Divergent Estimates in Gun Ownership Trends

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Only two organizations have provided continuous estimates of the proportion of American households that own a gun over an extended period of time. One of the these is the well-known pollster, The Gallup Organization. Much less known among ordinary Americans, but quite well known and respected by academic researchers, is the General Social Survey conducted every other year by the National Opinion Research Center at the University of Chicago.

These two survey organizations use very different methods to compile their samples of American adults. Gallup relies on telephone interviewing while the General Social Survey interviews people in their homes.  Naturally the two organizations’ estimates of gun ownership rates have differed over the years, but they have diverged sharply since about 2000.

As this graph shows, the GSS data* show a consistent decline in gun ownership rates since the late 1970s.  At that time estimates from the GSS actually exceeded those reported by Gallup.  However, beginning in the mid-1980s Gallup reported higher ownership rates than the GSS, a difference which has persisted ever since.  Despite this divergence both organizations documented a fairly steep decline in household ownership rates during the 1990s.

ownership-trends-compared

Starting around 1990, though, the two organizations began reporting quite different rates of gun ownership, and since 2000 the GSS has consistently reported ownership rates some seven or more points lower than Gallup’s.  The graph below reports differences between the two organization’s estimates for years where both surveys are available.  Since the “expanded” question was not asked by Gallup until 1991, I have relied on the narrower item, “do you have a gun in your home,” without including people who reported having a gun stored in a location outside the home.  Including those data would increase the divergence in estimates between the two organizations.

gss-gallup-gap

It is difficult to find other estimates of gun ownership rates to which we might compare the figures from Gallup and NORC.  The only data from a Federal agency that I can find so far is one set of figures for 2001 from the Behavioral Risk Factor Surveillance System maintained by the Centers for Disease Control. In 2001 a question on gun ownership was inserted into that survey of nearly 202,000 respondents nationwide.  That BRFSS reports an overall gun ownership rate for the United States of 32% in 2001, essentially identical to the 33.4% average of the figures from the 2000 and 2002 GSS. In contrast Gallup reported a 40% figure for 2001. I am continuing to review data from other Federal agencies, but, under pressure from the National Rifle Association, Congress has restricted the ability of agencies like the Centers for Disease Control to conduct studies of gun violence.

 

*The GSS figures come from my tabulation of the combined 1972-2010 GSS dataset used in this earlier posting on generational trends in gun ownership rates. I have treated the small number of refusals as missing data and report the percentage of people who gave a yes or no answer to the question about the presence of a gun in the household.  My estimates differ slightly from the rates reported by a similar tabulation from the Violence Policy Center.  Their figures may be based on the full cross-sectional samples for each year, but the VPC report does not provide details on exactly which dataset(s) they employed. (Return)

Millennials “just say no” to guns

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Updated, January 12, 2013

A person’s age can be thought of as summarizing two different sets of influences in our lives.  One set derives from our position in the life-cycle as we move from young adulthood through middle age and beyond.  These experiences should be rather similar no matter when someone is born.  However different generations share different formative experiences, so we might expect that people born in a particular historical period also share common beliefs and patterns of behavior.  These generational explanations rely on looking at date of birth, and not current age, when looking for patterns in survey data.

Disentangling the two factors is obviously impossible in just a single survey, but if we aggregate comparable surveys taken over many years we can separate out the effects of generational experiences and position in the life-cycle.  As it turns out, the General Social Survey provides exactly the kind of data we need if we want to study how gun ownership has varied over time and across age groups.

I have followed the Pew Research Center’s definitions of American political generations to label four “cohorts” who became adults after World War II.  Pew terms Americans who came of age between the War and the Kennedy Assassination the “Silent” generation.  They are followed by the “Boomers,” who came of age between 1964 and 1982, then “Generation X” (1983-1998) and finally the “Millennials,” who reached adulthood at the turn of the twenty-first century or thereafter.  To these groups I have added all Americans who came of age before 1946, which I call the “New Deal” generation since the Depression, FDR, and World War II were all significant influences on their adult lives.

In the table below I present the reported rate of household gun ownership for each of these five generations using the 33,154 respondents ever asked this question in the NORC General Social Survey since 1972.  With so many interviews conducted over such a long span of time we can disentangle the separate effects of both generation and life-cycle on gun ownership patterns.

generations-and-gun-main

Looking first at the highlighted column we see that gun ownership has declined substantially in each generation following the Silents, half of whom reported living in a household with a gun.  On average gun ownership has declined by about ten percent in every generation that came of age after 1963.  Gun ownership reaches its nadir in the youngest generation with only one-in-five “Millennials” reporting that they live in a household with a gun present.  If that trend continues over the next two generations, hardly any Americans coming of age in mid-century will choose to own a gun.

The right-hand side of the table shows how gun ownership varies over the course of the life cycle.  We might expect people to purchase guns as they age and become more settled, so that the low rate of ownership among current 18-29 year-old Millennials may not persist into their later years.  The GSS data provide no support for such a notion.  Within each generation gun ownership rates remain essentially flat across all age groups with perhaps a slight decline after retirement age.  That suggests the low rate of ownership among current Millennials should persist as they grow older.

As a validity check on these results, I compiled the same table for respondents to the 2010 General Social Survey.  I have excluded people who were interviewed as part of the time-series study and are thus included in the table above.  That leaves a separate sample of 2,857 people of whom 1,944 were asked the gun ownership question.  Respondents in the three youngest cohorts report slightly higher rates of gun ownership than we found in the time-series data, but the differences are small and the pattern identical to what we observed using all respondents interviewed since 1972.

generations-and-guns-2010xs

Patterns of Gun Ownership in the United States

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The events in Newtown, Connecticut, give us all pause.  It has led me to examining data on gun ownership beginning with the 2010 General Social Survey conducted every other year by the National Opinion Research Center at the University of Chicago.  In all some 3,207 respondents were asked in 2010 whether they owned a gun, and if so, whether they owned a rifle, shotgun or a “pistol”. I will use the more-common term “handgun” for “pistol.”

Let us begin with some basic information on patterns of gun ownership.  Many surveys like Gallup report figures for “gun” ownership without differentiating among the various kinds of guns people possess.  We might imagine a group of “sportsmen” who own rifles and shotguns but not handguns.  There are also people who own only a handgun,  people who own both handguns and long guns, and people who own no guns at all.

Two-thirds of the households report owning no guns at all.   Only six percent of Americans, or 18% of gun owners, possess just a handgun. Eleven percent of households own only a long gun, with preferences about equal for either a rifle or a shotgun or both.  A majority of handgun owners also have both a rifle and a shotgun in their possession.   Here is a reorganized version of the data above that divides American households into three categories — people who own only long guns, people who own only handguns, and people who own both.

About a fifth of American households own a handgun while a quarter own a rifle or shotgun.  Only three out of ten handgun owners do not also own a long gun.  The reverse combination is more common; 44% of long gun owners do not own a handgun.

Health Insurance Coverage in the American States – I

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With this post I begin a new series of reports based on my analysis of state-level data on health insurance coverage.  I began this research in the summer after reading this article on a blog over at the The Atlantic but put it aside during the furore of the election campaign. In that article Richard Florida examines a map of data on insurance coverage from Gallup and finds an “uninsured belt” running through “much of the deep south and the Sunbelt.” In contrast, states in the Northeast and the industrial Midwest have larger proportions of their populations who are covered by health insurance.

Mr. Florida then goes on to report positive correlations between the proportion of a state’s population without health insurance and political attitudes like the proportion of a state’s residents that identify themselves as conservatives in polling or that voted for McCain in the 2008 election. Patterns like these make it tempting to infer that health insurance coverage reflects a pattern of Republican “stinginess” and Democratic “generosity.” The truth, as it turns out, is much more complicated.

What is left out of these “big picture” analyses of ties between Republican opinion and healthcare coverage is any explanation of the mechanisms by which popular opinions are converted into actual policy outcomes like rates of insurance coverage. The most obvious hypothesis is that states with strong Republican leanings elect Republican legislatures and governors that implement policies leading to lower rates of coverage in the states they govern. While that might be the most obvious mechanism, it is simply untrue. There is no evidence that states where the Republicans controlled the levers of government have systematically lower levels of insurance coverage.

For this research I relied on the extensive set of state-level figures on insurance coverage based on Census data as maintained by the Kaiser Family Foundation.  I will start with the coverage rates based on the state’s total population.  Later I will narrow the focus to one specific group, adults 19-64 without dependents, who constitute the most difficult group to insure.

To test this hypothesis, I categorized states based on the number of years between 1990 and 2010 one party or the other controlled both houses of the state legislature and the governor’s office.  A state was scored +1 for each year both houses and the governorship were held by the Republicans; years where the Democrats were in control were scored as -1.  Any years of split control were coded zero.  I then averaged these figures over the two decades to construct an index of partisan control and computed the average percent of the population without insurance for five categories.  Here are the results:

As we might expect, states with consistent Republican control also had higher rates of support for John McCain in 2008.  However there is simply no relationship between partisan control of state governments and the percent of the population without health insurance.  What does influence health insurance coverage rates will be the subject of the postings to follow.

 

The New Delusion: Sandy Elected Obama

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Over the weekend Republican-leaning commentators at Nate Silver’s 538 Blog have been advancing the theory that President Obama’s victory can be attributed to “Superstorm” Sandy which hit the Northeast on October 29th, just a week before Election Day.  What especially irked Republicans was the reaction of New Jersey Governor Chris Christie who embraced the President in the wake of the storm despite having delivered the keynote speech at the Republican National Convention and being an active spokesman for Governor Romney.

The view that Christie “threw Romney under the bus” and thus assured the President’s re-election was the polar opposite of Republican commentary right after the storm hit.  Spokesmen like Rudy Giuliani criticized the President for campaigning in Western states like Nevada when New Jersey and New York had been ravaged. Liberal commentators like John Cassidy at The New Yorker naturally took the opposite view, suggesting that the President’s “handling of Sandy has raised his standing, and his poll ratings.”

I find neither of these arguments convincing.  First, many of the claims about a late Sandy-based surge in the polls rely on differences in the President’s level of support that fall well within the margin of error.  Cassidy, for instance, points to a three-point swing in Pew’s results as evidence, a change that could just as easily be attributed to sampling variability.  Including a term in my model for polls conducted after Sandy showed a  pro-Obama effect of one percent, but it, too, did not achieve statistical significance.

Rather than looking at the pre-election polls, I suggest we examine the exit polls taken on Election Day.  Looking at the national exit polls, we might conclude that Sandy had a large effect.  Fully 15% of voters reported that Sandy was “the most important factor” in determining how they voted, and 73% of those people reported voting for Obama.  To anyone accustomed to analyzing surveys, this 15% figure seems implausibly high. For 15% of the national electorate to attribute their vote to Sandy, when the vast majority of them lived outside the affected area, is hard to comprehend. Moreover, despite the obvious correlation between attitudes about the storm and support for the President, the direction of causality is questionable.  An equally plausible explanation for this correlation is that both answers reflect the partisanship of the person being interviewed.

We might thus wonder what effect the storm might have had on voters in swing states like Ohio, or even Florida with its history of hurricanes.  Unfortunately, if we drill down to the individual state exit polls, we find that this question was asked only among voters in New York and New Jersey, states whose outcomes were never in doubt.  Even voters in neighboring states like Pennsylvania and Connecticut were not asked about Sandy. (No exit polling was conducted in Louisiana this year so we cannot assess the claim that voters in that state might have been more attuned to the issue because of their experience with Katrina.  Mississippi voters were polled but not asked the Sandy question.)

In order for the hurricane to have affected the outcome of the election, we would need to see its effects in the swing states where the election was actually decided.  Since we do not have direct measures of peoples’ opinions about the effects of the storm in these states, and because such answers are inherently untrustworthy, we have to look at a more indirect measure of Sandy’s effects.  We can compare the opinions of voters in the swing states who reported deciding between the candidates in the last days before the election and see whether we can detect a late movement toward the President in the aftermath of the hurricane.  Here are the results from the exit polls in the nine swing states I examined before.

late-deciders-sandy-effect2

The chart shows quite clearly that, if anything, late deciders broke more in favor of Governor Romney than President Obama.  Of voters who made up their minds in October or earlier, the President’s average lead in these nine swing states was about five points.  Among voters who decided during “the last few days” before the election or on Election Day itself, his margin shrank to just over a percentage point.

More importantly, we can determine how large a contribution each group of voters made to the overall margin between the candidates.  Take Wisconsin as an example.  Late deciders made up 10% of the electorate in that state, and those voters favored Romney by eight points.  Taken together we can attribute an effect on the overall margin of 10% times 8%, or 0.8% in favor of Mr. Romney.  The other 90% of Wisconsin’s voters favored the President by 10%, for an overall effect on the margin of 9.0% in favor of the President.

The average effect of the decisions of late deciders in these nine states is zero compared to a four percent margin in the President’s direction among voters who decided in October or before.  Thus there is absolutely no measurable effect of Superstorm Sandy on the results in these swing states.  If anything voters who decided after the storm hit showed a slight preference for Mr. Romney in comparison to voters who decided before.

Gerrymandering: The Final Reckoning

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Over the past few posts I have narrowed down the list of states where we might claim gerrymandering affected the outcome of the 2012 Congressional election.  There is still one more task remaining — identifying those states where the partisan composition of the legislature and governorship, and the laws governing redistricting, enabled one party or the other to draw lines in a favorable manner.  This table combines information on each state’s constellation of partisanship and the method by which the state allocates Congressional seats.

There are four types of apportionment methods identified by Ballotpedia.  Most states place control over redistricting in the hands of the state legislature with the governor having a veto in all such states except North Carolina.  Nine states use nonpartisan commissions, and another five states can appoint a commission if the legislature fails to agree on a plan.  I treat those states as equivalent to states where the legislature is entirely in control.  Iowa reverses the backup system, with the legislature brought in only if the commission fails to come up with a plan.  I consider Iowa a commission state.

In the table above, I have divided the states into four groups.  At the top we have the eight states with unified Democratic control of state government and laws that grant the legislature and governor control over the apportionment process.  Some of the states identified in the last post as showing a pro-Democratic bias in their seat allocations like Massachusetts and Connecticut appear on this list of states with conditions favorable to Democratic gerrymandering.

Then follows a much larger group of states, nineteen, which had unified Republican state houses with control over redistricting.  Again we see some familiar faces from earlier tables like Pennsylvania and Ohio.  The third block of fourteen states have more uncertain gerrymandering conditions because of split partisan control either within the legislature or between the legislature and the governor.  The last group of nine states rely on nonpartisan commissions to draw their lines.  Both California and Arizona appear on this list despite showing a Republican and a Democratic excess of seats respectively.

So the last step is to combine the earlier list of states where gerrymandering might have taken place with the lists of states in the top two groups of the table above where the arrangement of political forces in the state might have encouraged gerrymandering.

Democratic Gerrymanders

Four of the eight states with pro-Democratic seat outcomes seem likely candidates for gerrymanderers.  All told, the Democrats probably won between four and six additional seats in Massachusetts, Connecticut, Illinois, and Maryland than they would have under a fair allocation of seats.  Gerrymandering seems much less likely to explain the additional Democratic victories in Georgia and Maine, where the Republicans were in control of the apportionment process, or in Arizona where district lines are drawn by a commission.

In Georgia, the entire process was controlled by the Republicans and was expected to produce a result favorable to that party.  However one of the targeted Democratic incumbents, John Barrow, moved after his district was redrawn, contested the 12th CD, and won with 54% of the vote.  Barrow’s dogged pursuit of his seat probably accounts for the “extra” Democrat in the Georgia delegation.

In New Hampshire, a Republican legislature faced off against a Democratic governor, though most of political struggles took place within Republican ranks.  Both seats had been captured from the Democrats in 2010, and the two new incumbents squabbled over the small changes that needed to be made to balance the two districts population.  In the event both seats were retaken by the Democrats in 2012.  This Democratic surge in New Hampshire probably has much to do with the regional trends to the Democrats across New England,  and the efforts by the Obama campaign to mobilize Democratic voters in a swing state.

Republican Gerrymanders

Two states that showed a pro-Republican bias had lines drawn by a commission rather than the legislature, while in Virginia legislative control was split between a Democratic Senate and a Republican House.  That leaves ten states with unified Republican control that show evidence of gerrymandering.  At the top of the list we have Pennsylvania, Ohio, Michigan and North Carolina, all states that have been repeatedly cited by observers as being heavily gerrymandered in the Republicans’ favor.  Unfortunately these observers often tend to claim the Republicans maintained control of the House entirely by gerrymandering and neglect the effects of incumbency.

At one extreme gerrymandering might have given the Republicans seventeen more seats in the House.  That figure combines the minimal estimate for Democratic gerrymanders, four, with the maximal estimate for the Republicans of twenty-one. If those seventeen seats had been won by Democrats, they would have eked out a one-seat majority in the House.  To achieve that value, though, we have to assume that urbanism only exerted effects in the Democratic states and had no effects in the Republican ones.  The opposite set of assumptions leads to an estimate of gerrymandering effects of just eight seats for the Republicans, far too few to have changed the outcome in the House.

These results provide a lower- and upper-bound on the effects of gerrymandering.  The actual effects probably lie somewhere in between.  Perhaps about a dozen seats remained with the Republicans because of gerrymanders, hardly an insignificant number to be sure, but not sufficient to explain why the Democrats could not win control of the House despite winning a slight majority of the popular vote for Congress.

 

Accounting for Geography

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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.

Was Gerrymandering the Culprit? — Part I

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Results updated on November 23, 2012, with final Congressional results for 434 races; NC 7th is still undecided.

It is now time to put some of the findings from earlier postings together and try to determine the extent of gerrymandering in the 2012 Congressional Elections.

Three factors should influence the number of House seats a party wins in a state Congressional election:

I have taken two separate measurements of the first item, the relationship between seats and votes.  I have calculated both a longitudinal measurement using elections from 1942 on, and a cross-sectional measurement using state results for 2012.  In both approaches I estimate the coefficients α and β of this “logit” model:

log(Democratic Seats/Republican Seats) = α + β log(Democratic Votes/Republican Votes)

The two models produce very different estimates for α, the seat “bias,” because it varies historically.  However the two estimates for β are nearly identical. The longitudinal estimate was 1.92; the cross-sectional estimate is 2.08.  For simplicity, I will just use two for the value of β.  (Mathematically, that implies that the ratio of Democratic to Republican seats varies in proportion to the square of the ratio of their votes.)

In this Technical Appendix, I explain why, if the Democrats win exactly half the vote, the only way they can win exactly half the seats is if the “bias” term α is zero. We can use this fact to create an “unbiased” distribution of seats.  I simply substitute two for β and apply it to the logit of the state-wide Democratic vote for Congress.  I will call this the “unbiased allocation.”  For each state I compare this estimate to the number of seats the Democrats actually won. Here are the results:

I have included all states where the difference between the predicted and actual number of Democratic seats was at least 0.7.  The state that gave us the word “gerrymander,” Massachusetts, shows the largest pro-Democratic deviation.  While the unbiased allocation model would award the Democrats only seven or eight of the nine seats in that state, not one Republican represents the Commonwealth of Massachusetts in Congress. The other state where Democrats did better than expected is Arizona, where they won a majority of the state’s Congressional seats with a minority of the popular vote.  Arizona had two of the closest races in the country, and they both fell to the Democrats by slim margins. All told, eight states including four New England states, have new Congressional delegations with an “extra” Democratic member in their numbers.

Many more states deviate from the unbiased allocation on the Republican side, with half-a-dozen states showing a pro-Republican bias of two, three, or, in the case of Pennsylvania, four seats. All told, sixteen states met our 0.7 criterion.  Compared to an unbiased allocation, the results in these sixteen states probably cost the Democrats 28 seats.  When we subtract out the eight extra seats the Democrats won in the pro-Democratic states, we get a net Democratic deficit in 2012 of some twenty seats compared to an “unbiased” allocation based solely on the popular vote for Congress in each state.

Before we start attributing all those seats to Republican gerrymandering, we first need to consider what other factors might influence the translation of Democratic votes to Democratic seats.  There is good reason to believe that the geographic distribution of Democratic voters by itself creates a pro-Republican bias when district lines are drawn.

 Accounting for Geography

 

Seats and Votes 2012: Evidence from the States

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Updated, November 23, 2012 with final results from all races except the NC 7th.

Last week I published a chart showing the historical relationship between House seats and votes since 1940.  Using the district-level level 2012 results compiled by David Wasserman, I plotted the percent of votes and seats for the Democrats in the 33 states where both parties had at least one member of the delegation.  That graph bears a striking similarity to the earlier chart depicting the historical relationship.

If I apply the same method to measure bias as I described here using the “logit” of the seats and votes shares, I get a slope that is nearly identical to what I estimated for the historical relationship.

1942-2010
Historical		 2012
States
Slope (β) 1.92 2.08
Intercept (α) 0.034 -0.189
Democratic Seat Bias 3.7 -20.5

However the intercept term, α, shows a much larger bias in the Republican direction than the four-seat pro-Democratic bias we found historically.  In these 32 states where both parties won at least one seat in 2012, the two parties garnered nearly identical numbers of votes, but the Democrats were rewarded with just 176 or 177 of the 388 seats (45.5%) in these states.

In the eighteen states with homogeneous delegations, the Democrats netted only one seat.  They swept all fourteen seats in Massachusetts and Connecticut and picked up ten more in six other states. The Republicans scored best in Oklahoma and Arkansas, but Massachusetts alone has as many seats (nine) as those two states combined.  All told the Republicans won twenty-three seats in ten of these homogeneous states.  The Democrats’ one-seat gain the homogenous states left them facing an estimated 19-20 seat deficit after adjusting for the votes they won.

The New Republican Bulwark in the House

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For only the third time since 1940, the winner of the national popular vote for Congress failed to take control of the House of Representatives.  The Democrats won a slim majority of the (two-party) vote, 50.2%, but failed to gain back the House, winning just 197 of the 431 seats currently decided,* or 45.7%. This is the largest adverse gap between the Democratic Party’s share of seats and its share of votes since the New Deal.

For most of the years between 1942 and 1994, the Democrats enjoyed a “bonus” in terms of the seats they were awarded in the House of Representatives.  A good portion of that bonus came from the workings of our electoral system.  Political scientists and statisticians have long known that the method of voting used in Congressional elections, called “plurality voting” or “first-past-the-post,” exaggerates the size of majorities in the elected assembly.  A party that wins 51% of the vote gains more than 51% of the seats, and the bonus increases as the party’s share of the popular vote grows.** This is not a uniquely American phenomenon; we see the same exaggeration at work in countries like the United Kingdom which also employ plurality voting.  The bonus is quite evident if we plot the share of seats won by the Democrats against their shares of the (two-party) popular vote.

The actual relationship between seats and votes is much steeper than the “parity” line which awards a party a share of the seats equal to its share of the vote.  Much of the Democratic advantage we saw in the first graph reflects this feature of our electoral system.  In the time-series plot the Democrats’ peak years of 1974 and 1976 appear to have garnered the Party dramatically oversized majorities in the House of Representatives.  Looked at in the context of the historical relationship between seats and votes in Congressional elections, the 1974 and 1976 contests were not out of line.

This relationship between seats and votes gives us a baseline for measuring how Congressional reapportionment might shift the balance of power in the House.  There are two major ways reapportionment might produce a bias for one party or another.  General demographic trends create the need to reallocate Congressional districts from states whose populations are relatively shrinking to states whose populations are relatively growing.  Even if Congressional district lines are drawn fairly, and even if the distribution of popular votes remain unchanged, taking seats away from Democratic bastions like Massachusetts and New York and awarding them to Republican strongholds like Texas could shift the margin in the House.

Besides demographic changes the other usual suspect is partisan gerrymandering.   American political institutions grant parties enormous power to reshape the structure of electoral competition every ten years because of Constitutionally-mandated reapportionment.  The Constitution also places the authority over how district lines will be drawn in the hands of state governments. Nine states, mostly in the West but also including New Jersey, use a nonpartisan commission to draw district lines.  Of the remaining 41, the state legislature and governor have full control of the redistricting process in 28 of them.  The other thirteen states use a “hybrid” approach with a commission that is usually subservient, and sometimes purely advisory, to the elected state legislature.

These institutions invest an enormous amount of power in one specific set of state legislators and governors, those holding office in a year ending in zero.  These partisan elected officials have the power to structure competition for the House of Representatives for the decade to come.  The development of sophisticated computer software combined with the geographic marketing databases places enormous power in the hands of determined modern gerrymanderers.

One way we might begin the measure the extent of partisan gerrymandering is to look at whether individual “apportionment periods,” the five elections conducted in the same seat boundaries, showed a bias toward one party or another.  We can use the seats/votes relationship to provide a baseline expectation of what seat outcomes ought to be, then calculate the average deviation for each five-election apportionment period like this.

I date apportionment periods by the date of the Census, so the 1940 apportionment covers elections held from 1942 through 1950. The vertical bars measure the difference between the average number of seats the Democrats won in the five Congressional elections following each Census and the number of seats they “should” have won in that period based on their share of the popular vote.  Remember that this technique already accounts for the bonus resulting from our plurality-voting electoral system.  These bars measure any additional partisan bias associated with a particular apportionment period.

The electoral system showed a distinct pro-Democratic bias of five to eight seats for most of the period between the New Deal and the 1990s.  The only exception came in the 1950s when the bonus fell to an estimated two seats, a value not statistically different from zero.  The Democrats’ bonus began to slip after 1960 and has moved in the Republicans’ direction ever since.  Extrapolating the pro-Republican trend from 1960 through 2010 would predict an advantage for the GOP of about three or four seats in 2012.

The actual Republican bias in 2012 looks closer to ten seats.  Not an auspicious start to the decade ahead if you are a Democrat.

While many commentators have pointed to Republican gerrymandering as the primary explanation for this result, I want to take things a bit more slowly and consider first how the process of reapportionment itself may have altered the balance of power in the House by shifting seats from Democratic states to Republican ones.  I begin that discussion in the next post.

The Effects of Reapportionment

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*I awarded two of the six undecided House races from the November 12th list at CNN. I gave both AZ 9 and FL 18 to the Democrats, leaving two races in California, and one each in Arizona and North Carolina as undecided. That puts the current tally at 234 to 197 in favor of the Republicans. All four of these uresolved contests show slim majorities for the Democrats.  If they took all four it would raise their total to 201.  Even with those seats added, that’ hypothetical mark of 46.2% of the seats would remain the worst result by a majority winner since the New Deal.  Both the 1942 Republicans and the 1996 Democrats failed to win the House, but they both won a larger share of the seats than the 2012 Democrats.  (Return)

**The claim that the bonus increases as the popular vote share increases holds true for values in the range observed historically.  Eventually the bonus must shrink as the popular vote approaches 100%.  In the historical period I am examining here, the Democrats never won less than 45.3% of the popular vote (1946) or more than 58.3% (1974). (Return)