Apportioning Seats in the U.S. House of Representatives Using the 2013 Estimated Citizen Population

October 30, 2015 (R41636)
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Summary

Congressional apportionment is the process of determining the number of Representatives to which each state is entitled in the U.S. House of Representatives based on the decennial census of population. Congressional redistricting, often confused with apportionment, is the process of revising the geographic boundaries of areas from which voters elect Representatives to the House. The apportionment process is a function of four factors: (1) population size, (2) the number of Representatives or seats to be apportioned, (3) the number of states, and (4) the method of apportionment.

Recently, some commentators and Members of Congress have called for a change in the nature of the population used to apportion seats in the U.S. House of Representatives, advocating a change from using all "persons" to using all "citizens." Section 2 of the 14th Amendment to the U.S. Constitution states that "Representatives shall be apportioned among the several States according to their respective numbers, counting the whole number of persons in each State, excluding Indians not taxed." Consequently, such a change would appear to necessitate a constitutional amendment.

This report examines the impact on the apportionment of seats in the House of Representatives if such a change were to occur, using an estimate of the 2013 citizen population in place of the 2010 apportionment population to determine the potential distribution of seats in the House of Representatives for the 114th Congress. In addition, the apportionment of the House of Representatives is shown using an estimate of the 2013 total apportionment population, as well.

If the apportionment of seats in the House of Representatives for the 114th Congress were to be based on the 2013 estimated citizen apportionment population rather than the 2010 total apportionment population, as required by the Constitution, it is estimated that seven seats would shift among 11 states. California would lose four seats relative to its actual distribution of seats as a result of the 2010 apportionment. Texas, Florida, and New York would each lose one seat relative to the number of seats received in the 2010 apportionment.

On the other hand, Louisiana, Missouri, Montana, North Carolina, Ohio, Oklahoma, and Virginia would each pick up a single seat, if the 2013 citizen population were used to apportion seats rather than the 2010 total apportionment population. Using citizenship status to apportion the seats in the U.S. House of Representatives tends to benefit states with smaller immigrant populations and cost states with larger immigrant populations.

For those seeking to change the current population standard for apportioning the seats in the House of Representatives, there appears to be at least three possible choices. First, and most obvious, amend the U.S. Constitution. Second, use the citizen population in the redistricting process to geographically define the congressional districts. Or third, change the apportionment law to adopt an apportionment formula that, when used with the total population, mimics the apportionment distribution that occurs when using the citizen population.


Apportioning Seats in the U.S. House of Representatives Using the 2013 Estimated Citizen Population

Introduction and Background

How seats in the U.S. House of Representatives are apportioned among the states is determined, in part, by the U.S. Constitution and, in part, by federal legislation and legal determinations by the courts. The U.S. Constitution determines the maximum and minimum size of the House of Representatives as well as the nature of the population upon which any apportionment is determined.

Historically, the issue of the nature of the population upon which the apportionment of the House of Representatives is based has been raised periodically. Rather than "persons," as is required in the Constitution, historical proposals have advocated "free Citizens," "legal voters," "male citizens," "the voting population," "citizens," or "exclude aliens" as the basis for the apportionment population.1

From time to time, commentators and Members of Congress raise the issue, proposing to change the population upon which the apportionment of House seats is based from "persons" to "citizens."2 This report examines the impact on the apportionment of seats in the House of Representatives if such a change were to occur, using an estimate of the 2013 citizen population in place of the 2010 apportionment population to determine the distribution of seats in the House of Representatives for the 114th Congress. In addition, the apportionment of seats in the 114th Congress is shown using an estimate of the 2013 total apportionment population as well.

Constitutional Issue

According to Section 2 of the 14th Amendment to the U.S. Constitution,

Representatives shall be apportioned among the several States according to their respective numbers, counting the whole number of persons in each State, excluding Indians not taxed. But when the right to vote at any election for the choice of electors for President and Vice President of the United States, Representatives in Congress, the Executive and Judicial officers of a State, or the members of the Legislature thereof, is denied to any of the male inhabitants of such State, being twenty-one years of age, and citizens of the United States, or in any way abridged, except for participation in rebellion, or other crime, the basis of representation therein shall be reduced in the proportion which the number of such male citizens shall bear to the whole number of male citizens twenty-one years of age in such State. (Emphasis added.)

As stated, it has been taken to mean that the apportionment population is all persons residing in the United States. As Section 1 of this same amendment defines U.S. citizenship, the use of the term "persons" rather than "citizens" has not been taken to be an oversight by most.3 Thus, changing the meaning of population in the apportionment process is, most likely, going to require a constitutional amendment.4

Practicalities

The U.S. Supreme Court has held that, for purposes of apportionment only, actual population counts from the census must be used. Population estimates based on sample surveys cannot be used to apportion the seats in the House of Representatives.5

Currently, as will be highlighted below, the only source for information on citizenship status is the U.S. Census Bureau's American Community Survey (ACS), a sample survey.6 Even assuming that a constitutional amendment were to be passed and ratified by the required number of states relatively quickly, without the Census Bureau conducting a special census prior to the scheduled 2020 census, the earliest that another apportionment using citizenship status information is likely to occur is 2020. If such an amendment were to be passed, presumably the Census Bureau would ask a question about citizenship status of all persons in the 2020 census.

Recent Congressional Interest7

While no legislation has been introduced in the most recent congresses, in the 111th Congress, Representative Candice Miller introduced H.J.Res. 11, a constitutional amendment that provided for the apportionment of seats in the House of Representatives based on the citizen population rather than total population.8 The proposed amendment had 28 cosponsors.

At the same time, Representative Virginia Foxx and Senator Robert F. Bennett introduced The Fairness in Representation Act (H.R. 3797/S. 1688). The proposed legislation would have amended Title 13 to require that the Census Bureau include on the 2010 census questionnaire "a checkbox or other similar option for respondents to indicate citizenship status or lawful presence in the United States." The proposed legislation further required that the Secretary of Commerce adjust the total population figures to assure that only the citizen population was used in apportioning seats to the House of Representatives.

In addition, Senator David Vitter introduced an amendment to the Commerce, Justice, Science, and Related Agencies Appropriations Act, 2010 (S.Amdt. 2635 to H.R. 2847). The amendment stated, in part, that "none of the funds provided in this Act or any other act for any fiscal year may be used for collection of census data that does not include questions regarding United States citizenship and immigration status." The amendment was subsequently ruled non-germane.

Prior to the 2010 Census, in the 111th Congress, there was also opposition to the idea of restricting the apportionment based on the citizen population. Representative Joe Baca introduced the Every Person Counts Act (H.R. 3855). This bill would have prevented the Census Bureau from collecting information about U.S. citizenship or immigration status in any census.

None of the above legislation came to a vote.

Potential Impact of Using the Citizen Population to Apportion Seats in the House

In the 1990 and 2000 censuses, estimates of citizenship status were derived from the results of such questions on the "long-form" questionnaires. Both censuses included two types of questionnaires, a "short-form" questionnaire, which included a few basic questions on age, sex , race, and Hispanic heritage, and a "long-form" questionnaire, which included all of the questions from the short form and a large number of other demographic questions, including citizenship status. The long-form questionnaire was sent to a probability sample of about one-sixth of the U.S. households. The other five-sixths received only the short form. For the 1990 and 2000 censuses, the information derived from the short form and the long form constituted the results of the census.

Plans for the 2010 census were dramatically different. While the basic set of similar questions from the short form would again be posed on the census questionnaire going to the American public on April 1, 2010, there would be no comparable long form sent out at the same time. Rather, the information originally collected on the long form now would be collected by the American Community Survey (ACS), a cumulative, rolling sample survey that would collect, starting in 2006, the same or similar information collected in the previous long form used in the 1990 and 2000 censuses.9 Again, like in 1990 and 2000, the ACS is the only source for geographically detailed information about citizenship status.

Estimating the Total and Citizen Apportionment Populations

Limitations and Caveats

Citizenship Verification

Counts of citizens derived from the ACS are based on how respondents answered a question related to their citizenship status.10 The Census Bureau does nothing to attempt to verify whether or not the person responding is or is not a citizen by asking for legal documentation that could establish this fact. And, unless such a request was required by law, it is very doubtful that, even if the Census Bureau were to include such a question on the 2020 Census form, they would attempt such legal verification.

Residence Measurement

Partly because the census collects information on the population for purposes of apportionment, the concept of "usual residence," as measured by the census is meant to measure the legal address of the respondent as of census day (i.e., a de jure measure). On the other hand, the concept of residence as used by the ACS is better described as "where the respondent is residing when he or she completes the questionnaire" (i.e., a de facto measure).11 Using the result of a survey that defines residence in one way to estimate a number for the census, which defines residence in a different way, may not prove meaningful. If one were to ask the citizenship status question on the census questionnaire, it is possible that there would be differences in the results (specifically where geographically the counts were to apply) and estimates based on the ACS might be due to differences in the definition of residence used by each survey. The possible impact of this difference on estimating the total 2013 citizen population from the ACS, is difficult, if not impossible, to gauge.

Sampling Error

The results of the decennial census are based on an attempt to count every person residing in the United States. The ACS, unlike this 100 percent count, is based on a sample survey, albeit a large sample survey. Sample surveys are, unlike censuses, subject to sampling error. Therefore, any estimate of the 2013 citizen population based on the ACS is also subject to sampling error. These sampling errors have been calculated and are shown in the tables below.

Different Time Frames between the Census and the ACS

While the decennial census figures are mythically based on the concept of collecting the information on a single day, April 1, 2010, in fact, the information for the census is collected over many months. However, this information, whether collected on April 1, or on September 9, 2010, refers back to the single date of April 1, 2010. This is the reference date for census data.12

The ACS, on the other hand, is designed very differently.13 Each month, a new, large sample of households (about 250,000) is mailed the ACS questionnaire. Over the course of a full year, about 3 million households receive the ACS questionnaires. Yearly estimates (ACS-1Yr)—the most frequently published figures—are based on the accumulated results from samples over the whole year—accurate for geographical areas containing populations of 65,000 persons or more. Thus, the time frame for the ACS-1Yr is not a single day, but a year of monthly household samples. Rather than a specific time reference as with the census, ACS results are analogous to a yearly average. Consequently, the time reference for the information collected differs between the 2010 census and the ACS. Like the difference with respect to residency, the possible impact of the difference in time references between the two sets of information on any estimated figures is difficult to gauge.

Estimation Method Used to Estimate the 2013 Total and Citizen Apportionment Population

Since 1970, with one exception, the apportionment population for each state has consisted of two components: (1) the state's resident population; and (2) the overseas military and civilian federal employee population and their dependents living with them.14

2010 Apportionment Population and Its Components

Table 1 shows this information for each state for the 2010 apportionment population. In addition, the ratio of the overseas population to the residential population in 2010 is calculated for each state. This ratio subsequently will be used to estimate the size of the overseas military and civilian federal employee population for 2013, under the assumption that the actual ratio calculated on the basis of the 2010 Census is the same as the ratio would be if one were to use the actual 2013 resident population and the actual 2013 overseas military and civilian federal employee population.

Table 1. 2010 Apportionment Population and Components

 

2010 Apportionment Population a

 

State

Total

Resident Population

U.S. Overseas Population

Ratio of Overseas to Resident Pop.

Alabama

4,802,982

4,779,736

23,246

0.004863449

Alaska

721,523

710,231

11,292

0.015899053

Arizona

6,412,700

6,392,017

20,683

0.003235755

Arkansas

2,926,229

2,915,918

10,311

0.003536108

California

37,341,989

37,253,956

88,033

0.002363051

Colorado

5,044,930

5,029,196

15,734

0.003128532

Connecticut

3,581,628

3,574,097

7,531

0.002107106

Delaware

900,877

897,934

2,943

0.003277524

Florida

18,900,773

18,801,310

99,463

0.005290216

Georgia

9,727,566

9,687,653

39,913

0.004119987

Hawaii

1,366,862

1,360,301

6,561

0.004823197

Idaho

1,573,499

1,567,582

5,917

0.003774603

Illinois

12,864,380

12,830,632

33,748

0.002630268

Indiana

6,501,582

6,483,802

17,780

0.002742218

Iowa

3,053,787

3,046,355

7,432

0.002439637

Kansas

2,863,813

2,853,118

10,695

0.003748531

Kentucky

4,350,606

4,339,367

11,239

0.002590009

Louisiana

4,553,962

4,533,372

20,590

0.004541873

Maine

1,333,074

1,328,361

4,713

0.003547981

Maryland

5,789,929

5,773,552

16,377

0.002836555

Massachusetts

6,559,644

6,547,629

12,015

0.001835015

Michigan

9,911,626

9,883,640

27,986

0.002831548

Minnesota

5,314,879

5,303,925

10,954

0.002065263

Mississippi

2,978,240

2,967,297

10,943

0.003687868

Missouri

6,011,478

5,988,927

22,551

0.003765449

Montana

994,416

989,415

5,001

0.005054502

Nebraska

1,831,825

1,826,341

5,484

0.003002725

Nevada

2,709,432

2,700,551

8,881

0.003288588

New Hampshire

1,321,445

1,316,470

4,975

0.003779045

New Jersey

8,807,501

8,791,894

15,607

0.001775158

New Mexico

2,067,273

2,059,179

8,094

0.003930693

New York

19,421,055

19,378,102

42,953

0.002216574

North Carolina

9,565,781

9,535,483

30,298

0.003177395

North Dakota

675,905

672,591

3,314

0.004927214

Ohio

11,568,495

11,536,504

31,991

0.002773024

Oklahoma

3,764,882

3,751,351

13,531

0.003606967

Oregon

3,848,606

3,831,074

17,532

0.004576262

Pennsylvania

12,734,905

12,702,379

32,526

0.002560623

Rhode Island

1,055,247

1,052,567

2,680

0.002546156

South Carolina

4,645,975

4,625,364

20,611

0.004456082

South Dakota

819,761

814,180

5,581

0.00685475

Tennessee

6,375,431

6,346,105

29,326

0.004621102

Texas

25,268,418

25,145,561

122,857

0.004885833

Utah

2,770,765

2,763,885

6,880

0.00248925

Vermont

630,337

625,741

4,596

0.007344892

Virginia

8,037,736

8,001,024

36,712

0.004588413

Washington

6,753,369

6,724,540

28,829

0.004287133

West Virginia

1,859,815

1,852,994

6,821

0.00368107

Wisconsin

5,698,230

5,686,986

11,244

0.001977146

Wyoming

568,300

563,626

4,674

0.008292733

Total

309,183,463

308,143,815

1,039,648

 

Source: U.S. Census Bureau, 2010 Census at http://www.census.gov/population/apportionment/data.

Note:

a. Includes the resident population for the 50 states, as ascertained by the 2010 Census under Title 13, U.S. Code, and counts of overseas U.S. military and federal civilian employees (and their dependents living with them) allocated to their home state, as reported by the employing federal agencies. The apportionment population does not include the resident or the overseas population of the District of Columbia.

The values in columns 2-4 in Table 1 were the population values used in determining the allocation of seats in the U.S. House of Representatives to the states for the 2012 apportionment process, which produced the seat distribution in the U.S. House of Representatives for the 113th Congress. Column 5, labelled "Ratio of Overseas to Resident Pop.," subsequently will be used to estimate the 2013 overseas population by multiplying this ratio by the 2013 estimated resident population.

Estimating the 2013 Apportionment Population

Table 2 shows the process of estimating the 2013 apportionment population for each of the states. The U.S. Census Bureau, using a demographic methodology referred to as a "cohort components method,"15 estimates the resident population of the United States, the states, the counties, and Puerto Rico every year between censuses.16

Column 3, labelled "Resident Population Estimate (as of July 1, 2013)," shows the U.S. Census Bureau's state population estimates as of July 1, 2013.17 Column 4 displays the "2010 Ratio of Overseas to Resident Pop.," computed in Table 1. Multiplying this ratio by the estimated 2013 resident population produces estimates of the 2013 overseas population for each state, shown in column 5. Adding the 2013 estimated resident population to the 2013 estimated overseas population produces the 2013 estimated apportionment population, shown in column 2.

Table 2. 2013 Estimated Apportionment Population by States

State

2013 Apportionment Population, Estimated a

Resident Population Estimate (as of July 1, 2013) b

2010 Ratio of Overseas to Resident Pop. c

2013 Overseas Population Estimate d

Alabama

4,857,506

4,833,996

0.004863449

23,510

Alaska

748,981

737,259

0.015899053

11,722

Arizona

6,656,466

6,634,997

0.003235755

21,469

Arkansas

2,969,228

2,958,765

0.003536108

10,463

California

38,522,208

38,431,393

0.002363051

90,815

Colorado

5,288,580

5,272,086

0.003128532

16,494

Connecticut

3,606,925

3,599,341

0.002107106

7,584

Delaware

928,272

925,240

0.003277524

3,032

Florida

19,704,001

19,600,311

0.005290216

103,690

Georgia

10,035,937

9,994,759

0.004119987

41,178

Hawaii

1,415,783

1,408,987

0.004823197

6,796

Idaho

1,618,931

1,612,843

0.003774603

6,088

Illinois

12,924,458

12,890,552

0.002630268

33,906

Indiana

6,588,731

6,570,713

0.002742218

18,018

Iowa

3,099,885

3,092,341

0.002439637

7,544

Kansas

2,906,656

2,895,801

0.003748531

10,855

Kentucky

4,410,978

4,399,583

0.002590009

11,395

Louisiana

4,650,310

4,629,284

0.004541873

21,026

Maine

1,333,416

1,328,702

0.003547981

4,714

Maryland

5,955,583

5,938,737

0.002836555

16,846

Massachusetts

6,721,185

6,708,874

0.001835015

12,311

Michigan

9,926,220

9,898,193

0.002831548

28,027

Minnesota

5,433,258

5,422,060

0.002065263

11,198

Mississippi

3,003,241

2,992,206

0.003687868

11,035

Missouri

6,067,679

6,044,917

0.003765449

22,762

Montana

1,019,994

1,014,864

0.005054502

5,130

Nebraska

1,874,581

1,868,969

0.003002725

5,612

Nevada

2,800,674

2,791,494

0.003288588

9,180

New Hampshire

1,327,614

1,322,616

0.003779045

4,998

New Jersey

8,927,321

8,911,502

0.001775158

15,819

New Mexico

2,095,098

2,086,895

0.003930693

8,203

New York

19,739,337

19,695,680

0.002216574

43,657

North Carolina

9,880,211

9,848,917

0.003177395

31,294

North Dakota

727,424

723,857

0.004927214

3,567

Ohio

11,604,094

11,572,005

0.002773024

32,089

Oklahoma

3,867,016

3,853,118

0.003606967

13,898

Oregon

3,946,044

3,928,068

0.004576262

17,976

Pennsylvania

12,814,024

12,781,296

0.002560623

32,728

Rhode Island

1,056,036

1,053,354

0.002546156

2,682

South Carolina

4,793,193

4,771,929

0.004456082

21,264

South Dakota

851,306

845,510

0.006854750

5,796

Tennessee

6,527,294

6,497,269

0.004621102

30,025

Texas

26,635,139

26,505,637

0.004885833

129,502

Utah

2,910,013

2,902,787

0.002489250

7,226

Vermont

631,459

626,855

0.007344892

4,604

Virginia

8,308,293

8,270,345

0.004588413

37,948

Washington

7,003,639

6,973,742

0.004287133

29,897

West Virginia

1,860,418

1,853,595

0.003681070

6,823

Wisconsin

5,754,308

5,742,953

0.001977146

11,355

Wyoming

588,060

583,223

0.008292733

4,837

Total

316,917,008

315,848,420

 

1,068,588

Source: Derived by CRS from 2010 Apportionment Population, U. S. Census Bureau, and Resident Population Estimates, 2010-2014, U.S. Census Bureau. U.S. Census Bureau, Population Division, "Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2014," May 2015.

Notes:

a. 2013 apportionment population consists of U.S. resident population as of July 1, 2013 plus the estimated 2013 overseas U.S. populations (i.e., sum of values in columns 3 and 5 for each state).

b. U.S. Census Bureau, Population Division, "Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2014," release date: May 2015.

c. Ratio computed using 2010 resident population and 2010 overseas population in Table 1.

d. 2013 overseas population estimate is based on multiplying the ratio of the 2010 overseas population to the 2010 resident population, derived from the 2010 census, by the July 1, 2013 U.S. Census Bureau resident population estimate. This implies that the distribution of the 2013 overseas population is distributed among the states as it was in 2010.

2013 American Community Survey Citizenship Status

Table 3 below displays the results from the "citizenship" question posed in the 2013 American Community Survey (ACS) for each state. In addition, as the results are based on a sample survey, each estimate is associated with a measurement of error (MoE).18 By adding or subtracting the value of the associated MoE to the estimate, one calculates the upper and lower bounds for that estimated value at the 90% confidence level.

According to the documentation for the 2013 ACS, citizenship status/U.S. citizenship status was defined in the following way:

The data on citizenship status were derived from answers to Question 8 in the 2013 American Community Survey (ACS). This question was asked about Persons 1 through 5 in the ACS.

Respondents were asked to select one of five categories: (1) born in the United States, (2) born in Puerto Rico, Guam, the U.S. Virgin Islands, or Northern Marianas, (3) born abroad of U.S. citizen parent or parents, (4) U.S. citizen by naturalization, or (5) not a U.S citizen. Respondents indicating they are a U.S. citizen by naturalization also are asked to print their year of naturalization. People born in American Samoa, although not explicitly listed, are included in the second response category.

For the Puerto Rico Community Survey, respondents were asked to select one of five categories: (1) born in Puerto Rico, (2) born in a U.S. state, District of Columbia, Guam, the U.S. Virgin Islands, or Northern Marianas, (3) born abroad of U.S. citizen parent or parents, (4) U.S. citizen by naturalization, or (5) not a U.S. citizen. Respondents indicating they are a U.S. citizen by naturalization also are asked to print their year of naturalization. People born in American Samoa, although not explicitly listed, are included in the second response category.

When no information on citizenship status was reported for a person, information for other household members, if available, was used to assign a citizenship status to the respondent.19

Table 3. 2013 American Community Survey (ACS), Citizen Population Estimates with 90% Measurement of Errors (MoE90)

 

Total U.S. population

U.S. citizen, born in United States

U.S. citizen, born in Puerto Rico or U.S. island areas

U.S. citizen, born abroad of American parent(s)

U.S. citizen by naturalization

Not a U.S. citizen

State

Estimate

MoE

Estimate

MoE90a

Estimate

MoE90a

Estimate

MoE90a

Estimate

MoE90a

Estimate

MoE90a

AL

4,833,722

*****

4,631,111

8,249

6,570

1,762

33,815

3,628

59,782

4,481

102,444

6,739

AK

735,132

*****

668,628

4,080

4,433

1,316

11,311

2,074

28,509

2,559

22,251

3,384

AZ

6,626,624

*****

5,649,584

18,091

14,163

2,665

66,567

4,612

342,265

11,609

554,045

17,068

AR

2,959,373

*****

2,804,722

6,755

3,099

1,285

17,664

2,799

43,677

4,184

90,211

5,360

CA

38,332,521

*****

27,543,007

52,221

79,653

5,398

398,661

9,591

5,006,979

29,801

5,304,221

48,531

CO

5,268,367

*****

4,693,854

12,075

7,168

1,329

66,711

4,468

197,600

7,513

303,034

10,144

CT

3,596,080

*****

2,971,430

12,464

88,069

5,401

36,938

3,254

244,730

7,996

254,913

11,997

DE

925,749

*****

833,503

4,459

9,674

2,114

5,804

1,243

34,625

3,031

42,143

3,706

FL

19,552,860

*****

15,085,372

35,380

449,721

16,458

219,705

9,889

2,028,738

27,417

1,769,324

28,881

GA

9,992,167

*****

8,891,411

18,901

36,621

4,973

93,156

5,844

375,460

10,785

595,519

16,129

HI

1,404,054

*****

1,118,050

10,918

12,466

2,480

27,074

2,984

139,732

6,178

106,732

7,673

ID

1,612,136

*****

1,502,000

6,361

1,532

867

13,079

2,360

35,903

3,561

59,622

4,402

IL

12,882,135

*****

10,943,606

23,904

50,918

4,405

80,143

5,403

852,962

17,158

954,506

21,734

IN

6,570,902

*****

6,212,385

10,199

10,692

2,655

33,324

2,976

110,657

5,837

203,844

9,173

IA

3,090,416

*****

2,925,682

7,387

2,118

777

13,494

2,126

55,195

4,323

93,927

6,079

KS

2,893,957

*****

2,674,173

8,174

2,485

900

19,126

2,179

66,850

4,793

131,323

7,374

KY

4,395,295

*****

4,216,441

7,527

4,924

1,666

24,914

2,691

56,085

4,121

92,931

5,579

LA

4,625,470

*****

4,412,731

7,904

6,321

1,651

23,859

2,803

76,033

4,780

106,526

6,599

ME

1,328,302

*****

1,269,681

3,487

1,078

480

12,856

1,541

25,351

2,768

19,336

2,805

MD

5,928,814

*****

5,000,878

17,115

18,442

3,266

67,244

4,092

420,344

11,398

421,906

11,434

MA

6,692,824

*****

5,475,165

18,340

113,620

6,250

57,884

3,661

549,009

14,653

497,146

14,628

MI

9,895,622

*****

9,206,167

12,843

11,334

2,117

61,335

4,192

315,064

9,394

301,722

10,578

MN

5,420,380

*****

4,978,189

9,717

5,003

1,604

33,674

2,719

207,945

8,017

195,569

8,357

MS

2,991,207

*****

2,908,659

5,986

5,292

1,290

14,357

2,748

24,044

2,757

38,855

4,889

MO

6,044,171

*****

5,772,869

9,174

5,394

1,954

32,647

3,444

105,387

5,456

127,874

7,704

MT

1,015,165

*****

987,034

2,902

615

427

8,051

1,564

11,127

1,756

8,338

1,402

NE

1,868,516

*****

1,730,401

5,014

1,441

631

13,492

1,657

41,774

3,508

81,408

5,157

NV

2,790,136

*****

2,215,002

10,319

11,839

2,452

34,131

4,494

250,949

7,732

278,215

9,255

NH

1,323,459

*****

1,234,128

4,865

3,966

1,460

10,190

1,751

40,448

3,147

34,727

4,082

NJ

8,899,339

*****

6,753,607

22,017

138,987

7,594

80,972

5,130

1,021,084

17,262

904,689

19,525

NM

2,085,287

*****

1,849,232

10,862

3,687

1,244

21,119

2,432

72,651

4,458

138,598

9,193

NY

19,651,127

*****

14,798,608

33,209

296,387

10,788

172,821

8,545

2,359,247

27,804

2,024,064

29,025

NC

9,848,060

*****

8,989,881

12,867

30,673

4,460

78,080

4,907

239,232

9,499

510,194

13,191

ND

723,393

*****

695,779

2,852

207

181

6,300

1,638

6,548

1,418

14,559

2,288

OH

11,570,808

*****

11,003,182

15,677

33,237

3,772

57,052

5,042

237,404

9,075

239,933

11,011

OK

3,850,568

*****

3,596,428

6,828

5,043

1,496

30,665

2,712

76,353

3,959

142,079

5,755

OR

3,930,065

*****

3,496,761

10,453

4,974

1,475

37,124

3,163

155,415

6,872

235,791

10,303

PA

12,773,801

*****

11,768,250

19,416

140,784

7,773

68,608

4,700

410,524

11,354

385,635

14,764

RI

1,051,511

*****

891,444

6,797

14,424

2,412

9,671

1,791

69,709

4,196

66,263

5,347

SC

4,774,839

*****

4,491,687

9,702

12,465

1,921

39,409

4,124

89,661

5,397

141,617

7,537

SD

844,877

*****

816,396

3,194

78

127

3,944

972

8,035

1,593

16,424

2,611

TN

6,495,978

*****

6,137,131

11,202

9,110

1,768

44,936

4,554

114,362

6,811

190,439

8,525

TX

26,448,193

*****

21,717,032

35,268

78,803

7,972

283,087

11,815

1,491,058

22,794

2,878,213

37,483

UT

2,900,872

*****

2,634,377

10,880

3,616

1,547

25,925

2,868

88,045

5,601

148,909

9,201

VT

626,630

*****

594,234

2,480

277

180

5,107

911

15,904

1,783

11,108

2,020

VA

8,260,405

*****

7,169,317

15,915

30,964

3,709

111,161

4,765

477,236

11,110

471,727

15,730

WA

6,971,406

*****

5,911,639

16,825

22,077

2,942

94,026

6,306

436,834

12,244

506,830

13,390

WV

1,854,304

*****

1,818,241

2,935

1,977

793

7,765

1,376

13,343

1,806

12,978

2,173

WI

5,742,713

*****

5,423,701

8,914

16,507

2,973

27,818

2,690

119,720

4,926

154,967

8,161

WY

582,658

*****

560,963

2,348

358

265

3,308

912

7,083

1,291

10,946

1,829

Source: U.S. Census Bureau, American Factfinder (http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml), American Community Survey (http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t). Select : Topics = People, Origin, Citizenship; Geographies = States, All states plus PR. This action produces Table ID B05001, ACS 2013 1-year estimates—file, ACS_13_1YR_B05001_with_ann.csv. When this .csv file is converted to an Excel .xlsx file, it equals the table, above. The values for Puerto Rico and the District of Columbia have been removed.

a. The measurement of error at the 90% confidence level. See the Appendix for a discussion.

Summing the estimated values in columns 4, 6, 8, and 10 from Table 3, one arrives at the 2013 estimated total resident citizen population based on the 2013 ACS survey. This sum is displayed in column 6 in Table 4, below. The calculation of the associated Margin of Error at the 95% confidence level (MoE95) for this calculated sum, and consequently, the upper and lower bound population estimates is discussed in the Appendix, and the MoE95 for the resident citizen population is shown in Table A-2.

Estimating the ACS 2013 Citizen Apportionment Population

Table 4 displays the 2013 ACS resident citizen population estimate and the upper and lower bound populations of that estimate for each state (columns 6, 7, and 8). In addition, column 5 displays the estimated 2013 overseas population initially calculated in Table 2 for each state. The apportionment population is the resident population plus the overseas population as defined by the U.S. Census Bureau. Consequently, summing the estimated 2013 overseas population with each of the resident citizen population values shown in columns 6, 7 and 8 above produces the matching apportionment population estimates as shown in columns 2, 3 and 4 in Table 4 below. The value in column 2, the 2013 citizen apportionment population estimate, is the state values used in this report to calculate the apportionment of seats for the U.S. House of Representatives.20

Table 4. 2013 Citizen Apportionment Estimates with 95%
Upper and Lower Error Bounds

Derived from 2013 American Community Survey (ACS)

 

2013 Citizen Apportionment Population

 

2013 ACS Resident Citizen Population

State

Estimateb

95% Upper Boundc

95% Lower Boundd

2013 Overseas Population Estimatea

Estimatee

95% Upper Boundf

95% Lower Boundf

Alabama

4,754,788

4,766,962

4,742,614

23,510

4,731,278

4,743,452

4,719,104

Alaska

724,603

731,045

718,161

11,722

712,881

719,323

706,439

Arizona

6,094,048

6,120,434

6,067,662

21,469

6,072,579

6,098,965

6,046,193

Arkansas

2,879,625

2,889,779

2,869,471

10,463

2,869,162

2,879,316

2,859,008

California

33,119,115

33,191,945

33,046,285

90,815

33,028,300

33,101,130

32,955,470

Colorado

4,981,827

4,999,659

4,963,995

16,494

4,965,333

4,983,165

4,947,501

Connecticut

3,348,751

3,367,928

3,329,574

7,584

3,341,167

3,360,344

3,321,990

Delaware

886,638

893,695

879,581

3,032

883,606

890,663

876,549

Florida

17,887,226

17,945,256

17,829,196

103,690

17,783,536

17,841,566

17,725,506

Georgia

9,437,826

9,465,319

9,410,333

41,178

9,396,648

9,424,141

9,369,155

Hawaii

1,304,118

1,319,764

1,288,472

6,796

1,297,322

1,312,968

1,281,676

Idaho

1,558,602

1,567,790

1,549,414

6,088

1,552,514

1,561,702

1,543,326

Illinois

11,961,535

11,997,564

11,925,506

33,906

11,927,629

11,963,658

11,891,600

Indiana

6,385,076

6,399,862

6,370,290

18,018

6,367,058

6,381,844

6,352,272

Iowa

3,004,033

3,014,582

2,993,484

7,544

2,996,489

3,007,038

2,985,940

Kansas

2,773,489

2,785,123

2,761,855

10,855

2,762,634

2,774,268

2,751,000

Kentucky

4,313,759

4,324,657

4,302,861

11,395

4,302,364

4,313,262

4,291,466

Louisiana

4,539,970

4,551,638

4,528,302

21,026

4,518,944

4,530,612

4,507,276

Maine

1,313,680

1,319,322

1,308,038

4,714

1,308,966

1,314,608

1,303,324

Maryland

5,523,754

5,549,036

5,498,472

16,846

5,506,908

5,532,190

5,481,626

Massachusetts

6,207,989

6,237,260

6,178,718

12,311

6,195,678

6,224,949

6,166,407

Michigan

9,621,927

9,641,694

9,602,160

28,027

9,593,900

9,613,667

9,574,133

Minnesota

5,236,009

5,251,483

5,220,535

11,198

5,224,811

5,240,285

5,209,337

Mississippi

2,963,387

2,972,032

2,954,742

11,035

2,952,352

2,960,997

2,943,707

Missouri

5,939,059

5,952,624

5,925,494

22,762

5,916,297

5,929,862

5,902,732

Montana

1,011,957

1,016,436

1,007,478

5,130

1,006,827

1,011,306

1,002,348

Nebraska

1,792,720

1,800,311

1,785,129

5,612

1,787,108

1,794,699

1,779,517

Nevada

2,521,101

2,537,631

2,504,571

9,180

2,511,921

2,528,451

2,495,391

New Hampshire

1,293,730

1,301,149

1,286,311

4,998

1,288,732

1,296,151

1,281,313

New Jersey

8,010,469

8,045,546

7,975,392

15,819

7,994,650

8,029,727

7,959,573

New Mexico

1,954,892

1,969,255

1,940,529

8,203

1,946,689

1,961,052

1,932,326

New York

17,670,720

17,724,868

17,616,572

43,657

17,627,063

17,681,211

17,572,915

North Carolina

9,369,160

9,389,789

9,348,531

31,294

9,337,866

9,358,495

9,317,237

North Dakota

712,401

716,674

708,128

3,567

708,834

713,107

704,561

Ohio

11,362,964

11,385,814

11,340,114

32,089

11,330,875

11,353,725

11,308,025

Oklahoma

3,722,387

3,732,489

3,712,285

13,898

3,708,489

3,718,591

3,698,387

Oregon

3,712,250

3,727,724

3,696,776

17,976

3,694,274

3,709,748

3,678,800

Pennsylvania

12,420,894

12,449,796

12,391,992

32,728

12,388,166

12,417,068

12,359,264

Rhode Island

987,930

998,098

977,762

2,682

985,248

995,416

975,080

South Carolina

4,654,486

4,668,782

4,640,190

21,264

4,633,222

4,647,518

4,618,926

South Dakota

834,249

838,659

829,839

5,796

828,453

832,863

824,043

Tennessee

6,335,564

6,352,234

6,318,894

30,025

6,305,539

6,322,209

6,288,869

Texas

23,699,482

23,752,319

23,646,645

129,502

23,569,980

23,622,817

23,517,143

Utah

2,759,189

2,774,277

2,744,101

7,226

2,751,963

2,767,051

2,736,875

Vermont

620,126

623,930

616,322

4,604

615,522

619,326

611,718

Virginia

7,826,626

7,850,845

7,802,407

37,948

7,788,678

7,812,897

7,764,459

Washington

6,494,473

6,520,616

6,468,330

29,897

6,464,576

6,490,719

6,438,433

West Virginia

1,848,149

1,852,670

1,843,628

6,823

1,841,326

1,845,847

1,836,805

Wisconsin

5,599,101

5,612,142

5,586,060

11,355

5,587,746

5,600,787

5,574,705

Wyoming

576,549

579,936

573,162

4,837

571,712

575,099

568,325

Source: Calculated by CRS from values in Table 2 and Table 3.

Notes:

a. See Table 2, column 5. For an explanation of why this value is used here, see footnote 14, above.

b. For each state, the sum of the value in column 6, the 2013 resident citizen population estimate, and the value in column 5, the 2013 overseas military and civilian federal employee population estimate.

c. For each state, the sum of the value in column 7, the 95% upper bound of the 2013 resident citizen population estimate (based on adding the MoE95 value to the 2013 resident citizen population estimate) and the value in column 5, the 2013 overseas military and civilian federal employee population estimate. For the value of MoE95, see Table A-2.

d. For each state, the sum of the value in column 8, the 95% lower bound of the 2013 resident citizen population estimate (based on subtracting the MoE95 value from the 2013 resident citizen population estimate) and the value in column 5, the 2013 overseas military and civilian federal employee population estimate. For the value of MoE95, see Table A-2.

e. For each state, the value is the sum of the counts for the different types of citizen populations shown in Table 3, above, columns 4, 6, 8, and 10 and in Table A-1.

f. For each state, the value shown constitutes either the addition to (column 7) or the subtraction from (column 8) of the MoE95 (the margin of error for the resident citizen population, MoECIT) for the sum of the counts for the different types of citizen populations shown in Table 3, above, columns 4, 6, 8, and 10 and in Table A-1, to the 2013 resident citizen population estimate, (column 6). The calculation of the MoE95 for the sum of the counts, often referred to as the square root of the sum of squared errors, is shown in Table A-2, and described in the Appendix.

Apportioning Seats to the House of Representatives Using Citizen Population Estimates

If the citizen population had been the basis of apportioning the seats in the House of Representatives after the 2000 census, it was estimated that nine seats would have shifted among 13 states relative to the actual apportionment.21 California would have received six fewer Representatives than it actually did. Florida and Texas, scheduled to receive two additional seats, each would have lost one of those two seats. New York, scheduled to lose two seats in the 2000 apportionment, would lose an additional seat if the 2000 citizen population had been used to apportion the seats in the House. And nine states would have gained one more Representatives than they actually received in the 2000 apportionment.

Columns 2 and 3 in Table 5 display the actual 2010 apportionment population, as well as the 2012 apportionment of seats in the U.S. House of Representatives (i.e., the current apportionment of seats).

Column 4 shows the 2013 total apportionment population estimate based upon the 2013 total state resident populations calculated in Table 2. Based on this estimated population, if an apportionment of the seats in the U.S. House of Representatives were to be conducted today, the distribution of seats among the states would be that shown in column 5 of Table 5. As can be seen in column 6, to the extent the estimated 2013 population reflects population changes among the states, then it would appear that Minnesota would lose a seat and North Carolina would gain a seat in an apportionment today, relative to the actual apportionment based on the 2010 population.

Column 7 displays the estimated state citizen population for 2013 as derived in Table 3. If the apportionment of the seats of the U.S. House of Representatives was to be conducted today, and, was based on the estimated 2013 population of U.S. citizens in each state, then the distribution of House seats among the states would be that shown in column 8 of Table 5. As can be seen in column 9, to the extent the 2013 citizen population estimate is an accurate representation of the citizen population in the states, the distribution of seats in the House based on that population would create a 7 seat change affecting 11 states, relative to the actual 2010 seat distribution among the states.

California would lose 4 seats, and Florida, New York, and Texas would each lose 1 seat. On the other hand, Louisiana, Missouri, Montana, North Carolina, Ohio, Oklahoma, and Virginia would each pick up a single seat, if the estimated 2013 citizen population were used to apportion seats today rather than the 2010 census population.22

Table 5. Impact of Apportioning Seats in the House of Representatives Using the Estimated 2013 Total and Citizen Population

 

2012 Actual Apportionment

2013 Apportionment

 

2013 Apportionment

 

State

2010 Census Apportion-ment Pop. a

Actual House Seats

2013 Total Apportion-ment Pop. Estimate b

House Seats

SEAT DIFFER-ENCE: Actual 2012 vs. 2013 based on Total Pop.

2013 Citizen Apportion-ment Pop. Estimate c

House Seats

SEAT DIFFER-ENCE: Actual 2012 vs. 2013 based on Citizen Pop.

AL

4,802,982

7

4,857,506

7

0

4,754,788

7

0

AK

721,523

1

748,981

1

0

724,603

1

0

AZ

6,412,700

9

6,656,466

9

0

6,094,048

9

0

AR

2,926,229

4

2,969,228

4

0

2,879,625

4

0

CA

37,341,989

53

38,522,208

53

0

33,119,115

49

-4

CO

5,044,930

7

5,288,580

7

0

4,981,827

7

0

CN

3,581,628

5

3,606,925

5

0

3,348,751

5

0

DE

900,877

1

928,272

1

0

886,638

1

0

FL

18,900,773

27

19,704,001

27

0

17,887,226

26

-1

GA

9,727,566

14

10,035,937

14

0

9,437,826

14

0

HI

1,366,862

2

1,415,783

2

0

1,304,118

2

0

ID

1,573,499

2

1,618,931

2

0

1,558,602

2

0

IL

12,864,380

18

12,924,458

18

0

11,961,535

18

0

IN

6,501,582

9

6,588,731

9

0

6,385,076

9

0

IA

3,053,787

4

3,099,885

4

0

3,004,033

4

0

KS

2,863,813

4

2,906,656

4

0

2,773,489

4

0

KY

4,350,606

6

4,410,978

6

0

4,313,759

6

0

LA

4,553,962

6

4,650,310

6

0

4,539,970

7

1

ME

1,333,074

2

1,333,416

2

0

1,313,680

2

0

MD

5,789,929

8

5,955,583

8

0

5,523,754

8

0

MA

6,559,644

9

6,721,185

9

0

6,207,989

9

0

MI

9,911,626

14

9,926,220

14

0

9,621,927

14

0

MN

5,314,879

8

5,433,258

7

-1

5,236,009

8

0

MS

2,978,240

4

3,003,241

4

0

2,963,387

4

0

MO

6,011,478

8

6,067,679

8

0

5,939,059

9

1

MT

994,416

1

1,019,994

1

0

1,011,957

2

1

NB

1,831,825

3

1,874,581

3

0

1,792,720

3

0

NV

2,709,432

4

2,800,674

4

0

2,521,101

4

0

NH

1,321,445

2

1,327,614

2

0

1,293,730

2

0

NJ

8,807,501

12

8,927,321

12

0

8,010,469

12

0

NM

2,067,273

3

2,095,098

3

0

1,954,892

3

0

NY

19,421,055

27

19,739,337

27

0

17,670,720

26

-1

NC

9,565,781

13

9,880,211

14

1

9,369,160

14

1

ND

675,905

1

727,424

1

0

712,401

1

0

OH

11,568,495

16

11,604,094

16

0

11,362,964

17

1

OK

3,764,882

5

3,867,016

5

0

3,722,387

6

1

OR

3,848,606

5

3,946,044

5

0

3,712,250

5

0

PA

12,734,905

18

12,814,024

18

0

12,420,894

18

0

RI

1,055,247

2

1,056,036

2

0

987,930

2

0

SC

4,645,975

7

4,793,193

7

0

4,654,486

7

0

SD

819,761

1

851,306

1

0

834,249

1

0

TN

6,375,431

9

6,527,294

9

0

6,335,564

9

0

TX

25,268,418

36

26,635,139

36

0

23,699,482

35

-1

UT

2,770,765

4

2,910,013

4

0

2,759,189

4

0

VT

630,337

1

631,459

1

0

620,126

1

0

VA

8,037,736

11

8,308,293

11

0

7,826,626

12

1

WA

6,753,369

10

7,003,639

10

0

6,494,473

10

0

WV

1,859,815

3

1,860,418

3

0

1,848,149

3

0

WI

5,698,230

8

5,754,308

8

0

5,599,101

8

0

WY

568,300

1

588,060

1

0

576,549

1

0

Totals

309,183,463

435

316,917,008

435

 

294,552,403

435

 

Source: All calculations performed by CRS.

Notes:

a. Includes the resident population for the 50 states, as ascertained by the Twenty-Third Decennial Census under Title 13, United States Code, and counts of overseas U.S. military and federal civilian employees and their dependents living with them.

b. See Table 2, above.

c. See Table 4, above.

Taking the Citizen Population into Account in the Apportionment Process: Some Possible Options

As is shown above, using the citizen population to apportion the seats in the House of Representatives, as some have advocated, would have an impact on the distribution of seats among the states. For those who favor the current method and outcome, no change in policy is necessary. However, for those who wish, for whatever reason, to make sure only the citizen population has an impact on the apportionment process, there are several options.

Constitutional Amendment

First, and most obviously, proponents of such a policy can propose and attempt to pass and ratify a constitutional amendment changing the term "persons" to "citizens" in the 14th Amendment. This strategy was apparent, for example, in the proposed legislation introduced by Representative Candice Miller in the 111th Congress (H.J.Res. 11). Short of this action, however, it would appear that apportioning the seats in the House of Representatives by using the citizen population is not likely to occur, as it most likely would be unconstitutional.23

Using the Citizen Population in the Redistricting Process Rather than in the Apportionment Process

The apportionment process determines the number of House seats that are allocated to each state (and, subsequently, the number of electoral votes). Once that process is completed, currently the next step, usually carried out by the state legislatures or state redistricting commissions, is to determine, within the state, what geographic area is to be represented by each seat. That is, the redistricting process draws the boundary lines for each of the congressional seats within each multi-member state. While the Constitution appears clear that the apportionment of seats is to be based on "persons," it is silent with respect to how congressional district boundaries are drawn and on what basis.

Legal Considerations

The federal courts have established criteria for the drawing of congressional districts (as well as state and local political jurisdictions), and it would appear that redistricting, currently, does not necessarily have to use total population, but could, if allowed by the state, use some other well-defined population—like the state's citizen population.24

Practicalities

Currently, the U.S. Census Bureau is required to deliver census information to be used in the redistricting process by one year following census day (i.e., most recently, by April 1, 2011). The information includes block level information on age, sex, race, and Hispanic-origin of all persons living in the states. It does not include citizenship status on all persons living in the states. Such information, if collected on the decennial census form, could be used to draw boundaries for congressional districts rather than total population. The Fairness in Representation Act (H.R. 3797/S. 1688), or something similar, proposed by Representative Foxx and Senator Bennett in the 111th Congress would require the Census Bureau to collect this information on the 100% census form.

However, the states do have information for very small areas like blocks and precincts. Most states have voter registration information at the address level. As one must be a citizen to vote in most elections, this information could serve as a surrogate for the citizen population.25 The major drawback would be that not every citizen is registered. However, it is very likely that many congressional and state legislative district boundaries already are based on much of this information. To the extent that boundaries are drawn to enhance the power positions of political parties, it is almost certain that voter registration information has been used by the map makers.26

Of course, Congress could pass legislation with respect to congressional redistricting requiring that the citizen population be used in the redistricting of seats for the U.S. House of Representatives in all states. Whether the states choose to follow this path or Congress chooses to, such a procedure could determine the population to be "represented" in the Congress, even if the number of seats for each state is determined by the total population.

Changing the Apportionment Method

The apportionment of the seats in the U.S. House of Representatives is determined by four factors: the population size within the states, the number of seats to be allocated, the method or formula used, and the number of states in which seats are apportioned. Currently, the method of equal proportions is used to apportion the seats. The method is defined by law and, consequently, can be changed by Congress.27

In a 1941 journal article, Walter F. Willcox, the leading proponent of the major fractions method of apportionment at the time, and a noted mathematician from Cornell University, proposed using the method of smallest divisors on the total population as a method that came closest to simulating the impact of using the citizen population with either the method of major fractions or the method of equal proportions.28 In his words,

Let me now explain why I have come to prefer the method of smallest divisors to any of the others, even that of major fractions which I advocated for many years.

My reasons are:

1. It secures the smallest average population per district and the narrowest range between the largest and the smallest average district.

2. It is the easiest method for the average citizen to understand and judge.

3. The theory underlying it is persuasive to the non-mathematical mind.

4. Its results based on the whole population come close to those of the method of major fractions or the method of equal proportions based on the citizen population.29

The method of smallest divisors (also referred to as the Adams method, after John Quincy Adams, a proponent) rounds up to the next seat for any fractional remainder. The rounding point between 1 and 2, for example, would be any fraction exceeding 1 with similar rounding points for all other integers. The method of smallest divisors (which has never been used in practice to apportion seats in the U.S. House of Representatives) may be defined in the following manner for a 435-seat House:

Find a number so that when it is divided into each state's population and resulting quotients that include fractions are rounded up, the total number of seats will sum to 435. (In all cases where a state would be entitled to less than one seat, it receives one anyway because of the constitutional entitlement.)30

The method of smallest divisors tends to favor states that are less populated. In general, with respect to the non-citizen population, the smallest divisors method tends to favor geographic areas where non-citizens are less likely to be located—less populated areas with fewer jobs or less-urban states. As a consequence, it could be argued that such a method is less representative than the current method.

Table 6, below, shows a comparison for 2013 between the apportionment of seats using the equal proportion method (the current method) for both the 2013 total population estimate and the 2013 citizen population estimate as compared to the smallest divisor method using the total 2013 total population estimate.

As can be seen in columns 7 and 8, while the distribution of seats based solely on the citizen population using the method of equal proportions is not exactly the same as that based on the total population using the method of smallest divisors, the impact of using the method of smallest divisors appears to fall somewhere between the distributions using the method of equal proportions on the 2013 total and citizen apportionment population estimates.

This is only one example. It is possible that other methods of apportioning the total population could be developed that would mimic the results one would get using the equal proportion methods with the citizen population more closely. The point is that an alternative to pursuing a constitutional amendment to replace the total population with the citizen population for apportionment purposes could be to change the apportionment method.

Table 6. Comparing the Seat Distributions: The Method of Equal Proportions (EqPro.) Using the Estimated 2013 Citizen Population to the Method of Smallest Divisor Using the Estimated 2013 Total Apportionment Population

State

2013 Total Apportion-ment Population Estimate a

2013 Seats Based on Total Pop. Using EqPro. Method b

2013 Citizen Apportion-ment Population Estimate c

2013 Seats Based on Citizens Pop. Using EqPro. Method b

SEAT DIFFER-ENCE Between Total & Citizen Pop. Using EqPro. Method

2013 Seats Based on Total Pop. Using Smallest Divisor Method d

SEAT DIFFER-ENCE Between EqPro. Method Using Citizen Pop. & Smallest Divisor Method Using Total Population

Alabama

4,857,506

7

4,754,788

7

0

7

0

Alaska

748,981

1

724,603

1

0

1

0

Arizona

6,656,466

9

6,094,048

9

0

9

0

Arkansas

2,969,228

4

2,879,625

4

0

4

0

California

38,522,208

53

33,119,115

49

-4

51

2

Colorado

5,288,580

7

4,981,827

7

0

7

0

Connecticut

3,606,925

5

3,348,751

5

0

5

0

Delaware

928,272

1

886,638

1

0

2

1

Florida

19,704,001

27

17,887,226

26

-1

26

0

Georgia

10,035,937

14

9,437,826

14

0

14

0

Hawaii

1,415,783

2

1,304,118

2

0

2

0

Idaho

1,618,931

2

1,558,602

2

0

3

1

Illinois

12,924,458

18

11,961,535

18

0

17

-1

Indiana

6,588,731

9

6,385,076

9

0

9

0

Iowa

3,099,885

4

3,004,033

4

0

5

1

Kansas

2,906,656

4

2,773,489

4

0

4

0

Kentucky

4,410,978

6

4,313,759

6

0

6

0

Louisiana

4,650,310

6

4,539,970

7

1

7

0

Maine

1,333,416

2

1,313,680

2

0

2

0

Maryland

5,955,583

8

5,523,754

8

0

8

0

Massachusetts

6,721,185

9

6,207,989

9

0

9

0

Michigan

9,926,220

14

9,621,927

14

0

13

-1

Minnesota

5,433,258

7

5,236,009

8

1

8

0

Mississippi

3,003,241

4

2,963,387

4

0

4

0

Missouri

6,067,679

8

5,939,059

9

1

8

-1

Montana

1,019,994

1

1,011,957

2

1

2

0

Nebraska

1,874,581

3

1,792,720

3

0

3

0

Nevada

2,800,674

4

2,521,101

4

0

4

0

New Hampshire

1,327,614

2

1,293,730

2

0

2

0

New Jersey

8,927,321

12

8,010,469

12

0

12

0

New Mexico

2,095,098

3

1,954,892

3

0

3

0

New York

19,739,337

27

17,670,720

26

-1

26

0

North Carolina

9,880,211

14

9,369,160

14

0

13

-1

North Dakota

727,424

1

712,401

1

0

1

0

Ohio

11,604,094

16

11,362,964

17

1

16

-1

Oklahoma

3,867,016

5

3,722,387

6

1

6

0

Oregon

3,946,044

5

3,712,250

5

0

6

1

Pennsylvania

12,814,024

18

12,420,894

18

0

17

-1

Rhode Island

1,056,036

2

987,930

2

0

2

0

South Carolina

4,793,193

7

4,654,486

7

0

7

0

South Dakota

851,306

1

834,249

1

0

2

1

Tennessee

6,527,294

9

6,335,564

9

0

9

0

Texas

26,635,139

36

23,699,482

35

-1

35

0

Utah

2,910,013

4

2,759,189

4

0

4

0

Vermont

631,459

1

620,126

1

0

1

0

Virginia

8,308,293

11

7,826,626

12

1

11

-1

Washington

7,003,639

10

6,494,473

10

0

10

0

West Virginia

1,860,418

3

1,848,149

3

0

3

0

Wisconsin

5,754,308

8

5,599,101

8

0

8

0

Wyoming

588,060

1

576,549

1

0

1

0

Totals

316,917,008

435

294,552,403

435

 

435

 

Source: Table 2 and Table 4. All seat apportionment calculations performed by CRS.

Notes:

a. See Table 2 for derivation.

b. For a description of the Method of Equal Proportions (EqPro.), the current apportionment formula, CRS Report R41357, The U.S. House of Representatives Apportionment Formula in Theory and Practice, by [author name scrubbed].

c. See Table 4 for derivation.

d. For a comparison of other formulas used in apportioning the U.S. House of Representatives over its history see, CRS Report R41382, The House of Representatives Apportionment Formula: An Analysis of Proposals for Change and Their Impact on States, by [author name scrubbed].

Calculating the Sampling Errors

The 2013 ACS total population, with respect to citizenship status is composed of five parts: (a) native born citizens (NB), (b) native born citizens born in Puerto Rico, Guam, the Virgin Islands, or the Northern Marianas (PR), (c) citizens born abroad of American parents (BA), (d) naturalized citizens (NAT), and (e) non-citizens (NON). The citizen population is made up of the first four parts of the total population (parts a-d). These values are presented in Table 3, along with the associated measurement of error (MoE), as the values are estimates derived from a sample survey.

As the ACS is a sample survey, estimates derived from the survey results are subject to sampling error. When constructing tables from the ACS, the American Factfinder application produces the margin of error (MOE) for all appropriate estimates at a 90 percent confidence level. For purposes of this paper, all MoEs for population estimates have been converted to MoEs at the 95 percent confidence level using the formula described in the U.S. Census Bureau's description of how to use ACS data.31 To create MoEs at the 95 percent confidence level, one multiplies each of the MoE values provided by the Census Bureau by the ratio (1.960/1.645). These values (MoE95), along with the values provided by the U.S. Census Bureau (the estimates and the matching MoE90) are displayed in Table A-1.

Calculating the Measurement Errors for the Citizen Population Estimate

As noted above, the citizen population estimate is composed of the sum of the persons responding to the four categories in the citizenship status question (native born; native born in Puerto Rico, Guam, the Virgin Islands, and the Northern Marianas; born abroad to American parents; and naturalized). Consequently, the estimate of the citizen population merely consists of the sum of the values in these four categories. However, estimates of the sampling error for this sum are somewhat more complicated. The first step in calculating the MOEs for the aggregated counts for each state consists of using the following general formula,

the MOECIT for the citizen population is

MOECIT = ± √ (MOENB2 + MOEPR2 + MOEBA2 +MOENAT2)

for each state.32

These calculations for the MoE90 and MoE95 for the sum are shown in the last two columns of Table A-2, below. Estimating the upper and lower bound for any estimate consists of adding and subtracting the value of the MoE to the estimate. Thus, the 95% upper and lower bound for the 2013 ACS resident citizen population estimate for each state shown in Table 4 in the text above consists of adding (upper bound) and subtracting (lower bound) the matching state value for the MoE95 shown in column 11 of Table A-2 to the 2013 ACS resident citizen population estimated value for each state in Table 4.

Table A-1. 2013 American Community Survey (ACS), Citizen Population Estimates with Measurement of Errors (MoE)

(Error Levels at the 90% and 95%)

 

Total U.S. Population

U.S. citizen, born in United States

U.S. citizen, born in Puerto Rico or U.S. Island Areas

U.S. citizen, born abroad of American parent(s)

U.S. citizen by naturalization

Not a U.S. citizen

State

Estimate

MoE

Estimate

MoE90

MoE95

Estimate

MoE90

MoE95

Estimate

MoE90

MoE95

Estimate

MoE90

MoE95

Estimate

MoE90

MoE95

AL

4,833,722

*****

4,631,111

8,249

9,829

6,570

1,762

2,099

33,815

3,628

4,323

59,782

4,481

5,339

102,444

6,739

8,029

AK

735,132

*****

668,628

4,080

4,861

4,433

1,316

1,568

11,311

2,074

2,471

28,509

2,559

3,049

22,251

3,384

4,032

AZ

6,626,624

*****

5,649,584

18,091

21,555

14,163

2,665

3,175

66,567

4,612

5,495

342,265

11,609

13,832

554,045

17,068

20,336

AR

2,959,373

*****

2,804,722

6,755

8,049

3,099

1,285

1,531

17,664

2,799

3,335

43,677

4,184

4,985

90,211

5,360

6,386

CA

38,332,521

*****

27,543,007

52,221

62,221

79,653

5,398

6,432

398,661

9,591

11,428

5,006,979

29,801

35,508

5,304,221

48,531

57,824

CO

5,268,367

*****

4,693,854

12,075

14,387

7,168

1,329

1,583

66,711

4,468

5,324

197,600

7,513

8,952

303,034

10,144

12,086

CT

3,596,080

*****

2,971,430

12,464

14,851

88,069

5,401

6,435

36,938

3,254

3,877

244,730

7,996

9,527

254,913

11,997

14,294

DE

925,749

*****

833,503

4,459

5,313

9,674

2,114

2,519

5,804

1,243

1,481

34,625

3,031

3,611

42,143

3,706

4,416

FL

19,552,860

*****

15,085,372

35,380

42,155

449,721

16,458

19,610

219,705

9,889

11,783

2,028,738

27,417

32,667

1,769,324

28,881

34,411

GA

9,992,167

*****

8,891,411

18,901

22,520

36,621

4,973

5,925

93,156

5,844

6,963

375,460

10,785

12,850

595,519

16,129

19,218

HI

1,404,054

*****

1,118,050

10,918

13,009

12,466

2,480

2,955

27,074

2,984

3,555

139,732

6,178

7,361

106,732

7,673

9,142

ID

1,612,136

*****

1,502,000

6,361

7,579

1,532

867

1,033

13,079

2,360

2,812

35,903

3,561

4,243

59,622

4,402

5,245

IL

12,882,135

*****

10,943,606

23,904

28,481

50,918

4,405

5,249

80,143

5,403

6,438

852,962

17,158

20,444

954,506

21,734

25,896

IN

6,570,902

*****

6,212,385

10,199

12,152

10,692

2,655

3,163

33,324

2,976

3,546

110,657

5,837

6,955

203,844

9,173

10,930

IA

3,090,416

*****

2,925,682

7,387

8,802

2,118

777

926

13,494

2,126

2,533

55,195

4,323

5,151

93,927

6,079

7,243

KS

2,893,957

*****

2,674,173

8,174

9,739

2,485

900

1,072

19,126

2,179

2,596

66,850

4,793

5,711

131,323

7,374

8,786

KY

4,395,295

*****

4,216,441

7,527

8,968

4,924

1,666

1,985

24,914

2,691

3,206

56,085

4,121

4,910

92,931

5,579

6,647

LA

4,625,470

*****

4,412,731

7,904

9,418

6,321

1,651

1,967

23,859

2,803

3,340

76,033

4,780

5,695

106,526

6,599

7,863

ME

1,328,302

*****

1,269,681

3,487

4,155

1,078

480

572

12,856

1,541

1,836

25,351

2,768

3,298

19,336

2,805

3,342

MD

5,928,814

*****

5,000,878

17,115

20,392

18,442

3,266

3,891

67,244

4,092

4,876

420,344

11,398

13,581

421,906

11,434

13,623

MA

6,692,824

*****

5,475,165

18,340

21,852

113,620

6,250

7,447

57,884

3,661

4,362

549,009

14,653

17,459

497,146

14,628

17,429

MI

9,895,622

*****

9,206,167

12,843

15,302

11,334

2,117

2,522

61,335

4,192

4,995

315,064

9,394

11,193

301,722

10,578

12,604

MN

5,420,380

*****

4,978,189

9,717

11,578

5,003

1,604

1,911

33,674

2,719

3,240

207,945

8,017

9,552

195,569

8,357

9,957

MS

2,991,207

*****

2,908,659

5,986

7,132

5,292

1,290

1,537

14,357

2,748

3,274

24,044

2,757

3,285

38,855

4,889

5,825

MO

6,044,171

*****

5,772,869

9,174

10,931

5,394

1,954

2,328

32,647

3,444

4,103

105,387

5,456

6,501

127,874

7,704

9,179

MT

1,015,165

*****

987,034

2,902

3,458

615

427

509

8,051

1,564

1,863

11,127

1,756

2,092

8,338

1,402

1,670

NE

1,868,516

*****

1,730,401

5,014

5,974

1,441

631

752

13,492

1,657

1,974

41,774

3,508

4,180

81,408

5,157

6,145

NV

2,790,136

*****

2,215,002

10,319

12,295

11,839

2,452

2,922

34,131

4,494

5,355

250,949

7,732

9,213

278,215

9,255

11,027

NH

1,323,459

*****

1,234,128

4,865

5,797

3,966

1,460

1,740

10,190

1,751

2,086

40,448

3,147

3,750

34,727

4,082

4,864

NJ

8,899,339

*****

6,753,607

22,017

26,233

138,987

7,594

9,048

80,972

5,130

6,112

1,021,084

17,262

20,567

904,689

19,525

23,264

NM

2,085,287

*****

1,849,232

10,862

12,942

3,687

1,244

1,482

21,119

2,432

2,898

72,651

4,458

5,312

138,598

9,193

10,953

NY

19,651,127

*****

14,798,608

33,209

39,568

296,387

10,788

12,854

172,821

8,545

10,181

2,359,247

27,804

33,128

2,024,064

29,025

34,583

NC

9,848,060

*****

8,989,881

12,867

15,331

30,673

4,460

5,314

78,080

4,907

5,847

239,232

9,499

11,318

510,194

13,191

15,717

ND

723,393

*****

695,779

2,852

3,398

207

181

216

6,300

1,638

1,952

6,548

1,418

1,690

14,559

2,288

2,726

OH

11,570,808

*****

11,003,182

15,677

18,679

33,237

3,772

4,494

57,052

5,042

6,007

237,404

9,075

10,813

239,933

11,011

13,119

OK

3,850,568

*****

3,596,428

6,828

8,135

5,043

1,496

1,782

30,665

2,712

3,231

76,353

3,959

4,717

142,079

5,755

6,857

OR

3,930,065

*****

3,496,761

10,453

12,455

4,974

1,475

1,757

37,124

3,163

3,769

155,415

6,872

8,188

235,791

10,303

12,276

PA

12,773,801

*****

11,768,250

19,416

23,134

140,784

7,773

9,261

68,608

4,700

5,600

410,524

11,354

13,528

385,635

14,764

17,591

RI

1,051,511

*****

891,444

6,797

8,099

14,424

2,412

2,874

9,671

1,791

2,134

69,709

4,196

4,999

66,263

5,347

6,371

SC

4,774,839

*****

4,491,687

9,702

11,560

12,465

1,921

2,289

39,409

4,124

4,914

89,661

5,397

6,430

141,617

7,537

8,980

SD

844,877

*****

816,396

3,194

3,806

78

127

151

3,944

972

1,158

8,035

1,593

1,898

16,424

2,611

3,111

TN

6,495,978

*****

6,137,131

11,202

13,347

9,110

1,768

2,107

44,936

4,554

5,426

114,362

6,811

8,115

190,439

8,525

10,157

TX

26,448,193

*****

21,717,032

35,268

42,021

78,803

7,972

9,499

283,087

11,815

14,077

1,491,058

22,794

27,159

2,878,213

37,483

44,661

UT

2,900,872

*****

2,634,377

10,880

12,963

3,616

1,547

1,843

25,925

2,868

3,417

88,045

5,601

6,674

148,909

9,201

10,963

VT

626,630

*****

594,234

2,480

2,955

277

180

214

5,107

911

1,085

15,904

1,783

2,124

11,108

2,020

2,407

VA

8,260,405

*****

7,169,317

15,915

18,963

30,964

3,709

4,419

111,161

4,765

5,677

477,236

11,110

13,237

471,727

15,730

18,742

WA

6,971,406

*****

5,911,639

16,825

20,047

22,077

2,942

3,505

94,026

6,306

7,514

436,834

12,244

14,589

506,830

13,390

15,954

WV

1,854,304

*****

1,818,241

2,935

3,497

1,977

793

945

7,765

1,376

1,639

13,343

1,806

2,152

12,978

2,173

2,589

WI

5,742,713

*****

5,423,701

8,914

10,621

16,507

2,973

3,542

27,818

2,690

3,205

119,720

4,926

5,869

154,967

8,161

9,724

WY

582,658

*****

560,963

2,348

2,798

358

265

316

3,308

912

1,087

7,083

1,291

1,538

10,946

1,829

2,179

Source: U.S. Census Bureau, American Factfinder (http://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml), American Community Survey (http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t). Select : Topics = People, Origin, Citizenship; Geographies = States, All states plus PR. This produces Table ID B05001, ACS 2013 1-year estimates—file, ACS_13_1YR_B05001_with_ann.csv. When this .csv file is converted to an Excel .xlsx file, it equals the table, above, except for the MoE95 values. These values, for each cell, are equal to ((1.96/1.645)*(the population estimate)).

Table A-2. Margin of Error (MOE) at the 90 and 95 Percent Level
for 2013 Citizenship Status

Component Parts and Total

State

Native Born U.S. Citizens (MOE90 (+/-))

Native Born U.S. Citizens (MOE95 (+/-))

U.S. Citizens, Born in Puerto Rico or Islands (MOE90 (+/-))

U.S. Citizens, Born in Puerto Rico or Islands (MOE95 (+/-))

U.S. Citizens, Born Abroad of Amer. Parent(s) (MOE90 (+/-))

U.S. Citizens, Born Abroad of Amer. Parent(s) (MOE95 (+/-))

U.S. Citizen by Natural-ization (MOE90 (+/-))

U.S. Citizen by Natural-ization (MOE95 (+/-))

2013 Estimated Total Citizen Population (Margin of Error (+/-) for 90%

2013 Estimated Total Citizen Population (Margin of Error (+/-) for 95%

AL

8,249

9,829

1,762

2,099

3,628

4,323

4,481

5,339

10,217

12,174

AK

4,080

4,861

1,316

1,568

2,074

2,471

2,559

3,049

5,406

6,442

AZ

18,091

21,555

2,665

3,175

4,612

5,495

11,609

13,832

22,146

26,386

AR

6,755

8,049

1,285

1,531

2,799

3,335

4,184

4,985

8,522

10,154

CA

52,221

62,221

5,398

6,432

9,591

11,428

29,801

35,508

61,125

72,830

CO

12,075

14,387

1,329

1,583

4,468

5,324

7,513

8,952

14,966

17,832

CT

12,464

14,851

5,401

6,435

3,254

3,877

7,996

9,527

16,095

19,177

DE

4,459

5,313

2,114

2,519

1,243

1,481

3,031

3,611

5,923

7,057

FL

35,380

42,155

16,458

19,610

9,889

11,783

27,417

32,667

7,342

8,748

GA

18,901

22,520

4,973

5,925

5,844

6,963

10,785

12,850

48,704

58,030

HI

10,918

13,009

2,480

2,955

2,984

3,555

6,178

7,361

23,075

27,493

ID

6,361

7,579

867

1,033

2,360

2,812

3,561

4,243

13,131

15,646

IL

23,904

28,481

4,405

5,249

5,403

6,438

17,158

20,444

7,711

9,188

IN

10,199

12,152

2,655

3,163

2,976

3,546

5,837

6,955

30,239

36,029

IA

7,387

8,802

777

926

2,126

2,533

4,323

5,151

12,410

14,786

KS

8,174

9,739

900

1,072

2,179

2,596

4,793

5,711

8,853

10,549

KY

7,527

8,968

1,666

1,985

2,691

3,206

4,121

4,910

9,764

11,634

LA

7,904

9,418

1,651

1,967

2,803

3,340

4,780

5,695

9,146

10,898

ME

3,487

4,155

480

572

1,541

1,836

2,768

3,298

9,793

11,668

MD

17,115

20,392

3,266

3,891

4,092

4,876

11,398

13,581

4,736

5,642

MA

18,340

21,852

6,250

7,447

3,661

4,362

14,653

17,459

21,219

25,282

MI

12,843

15,302

2,117

2,522

4,192

4,995

9,394

11,193

24,567

29,271

MN

9,717

11,578

1,604

1,911

2,719

3,240

8,017

9,552

16,590

19,767

MS

5,986

7,132

1,290

1,537

2,748

3,274

2,757

3,285

12,987

15,474

MO

9,174

10,931

1,954

2,328

3,444

4,103

5,456

6,501

7,256

8,645

MT

2,902

3,458

427

509

1,564

1,863

1,756

2,092

11,385

13,565

NE

5,014

5,974

631

752

1,657

1,974

3,508

4,180

3,759

4,479

NV

10,319

12,295

2,452

2,922

4,494

5,355

7,732

9,213

6,371

7,591

NH

4,865

5,797

1,460

1,740

1,751

2,086

3,147

3,750

13,873

16,530

NJ

22,017

26,233

7,594

9,048

5,130

6,112

17,262

20,567

6,227

7,419

NM

10,862

12,942

1,244

1,482

2,432

2,898

4,458

5,312

29,440

35,077

NY

33,209

39,568

10,788

12,854

8,545

10,181

27,804

33,128

12,055

14,363

NC

12,867

15,331

4,460

5,314

4,907

5,847

9,499

11,318

45,446

54,148

ND

2,852

3,398

181

216

1,638

1,952

1,418

1,690

17,314

20,629

OH

15,677

18,679

3,772

4,494

5,042

6,007

9,075

10,813

3,586

4,273

OK

6,828

8,135

1,496

1,782

2,712

3,231

3,959

4,717

19,177

22,850

OR

10,453

12,455

1,475

1,757

3,163

3,769

6,872

8,188

8,479

10,102

PA

19,416

23,134

7,773

9,261

4,700

5,600

11,354

13,528

12,987

15,474

RI

6,797

8,099

2,412

2,874

1,791

2,134

4,196

4,999

24,257

28,902

SC

9,702

11,560

1,921

2,289

4,124

4,914

5,397

6,430

8,534

10,168

SD

3,194

3,806

127

151

972

1,158

1,593

1,898

11,998

14,296

TN

11,202

13,347

1,768

2,107

4,554

5,426

6,811

8,115

3,701

4,410

TX

35,268

42,021

7,972

9,499

11,815

14,077

22,794

27,159

13,991

16,670

UT

10,880

12,963

1,547

1,843

2,868

3,417

5,601

6,674

44,346

52,837

VT

2,480

2,955

180

214

911

1,085

1,783

2,124

12,664

15,088

VA

15,915

18,963

3,709

4,419

4,765

5,677

11,110

13,237

3,192

3,804

WA

16,825

20,047

2,942

3,505

6,306

7,514

12,244

14,589

20,327

24,219

WV

2,935

3,497

793

945

1,376

1,639

1,806

2,152

21,941

26,143

WI

8,914

10,621

2,973

3,542

2,690

3,205

4,926

5,869

3,794

4,521

WY

2,348

2,798

265

316

912

1,087

1,291

1,538

10,945

13,041

Source: Table A-1 above. Calculation performed by CRS.

Author Contact Information

[author name scrubbed], Specialist in American National Government ([email address scrubbed], [phone number scrubbed])

Footnotes

1.

For the Constitutional Convention and the debate over the 14th Amendment to the Constitution, see Charles A. Kromkowski, Recreating the American Republic, Rules of Apportionment, Constitutional Change, and American Political Development, 1700-1860 (Cambridge: Cambridge University Press, 2002), pp. 275, 378-379, 414-416; for the debate over the apportionment bills of the 1920s and 1930s, see Charles W. Eagles, Democracy Delayed, Congressional Reapportionment and Urban-Rural Conflict in the 1920's (Athens, GA: University of Georgia Press, 1990), pp. 28, 34, 70-71, 77-78, 80, 118.

2.

See, John S. Baker and Elliott Stonecipher, "Our Unconstitutional Census," Wall Street Journal, August 9, 2009; Dudley L. Poston, Jr., Steven A. Camarota, and Amanda K. Baumle, Remaking the Political Landscape, The Impact of Illegal and Legal Immigration on Congressional Apportionment, Center for Immigration Studies, Backgrounder, Washington, DC, October 2003; Charles Wood, "Losing Control of America's Future—The Census, Birthright Citizenship and Illegal Aliens," Harvard Journal of Law and Public Policy, vol. 22, no. 2 (Spring 1999), pp. 465-522; Michael Regan, "2010 Census: Who Should Count?," The Hartford Courant, September 30, 2007, at http://www.courant.com/news/local/hc-reapportion0930.artsep30,0,1255793.story; Jack Martin, Who Represents Illegal Aliens?, Federation for American Immigration Reform (FAIR), Washington, DC, September 2008, at http://www.fairus.org/site/News2?page=NewsArticle&id=21695&security=1601&news_iv_ctrl=1007.

3.

CRS Report R41048, Constitutionality of Excluding Aliens from the Census for Apportionment and Redistricting Purposes, by [author name scrubbed] and [author name scrubbed].

4.

Ibid., pp. 3-7.

5.

525 U.S. 316, 119 S.Ct. 765 (1999). Also see, CRS Report RL30870, Census 2000: Legal Issues re: Data for Reapportionment and Redistricting, by [author name scrubbed].

6.

CRS Report R41532, The American Community Survey: Development, Implementation, and Issues for Congress, by [author name scrubbed].

7.

For a brief review of selected legislation on this topic from previous Congresses, see the Appendix of CRS Report R41048, Constitutionality of Excluding Aliens from the Census for Apportionment and Redistricting Purposes, pp. 11-13.

8.

The amendment reads as follows, "Representatives shall be apportioned among the several States according to their respective numbers, which shall be determined by counting the number of persons in each State who are citizens of the United States."

9.

For a full description of the history and nature of the ACS, see CRS Report R41532, The American Community Survey: Development, Implementation, and Issues for Congress, by [author name scrubbed].

10.

The ACS question on citizenship status reads as follows: "Is this person a citizen of the United States?" There are five response categories: (1) Yes, born in the United States; (2) Yes, born in Puerto Rico, Guam, the U.S. Virgin Islands, or Northern Marianas; (3) Yes, born abroad of U.S. citizen parent or parents; (4) Yes, U.S. citizen by naturalization (year of naturalization requested); and (5) No, not a U.S. citizen. For purposes of this report, the first four categories constitute the citizen population.

11.

For a thorough discussion of this issue, see Once, Only Once, and in the Right Place: Residence Rules in the Decennial Census, ed. Daniel L. Cork and Paul R. Voss, 1st ed. (Washington, DC: National Research Council, 2006).

12.

If a household fails to return the mail form, an interviewer follows up with a personal visit to collect information. If a child is born after April 1 to the household, but before the follow-up interview, the interviewer is instructed to not count that child in the census because the child was not a resident on census day. Similarly, if a person in the household dies after April 1, but before the follow-up interview, that person is counted because, on Census Day, that person was alive and a resident of the household.

13.

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data: What General Data Users Need to Know, U.S. Government Printing Office, Washington, DC, pp. 1-4.

14.

Only the resident population was used to apportion seats in 1980. Theoretically, all of the overseas U. S. population could be used in the apportionment of seats to the states. However, there is no dependable source of information about "home" state of residence for the overseas population. The overseas military and civilian federal employee population and their dependents, on the other hand, are required to designate their home state of residence. This information is available to the U.S. Census Bureau, and, consequently, allows the U.S. Census Bureau to add this overseas population appropriately.

15.

U.S. Department of Commerce, U.S. Census Bureau, Methodology for the United States Population Estimates: Vintage 2014, Washington, DC, 2015, at http://www.census.gov/popest/methodology/index.html.

16.

U.S. Department of Commerce, U.S. Census Bureau, Population and Housing Unit Estimates, Population Estimates, Washington, DC, at http://www.census.gov/popest/index.html.

17.

The most recent population estimates from the U.S. Census Bureau's population estimation program are as of July 1, 2014. However, as the estimates for the citizen population from the American Community Survey (ACS) are for the year 2013, it was felt by the author that total population estimates should correspond. It should be noted that estimates of the population derived from the U.S. Census Bureau's population estimation program are considered by the Bureau as the "official" population estimates. While it is possible to derive population estimates from the ACS, these are not considered to be "official" by the Bureau.

18.

This table is a subset of Table A-1 in the Appendix and displays MoE for a 90% estimate of error (MoE90). The MoE and its derivation are discussed more fully in the Appendix.

19.

U.S. Department of Commerce, U.S. Census Bureau, American Community Survey and Puerto Rico Community Survey, 2013 Subject Definitions, Washington, DC, 2014, p. 54, at http://www2.census.gov/programs-surveys/acs/tech_docs/subject_definitions/2013_ACSSubjectDefinitions.pdf.

20.

Both the upper and lower bound values were also used to calculate the seat distribution, as well. Using these two populations resulted in no difference in the seat distribution from that of using the estimate. However, that does not necessarily mean that sampling error would have no effect. Each state estimate ranges from a high value to a low value. While calculating the impact of all the low values for all states or for all the high values at once did not reveal an impact, a distribution of state values with a high in some states combined with low values in other states might produce such a difference.

21.

[author name scrubbed], Apportioning Representatives Among the States by Citizen Population Instead of Total State Population, Congressional Research Service, Government & Finance Division, CRS Congressional Distribution Memorandum, Washington, DC, May 11, 2005, pp. 1-2. This report is available from the author upon request.

22.

It should be noted that the magnitude of the impact of using the citizen population as opposed to the resident population for apportionment is a one-time event. If the citizen population were used in multiple apportionments, such dramatic changes in the number of seats would be rare from apportionment to apportionment.

23.

CRS Report R41048, Constitutionality of Excluding Aliens from the Census for Apportionment and Redistricting Purposes, 3-7. Also see, CRS Report R42483, Legal Issues Regarding Census Data for Reapportionment and Redistricting, by [author name scrubbed].

24.

CRS Report R41048, Constitutionality of Excluding Aliens from the Census for Apportionment and Redistricting Purposes, by [author name scrubbed] and [author name scrubbed] , pp. 7-9. Also see, CRS Report R42483, Legal Issues Regarding Census Data for Reapportionment and Redistricting, by [author name scrubbed].

25.

The Court may come to a more definitive conclusion about the issue next year. In May 26, 2015, the U.S. Supreme Court agreed to hear an appeal in Evanwel v. Abbott, a one-person, one vote case involving the population used in the creation of Texas senate districts (i.e., in the redistricting process). Although, strictly speaking, the issue in this case is about redistricting, the Equal Protection clause, and "one-person, one-vote," and the use of the eligible voter population to construct boundaries, one must be a citizen to vote in all elections in the State of Texas. See, CRS Legal Sidebar WSLG1325, Supreme Court Agrees to Consider Redefinition of One-Person, One-Vote in State Legislative Redistricting in Evenwel v. Abbott, by Dennis W. Polio.

26.

It is difficult to imagine how one could politically gerrymander without available political information like voter registration or voting data.

27.

For a full discussion of the method, see CRS Report R41357, The U.S. House of Representatives Apportionment Formula in Theory and Practice, by [author name scrubbed]. Also see, CRS Report R41382, The House of Representatives Apportionment Formula: An Analysis of Proposals for Change and Their Impact on States, by [author name scrubbed].

28.

Walter F. Willcox, "A Role of Mathematics in Congressional Apportionment: A Reply," Sociometry, vol. 4, no. 3 (August 1941), pp. 293-298.

29.

Ibid., p. 294.

30.

CRS Report R41382, The House of Representatives Apportionment Formula: An Analysis of Proposals for Change and Their Impact on States, p. 11, and also see Table 2 in that report for a comparison of its impact relative to other methods of apportionment, pp. 13-15.

31.

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data: What General Data Users Need to Know, "Appendix 3. Measures of Sampling Error," (GPO: Washington, October 2008), p. A-12.

32.

Ibid., p. A-14.