Cambridge University Press
The Dynamics of Racial Resentment Across the 50 U.S. StatesAppendix for ReflectionAppendix A: Detailed Figures Referenced in TextA1. Racial Resentment in Individual States, Over TimeA2. Racial Resentment by Region, Over TimeA3. Mapping Annual Racial Resentment in the States, Relative to Other StatesNote: This is similar to Figure 3, but with deciles in lieu of quintiles. Quintiles for each map are calculated using data from that year only. As such, the individual maps should not be directly compared to each other. However, it is appropriate to compare to maps to observe patterns in which states have racial resentment scores in the higher and low quintiles of the data.A4. Changes in Estimated Racial Resentment in Over Time, Sort by Racial Resentment in 2016Note: This is similar to Figure 4, but instead sorted by estimated racial resentment in 2016.A5. Mapping Annual Racial Resentment in the States, Relative to Other StatesNote: This is similar to Figure 5, but with deciles in lieu of quintiles. Quintiles for each map are calculated using data from that year only. As such, the individual maps should not be directly compared to each other. However, it is appropriate to compare to maps to observe patterns in which states have racial resentment scores in the higher and low quintiles of the data.Appendix B: The Use of MRP to Create Subnational Estimates of Public OpinionMultilevel regression with post-stratification weighting (MRP) is an approach to estimating public opinion that brings together three pieces of information: census data, survey data containing a measure of the attitude one is interested in measuring, and data on state-level variables that may have an impact on those attitudes. Public opinion is modeled as a function of demographic characteristics and state-level variables, and the responses are weighted using frequencies of demographic types from the census. The method has been vigorously tested and validated across a range of data, and several groups of scholars have created useful sets of guidelines and cautions to those using MRP ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2013</Year><RecNum>39</RecNum><DisplayText>(Lax and Phillips 2013, Buttice and Highton 2013)</DisplayText><record><rec-number>39</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519835380">39</key></foreign-keys><ref-type name="Conference Proceedings">10</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate sub-national opinion using MRP? Preliminary findings and recommendations</title><secondary-title>annual meeting of the Midwest Political Science Association, Chicago</secondary-title></titles><dates><year>2013</year></dates><urls></urls></record></Cite><Cite><Author>Buttice</Author><Year>2013</Year><RecNum>25</RecNum><record><rec-number>25</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519246359">25</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Buttice, Matthew K</author><author>Highton, Benjamin</author></authors></contributors><titles><title>How does multilevel regression and poststratification perform with conventional national surveys?</title><secondary-title>Political Analysis</secondary-title></titles><periodical><full-title>Political Analysis</full-title></periodical><pages>449-467</pages><volume>21</volume><number>4</number><dates><year>2013</year></dates><isbn>1476-4989</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2013, Buttice and Highton 2013). MRP can be used to create state level estimates with a single national survey of at least 1,400 or so respondents PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MYXg8L0F1dGhvcj48WWVhcj4yMDA5PC9ZZWFyPjxSZWNO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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5MYXg8L0F1dGhvcj48WWVhcj4yMDA5PC9ZZWFyPjxSZWNO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ADDIN EN.CITE.DATA (Lax and Phillips 2009b, Park, Gelman, and Bafumi 2004, Pacheco 2011) and congressional districts with just a few thousand respondents ADDIN EN.CITE <EndNote><Cite><Author>Warshaw</Author><Year>2012</Year><RecNum>24</RecNum><DisplayText>(Warshaw and Rodden 2012)</DisplayText><record><rec-number>24</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519246298">24</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Warshaw, Christopher</author><author>Rodden, Jonathan</author></authors></contributors><titles><title>How should we measure district-level public opinion on individual issues?</title><secondary-title>The Journal of Politics</secondary-title></titles><periodical><full-title>The Journal of Politics</full-title></periodical><pages>203-219</pages><volume>74</volume><number>1</number><dates><year>2012</year></dates><isbn>0022-3816</isbn><urls></urls></record></Cite></EndNote>(Warshaw and Rodden 2012). MRP produces more robust and precise estimates than simple survey disaggregation, particularly when sample sizes are relatively small PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYXVkZW5idXNoPC9BdXRob3I+PFllYXI+MjAwMjwvWWVh
cj48UmVjTnVtPjM1PC9SZWNOdW0+PERpc3BsYXlUZXh0PihSYXVkZW5idXNoIGFuZCBCcnlrIDIw
MDIsIFNuaWpkZXJzIDIwMTEsIFN0ZWVuYmVyZ2VuIGFuZCBKb25lcyAyMDAyLCBMYXggYW5kIFBo
aWxsaXBzIDIwMDliLCBQYXJrLCBHZWxtYW4sIGFuZCBCYWZ1bWkgMjAwNCk8L0Rpc3BsYXlUZXh0
PjxyZWNvcmQ+PHJlYy1udW1iZXI+MzU8L3JlYy1udW1iZXI+PGZvcmVpZ24ta2V5cz48a2V5IGFw
cD0iRU4iIGRiLWlkPSJ3d3Y1YWVwdHZmMndhYWV2ZTJscGRlc3dhdHRyZHN2dzJldDUiIHRpbWVz
dGFtcD0iMTUxOTgzMTEzOCI+MzU8L2tleT48L2ZvcmVpZ24ta2V5cz48cmVmLXR5cGUgbmFtZT0i
Qm9vayI+NjwvcmVmLXR5cGU+PGNvbnRyaWJ1dG9ycz48YXV0aG9ycz48YXV0aG9yPlJhdWRlbmJ1
c2gsIFN0ZXBoZW4gVzwvYXV0aG9yPjxhdXRob3I+QnJ5aywgQW50aG9ueSBTPC9hdXRob3I+PC9h
dXRob3JzPjwvY29udHJpYnV0b3JzPjx0aXRsZXM+PHRpdGxlPkhpZXJhcmNoaWNhbCBsaW5lYXIg
bW9kZWxzOiBBcHBsaWNhdGlvbnMgYW5kIGRhdGEgYW5hbHlzaXMgbWV0aG9kczwvdGl0bGU+PC90
aXRsZXM+PHZvbHVtZT4xPC92b2x1bWU+PGRhdGVzPjx5ZWFyPjIwMDI8L3llYXI+PC9kYXRlcz48
cHVibGlzaGVyPlNhZ2U8L3B1Ymxpc2hlcj48aXNibj4wNzYxOTE5MDRYPC9pc2JuPjx1cmxzPjwv
dXJscz48L3JlY29yZD48L0NpdGU+PENpdGU+PEF1dGhvcj5TbmlqZGVyczwvQXV0aG9yPjxZZWFy
PjIwMTE8L1llYXI+PFJlY051bT4zNzwvUmVjTnVtPjxyZWNvcmQ+PHJlYy1udW1iZXI+Mzc8L3Jl
Yy1udW1iZXI+PGZvcmVpZ24ta2V5cz48a2V5IGFwcD0iRU4iIGRiLWlkPSJ3d3Y1YWVwdHZmMndh
YWV2ZTJscGRlc3dhdHRyZHN2dzJldDUiIHRpbWVzdGFtcD0iMTUxOTgzMTI2NiI+Mzc8L2tleT48
L2ZvcmVpZ24ta2V5cz48cmVmLXR5cGUgbmFtZT0iQm9vayBTZWN0aW9uIj41PC9yZWYtdHlwZT48
Y29udHJpYnV0b3JzPjxhdXRob3JzPjxhdXRob3I+U25pamRlcnMsIFRvbSBBQjwvYXV0aG9yPjwv
YXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48dGl0bGVzPjx0aXRsZT5NdWx0aWxldmVsIGFuYWx5c2lz
PC90aXRsZT48c2Vjb25kYXJ5LXRpdGxlPkludGVybmF0aW9uYWwgZW5jeWNsb3BlZGlhIG9mIHN0
YXRpc3RpY2FsIHNjaWVuY2U8L3NlY29uZGFyeS10aXRsZT48L3RpdGxlcz48cGFnZXM+ODc5LTg4
MjwvcGFnZXM+PGRhdGVzPjx5ZWFyPjIwMTE8L3llYXI+PC9kYXRlcz48cHVibGlzaGVyPlNwcmlu
Z2VyPC9wdWJsaXNoZXI+PHVybHM+PC91cmxzPjwvcmVjb3JkPjwvQ2l0ZT48Q2l0ZT48QXV0aG9y
PlN0ZWVuYmVyZ2VuPC9BdXRob3I+PFllYXI+MjAwMjwvWWVhcj48UmVjTnVtPjM2PC9SZWNOdW0+
PHJlY29yZD48cmVjLW51bWJlcj4zNjwvcmVjLW51bWJlcj48Zm9yZWlnbi1rZXlzPjxrZXkgYXBw
PSJFTiIgZGItaWQ9Ind3djVhZXB0dmYyd2FhZXZlMmxwZGVzd2F0dHJkc3Z3MmV0NSIgdGltZXN0
YW1wPSIxNTE5ODMxMTc0Ij4zNjwva2V5PjwvZm9yZWlnbi1rZXlzPjxyZWYtdHlwZSBuYW1lPSJK
b3VybmFsIEFydGljbGUiPjE3PC9yZWYtdHlwZT48Y29udHJpYnV0b3JzPjxhdXRob3JzPjxhdXRo
b3I+U3RlZW5iZXJnZW4sIE1hcmNvIFI8L2F1dGhvcj48YXV0aG9yPkpvbmVzLCBCcmFkZm9yZCBT
PC9hdXRob3I+PC9hdXRob3JzPjwvY29udHJpYnV0b3JzPjx0aXRsZXM+PHRpdGxlPk1vZGVsaW5n
IG11bHRpbGV2ZWwgZGF0YSBzdHJ1Y3R1cmVzPC90aXRsZT48c2Vjb25kYXJ5LXRpdGxlPmFtZXJp
Y2FuIEpvdXJuYWwgb2YgcG9saXRpY2FsIFNjaWVuY2U8L3NlY29uZGFyeS10aXRsZT48L3RpdGxl
cz48cGVyaW9kaWNhbD48ZnVsbC10aXRsZT5BbWVyaWNhbiBKb3VybmFsIG9mIFBvbGl0aWNhbCBT
Y2llbmNlPC9mdWxsLXRpdGxlPjwvcGVyaW9kaWNhbD48cGFnZXM+MjE4LTIzNzwvcGFnZXM+PGRh
dGVzPjx5ZWFyPjIwMDI8L3llYXI+PC9kYXRlcz48aXNibj4wMDkyLTU4NTM8L2lzYm4+PHVybHM+
PC91cmxzPjwvcmVjb3JkPjwvQ2l0ZT48Q2l0ZT48QXV0aG9yPkxheDwvQXV0aG9yPjxZZWFyPjIw
MDk8L1llYXI+PFJlY051bT4xMjwvUmVjTnVtPjxyZWNvcmQ+PHJlYy1udW1iZXI+MTI8L3JlYy1u
dW1iZXI+PGZvcmVpZ24ta2V5cz48a2V5IGFwcD0iRU4iIGRiLWlkPSJ3d3Y1YWVwdHZmMndhYWV2
ZTJscGRlc3dhdHRyZHN2dzJldDUiIHRpbWVzdGFtcD0iMTUxOTI0NTg5NCI+MTI8L2tleT48L2Zv
cmVpZ24ta2V5cz48cmVmLXR5cGUgbmFtZT0iSm91cm5hbCBBcnRpY2xlIj4xNzwvcmVmLXR5cGU+
PGNvbnRyaWJ1dG9ycz48YXV0aG9ycz48YXV0aG9yPkxheCwgSmVmZnJleSBSPC9hdXRob3I+PGF1
dGhvcj5QaGlsbGlwcywgSnVzdGluIEg8L2F1dGhvcj48L2F1dGhvcnM+PC9jb250cmlidXRvcnM+
PHRpdGxlcz48dGl0bGU+SG93IHNob3VsZCB3ZSBlc3RpbWF0ZSBwdWJsaWMgb3BpbmlvbiBpbiB0
aGUgc3RhdGVzPzwvdGl0bGU+PHNlY29uZGFyeS10aXRsZT5BbWVyaWNhbiBKb3VybmFsIG9mIFBv
bGl0aWNhbCBTY2llbmNlPC9zZWNvbmRhcnktdGl0bGU+PC90aXRsZXM+PHBlcmlvZGljYWw+PGZ1
bGwtdGl0bGU+QW1lcmljYW4gSm91cm5hbCBvZiBQb2xpdGljYWwgU2NpZW5jZTwvZnVsbC10aXRs
ZT48L3BlcmlvZGljYWw+PHBhZ2VzPjEwNy0xMjE8L3BhZ2VzPjx2b2x1bWU+NTM8L3ZvbHVtZT48
bnVtYmVyPjE8L251bWJlcj48ZGF0ZXM+PHllYXI+MjAwOTwveWVhcj48L2RhdGVzPjxpc2JuPjE1
NDAtNTkwNzwvaXNibj48dXJscz48L3VybHM+PC9yZWNvcmQ+PC9DaXRlPjxDaXRlPjxBdXRob3I+
UGFyazwvQXV0aG9yPjxZZWFyPjIwMDQ8L1llYXI+PFJlY051bT4yMTwvUmVjTnVtPjxyZWNvcmQ+
PHJlYy1udW1iZXI+MjE8L3JlYy1udW1iZXI+PGZvcmVpZ24ta2V5cz48a2V5IGFwcD0iRU4iIGRi
LWlkPSJ3d3Y1YWVwdHZmMndhYWV2ZTJscGRlc3dhdHRyZHN2dzJldDUiIHRpbWVzdGFtcD0iMTUx
OTI0NjE5NSI+MjE8L2tleT48L2ZvcmVpZ24ta2V5cz48cmVmLXR5cGUgbmFtZT0iSm91cm5hbCBB
cnRpY2xlIj4xNzwvcmVmLXR5cGU+PGNvbnRyaWJ1dG9ycz48YXV0aG9ycz48YXV0aG9yPlBhcmss
IERhdmlkIEs8L2F1dGhvcj48YXV0aG9yPkdlbG1hbiwgQW5kcmV3PC9hdXRob3I+PGF1dGhvcj5C
YWZ1bWksIEpvc2VwaDwvYXV0aG9yPjwvYXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48dGl0bGVzPjx0
aXRsZT5CYXllc2lhbiBtdWx0aWxldmVsIGVzdGltYXRpb24gd2l0aCBwb3N0c3RyYXRpZmljYXRp
b246IHN0YXRlLWxldmVsIGVzdGltYXRlcyBmcm9tIG5hdGlvbmFsIHBvbGxzPC90aXRsZT48c2Vj
b25kYXJ5LXRpdGxlPlBvbGl0aWNhbCBBbmFseXNpczwvc2Vjb25kYXJ5LXRpdGxlPjwvdGl0bGVz
PjxwZXJpb2RpY2FsPjxmdWxsLXRpdGxlPlBvbGl0aWNhbCBBbmFseXNpczwvZnVsbC10aXRsZT48
L3BlcmlvZGljYWw+PHBhZ2VzPjM3NS0zODU8L3BhZ2VzPjx2b2x1bWU+MTI8L3ZvbHVtZT48bnVt
YmVyPjQ8L251bWJlcj48ZGF0ZXM+PHllYXI+MjAwNDwveWVhcj48L2RhdGVzPjxpc2JuPjE0NzYt
NDk4OTwvaXNibj48dXJscz48L3VybHM+PC9yZWNvcmQ+PC9DaXRlPjwvRW5kTm90ZT4A
ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5SYXVkZW5idXNoPC9BdXRob3I+PFllYXI+MjAwMjwvWWVh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 EN.CITE.DATA (Raudenbush and Bryk 2002, Snijders 2011, Steenbergen and Jones 2002, Lax and Phillips 2009b, Park, Gelman, and Bafumi 2004). Park, Gelman and Bafumi (2006) compare MRP estimates of opinion to older approaches to modeling state opinion and show that MRP substantially outperforms approaches that do not partially pool information across respondents. The subsequent process of post-stratification corrects for over-sampling or under-sampling of demographic categories ADDIN EN.CITE <EndNote><Cite><Author>Voss</Author><Year>1995</Year><RecNum>40</RecNum><DisplayText>(Voss, Gelman, and King 1995)</DisplayText><record><rec-number>40</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519836439">40</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Voss, Stephen</author><author>Gelman, Andrew</author><author>King, Gary</author></authors></contributors><titles><title>The polls—A review: Preelection survey methodology: Details from eight polling organizations, 1988 and 1992</title><secondary-title>Public Opinion Quarterly</secondary-title></titles><periodical><full-title>Public Opinion Quarterly</full-title></periodical><pages>98-132</pages><volume>59</volume><number>1</number><dates><year>1995</year></dates><isbn>1537-5331</isbn><urls></urls></record></Cite></EndNote>(Voss, Gelman, and King 1995). The pooling of information across states and years also allows for accurate estimation of opinion for smaller states, which are often excluded from national surveys or have a small state sample ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText>(Lax and Phillips 2009b, Pacheco 2011)</DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245894">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate public opinion in the states?</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>107-121</pages><volume>53</volume><number>1</number><dates><year>2009</year></dates><isbn>1540-5907</isbn><urls></urls></record></Cite><Cite><Author>Pacheco</Author><Year>2011</Year><RecNum>15</RecNum><record><rec-number>15</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245995">15</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Using national surveys to measure dynamic US state public opinion: A guideline for scholars and an application</title><secondary-title>State Politics & Policy Quarterly</secondary-title></titles><periodical><full-title>State Politics & Policy Quarterly</full-title></periodical><pages>415-439</pages><volume>11</volume><number>4</number><dates><year>2011</year></dates><isbn>1532-4400</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009b, Pacheco 2011). In general, MRP yields smaller errors, higher correlations, and more reliable estimates than disaggregation ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText>(Lax and Phillips 2009b)</DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245894">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate public opinion in the states?</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>107-121</pages><volume>53</volume><number>1</number><dates><year>2009</year></dates><isbn>1540-5907</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009b).Because one doesn’t need as many survey responses to produce reliable state estimates compared to disaggregation, scholars have used MRP to create measures of sub-national and sub-state attitudes on a range of issues, including same-sex marriage ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>1779</RecNum><DisplayText>(Lax and Phillips 2009a, Lewis and Jacobsmeier 2017)</DisplayText><record><rec-number>1779</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">1779</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>Gay rights in the states: Public opinion and policy responsiveness</title><secondary-title>American Political Science Review</secondary-title></titles><periodical><full-title>American Political Science Review</full-title></periodical><pages>367-386</pages><volume>103</volume><number>3</number><dates><year>2009</year></dates><isbn>1537-5943</isbn><urls></urls></record></Cite><Cite><Author>Lewis</Author><Year>2017</Year><RecNum>65</RecNum><record><rec-number>65</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">65</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lewis, Daniel C</author><author>Jacobsmeier, Matthew L</author></authors></contributors><titles><title>Evaluating Policy Representation with Dynamic MRP Estimates: Direct Democracy and Same-Sex Relationship Policies in the United States</title><secondary-title>State Politics & Policy Quarterly</secondary-title></titles><periodical><full-title>State Politics & Policy Quarterly</full-title></periodical><pages>441-464</pages><volume>17</volume><number>4</number><dates><year>2017</year></dates><isbn>1532-4400</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009a, Lewis and Jacobsmeier 2017), ideology and partisanship PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Fbm5zPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48UmVj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=
ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5Fbm5zPC9BdXRob3I+PFllYXI+MjAxNTwvWWVhcj48UmVj
TnVtPjY4PC9SZWNOdW0+PERpc3BsYXlUZXh0PihFbm5zIGFuZCBLb2NoIDIwMTUsIDIwMTMsIFBh
Y2hlY28gMjAxMSk8L0Rpc3BsYXlUZXh0PjxyZWNvcmQ+PHJlYy1udW1iZXI+Njg8L3JlYy1udW1i
ZXI+PGZvcmVpZ24ta2V5cz48a2V5IGFwcD0iRU4iIGRiLWlkPSJlZHZhOTlhemFzendzYmVmc3B1
cGE1YTcwZnRyenNwMDk5MDAiIHRpbWVzdGFtcD0iMTUyMjY4Mzg5NyI+Njg8L2tleT48L2ZvcmVp
Z24ta2V5cz48cmVmLXR5cGUgbmFtZT0iSm91cm5hbCBBcnRpY2xlIj4xNzwvcmVmLXR5cGU+PGNv
bnRyaWJ1dG9ycz48YXV0aG9ycz48YXV0aG9yPkVubnMsIFBldGVyIEs8L2F1dGhvcj48YXV0aG9y
PktvY2gsIEp1bGlhbm5hPC9hdXRob3I+PC9hdXRob3JzPjwvY29udHJpYnV0b3JzPjx0aXRsZXM+
PHRpdGxlPlN0YXRlIHBvbGljeSBtb29kOiBUaGUgaW1wb3J0YW5jZSBvZiBvdmVyLXRpbWUgZHlu
YW1pY3M8L3RpdGxlPjxzZWNvbmRhcnktdGl0bGU+U3RhdGUgUG9saXRpY3MgJmFtcDsgUG9saWN5
IFF1YXJ0ZXJseTwvc2Vjb25kYXJ5LXRpdGxlPjwvdGl0bGVzPjxwZXJpb2RpY2FsPjxmdWxsLXRp
dGxlPlN0YXRlIFBvbGl0aWNzICZhbXA7IFBvbGljeSBRdWFydGVybHk8L2Z1bGwtdGl0bGU+PC9w
ZXJpb2RpY2FsPjxwYWdlcz40MzYtNDQ2PC9wYWdlcz48dm9sdW1lPjE1PC92b2x1bWU+PG51bWJl
cj40PC9udW1iZXI+PGRhdGVzPjx5ZWFyPjIwMTU8L3llYXI+PC9kYXRlcz48aXNibj4xNTMyLTQ0
MDA8L2lzYm4+PHVybHM+PC91cmxzPjwvcmVjb3JkPjwvQ2l0ZT48Q2l0ZT48QXV0aG9yPkVubnM8
L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFyPjxSZWNOdW0+NzE8L1JlY051bT48cmVjb3JkPjxyZWMt
bnVtYmVyPjcxPC9yZWMtbnVtYmVyPjxmb3JlaWduLWtleXM+PGtleSBhcHA9IkVOIiBkYi1pZD0i
ZWR2YTk5YXphc3p3c2JlZnNwdXBhNWE3MGZ0cnpzcDA5OTAwIiB0aW1lc3RhbXA9IjE1MjI2ODM4
OTciPjcxPC9rZXk+PC9mb3JlaWduLWtleXM+PHJlZi10eXBlIG5hbWU9IkpvdXJuYWwgQXJ0aWNs
ZSI+MTc8L3JlZi10eXBlPjxjb250cmlidXRvcnM+PGF1dGhvcnM+PGF1dGhvcj5Fbm5zLCBQZXRl
ciBLPC9hdXRob3I+PGF1dGhvcj5Lb2NoLCBKdWxpYW5uYTwvYXV0aG9yPjwvYXV0aG9ycz48L2Nv
bnRyaWJ1dG9ycz48dGl0bGVzPjx0aXRsZT5QdWJsaWMgT3BpbmlvbiBpbiB0aGUgVVMgU3RhdGVz
OiAxOTU2IHRvIDIwMTA8L3RpdGxlPjxzZWNvbmRhcnktdGl0bGU+U3RhdGUgUG9saXRpY3MgJmFt
cDsgUG9saWN5IFF1YXJ0ZXJseTwvc2Vjb25kYXJ5LXRpdGxlPjwvdGl0bGVzPjxwZXJpb2RpY2Fs
PjxmdWxsLXRpdGxlPlN0YXRlIFBvbGl0aWNzICZhbXA7IFBvbGljeSBRdWFydGVybHk8L2Z1bGwt
dGl0bGU+PC9wZXJpb2RpY2FsPjxwYWdlcz4zNDktMzcyPC9wYWdlcz48dm9sdW1lPjEzPC92b2x1
bWU+PG51bWJlcj4zPC9udW1iZXI+PGRhdGVzPjx5ZWFyPjIwMTM8L3llYXI+PC9kYXRlcz48aXNi
bj4xNTMyLTQ0MDA8L2lzYm4+PHVybHM+PC91cmxzPjwvcmVjb3JkPjwvQ2l0ZT48Q2l0ZT48QXV0
aG9yPlBhY2hlY288L0F1dGhvcj48WWVhcj4yMDExPC9ZZWFyPjxSZWNOdW0+MTAxPC9SZWNOdW0+
PHJlY29yZD48cmVjLW51bWJlcj4xMDE8L3JlYy1udW1iZXI+PGZvcmVpZ24ta2V5cz48a2V5IGFw
cD0iRU4iIGRiLWlkPSJlZHZhOTlhemFzendzYmVmc3B1cGE1YTcwZnRyenNwMDk5MDAiIHRpbWVz
dGFtcD0iMTUyMjY4Mzg5NyI+MTAxPC9rZXk+PC9mb3JlaWduLWtleXM+PHJlZi10eXBlIG5hbWU9
IkpvdXJuYWwgQXJ0aWNsZSI+MTc8L3JlZi10eXBlPjxjb250cmlidXRvcnM+PGF1dGhvcnM+PGF1
dGhvcj5QYWNoZWNvLCBKdWxpYW5uYTwvYXV0aG9yPjwvYXV0aG9ycz48L2NvbnRyaWJ1dG9ycz48
dGl0bGVzPjx0aXRsZT5Vc2luZyBuYXRpb25hbCBzdXJ2ZXlzIHRvIG1lYXN1cmUgZHluYW1pYyBV
UyBzdGF0ZSBwdWJsaWMgb3BpbmlvbjogQSBndWlkZWxpbmUgZm9yIHNjaG9sYXJzIGFuZCBhbiBh
cHBsaWNhdGlvbjwvdGl0bGU+PHNlY29uZGFyeS10aXRsZT5TdGF0ZSBQb2xpdGljcyAmYW1wOyBQ
b2xpY3kgUXVhcnRlcmx5PC9zZWNvbmRhcnktdGl0bGU+PC90aXRsZXM+PHBlcmlvZGljYWw+PGZ1
bGwtdGl0bGU+U3RhdGUgUG9saXRpY3MgJmFtcDsgUG9saWN5IFF1YXJ0ZXJseTwvZnVsbC10aXRs
ZT48L3BlcmlvZGljYWw+PHBhZ2VzPjQxNS00Mzk8L3BhZ2VzPjx2b2x1bWU+MTE8L3ZvbHVtZT48
bnVtYmVyPjQ8L251bWJlcj48ZGF0ZXM+PHllYXI+MjAxMTwveWVhcj48L2RhdGVzPjxpc2JuPjE1
MzItNDQwMDwvaXNibj48dXJscz48L3VybHM+PC9yZWNvcmQ+PC9DaXRlPjwvRW5kTm90ZT5=
ADDIN EN.CITE.DATA (Enns and Koch 2015, 2013, Pacheco 2011), immigration ADDIN EN.CITE <EndNote><Cite><Author>Butz</Author><Year>2016</Year><RecNum>119</RecNum><DisplayText>(Butz and Kehrberg 2016)</DisplayText><record><rec-number>119</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683898">119</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Butz, Adam M</author><author>Kehrberg, Jason E</author></authors></contributors><titles><title>Estimating anti-immigrant sentiment for the American states using multi-level modeling and post-stratification, 2004–2008</title><secondary-title>Research & Politics</secondary-title></titles><periodical><full-title>Research & Politics</full-title></periodical><pages>2053168016645830</pages><volume>3</volume><number>2</number><dates><year>2016</year></dates><isbn>2053-1680</isbn><urls></urls></record></Cite></EndNote>(Butz and Kehrberg 2016), gender mood ADDIN EN.CITE <EndNote><Cite><Author>Koch</Author><Year>2017</Year><RecNum>92</RecNum><DisplayText>(Koch and Thomsen 2017)</DisplayText><record><rec-number>92</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683897">92</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Koch, Julianna</author><author>Thomsen, Danielle M</author></authors></contributors><titles><title>Gender Equality Mood across States and over Time</title><secondary-title>State Politics & Policy Quarterly</secondary-title></titles><periodical><full-title>State Politics & Policy Quarterly</full-title></periodical><pages>351-360</pages><volume>17</volume><number>4</number><dates><year>2017</year></dates><isbn>1532-4400</isbn><urls></urls></record></Cite></EndNote>(Koch and Thomsen 2017), Supreme Court nominees and decisions PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYXN0ZWxsZWM8L0F1dGhvcj48WWVhcj4yMDEwPC9ZZWFy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=
ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYXN0ZWxsZWM8L0F1dGhvcj48WWVhcj4yMDEwPC9ZZWFy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=
ADDIN EN.CITE.DATA (Kastellec, Lax, and Phillips 2010, Franko 2017, Caldarone, Canes-Wrone, and Clark 2009), income inequality ADDIN EN.CITE <EndNote><Cite><Author>Franko</Author><Year>2017</Year><RecNum>36</RecNum><DisplayText>(Franko 2017)</DisplayText><record><rec-number>36</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">36</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Franko, William W</author></authors></contributors><titles><title>Understanding public perceptions of growing economic inequality</title><secondary-title>State Politics & Policy Quarterly</secondary-title></titles><periodical><full-title>State Politics & Policy Quarterly</full-title></periodical><pages>319-348</pages><volume>17</volume><number>3</number><dates><year>2017</year></dates><isbn>1532-4400</isbn><urls></urls></record></Cite></EndNote>(Franko 2017), roll call voting PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYXN0ZWxsZWM8L0F1dGhvcj48WWVhcj4yMDE1PC9ZZWFy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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5LYXN0ZWxsZWM8L0F1dGhvcj48WWVhcj4yMDE1PC9ZZWFy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ADDIN EN.CITE.DATA (Kastellec et al. 2015, Kastellec, Lax, and Phillips 2010, Krimmel, Lax, and Phillips 2016), the Affordable Health Care act ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2017</Year><RecNum>77</RecNum><DisplayText>(Pacheco and Maltby 2017)</DisplayText><record><rec-number>77</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683897">77</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author><author>Maltby, Elizabeth</author></authors></contributors><titles><title>The Role of Public Opinion—Does It Influence the Diffusion of ACA Decisions?</title><secondary-title>Journal of health politics, policy and law</secondary-title></titles><periodical><full-title>Journal of health politics, policy and law</full-title></periodical><pages>309-340</pages><volume>42</volume><number>2</number><dates><year>2017</year></dates><isbn>0361-6878</isbn><urls></urls></record></Cite></EndNote>(Pacheco and Maltby 2017), smoking bans ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2012</Year><RecNum>56</RecNum><DisplayText>(Pacheco 2012)</DisplayText><record><rec-number>56</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">56</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>The social contagion model: Exploring the role of public opinion on the diffusion of antismoking legislation across the American states</title><secondary-title>The Journal of Politics</secondary-title></titles><periodical><full-title>The Journal of Politics</full-title></periodical><pages>187-202</pages><volume>74</volume><number>1</number><dates><year>2012</year></dates><isbn>0022-3816</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2012), abortion ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2014</Year><RecNum>2079</RecNum><DisplayText>(Pacheco 2014)</DisplayText><record><rec-number>2079</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522586418">2079</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Measuring and Evaluating Changes in State Opinion across Eight Issues</title><secondary-title>American Politics Research</secondary-title></titles><periodical><full-title>American Politics Research</full-title></periodical><pages>986-1009</pages><volume>42</volume><number>6</number><dates><year>2014</year></dates><isbn>1532-673X</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2014), death penalty ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2014</Year><RecNum>2079</RecNum><DisplayText>(Pacheco 2014)</DisplayText><record><rec-number>2079</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522586418">2079</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Measuring and Evaluating Changes in State Opinion across Eight Issues</title><secondary-title>American Politics Research</secondary-title></titles><periodical><full-title>American Politics Research</full-title></periodical><pages>986-1009</pages><volume>42</volume><number>6</number><dates><year>2014</year></dates><isbn>1532-673X</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2014), and welfare spending ADDIN EN.CITE <EndNote><Cite><Author>Pacheco</Author><Year>2014</Year><RecNum>2079</RecNum><DisplayText>(Pacheco 2014)</DisplayText><record><rec-number>2079</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522586418">2079</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Pacheco, Julianna</author></authors></contributors><titles><title>Measuring and Evaluating Changes in State Opinion across Eight Issues</title><secondary-title>American Politics Research</secondary-title></titles><periodical><full-title>American Politics Research</full-title></periodical><pages>986-1009</pages><volume>42</volume><number>6</number><dates><year>2014</year></dates><isbn>1532-673X</isbn><urls></urls></record></Cite></EndNote>(Pacheco 2014), to name a few. Indeed, MRP is “emerging as a widely used gold standard for estimating preferences from national surveys” ADDIN EN.CITE <EndNote><Cite><Author>Selb</Author><Year>2011</Year><RecNum>37</RecNum><Pages>456</Pages><DisplayText>(Selb and Munzert 2011, 456)</DisplayText><record><rec-number>37</rec-number><foreign-keys><key app="EN" db-id="edva99azaszwsbefspupa5a70ftrzsp09900" timestamp="1522683896">37</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Selb, Peter</author><author>Munzert, Simon</author></authors></contributors><titles><title>Estimating constituency preferences from sparse survey data using auxiliary geographic information</title><secondary-title>Political Analysis</secondary-title></titles><periodical><full-title>Political Analysis</full-title></periodical><pages>455-470</pages><volume>19</volume><number>4</number><dates><year>2011</year></dates><isbn>1476-4989</isbn><urls></urls></record></Cite></EndNote>(Selb and Munzert 2011, 456).Implementing MRP to Measure Racial ResentmentRacial resentment is most frequently measured with a battery of four questions, and combined into an additive scale ADDIN EN.CITE <EndNote><Cite><Author>Kinder</Author><Year>1996</Year><RecNum>452</RecNum><DisplayText>(Kinder and Sanders 1996)</DisplayText><record><rec-number>452</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">452</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Kinder, Donald R.</author><author>Sanders, Lynn M.</author></authors></contributors><titles><title>Divided by Color: Racial Politics and Democratic Ideals</title></titles><dates><year>1996</year></dates><pub-location>Chicago</pub-location><publisher>The University of Chicago Press</publisher><urls></urls></record></Cite></EndNote>(Kinder and Sanders 1996). Scholars typically use this battery to create a measure of an individual’s level of racial resentment, and then in turn, may use that measure as an independent variable when assessing someone’s opinion on racialized policy PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EZVNhbnRlPC9BdXRob3I+PFllYXI+MjAxMzwvWWVhcj48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 EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5EZVNhbnRlPC9BdXRob3I+PFllYXI+MjAxMzwvWWVhcj48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 EN.CITE.DATA (DeSante 2013, Feldman and Huddy 2005, Filindra and Kaplan , Tuch and Hughes 2011). Here, we use a multilevel model to create individualized scores of racial resentment based on demographic and geographic markers, and weight the occurrence of those factors appropriately to create a state level measure. That measure represents the level of racial resentment in each state in a particular year.In order to implement MRP, we collected public opinion survey data on racial resentment over time using the American National Election Survey (ANES) and pooled that data into a single dataset. The ANES uses a cluster sampling design. Some scholars have articulated concerns about the use of clustered data to create estimates of state opinion because of the non-representativeness of clustered data ADDIN EN.CITE <EndNote><Cite><Author>Brace</Author><Year>2002</Year><RecNum>3942</RecNum><DisplayText>(Brace et al. 2002)</DisplayText><record><rec-number>3942</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1556634164">3942</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Brace, Paul</author><author>Sims-Butler, Kellie</author><author>Arceneaux, Kevin</author><author>Johnson, Martin</author></authors></contributors><titles><title>Public opinion in the American states: New perspectives using national survey data</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>173-189</pages><dates><year>2002</year></dates><isbn>0092-5853</isbn><urls></urls></record></Cite></EndNote>(Brace et al. 2002). The problem of clustered sampling can be mitigated by including state level variables in MRP, and the final step of post-stratification in particular corrects for this bias ADDIN EN.CITE <EndNote><Cite><Author>Lax</Author><Year>2009</Year><RecNum>12</RecNum><DisplayText>(Lax and Phillips 2009b)</DisplayText><record><rec-number>12</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245894">12</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Lax, Jeffrey R</author><author>Phillips, Justin H</author></authors></contributors><titles><title>How should we estimate public opinion in the states?</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>107-121</pages><volume>53</volume><number>1</number><dates><year>2009</year></dates><isbn>1540-5907</isbn><urls></urls></record></Cite></EndNote>(Lax and Phillips 2009b). Many other scholars have effectively used clustered data to create state estimates of opinion using MRP (for instance PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdG9sbHdlcms8L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFy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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5TdG9sbHdlcms8L0F1dGhvcj48WWVhcj4yMDEzPC9ZZWFy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ADDIN EN.CITE.DATA (Stollwerk 2013, Koch and Thomsen 2017, Butz and Kehrberg 2016).The ANES asked the standard battery of four questions to gauge racial resentment in 1988, 1990, 1992, 1994, 2000, 2004, 2008, 2012, and 2016. Specifically, respondents were asked if they agree strongly, agree somewhat, neither agree nor disagree, disagree somewhat or disagree strongly with the following four statements: 1) Irish, Italians, Jewish and many other minorities overcame prejudice and worked their way up. Blacks should do the same without any special favors. 2) Generations of slavery and discrimination have created conditions that make it difficult for blacks to work their way out of the lower class. 3)Over the past few years, blacks have gotten less than they deserve. 4)It’s really a matter of some people not trying hard enough, if blacks would only try harder they could be just as well off as whites. We follow conventions established by both scholars of MRP and racial resentment in our treatment of these questions in our analysis. We code each of the four racial resentment questions such that agreement with the prompts are coded on a five-point scale, with higher scores indicating a higher level of racial resentment. We then combined responses to the four prompts and divided that number by 16, so that an individual’s racial resentment score ranges from 0 to 1, with 1 representing the highest possible level of racial resentment.Next, we use a multilevel linear regression model to predict the level of racial resentment using a series of demographic and state-level factors used in the extant MRP literature. This includes measures of a series of binary categorical measures for age (18-29, 30-44, 45-64, or 65+), education (less than high school graduate, high school graduate, some college, or college graduate), and state of residence (each of the 50 states). Rather than include a race-gender interaction, we follow scholars who have sought to take seriously the intersectionality literature in quantitative data analysis; specifically, we include indicators of each race-gender combination of respondents (non-Hispanic white man, non-Hispanic white woman, non-Hispanic black man, non-Hispanic black woman, men of “other” race, women of “other” race) PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYW5jb2NrPC9BdXRob3I+PFllYXI+MjAwNzwvWWVhcj48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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5IYW5jb2NrPC9BdXRob3I+PFllYXI+MjAwNzwvWWVhcj48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ADDIN EN.CITE.DATA (Hancock 2007, Masuoka and Junn 2013, Reingold and Smith 2012). Scholars often exclude public opinion data from non-whites in their analysis of racial resentment. While blacks and other people of color may not exhibit the same levels of anti-Black animus, they are neither immune from relying on individual rather than structural explanations of racial inequality ADDIN EN.CITE <EndNote><Cite><Author>Smith</Author><Year>2014</Year><RecNum>1355</RecNum><DisplayText>(Smith 2014, Nunnally and Carter 2012)</DisplayText><record><rec-number>1355</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">1355</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Smith, Candis Watts</author></authors></contributors><titles><title>Shifting From Structural to Individual Attributions of Black Disadvantage Age, Period, and Cohort Effects on Black Explanations of Racial Disparities</title><secondary-title>Journal of Black Studies</secondary-title></titles><periodical><full-title>Journal of Black Studies</full-title></periodical><pages>432-452</pages><volume>45</volume><number>5</number><dates><year>2014</year></dates><isbn>0021-9347</isbn><urls></urls></record></Cite><Cite><Author>Nunnally</Author><Year>2012</Year><RecNum>945</RecNum><record><rec-number>945</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">945</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Nunnally, Shayla C.</author><author>Carter, Niambi M.</author></authors></contributors><titles><title>Moving from Victims to Victors: African American Attitudes on the "Culture of Poverty" and Black Blame</title><secondary-title>Journal of African American Studies</secondary-title></titles><periodical><full-title>Journal of African American Studies</full-title></periodical><pages>423-455</pages><volume>`16</volume><number>3</number><dates><year>2012</year></dates><urls></urls></record></Cite></EndNote>(Smith 2014, Nunnally and Carter 2012) nor impervious to a reliance on the dominant racial logic of the time ADDIN EN.CITE <EndNote><Cite><Author>Bonilla-Silva</Author><Year>2014 [2003]</Year><RecNum>1045</RecNum><DisplayText>(Bonilla-Silva 2014 [2003])</DisplayText><record><rec-number>1045</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">1045</key></foreign-keys><ref-type name="Book">6</ref-type><contributors><authors><author>Bonilla-Silva, Eduardo</author></authors></contributors><titles><title>Racism without Racists: Color-blind Racism and the Persistence of Racial Inequality in the United States</title></titles><edition>4th</edition><dates><year>2014 [2003]</year></dates><pub-location>Lanham</pub-location><publisher>Rowman and LIttlefield Publishers, Inc.</publisher><urls></urls></record></Cite></EndNote>(Bonilla-Silva 2014 [2003]). Henry and Sears ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Henry</Author><Year>2002</Year><RecNum>30</RecNum><DisplayText>(2002)</DisplayText><record><rec-number>30</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="0">30</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Henry, P.J.</author><author>Sears, David O.</author></authors></contributors><titles><title>The Symbolic Racism 2000 Scale</title><secondary-title>Political Psychology</secondary-title></titles><periodical><full-title>Political Psychology</full-title></periodical><pages>253-283</pages><volume>23</volume><number>2</number><dates><year>2002</year></dates><urls></urls></record></Cite></EndNote>(2002) show that the racial resentment measure is reliable for Blacks, and Kam and Burge ADDIN EN.CITE <EndNote><Cite ExcludeAuth="1"><Author>Kam</Author><Year>2018</Year><RecNum>2075</RecNum><DisplayText>(2018)</DisplayText><record><rec-number>2075</rec-number><foreign-keys><key app="EN" db-id="ztrzt5vxks92dqe5f9c5axpitsp9vxed9dxt" timestamp="1522552616">2075</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Kam, Cindy D</author><author>Burge, Camille D</author></authors></contributors><titles><title>Uncovering Reactions to the Racial Resentment Scale across the Racial Divide</title><secondary-title>The Journal of Politics</secondary-title></titles><periodical><full-title>The Journal of Politics</full-title></periodical><pages>314-320</pages><volume>80</volume><number>1</number><dates><year>2018</year></dates><isbn>0022-3816</isbn><urls></urls></record></Cite></EndNote>(2018) find that white and Black Americans relate to the four questions of the measurement in similar ways. Finally, we include a state-level variable representing state ideology ADDIN EN.CITE <EndNote><Cite><Author>Berry</Author><Year>1998</Year><RecNum>18</RecNum><DisplayText>(Berry et al. 1998)</DisplayText><record><rec-number>18</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519246096">18</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Berry, William D</author><author>Ringquist, Evan J</author><author>Fording, Richard C</author><author>Hanson, Russell L</author></authors></contributors><titles><title>Measuring citizen and government ideology in the American states, 1960-93</title><secondary-title>American Journal of Political Science</secondary-title></titles><periodical><full-title>American Journal of Political Science</full-title></periodical><pages>327-348</pages><dates><year>1998</year></dates><isbn>0092-5853</isbn><urls></urls></record></Cite></EndNote>(Berry et al. 1998) and an indicator variable for the year of the survey. The results of the model are used to predict the level of racial resentment for each of “types” of individuals based on combinations of the demographic and geographic identifiers. By “type” we are referring to the 4,800 permutations of individuals (3 race groups x 2 genders x 4 age groups x 4 education groups x 50 states) that we are able to ascertain within the constraints of the U.S. Census data. For instance, we predict the level of racial resentment for the average non-Hispanic white, male, between the ages of 18-29, with less than a high school education in Alabama, as well as the level of racial resentment of a non-Hispanic black, woman, older than 65, with a college education in Wyoming. We are interested in estimating racial resentment in the states over time. Previous work with dynamic MRP takes two different approaches to incorporating an over-time element to the analysis. One approach to incorporating time is to run the analysis iteratively with small windows of time. For instance, Pacheco (2011) pools survey data in three and five year floating blocks to create an annual estimate of opinion. On the one hand, this approach may have the benefit of incorporating information from neighboring years to mitigate potential effects of outlier survey years. On the other hand, this approach is relatively inefficient and may ignore as significant amounts of data ADDIN EN.CITE <EndNote><Cite><Author>Gelman</Author><Year>2016</Year><RecNum>8</RecNum><DisplayText>(Gelman et al. 2016)</DisplayText><record><rec-number>8</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245720">8</key></foreign-keys><ref-type name="Unpublished Work">34</ref-type><contributors><authors><author>Gelman, Andrew</author><author>Lax, Jeffrey</author><author>Phillips, Justin</author><author>Gabry, Jonah</author><author>Trangucci, Robert</author></authors></contributors><titles><title>Using Multilevel Regression and Poststratification to Estimate Dynamic Public Opinion</title></titles><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(Gelman et al. 2016). Not all scholars using this iterative approach create short “windows” of time. For example, in their creation of state level partisanship and ideology, Enns and Koch (2013) create annual estimates by only using data for a single given year. By using only information from a single year, this approach makes use of an even smaller percent of the total data. The other approach to dynamic MRP pools all of the survey data into one analysis and includes indicator variables for years PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HZWxtYW48L0F1dGhvcj48WWVhcj4yMDE2PC9ZZWFyPjxS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ADDIN EN.CITE PEVuZE5vdGU+PENpdGU+PEF1dGhvcj5HZWxtYW48L0F1dGhvcj48WWVhcj4yMDE2PC9ZZWFyPjxS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ADDIN EN.CITE.DATA (Gelman et al. 2016, Franko 2017). This approach generates comparable estimates to those generated in this first method, but because it incorporates the complete set of information, is a more efficient model with tighter standard errors ADDIN EN.CITE <EndNote><Cite><Author>Gelman</Author><Year>2016</Year><RecNum>8</RecNum><DisplayText>(Gelman et al. 2016)</DisplayText><record><rec-number>8</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1519245720">8</key></foreign-keys><ref-type name="Unpublished Work">34</ref-type><contributors><authors><author>Gelman, Andrew</author><author>Lax, Jeffrey</author><author>Phillips, Justin</author><author>Gabry, Jonah</author><author>Trangucci, Robert</author></authors></contributors><titles><title>Using Multilevel Regression and Poststratification to Estimate Dynamic Public Opinion</title></titles><dates><year>2016</year></dates><urls></urls></record></Cite></EndNote>(Gelman et al. 2016). We take the latter approach here, pooling all survey data and including indicator variables for specific years.The results in the model reflect our expectations and the broad literature on predicting racial resentment. In brief, we find that age has a positive relationship with racial resentment, meaning that older people are more racially resentful (Nteta and Greenlee 2013). We find that higher education is associated with lower racial resentment. Finally, the results corroborate studies that show that white men tend to have higher levels of racial animus than other race-gendered groups (e.g. ADDIN EN.CITE <EndNote><Cite><Author>Smith</Author><Year>2013</Year><RecNum>134</RecNum><DisplayText>(Smith, Senter, and Strachan 2013)</DisplayText><record><rec-number>134</rec-number><foreign-keys><key app="EN" db-id="wwv5aeptvf2waaeve2lpdeswattrdsvw2et5" timestamp="1525110367">134</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Smith, Justin M</author><author>Senter, Mary</author><author>Strachan, J Cherie</author></authors></contributors><titles><title>Gender and white college students' racial attitudes</title><secondary-title>Sociological Inquiry</secondary-title></titles><periodical><full-title>Sociological Inquiry</full-title></periodical><pages>570-590</pages><volume>83</volume><number>4</number><dates><year>2013</year></dates><isbn>1475-682X</isbn><urls></urls></record></Cite></EndNote>(Smith, Senter, and Strachan 2013). The state-level ideology variable is also significant.Table B1. Predicting Racial Resentment CoeffStd. Err.Age (30-44)0.43885***0.06361Age (45-64)0.57282***0.06307Age (65+)0.46422***0.0719Educ (HS grad)0.25779*0.10734Educ (some college)-0.058440.1116Educ (college grad)-1.55261***0.11268Race - Black-3.82843***0.09855Race - Other-0.82888***0.8933Female-5.85958***1.38478Female*BlackFemale*OtherIdeology-0.01383*0.00537Intercept13.1919***1.41610 '***' 0.001 '**' 0.01 '*' 0.05?N: 25138 (787 obs missing due to missingness)Multiple R-squared:? 0.1616, Adjusted R-squared:? 0.1592?State indicator variables omitted from table for brevityAfter estimating the multilevel linear regression model, we predicted the level of racial resentment for each of the 4800 “types” of people using the full slate of independent variables in the model. We then weight these person types by the frequency of those person types by each state, incorporating this information from the U.S. Census. By combining the predicted level of racial resentment for each “type” of person by how many of those “types” of people there are in each state, we are able to construct a measure of the state-level racial resentment. The estimates of state racial resentment are reported in Table B2.Table B2. Racial Resentment Scores in the US States, 1988-2016State198819901992199420002004200820122016AK0.67930.62630.66840.68920.70320.71450.71250.72710.6589AL0.69680.65510.69100.70720.71690.70050.69340.71580.6597AR0.71380.66680.70780.73100.73610.72930.74160.75980.7045AZ0.68000.63110.66730.69120.68830.70270.71470.71630.6609CA0.62830.58220.62400.64490.64460.64660.65580.66610.6082CO0.62500.57500.61500.63400.63510.65240.66280.67050.6131CT0.64780.59480.63910.66340.66760.67430.67420.69220.6216DE0.66500.59900.63350.65960.67130.65720.65700.66790.6117FL0.68910.63810.67860.69470.69810.70340.70360.71290.6554GA0.68900.63730.67800.70100.70480.68250.68680.69910.6296HI0.61740.57050.61350.62520.63620.63750.64540.65790.5943IA0.62520.57530.61820.63930.65060.67740.68360.69610.6370ID0.60660.55800.58930.61270.62330.65550.65550.66360.6105IL0.64990.59860.63610.65990.66040.66330.66860.67690.6267IN0.66040.61400.65470.68000.68330.69940.70630.71940.6571KS0.63590.58280.62190.64880.65870.67090.67640.68320.6297KY0.65770.60180.64610.67160.68760.69640.69970.71670.6560LA0.70190.64270.69120.70840.71780.69330.69670.69570.6436MA0.59500.54710.59340.62450.62090.64020.64210.65530.5926MD0.63220.58870.63120.65670.65430.63530.64100.65320.5906ME0.64990.60800.64580.66680.67580.70540.70070.71880.6589MI0.64250.59610.64240.66650.66780.67820.67760.69380.6334MN0.60770.56520.60200.63050.63310.66380.65990.67570.6144MO0.65450.60470.64660.66910.67450.69210.69530.70530.6484MS0.68770.64140.68700.70200.70890.68050.67340.67590.6288MT0.63090.57870.61300.63960.64040.67630.67530.68060.6295NC0.66590.61980.65910.67510.68200.67560.67160.68870.6329ND0.64450.59430.64140.66180.66690.69800.70400.71840.6622NE0.66460.60810.63440.66630.67970.70550.70740.71480.6560NH0.61230.55940.59900.61680.62080.64690.65240.66210.6024NJ0.64750.60570.64870.67110.66780.66570.67760.69020.6260NM0.64390.58650.63130.65410.65620.66070.66300.67810.6200NV0.68470.63870.67340.68750.68490.69880.70370.71190.6584NY0.62700.57500.61420.63940.64440.64090.64540.66170.5971OH0.66480.61470.65660.68230.68280.69200.70350.71670.6575OK0.65040.60470.64320.67360.67630.68950.69900.71410.6584OR0.59010.53370.57110.59340.59880.62400.62700.63970.5834PA0.64950.60540.64870.66570.67630.69200.69460.71040.6472RI0.58650.54880.59840.61640.60410.63070.63370.64290.5824SC0.67210.63180.67290.69030.69350.67160.68220.69150.6344SD0.64790.60520.64450.66880.67400.70250.71140.72100.6626TN0.68410.63750.67570.70980.70800.70910.72680.73560.6693TX0.66900.61980.65770.68430.67900.68280.69010.69960.6406UT0.58790.53750.58130.60630.60540.63640.64920.66200.6024VA0.63740.58920.63180.65570.65630.64580.64910.66270.6062VT0.54520.50800.54810.55140.56300.58970.60040.60350.5436WA0.59900.55520.58670.61820.62660.64150.64390.65850.5993WI0.63790.59210.62420.65090.65530.67790.68700.69860.6445WV0.67730.62820.66160.68100.69370.72190.72790.74480.6894WY0.64530.59160.62670.65360.65850.69040.70280.71150.6503Notes on Change Over TimeThe correlation of state racial resentment scores within a state over time is very high, ranging from .80 to .99. This means that the average level of racial resentment in states does not vary substantially, and varies more in some states than others. We also compared the relative ranking of states (from 1 to 50) in terms of level of racial resentment. The rankings of state racial resentment are also high over time, though the correlation is lower than the racial resentment scores themselves. That means that there is some variability in the relative rank ordering of states’ racial resentment, though not a lot. Tables B3 and B4 show correlation for racial resentment scores and rankings, respectively, below.Table B3. Correlation within Racial Resentment Scores1988199019921994200020042008201220161988119900.987119920.9750.99119940.9760.9830.986120000.9820.9810.9780.986120040.840.8390.8130.840.864120080.8170.8190.7930.8250.8380.982120120.8010.8060.7820.8170.8340.9780.988120160.8140.8170.7880.8180.8360.9810.9860.9881 Table B4. Correlation within Racial Resentment Ranking1988199019921994200020042008201220161988119900.981119920.9660.985119940.9680.9820.987120000.9770.9780.9750.981120040.80.8140.7860.7980.833120080.7560.7710.7460.7640.7890.972120120.7370.7540.740.7570.7830.9660.979120160.7470.7670.7440.7590.7830.9650.9750.981It is common in MRP scholarship to assess validity by comparing the newly generated scores with widely accepted measures of the same concept. We are unable to do so here, as there are not other state-level measures of racial animus over time. Instead, we compare the estimated racial resentment to presidential vote (in presidential voting years), and to the Enns and Koch (2013) measures of state partisanship and ideology. These scores are more highly correlated with the Enns and Koch scores of ideology (which ranges from correlated at .33 in 1992 to .65 in 2008) than their measure of state partisanship (which ranges from around 0 in 1992 to .56 in 2008). We hesitate to read too much into these patterns, as previous literature indicates that racial resentment is a concept distinct from partisanship and ideology (Henry & Sears, 2002). Works Cited in Appendix ADDIN EN.REFLIST Berry, William D, Evan J Ringquist, Richard C Fording, and Russell L Hanson. 1998. "Measuring citizen and government ideology in the American states, 1960-93." American Journal of Political Science:327-348.Bonilla-Silva, Eduardo. 2014 [2003]. Racism without Racists: Color-blind Racism and the Persistence of Racial Inequality in the United States. 4th ed. Lanham: Rowman and LIttlefield Publishers, Inc.Brace, Paul, Kellie Sims-Butler, Kevin Arceneaux, and Martin Johnson. 2002. "Public opinion in the American states: New perspectives using national survey data." American Journal of Political Science:173-189.Buttice, Matthew K, and Benjamin Highton. 2013. "How does multilevel regression and poststratification perform with conventional national surveys?" Political Analysis 21 (4):449-467.Butz, Adam M, and Jason E Kehrberg. 2016. "Estimating anti-immigrant sentiment for the American states using multi-level modeling and post-stratification, 2004–2008." Research & Politics 3 (2):2053168016645830.Caldarone, Richard P, Brandice Canes-Wrone, and Tom S Clark. 2009. "Partisan labels and democratic accountability: An analysis of State Supreme Court abortion decisions." The Journal of Politics 71 (2):560-573.DeSante, Christopher D. 2013. "Working Twice as Hard to Get Half as Far: Race, Work Ethic, and America’s Deserving Poor." American Journal of Political Science 57 (2):342-356.Enns, Peter K, and Julianna Koch. 2013. "Public Opinion in the US States: 1956 to 2010." State Politics & Policy Quarterly 13 (3):349-372.Enns, Peter K, and Julianna Koch. 2015. "State policy mood: The importance of over-time dynamics." State Politics & Policy Quarterly 15 (4):436-446.Feldman, Stanley, and Leonie Huddy. 2005. "Racial Resentment and White Opposition to Race‐Conscious Programs: Principles or Prejudice?" American Journal of Political Science 49 (1):168-183.Filindra, Alexandra, and Noah J Kaplan. "Racial Resentment and Whites’ Gun Policy Preferences in Contemporary America." Political Behavior 32 (2):255-275.Franko, William W. 2017. "Understanding public perceptions of growing economic inequality." State Politics & Policy Quarterly 17 (3):319-348.Gelman, Andrew, Jeffrey Lax, Justin Phillips, Jonah Gabry, and Robert Trangucci. 2016.Hancock, Ange-Marie. 2007. "When Multiplication Doesn't Equal Quick Addition: Examining Intersectionality as a Research Paradigm." Perspectives on Politics 5 (1):63-79.Henry, P.J., and David O. Sears. 2002. "The Symbolic Racism 2000 Scale." Political Psychology 23 (2):253-283.Kam, Cindy D, and Camille D Burge. 2018. "Uncovering Reactions to the Racial Resentment Scale across the Racial Divide." The Journal of Politics 80 (1):314-320.Kastellec, Jonathan P, Jeffrey R Lax, Michael Malecki, and Justin H Phillips. 2015. "Polarizing the electoral connection: partisan representation in Supreme Court confirmation politics." The Journal of Politics 77 (3):787-804.Kastellec, Jonathan P, Jeffrey R Lax, and Justin H Phillips. 2010. "Public opinion and Senate confirmation of Supreme Court nominees." The Journal of Politics 72 (3):767-784.Kinder, Donald R., and Lynn M. Sanders. 1996. Divided by Color: Racial Politics and Democratic Ideals. Chicago: The University of Chicago Press.Koch, Julianna, and Danielle M Thomsen. 2017. "Gender Equality Mood across States and over Time." State Politics & Policy Quarterly 17 (4):351-360.Krimmel, Katherine, Jeffrey R Lax, and Justin H Phillips. 2016. "Gay rights in Congress: Public opinion and (mis) representation." Public Opinion Quarterly 80 (4):888-913.Lax, Jeffrey R, and Justin H Phillips. 2009a. "Gay rights in the states: Public opinion and policy responsiveness." American Political Science Review 103 (3):367-386.Lax, Jeffrey R, and Justin H Phillips. 2009b. "How should we estimate public opinion in the states?" American Journal of Political Science 53 (1):107-121.Lax, Jeffrey R, and Justin H Phillips. 2013. "How should we estimate sub-national opinion using MRP? Preliminary findings and recommendations." annual meeting of the Midwest Political Science Association, Chicago.Lewis, Daniel C, and Matthew L Jacobsmeier. 2017. "Evaluating Policy Representation with Dynamic MRP Estimates: Direct Democracy and Same-Sex Relationship Policies in the United States." State Politics & Policy Quarterly 17 (4):441-464.Masuoka, Natalie, and Jane Junn. 2013. The Politics of Belonging: Race, Public Opinion, and Immigration. Chicago, IL: University of Chicago Press.Nunnally, Shayla C., and Niambi M. Carter. 2012. "Moving from Victims to Victors: African American Attitudes on the "Culture of Poverty" and Black Blame." Journal of African American Studies `16 (3):423-455.Pacheco, Julianna. 2011. "Using national surveys to measure dynamic US state public opinion: A guideline for scholars and an application." State Politics & Policy Quarterly 11 (4):415-439.Pacheco, Julianna. 2012. "The social contagion model: Exploring the role of public opinion on the diffusion of antismoking legislation across the American states." The Journal of Politics 74 (1):187-202.Pacheco, Julianna. 2014. "Measuring and Evaluating Changes in State Opinion across Eight Issues." American Politics Research 42 (6):986-1009.Pacheco, Julianna, and Elizabeth Maltby. 2017. "The Role of Public Opinion—Does It Influence the Diffusion of ACA Decisions?" Journal of health politics, policy and law 42 (2):309-340.Park, David K, Andrew Gelman, and Joseph Bafumi. 2004. "Bayesian multilevel estimation with poststratification: state-level estimates from national polls." Political Analysis 12 (4):375-385.Raudenbush, Stephen W, and Anthony S Bryk. 2002. Hierarchical linear models: Applications and data analysis methods. Vol. 1: Sage.Reingold, Beth, and Adrienne R Smith. 2012. "Welfare policymaking and intersections of race, ethnicity, and gender in US state legislatures." American Journal of Political Science 56 (1):131-147.Selb, Peter, and Simon Munzert. 2011. "Estimating constituency preferences from sparse survey data using auxiliary geographic information." Political Analysis 19 (4):455-470.Smith, Candis Watts. 2014. "Shifting From Structural to Individual Attributions of Black Disadvantage Age, Period, and Cohort Effects on Black Explanations of Racial Disparities." Journal of Black Studies 45 (5):432-452.Smith, Justin M, Mary Senter, and J Cherie Strachan. 2013. "Gender and white college students' racial attitudes." Sociological Inquiry 83 (4):570-590.Snijders, Tom AB. 2011. "Multilevel analysis." In International encyclopedia of statistical science, 879-882. Springer.Steenbergen, Marco R, and Bradford S Jones. 2002. "Modeling multilevel data structures." american Journal of political Science:218-237.Stollwerk, Alissa. 2013. "The application of multilevel regression with post-stratification to cluster sampled polls: Challenges and suggestions." 71st Annual Meeting of the Midwest Political Science Association, Chicago, IL.Tuch, Steven A, and Michael Hughes. 2011. "Whites’ racial policy attitudes in the twenty-first century: The continuing significance of racial resentment." The ANNALS of the American Academy of Political and Social Science 634 (1):134-152.Voss, Stephen, Andrew Gelman, and Gary King. 1995. "The polls—A review: Preelection survey methodology: Details from eight polling organizations, 1988 and 1992." Public Opinion Quarterly 59 (1):98-132.Warshaw, Christopher, and Jonathan Rodden. 2012. "How should we measure district-level public opinion on individual issues?" The Journal of Politics 74 (1):203-219. ................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related searches
- minecraft online no download just press p
- minecraft online no download just press play
- syneos press release
- dry cleaning press machine
- dry cleaner press machine
- us steel press release
- used dry clean press machine
- cold press coffee recipe
- department of education press office
- cell press journal impact factor
- small business administration press release
- cell press impact factor