I’m a committeeperson in the 24th Ward, which serves West Philly’s Mantua, Parkside, and Powelton neighborhoods. In May 2022, we voted to become an Open Ward. What does that mean? There’s no single definition, and each Open Ward establishes its own bylaws. But at a high level, being “Open” means that Committeepeople vote on which candidates to endorse and on other Ward operations, rather than simply following the Ward Leader. In addition to being more democratic, we also hope that by empowering committeepeople in this way, we can increase participation and Ward capacity for Get Out the Vote and other efforts.
Many of these benefits are fuzzy, but an increase in voter participation should be measurable. So, do we have any evidence that this works? Do Open Wards increase turnout? A number of wards have been open for years (some even for decades). Today, I’m doing some statistical analyses to measure if that’s true.
First, what wards are “Open”? As I said, there’s no single definition. But for this analysis, I’m using the set of Wards that have bylaws allowing committeepeople to vote on endorsements. Those I’m considering Open are:
| Ward | Year Opened |
|---|---|
| 1 | 2018 |
| 2 | 2018 |
| 5 | 60s or 70s |
| 8 | 60s or 70s |
| 9 | 60s or 70s |
| 15 | 2022 |
| 18 | 2018 |
| 24 | 2022 |
| 27 | 60s or 70s |
| 30 | 1998 |
| 39a | 2022 |
(If you want to contest these definitions or years, my email is open.)
View code
library(sf)
library(dplyr)
library(tidyr)
library(ggplot2)
source("../../admin_scripts/theme_sixtysix.R")
source("../../admin_scripts/util.R")
asnum <- function(x) as.numeric(as.character(x))
divs <- st_read("../../data/gis/warddivs/202011/Political_Divisions.shp")ward_from_div <- function(warddiv){
ward <- substr(warddiv, 1, 2)
div <- substr(warddiv, 4,5)
ward <- case_when(
ward == "39" & asnum(div) >= 25 ~ "39a",
ward == "39" & asnum(div) < 25 ~ "39b",
TRUE ~ ward
)
return(ward)
}
divs <- divs |>
mutate(
warddiv = pretty_div(DIVISION_N),
ward = ward_from_div(warddiv)
)
wards <- divs |> group_by(ward) |> summarise() |> st_make_valid()
open_wards <- tribble(
~ward, ~year_opened,
"01", "2018",
"02", "2018",
"05", "60s or 70s",
"08", "60s or 70s",
"09", "60s or 70s",
"15", "2022",
"18", "2018",
"24", "2022",
"27", "60s or 70s",
"30", "1998",
"39a", "2022"
)
wards <- wards |> mutate(
is_open = ward %in% open_wards$ward,
is_open_pre_22 = ward %in% (
open_wards |> filter(year_opened != "2022") |> with(ward)
)
)
ggplot(wards) +
geom_sf(aes(fill = is_open)) +
scale_fill_manual(
values=c(`TRUE` = light_blue, `FALSE` = light_grey),
guide=FALSE
) +
geom_text(
data = wards |> filter(is_open) |>
mutate(
x = st_coordinates(st_centroid(geometry))[,"X"],
y = st_coordinates(st_centroid(geometry))[,"Y"]
),
aes(
x = x,
y = y,
label = ward
),
size = 3
) +
theme_map_sixtysix() +
labs(
title="Philadelphia's Open Wards"
)
The first thing you’ll notice is that these wards cluster around Center City. As such, they tend to be wealthier and whiter than the city as a whole; the same demographics that have seen a generalized surge in turnout since 2016. That makes the causality here difficult to measure: does the openness of a ward increase turnout, or did these wards become open thanks to local political engagement, and turnout there would have increased regardless?
There’s another complication in answering the question: how should we define turnout? I don’t love the usual measure, votes cast divided by registered voters; that number will be alarmingly low because of inactive voters still on the rolls, and the denominator moves for all sorts of reasons, including slow cleaning of the voter rolls or registration drives (I’ve called this the Turnout Funnel). And wards might have very different populations with different propensities to vote, or even be eligible to vote, which we want to control for.
I’ll use my favorite metric: turnout in an off-year election as a percentage of Presidential turnout. Off-year turnout is more malleable to local GOTV efforts: there’s more need just to remind people when Election Day is, and people get less communication about the election from other channels. A division’s Presidential turnout gives a ceiling on plausible off-year turnout. In this case, I’ll measure turnout in November 2022 divided by turnout in November 2020.
View code
df_major <- readRDS("../../data/processed_data/df_major_20230116.Rds")
turnout <- df_major |> filter(is_topline_office, ward != "99") |>
group_by(warddiv, year, election_type) |>
summarise(votes=sum(votes))
turnout <- turnout |> left_join(
divs |> as.data.frame() |> select(warddiv, ward),
by="warddiv"
) |>
left_join(
wards |> as.data.frame() |> select(ward, is_open, is_open_pre_22),
by="ward"
)
div_turnout <- turnout |>
pivot_wider(
names_from=c("election_type", "year"),
values_from = "votes"
)
ward_turnout <- turnout |>
group_by(ward, year, election_type, is_open, is_open_pre_22) |>
summarise(votes=sum(votes)) |>
pivot_wider(names_from=c("election_type", "year"), values_from="votes")
ward_turnout |>
mutate(row = case_when(is_open ~ ward, TRUE ~ "Closed Wards")) |>
group_by(is_open) |>
summarise(votes_20 = sum(general_2020), votes_22 = sum(general_2022, na.rm=T)) |>
mutate(ratio = votes_22 / votes_20)View code
ggplot(wards |> left_join(ward_turnout, by="ward", suffix=c("", "_w"))) +
geom_sf(aes(fill = general_2022 / general_2020)) +
geom_sf(aes(color=is_open), fill = NA, lwd=1) +
scale_color_manual(guide=FALSE, values=c(`TRUE` = "white", `FALSE` = alpha("white", 0))) +
scale_fill_viridis_c() +
theme_map_sixtysix() %+replace%
theme(legend.position = "right") +
labs(
title="Turnout in 2022 vs 2020",
subtitle="Open Wards are outlined.",
fill = "Votes cast \n2022 / 2020"
)
Overall, the Open Wards (including those that only became Open later) cast 148,000 votes for President in 2020, and then 118,000 for Governor in 2022, an 80% rate. The other wards cast 594,000 and 380,000, a 64% rate. That’s a 16 percentage point difference!
But it might not be causal. We need to control for the ways Open Wards are systematically difference from Closed ones. We could do this by adding a bunch of control variables to a regression, but I prefer a stronger approach: using spatial boundary discontinuities. We can look at neighboring divisions on opposite sides of a ward boundary, one in a closed ward and one in an Open Ward) and compare the differences. By using only neighboring divisions, we create a useful apples-to-apples comparison; we assume that any systematic differences between these divisions is the effect of Open Wards. (I did a similar analysis in 2019, measuring the power of ward endorsements.)
For this analysis, I’ll limit to wards that were only Open before 2022 (sadly excluding my own 24). I filter down so each division is only in a single pair, and control for the proportions Black and Hispanic in the divisions (the pairing of divisions across boundaries should mean these proportions are not that different, but this will control for any lingering differences that do exist).
View code
block_data <- readr::read_csv(
"../../data/census/decennial_2020_poprace_phila_blocks/DECENNIALPL2020.P4_data_with_overlays_2021-09-22T083940.csv",
skip=1
)
block_shp <- sf::st_read(
"../../data/gis/census/tl_2020_42101_tabblock/tl_2020_42_tabblock20.shp"
)block_shp <- st_transform(block_shp, st_crs(wards))
block_cents <- block_shp |> st_centroid()
st_contains_df <- function(shp1, shp2, id1, id2){
raw <- st_contains(shp1, shp2)
res <- list()
for(i in 1:length(id1)){
res[[i]] <- data.frame(
id1 = id1[i],
idx2 = raw[[i]],
id2 = id2[raw[[i]]]
)
}
return(bind_rows(res))
}
blocks_to_wards <- st_contains_df(wards, block_cents, wards$ward, block_cents$GEOID20) |>
rename(ward = id1, block_i = idx2, geoid = id2)
res <- list()
for(ward in unique(blocks_to_wards$ward)){
blocks_w <- block_cents[
blocks_to_wards |> filter(ward == !!ward) |> with(block_i),
]
divs_w <- divs |> filter(ward == !!ward)
blocks_to_divs <- st_contains_df(divs_w, blocks_w, divs_w$warddiv, blocks_w$GEOID20) |>
select(-idx2) |>
rename(warddiv = id1, geoid = id2)
res[[ward]] <- blocks_to_divs
}
blocks_to_divs <- bind_rows(res)
div_pops <- blocks_to_divs |>
inner_join(block_data |> mutate(geoid = substr(id, 10,24)), by="geoid") |>
group_by(warddiv) |>
summarise(
pop = sum(`!!Total:`),
hisp = sum(`!!Total:!!Hispanic or Latino`),
nhw = sum(`!!Total:!!Not Hispanic or Latino:!!Population of one race:!!White alone`),
nhb = sum(`!!Total:!!Not Hispanic or Latino:!!Population of one race:!!Black or African American alone`),
nha = sum(`!!Total:!!Not Hispanic or Latino:!!Population of one race:!!Asian alone`)
)neighboring_wards <- st_touches(wards)
neighboring_wards <- neighboring_wards |>
lapply(as.data.frame) |>
bind_rows(.id = "id0") |>
mutate(id0 = asnum(id0)) |>
rename(id1 = `X[[i]]`) |>
filter(id0 < id1) |>
mutate(ward0 = wards$ward[id0], ward1 = wards$ward[id1])
neighboring_divs <- vector(mode="list", length=nrow(neighboring_wards))
for(row in 1:nrow(neighboring_wards)){
ward0 <- neighboring_wards$ward0[row]
ward1 <- neighboring_wards$ward1[row]
divs0 <- divs |> filter(ward == ward0)
divs1 <- divs |> filter(ward == ward1)
touches <- st_touches(divs0, divs1) |>
lapply(as.data.frame) |>
bind_rows(.id = "id0") |>
mutate(id0 = asnum(id0)) |>
rename(id1 = `X[[i]]`) |>
mutate(div0 = divs0$warddiv[id0], div1 = divs1$warddiv[id1])
neighboring_divs[[row]] <- touches |> select(div0, div1)
}
neighboring_divs <- bind_rows(neighboring_divs)
neighboring_divs <- neighboring_divs |>
left_join(
div_turnout, by=c("div0"="warddiv")
) |>
left_join(
div_turnout, by=c("div1"="warddiv"),
suffix=c("_0", "_1")
) |>
left_join(
div_pops, by=c("div0" = "warddiv")
)|>
left_join(
div_pops, by=c("div1" = "warddiv"),
suffix=c("_0", "_1")
)
neighboring_divs <- neighboring_divs |>
mutate(
prop_22_over_20_0 = general_2022_0 / general_2020_0,
prop_22_over_20_1 = general_2022_1 / general_2020_1,
signed_is_open = is_open_1 - is_open_0,
signed_is_open_pre_22 = is_open_pre_22_1 - is_open_pre_22_0
)
# Grab arbitrary unique pairs
set.seed(215)
neighboring_divs_lm <- neighboring_divs |>
group_by(div0) |>
filter(sample.int(n()) == 1) |>
group_by(div1) |>
filter(sample.int(n()) == 1)
form <- as.formula(
"prop_22_over_20_1 - prop_22_over_20_0 ~ -1 + signed_is_open_pre_22 + I(hisp_1 / pop_1 - hisp_0 / pop_0) + I(nhb_1 / pop_1 - nhb_0 / pop_0)"
)
fit <- lm(form, data = neighboring_divs_lm)
summary(fit)##
## Call:
## lm(formula = form, data = neighboring_divs_lm)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.31756 -0.05978 -0.00070 0.04634 0.32425
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## signed_is_open_pre_22 0.01738 0.01503 1.156 0.248
## I(hisp_1/pop_1 - hisp_0/pop_0) -0.26663 0.06638 -4.017 7.38e-05 ***
## I(nhb_1/pop_1 - nhb_0/pop_0) -0.24061 0.03274 -7.349 1.72e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.09265 on 316 degrees of freedom
## Multiple R-squared: 0.1924, Adjusted R-squared: 0.1847
## F-statistic: 25.1 on 3 and 316 DF, p-value: 1.37e-14The result: divisions in Open Wards had 1.7pp stronger turnout than divisions just across the boundary, but this result has a p-value of 0.25 and is not statistically significant (meaning we don’t have enough data to be confident it’s different from zero). This provides a confidence interval of (-1pp, 5pp).
A few notes about this analysis:
- The result was much bigger (about 5pp) when I didn’t control for race! This means that even when comparing only division across boundaries, there are racial differences between the divisions in Open Wards and those not.
- I’m only using one election comparison (2022 vs 2020), and we obviously have a lot more. I’ll think about ways to compellingly use more elections without introducing complications. Quick checks using 2018 vs 2016 did not substantively change the results.
So, what does this mean? Our best estimate for Open Wards’ change in turnout is a small, positive effect, but we don’t have enough data to be confident this is different from zero. Open Wards of course have other claims to benefits than just turnout: more democratic decision-making, for example. And a 2 percentage point increase in Philadelphia’s turnout, if it existed, wouldn’t be nothing: if scaled across the city, it would have meant 4,000 votes in 2019’s Superior Court election, which was decided by 17,000.
Hey, long-time fan, first-time commenter, etc. This is awesome. That said, if you’re looking to break this down a bit more:
There’s over-time variation that you’re not using here. There aren’t enough treated units or variation in treatment timing to do a difference-in-differences, but you could definitely do a synthetic controls estimation. You’d compare turnout pre-/post-opening in each of the listed districts to turnout pre-/post-opening in the average of some set of control districts—where that set could be just neighboring districts, or all districts. (You could still include demographic controls as part of the synth calibration.) It would be interesting to see the effects over time for each opening ward.