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New year, new election.

This time around, May’s election will have the Mayoral race at the top of the ballot, and City Council races. Odd-year primaries also bring Philadelphia’s lesser-followed judicial elections. These elections are problematic: we vote for multiple candidates each election, upwards of 7, and even the most educated voter really doesn’t know anything about any of them. In these low-information elections, the most important factor in getting elected is whether a candidate ends up in the first column of the ballot. Ballot position is decided by a lottery. Philadelphia elects its judges at random.

I’ve looked into this before, measuring the impact of ballot position on the Court of Common Pleas, and then that impact by neighborhood. We even ran an experiment to see if we could improve the impact of the Philadelphia Bar’s Recommendations. But first, let’s revisit the story, update it with 2017 results, and establish the gruesome baseline.

The Court of Common Pleas
The most egregious race is for the city’s Court of Common Pleas. That court is responsible for major civil and criminal trials, juvenile and domestic relations, and orphans, so the bulk of our judicial cases. Common Pleas judges are elected to 10-year terms.

The field for Common Pleas is huge; we don’t know how many openings there will be this year, but in the five elections since 2009, there was an average of 31 candidates competing for 9 openings. Even the most informed voter doesn’t know the name of most of these candidates when she enters the voting booth, and ends up either relying on others’ recommendations or voting at random. This is exactly the type of election where other factors–flyers handed out in front of a polling place, the order of names on the ballot–will dominate.

Because of this low attention, some of the judges that do end up winning come with serious allegations against them. In 2015, Scott Diclaudio won; months later he would be censured for misconduct and negligence, and then get caught having given illegal donations in the DA Seth Williams probe. The 2015 race also elected Judge Lyris Younge, who has since been removed from Family Court for violating family rights, and just in October made headlines by evicting a full building of residents with less than a week’s notice. In 2016, Mark Cohen was voted out as state rep after reports on his excessive use of per-diem expenses. In 2017, he was elected to the Court of Common Pleas.
​
Every one of those candidates—Diclaudio, Younge, and Cohen—was in the first column of the ballot.

Random luck is more important than Democratic Endorsement or Quality
The order of names on the ballot is entirely random, so this gives us a rare chance to causally estimate the importance of order, compared to other factors.

First, what do I mean by ballot order? Here’s a picture of the 2017 Democratic ballot for the Court of Common Pleas.

There are so many candidates that they need to be arranged in a rectangle. In 2017, candidates were organized into 11 rows and 3 columns. Six of the nine winners came from the first column. (The empty spaces are candidates who dropped out of the race after seeing their lottery pick.)

​The shape of the ballot changes wildly over years. Sometimes it’s short and wide, in 2017 it was tall and thin. But in every year, candidates in the first column fare better than others. The results year over year show that the first column consistently receives more votes:

As the above makes clear, candidates do win from later columns every year. But first-column candidates win more than twice as often as if all columns won equally. Below, I collapse the columns for each year, and calculate the number of actual winners from each column, compared to the expected winners if winners were completely at random. The 2011 election marked the high-water mark, when more than three times as many winners came from the first column than should have.

This effect means that Philadelphia ends up with unqualified judges. Let’s use recommendations by the Philadelphia Bar Association as a measure of candidate quality. The Bar Association’s Judicial Commission does an in-depth review of candidates each cycle, interviewing candidates and people who have worked with them. They then rate candidates Recommended or Not Recommended. A Bar Association Recommendation should be considered a low bar; on average, they Recommend two thirds of candidates and more than twice as many candidates as are able to win in a given year. While the Bar Association maintains confidentiality about why they Recommend or don’t Recommend a given candidate (giving candidates wiggle room to complain about its decisions), my understanding is that there have to be serious concerns to be Not Recommended: a history of corruption, gross incompetence, etc.

In short, it’s worrisome when Not Recommended candidates win. But win they do. In the last two elections, a total of six Not Recommended candidates won. They were all from the first column.

Note that, from a causal point, this analysis could be done. Ballot position is randomized, so there is no need to control for other factors. The effect we see has the benefit of combining the various ways ballot position might matter: clearly because voters are more likely to push buttons in the first column, but also perhaps if endorsers take ballot position into account, or candidates campaign harder once they see their lottery result. This analysis is uncommonly clean, and we could be done.

But clearly other factors affect who wins, too. How does the strength of ballot position compare to those others?

Let’s compare the strength of ballot position to three other explanatory features: endorsement by the Democratic City Committee, recommendation by the Philadelphia Bar Association, and endorsement by the Philadelphia Inquirer. (There are two other obvious ones, Ward-level endorsements and money spent. I don’t currently have data on that, but maybe I’ll get around to it!)

​I regress the log of each candidate’s total votes on their ballot position (being in the first, second, or third column, versus later ones, and being in the first or second row), endorsements from the Democratic City Committee and the Inquirer, and Recommendation by the Philadelphia Bar, using year fixed effects.

Being in the first column nearly triples your votes versus being in the fourth or later. The second largest effect is the Democratic City Committee endorsement, which doubles your votes versus not having it. (Notice that the DCC correlation isn’t plausibly causal, the Committee certainly takes other factors into its consideration. Perhaps this correlation is absorbing candidate’s abilities to raise money or campaign. And it almost certainly is absorbing some of the first-column effect: the DCC is known to take ballot position into consideration!)

The Philadelphia Bar Association’s correlation is sadly indistinguishable from none at all, but with a *big* caveat: the Philadelphia Inquirer has begun simply adopting the Bar’s recommendations as its own, so that Inquirer correlation represents entirely Recommended candidates.

​While this analysis doesn’t include candidate fundraising or Ward endorsements, those certainly matter, and would probably rank high on this list if we had the data. Two years ago, I found tentative evidence that Ward endorsements may matter even more than the first column. And Ward endorsements, in turn, cost money.

What are the solutions?
There are obvious solutions to this. I personally think holding elections for positions that no-one pays attention to is an invitation for bad results. It’s better to have those positions be appointed by higher profile officials; it concentrates power in fewer hands, but at least the Mayor or Council know that voters are watching.

A more plausible solution than eliminating judicial elections altogether might be to just randomize ballot positions between divisions, rather than use the same ones across the city. That would mean that all candidates would get the first-column benefit in some divisions and not in others, and would wash away its effect, allowing other factors to determine who wins.

Unfortunately, while these are easy in theory, any changes would require a change to the Pennsylvania Election Code, and may be a hard haul. But one thing everyone seems to agree on is that our current system is bad.

The turnout in November was unprecedented. Nationally, we had the highest midterm turnout since the passage of the Voting Rights Act. In Philadelphia some 53% of registered voters voted a huge increase over 4 years ago, or really any midterm election in recent memory. Turnout can be hard to wrap your mind around, with a lot of factors that affect who votes and in what elections. What drives those numbers? Is it registration deviations? Or variations in the profile of the election? And how is that structured by neighborhood?

Decomposing the Funnel
I’m conceptualizing the entire process that filters down the whole population as a funnel, with four stages at which a fraction of the population drops out.

Let’s break down the funnel using the following accounting equation:

Voters per mile =
(Population over 18 per mile) x
(Proportion of over-18 population that are citizens) x
(Proportion of citizens that are registered to vote) x
(Proportion of registered voters that voted in 2016) x
(Proportion of those who voted in 2016 that voted in 2018).

The implications are very different if a neighborhood lags in a given one of these steps. If a ward has a low proportion of citizens that are registered, registration drives make sense. If a neighborhood has very low turnout in midterms vs presidential elections, then awareness and motivation is what matters.

We are also going to see that using metrics based on Registered Voters in Philadelphia is flawed. The lack of removal of voters from the rolls—which is actually a good practice, since the alternative is to aggressively remove potential voters—means that the proportion of registered voters that vote is not a useful metric.

Funnel Maps
Let’s walk through the equation above, map by map.

Overall, 44% of Philadelphia’s adults voted in 2018. That represented high turnout (measured as a proportion of adults) from the usual suspects: the ring around Center City, Chestnut Hill, Mount Airy, and the Oak Lanes.

How does the funnel decompose these difference?

First, consider something not in that map: population density. This is obviously, almost definitionally, important. Wards with more population will have more voters. ​

​The ring around Center City, North Philly, and the lower Northeast have much higher densities than the rest of the city. [Note: See the appendix for a discussion of how I mapped Census data to Wards]

​We don’t allow all of those adults to vote. I don’t have data on explicit voter eligibility, but we can at least look at citizenship.

The city as a whole is 92% citizens, and some 53 of the city’s 66 wards are more than 90%. Notable exceptions include University City, and immigrant destinations in South Philly and the lower Northeast.

The next step in the funnel: what fraction of those citizens are registered to vote? Here’s where things get hairy.

Overall in the city, there are 93% as many registered voters as there are adult citizens. That’s high, and a surprising 22 wards have *more registered voters than the census thinks there are adult citizens*. What’s up? Philadelphia is very slow to remove voters from the rolls, and this imbalance likely represents people who have moved, but haven’t been removed.

While people tend to use facts like this to suggest conspiracy theories, Philadelphia’s ratio is actually quite typical for the state, across wealthy and poor, Republican and Democratic counties. [See below!]

And it’s very (very) important to point out that this is good for democracy: being too aggressive in removing voters means disenfranchising people who haven’t moved. And we have no evidence that anybody who has moved is actually *voting*, just that their name remains in the database.

It does, however, mean that measuring turnout as a fraction of registered voters is misleading.

I explore the registered voter question below, but for the time being let’s remove the registration process from the equation above to sidestep the issue:

Voters per mile =
(Population over 18 per mile) x
(Proportion of Over 18 pop that are citizens) x
(Proportion of citizens that voted in 2016) x
(Proportion of those who voted in 2016 that voted in 2018).
​
Why break out 2016 voters first, then 2018? Because there are deep differences in the processes that lead people to voting in Presidential elections versus midterms, and low participation in 2016 and 2018 have different solutions. Presidential elections are obviously high-attention, high-energy affairs. If you didn’t vote in 2016, didn’t turn out in the highest-profile, biggest-budget election of the cycle, you either have steep barriers to voting (time constraints, bureaucratic blockers, awareness), or are seriously disengaged. If someone didn’t vote in 2016, it’s hard to imagine you’d get them to vote in 2018.

Compare that to people who voted in 2016 but not in 2018. These voters (a) are all registered, (b) are able to get to the polling place, and (c) know where their polling place is. This group is potentially easier to get to vote in a midterm: they’re either unaware of or uninterested in the lower-profile races of the midterm, or have made the calculated decision to skip it. Whichever the reason, it seems a lot easier to get presidential voters to vote in a midterm than to get the citizens who didn’t even vote in the Presidential race.

So which citizens voted in 2016?

Overall, 63% of Philadelphia’s adult citizens voted. This percentage has large variation across wards, ranging from the low 50s along the river, to 81% in Grad Hospital’s Ward 30 [Scroll to the bottom for a map with Ward numbers]. Wards 28 and 47 in Strawberry Mansion and North Philly have high percentages here, and also had high percentages in the prior map of registered voters, which to me suggests a combination of high registration rates in the neighborhood and a low population estimate from the ACS (see the discussion of registered voters below).

How many of these 2016 voters came out again two years later?

This map is telling. While there were fine-grained changes in the map versus 2014’s midterm, the overall pattern of who votes in midterms didn’t fundamentally change: it’s the predominantly White neighborhoods in Center City, its neighbors, and Chestnut Hill and Mount Airy, coupled with some high-turnout predominantly Black Wards in Overbrook, Wynnefield, and West Oak Lane.

The dark spot is the predominantly Hispanic section of North Philly. It’s not entirely surprising that this region would have low turnout in a midterm, but remember that this map has as its denominator *people who voted in 2016*! So there’s even disproportionate fall-off among people who voted just two years ago.

So that’s the funnel. We have some small variations in citizenship, confusing variations in proportions registered, steep differences in who voted in 2016, and then severely class- and race-based differences in who comes out in the midterm. Chestnut Hill and Center City have high scores on basically all of these dimensions, leading to their electoral dominance (except for Chestnut Hill’s low population density and Center City’s relatively high proportion of non-citizens). Upper North Philly and the Lower Northeast are handcuffed at each stage, with telling correlations between non-citizen residents and later voting patterns, even those which could have been unrelated, such as the turnout among citizens.

What’s going on with the Registered Voters?
It’s odd to see more registered voters than the Census claims there are adult citizens. I’ve claimed this is probably due to failing to remove from the the rolls people who move, so let’s look at some evidence.

First, let’s consider the problem of uncertainty in the population estimates. The American Community Survey is a random sample, meaning the population counts have uncertainty. I’ve used the 5-year estimates to minimize that uncertainty, but some still exists. The median margin of error in the population estimates among the wards is +/- 7%. This uncertainty is larger for less populous wards: the margin of error for Ward 28’s population is +/-12%. So the high registration-to-population ratios may be partially driven by an unlucky sample giving a small population estimate. But the size of the uncertainty isn’t enough to explain the values that are well above one, and even if it were large enough to bring the ratios just below one, the idea that nearly 100% of the population would be registered would still be implausibly high.

So instead, let’s look at evidence that these high ratios might be systematic, and due to lags in removal. ​First, consider renters.

Wards with more renters have higher registration rates in the population, including many rates over 100%. On the one hand, this is surprising because we actually expect renters to be *less* likely to vote; they are often newer to thei
r home and to the city, and less invested in local politics. On the other hand, they have higher turnover, so any lag in removing voters will mean more people are registered at a given address. The visible positive correlation suggests that the second is so strong as to even overcome the first.

​(For reference, here’s a map of Ward numbers)

There’s an even stronger signal than renters, though. Consider the Wards that lost population between 2010 and 2016, according to the Census.

The group of Wards that saw the greatest declines in population also have the highest proportions of registered voters (except for 65, which is a drastic outlier). This correlation suggests that the leaving residents may not be removed from the rolls, further pointing the finger at that as the culprit.

Finally, let’s look at Philadelphia versus *the rest of the state*. It turns out that Philadelphia’s registered voter to adult citizen ratio is typical of the state, especially when you consider its high turnover.

First, there isn’t a strong correlation between Registered Voters per adult citizen and Democratic counties.

Allegheny and Philadelphia, to two most Democratic counties are 1st and 6th, respectively, but predominantly-Republican Pike County, outside of Scranton, is second, and the rest of Philadelphia’s wealthier suburbs are 3, 4, 5, and 7. (I’m not totally sure what’s going on with Forest County, at the bottom of the plot, but the next scatterplot helps a little bit).

​More telling is a county’s fraction of the residents that have moved in since 2000: it looks like counties with higher turnover has higher ratios, which we would expect if the culprit were lagging removal.

Philadelphia here looks entirely normal: counties with more recent arrivals have higher ratios. This also sheds some light on Forest County. With a population of only 7,000, it’s an outlier in terms of long-term residents, and thus has a *very* low registration-to-citizen ratio.

Apologies for the long explanation. I didn’t want to just ignore this finding, but I’m terrified it’ll be discovered and used by some conspiracy theorist.

Exactly where the voters fall out of the funnel matters
Philadelphia’s diverse wards have a diverse array of turnout challenges. Unfortunately, the voter registration rolls are pretty unhelpful as a signal, at least in the simplistic aggregate way I considered them here. (Again: good for democracy, bad for blogging).

Which stage of the funnel matters depends on two things: their respective sizes, and how easy it is to move each. Is it more plausible to get out the citizens who didn’t vote in 2016? Or is it more plausible to get the 2016 voters out again in 2018? Where will registration drives help, and where is that not the issue?

​Next year’s mayoral primary, a local election with an incumbent mayor, will likely be an exaggerated version of the midterm, with even more significant fall-off than we saw in November. More on that later.

Appendix: Merging Census data with Wards
I use population data from the Census Bureau’s American Community Survey, an annual sample of the U.S. population with an extensive questionnaire on demographics and housing. Because it’s a sample, I use the five-year aggregate collected from 2012-2016. This will minimize the sampling uncertainty in the estimates, but mean there could be some bias in regions where the population has been changing since that period.

The Census collects data in its own regions: block groups and tracts. These don’t line up with Wards, so I’ve built a crosswalk from census geographies to political ones:
– I map the smallest geography–Census Blocks–to the political wards.
– Using that mapping, I calculate the fraction of each block group’s 2010 population in each Ward. I use 2010 because blocks are only released with the Decennial Census. Largely, any changes over time in block populations are swamped by across-block variations in populations that are consistent through time.
– I then apportion each block group’s ACS counts–of adults, of citizens–according to those proportions.
This method is imperfect; it will be off wherever block groups are split by a ward and the block group is internally segregated, but I think the best available approach.