2022

Posted on October 11, 2022February 2, 2023 by Haystaq DNA

Expanding Democrat’s Holdings in State Legislatures by Leveraging Abortion

Download a PDF of this Report

This white paper takes advantage of Haystaq’s national issue scores to identify expansion targets for the Democratic Party in state legislative elections.^[1] Based on feedback we have received, we focused on the following chambers in bold that we believe are pick up opportunities when using abortion related messaging. This analysis considers all seats being contested by the two major parties, excluding only those rated as “Solid D” by CNalysis (whose seat ratings this paper adopts).

Expansion Targets 2022^[2]	Protects 2022 Cycle	Long-term Opportunities
Michigan House	Nevada House, and Senate	Georgia House only
Pennsylvania House	Maine Senate	North Carolina House
Arizona Senate	Minnesota House	North Carolina Senate
Michigan Senate

The table below briefly summarizes the legal status of abortion in the targeted states and public option in the state overall. It is quite possible that abortion may prove to be a more salient issue in states where the current abortion policy is at odds with the opinion of the majority of voters.

State	Legal	Constitutional Protection	Legislative Control	% supporting Abortion Statewide	Abortion Rights Under Threat
Arizona	No (pending court challenges)	No	GOP	56%	Yes
Pennsylvania	Yes	No	GOP	56%	Yes^[3]
Minnesota	Yes	Yes (non-explicit)	Split	54%	No
Michigan	Yes (pending court action)	No	GOP	55%	No
Nevada	Yes	Yes	Dem	63%	No
Maine	Yes	No	Dem	62%	No
North Carolina	Yes (viability)	No	GOP	49%	Yes
Georgia	Yes (6-weeks)	No	GOP	49%	Yes
Texas	No	No	GOP	46%	Yes

The key finding is, given that the vast majority of Americans disapprove of the overturning of Roe versus Wade, there are several dozen GOP held state legislative seats with pro-choice majorities and where Haystaq’s data can be used to id and target pro-choice voters, and there are many more seats where vulnerable Democrats can use the abortion issue to bolster their electoral position. If Democrats succeed in unifying the pro-choice vote, the party could gain control over both houses of the Michigan legislature along with the Pennsylvania House and the Arizona Senate. Of these legislative chambers, the Michigan Senate will be the easiest to flip, followed by the PA House and AZ Senate.^[4] Evidence from field testing and from recent special elections shows that this issue indeed moves vote choice.

The tables below highlight seats in each legislative chamber where abortion related messaging can make a difference with expansion opportunity seats in bold and districts ranked by the salience of the abortion issue. The race ratings key below is taken from CNalysis. For each chamber, the number of seats currently held by the GOP which have Pro Choice majorities is noted, and the information is underlined when the number of seats is enough to flip control of the chamber to the Democrats.

Race Ratings Key

Solid R

Very Likely R

Likely R

Lean R

Tilt R

Tossup

Tilt D

Lean D

Likely D

Very Likely D

Expansion Targets

MI Senate (Chamber Rating: toss-up)

3 flips possible
3 seats need for control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
13	D	203,108	96,391	124,993	59,925	62.2%
21	R	198,356	72,090	124,412	42,377	58.8%
4	D	209,569	71,730	121,671	41,947	58.5%
14	R	201,075	82,691	109,008	47,801	57.8%
28	R	181,207	74,818	104,858	42,582	56.9%
9	D	185,843	75,889	102,357	40,839	53.8%
11	D	198,566	64,910	108,308	33,591	51.8%

PA House (Chamber Rating likely R)

15 flips identified
12 seats needed for control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
172	D	33,927	10,996	22,451	7,861	71.5%
45	D	43,874	15,931	27,537	10,751	67.5%
121	D	31,109	9,160	19,974	6,054	66.1%
25	D	42,922	15,281	25,405	9,733	63.7%
16	D	40,813	14,148	21,510	8,880	62.8%
118	D	42,605	17,678	23,913	11,075	62.6%
33	R	42,033	17,389	22,957	10,729	61.7%
2	D	40,142	15,108	22,620	9,219	61.0%
151	R	45,408	20,190	25,941	12,161	60.2%
61	D	45,892	22,495	25,552	12,769	56.8%
29	R	49,874	24,003	26,528	13,555	56.5%
137	R	44,561	15,873	23,385	8,956	56.4%
189	R	37,397	8,393	20,618	4,691	55.9%
53	D	40,307	14,872	22,866	8,240	55.4%
115	D	37,081	8,163	20,666	4,517	55.3%
39	R	44,855	17,716	21,461	9,763	55.1%
146	D	41,054	12,209	22,100	6,662	54.6%
30	R	44,426	20,948	22,894	11,429	54.6%
168	R	42,387	19,484	22,816	10,466	53.7%
3	D	44,772	19,822	22,179	10,629	53.6%
26	R	42,572	16,872	22,166	9,037	53.6%
142	R	43,865	17,959	22,188	9,544	53.1%
18	R	39,719	12,975	22,449	6,835	52.7%
51	R	38,160	12,060	16,212	6,323	52.4%
144	R	45,006	18,380	22,831	9,625	52.4%
74	D	37,401	10,581	18,538	5,449	51.5%
82	R	37,986	13,096	20,020	6,678	51.0%
120	R	39,922	15,036	18,435	7,586	50.5%

AZ Senate (Chamber Rating: Very Likely R)

3 flips possible
2 seats needed for control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
11	R	112,243	21,923	79,713	16,064	73.3%
18	D	158,642	75,347	111,147	48,576	64.5%
8	R	128,059	36,355	87,767	21,919	60.3%
23	R	120,100	24,701	68,004	12,805	51.8%

MI House (Chamber Rating: lean R)

8 flips possible
3 seats needed to gain control

bgcolor=”blue”

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
20	D	74,703	33,317	44,633	21,164	63.5%
2	D	65,447	18,182	40,242	10,875	59.8%
21	D	58,957	28,645	36,086	16,881	58.9%
40	D	67,752	30,234	41,519	17,688	58.5%
69	D	72,560	22,945	41,253	13,418	58.5%
22	D	74,522	38,574	42,596	21,876	56.7%
73	R	55,292	23,075	31,876	13,015	56.4%
80	R	66,445	27,813	38,367	15,675	56.4%
81	R	68,707	30,413	38,364	16,737	55.0%
27	D	71,170	27,671	38,741	15,117	54.6%
31	D	72,470	25,464	38,074	13,908	54.6%
55	D	66,620	31,869	38,131	17,284	54.2%
28	D	69,534	22,056	36,538	11,800	53.5%
83	R	59,140	14,944	36,566	7,973	53.4%
54	D	69,211	32,757	40,086	17,424	53.2%
84	R	66,713	23,496	38,486	12,497	53.2%
57	R	63,492	19,714	31,956	10,376	52.6%
58	R	65,522	21,407	34,247	11,175	52.2%
48	R	73,170	35,774	36,904	18,598	52.0%
61	D	71,308	24,653	38,596	12,660	51.4%
76	D	70,433	29,252	37,021	14,962	51.1%
68	D	73,016	26,474	39,197	13,513	51.0%

Protects

NV House (Chamber Rating: Lean D)

1 flip possible

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
42	D	44,553	12,768	25,434	7,464	58.5%
8	D	45,109	11,885	24,845	6,932	58.3%
1	D	51,497	17,487	28,641	10,081	57.6%
16	D	44,548	11,222	26,180	6,403	57.1%
9	D	45,980	14,436	24,380	7,992	55.4%
34	D	46,428	14,224	26,023	7,855	55.2%
41	D	49,126	15,291	26,150	8,440	55.2%
35	D	49,196	15,937	25,225	8,609	54.0%
5	D	46,656	15,138	25,280	8,115	53.6%
29	D	47,750	15,236	25,112	8,103	53.2%
3	D	43,360	12,115	24,085	6,425	53.0%
25	R	49,102	25,773	25,767	13,467	52.3%
21	D	49,808	18,523	25,623	9,670	52.2%
12	D	43,998	13,569	24,805	6,950	51.2%

NV Senate (Chamber Rating: Very Likely D)

0 flips possible
None of the districts up for election this cycle have a majority of voters that are pro-choice, the below districts are analyzed based on registered rather than likely voters

District With CN Analysis Rating	Current Hold	Reg Voters	Count Pro Choice	Percent Pro Choice
8	D	96,819	48,480	50%
9	D	90,550	49,824	55%
12	R	98,969	51,790	52.3%

ME Senate (Chamber Rating: Toss-Up)

0 flips possible
GOP needs nine seats to gain control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
21	D	25,931	7,124	17,802	4,036	56.7%
32	D	26,344	8,889	14,976	4,884	54.9%
31	D	28,733	9,933	15,916	5,400	54.4%
34	D	31,246	12,996	15,178	6,576	50.6%

MN House (Tilt R)

0 flips possible
GOP needs four seats to gain control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
53B	D	25,309	7,593	16,415	4,114	54.2%

Long Term Opportunities

NC House (Chamber Rating: Very Likely R)

8 flips possible
18 seats needed for control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
50	D	59,865	27,811	38,018	19,911	71.6%
54	D	61,270	30,619	38,775	21,297	69.6%
36	D	61,463	25,334	39,993	17,206	67.9%
40	D	65,822	31,750	41,228	20,272	63.8%
2	R	59,451	25,007	34,722	15,938	63.7%
115	D	60,045	24,852	35,735	15,817	63.6%
47	D	47,726	11,876	25,628	7,539	63.5%
35	D	60,762	24,066	37,173	15,226	63.3%
48	D	48,841	13,585	28,474	8,535	62.8%
45	R	47,078	12,215	28,417	7,652	62.6%
105	D	56,324	20,484	35,701	12,659	61.8%
98	R	60,543	22,631	34,947	13,450	59.4%
104	D	59,160	27,187	35,184	16,127	59.3%
9	D	55,901	19,298	32,233	11,185	58.0%
103	D	60,482	26,478	35,050	15,210	57.4%
62	R	64,967	28,217	38,258	16,097	57.0%
32	D	52,567	17,380	29,592	9,815	56.5%
37	R	61,579	23,314	33,283	12,959	55.6%
73	R	51,777	17,394	27,506	9,460	54.4%
43	R	52,756	16,831	27,273	8,767	52.1%
74	R	62,114	26,190	32,873	13,619	52.0%
20	R	62,597	24,229	30,376	12,350	51.0%
24	D	54,889	18,154	28,798	9,097	50.1%

NC Senate (Chamber Rating: Very Likely R)

2 flips possible
4 seats need for control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
17	D	136,972	53,338	81,240	32,867	61.6%
24	R	115,536	30,281	63,238	18,564	61.3%
18	D	135,437	53,694	79,153	32,830	61.1%
19	D	128,229	37,142	76,536	22,350	60.2%
42	R	143,920	59,588	87,923	35,736	60.0%
3	D	136,151	47,483	69,517	24,703	52.0%

GA House (Chamber Rating: Very Likely R)

1 flip possible
14 seats needed for control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
54	D	38,541	14,555	28,391	9,010	61.9%
106	D	39,923	15,464	22,184	8,580	55.5%
50	D	36,503	13,086	23,946	7,105	54.3%
101	D	38,378	12,387	21,243	6,550	52.9%
35	R	37,535	11,982	19,972	6,280	52.4%
154	D	40,566	14,684	25,057	7,670	52.2%
105	D	38,221	12,695	20,704	6,582	51.8%

TX House (Chamber Rating: Very Likely R)

2 flips possible
10 seats needed for control

District With CN Analysis Rating	Current Hold	Reg Voters	Likely Voters	Count Pro Choice	Count Pro Choice Likely Voters	Percent Pro Choice
35	D	72,311.00	12,898.00	62,774.00	10,016.00	77.7%
41	D	97,046.00	24,425.00	81,649.00	17,763.00	72.7%
74	D	109,198.00	25,412.00	87,426.00	17,676.00	69.6%
47	D	129,733.00	70,610.00	83,307.00	43,880.00	62.1%
135	D	99,870.00	25,133.00	66,184.00	15,587.00	62.0%
37	D	96,832.00	23,604.00	75,297.00	14,532.00	61.6%
148	D	89,635.00	22,825.00	59,411.00	13,594.00	59.6%
45	D	121,123.00	43,602.00	74,356.00	24,199.00	55.5%
105	D	75,258.00	20,157.00	50,794.00	11,005.00	54.6%
34	D	103,130.00	24,252.00	46,370.00	12,661.00	52.2%
118	R	113,023.00	29,691.00	70,522.00	15,032.00	50.6%
31	R	111,130.00	35,801.00	71,456.00	18,112.00	50.6%

Works Cited

“Abortion in Nevada.” Wikipedia, 3 Aug. 2022, en.wikipedia.org/wiki/Abortion_in_Nevada#History. Accessed 9 Aug. 2022.

“Arizona Senate.” Wikipedia, 25 July 2022, en.wikipedia.org/wiki/Arizona_Senate. Accessed 9 Aug. 2022.

“Arizona State Legislative.” Projects.cnalysis.com, projects.cnalysis.com/21-22/state-legislative/arizona. Accessed 9 Aug. 2022.

Center for Reproductive Rights. “Abortion Laws by State.” Center for Reproductive Rights, reproductiverights.org/maps/abortion-laws-by-state/.

Cohn, Nate. “Do Americans Support Abortion Rights? Depends on the State.” The New York Times, 4 May 2022, www.nytimes.com/2022/05/04/upshot/polling-abortion-states.html.

“Georgia House of Representatives.” Wikipedia, 16 June 2022, en.wikipedia.org/wiki/Georgia_House_of_Representatives. Accessed 16 Aug. 2022.

“Maine State Legislative.” Projects.cnalysis.com, projects.cnalysis.com/21-22/state-legislative/maine. Accessed 9 Aug. 2022.

“Michigan Senate.” Wikipedia, 18 May 2022, en.wikipedia.org/wiki/Michigan_Senate. Accessed 9 Aug. 2022.

“Minnesota House of Representatives.” Wikipedia, 24 Mar. 2022, en.wikipedia.org/wiki/Minnesota_House_of_Representatives. Accessed 9 Aug. 2022.

“Minnesota State Legislative.” Projects.cnalysis.com, projects.cnalysis.com/21-22/state-legislative/minnesota. Accessed 9 Aug. 2022.

“Nevada Assembly.” Wikipedia, 7 June 2021, en.wikipedia.org/wiki/Nevada_Assembly.

“Nevada State Legislative.” Projects.cnalysis.com, projects.cnalysis.com/21-22/state-legislative/nevada. Accessed 9 Aug. 2022.

“North Carolina House of Representatives.” Wikipedia, 1 Aug. 2022, en.wikipedia.org/wiki/North_Carolina_House_of_Representatives. Accessed 16 Aug. 2022.

“North Carolina Senate.” Wikipedia, 8 Oct. 2020, en.wikipedia.org/wiki/North_Carolina_Senate.

“North Carolina State Legislative.” Projects.cnalysis.com, projects.cnalysis.com/21-22/state-legislative/north-carolina. Accessed 16 Aug. 2022.

“Pennsylvania House of Representatives.” Wikipedia, 30 July 2022, en.wikipedia.org/wiki/Pennsylvania_House_of_Representatives. Accessed 9 Aug. 2022.

“Pennsylvania State Legislative.” Projects.cnalysis.com, projects.cnalysis.com/21-22/state-legislative/pennsylvania. Accessed 9 Aug. 2022.

Wikipedia Contributors. “Michigan House of Representatives.” Wikipedia, Wikimedia Foundation, 31 Oct. 2019, en.wikipedia.org/wiki/Michigan_House_of_Representatives. Accessed 7 Nov. 2019.

—. “Texas House of Representatives.” Wikipedia, Wikimedia Foundation, 25 Nov. 2019, en.wikipedia.org/wiki/Texas_House_of_Representatives.

[1] In Wyoming and Oklahoma Democrats are not contesting enough seats to take control of the legislature. In West Virginia, the Dakotas, Tenensee, Kentucky, Indiana, and Utah not enough Democratic candidates filed to take control of the upper houses of the legislature. In Massachusetts the GOP is not contesting either house of the legislature and in California the GOP is not contesting the state senate.

[2] The AZ house was not analyzed because CNalysis does not rate these races.

[3] Both houses of the PA leg. are controlled by anti-abortion forces and GOP gubernational nominee Doug Mastriano supports a total ban on abortion

[4] The Michigan house will be difficult to flip because the Dems need to play defense in several challenging seats.

Report on Racial Bloc Voting Analysis for the State of Montana 10/03/2022

Posted on October 3, 2022February 2, 2023 by Haystaq DNA

Report on Racial Bloc Voting Analysis for the State of Montana

Prepared for:

The Montana Districting and Apportionment Commission

Download a PDF of this Report

May 6, 2022

Synopsis

Analysis of the State of Montana’s election results going back to 2014 shows evidence of racial bloc voting (RBV) in the five regions solicited by the Montana Districting and Apportionment Commission. This analysis used general and party primary elections for ten races going back to 2014. We show the results of Homogeneous Precinct Analysis, Bivariate Regression Analysis, and Ecological Inference Analysis to support these findings. All analyses were run using two definitions of American Indian, “American Indian Any” and “American Indian Alone”, using Citizen Voting Age Population (CVAP) data. In the following report, we will walk you through our findings.

Introduction

Figure 1: Map of the 5 regions for analysis defined by the MT Redistricting and Apportionment Commission

Since 2004, Montana’s legislative maps have included 6 majority-minority House Districts (out of 100 districts total) and 3 majority-minority Senate Districts (out of 50 districts total). These majority-minority House and Senate Districts cover all or part of 7 reservations in Montana, which guided our RBV analysis. In addition to the three regions spelled out below that surround current majority-minority legislative districts, the Montana Districting and Apportionment Commission has requested that Haystaq DNA analyze the two major cities with the highest American Indian populations, Great Falls and Billings.

As of the 2016-2020 American Community Survey 5-year Estimates released by the U.S. Census, Montana has a citizen voting age population statewide that is 6.53% “American Indian Alone” and 8.09% “Any Part American Indian.” For minority demographic groups, it was requested that HaystaqDNA look at citizen voting age populations for “Any Part American Indian,” as well as “American Indian Alone” to determine if racially polarized voting exists and to what extent amongst those two categories.

Regions

HaystaqDNA performed the analysis on five regions in the State of Montana to determine if there was RBV in past elections. Our analysis includes all precincts within 17 counties, broken down into the following five regions at the request of the Redistricting Commission. While regions 1 through 3 were selected because they encompass current majority-minority House and Senate Districts, it was proposed that our analysis include all precincts within the 17 counties listed below to ensure coverage of precincts both on and off reservation lands and both within and outside of current majority-minority legislative districts.

Region 1 – Blackfeet & Flathead Reservations (SD 8)

Reservation	Senate District	House District	County
Blackfeet	SD 8	HDs 15 & 16	Glacier Co. Precincts
Blackfeet	SD 8	HD 15	Pondera Co. Precincts
Flathead	SD 8	HD 15	Lake Co. Precincts
Flathead			Sanders Co. Precincts

Region 2 – Rocky Boy’s, Fort Belknap, & Fort Peck Reservations (SD 16)

Reservation	Senate District	House District	County
Rocky Boy’s	SD 16	HD 32	Hill Co. Precincts
Rocky Boy’s	SD 16	HD 32	Chouteau Co. Precincts
Fort Belknap	SD 16	HD 32	Blaine Co. Precincts
Fort Belknap	SD 16	HD 32	Phillips Co. Precincts
Fort Peck	SD 16	HD 31	Roosevelt Co. Precincts
Fort Peck	SD 16	HD 31	Valley Co. Precincts
Fort Peck			Daniels Co. Precincts
Fort Peck			Sheridan Co. Precincts

Region 3 – Crow & Northern Cheyenne Reservations (SD 21)

Reservation	Senate District	House District	County
Crow	SD 21	HDs 41 & 42	Big Horn Co. Precincts
Crow	SD 21	HD 42	Yellowstone Co. Precincts 42.1 and 42.2 only (remaining Yellowstone Co. Precincts part of Billings Region)
Crow	SD 21	HD 41	Rosebud Co. Precincts
	SD 21	HD 41	Powder River Co. Precincts

Region 4 – City of Billings

Reservation	Senate District	House District	County
			Yellowstone County Precincts (excluding Precincts 42.1 and 42.2 which are included in a majority-minority House and Senate District and part of Region 3)

Region 5 – City of Great Falls (Little Shell)

Reservation	Senate District	House District	County
			Cascade County Precincts

Elections

The following elections were analyzed using Ecological Inference, Homogeneous Precinct Analysis and Bivariate Regression Analysis in each of the 5 regions. There were 10 races for analysis total, with 4 from each presidential election cycle and 1 from each midterm between 2014 and 2020. For each of the races listed below, we analyzed the election results from the General, Democratic Primary, and Republican Primary elections where applicable.

2014 U.S. Senate
2016 President
2016 Congressional
2016 Governor
2016 Attorney General
2018 U.S. Senate
2020 President
2020 U.S. Senate
2020 Attorney General
2020 Auditor

Data

Election Results & Precinct Shapefiles

Our process began with creating precinct shapefiles joined to election results for each year of elections, to reflect precinct geographies in place at the time of the election. We obtained the shapefile of Montana Voting Precincts from the Montana State Library Services repository. A shapefile is a geospatial vector data format for geographic information system (GIS) software. We use these shapefiles to spatially analyze the election results in comparison with population data. We obtained historical election results at the precinct level from the Montana Secretary of State website. Using the information below, we manually consolidated election results or precinct geographies.

Four of the 17 counties included in the region of study have had a precinct change between 2014 and 2020. The following is a list of the Precinct Changes between 2014 and 2020 which were made manually on the 2020 precinct shapefile before running the rest of the analysis.

Precinct changes between 2014 and 2016

Yellowstone County (Region 4 – City of Billings)

o Consolidation: Precincts 40.2 and 45.1 consolidated into Precinct 40-45

Precinct Changes between 2016 and 2018

Lake County (Region 1 – Flathead Reservation)

o Split: Precinct Pab 1 split from Ron 1

o Split: Precinct Pab 2 split from Ron 2

*The precinct geography for Ron 1 in 2014 and 2016 was equivalent to the current Pab 1 and Ron 1 combined on the Census Bureau’s 2020 Lake County VTDs. The precinct geography for Ron 2 in 2014 and 2016 was equivalent to the current Pab 2 and Ron 2 combined on the Census Bureau’s 2020 Lake County VTDs.

Phillips County (Region 2 – Fort Belknap Reservation)

o Consolidation: Precincts 2s, 5, 6, 8s, 11 and 12s consolidated into 11S

o Consolidation: Precincts 2n, 7, 8n, 9-1, 9-2, 12n and 16 consolidated into 11N

Valley County (Region 2 – Fort Peck Reservation)

o Consolidation: Precincts 1 and 2 consolidated into 31

o Consolidation: Precincts 2 and 4 consolidated int 33

o Consolidation: Precincts 5, 6, 7 and 8 consolidated into 34

Population Data

For the demographic population data, we used Citizen Voting Age Population (CVAP) from the American Community Survey 5-year Estimates released by the U.S. Census. These data are available at the Block Group level. We disaggregated the data from the Block Group level to the Block level, and then aggregated the data to precincts. This same process was followed for every election year, following the manual modifications made to the precinct shapefile reflecting the consolidations and changes made to precinct geography outlined above, to join CVAP from the year of the election to election results.

We performed the analysis using the “American Indian Alone” category, and also on an “Any Part American Indian” category that we created by combining “American Indian Alone”, “American Indian or White” and “American Indian and Black or African American.” We compared these American Indian categories to “White Alone” and “Other”, which is the combination of all remaining variables.

Methodologies

Homogeneous Precinct Analysis

Homogeneous precinct analysis is a method for estimating voting behavior by race or ethnicity by comparing voting patterns in “homogeneous precincts,” i.e. precincts that are composed of a single racial or ethnic group.

For example, if there is a precinct composed entirely of American Indian voters, and the voters within that precinct give 90% of their votes to Candidate X, then we know that 90% of the American Indian voters supported Candidate X. Since precincts are usually not exclusively one race or ethnicity, precincts that are 90% or more of a single race or ethnicity are usually considered homogeneous for the purposes of this analysis.

A drawback of homogeneous precinct analysis is that we are only able to perform it in a given region that has homogeneous precincts. For example, if a region does not have any precincts that are over 90% CVAP for the race or ethnicity of interest, we are unable to perform homogeneous precinct analysis.

For the purposes of our analysis, we define a homogeneous American Indian precinct to be any precinct that is 90% or more American Indian Alone or American Indian Any.

In Figure 2, we have an example of homogeneous precinct analysis from the 2016 General Election for Attorney General in Region 1.

Figure 2: Bivariate regression plot: Attorney General, 2016 General Election, Region 1.

In Figure 2, we see in the first table that in all of Region 1, there are 3 homogeneous American Indian precincts, all in Glacier county. We see in the Total row that these precincts are overall 96.2% American Indian Any, and the vast majority of voters, 85.4%, voted for Larry Jent. In the second table, we can see that there are many more homogeneous white precincts in Region 1, and overall they are 94.6% white. The majority of voters in these precincts, 78.8%, voted for Tim Fox. These results show evidence of racial bloc voting in this election and region.

In the results section, we consider evidence of RBV to be present when we see greater than a 50% preference for the American Indian and White candidate of choice, and when those candidates differ.

Bivariate Regression Analysis

Bivariate regression analysis provides estimates of voting patterns by race or ethnicity across precincts, regardless of the existence of homogeneous precincts. The analysis shows the relationship between each candidate’s precinct-level vote share and the precinct-level CVAP for each race or ethnicity.

For example, in Figure 3, we can see the plot of % American Indian Alone, White Alone, and Other CVAP in comparison with the % of Votes for Tim Fox and Larry Jent in the 2016 General Election for Attorney General in Region 1.

Figure 3: Bivariate regression plot: Attorney General, 2016 General Election, Region 1.

In Figure 3, each point represents a precinct and its share of CVAP on the x-axis vs the candidate votes in that precinct on the y-axis. In the top left, we can see as the proportion of American Indian Alone CVAP % increases, the share of votes for Tim Fox decreases. In the bottom right, we can see that as the proportion of American Indian Alone CVAP increases, the share of votes for Larry Jent increases. The inverse is true for the Whie Alone category. For the Other category, the % CVAP is so small that we cannot draw any conclusions.

In Figure 4, we can see the correlation coefficients for each bivariate relationship shown in Figure 3.

Figure 4: Bivariate regression correlation coefficients: Attorney General, 2016 General Election, Region 1.

For each plot, a correlation coefficient can be between -1 to 1, where -1 is a perfect negative correlation and 1 is a perfect positive correlation, or slope of the graph if we were to draw a line of best fit on each plot in Figure 3. Here, in Region 1, we see a strong positive correlation between American Indian Alone CVAP and votes for Larry Jent, with a coefficient of 0.9265. We also see a strong positive correlation between White Alone CVAP and votes for Tim Fox in the 2016 Attorney General’s race. These results show evidence of racial bloc voting in this election in Region 1.

In the results section, we consider evidence of RBV to be present when we see a strong positive correlation of greater than 0.5 for the American Indian and White candidate of choice, and when those candidates differ.

Ecological Inference Analysis

Ecological Inference (EI) is the process of drawing conclusions about individual-level behavior from aggregate-level data. The process involves using aggregate (historically called “ecological”) data to draw conclusions about individual-level behavior when no individual-level data are available. The fundamental difficulty with such inferences is that many different possible relationships at the individual level can generate the same observation at the aggregate level. For example, there are a very large number of ways in which electoral support for a political candidate can break down among individual voters and still produce the same aggregate level of support. In the absence of individual-level measurement (for example in the form of surveys), such information needs to be inferred.

EI analysis builds on ecological regression analysis by incorporating method of bounds and maximum likelihood estimation statistical techniques. For our analysis, we use the eiCompare package in R, which builds on Gary King’s ei package in R.

Figure 5 provides an example of EI analysis for the same election as the previous examples, the 2016 Attorney General election in Region 1.

Figure 5: Ecological Inference Plot: Attorney General, 2016 General Election, Region 1.

In Figure 5, we see the estimates of the EI analysis. The green dots represent the EI estimate, and the lines on either side of the dots represent the confidence interval of the statistical estimate. When looking at the estimates for American Indian Alone in the top box, you can see that the estimate of votes is strongly for Larry Jent. For White CVAP, the preference is for Tim Fox.

Figure 6: Ecological Inference Estimates: Attorney General, 2016 General Election, Region 1.

Figure 6 gives the precise estimates shown in the chart above. In the above example, the predicted support for Larry Jent among American Indian Alone CVAP was 90% and for White CVAP was 14.8%. The predicted support for Tim Fox among White CVAP was 85%. The confidence intervals (ci_95_lower and ci_95_upper) indicate that for this estimate, there is a 95% confidence that the true value of this statistically predicted support for Tim Fox among White CVAP is between 83.75% and 86.63%. These results show evidence of racial bloc voting in this election and region.

In the results section, we consider evidence of RBV to be present when we see a clear majority preference for both the American Indian and White candidate of choice and when those candidates differ.

Results

In looking at elections in Montana going back to 2014, we found evidence of racial bloc voting in each of the five regions we analyzed. That is, we found evidence that using either the “American Indian Alone” or the “American Indian Any” definition, American Indian voters vote cohesively in support of their candidate of choice, and that White voters often vote in a bloc for a different candidate of choice.

Across regions, we only saw a few instances where there was RBV for only one of “American Indian Alone” or “American Indian Any”, but not for both. Overall, the results were very similar between the two definitions of American Indian.

In the following sections, we provide summary level results of our findings for each Region that we analyzed. We are also providing a Supporting Appendix with the charts for each methodology and definition of American Indian where we found evidence of RBV.

Region 1 – Blackfeet & Flathead Reservations (SD 8)

Figure 7: Region 1

Region 1 consists of all precincts in Glacier, Pondera, Lake, and Sanders counties. Region 1 has a total CVAP of approximately 47,434. The American Indian Alone CVAP is approximately 12,693, or 26.76%, and the American Indian Any CVAP is 14,021, or 29.57% of the total CVAP.

The examples provided in the methodology show evidence of racial bloc voting in the 2016 General Election for Attorney General.

In Region 1, the Homogeneous Precinct Analysis, Bivariate Regression Analysis, and Ecological Inference Analysis all showed evidence of RBV in the following elections, using both the American Indian Alone and American Indian Any definitions:

2014 U.S. Senate, General Election
2016 President, General Election
2016 Congressional, General Election
2016 Attorney General, General Election
2016 Governor, General Election
2018 U.S. Senate, General Election
2020 President, General Election
2020 U.S. Senate, General Election
2020 Attorney General, General Election
2020 Auditor, General Election

In addition, we found evidence of RBV with the following methods and elections:

Homogeneous Precinct Analysis showed RBV in the 2016 Presidential Democratic Primary and the 2020 Attorney General Republican Primary.
Bivariate Regression Analysis showed RBV in the 2014 US Senate Republican Primary and the 2016 Gubernatorial Republican Primary for both American Indian Alone and American Indian Any definitions.

Our analysis did not find evidence of RBV using Ecological Inference in any primary elections in Region 1.

Region 2 – Rocky Boy’s, Fort Belknap, & Fort Peck Reservations (SD 16)

Figure 8: Region 2

Region 2 consists of all precincts in Hill, Chouteau, Blaine, Phillips, Roosevelt, Valley, Daniels, and Sheridan counties. Region 2 has a total CVAP of approximately 40,957. The American Indian Alone CVAP is approximately 10,590, or 25.9%, and the American Indian Any CVAP is 11,581, or 28.3% of the total CVAP.

In Region 2, the Homogeneous Precinct Analysis, Bivariate Regression Analysis, and Ecological Inference Analysis all showed evidence of RBV in the following elections, using both the American Indian Alone and American Indian Any definitions:

2014 U.S. Senate, General Election
2016 President, General Election
2016 Congressional, General Election
2016 Governor, General Election
2016 Attorney General, General Election
2018 U.S. Senate, General Election
2020 President, General Election
2020 U.S. Senate, General Election
2020 Attorney General, General Election
2020 Auditor, General Election
2020 Auditor, Democratic Primary

The 2016 Democratic Primary Election for President showed evidence of RBV using the Homogeneous Precinct Analysis and Ecological Inference Analysis, using both the American Indian Alone and American Indian Any definitions.

Region 3 – Crow & Northern Cheyenne Reservations (SD 21)

Figure 9: Region 3

Region 3 consists of all precincts in Big Horn, Rosebud, and Powder River Counties. Region 3 also includes Yellowstone County Precincts 42.1 and 42.2 (remaining Yellowstone County precincts not in the Billings region). Region 3 has a total CVAP of approximately 17,532. The American Indian Alone CVAP is approximately 7,615, or 43.4%, and the American Indian Any CVAP is 8,016, or 45.7% of the total CVAP.

In Region 3, the Homogeneous Precinct Analysis, Bivariate Regression Analysis, and Ecological Inference Analysis all showed evidence of RBV in the following elections, using both the American Indian Alone and American Indian Any definitions:

2014 U.S. Senate, General Election
2016 President, General Election
2016 Congressional, General Election
2016 Attorney General, General Election
2016 Governor, General Election
2018 U.S. Senate, General Election
2020 President, General Election
2020 U.S. Senate, General Election
2020 Attorney General, General Election
2020 Auditor, General Election

In addition, we found evidence of RBV with the following methods and elections:

Ecological Inference showed evidence of RBV in the 2020 Attorney General Democratic Primary for both American Indian Alone and American Indian Any definitions.
Bivariate Regression Analysis showed evidence of RBV in the 2016 Presidential Republican Primaries for both American Indian Alone and American Indian Any definitions.
Homogeneous Precinct Analysis showed evidence of RBV in the 2016 Gubernatorial Republican Primary.

Region 4 – City of Billings

Figure 10: Region 4

Region 4 consists of all precincts in Yellowstone County, except for precincts 42.1 and 42.2 which are included in Region 3. Region 4 has a total CVAP of approximately 121,688. The American Indian Alone CVAP is approximately 5,484, or 4.5%, and the American Indian Any CVAP is 7,351, or 6% of the total CVAP.

In Region 4, we were not able to perform Homogeneous Precinct Analysis because there were no homogeneous American Indian Alone or American Indian Any precincts, where the American Indian CVAP was 90% or more of the total CVAP.

The Bivariate Regression Analysis and Ecological Inference Analysis showed evidence of RBV in the following elections, using both the American Indian Alone and American Indian Any definitions:

2014 U.S. Senate, General Election
2016 President, General Election
2016 Congressional, General Election
2016 Governor, General Election
2016 Attorney General, General Election
2018 U.S. Senate, General Election
2020 President, General Election
2020 U.S. Senate, General Election
2020 Attorney General, General Election
2020 Auditor, General Election

Additionally, the Ecological Inference alone showed evidence of RBV in the following elections, using both the American Indian Alone and American Indian Any definitions:

2014 U.S. Senate, Democratic Primary
2016 President, Democratic Primary
2016 Governor, Republican Primary
2018 U.S. Senate, Republican Primary
2020 President, Democratic Primary
2020 Attorney General, Democratic Primary
2020 Attorney General, Republican Primary
2020 Auditor, Democratic Primary

Using only the American Indian Alone definition, the Ecological Inference showed RBV in the following elections as well:

2014 U.S. Senate, Republican Primary
2020 Auditor, Republican Primary

Using only the American Indian Any definition, the Ecological Inference showed RBV in the following election as well:

2016 President, Republican Primary

The Bivariate Regression also shows RBV in the 2020 U.S. Senate Republican Primary for American Indian Alone and American Indian Any.

Region 5 – City of Great Falls (Little Shell)

Figure 11: Region 5

Region 5 consists of all precincts in Cascade County. Region 5 has a total CVAP of approximately 63,032. The American Indian Alone CVAP is approximately 3,434, or 5.5%, and the American Indian Any CVAP is 4,697, or 7.5% of the total CVAP.

In Region 5, we were not able to perform Homogeneous Precinct Analysis because there were no homogeneous American Indian Alone or American Indian Any precincts, where the American Indian CVAP was 90% or more of the total CVAP.

The Ecological Inference Analysis showed evidence of RBV in the following elections, using both the American Indian Alone and American Indian Any definitions:

2014 U.S. Senate, General Election
2016 President, General Election
2016 Congressional, General Election
2016 Attorney General, General Election
2018 U.S. Senate, General Election
2020 President, General Election
2020 U.S. Senate, General Election
2020 Attorney General, General Election
2020 Attorney General, Democratic Primary
2020 Auditor, General Election
2020 Auditor, Democratic Primary

Additionally, the Ecological Inference showed evidence of RBV in the 2016 Presidential Democratic Primary using American Indian Alone only.

Supporting Appendices

Attached to this report is a folder of Supporting Appendices that has the following structure:

Supporting Appendices

Bivariate Regression and Ecological Inference Analysis – this folder contains all charts as shown in the methodology section for these analysis, where we found evidence of RBV. The charts are broken out by Region.

Region 1

Files ending in ‘Bivariate Plot’ and ‘Bivariate Coefficients’ show the results of the Bivariate Regression Analysis.

Files ending in ‘EI Plot’ and ‘EI Estimates’ show the results of the Ecological Inference Analysis.

G stands for General Election, DemP stands for Democratic Primary, RepP stands for Republican Primary in the file names.

AI stands for American Indian in the file names.

Region 2

Region 3

Region 4

Region 5

Homogeneous Precinct Analysis – this folder contains data files (in CSV format) of the results of the homogeneous precinct analysis for all regions that had precincts that were 90% or more American Indian Any/Alone or White.

G stands for General Election, DemP stands for Democratic Primary, RepP stands for Republican Primary in the file names.

AI stands for American Indian in the file names.