>
    <tr>
      <td>S59</td>
      <td>Relative agreement: "My taxes should go towards solving global problems"</td>
      <td>92</td>
    </tr>
    <tr>
      <td>S60</td>
      <td>Absolute agreement: "My taxes should go towards solving global problems"</td>
      <td>93</td>
    </tr>
    <tr>
      <td>S61</td>
      <td>Moral circle</td>
      <td>93</td>
    </tr>
    <tr>
      <td>S62</td>
      <td>Feeling that the survey was politically biased</td>
      <td>94</td>
    </tr>
    <tr>
      <td>S63</td>
      <td>Manual classification of feedback fields</td>
      <td>94</td>
    </tr>
    <tr>
      <td>S64</td>
      <td>Likelihood of becoming a millionaire</td>
      <td>95</td>
    </tr>
    <tr>
      <td>S65</td>
      <td>Foreign origin</td>
      <td>95</td>
    </tr>
    <tr>
      <td>S66</td>
      <td>Vote in the last election compared to actual results (among voters)</td>
      <td>96</td>
    </tr>
    <tr>
      <td>S67</td>
      <td>Vote in the last election compared to actual results (entire population)</td>
      <td>97</td>
    </tr>
    <tr>
      <td>S68</td>
      <td>Variance decomposition</td>
      <td>136</td>
    </tr>
    <tr>
      <td>S69</td>
      <td>Algorithm for the custom redistribution</td>
      <td>140</td>
    </tr>
    <tr>
      <td>S70</td>
      <td>Tax schedule of radical and custom redistributions</td>
      <td>142</td>
    </tr>
    <tr>
      <td>S71</td>
      <td>[On selected demographics] Absolute support for broad global redistribution</td>
      <td>147</td>
    </tr>
    <tr>
      <td>S72</td>
      <td>[On selected demographics] Acceptance of plausible global policies</td>
      <td>148</td>
    </tr>
    <tr>
      <td>S73</td>
      <td>[On selected demographics] Average synthetic indicators of support</td>
      <td>148</td>
    </tr>
    <tr>
      <td>S74</td>
      <td>[Weighted by vote] Support for the NCS, GCS, ICS</td>
      <td>149</td>
    </tr>
    <tr>
      <td>S75</td>
      <td>[Weighted by vote] Support for an int'l wealth tax depending on coverage</td>
      <td>149</td>
    </tr>
    <tr>
      <td>S76</td>
      <td>[Weighted by vote] Conjoint analysis</td>
      <td>150</td>
    </tr>
    <tr>
      <td>S77</td>
      <td>[Weighted by vote] Testing warm glow</td>
      <td>150</td>
    </tr>
    <tr>
      <td>S78</td>
      <td>[Weighted by vote] Acceptance of plausible global policies</td>
      <td>151</td>
    </tr>
    <tr>
      <td>S79</td>
      <td>[Weighted by vote] Support for broad or radical global redistribution</td>
      <td>151</td>
    </tr>
    <tr>
      <td>S80</td>
      <td>[Extended sample] Support for the NCS, GCS, ICS</td>
      <td>152</td>
    </tr>
    <tr>
      <td>S81</td>
      <td>[Extended sample] Support for an int'l wealth tax depending on coverage</td>
      <td>152</td>
    </tr>
    <tr>
      <td>S82</td>
      <td>[Extended sample] Conjoint analysis</td>
      <td>153</td>
    </tr>
    <tr>
      <td>S83</td>
      <td>[Extended sample] Testing warm glow</td>
      <td>153</td>
    </tr>
    <tr>
      <td>S84</td>
      <td>[Extended sample] Acceptance of plausible global policies</td>
      <td>154</td>
    </tr>
    <tr>
      <td>S85</td>
      <td>[Extended sample] Support for broad or radical global redistribution</td>
      <td>154</td>
    </tr>
  </tbody>
</table>