In the United States Court of Appeals for the Seventh Circuit

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1 Nos & In the United States Court of Appeals for the Seventh Circuit RUTHELLE FRANK, et al., v. Plaintiffs-Appellees, SCOTT WALKER, in his official capacity as Governor of State of Wisconsin, et al., Defendants-Appellants. LEAGUE OF UNITED LATIN AMERICAN CITIZENS OF WISCONSIN, et al., Plaintiffs-Appellees, v. DAVID G. DEININGER, et al., Defendants-Appellants. On Appeal from the United States District Court for the Eastern District of Wisconsin, Nos. 2:11-cv LA & 2:12-cv LA. The Honorable Lynn S. Adelman, Judge Presiding. JOINT SUPPLEMENTAL APPENDIX OF ALL PLAINTIFFS-APPELLEES VOLUME I OF II KARYN L. ROTKER (Counsel of Record) LAURENCE J. DUPUIS AMERICAN CIVIL LIBERTIES UNION OF WISCONSIN FOUNDATION 207 East Buffalo Street, Suite 325 Milwaukee, WI Phone: (414) krotker@aclu-wi.org ldupuis@aclu-wi.org Attorneys for Frank Plaintiffs-Appellees CHARLES G. CURTIS, JR. ARNOLD & PORTER LLP 16 North Carroll Street Suite 620 Madison, Wisconsin Phone: (608) charles.curtis@aporter.com Attorney for LULAC Plaintiffs-Appellees (ADDITIONAL COUNSEL LISTED ON REVERSE SIDE) COUNSEL PRESS (866) PRINTED ON RECYCLED PAPER

2 JEREMY ROSEN NATIONAL LAW CENTER ON HOMELESSNESS & POVERTY 2000 M Street NW, Suite 210 Washington, DC Phone: (202) jrosen@nlchp.org CRAIG G. FALLS DECHERT LLP 1900 K Street NW Washington, DC Phone: (202) craig.falls@dechert.com ANGELA M. LIU DECHERT LLP 77 West Wacker Drive, Suite 3200 Chicago, IL (312) angela.liu@dechert.com NEIL A. STEINER DECHERT LLP 1095 Avenue of the Americas New York, NY Phone: (212) neil.steiner@dechert.com DALE E. HO SEAN J. YOUNG AMERICAN CIVIL LIBERTIES UNION FOUNDATION, INC. 125 Broad Street, 18th Floor New York, NY (212) dale.ho@aclu.org syoung@aclu.org Attorneys for Frank Plaintiffs-Appellees PENDA D. HAIR JAMES EICHNER KATHERINE CULLITON-GONZÁLEZ LEIGH M. CHAPMAN ADVANCEMENT PROJECT 1220 L Street, N.W., Suite 850 Washington, D.C Phone: (202) phair@advancementproject.org jeichner@advancementproject.org kcullitongonzalez@advancementproject.org lchapman@advancementproject.org NATHAN D. FOSTER ARNOLD & PORTER LLP th Street, Suite 4400 Denver, Colorado Phone: (303) nathan.foster@aporter.com JOHN C. ULIN (Counsel of Record) MARCO J. MARTEMUCCI ARNOLD & PORTER LLP 777 South Figueroa Street, 44th Floor Los Angeles, California Phone: (213) john.ulin@aporter.com marco.martemucci@aporter.com DANIEL OSTROW ARNOLD & PORTER LLP 399 Park Avenue New York, New York Phone: (212) daniel.ostrow@aporter.com CARL S. NADLER ETHAN J. CORSON ARNOLD & PORTER LLP 555 Twelfth Street, N.W. Washington, D.C Phone: (202) carl.nadler@aporter.com corson@aporter.com Attorneys for LULAC Plaintiffs-Appellees

3 TABLE OF CONTENTS TO JOINT SUPPLEMENTAL APPENDIX OF ALL PLAINTIFFS-APPELLEES VOLUME I Frank Exhibit 600, Expert Report of Matt A. Barreto, Rates of Possession of Accepted Photo Identification, Among Different Subgroups in the Eligible Voter Population, Milwaukee County, Wisconsin (April 23, 2012), date admitted: November 5, JSA-001 LULAC Exhibit 002, Declaration of Leland Beatty, (April 12, 2012), date admitted: November 6, JSA-079 LULAC Exhibit 202, Rebuttal Declaration of Leland Beatty, (June 20, 2012), date admitted: November 6, JSA-097 LULAC Exhibit 817, Declaration of Leland Beatty, (October 28, 2013), date admitted: November 6, JSA-105 LULAC Exhibit 68, Declaration of Lorraine C. Minnite, (April 19, 2012) date admitted: November 7, JSA-107 LULAC Exhibit 812, Supplemental Report by Lorraine C. Minnite, (October 23, 2013), date admitted: November 7, JSA-134 LULAC Exhibit 873, Lorraine Carol Minnite Curriculum Vitae, date admitted: November 7, JSA-150 VOLUME II LULAC Exhibit 236, State of Wisconsin Government Accountability Board, Obtaining a Wisconsin State ID Card for FREE, date admitted: November 7, JSA-162 Frank Exhibit 412, GAB Flow Chart, date admitted: November 8, JSA-164 i

4 Frank Exhibit 578, Expert Report of Marc V. Levine, Racial Disparities, Socioeconomic Status, and Racialized Politics in Milwaukee and Wisconsin: An Analysis of Senate Factors Five and Six of the Voting Rights Act, (May 18, 2012, revised October 18, 2013), date admitted: November 8, JSA-165 LULAC Exhibit 811, Declaration of Barry C. Burden, Ph.D., (October 23, 2013), date admitted: November 8, JSA-231 LULAC Exhibit 813, Barry C. Burden Curriculum Vitae, date admitted: November 8, JSA-251 ii

5 JSA-001

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83 IN THE UNITED STATES DISTRICT COURT FOR THE EASTERN DISTRICT OF WISCONSIN BETTYE JONES, et al., Plaintiffs, v. Case No. 2:12-cv LA JUDGE DAVID G. DEININGER, et al., (all sued in their official capacity), Defendants. DECLARATION OF LELAND BEATTY Leland Beatty hereby declares as follows: 1. My name is Leland Beatty. I am a resident of Austin, TX. I am currently employed by commercial and political clients as a statistical marketing consultant. Previously, I served as general manager of Texas Rural Communities, Inc., a non-profit economic development corporation, and as director of research at the Texas Department of Agriculture. My resume is attached. 2. I have done extensive research on voting behaviors including minority participation in Texas, Minnesota, New York and Wisconsin, including study of the impact of photo identification requirements on the voting age population and on registered voters in Texas. 3. I have been asked to test the effect of Wisconsin voter identification requirements by race, using individual-level data from voter registration, driver's license and state-issued identification card records. This data was augmented with Census voting age population counts Case 2:12-cv LA Filed 04/23/12 Page 1 of 18 Document 24 JSA-079

84 at the county and zip code levels, and with commercially obtained individual-level ethnicity identification. 4. As explained below, my work primarily relies on data provided by the State of Wisconsin, specifically by its Department of Motor Vehicles and its Government Accountability Board. The State s data show a disparate impact that is stark and clear, bearing out what earlier analyses showed: minority voters are at a substantial disadvantage under Wisconsin s voter ID law, and the effect of that law imprints an unavoidable disparate impact on minority election participation. 5. My opinions are based on technical and specialized knowledge that I have gained from my education, training and experience and are developed based on widely accepted and reliable quantitative social science methods. I. Conclusions 6. Non-white Wisconsin residents will be significantly and adversely impacted by the Wisconsin voter identification law. Non-White Wisconsin registered voters are significantly less likely to possess a driver's license or state identification that matches their voter registration data % of registered White voters do not have a matching driver's license or state identification, compared to 15.8% of registered Asian voters, 16.2% of registered African American voters, and 24.8% of registered Hispanic voters. 8. Overall, some 11.1% of Wisconsin registered voters do not have a matching driver's license or state identification. African American registered voters are 1.7 times as likely as White registered voters to be without a matching driver's license or state identification Case 2:12-cv LA Filed 04/23/12 Page 2 of 18 Document 24 JSA-080

85 Hispanic registered voters are 2.6 times as likely as White registered voters to be without a matching driver's license or state identification. 9. Although African-American voters represent 5.3% of the registered voters in Wisconsin, they constituted 7.8% of the registered voters who lack a driver s license or a State identification card. Although Hispanic voters represent 1.6% of the registered voters in Wisconsin, they constituted 3.6% of the registered voters who lack a driver s license or a State identification card. 10. The number of registered voters without matching driver's license or state id is large enough to change the outcome of many Wisconsin elections. For example, the number of registered voters without matching driver's license or state id is larger than the vote margin that decided the elections of the U.S. Senator, Governor, Attorney General, Secretary of State, Treasurer, 3 of 8 Congressional districts, 9 of 17 State Senate districts, and 34 of 99 State Assembly Districts. The deciding margin in some districts was less than 5% of the number of registered voters without matching driver's license or state id. (See Tables 1 thru 4, attached at end of document). II. Data Sources 11. I worked with the following data sources in forming my opinions. 12. I obtained voting age population (VAP) data from the 2010 Census Redistricting Data Summary File. The U.S. Census reports total Wisconsin voting age population to be 4,347, I obtained driver's license (DL) and State issued identification card (State ID) data from the Wisconsin Department of Motor Vehicles Case 2:12-cv LA Filed 04/23/12 Page 3 of 18 Document 24 JSA-081

86 14. Wisconsin Department of Motor Vehicles initially provided a county-level report of the number of persons with either a driver s license or a State ID. This report states 4,356,681 voting age Wisconsin residents possessed either driver s licenses or State IDs. 15. Subsequently, WI DMV produced a list of all Wisconsin driver's license holders, and a list of State ID holders. These files included, for each person with driver s license or State ID, thirteen variables: 1. Run Time Stamp (of administrative value only) 2. First name 3. Middle initial 4. Last Name 5. Gender 6. Date of Birth 7. Race 8. Zip Code 9. County name. 10. Current DL or State ID number 11. Date of issuance 12. Date of expiration 13. Current DL/State ID status III. Methodology and Results 16. To determine which registered voters possessed a matching driver's license or state identification, I created match strings from basic voter and driver's license/state identification records, then counted the matches between the two files. Three different strategies were used to qualify matches between registered voters and driver's license/state identification holders: 1) If the voter's first name, last name, date of birth, residence county and zip code uniquely matched the same information in a driver's license record; 2) if the voter's first name, last and date of birth uniquely match the same information in a driver's license record; or 3) if the Case 2:12-cv LA Filed 04/23/12 Page 4 of 18 Document 24 JSA-082

87 voter's last name, date of birth and zip code uniquely matched the same information in a driver's license/state identification record, that voter was considered to have an identification match. 1. Creating Records That Allowed Me to Match Persons With Wisconsin Driver s Licenses or State Identification Cards With Registered Voters 17. I wanted to create data files that would allow me to match Wisconsin residents who had either a Wisconsin driver s license or a State identification card with registered Wisconsin voters. To do that, I created the following data files. a. Persons With Driver s Licenses or State Identification Cards. 18. The Wisconsin Driver s License list included 4,394,270 people, all with unique identification numbers. I created a unique identity string (IDstring0) for each record, composed of First Name, Last Name, Date of Birth, Gender, Ethnicity, County and Zip Code. In doing so, I discovered that 13 DL holders had an identical Idstring0 with another person. I eliminated the duplicate records. 19. The Wisconsin State ID list included 420,416 people, all with unique ID numbers. A unique identity string (IDstring0) was created for each record, composed of First Name, Last Name, Date of Birth, Gender, Ethnicity, County and Zip Code. Three State-ID holders had identical IDstring0 with one other ID holder. I eliminated the duplicate records ,397 State ID holders had matching identification numbers with persons in the driver s license file, and well as identical IDstring0. I considered these records to be duplicates and eliminated them, leaving 308,016 unique State ID holders. 21. I then combined the driver s license and State ID files into one list with 4,702,273 unique driver s license or State ID holders (DL_ID) Case 2:12-cv LA Filed 04/23/12 Page 5 of 18 Document 24 JSA-083

88 22. Finally, I deleted 133,836 persons who were under 18 years of age from the combined driver s license/state ID (DL_ID) list, leaving 4,568,437 voting age persons with DL or State ID. b. Persons Registered to Vote in Wisconsin 23. The Wisconsin Government Accountability Board, which oversees voter registration, produced a complete list of 3,255,377 Wisconsin registered voters with 74 variables for each voter. No race or gender identification variables are included in the voter registration file. Each voter has a unique voter identification number. 24. Among the variables in the file were: a. An administrative id number b. First Name c. Middle Name d. Last Name e. Name Suffix f. Date of Birth g. State ID h. Voter Registration Number i. 13 Address variables, including variables for each address part, as well as the address parts combined into Address1 and Address2 variables. j. Zip Code k. Five voter status variables, including current status, status reason, application source, application date and effective date l. 23 jurisdictional and district variables, including county, ward, assembly districts, court districts and municipalities. m. Phone n. Permanent Absentee o. 22 election participation history variables 1 1 The State ID field appears to be identical to the State ID field in the DL_ID file, and 66.1% of registered voters have a State ID number. These voters uniquely match to specific DL_ID holders Case 2:12-cv LA Filed 04/23/12 Page 6 of 18 Document 24 JSA-084

89 2. Using These Data Files to Calculate the Percent of Wisconsin Registered Voters, By Race, With a Driver s License or a State Identification Card 25. I next wanted to determine how many of the individuals in the Wisconsin registered voter files I could match to those in the file of individuals who had a Wisconsin driver s license or State ID. By eliminating the individuals for whom I could make this match, I would identify the Wisconsin registered voters who did not have a driver s license or a State ID. a. Creating Sub-Files to Compare 26. I started by sorting the data in both the voter registration file and the combined driver s license/state ID file (DL_ID) into three sub-files of data. The first sub-file (IDstring1) identified individual in both sets of files (voter registration and combined driver s license/state ID (DL_ID) by their first name, last name, date of birth, county and zip code data. This data sort uniquely identified 99.9% of registered voters, and 99.9% of DL_ID holders. 27. The second sub-file of data (IDstring2) identified individuals in both the voter registration data and the combined driver s license/state ID file (DL_ID) by their first name, last name and date of birth. This string uniquely identified 99.7% of registered voters, and 99.9% of DL_ID holders. 28. The third sub-file of data (IDstring3) identified individuals in both the voter registration data and the combined driver s license/state ID file (DL_ID) by their last name, date of birth and zip code. This string uniquely identified 99.4% of registered voters, and 99.5% of DL_ID holders Case 2:12-cv LA Filed 04/23/12 Page 7 of 18 Document 24 JSA-085

90 b. Running the Comparison 29. I then did three rounds of comparisons. Individuals who appeared in the voter registration data but could not be matched to the driver s license/state ID data after all three rounds of comparison represent registered voters who have neither a Wisconsin driver s license nor a Wisconsin state identification card. 30. First Round Match. The first round of matching I conducted was based on the sub-file that identified individuals in both the voter registration and combined driver s license/state ID (DL_ID) by their first name, last name, date of birth, county and zip code data. (IDstring1) ,209 registered voters (0.04%) had an identical IDstring1 with at least one other voter. I eliminated these duplicate entries. I eliminated these duplicate entries. Only 2 persons in the DL_ID file had identical IDstring1 with another DL_ID holder. I also eliminated these duplicate entries % of registered voters matched at least 1 DL_ID record on IDstring1, or 2,338,535 voters, leaving 916,505 voters unmatched. 33. Second Round Match. I next attempted to match the 916,505 registered voters that I did not match in the first round using the second data sub-file that was based on individuals in both the voter registration data and the combined driver s license/state ID file (DL_ID) by their first name, last name and date of birth (IDstring2). 34. Of the 916,505 registered voters unmatched after the IDstring1 comparison, some 99.9% (915,362) had a unique IDstring2. I eliminate the duplicate records Case 2:12-cv LA Filed 04/23/12 Page 8 of 18 Document 24 JSA-086

91 35. 2,230,887 DL_ID holders were unmatched after IDstring % (2,230,584) had a unique IDstring2. I eliminated the duplicate records. 36. In the second round match based on IDstring2, some 483,709 voters matched IDstring2 with at least one DL_ID holder. 37. After this second round match process, some 87% of registered voters had matched at least 1 DL_ID holder, leaving 432,796 registered voters unmatched with an entry in the driver s license/state ID data. 38. Third Round Match. My final effort to match registered voters with an individual identified in the combined driver s license/state ID data was based on the sub-file of data including individuals in both the voter registration data and the combined driver s license/state ID file (DL_ID) by their last name, date of birth and zip code (IDstring3) % of registered voters unmatched after IDString2 had a unique IDstring3. I eliminated the duplicates ,747,520 DL_ID holders remained unmatched after IDstring2, and 99.6% of DL_ID holders unmatched after IDstring2 had a unique IDstring ,283 voters matched on IDstring3, leaving 356,512 voters (10.95%) unmatched to a DL_ID holder. 3. Identifying the Race of the Un-matched Individuals in the Voter Registration Data. 42. The individuals in the voter registration data that I was unable to match to a driver s license or a State ID after three rounds of trying represent registered Wisconsin voters who have neither a driver s license or a State identification card Case 2:12-cv LA Filed 04/23/12 Page 9 of 18 Document 24 JSA-087

92 43. I submitted the 356,512 unmatched voters registered voters that I could not uniquely match to a driver s license or a voter identification card to Ethnic Technologies, a company that maintains proprietary data on race and ethnicity. One of the services Ethnic Technologies offers is the ability to identify the race of individuals based on identifying data such as that contained in the Wisconsin voter registration data Ethnic Technologies was able to identify the race of 91.6 percent of the unmatched voters those who are registered to vote in Wisconsin but who lack a driver s license as demonstrated by my inability to match them in the driver s license/state ID file (DL_ID). 45. The end result of my work is summarized in the following table: Registered Voters by Race, With Count Not Matching Driver's License or State ID, and Race Share Without Matching Driver's License or State ID Share of Race Race ASIAN Registered Voters 37,597 Share of All Voters 1.2% Voters Without Matching Driver's License or State ID 5,929 Share of All Unmatched Voters 1.6% Without Matching Driver's License or State ID 15.8% BLACK 172, % 27, % 16.2% HISPANIC 51, % 12, % 24.8% INDIAN 15, % % 5.6% OTHER 32, % 32, % 100.0% WHITE 2,945, % 280, % 9.5% Total 3,255, % 360, % 11.1% 2 I also submitted to Ethnic Technologies some 3,650 entries from the voter registration data file were not unique records because they had multiple matches in the driver s license/state ID file (DL_ID). My hope was that by identifying these entries based on race, I could isolate unique entries. This was successfully in only a small number of entries. Where it was successful, I added the unmatched entries to my calculation of registered voters who lack either driver s license or a State identification card Case 2:12-cv LA Filed 04/23/12 Page 10 of 18 Document 24 JSA-088

93 IV. The Results of My Work are Consistent With Prior Research 46. Disparate effects by ethnicity, age and education status from requirements to produce driver's licenses and state-issued identification were documented in a 2005 study by John Pawasarat, Director of the Employment of Training Institute at the University of Wisconsin- Milwaukee. (Pawasarat, Employment and Training Institute, Univeristy of Wisconsin- Milwaukee, June 2005) 47. Pawasarat's study was based on complete data on all driver's license holders in the State of Wisconsin, provided by the Wisconsin Department of Transportation, as well as complete statewide counts of persons with state id cards. This data allowed Pawasarat to compare counts of persons with driver's licenses by county and zip code, broken out by age, gender and ethnicity, with counts of voting age Wisconsin residents from the United States Census. 48. Pawasarat concluded, Minorities and poor are the most likely to have driver s license problems, with African Americans and Latinos almost four times as likely as whites to be without a valid driver s license. Statewide, 55% of African-American males and 46% of Latino males lack a driver s license, compared with 16% of White males. 49. Among females, 49% of African-Americans, 59% of Latinas, and 17% of Whites lacked a driver s license. 50. Minority young adults ages are impacted even more severely. Pawasarat documented that 78% of young African American males and 66% of young African American females did not have driver s licenses at the time of his study, compared to 43% of young White males and 31% of young White females Case 2:12-cv LA Filed 04/23/12 Page 11 of 18 Document 24 JSA-089

94 51. Among young Latinos 57% of males and 63% of females did not possess valid driver s licenses. 52. Statewide, 167,687 African Americans and Latinos were found to be without a driver s license in 2002, or 4% of the voting age population. Many Wisconsin elections are decided by margins no larger than this, including the 2010 Secretary of State contest. 53. Pawasarat concluded that the number of African Americans without driver's licenses would continue to grow over time. 54. A number of studies conducted in other states also found that photographic voter identification laws created disparate impact by ethnicity. 55. The Disproportionate Impact of Indiana Voter ID Requirements on the Electorate by Barreto, et al determined that in Indiana 17.5% of registered voters with annual incomes under $40,000 did not possess qualifying identification, compared to 11.2% of registered voters with annual incomes over $40, In Wisconsin, only 38% of white persons have incomes under $40,000, while 68% of African Americans and 55% of Latinos have incomes under $40,000. Minority voters in Wisconsin are significantly impacted by income disparity, according to the U.S. Census, which means they are significantly more likely to be without acceptable identification for voting. Wisconsin Household Income Group Share by Ethnicity African Income Group White American Latino Under $40, % 68.0% 55.4% $40,000 and Over 61.6% 32.0% 44.6% Case 2:12-cv LA Filed 04/23/12 Page 12 of 18 Document 24 JSA-090

95 56. Barreto's estimates, if applied to Wisconsin, would mean that African Americans are 14% more likely than Whites to be without acceptable voting identification, and Latinos are 8% more likely. 57. Most research shows additional disproportionate impacts on minority voters. Barreto, et al, discovered that 18.1% of minority voters in Indiana did not possess acceptable photo identification, compared to only 11.5% of white voters. 58. From a study of Georgia registered voters, M.V. Hood III and Charles S. Bullock III of the University of Georgia concluded that minority voters are almost twice as likely to be disenfranchised by photo identification requirements as were white voters In Voter IDs Are Not the Problem: A Survey of Three States, Pastor, et al discovered that African American voters in three states were almost 2.5 times as likely to be without acceptable photo identification as white voters The table below shows estimates from all three studies of the share of the voting age population without acceptable id for voting, by race. Share Without Acceptable Voter ID: Three Studies Ethnicity Hood Study (Georgia) Pastor Study (Indiana, Maryland and Mississippi) Barreto Study (Indiana) African American 6.8% 2.2% 18.1% Latino 7.3% Not Calculated Not Calculated White 3.7% 0.9% 11.5% Other/Missing 4.9% Not Calculated Not Calculated Case 2:12-cv LA Filed 04/23/12 Page 13 of 18 Document 24 JSA-091

96 61. I declare under penalty of perjury that the foregoing is fiue and correct. Executed on April 22,2012 in Austin, Texas. -t4- Case 2:12-cv LA Filed 04/23/12 Page 14 of 18 Document 24 JSA-092

97 Tables 1 thru 4: 2010 General Election Statewide, Congressional and Legislative Contests Decided by Margin Smaller than Number of Registered Voters Without Matching ID Table 1: Statewide Offices Congressional Districts, Registered Voters Without Matching State ID Compared to 2010 Election Margin Ratio of Registered Voters Without Contest Congressional District 3 Registered Voters Without Matching Driver's License/State ID 43, General Election Vote Margin 9,542 Matching Driver's License or State ID to 2010 General Election Margin 4.5 Congressional District 7 43,648 19, Congressional District 8 41,604 25, Table 2: Congressional Districts Statewide Offices, Registered Voters Without Matching State ID Compared to 2010 Election Margin Ratio of Registered Voters Without Contest Governor Registered Voters Without Matching Driver's License/State ID 360, General Election Vote Margin 124,638 Matching Driver's License or State ID to 2010 General Election Margin 2.9 AG 360, , SecState 360,155 21, Treasurer 360, , US Senate 360, , Case 2:12-cv LA Filed 04/23/12 Page 15 of 18 Document 24 JSA-093

98 Table 3: State Senate Districts State Senate Districts, Registered Voters Without Matching State ID Compared to 2010 Election Margin Ratio of Registered Voters Without Contest State Senate 3 Registered Voters Without Matching Driver's License/State ID 10, General Election Vote Margin 8,605 Matching Driver's License or State ID to 2010 General Election Margin 1.2 State Senate 5 11,791 3, State Senate 7 13, State Senate 15 9,893 9, State Senate 21 10,650 3, State Senate 23 9,881 5, State Senate 25 11,725 1, State Senate 29 8,672 2, State Senate 31 10, Case 2:12-cv LA Filed 04/23/12 Page 16 of 18 Document 24 JSA-094

99 Table 4: State Assembly Districts State Assembly Districts, Registered Voters Without Matching State ID Compared to 2010 Election Margin Ratio of Registered Voters Registered Voters Without Matching 2010 General Without Matching Driver's License or State ID to Driver's Election Vote 2010 General Election Contest License/State ID Margin Margin Assembly 1 4,277 4, Assembly 5 4,358 3, Assembly 7 3,788 3, Assembly 15 3, Assembly 20 4,427 1, Assembly 26 3, Assembly 28 3,577 3, Assembly 36 3,618 3, Assembly 37 2,856 1, Assembly 42 2, Assembly 43 3,659 1, Assembly 44 2, Assembly 45 3,488 1, Assembly 47 4,313 3, Assembly 49 3,310 2, Assembly 51 2, Assembly 54 3,345 2, Assembly 57 3,636 1, Assembly 62 3,708 1, Assembly 68 2, Assembly 70 3,288 1, Assembly 71 3,739 3, Assembly 72 2,877 1, Assembly 73 4,066 2, Assembly 74 3,998 1, Assembly 75 3, Assembly 77 6,978 4, Assembly 80 2,989 1, Assembly 85 3,167 1, Assembly 88 4, Assembly 90 3,585 3, Assembly 91 4,423 3, Assembly 92 2, Assembly 94 4,412 4, Case 2:12-cv LA Filed 04/23/12 Page 17 of 18 Document 24 JSA-095

100 Leland Beatty Predictive Analytics 1103 Upland Drive. Austin, TX (512) Employment 1/00-present Consultant, Predictive Analytics and Business Process. Austin, TX Voter Participation and Preference Modeling Highly accurate predictive modeling. In the past four election cycles, actual results varied less than 2% from predicted results. This information advantage has consistently helped candidates win against much better funded opponents. Services include innovative low-cost polling, detailed market segmentation, and integration with campaign resources, including budgeting, message, canvas, mail and phone programs, and media buying. Marketing Planning and Management Create targeted marketing plans based on data mining analysis of complex consumer data. Product includes detailed market segments with strategies and metrics for each segment. Clients include national big-box retailers, insurance marketers and direct mail vendors. 6/89-12/99 Texas Rural Communities, Inc. Austin, TX General Manager Highlights: Created adult continuing education programs, including a farm financial management training program which became a requirement for all Texas farmers seeking a federal farm loan. Delivered program via community colleges and the Internet. Customer satisfaction levels topped 90% in a course expected to be unpopular Operated a grant program targeting rural, minority children which funded innovative social programs, such as heritage-based mentor programs for at-risk rural minority children. Investment fiduciary for Heartland Lloyds, a start-up insurance company targeted to the insurance needs of underserved rural counties; served on Heartland s Board of Directors for five years Operated an active small business loan and technical assistance program Managed operations of five endangered Texas state parks. Tripled income and reduced deficits by 50% in two years Created a set of private-industry tourism promotions that increased revenues of participants by more than 33% in first season. 5/83-5/89 Texas Department of Agriculture Austin, TX Director, Farmer Assistance Programs Created and delivered programs to increase income and provide legal and mental health assistance to farmers and other rural businesses during the worst agricultural economic downturn since the Great Depression Led staff of 6 to 12 researchers and program specialists Producer for Willie Nelson s FarmAid II Primary speechwriter for Agriculture Commissioner Jim Hightower 3/80-5/83 Sweetwater Reporter Sweetwater, TX Managing Editor of award-winning daily newspaper serving 17,000 subscribers 9/77-5/79 Weslaco Independent School District Weslaco, TX Bilingual Teacher for migrant high schoolers with English language skills at primary levels Education The University of Texas at Austin, McCombs School of Business Master of Business Administration Columbia University, New York, New York Bachelor of Arts in Literature, History and Art History Case 2:12-cv LA Filed 04/23/12 Page 18 of 18 Document 24 JSA-096

101 IN THE UNITED STATES DISTRICT COURT FOR THE EASTERN DISTRICT OF WISCONSIN BETTYE JONES, et al., Plaintiffs, v. Case No. 2:12-cv LA JUDGE DAVID G. DEININGER, et al., (all sued in their official capacity), Defendants. REBUTTAL DECLARATION OF LELAND BEATTY Leland Beatty hereby declares as follows: 1. I previously submitted a declaration in this case that explained my analysis of how the Wisconsin voter identification requirements impact different ethnic populations in Wisconsin. See Declaration of Leland Beatty (dated Apr. 22, 2012) ( Beatty ). I submit this Rebuttal Declaration to respond to the May 31, 2012 Declaration of M.V. (Trey) Hood III ( Hood ). A. Differences Between Our Identification of Individuals Without a Current Driver s License or State Identification Card 2. Professor Hood used a different matching methodology and came up with a lower percentage of Wisconsin registered voters who lack a driver s license or state identification card ( proper identification ). Specifically, my estimate is that 11.1 percent of Wisconsin registered voters lack proper identification. Beatty 8. Professor Hood estimates that between 6.15 Case 2:12-cv LA Filed 06/27/12 Page 1 of 8 Document 59 JSA-097

102 percent and 9.34 percent of Wisconsin registered voters lack proper identification. See Hood, at 7 (Table 1). 3. When Professor Hood and I sought to match individuals appearing in the Government Accountability Board s ( GAB s ) registered voter files with those appearing in the State Department of Motor Vehicles ( DMV ) records of individuals with proper identification based on first name, last name, and date of birth, our match rate was virtually identical. I found that percent of Wisconsin registered voters could be matched based on these variables. Professor Hood found that percent of Wisconsin registered voters could be matched based on these variables. See Hood, at 7 (Table 1). 4. I estimated that 11.1 percent of Wisconsin registered voters a total of 360,387 registered voters lack a Wisconsin driver s license or state identification card. Professor Hood estimated that 6.15 to 9.34 percent a total of between 200,069 and 306,894 registered voters lack such identification Accordingly, my estimate of registered voters differed from Professor Hood s estimate by somewhere between 160,318 registered voters (using Professor Hood s 6.15 percent estimate) and 53,493 registered voters (using his 9.34 percent estimate). The differences between our two estimates can be explained almost completely by two methodological differences. 1. Individuals With Matching Driver s License Numbers But Not Non- Matching Personal Identification Data 5. First, Professor Hood treated an individual as having proper identification if there was a match between the drivers license number contained in the voter registration files and the DMV files. In conducting this match, Professor Hood did not require that there be any match Case 2:12-cv LA Filed 06/27/12 Page 2 of 8 Document 59 JSA-098

103 between the individuals name or date of birth. Accordingly, Professor Hood would treat as a match a GAB record and a DMV record with the same drivers license number even if those files were for people with different names or dates of birth. Indeed, because I treated as matched GAB and DMV files reflecting the same name and date of birth, Professor Hood s methodology would make a difference here only for files that matched driver s license numbers but did not match names or dates of birth To give one example, in the voter file State ID number Redacted belongs to, born Redacted Redacted 1980, residing in Milwaukee County. In the Driver s License/State ID file, this state ID number is assigned to Redacted, born Redacted 1971, residing in Marinette County. 7. This methodological difference accounts for Professor Hood having treated 2.6 percent of Wisconsin registered voters a total of 85,622 registered voters as having proper identification when I classified them as not having proper identification. 8. I am uncomfortable treating an individual as having proper identification simply because they have a matching driver s license number in GAB and DMV files if their names or dates of birth do not match. The fact that their personal identification is different undermines any confidence that this is really the same person. 1 I also treated as matched any registered voter if their Last Name, Date of Birth and Zip Code in the GAB registered voter file matched a unique record in the DMV s driver's license and state identification card file. This produced a match for an additional 76,283 registered voters. Professor Hood treated all these voters as matched also I mention it here because my treating these voters as matched, because they did have a match for significant personal identification data, reduced the extent to which my estimate would have varied from Professor Hood s had I not treated these individuals as matched Case 2:12-cv LA Filed 06/27/12 Page 3 of 8 Document 59 JSA-099

104 2. Driver s License in the GAB Records But Not the DMV Records 9. The second main reason for a difference in my estimate as compared to Professor Hood s estimate is that Professor Hood treated an individual as having a Wisconsin driver s license or state identification number if there was a license or identification number in their GAB files, even if there was not a record of that individual having a current driver s license or state identification at DMV. See Hood This methodological difference accounts for Professor Hood treating 3.3 percent of Wisconsin registered voters a total of 106,825 such voters as having proper identification, when I did not treat those voters as having proper identification. 11. Professor Hood seems to recognize that these individual do not really have a current Wisconsin driver s license. Instead, he states that they should be treated as having one because: (a) an expired license can still be used to vote so long as it expired after the last general election; and (b) the fact that these registrants at one time were in possession of a driver s license or state ID card is an indication that they could obtain one again. See Hood I believe it is more appropriate to treat these individuals as not having a Wisconsin driver s license or state identification card. At best, their card is expired and they are thus impacted by the Wisconsin voter identification law. If they wish to continue voting in Wisconsin, they will have to go through the steps of securing a new driver s license or state identification card Case 2:12-cv LA Filed 06/27/12 Page 4 of 8 Document 59 JSA-100

105 3. These Two Methodological Issues Explain Virtually the Entire Difference Between My Estimates and Professor Hoods 13. Between them, these three methodological issues result in Professor Hood treating 195,599 registered voters as having a Wisconsin driver s license or state identification card that I did not treat as having such identification. Since the maximum difference in our estimates was 198,578 registered voters (using Professor Hood s lower estimate of 6.1 percent lacking proper identification), these two methodological issues are responsible for 98.5% percent of the difference between my estimate and Professor Hood s estimate. B. Even Using Professor Hood s Methodology, It is Clear That the Wisconsin Voter Identification Requirement Has a Disparate Impact on Non-White Voters 14. Professor Hood s analysis came up with a lower percentage of Wisconsin registered voters who lack a Wisconsin driver s license or state identification card. But Professor Hood did not follow up to examine whether his lower estimate ultimately contradicts my conclusion that non-white Wisconsin registered voters are significantly less likely to possess these forms of identification. 15. Had Professor Hood done so, he would have found that his methodology confirms my conclusion. I used his exact methodology, and then examined the race of those registered voters lacking proper identification using the data from Ethnic Technologies discussed in my original report. I found that percent of non-white Wisconsin registered voters lack a driver s license or state identification card as compared to only 4.36 percent of white voters: Case 2:12-cv LA Filed 06/27/12 Page 5 of 8 Document 59 JSA-101

106 Hood Match Scheme: Share Without DL_ID Match by White/NonWhite No DL_ID Match Share Without Match Race Total Non-White 309,714 36, % White 2,945, , % Total 3,255, , % 16. Further, using Professor Hood s methodology together with the race data from Ethnic Technologies, I determined that African Americans and Hispanics are more than twice as likely to lack proper identification : Hood Match Scheme: Share Without DL_ID Match by Race Race Total No DL_ID Match Share Without Match African American 172,251 15, % Hispanic 51,974 5, % White 2,945, , % Other/Unknown 84,601 15, % 17. There is accordingly a significant disparity the percentage of non-white registered voters who lack the identification required by the Wisconsin law whether one uses the matching methodology endorsed by my original declaration or by Professor Hood. C. Use of Ethnic Technologies 18. Finally, I note that Professor Hood expresses concern that I did not explain the methodology that Ethnic Technology used to determine the race of the Wisconsin registered voters who lack a Wisconsin driver s license or state identification card. 19. It is a standard practice in the field of elections analysis to use a firm like Ethnic Technologies to analyze the ethnic make-up of a particular group of voters. This is particularly important in well-integrated places such as Houston and Dallas, Texas, where Ethnic Case 2:12-cv LA Filed 06/27/12 Page 6 of 8 Document 59 JSA-102

107 Technologies match work has made significant differences in my work. To my knowledge there are only two firms in the country that provide this sort of information (the other being CPM Technologies) and Ethnic Technologies has the reputation of being the more reliable. 20. Ethnic Technologies determines the race of voters based on an analysis of surnames, as well as by geocoding, an analysis of where an individual lives and whether their neighborhood location helps identify their ethnicity. 2 There is an extensive body of academic literature that confirms the accuracy of the methods the firm uses. 21. In 2007, before Ethnic Technologies implemented its newest version of first-name analysis, the National Cancer Institute commissioned a study that examined Ethnic Technologies methodology that concluded Ethnic Technologies methodology was highly accurate but likely under-identified African Americans. See Triangulating Differential Nonresponse by Race in a Telephone Survey, by DeFrank, Bowling, Rimer, Gierisch, and Skinner, Preventing Chronic Disease, Vol. 4 No. 3, July Other scholarly articles that confirm that accuracy of Ethnic Technologies methodology include: (1) Mateos, P. (2007), A review of name-based ethnicity classification methods and their potential in population studies, Population Space Place, 13: 243ñ263. doi: /psp.457; (2) Mateos, P., Webber, R. & Longley, P (2006) How segregated are name origins? A new method of measuring ethnic residential segregation, in: Priestnall, G and Aplin, P, (eds.) Proceedings of the 14th GIScience Research UK Annual Conference (GISRUK 2006). (pp ) University of Nottingham: Nottingham, UK; and 2 Ethnic Technologies has a detailed explanation of its methodology for determining ethnicity on its website. See Case 2:12-cv LA Filed 06/27/12 Page 7 of 8 Document 59 JSA-103

108 (3) Onomastics and lts Uses, Joel T. Rosenthal, Journal of Interdisciplinary History, Summer 2005, Vol. 36, No. I, P4ges I declare under penalty of perjury that the foregoing is true and correct. Executed on June in Austin Texas. -8 Case 2:12-cv LA Filed 06/27/12 Page 8 of 8 Document 59 JSA-104

109 Declaration of Leland Beatty 1. My name is Leland Beatty. I am a resident of Austin, TX. I have previously submitted declarations in this case which detailed a match between the Wisconsin voter registration database and the Wisconsin driver's license and state ID databases, which demonstrated that Wisconsin minority voters are at a substantial disadvantage under Wisconsin's voter ID law, and that the effect of that law imprints an unavoidable disparate impact on minority election participation. 2. I have been asked to match the current Wisconsin voter registration database and the current Wisconsin driver's license and state ID databases to determine if there has been a substantial change in the disparate adverse impact on minority voters. Conclusions 3. Current data support the prior conclusions that Non-White Wisconsin residents will be significantly and adversely impacted by the Wisconsin voter identification law, as well as the other conclusions presented in the prior declarations. Non-White registered voters are significantly less likely to possess a driver's license or state identification that matches their voter registration data. 4. Hispanic voters are 2.3 times as likely as White voters to not possess a matching driver's license or state ID; Asian voters 1.5 time as likely as White voters; and African American voters 1.4 times as likely as White voters. Methodology 5. I used the same process and methods detailed in my earlier declarations, adding a match based on first name, middle initial, last name and date of birth, as well as attempting to match various combinations of first and last names which include multiple words ,735 registered voters did not match a driver's license or state ID. These voter record were submitted to Ethnic Technologies, a company that maintains proprietary data on race and ethnicity, to identify the race of individuals based on identifying data contained in the Wisconsin voter registration database. 7. I have worked with Ethnic Technologies data on many occasions in the past, and have found their work to be reliably accurate on each occasion. I have had the opportunity to review the onomastic research on which their methods are based, and have found the research to strongly support the Ethnic Technologies methodology. For these reasons, and as I explained more fully in the Reply Declaration of Leland Beatty that I submitted back in April, 2012, I am confident using their work product in both my marketing and political work ,796 submitted voters were returned from Ethnic Technologies with race identification (93.1%). Race for 21,939 voters could not be determined. JSA-105

110 9. The end result of my work is summarized in the following table: Race ASIAN BLACK HISPANIC INDIAN WHTTE Total License and State ld Records, bv Race fotalvoten 4,31( 199,39t 62,49t 17,9? 3,049,61f 3,373,741 TotalVoters Matchd to DL / ld Record 38,91S 176,51( 50,49{ 16,79i 2,7U5,21t 3,077,951 Iotal UnmatcheC todl/ld Recod, Rme klentified 5,391 n,g7s 11,9W 1,13i 2V, ,79 share of Race Wthout Matchirg u_/td 12.20/t i( 6.3oi % 10. I declare under penalty of perjury that the forgoing is true and correct. Executed on October 28, 2013 inaustin. Texas. JSA-106

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