Updated, November 24, 2012, with complete results for all seats except the NC 7th.
If seats in Congress were allocated in an unbiased fashion, the Democrats might have won as many as twenty additional seats than they did on November 6th and would have taken control of the House of Representatives with 221 seats.
Where did this large Democratic deficit come from? Democratic politicians and left-leaning pundits point their fingers at partisan gerrymandering by Republican state governments elected in the off-year landslide of 2010. Students of the redistricting process itself point to a more fundamental problem for the Democrats, the geographic distribution of their supporters.
In an intriguing paper based on a careful simulation model of the redistricting process, political scientists Jowei Chen and Jonathan Rodden show that the tendency for Democratic voters to be tightly clustered in urban areas naturally advantages the Republicans when lines are drawn:
We show that in many urbanized states, Democrats are highly clustered in dense central city areas, while Republicans are scattered more evenly through the suburban, exurban, and rural periphery. Precincts in which Democrats typically form majorities tend to be more homogeneous and extreme than Republican-leaning precincts. When these Democratic precincts are combined with neighboring precincts to form legislative districts, the nearest neighbors of extremely Democratic precincts are more likely to be similarly extreme than is true for Republican precincts. As a result, when districting plans are completed, Democrats tend to be inefficiently packed in homogeneous districts.
In another study of the 2012 redistricting Nicholas Goedert observes that measures of urbanization correlate with the degree to which the Democrats gain a smaller or larger share of seats than what their votes share would predict. So before we join the critics in claiming Republican gerrymandering as the source of the Democratic seat deficit, we need to first consider the role of urbanism.
The Census Bureau defines two types of urban areas — “urbanized areas” which contain a minimum of 50,000 people, and “urban clusters” which contain between 2,500 and 50,000 inhabitants. The Bureau provides detailed information by state for both these types of urban areas. I have tested a variety of these measures of urbanism by adding them to the baseline logit model for Democratic seats and votes. Typically the measures for urban clusters have no significant effect on either vote or seat shares, but the data for urbanized areas, places with at least 50,000 people, matter considerably.
To get a sense of how urbanized areas and urban clusters are distributed across the country I recommend looking at two maps on this page at the Census Bureau website. The map on the left displays the density of the urbanized areas and urban clusters. We can easily identify the large urban conglomerates like the Northeast Corridor, Atlanta, Chicago, Houston, Los Angeles, and Seattle. The second map codes entire counties and shows how California’s geography differs from most of the rest of the nation. Whole counties stretching back to the Nevada border are counted as urbanized even though most of the population living in the urbanized areas are along the coast. California also dominates the list of urbanized areas when they are sorted by population density. Of the top-thirty urbanized areas ranked by population density only six are outside California.
It is certainly the case that Democrats do better in states with a larger percentage of their populations living in urbanized areas. About fourteen percent of the variation in Democratic Congressional vote across states can be accounted for by the proportion living in urbanized areas. When it comes to the relationship betweens seats and votes, however, simply measuring how urbanized a state is does not affect the share of seats a party receives. What turns out to matter much more is the population density of urbanized areas. Adding that variable to our simple seats and votes model significantly improves our ability to predict the share of Democratic seats in a state given their share of its votes. It also makes theoretical sense that urban density should play an important role given the relationship between clustering and apportionment bias Chen and Rodden explore.
To see how urban density influences affects the distribution of Congressional seats, look at this table which shows the expected Democratic share of the seats given different values of the predictors.
Look first at the 50% column. Even if the Democrats win half the vote in a state, they can only be assured of winning half the seats in the most heavily urbanized states. Even in states like Maryland or Texas, with levels of urbanism higher than three-quarters of the states, winning half the vote does not guarantee a commensurate share of seats. The effects of urban population density give the Democrats a boost in the most urbanized states, but they are few in number. There are many more states where the Democrats need to poll well above 50% to claim half the seats in those states.
Given this powerful effect of urban density, I have rerun my seat estimates adjusting for the effect of urban density. Not surprisingly, the Democratic deficit compared to the unbiased allocation shrinks when political geography is taken into account, but the amount of shrinkage is striking.
Let us start with the totals at the bottom of the table. Using the method of “unbiased allocations” I estimate an 17 seat deficit for the Democrats in these states based solely on the share of the vote they won. Adjusting for urban density accounts for fully 12 of those seats leaving a total deficit of just five.
Two of those five seats are in California, where a nonpartisan commission draws district boundaries. As the maps above attest, the definition of “urbanism” applies rather differently to California than to the other states with densely populated urbanized areas. So we might be a bit hesitant to claim that those two seats reflect gerrymandering.
