The Electoral College, let’s stipulate, has no place in a modern election. What made sense for a set of states who considered themselves mostly sovereign in the 1780s, at a time when protecting the power of people who owned other humans for profit was a major feature of political negotiation, certainly does not make sense today. Even leaving aside the weak federation and the strong slavocracy that prevailed a quarter millennium ago, the people who framed the U.S. Constitution could hardly have imagined the political geography of a hemispheric empire, with 50 states of dramatically unequal size and urbanity. The time to reform or abolish the Electoral College (and the Senate) has long since arrived.
But let’s also note that there is a shred of a principle worth holding onto in the territorial apportionment of electoral power. The right wing’s objection that the voting power of cities will run roughshod over the interests of the heartland is almost entirely a cocktail of racist delusion and pure political cynicism. (After all, what we have is the exact opposite—a system where the voting power of the cities, along with a legitimate democratic majority, is itself run over roughshod.) Yet in a democratic system which retains protection for minorities, it is worth considering how and why we might want to reserve votes for specifically-defined geographic blocs.
This led me to think: what if we gave credit to the principle that the Electoral College should in fact protect rural voters by not simply tagging them on as a permanently-outvoted rump in a popular election, but designed a system which didn’t over-represent rural areas in respect to their actual numbers, as the current Electoral College does? What if, instead, we divided the population into equal-size sets, and arranged these sets so that the most urban voters voted together in a single bloc, followed by a bloc of the next-most-urban voters, all the way down to the most rural voters?
The “districts” in such a system would not be territorially-continuous areas, but instead segments of the population along an urban-rural spectrum. Voters in Manhattan and the Chicago Loop would share a “district” and an electoral vote, and so would voters in eastern Montana and northern Maine. The most rural slice of the population would thus be guaranteed an electoral vote—but so would the same number of voters in the most urban slice of the population.
I was curious what this would look like, so I ran some models based on the 2016 election. In these models, I’ve taken raw population density as a proxy for urbanity, which is a highly imperfect measure. (Los Angeles County, for instance, is less dense than Orange County, because the former includes a lot of uninhabited land area in mountains and desert—even though it is unquestionably more “urban” than the latter.) I’ve also run the analysis with counties as the geographic unit, which is also problematic: counties in the United States vary considerably in their makeup. But counties are one of the only units where we have good election data, and density is a crude but nevertheless usable index of urbanity.
The analysis is available in this Binder notebook for replicability and exploring. Unsurprisingly, the voting patterns of our density-sliced districts exhibit the pattern that has become a familiar part of political geography of the U.S.: a linear trend from Democrat-voting cities to Republican-voting rural areas. Here’s the result with 9 “districts” of equal size. In these tables, the districts are defined by their most-dense county (their upper bound on the density spectrum) and their least-dense county (their lower bound). Thus, in this example, District 1 includes all the counties whose densities range from New York County (Manhattan) to DuPage County (the inner suburbs of Chicago).
District | Total Population | Densest County | Least Dense County | Dem % | Rep % | Winner |
---|---|---|---|---|---|---|
1 | 37,299,101 | New York County, New York | DuPage County, Illinois | 69.36 | 26.7 | D |
2 | 34,210,307 | DeKalb County, Georgia | Clayton County, Georgia | 63.11 | 32.31 | D |
3 | 36,929,333 | Middlesex County, Massachusetts | Hillsborough County, Florida | 59.26 | 35.89 | D |
4 | 35,956,241 | Monmouth County, New Jersey | Vanderburgh County, Indiana | 51.63 | 43.23 | D |
5 | 32,504,452 | Hampden County, Massachusetts | Kalamazoo County, Michigan | 46.64 | 48.25 | R |
6 | 38,893,609 | Maricopa County, Arizona | Saratoga County, New York | 42.79 | 51.48 | R |
7 | 35,685,254 | Hinds County, Mississippi | Moore County, North Carolina | 37.47 | 57.77 | R |
8 | 35,677,509 | Lapeer County, Michigan | Allen County, Kentucky | 34.34 | 60.65 | R |
9 | 35,747,224 | Bonneville County, Idaho | Yukon-Koyukuk Census Area, Alaska | 33.46 | 59.97 | R |
Most interestingly, though, Donald Trump still wins this version of the 2016 election, even though the constituencies are equally apportioned (or roughly so—because the rank order of counties has to be cut at distinct breakpoints, it’s impossible to make perfectly-equally sized districts, especially at the higher end of the spectrum, where each county may account for several million people). Here, he squeaks out a victory by winning just under a 2% margin in the middle-density District 5, which ranges in county densities from the Pioneer Valley of Massachusetts to Kalamazoo, Michigan. Of course, if this really were how electors were apportioned, the campaigns would have been fought differently, so it’s hard to make any assumptions about how voters might have behaved under such a system. But it still shows something interesting and important: that the Republicans have an advantage not only in rural counties, but in just enough counties that are exactly down the middle of the density spectrum for them to hold an advantage.
As is the case with any attempt to divide voters into sets and then aggregate their choices into winner-take-all blocs, the number of districts we draw has some surprising consequences. For instance, if we run the same election with 21 districts rather than 9, Hillary Clinton wins the election by edging out Trump in District 11:
District | Total Population | Densest County | Least Dense County | Dem % | Rep % | Winner |
---|---|---|---|---|---|---|
1 | 14,861,227 | New York County, New York | Essex County, New Jersey | 80.64 | 16.47 | D |
2 | 15,912,247 | Cook County, Illinois | Winchester city, Virginia | 63.17 | 32.35 | D |
3 | 14,800,056 | Dallas County, Texas | Newport News city, Virginia | 61.62 | 34.28 | D |
4 | 16,320,929 | Los Angeles County, California | Westchester County, New York | 64.97 | 30.27 | D |
5 | 15,597,047 | Hennepin County, Minnesota | Macomb County, Michigan | 63.5 | 31.64 | D |
6 | 15,628,239 | Norfolk County, Massachusetts | Providence County, Rhode Island | 58.99 | 36.55 | D |
7 | 15,318,996 | Fairfield County, Connecticut | Hillsborough County, Florida | 57.05 | 37.61 | D |
8 | 15,117,251 | Monmouth County, New Jersey | Clarke County, Georgia | 52.52 | 42.3 | D |
9 | 15,533,076 | Bucks County, Pennsylvania | Washington County, Oregon | 50.7 | 44.46 | D |
10 | 15,060,023 | Hamilton County, Indiana | Genesee County, Michigan | 48.68 | 45.9 | D |
11 | 15,417,696 | Greenville County, South Carolina | Cleveland County, Oklahoma | 48.15 | 46.57 | D |
12 | 15,585,913 | Sedgwick County, Kansas | Leon County, Florida | 46.06 | 49.04 | R |
13 | 15,316,510 | Putnam County, New York | Cumberland County, Maine | 41.33 | 52.86 | R |
14 | 15,323,833 | Erie County, Pennsylvania | Saratoga County, New York | 40.9 | 53.15 | R |
15 | 15,186,794 | Hinds County, Mississippi | Tompkins County, New York | 39.57 | 55.79 | R |
16 | 15,404,525 | Clackamas County, Oregon | Boone County, Indiana | 36.02 | 59.16 | R |
17 | 15,316,483 | Belknap County, New Hampshire | Barry County, Michigan | 35.38 | 59.55 | R |
18 | 15,234,397 | Fulton County, New York | Putnam County, Indiana | 35.73 | 59.36 | R |
19 | 15,336,889 | Santa Fe County, New Mexico | Preston County, West Virginia | 32.32 | 62.59 | R |
20 | 15,320,870 | Huntingdon County, Pennsylvania | Edwards County, Illinois | 32.91 | 62.26 | R |
21 | 15,310,029 | Franklin County, Arkansas | Yukon-Koyukuk Census Area, Alaska | 33.65 | 58.71 | R |
But turn the dial back down to 17 districts, and Trump wins again, with 9 electoral votes to Clinton’s 8:
District | Total Population | Densest County | Least Dense County | Dem % | Rep % | Winner |
---|---|---|---|---|---|---|
1 | 20,084,946 | New York County, New York | Cook County, Illinois | 78.83 | 17.9 | D |
2 | 19,239,594 | Union County, New Jersey | Fredericksburg city, Virginia | 60.91 | 34.62 | D |
3 | 18,684,112 | Passaic County, New Jersey | Colonial Heights city, Virginia | 63.99 | 31.35 | D |
4 | 18,614,150 | Tarrant County, Texas | Radford city, Virginia | 63.41 | 31.88 | D |
5 | 18,562,096 | Macomb County, Michigan | Salt Lake County, Utah | 57.21 | 37.4 | D |
6 | 19,226,969 | Santa Clara County, California | Hartford County, Connecticut | 57.15 | 38.61 | D |
7 | 19,387,526 | Travis County, Texas | Erie County, New York | 50.33 | 44.5 | D |
8 | 18,888,412 | Galveston County, Texas | Chatham County, Georgia | 49.77 | 44.9 | D |
9 | 18,836,123 | Hamilton County, Tennessee | Manatee County, Florida | 46.97 | 47.75 | R |
10 | 18,990,427 | Marin County, California | Douglas County, Colorado | 44.67 | 49.78 | R |
11 | 18,936,579 | Winnebago County, Wisconsin | Hampshire County, Massachusetts | 42.36 | 52.46 | R |
12 | 18,869,503 | Benton County, Arkansas | Sangamon County, Illinois | 39.8 | 54.72 | R |
13 | 18,979,897 | Windham County, Connecticut | Albemarle County, Virginia | 35.99 | 59.3 | R |
14 | 18,883,361 | Cass County, Missouri | Stokes County, North Carolina | 36.74 | 58.04 | R |
15 | 18,908,564 | Pike County, Pennsylvania | Cherokee County, Oklahoma | 33.37 | 61.7 | R |
16 | 18,909,925 | Coshocton County, Ohio | Buena Vista County, Iowa | 32.27 | 62.89 | R |
17 | 18,900,846 | Breckinridge County, Kentucky | Yukon-Koyukuk Census Area, Alaska | 33.69 | 58.97 | R |
And if we crank all the way up to 27 districts, we see a case where the partisan sort order isn’t perfectly cut down the middle. Here, Trump wins District 13 (by 0.2%), while Clinton edges him out in the just-a-bit-more-rural District 14 (by 0.6%).
District | Total Population | Densest County | Least Dense County | Dem % | Rep % | Winner |
---|---|---|---|---|---|---|
1 | 13,422,482 | New York County, New York | Richmond County, New York | 80.69 | 16.43 | D |
2 | 10,840,874 | Baltimore city, Maryland | Bergen County, New Jersey | 69.68 | 26.5 | D |
3 | 13,035,745 | Orange County, California | DuPage County, Illinois | 59.33 | 35.73 | D |
4 | 8,274,429 | DeKalb County, Georgia | Newport News city, Virginia | 60.68 | 35.71 | D |
5 | 15,352,114 | Los Angeles County, California | Camden County, New Jersey | 64.98 | 30.12 | D |
6 | 10,583,764 | Westchester County, New York | Clayton County, Georgia | 62.7 | 32.47 | D |
7 | 13,012,875 | Middlesex County, Massachusetts | Suffolk County, New York | 60.63 | 34.87 | D |
8 | 10,662,615 | Broward County, Florida | Salt Lake County, Utah | 59.11 | 34.59 | D |
9 | 13,253,843 | Santa Clara County, California | Hillsborough County, Florida | 57.92 | 37.92 | D |
10 | 11,608,274 | Monmouth County, New Jersey | Fayette County, Kentucky | 53.41 | 41.23 | D |
11 | 12,136,236 | Collin County, Texas | Gloucester County, New Jersey | 51.02 | 44.35 | D |
12 | 12,211,731 | Knox County, Tennessee | Vanderburgh County, Indiana | 50.41 | 44.11 | D |
13 | 11,904,288 | Hampden County, Massachusetts | Clay County, Missouri | 47.33 | 47.52 | R |
14 | 11,764,983 | Onondaga County, New York | Pulaski County, Arkansas | 47.73 | 47.16 | D |
15 | 8,835,181 | Washtenaw County, Michigan | Kalamazoo County, Michigan | 44.18 | 50.77 | R |
16 | 15,030,062 | Maricopa County, Arizona | Washington County, Rhode Island | 44.94 | 49.3 | R |
17 | 11,554,247 | Lebanon County, Pennsylvania | Livingston County, Michigan | 41.15 | 53.84 | R |
18 | 12,309,300 | Riverside County, California | Saratoga County, New York | 41.73 | 51.82 | R |
19 | 11,792,515 | Hinds County, Mississippi | Wilson County, Tennessee | 40.33 | 55.0 | R |
20 | 11,828,605 | Minnehaha County, South Dakota | Nash County, North Carolina | 35.53 | 59.93 | R |
21 | 12,064,134 | Anchorage Municipality, Alaska | Moore County, North Carolina | 36.64 | 58.3 | R |
22 | 10,757,729 | Lapeer County, Michigan | Fauquier County, Virginia | 35.46 | 59.42 | R |
23 | 12,877,087 | San Bernardino County, California | Upson County, Georgia | 34.25 | 60.85 | R |
24 | 12,042,693 | Lane County, Oregon | Allen County, Kentucky | 33.47 | 61.5 | R |
25 | 11,948,202 | Bonneville County, Idaho | Meriwether County, Georgia | 32.98 | 62.07 | R |
26 | 11,889,133 | Orange County, Vermont | Cass County, Iowa | 32.3 | 62.86 | R |
27 | 11,909,889 | Izard County, Arkansas | Yukon-Koyukuk Census Area, Alaska | 34.16 | 57.86 | R |
What I think is most notable about these results is that the structural disadvantage for Democrats in our real Electoral College isn’t solely limited to the fact that rural states have more electors per voter than urban states. Instead, it’s the fact that in any territorial apportionment of voters, Democrats are at a disadvantage due to the fact that they are “inefficiently” clustered together in cities. And this phenomenon is really one that’s exclusively about the biggest of big cities. In these models, the second and third districts (that is, the next-to-most urban districts) are usually about as Democrat-leaning as the highest-numbered districts (that is, the most rural districts) are Republican-leaning. But the most urban district is much more Democrat-leaning than the most rural district. Thus Democrats have a surplus of voters in these handful of large cities, running up their margins in places which translates into no electoral advantage.
Obviously, this sort of an electoral system is never going to be instituted. But it does suggest something about the geography of partisanship that cuts against the “urban–rural” divide that has become so familiar. If a few hundred thousand Democratic voters were to decamp from the densest cities and move, not to the true rural hinterland, but to the kinds of counties which are right at the middle of U.S. density patterns—to cities like Chattanooga, Tallahassee, and Springfield, MA, all of which are in tipping-point districts in the models above—they would go a long ways towards shifting the geographic terrain of political campaigns in their favor.