Electoral College
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The Electoral College after Census 2010 and 2020: The Political Impact of Population Growth and Redistribution
Edward M. Burmila
The combined effects of an aging population, domestic migration, and the geographically heterogeneous effects of foreign immigration are producing politically significant changes in the distribution of the American population. Using statistical projections of state populations in the 2010 and 2020 US Censuses combined with statewide estimates of the normal vote based on the last five presidential elections (1992–2008), I show that by 2024 Republican presidential candidates will receive a net benefit of at least eight electoral votes due to the declining population of the Northeast and upper Midwest relative to the rapidly-growing Sun Belt. Democratic presidential candidates will find it increasingly difficult to win elections without having some success in the South and Southwest as Barack Obama did in 2008 but many previous candidates failed to do. While migration will also benefit some solid Democratic states such as California, on balance Republican presidential candidates are poised to benefit from the status of Sun Belt states as magnets for both foreign immigration and domestic migration from a retirement cohort of unprecedented size.
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n a system of representation based on population and geography such as the House of Representatives and subsequently the Electoral College, heterogeneity among state population growth rates has demonstrable political consequences. Of particular relevance to political scientists and the political process is the impact that the coming retirement of a generation of unprecedented size, the “baby boomers,” will have on the dynamics of presidential elections. Migration often accompanies retirement 1 and the combined effects of domestic migration and foreign immigration, which disproportionately affects a limited number of states, will measurably affect the balance of power in the Electoral College. Traditional Democratic strongholds in the upper Midwest, Mid-Atlantic, and Northeast have lost and will continue to lose influence in the Electoral College while rapidly growing Sun Belt states are poised to assume the positions of prominence once enjoyed by states such as Michigan, Ohio, and Pennsylvania. This project uses statistical projections of the 2010 and 2020 US Censuses to create two Electoral College maps: one covering the presidential elections of 2012, 2016, and
2020, and a second for 2024 and 2028. These maps are combined with state-level estimates of the normal vote based on the last five presidential elections (1992–2008) using a method similar to Lewis-Beck and Rice in their research on the home state advantage for presidential candidates.2 Together these data show that the next two censuses will produce Electoral College landscapes that offer net benefits to Republican candidates. Democratic candidates will find it increasingly difficult to win without some non-zero degree of success in Sun Belt states, as Barack Obama achieved in 2008. Conversely Republican candidates, who have performed well in the South and Southwest since the decline of the New Deal Democratic coalition, face the challenge of maintaining support in states with rapidly growing populations, such as Florida, Arizona, Texas, and Nevada. The coming changes are insufficiently large to alter the outcome of one-sided elections but in closely fought races such as 2000 or 2004, the net benefit of approximately eight electoral votes for the GOP is meaningful.
Migrating Boomers and Components of Population Change
Making estimates of the Electoral College in the near future requires an understanding of population dynamics at both the state and national levels. That is, the growth of each state relative to the nation as a whole ultimately determines changes in apportionment and thus the distribution of electoral votes. Demographers have long understood these dynamics by isolating the three components of
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Edward M. Burmila ([email protected]) is a Visiting Assistant Professor in the Department of Political Science at the University of Georgia, 104 Baldwin Hall, Athens, GA 30602. The author thanks Marjorie Hershey, Ted Carmines, Bill Bianco, John Hulsey, and the anonymous reviewers for their time and feedback.
doi:10.1017/S1537592709991836
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The Electoral College after Census 2010 and 2020
bers of immigrants each year, Sun Belt states such as California, Arizona, Florida, and Texas are magnets for migrants both foreign and domestic. The final component of population change is the rate of natural replacement, which is simply the number of births relative to deaths in a given population. Nationally, the rate of natural replacement is and has always been positive in the United States. While there is variance among states, rates of replacement for individual states are also positive with one exception. Only West Virginia experienced a “natural decrease” (births Ͻ deaths) during the 2000–2007 time period, albeit a very small decrease of Ϫ0.5 per 1000.9 Because rates of replacement are almost universally positive and clustered in a relatively small range (more than 70 percent of states have replacement rates between 20 and 50 per 1,000),10 this component takes a backseat to immigration as a driver of short-term population change. The data show that the effects of replacement are relatively uniform whereas immigration rates vary widely by state. Some states gained more than 100 migrants per 1000 while others experienced net losses due to high rates of emigration. The combined effects of all sources of population change by state are summarized in table 1. Only two, Louisiana 11 and North Dakota, actually lost absolute population between 2000 and 2007. It is therefore inappropriate to describe the populations of Frost Belt states as declining or shrinking. Nearly every state is growing; those in the Sun Belt are simply growing at a much faster rate. Heterogeneity of population growth rates among states is the driving force behind the changes in the political landscape in the context of the Electoral College.
population change: interstate domestic migration, foreign immigration, and the rate of natural replacement (births relative to deaths) among static population.3 There is considerable heterogeneity among states, each having a unique balance of these three factors. But with the decennial Census providing ample data at multiple geographic levels of analysis, it is possible to identify each component and make accurate state-level estimates of population change with a short-to-medium time horizon. Colloquially, media coverage and analysis of population dynamics focus on the baby boom generation— children of the immediate post-World War II era who will begin retiring en masse between 2010 and 2020. In this narrative, the aging portion of major Midwestern and Northeastern population centers (the “Frost Belt”) will reach retirement age and relocate to the Sun Belt, or warmwinter areas in the South and Southwest such as Florida, Arizona, Texas, and Nevada. The boomers highlight the importance of interstate domestic migration to population balance among states and regions. Census data on net domestic migration (immigrants minus emigrants) by state between 2000 and 2007 show that seven of the ten biggest gainers are Sun Belt states while the list of the ten biggest losers is composed almost entirely of states in the Midwest, Northeast, and Plains.4 On this point empirical data largely confirm the conventional wisdom: domestic migrants moving across state lines result in net growth in Sun Belt states at the expense of traditional northern population centers. Given the number of potential retirees in the next decade and the established phenomenon of retirement-driven interstate migration, the trend is expected to continue or intensify as the boomers reach age 65.5 It is important to note that existing data do not suggest that boomers are any more or less prone to migrate than other generations.6 It is the size of the cohort, not anomalies in its behavior, that will affect the balance of population among states. Foreign immigration, both legal and undocumented,7 is also influential to short-term population dynamics. The Department of Homeland Security estimates that as of 2007 there were nearly twelve million undocumented and nineteen million legal non-US citizens residing in the United States. Of all undocumented immigrants, 51 percent reside in one of four states: California, Texas, Florida, and Arizona.8 In comparison the two states outside of the Sun Belt with the highest undocumented immigrant populations, New York and Illinois, combine for only 10 percent of the total. Among legal immigrants and permanent residents, 58 percent of those entering the country between 2000 and 2007 resided in one of five states: California, Texas, Florida, New York, and Illinois. In short, foreign immigration is an important component of American population dynamics but its effects are focused heavily on a small number of states. While some northern states such as Illinois, New York, and New Jersey add substantial num838 Perspectives on Politics
Looking Ahead: Census 2010 and 2020
Understanding the components of population change makes it possible to derive accurate estimates of population in the near future based on existing data. An important caveat to population projection is that it reflects current trends but it does not predict future trends. In other words, this exercise is prospective in nature but based on retrospective data. In most cases it is not possible to predict trends that will develop in the future with any degree of accuracy. Events that are currently unforeseeable will ultimately influence the accuracy of these projections. Hurricane Katrina, for example, rendered inaccurate all previous estimates of population growth in Louisiana between 2000 and 2010. While perfect accuracy is not possible when making projections, Census projections based on contemporaneous data and trends have proven to be reliable over short-time horizons and as predictors of total population for large geographic units such as states.12 Literature from demography and human geography supports the short-term accuracy of
Table 1 Net population change, all sources, by state: 2000–2007
Total Pop. Change Net AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS 56,547 180,500 161,399 1,208,140 2,681,560 559,496 96,707 16,233 81,164 2,268,419 1,357,934 71,851 61,664 205,446 432,901 264,768 87,173 199,193 −175,754 100,650 321,836 42,286 133,340 278,129 281,732 74,129 Rate 90.20 40.59 60.37 235.48 79.17 130.08 28.40 28.38 103.58 141.93 165.88 59.31 21.07 158.77 34.86 43.54 32.43 49.28 −39.33 15.85 60.76 33.17 13.42 56.54 50.35 26.06 MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY Total Pop. Change Net 55,666 1,014,541 −2,485 63,306 80,042 271,573 150,869 567,125 320,908 113,772 166,662 326,019 151,738 9,513 395,893 41,370 467,457 3,052,581 412,132 633,061 12,427 574,284 237,925 3,685 29,048 Rate 61.70 126.04 −3.87 36.99 64.77 32.27 82.94 283.81 16.91 10.02 48.30 95.29 12.36 9.07 98.68 54.81 82.16 146.39 184.55 89.43 20.41 97.43 44.36 2.04 58.83
Source: Census 2000 and “Table 4: Cumulative Estimates of the Components of Population Change for the United States, Regions, and States: April 1, 2000 to July 1, 2007” (NSTEST2007-04); author’s calculations. Net Population Change = (Net Domestic Immigration + Net Foreign Immigration + Natural Increase). Rate calculated as events per 1000 static population at beginning of interval (Census 2000).
Census utilizes a cohort-component method widely used by demographers and population researchers.16 This method treats all individuals born in year n as a cohort and calculates rates for all major components of population change— fertility, mortality, and migration—for each age-gender group while continuously updating as new data become available. The technique is useful because it models variance among age cohorts while also recognizing trends that members within each cohort share in common. The data from the ISPP are summarized in table 2. The cohortcomponent method is simple compared to some examples of recently-developed probabilistic population forecasting methodology,17 but a long-running debate in demography literature emphasizes that more complex methods are not necessarily more accurate.18 Based on these population projections, Electoral College maps are derived using the same methodology used in decennial Congressional reapportionment. Since 1940 Congress has used the Huntington-Hill method to apportion House seats. This method assigns seats by rankordering state populations based on a divisor dn representing the inverse of the geometric mean for each additional seat n. A complete description of the methodology is found in the Appendix. Although somewhat cumbersome, Congress has used this method for more than a half-century and, barring legislation in the near future, will use it again in 2011 and 2021.19
Normal Vote in Presidential Elections
Putting these Electoral College changes in a relevant political context requires an estimate of the state-level normal vote in presidential elections. While this idea of a “baseline” partisan balance in a given unit of geography is one of the oldest and most familiar concepts in the study of American politics 20 it can be difficult to apply to presidential elections. Modern presidential elections are largely personality-driven, with substantial candidate-specific effects which make past results imperfect predictors of future performance.21 Nonetheless, estimating the normal vote based on recent results is necessary in a prospective exercise. There is consensus among existing election forecasting models that previous electoral outcomes, approval of the incumbent president, and current economic conditions are all relevant to accurate forecasting. In this instance it is not possible to predict future economic conditions or public opinion about Obama and the unknown incumbents who succeed him, so a modified version of the normal vote estimate in Lewis-Beck and Rice (1983) is used here.22 Those authors suggested using an average of the vote in the past five presidential elections, but two modifications are necessary. First, because of the presence of a significant thirdparty candidate in 1992 and 1996, the share of the vote received by the Democratic and Republican presidential
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historical Census population projections.13 Long-term predictions suffer from the inherent unpredictability of future rate statistics, notably fertility rates.14 Errors in these estimates are compounded over time, drastically reducing the usefulness of fifty- or hundred-year projections. However, short-term predictions tend to be as strong as the current data upon which they are based and suffer less from errors in estimates of future rate statistics. It is easier to estimate growth rates when they can be strongly anchored to current data, as is possible when making ten- or twenty-year projections. Projections in this project are based on the US Census Bureau Interim State Population Projections—2005, 15 meaning that the population estimates for 2010 and 2020 require only five and fifteen years of projection, respectively. Not only have historical Census projections been proven useful, but contemporary improvements in data quality combined with the use of proven methodology are further increasing the accuracy of current forecasts. Briefly, the
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The Electoral College after Census 2010 and 2020
Table 2 Projections of the total US population by state: 2000–2020
Area US Total Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware DC Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Census 2000 281,421,906 4,447,100 626,932 5,130,632 2,673,400 33,871,648 4,301,261 3,405,565 783,600 572,059 15,982,378 8,186,453 1,211,537 1,293,953 12,419,293 6,080,485 2,926,324 2,688,418 4,041,769 4,468,976 1,274,923 5,296,486 6,349,097 9,938,444 4,919,479 2,844,658 5,595,211 902,195 1,711,263 1,998,257 1,235,786 8,414,350 1,819,046 18,976,457 8,049,313 642,200 11,353,140 3,450,654 3,421,399 12,281,054 1,048,319 4,012,012 754,844 5,689,283 20,851,820 2,233,169 608,827 7,078,515 5,894,121 1,808,344 5,363,675 493,782 2010 (Projected) 308,935,581 4,596,330 694,109 6,637,381 2,875,039 38,067,134 4,831,554 3,577,490 884,342 529,785 19,251,691 9,589,080 1,340,674 1,517,291 12,916,894 6,392,139 3,009,907 2,805,470 4,265,117 4,612,679 1,357,134 5,904,970 6,649,441 10,428,683 5,420,636 2,971,412 5,922,078 968,598 1,768,997 2,690,531 1,385,560 9,018,231 1,980,225 19,443,672 9,345,823 636,623 11,576,181 3,591,516 3,790,996 12,584,487 1,116,652 4,446,704 786,399 6,230,852 24,648,888 2,595,013 652,512 8,010,245 6,541,963 1,829,141 5,727,426 519,886 Growth (2000–2010) 9.78% 3.36% 10.72% 29.37% 7.54% 12.39% 12.33% 5.05% 12.86% −7.39% 20.46% 17.13% 10.66% 17.26% 4.01% 5.13% 2.86% 4.35% 5.53% 3.22% 6.45% 11.49% 4.73% 4.93% 10.19% 4.46% 5.84% 7.36% 3.37% 34.64% 12.12% 7.18% 8.86% 2.46% 16.11% −0.87% 1.96% 4.08% 10.80% 2.47% 6.52% 10.83% 4.18% 9.52% 18.21% 16.20% 7.18% 13.16% 10.99% 1.15% 6.78% 5.29% 2020 (Projected) 335,804,546 4,728,915 774,421 8,456,448 3,060,219 42,206,743 5,278,867 3,675,650 963,209 480,540 23,406,525 10,843,753 1,412,373 1,741,333 13,236,720 6,627,008 3,020,496 2,890,566 4,424,431 4,719,160 1,408,665 6,497,626 6,855,546 10,695,993 5,900,769 3,044,812 6,199,882 1,022,735 1,802,678 3,452,283 1,524,751 9,461,635 2,084,341 19,576,920 10,709,289 630,112 11,644,058 3,735,690 4,260,393 12,787,354 1,154,230 4,822,577 801,939 6,780,670 28,634,896 2,990,094 690,686 8,917,395 7,432,136 1,801,112 6,004,954 530,948 Growth (2010–2020) 8.70% 2.88% 11.57% 27.41% 6.44% 10.87% 9.26% 2.74% 8.92% −9.30% 21.58% 13.08% 5.35% 14.77% 2.48% 3.67% 0.35% 3.03% 3.74% 2.31% 3.80% 10.04% 3.10% 2.56% 8.86% 2.47% 4.69% 5.59% 1.90% 28.31% 10.05% 4.92% 5.26% 0.69% 14.59% −1.02% 0.59% 4.01% 12.38% 1.61% 3.37% 8.45% 1.98% 8.82% 16.17% 15.22% 5.85% 11.32% 13.61% −1.53% 4.85% 2.13%
Source: US Census Bureau Population Division Interim State Population Projections—2005, “Table A1: Interim Projections of the Total Population for the United States and States: April 1, 2000 to July 1, 2030”.
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Table 3 Normal vote as Democratic share of two-party vote, presidential elections 1992–2008
Strong R State UT WY ID AK OK NE KS AL ND TX MS SD KY MT SC IN LA TN w-avg 0.3198 0.3360 0.3421 0.3675 0.3746 0.3759 0.4020 0.4065 0.4082 0.4226 0.4263 0.4293 0.4344 0.4377 0.4383 0.4511 0.4514 0.4549 State GA AZ AR NC WV VA Lean R w-avg 0.4560 0.4656 0.4664 0.4673 0.4703 0.4851 MO CO FL OH Toss Up State w-avg 0.4957 0.5002 0.5009 0.5057 State NV NH IA NM WI PA OR MN Lean D w-avg 0.5159 0.5246 0.5252 0.5332 0.5340 0.5357 0.5390 0.5423 MI WA CT NJ ME DE CA IL MD NY VT HI RI MA DC Strong D State w-avg 0.5506 0.5597 0.5606 0.5642 0.5677 0.5786 0.5819 0.5860 0.5922 0.6215 0.6224 0.6313 0.6398 0.6403 0.9141
Source data: Federal Election Commission; author’s calculations. Normal Vote calculated as weighted average of the Democratic share of the two-party popular vote in Elections 1992 through 2008.
candidates is not comparable to elections such as 2000, 2004, and 2008, which were traditional two-way races. Accordingly, the estimate used here is of the Democratic share of the two-party vote in each election. Second, for the purpose of making predictions in the near future it is logical to give greater weight to more recent elections—certainly 2008 should be more relevant than 1992 to the context of the 2012 election.Thus the Lewis-Beck and Rice model is modified as a weighted average of the past five elections, with each successive election after 1992 given additional weight such that 2008 is ultimately weighted at 500% of the 1992 results. So, the state-level normal vote is estimated as:
Strong D ~Va Ͼ 0.55). The normal vote scores and groups are summarized in table 3.
Assumptions
Using the methods described in the two previous sections requires two assumptions which qualify the results. First, it presumes no major changes in the party system over the time period covered by the projections. If the ideological positions of or coalitions making up the major parties change, normal vote-based estimates will be inaccurate. Second, it assumes no significant changes in the Electoral College at the state or national levels. Currently all but two states, Maine and Nebraska, award Electoral Votes on a winner-take-all basis to the plurality winner of the popular vote. Some states such as Colorado have held unsuccessful referenda recently on altering their winner-take-all systems.23 Multiple proposals also exist to alter the entire Electoral College to guarantee the election of the national popular vote winner.24 However, no such efforts have been successful and no changes appear imminent at the time of this writing. Rather than attempt to predict changes to an Electoral College system that has proven remarkably resistant to change over time, I will make the assumption of the status quo explicit so that the results presented here can be interpreted in light of any changes that take place during the next five elections. Additionally, an analysis based on a historical measure of the normal vote may overestimate the likelihood that
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va ϭ
( i * ~ Di ϩ Ri ! iϭ1 (n iϭ1
n
n
Di
where Va is the normal vote in state a as the Democratic share of the two-party presidential vote in presidential elections from 1992 to 2008; Di and Ri are the total number of votes received by the Democratic and Republican candidates, respectively, in year i , and i is an index variable for presidential elections 1 (1992) through 5 (2008). The states were sorted into five groups based on their estimated normal vote, in which higher values represented a greater Democratic share of the two-party vote: Strong R ~Va Ͻ 0.45), Lean R (0.45 Ͻ Va Ͻ 0.49), Toss-Up (0.49 Ͻ Va Ͻ 0.51), Lean D (0.51 Ͻ Va Ͻ 0.55), and
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The Electoral College after Census 2010 and 2020
Figure 1 Projected Electoral College post-Census 2010: presidential elections 2012–2020
Source data: US Census Bureau, Interim State Population Projection—2005, from USCB Population Estimates Program; TerraServer; author’s calculations; image created by author in ArcMap 9.2. Alaska and Hawaii experience no change in the projection and are excluded from the image for simplicity.
states will behave in future elections as they have in recent ones. In light of the convincing Obama victory in 2008 some popular commentators have posed questions about whether traditional Republican strongholds in the Sun Belt like Arizona and Texas are trending toward becoming competitive states.25 Such arguments are provocative but not well supported. While Arizona and Texas will certainly gain enough new residents by 2020 to alter the balance of an election similar to 2008 in theory—for example, McCain defeated Obama by about 951,000 votes in Texas while the state’s population is projected to grow by nearly 8,000,000 by Census 2020—assuming that this growth will alter the partisan balance in the state to the advantage of the Democratic Party is speculative and not well supported by the available evidence. Existing research on the effects of migration on political behavior has produced three different conclusions, none of which support this argument. One strain of research suggests that political behavior among migrants is shaped by their new environment, mitigating any partisan identification linked to their former states of residence.26 Other research has argued that Republicans will benefit in the Sun Belt due to the economic bias inherent in migration.27 Those who can afford the costs of relocation and retirement are of high socioeconomic status and therefore
842 Perspectives on Politics
presumed to lean conservative. A third argument is that partisanship is fundamentally stable, a function of either one’s social identity or core ideological beliefs 28 and thus unlikely to change with migration. This question of the political behavior of domestic migrants is an important one deserving of more attention in future research. Nevertheless, the argument posed in Todd and Gawiser is interesting but flawed.29 If anything, the available empirical evidence suggests that retirement migration will make the Sun Belt less competitive for Democrats, not more so.
Results and Discussion: The Electoral College Post-Census 2010, 2020
The projected changes in the Electoral College after the next two Censuses are summarized in figures 1 through 3. Figure 1 is coded to show the net change in electoral votes for the lower 48 states between the recent 2008 presidential election and 2012.30 Figure 2 follows with the net changes between the 2012–2020 elections (figure 2) and the elections of 2024 and 2028. Finally, figure 3 represents the total change between 2008 and 2024.31 In these figures the general trend of population movement away from the upper Midwest and Northeast and toward the Sun Belt is clear, with states that are
Figure 2 Projected Electoral College post-Census 2020: Presidential elections 2024–2028
Source data: U.S Census Bureau, Interim State Population Projection—2005, from USCB Population Estimates Program; TerraServer; author’s calculations; image created by author in ArcMap 9.2. Alaska and Hawaii experience no change in the projection and are excluded from the image for simplicity.
magnets for immigrants and retirees such as Texas, Florida, and Arizona making the biggest gains. Based on the statewide estimates of the normal vote from the previous section, table 4 provides a summary of the partisan balance in the current and future Electoral Colleges. The results show that the proportional decline of traditional Democratic strongholds such as New York, Illinois, Pennsylvania, and the smaller states of New England will decrease the value of Strong Democratic states by seven electoral votes and Lean Democratic states by two after Census 2020. Conversely, states that are either Strong or Leaning Republican will gain eight electoral votes (with the ninth ex-Democratic vote representing a net gain among the four Toss-up states). While not all states that are expected to gain electoral votes during this time period are favorable turf for the GOP, the net gain of eight electoral votes between Arizona and Texas alone provides the party’s candidates an advantage equal in value to a mediumsized state. No Republican candidate has lost Arizona or Texas in a two-way race since 1976.32 Two states in the Toss-up category, Ohio and Florida, were among the most fiercely contested states in the three most recent elections. The states’ populations are heading
in opposite directions, with Florida gaining electoral votes more rapidly than Ohio is shedding them. These two states (along with Missouri and Colorado) show no clear advantage for either party based on the measures used here and are likely to remain very competitive in 2012 and beyond. Among the group, however, the relative value of Florida is set to increase rapidly, gaining five electoral votes before the 2024 election due to the combined effects of foreign immigration and an influx of retiring boomers. The political allegiances of these new residents will be crucially important to both parties in Florida’s competitive electoral landscape. A normal vote-based analysis is admittedly an imperfect estimate of future electoral outcomes, which will depend not only on past results but also on candidateand context-specific effects, which are inherently unpredictable. Furthermore, the changes described here are insufficient to affect the outcome of uncompetitive races. Nonetheless the shift in population toward states in which Republicans traditionally do well will influence highly competitive elections similar to 2000 or 2004. To put the projected change in context, the nine electoral votes that the Democratic-leaning states will shed are equal in
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The Electoral College after Census 2010 and 2020
Figure 3 Cumulative net Electoral College changes, 2008–2024 (Projected)
Source data: U.S Census Bureau, Interim State Population Projection—2005, from USCB Population Estimates Program; TerraServer; author’s calculations; image created by author in ArcMap 9.2. Alaska and Hawaii experience no change in the projection and are excluded from the image for simplicity.
Table 4 Distribution of Electoral College based on estimated normal vote: Post-Census 2010 and 2020
Current Strong R Lean R Toss-up Lean D Strong D 138 64 67 69 200 2012–20 141 66 66 68 197 2024–28 141 69 68 67 193
Source: US Census Bureau, Federal Election Commission; author’s calculations.
value to Colorado. In a true struggle to reach 270, future Democratic candidates will need to find an additional state to win to make up for losses in their traditional strongholds. The coming redistribution of the American population will present challenges to candidates of both major parties. For Democratic candidates, the challenge is to become more competitive in the rapidly-growing Sun Belt states. The West Coast, New England, and upper Midwest will not be sufficient to win elections. Democrats must have at
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least modest success in the South and Southwest to win close races. For the GOP, the challenge will be to hold their ground in the Sun Belt as new voters from other areas of the country immigrate to key states like Texas, Florida, Nevada, and Arizona. The political environment in these states will be affected by an influx of northern retirees whose political attitudes and allegiances will differ from the established population. Nowhere will this be more important than in Florida, where a simple population pyramid shows that the migration of New England and Mid-Atlantic retirees will make individuals over the age of 60 the dominant age-demographic in the state.33 Political science research, as discussed earlier, has provided conflicting arguments about how migration will affect the political environment of fast-growing states, variously concluding that it will have no impact or will benefit one party. This is a question in need of further research; understanding the political behavior of domestic migrants will be crucial as the aging American population moves into retirement.
Summary
This article presents evidence that the victories Republican presidential candidates currently enjoy in states like Texas and Arizona are about to become more valuable. In
the next twelve years, two Censuses will affect apportionment and subsequently the Electoral College in ways, based on the current political landscape, that benefit Republican candidates. The available data support the common wisdom about the coming retirement of the baby boomers and its effect on the distribution of the American population. Frost Belt states—the Upper Midwest, Northeast, and Mid-Atlantic—will lose representation and electoral votes to rapidly-growing Sun Belt states such as Florida, Texas, and Arizona. By the presidential election of 2024 Republican candidates will receive a net benefit of at least eight electoral votes compared to 2008 due to the proportional loss of population in states like New York, Michigan, and Illinois. While no states are expected to experience an absolute loss of population, Sun Belt states, as magnets for both domestic and foreign migration, will simply grow much faster than states in other regions. Democratic candidates will need to have some success in the Sun Belt, which post-New Deal Democrats have often struggled to do. Republican candidates will be challenged to hold their ground in the South as millions of retirees flee the weather, but perhaps not the partisan tendencies, of northern states.
The population of each state Pi is divided by the divisor dn for all values of n between 2 and 60. The resulting 2950 products (50 states x 59 values n ! are then ranked in descending order with the largest value receiving the 51st House seat and so on until the 435th and final House seat is awarded. For example, in projections for 2010 the 51st House seat was awarded to California using this method, the 52nd to Texas, the 53rd to California, and so on until the 435th and final seat was awarded to Georgia.
Notes
1 2 3 4 Serow 2001; Longino and Fox 1995. Lewis-Beck and Rice 1983. Bogue 1969; Preston, Heuveline, and Guillot 2001. See “Table 4: Cumulative Estimates of the Components of Population Change for the United States, Regions, and States: April 1, 2000 to July 1, 2007” (NST-EST2007-04). This and all other Census data referenced in this article are available from the author upon request. Haas and Serow 2000. Ibid., 158. The Census is a count of residents in a given area, not a tally of US citizens or legal residents. While the rate of undocumented immigrants who fill out and return Census questionnaires announcing their residency is likely to be small, it is controversial but true that non-citizens both legally and illegally living in the US affect Census population counts. Seats in Congress and, subsequently, votes in the Electoral College are based on population figures which include these non-citizens. Hoefer, Rytina, and Baker 2008. Rates of population change are traditionally recorded as the number of events per 1,000 individuals of static population, i.e., population neither emigrating nor immigrating during the given time period. In this instance the rate of Ϫ0.5/1000 ϭ Ϫ1/2000, meaning that for a given group of West Virginians in this period the number of deaths exceeded births by one. Positive replacement rate in 36 of 51 states, including the District of Columbia. Hurricane Katrina resulted in a net decrease, considering all sources of in- and out-migration, of nearly 313,000 residents. Smith and Tayman 2003 (746-48) note that Census methodology often results in considerable error in predicting populations of specific groups—children under the age of five or African-American females, for example—but overall population predictions are accurate to a Mean Absolute Percentage Error (MAPE) between 5 and 10. MAPE is a measure of the accuracy of predictions without regard to the direction of errors.
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Appendix
The Huntington-Hill method assigns seats by rankordering state populations based on a divisor dn representing the inverse of the geometric mean for each additional seat n. The geometric mean differs from the arithmetic mean, a distinction which is important for “rounding up” to the next seat. An arithmetic mean between any two consecutive whole numbers is simply @ n ϩ ~ n ϩ 1!# / 2, whereas the geometric mean is the square-root of the product of the upper and lower bounds or !n ~ n Ϫ 1! . Using the inverse of this mean produces a divisor for each additional House seat for a given state after each has been given the one automatic House seat mandated by the Constitution. Divisors dn are calculated for a range of values n representing the lowest and highest number of seats a multidistrict state might be expected to receive. In this project, the range of n is (2, 60). So: dn ϭ where Pi ϭ population of state i in the base year of apportionment n ϭ range of values representing the number of seats state Pi would have if it gained a seat over a range of (2, 60) jx ϭ the next available House seat after each state has been awarded the Constitutionally-mandated minimum of one seat, giving a range of (1, 385) in a House of 435.
5 6 7
8 9
!n ~n Ϫ 1!
1
jx ϭ Pi /dn
10 11 12
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The Electoral College after Census 2010 and 2020 References
Abramowitz, Alan, and Kyle L. Saunders. 2006. Exploring the bases of partisanship in the American electorate: Social identity vs. ideology. Political Research Quarterly 59 (2): 175–187. Ahlburg, Dennis A. 1982. How accurate are the US Bureau of the Census projections of total live births? Journal of Forecasting 1 (4): 365–374. Bogue, Donald J. 1969. Principles of Demography. New York: Wiley & Sons. Brown, Thad A. 1988. Migration and Politics: The Impact of Population Mobility on American Voting Behavior. Chapel Hill, NC: University of North Carolina Press. Converse, Philip E. 1966. The concept of a normal vote. In Elections and the Political Order, ed. P. Converse, A. Campbell, W. Miller, and D. Stokes. New York: Wiley. Gimpel, James G., and Jason E. Schuknecht. 2001. Interstate migration and electoral politics. Journal of Politics 63 (1): 207–231. Green, Donald P., Bradley Palmquist, and Eric Schickler. 2002. Partisan Hearts and Minds: Political Parties and the Social Identities of Voters. New Haven, CT: Yale University Press. Haas, William H., and William J. Serow. 2000. The retirement rush. Forum for Applied Research and Public Policy 15 (4): 56–59. Haas, William H., III, and William J. Serow. 2002. The baby boom, amenity retirement migration, and retirement communities: Will the golden age of retirement continue? Research on Aging 24 (1): 150–164. Hoefer, Michael, Nancy Rytina, and Bryan C. Baker. 2008. “Estimates of the Unauthorized Immigrant Population Residing in the United States: January 2007.” Department of Homeland Security Office of Immigration Statistics, 7. Washington, DC. Huckfeldt, Robert, and John Sprague. 1995. Citizens, Contexts, and Social Communication. Cambridge: Cambridge University Press. Lewis-Beck, Michael S. 2005. Election forecasting: Principles and practice. British Journal of Politics & International Relations 7 (2): 145–164. Lewis-Beck, Michael S., and Tom W. Rice. 1983. Localism in presidential elections: The home state advantage. American Journal of Political Science 27 (3): 548–556. Long, J.F. 1995. Complexity, accuracy and utility of official population projections. Mathematical Population Studies 5 (3): 203–216. Longino, Charles F., and R. Alan Fox. 1995. Retirement Migration in America. Houston, TX: Vacation Publications. MacKuen, Michael, and Courtney Brown. 1987. Political context and attitude change. American Political Science Review 81(2): 471–490.
13 Stoto 1983; Mulder 2001. 14 Ahlburg 1982. 15 See “Table 6: Interim Projections: Total Population for Regions, Divisions, and States: 2005 to 2030.” 16 Smith et al. 2001. 17 Wilson and Rees 2005. 18 Stoto 1983; Long 1995; Smith 1997; Smith, Tayman and Swanson 2001; Rayer 2007. 19 For additional information and examples please see US Census Bureau, “Computing Reapportionment” at http://www.census.gov/population/www/ censusdata/apportionment/computing.html; accessed 3/15/09. 20 Converse 1966. 21 Steger 2000; Lewis-Beck 2005. 22 Lewis-Beck and Rice 1983. 23 Colorado Amendment 36 (2004) was defeated with only 34.1 percent support. 24 The National Popular Vote Interstate Compact is a plan under which supporters are attempting to get enough states to constitute an Electoral College majority (270 EV) to agree to award their state’s electoral votes to whoever wins the national popular vote. Thus far only four states totaling 50 electoral votes (as of 2008) have signed on to the compact. Since it does not go into effect until enough states have adopted it to constitute 270 electoral votes or more, the compact has yet to have any effect. Additional states are considering the plan, but two states which have signed on (Maryland and New Jersey) are also considering withdrawal. 25 Todd and Gawiser 2009. 26 MacKuen and Brown 1987; Brown 1988; Huckfeldt and Sprague 1995. 27 Gimpel and Schuknecht 2001. 28 Green, Palmquist, and Schickler 2002; Abramowitz and Saunders 2006. 29 Todd and Gawiser 2009. 30 Alaska and Hawaii undergo no changes in these projections. They were therefore eliminated from the maps in figures 1 through 3 to simplify the visual representation of the results. 31 For reference, the Electoral College map used in the 2004 and 2008 elections is available at http:// www.uselectionatlas.org, as well as at many other sites. 32 In 1996, Bill Clinton won a narrow plurality in Arizona with 8 percent of the popular vote going to Ross Perot. 33 Population pyramids for each state and for various years across the time period of these projections are available from the Census Bureau in the Interim State Population Projection—2005 report referenced throughout the paper.
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Mulder, Tammany. 2001. Accuracy of the US Census Bureau National population projections and their respective components of change. In The Direction of Fertility in the United States. Alexandria, VA: United States Census Bureau. Preston, Samuel H., Patrick Heuveline, and Michel Guillot. 2001. Demography: Measuring and Modeling Population Processes. London, UK: Blackwell Publishing. Rayer, Stefan. 2007. Population forecast accuracy: does the choice of summary measure of error matter? Population Research and Policy Review 26 (2): 163–184. Serow, William J. 2001. Retirement migration counties in the southeastern United States: Geographic, demographic, and economic correlates. Gerontologist 41 (2): 220–228. Smith, Stanley K. 1997. Further thoughts on simplicity and complexity in population projection models. International Journal of Forecasting 13 (4): 557–565.
Smith, Stanley K., and Jeff Tayman. 2003. An evaluation of population projections by age. Demography 40 (4): 741–757. Smith, Stanley K., Jeff Tayman, and David A. Swanson. 2001. State and Local Population Projections: methodology and analysis. New York: Kluwer Academic/ Plenum Publishers. Steger, Wayne P. 2000. Do primary voters draw from a stacked deck? Presidential nominations in an era of candidate-centered campaigns. Presidential Studies Quarterly 30 (4): 727–753. Stoto, Michael A. 1983. The accuracy of population projections. Journal of the American Statistical Association 78(381): 13–20. Todd, Chuck, and Sheldon Gawiser. 2009. How Barack Obama Won: A State-by-State Guide to the Historic 2008 Presidential Election. New York: Vintage. Wilson, Tom, and Phil Rees. 2005. Recent developments in population projection methodology: a review. Population, Space and Place 11 (5): 337–360.
December 2009 | Vol. 7/No. 4 847
The Electoral College after Census 2010 and 2020: The Political Impact of Population Growth and Redistribution
Edward M. Burmila
The combined effects of an aging population, domestic migration, and the geographically heterogeneous effects of foreign immigration are producing politically significant changes in the distribution of the American population. Using statistical projections of state populations in the 2010 and 2020 US Censuses combined with statewide estimates of the normal vote based on the last five presidential elections (1992–2008), I show that by 2024 Republican presidential candidates will receive a net benefit of at least eight electoral votes due to the declining population of the Northeast and upper Midwest relative to the rapidly-growing Sun Belt. Democratic presidential candidates will find it increasingly difficult to win elections without having some success in the South and Southwest as Barack Obama did in 2008 but many previous candidates failed to do. While migration will also benefit some solid Democratic states such as California, on balance Republican presidential candidates are poised to benefit from the status of Sun Belt states as magnets for both foreign immigration and domestic migration from a retirement cohort of unprecedented size.
I
n a system of representation based on population and geography such as the House of Representatives and subsequently the Electoral College, heterogeneity among state population growth rates has demonstrable political consequences. Of particular relevance to political scientists and the political process is the impact that the coming retirement of a generation of unprecedented size, the “baby boomers,” will have on the dynamics of presidential elections. Migration often accompanies retirement 1 and the combined effects of domestic migration and foreign immigration, which disproportionately affects a limited number of states, will measurably affect the balance of power in the Electoral College. Traditional Democratic strongholds in the upper Midwest, Mid-Atlantic, and Northeast have lost and will continue to lose influence in the Electoral College while rapidly growing Sun Belt states are poised to assume the positions of prominence once enjoyed by states such as Michigan, Ohio, and Pennsylvania. This project uses statistical projections of the 2010 and 2020 US Censuses to create two Electoral College maps: one covering the presidential elections of 2012, 2016, and
2020, and a second for 2024 and 2028. These maps are combined with state-level estimates of the normal vote based on the last five presidential elections (1992–2008) using a method similar to Lewis-Beck and Rice in their research on the home state advantage for presidential candidates.2 Together these data show that the next two censuses will produce Electoral College landscapes that offer net benefits to Republican candidates. Democratic candidates will find it increasingly difficult to win without some non-zero degree of success in Sun Belt states, as Barack Obama achieved in 2008. Conversely Republican candidates, who have performed well in the South and Southwest since the decline of the New Deal Democratic coalition, face the challenge of maintaining support in states with rapidly growing populations, such as Florida, Arizona, Texas, and Nevada. The coming changes are insufficiently large to alter the outcome of one-sided elections but in closely fought races such as 2000 or 2004, the net benefit of approximately eight electoral votes for the GOP is meaningful.
Migrating Boomers and Components of Population Change
Making estimates of the Electoral College in the near future requires an understanding of population dynamics at both the state and national levels. That is, the growth of each state relative to the nation as a whole ultimately determines changes in apportionment and thus the distribution of electoral votes. Demographers have long understood these dynamics by isolating the three components of
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Edward M. Burmila ([email protected]) is a Visiting Assistant Professor in the Department of Political Science at the University of Georgia, 104 Baldwin Hall, Athens, GA 30602. The author thanks Marjorie Hershey, Ted Carmines, Bill Bianco, John Hulsey, and the anonymous reviewers for their time and feedback.
doi:10.1017/S1537592709991836
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bers of immigrants each year, Sun Belt states such as California, Arizona, Florida, and Texas are magnets for migrants both foreign and domestic. The final component of population change is the rate of natural replacement, which is simply the number of births relative to deaths in a given population. Nationally, the rate of natural replacement is and has always been positive in the United States. While there is variance among states, rates of replacement for individual states are also positive with one exception. Only West Virginia experienced a “natural decrease” (births Ͻ deaths) during the 2000–2007 time period, albeit a very small decrease of Ϫ0.5 per 1000.9 Because rates of replacement are almost universally positive and clustered in a relatively small range (more than 70 percent of states have replacement rates between 20 and 50 per 1,000),10 this component takes a backseat to immigration as a driver of short-term population change. The data show that the effects of replacement are relatively uniform whereas immigration rates vary widely by state. Some states gained more than 100 migrants per 1000 while others experienced net losses due to high rates of emigration. The combined effects of all sources of population change by state are summarized in table 1. Only two, Louisiana 11 and North Dakota, actually lost absolute population between 2000 and 2007. It is therefore inappropriate to describe the populations of Frost Belt states as declining or shrinking. Nearly every state is growing; those in the Sun Belt are simply growing at a much faster rate. Heterogeneity of population growth rates among states is the driving force behind the changes in the political landscape in the context of the Electoral College.
population change: interstate domestic migration, foreign immigration, and the rate of natural replacement (births relative to deaths) among static population.3 There is considerable heterogeneity among states, each having a unique balance of these three factors. But with the decennial Census providing ample data at multiple geographic levels of analysis, it is possible to identify each component and make accurate state-level estimates of population change with a short-to-medium time horizon. Colloquially, media coverage and analysis of population dynamics focus on the baby boom generation— children of the immediate post-World War II era who will begin retiring en masse between 2010 and 2020. In this narrative, the aging portion of major Midwestern and Northeastern population centers (the “Frost Belt”) will reach retirement age and relocate to the Sun Belt, or warmwinter areas in the South and Southwest such as Florida, Arizona, Texas, and Nevada. The boomers highlight the importance of interstate domestic migration to population balance among states and regions. Census data on net domestic migration (immigrants minus emigrants) by state between 2000 and 2007 show that seven of the ten biggest gainers are Sun Belt states while the list of the ten biggest losers is composed almost entirely of states in the Midwest, Northeast, and Plains.4 On this point empirical data largely confirm the conventional wisdom: domestic migrants moving across state lines result in net growth in Sun Belt states at the expense of traditional northern population centers. Given the number of potential retirees in the next decade and the established phenomenon of retirement-driven interstate migration, the trend is expected to continue or intensify as the boomers reach age 65.5 It is important to note that existing data do not suggest that boomers are any more or less prone to migrate than other generations.6 It is the size of the cohort, not anomalies in its behavior, that will affect the balance of population among states. Foreign immigration, both legal and undocumented,7 is also influential to short-term population dynamics. The Department of Homeland Security estimates that as of 2007 there were nearly twelve million undocumented and nineteen million legal non-US citizens residing in the United States. Of all undocumented immigrants, 51 percent reside in one of four states: California, Texas, Florida, and Arizona.8 In comparison the two states outside of the Sun Belt with the highest undocumented immigrant populations, New York and Illinois, combine for only 10 percent of the total. Among legal immigrants and permanent residents, 58 percent of those entering the country between 2000 and 2007 resided in one of five states: California, Texas, Florida, New York, and Illinois. In short, foreign immigration is an important component of American population dynamics but its effects are focused heavily on a small number of states. While some northern states such as Illinois, New York, and New Jersey add substantial num838 Perspectives on Politics
Looking Ahead: Census 2010 and 2020
Understanding the components of population change makes it possible to derive accurate estimates of population in the near future based on existing data. An important caveat to population projection is that it reflects current trends but it does not predict future trends. In other words, this exercise is prospective in nature but based on retrospective data. In most cases it is not possible to predict trends that will develop in the future with any degree of accuracy. Events that are currently unforeseeable will ultimately influence the accuracy of these projections. Hurricane Katrina, for example, rendered inaccurate all previous estimates of population growth in Louisiana between 2000 and 2010. While perfect accuracy is not possible when making projections, Census projections based on contemporaneous data and trends have proven to be reliable over short-time horizons and as predictors of total population for large geographic units such as states.12 Literature from demography and human geography supports the short-term accuracy of
Table 1 Net population change, all sources, by state: 2000–2007
Total Pop. Change Net AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS 56,547 180,500 161,399 1,208,140 2,681,560 559,496 96,707 16,233 81,164 2,268,419 1,357,934 71,851 61,664 205,446 432,901 264,768 87,173 199,193 −175,754 100,650 321,836 42,286 133,340 278,129 281,732 74,129 Rate 90.20 40.59 60.37 235.48 79.17 130.08 28.40 28.38 103.58 141.93 165.88 59.31 21.07 158.77 34.86 43.54 32.43 49.28 −39.33 15.85 60.76 33.17 13.42 56.54 50.35 26.06 MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY Total Pop. Change Net 55,666 1,014,541 −2,485 63,306 80,042 271,573 150,869 567,125 320,908 113,772 166,662 326,019 151,738 9,513 395,893 41,370 467,457 3,052,581 412,132 633,061 12,427 574,284 237,925 3,685 29,048 Rate 61.70 126.04 −3.87 36.99 64.77 32.27 82.94 283.81 16.91 10.02 48.30 95.29 12.36 9.07 98.68 54.81 82.16 146.39 184.55 89.43 20.41 97.43 44.36 2.04 58.83
Source: Census 2000 and “Table 4: Cumulative Estimates of the Components of Population Change for the United States, Regions, and States: April 1, 2000 to July 1, 2007” (NSTEST2007-04); author’s calculations. Net Population Change = (Net Domestic Immigration + Net Foreign Immigration + Natural Increase). Rate calculated as events per 1000 static population at beginning of interval (Census 2000).
Census utilizes a cohort-component method widely used by demographers and population researchers.16 This method treats all individuals born in year n as a cohort and calculates rates for all major components of population change— fertility, mortality, and migration—for each age-gender group while continuously updating as new data become available. The technique is useful because it models variance among age cohorts while also recognizing trends that members within each cohort share in common. The data from the ISPP are summarized in table 2. The cohortcomponent method is simple compared to some examples of recently-developed probabilistic population forecasting methodology,17 but a long-running debate in demography literature emphasizes that more complex methods are not necessarily more accurate.18 Based on these population projections, Electoral College maps are derived using the same methodology used in decennial Congressional reapportionment. Since 1940 Congress has used the Huntington-Hill method to apportion House seats. This method assigns seats by rankordering state populations based on a divisor dn representing the inverse of the geometric mean for each additional seat n. A complete description of the methodology is found in the Appendix. Although somewhat cumbersome, Congress has used this method for more than a half-century and, barring legislation in the near future, will use it again in 2011 and 2021.19
Normal Vote in Presidential Elections
Putting these Electoral College changes in a relevant political context requires an estimate of the state-level normal vote in presidential elections. While this idea of a “baseline” partisan balance in a given unit of geography is one of the oldest and most familiar concepts in the study of American politics 20 it can be difficult to apply to presidential elections. Modern presidential elections are largely personality-driven, with substantial candidate-specific effects which make past results imperfect predictors of future performance.21 Nonetheless, estimating the normal vote based on recent results is necessary in a prospective exercise. There is consensus among existing election forecasting models that previous electoral outcomes, approval of the incumbent president, and current economic conditions are all relevant to accurate forecasting. In this instance it is not possible to predict future economic conditions or public opinion about Obama and the unknown incumbents who succeed him, so a modified version of the normal vote estimate in Lewis-Beck and Rice (1983) is used here.22 Those authors suggested using an average of the vote in the past five presidential elections, but two modifications are necessary. First, because of the presence of a significant thirdparty candidate in 1992 and 1996, the share of the vote received by the Democratic and Republican presidential
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historical Census population projections.13 Long-term predictions suffer from the inherent unpredictability of future rate statistics, notably fertility rates.14 Errors in these estimates are compounded over time, drastically reducing the usefulness of fifty- or hundred-year projections. However, short-term predictions tend to be as strong as the current data upon which they are based and suffer less from errors in estimates of future rate statistics. It is easier to estimate growth rates when they can be strongly anchored to current data, as is possible when making ten- or twenty-year projections. Projections in this project are based on the US Census Bureau Interim State Population Projections—2005, 15 meaning that the population estimates for 2010 and 2020 require only five and fifteen years of projection, respectively. Not only have historical Census projections been proven useful, but contemporary improvements in data quality combined with the use of proven methodology are further increasing the accuracy of current forecasts. Briefly, the
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Table 2 Projections of the total US population by state: 2000–2020
Area US Total Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware DC Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Census 2000 281,421,906 4,447,100 626,932 5,130,632 2,673,400 33,871,648 4,301,261 3,405,565 783,600 572,059 15,982,378 8,186,453 1,211,537 1,293,953 12,419,293 6,080,485 2,926,324 2,688,418 4,041,769 4,468,976 1,274,923 5,296,486 6,349,097 9,938,444 4,919,479 2,844,658 5,595,211 902,195 1,711,263 1,998,257 1,235,786 8,414,350 1,819,046 18,976,457 8,049,313 642,200 11,353,140 3,450,654 3,421,399 12,281,054 1,048,319 4,012,012 754,844 5,689,283 20,851,820 2,233,169 608,827 7,078,515 5,894,121 1,808,344 5,363,675 493,782 2010 (Projected) 308,935,581 4,596,330 694,109 6,637,381 2,875,039 38,067,134 4,831,554 3,577,490 884,342 529,785 19,251,691 9,589,080 1,340,674 1,517,291 12,916,894 6,392,139 3,009,907 2,805,470 4,265,117 4,612,679 1,357,134 5,904,970 6,649,441 10,428,683 5,420,636 2,971,412 5,922,078 968,598 1,768,997 2,690,531 1,385,560 9,018,231 1,980,225 19,443,672 9,345,823 636,623 11,576,181 3,591,516 3,790,996 12,584,487 1,116,652 4,446,704 786,399 6,230,852 24,648,888 2,595,013 652,512 8,010,245 6,541,963 1,829,141 5,727,426 519,886 Growth (2000–2010) 9.78% 3.36% 10.72% 29.37% 7.54% 12.39% 12.33% 5.05% 12.86% −7.39% 20.46% 17.13% 10.66% 17.26% 4.01% 5.13% 2.86% 4.35% 5.53% 3.22% 6.45% 11.49% 4.73% 4.93% 10.19% 4.46% 5.84% 7.36% 3.37% 34.64% 12.12% 7.18% 8.86% 2.46% 16.11% −0.87% 1.96% 4.08% 10.80% 2.47% 6.52% 10.83% 4.18% 9.52% 18.21% 16.20% 7.18% 13.16% 10.99% 1.15% 6.78% 5.29% 2020 (Projected) 335,804,546 4,728,915 774,421 8,456,448 3,060,219 42,206,743 5,278,867 3,675,650 963,209 480,540 23,406,525 10,843,753 1,412,373 1,741,333 13,236,720 6,627,008 3,020,496 2,890,566 4,424,431 4,719,160 1,408,665 6,497,626 6,855,546 10,695,993 5,900,769 3,044,812 6,199,882 1,022,735 1,802,678 3,452,283 1,524,751 9,461,635 2,084,341 19,576,920 10,709,289 630,112 11,644,058 3,735,690 4,260,393 12,787,354 1,154,230 4,822,577 801,939 6,780,670 28,634,896 2,990,094 690,686 8,917,395 7,432,136 1,801,112 6,004,954 530,948 Growth (2010–2020) 8.70% 2.88% 11.57% 27.41% 6.44% 10.87% 9.26% 2.74% 8.92% −9.30% 21.58% 13.08% 5.35% 14.77% 2.48% 3.67% 0.35% 3.03% 3.74% 2.31% 3.80% 10.04% 3.10% 2.56% 8.86% 2.47% 4.69% 5.59% 1.90% 28.31% 10.05% 4.92% 5.26% 0.69% 14.59% −1.02% 0.59% 4.01% 12.38% 1.61% 3.37% 8.45% 1.98% 8.82% 16.17% 15.22% 5.85% 11.32% 13.61% −1.53% 4.85% 2.13%
Source: US Census Bureau Population Division Interim State Population Projections—2005, “Table A1: Interim Projections of the Total Population for the United States and States: April 1, 2000 to July 1, 2030”.
840 Perspectives on Politics
Table 3 Normal vote as Democratic share of two-party vote, presidential elections 1992–2008
Strong R State UT WY ID AK OK NE KS AL ND TX MS SD KY MT SC IN LA TN w-avg 0.3198 0.3360 0.3421 0.3675 0.3746 0.3759 0.4020 0.4065 0.4082 0.4226 0.4263 0.4293 0.4344 0.4377 0.4383 0.4511 0.4514 0.4549 State GA AZ AR NC WV VA Lean R w-avg 0.4560 0.4656 0.4664 0.4673 0.4703 0.4851 MO CO FL OH Toss Up State w-avg 0.4957 0.5002 0.5009 0.5057 State NV NH IA NM WI PA OR MN Lean D w-avg 0.5159 0.5246 0.5252 0.5332 0.5340 0.5357 0.5390 0.5423 MI WA CT NJ ME DE CA IL MD NY VT HI RI MA DC Strong D State w-avg 0.5506 0.5597 0.5606 0.5642 0.5677 0.5786 0.5819 0.5860 0.5922 0.6215 0.6224 0.6313 0.6398 0.6403 0.9141
Source data: Federal Election Commission; author’s calculations. Normal Vote calculated as weighted average of the Democratic share of the two-party popular vote in Elections 1992 through 2008.
candidates is not comparable to elections such as 2000, 2004, and 2008, which were traditional two-way races. Accordingly, the estimate used here is of the Democratic share of the two-party vote in each election. Second, for the purpose of making predictions in the near future it is logical to give greater weight to more recent elections—certainly 2008 should be more relevant than 1992 to the context of the 2012 election.Thus the Lewis-Beck and Rice model is modified as a weighted average of the past five elections, with each successive election after 1992 given additional weight such that 2008 is ultimately weighted at 500% of the 1992 results. So, the state-level normal vote is estimated as:
Strong D ~Va Ͼ 0.55). The normal vote scores and groups are summarized in table 3.
Assumptions
Using the methods described in the two previous sections requires two assumptions which qualify the results. First, it presumes no major changes in the party system over the time period covered by the projections. If the ideological positions of or coalitions making up the major parties change, normal vote-based estimates will be inaccurate. Second, it assumes no significant changes in the Electoral College at the state or national levels. Currently all but two states, Maine and Nebraska, award Electoral Votes on a winner-take-all basis to the plurality winner of the popular vote. Some states such as Colorado have held unsuccessful referenda recently on altering their winner-take-all systems.23 Multiple proposals also exist to alter the entire Electoral College to guarantee the election of the national popular vote winner.24 However, no such efforts have been successful and no changes appear imminent at the time of this writing. Rather than attempt to predict changes to an Electoral College system that has proven remarkably resistant to change over time, I will make the assumption of the status quo explicit so that the results presented here can be interpreted in light of any changes that take place during the next five elections. Additionally, an analysis based on a historical measure of the normal vote may overestimate the likelihood that
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va ϭ
( i * ~ Di ϩ Ri ! iϭ1 (n iϭ1
n
n
Di
where Va is the normal vote in state a as the Democratic share of the two-party presidential vote in presidential elections from 1992 to 2008; Di and Ri are the total number of votes received by the Democratic and Republican candidates, respectively, in year i , and i is an index variable for presidential elections 1 (1992) through 5 (2008). The states were sorted into five groups based on their estimated normal vote, in which higher values represented a greater Democratic share of the two-party vote: Strong R ~Va Ͻ 0.45), Lean R (0.45 Ͻ Va Ͻ 0.49), Toss-Up (0.49 Ͻ Va Ͻ 0.51), Lean D (0.51 Ͻ Va Ͻ 0.55), and
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Figure 1 Projected Electoral College post-Census 2010: presidential elections 2012–2020
Source data: US Census Bureau, Interim State Population Projection—2005, from USCB Population Estimates Program; TerraServer; author’s calculations; image created by author in ArcMap 9.2. Alaska and Hawaii experience no change in the projection and are excluded from the image for simplicity.
states will behave in future elections as they have in recent ones. In light of the convincing Obama victory in 2008 some popular commentators have posed questions about whether traditional Republican strongholds in the Sun Belt like Arizona and Texas are trending toward becoming competitive states.25 Such arguments are provocative but not well supported. While Arizona and Texas will certainly gain enough new residents by 2020 to alter the balance of an election similar to 2008 in theory—for example, McCain defeated Obama by about 951,000 votes in Texas while the state’s population is projected to grow by nearly 8,000,000 by Census 2020—assuming that this growth will alter the partisan balance in the state to the advantage of the Democratic Party is speculative and not well supported by the available evidence. Existing research on the effects of migration on political behavior has produced three different conclusions, none of which support this argument. One strain of research suggests that political behavior among migrants is shaped by their new environment, mitigating any partisan identification linked to their former states of residence.26 Other research has argued that Republicans will benefit in the Sun Belt due to the economic bias inherent in migration.27 Those who can afford the costs of relocation and retirement are of high socioeconomic status and therefore
842 Perspectives on Politics
presumed to lean conservative. A third argument is that partisanship is fundamentally stable, a function of either one’s social identity or core ideological beliefs 28 and thus unlikely to change with migration. This question of the political behavior of domestic migrants is an important one deserving of more attention in future research. Nevertheless, the argument posed in Todd and Gawiser is interesting but flawed.29 If anything, the available empirical evidence suggests that retirement migration will make the Sun Belt less competitive for Democrats, not more so.
Results and Discussion: The Electoral College Post-Census 2010, 2020
The projected changes in the Electoral College after the next two Censuses are summarized in figures 1 through 3. Figure 1 is coded to show the net change in electoral votes for the lower 48 states between the recent 2008 presidential election and 2012.30 Figure 2 follows with the net changes between the 2012–2020 elections (figure 2) and the elections of 2024 and 2028. Finally, figure 3 represents the total change between 2008 and 2024.31 In these figures the general trend of population movement away from the upper Midwest and Northeast and toward the Sun Belt is clear, with states that are
Figure 2 Projected Electoral College post-Census 2020: Presidential elections 2024–2028
Source data: U.S Census Bureau, Interim State Population Projection—2005, from USCB Population Estimates Program; TerraServer; author’s calculations; image created by author in ArcMap 9.2. Alaska and Hawaii experience no change in the projection and are excluded from the image for simplicity.
magnets for immigrants and retirees such as Texas, Florida, and Arizona making the biggest gains. Based on the statewide estimates of the normal vote from the previous section, table 4 provides a summary of the partisan balance in the current and future Electoral Colleges. The results show that the proportional decline of traditional Democratic strongholds such as New York, Illinois, Pennsylvania, and the smaller states of New England will decrease the value of Strong Democratic states by seven electoral votes and Lean Democratic states by two after Census 2020. Conversely, states that are either Strong or Leaning Republican will gain eight electoral votes (with the ninth ex-Democratic vote representing a net gain among the four Toss-up states). While not all states that are expected to gain electoral votes during this time period are favorable turf for the GOP, the net gain of eight electoral votes between Arizona and Texas alone provides the party’s candidates an advantage equal in value to a mediumsized state. No Republican candidate has lost Arizona or Texas in a two-way race since 1976.32 Two states in the Toss-up category, Ohio and Florida, were among the most fiercely contested states in the three most recent elections. The states’ populations are heading
in opposite directions, with Florida gaining electoral votes more rapidly than Ohio is shedding them. These two states (along with Missouri and Colorado) show no clear advantage for either party based on the measures used here and are likely to remain very competitive in 2012 and beyond. Among the group, however, the relative value of Florida is set to increase rapidly, gaining five electoral votes before the 2024 election due to the combined effects of foreign immigration and an influx of retiring boomers. The political allegiances of these new residents will be crucially important to both parties in Florida’s competitive electoral landscape. A normal vote-based analysis is admittedly an imperfect estimate of future electoral outcomes, which will depend not only on past results but also on candidateand context-specific effects, which are inherently unpredictable. Furthermore, the changes described here are insufficient to affect the outcome of uncompetitive races. Nonetheless the shift in population toward states in which Republicans traditionally do well will influence highly competitive elections similar to 2000 or 2004. To put the projected change in context, the nine electoral votes that the Democratic-leaning states will shed are equal in
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The Electoral College after Census 2010 and 2020
Figure 3 Cumulative net Electoral College changes, 2008–2024 (Projected)
Source data: U.S Census Bureau, Interim State Population Projection—2005, from USCB Population Estimates Program; TerraServer; author’s calculations; image created by author in ArcMap 9.2. Alaska and Hawaii experience no change in the projection and are excluded from the image for simplicity.
Table 4 Distribution of Electoral College based on estimated normal vote: Post-Census 2010 and 2020
Current Strong R Lean R Toss-up Lean D Strong D 138 64 67 69 200 2012–20 141 66 66 68 197 2024–28 141 69 68 67 193
Source: US Census Bureau, Federal Election Commission; author’s calculations.
value to Colorado. In a true struggle to reach 270, future Democratic candidates will need to find an additional state to win to make up for losses in their traditional strongholds. The coming redistribution of the American population will present challenges to candidates of both major parties. For Democratic candidates, the challenge is to become more competitive in the rapidly-growing Sun Belt states. The West Coast, New England, and upper Midwest will not be sufficient to win elections. Democrats must have at
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least modest success in the South and Southwest to win close races. For the GOP, the challenge will be to hold their ground in the Sun Belt as new voters from other areas of the country immigrate to key states like Texas, Florida, Nevada, and Arizona. The political environment in these states will be affected by an influx of northern retirees whose political attitudes and allegiances will differ from the established population. Nowhere will this be more important than in Florida, where a simple population pyramid shows that the migration of New England and Mid-Atlantic retirees will make individuals over the age of 60 the dominant age-demographic in the state.33 Political science research, as discussed earlier, has provided conflicting arguments about how migration will affect the political environment of fast-growing states, variously concluding that it will have no impact or will benefit one party. This is a question in need of further research; understanding the political behavior of domestic migrants will be crucial as the aging American population moves into retirement.
Summary
This article presents evidence that the victories Republican presidential candidates currently enjoy in states like Texas and Arizona are about to become more valuable. In
the next twelve years, two Censuses will affect apportionment and subsequently the Electoral College in ways, based on the current political landscape, that benefit Republican candidates. The available data support the common wisdom about the coming retirement of the baby boomers and its effect on the distribution of the American population. Frost Belt states—the Upper Midwest, Northeast, and Mid-Atlantic—will lose representation and electoral votes to rapidly-growing Sun Belt states such as Florida, Texas, and Arizona. By the presidential election of 2024 Republican candidates will receive a net benefit of at least eight electoral votes compared to 2008 due to the proportional loss of population in states like New York, Michigan, and Illinois. While no states are expected to experience an absolute loss of population, Sun Belt states, as magnets for both domestic and foreign migration, will simply grow much faster than states in other regions. Democratic candidates will need to have some success in the Sun Belt, which post-New Deal Democrats have often struggled to do. Republican candidates will be challenged to hold their ground in the South as millions of retirees flee the weather, but perhaps not the partisan tendencies, of northern states.
The population of each state Pi is divided by the divisor dn for all values of n between 2 and 60. The resulting 2950 products (50 states x 59 values n ! are then ranked in descending order with the largest value receiving the 51st House seat and so on until the 435th and final House seat is awarded. For example, in projections for 2010 the 51st House seat was awarded to California using this method, the 52nd to Texas, the 53rd to California, and so on until the 435th and final seat was awarded to Georgia.
Notes
1 2 3 4 Serow 2001; Longino and Fox 1995. Lewis-Beck and Rice 1983. Bogue 1969; Preston, Heuveline, and Guillot 2001. See “Table 4: Cumulative Estimates of the Components of Population Change for the United States, Regions, and States: April 1, 2000 to July 1, 2007” (NST-EST2007-04). This and all other Census data referenced in this article are available from the author upon request. Haas and Serow 2000. Ibid., 158. The Census is a count of residents in a given area, not a tally of US citizens or legal residents. While the rate of undocumented immigrants who fill out and return Census questionnaires announcing their residency is likely to be small, it is controversial but true that non-citizens both legally and illegally living in the US affect Census population counts. Seats in Congress and, subsequently, votes in the Electoral College are based on population figures which include these non-citizens. Hoefer, Rytina, and Baker 2008. Rates of population change are traditionally recorded as the number of events per 1,000 individuals of static population, i.e., population neither emigrating nor immigrating during the given time period. In this instance the rate of Ϫ0.5/1000 ϭ Ϫ1/2000, meaning that for a given group of West Virginians in this period the number of deaths exceeded births by one. Positive replacement rate in 36 of 51 states, including the District of Columbia. Hurricane Katrina resulted in a net decrease, considering all sources of in- and out-migration, of nearly 313,000 residents. Smith and Tayman 2003 (746-48) note that Census methodology often results in considerable error in predicting populations of specific groups—children under the age of five or African-American females, for example—but overall population predictions are accurate to a Mean Absolute Percentage Error (MAPE) between 5 and 10. MAPE is a measure of the accuracy of predictions without regard to the direction of errors.
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Appendix
The Huntington-Hill method assigns seats by rankordering state populations based on a divisor dn representing the inverse of the geometric mean for each additional seat n. The geometric mean differs from the arithmetic mean, a distinction which is important for “rounding up” to the next seat. An arithmetic mean between any two consecutive whole numbers is simply @ n ϩ ~ n ϩ 1!# / 2, whereas the geometric mean is the square-root of the product of the upper and lower bounds or !n ~ n Ϫ 1! . Using the inverse of this mean produces a divisor for each additional House seat for a given state after each has been given the one automatic House seat mandated by the Constitution. Divisors dn are calculated for a range of values n representing the lowest and highest number of seats a multidistrict state might be expected to receive. In this project, the range of n is (2, 60). So: dn ϭ where Pi ϭ population of state i in the base year of apportionment n ϭ range of values representing the number of seats state Pi would have if it gained a seat over a range of (2, 60) jx ϭ the next available House seat after each state has been awarded the Constitutionally-mandated minimum of one seat, giving a range of (1, 385) in a House of 435.
5 6 7
8 9
!n ~n Ϫ 1!
1
jx ϭ Pi /dn
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The Electoral College after Census 2010 and 2020 References
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13 Stoto 1983; Mulder 2001. 14 Ahlburg 1982. 15 See “Table 6: Interim Projections: Total Population for Regions, Divisions, and States: 2005 to 2030.” 16 Smith et al. 2001. 17 Wilson and Rees 2005. 18 Stoto 1983; Long 1995; Smith 1997; Smith, Tayman and Swanson 2001; Rayer 2007. 19 For additional information and examples please see US Census Bureau, “Computing Reapportionment” at http://www.census.gov/population/www/ censusdata/apportionment/computing.html; accessed 3/15/09. 20 Converse 1966. 21 Steger 2000; Lewis-Beck 2005. 22 Lewis-Beck and Rice 1983. 23 Colorado Amendment 36 (2004) was defeated with only 34.1 percent support. 24 The National Popular Vote Interstate Compact is a plan under which supporters are attempting to get enough states to constitute an Electoral College majority (270 EV) to agree to award their state’s electoral votes to whoever wins the national popular vote. Thus far only four states totaling 50 electoral votes (as of 2008) have signed on to the compact. Since it does not go into effect until enough states have adopted it to constitute 270 electoral votes or more, the compact has yet to have any effect. Additional states are considering the plan, but two states which have signed on (Maryland and New Jersey) are also considering withdrawal. 25 Todd and Gawiser 2009. 26 MacKuen and Brown 1987; Brown 1988; Huckfeldt and Sprague 1995. 27 Gimpel and Schuknecht 2001. 28 Green, Palmquist, and Schickler 2002; Abramowitz and Saunders 2006. 29 Todd and Gawiser 2009. 30 Alaska and Hawaii undergo no changes in these projections. They were therefore eliminated from the maps in figures 1 through 3 to simplify the visual representation of the results. 31 For reference, the Electoral College map used in the 2004 and 2008 elections is available at http:// www.uselectionatlas.org, as well as at many other sites. 32 In 1996, Bill Clinton won a narrow plurality in Arizona with 8 percent of the popular vote going to Ross Perot. 33 Population pyramids for each state and for various years across the time period of these projections are available from the Census Bureau in the Interim State Population Projection—2005 report referenced throughout the paper.
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Mulder, Tammany. 2001. Accuracy of the US Census Bureau National population projections and their respective components of change. In The Direction of Fertility in the United States. Alexandria, VA: United States Census Bureau. Preston, Samuel H., Patrick Heuveline, and Michel Guillot. 2001. Demography: Measuring and Modeling Population Processes. London, UK: Blackwell Publishing. Rayer, Stefan. 2007. Population forecast accuracy: does the choice of summary measure of error matter? Population Research and Policy Review 26 (2): 163–184. Serow, William J. 2001. Retirement migration counties in the southeastern United States: Geographic, demographic, and economic correlates. Gerontologist 41 (2): 220–228. Smith, Stanley K. 1997. Further thoughts on simplicity and complexity in population projection models. International Journal of Forecasting 13 (4): 557–565.
Smith, Stanley K., and Jeff Tayman. 2003. An evaluation of population projections by age. Demography 40 (4): 741–757. Smith, Stanley K., Jeff Tayman, and David A. Swanson. 2001. State and Local Population Projections: methodology and analysis. New York: Kluwer Academic/ Plenum Publishers. Steger, Wayne P. 2000. Do primary voters draw from a stacked deck? Presidential nominations in an era of candidate-centered campaigns. Presidential Studies Quarterly 30 (4): 727–753. Stoto, Michael A. 1983. The accuracy of population projections. Journal of the American Statistical Association 78(381): 13–20. Todd, Chuck, and Sheldon Gawiser. 2009. How Barack Obama Won: A State-by-State Guide to the Historic 2008 Presidential Election. New York: Vintage. Wilson, Tom, and Phil Rees. 2005. Recent developments in population projection methodology: a review. Population, Space and Place 11 (5): 337–360.
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