Story 1
Daniel DeBonis
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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library(readxl)The attached Excel file contains data on the present allocation of the Infrastructure Investment and Jobs Act funding by State and Territory.
Your story (Data Visualization(s) ) should address the following questions:
Is the allocation equitable based on the population of each of the States and Territories, or is bias apparent?
Does the allocation favor the political interests of the Biden administration?
Importing and Finding Relevant Data
allocation <- read_excel("C:/Users/ddebo/Downloads/IIJA FUNDING AS OF MARCH 2023.xlsx")We have the amounts allocated per state or territory, now we need to find information on population and 2020 election totals per state and territory. For population totals, I used the census.gov website since I would imagine it to be the most accurate. The table containing the information is found at https://www.census.gov/data/tables/time-series/demo/popest/2020s-state-total.html
population <- read.csv("C:/Users/ddebo/Downloads/NST-EST2024-ALLDATA.csv")population <- population |>
select(NAME, POPESTIMATE2020) |>
rename(state = NAME, population = POPESTIMATE2020)population2 <- population[-(1:14), ]One major issue with this resource is that it does not contain population information for all territories of the US, so I need to supplement it with this other resource from the Census Bureau https://www.census.gov/programs-surveys/decennial-census/decade/2020/planning-management/release/2020-island-areas-data. I’ll just write the values from here in a small table to b added to the one we are already using. However, the citizens of the territories do not get to participate in the presidential election, so they won’t be considered for the second part of the question.
library(dplyr)
library(tibble)
territories <- tribble(
~state, ~population,
"American Samoa", 49710,
"Northern Mariana Islands", 47329,
"Guam", 153836,
"U.S. Virgin Islands", 87146
)
popall <- rbind(population2, territories)popall$state <- toupper(popall$state)The next thing to add to the data frame is the vote totals from the 2020 election. I got the vote totals from the MIT Election Data and Science Lab, which transformed the official document from the Federal Election Commission to a csv format that is easier to import into R. Unfortunately, this data is available in the form of a dataset including results from every presidential election since 1976, so in Excel I reorganized the data to fit the format of the data frame we are using.
allocation <- allocation |>
rename(state = `State, Teritory or Tribal Nation`)elect <- read.csv("C:/Users/ddebo/Documents/DSMS/2020preselect.csv")
df <- merge(popall, elect, by = 'state', all = TRUE)
full <- merge(df, allocation, by = 'state', all = TRUE)With the data frame formed, I can start thinking of visualizations. In fact, I had to add columns to represent vote percentages (done in Excel) and allocations per capita. ### Is Allocation Equitable?
full <- full |>
mutate(all_per_cap = `Total (Billions)`/population) |>
mutate(all_per_thou = all_per_cap * 1000) |>
mutate(all_per_ten = all_per_cap *10000)cor_test <- cor.test(full$`Total (Billions)`, full$population, use = 'complete.obs')
cor_test##
## Pearson's product-moment correlation
##
## data: full$`Total (Billions)` and full$population
## t = 28.072, df = 54, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9447920 0.9808445
## sample estimates:
## cor
## 0.9674029We have an incredibly high correlation between population and the amount allocated under the bill. A correlation value of .967 is very strong evidence that the money was allocated fairly based on the population of the state since population can explain almost the entirety of the variance in allocated monies.
ggplot(full, aes(x = `Total (Billions)`, y = population, label = state)) +
geom_point() +
geom_smooth(method = 'lm') +
labs(x = "Amount Allocated ($)", y = "State Population", title = "Funding Allocation by State Population")## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: The following aesthetics were dropped during statistical transformation: label.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
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## - attr(*, "validate")= logi TRUETo get a better sense of this allocation, which states are those most disproportionately benefiting? To make the scale more interpretable, I decided to use 1000 residents as the standard instead of one.
full|>
arrange(all_per_thou) |>
mutate(state = factor(state, levels = unique(state))) |>
top_n(20) |>
ggplot(aes(x = state, y = all_per_thou)) +
geom_col() +
coord_flip() +
labs(title = "IIJA Allocation per 1,000 Residents by State/Territory",
y = "Allocation per 1,000 Residents ($)",
x = "") +
theme_minimal()## Selecting by all_per_ten
It should not be too surprising to see many of the territories that benefited the most per capita are the ones with some of the smallest populations, including both states and territories. These are generally places with more rural populations or in more remote locations.
Are Allocations Politically Motivated?
I’ll start by revisiting the list of states in order of allocation per 1000 residents, this time removing the territories since their voters cannot participate in presidential elections. Then, I can color code the states based on their 2020 voting history.
full|>
arrange(all_per_thou) |>
mutate(state = factor(state, levels = unique(state))) |>
na.omit() |>
top_n(20)|>
ggplot(aes(x = state, y = all_per_thou, fill = voted_for)) +
geom_col() +
coord_flip() +
labs(title = "IIJA Allocation per 1,000 Residents by State/Territory",
y = "Allocation per 1,000 Residents ($)",
x = "") +
scale_fill_manual(values = c("blue", "red"))## Selecting by all_per_ten
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## - attr(*, "validate")= logi TRUEIf anything, there seems to be a bias towards states that did not support the Biden administration during the previous election. Could this be interpreted as them trying to sway voters by increasing investment in their state’s infrastructure. The states that benefited were not swing states where the good publicity a project brings might just sway voters; Alaska had the highest percentage of Biden voters at 43%.
cor_test <- cor.test(full$all_per_cap,full$Biden_pct, use = 'complete.obs')
cor_test##
## Pearson's product-moment correlation
##
## data: full$all_per_cap and full$Biden_pct
## t = -1.6525, df = 49, p-value = 0.1048
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.47525118 0.04892186
## sample estimates:
## cor
## -0.2297594A quick correlation test seems to support the idea the opposite of the posed question to be true. Allocations were weakly negatively correlated with Biden’s vote share, showing greater investments in areas that did not provide him with the support needed to be elected president.
full |>
mutate(margin = Biden_pct - Trump_pct) |>
na.omit() |>
ggplot(aes(x = margin, y = all_per_thou, color = margin > 0)) +
geom_point(size = 3, alpha = 0.7) +
geom_hline(yintercept = mean(df$all_per_thou, na.rm = TRUE),
linetype = "dashed", color = "gray40") +
geom_vline(xintercept = 0, linetype = "dashed", color = "gray40") +
scale_color_manual(values = c("red", "blue"),
labels = c("Trump-won", "Biden-won")) +
labs(
title = "Infrastructure Allocation per Capita vs. 2020 Presidential Margin",
x = "Biden Margin of Victory (%)",
y = "Allocation per 1,000 Residents ($)",
color = "2020 Winner"
) +
theme_minimal()## Warning in mean.default(df$all_per_thou, na.rm = TRUE): argument is not numeric
## or logical: returning NA## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_hline()`).
This graph supports what was previously believed, that there was no particular benefit for swing states, neither as a reward nor motivation to sway swing state voters.
ggplot(full, aes(x = Biden_pct, y = all_per_ten, label = state)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
labs(title = "Allocation vs. Biden 2020 Vote Share",
x = "Biden 2020 vote share (%)",
y = "Allocation per 10,000 people ($)") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: The following aesthetics were dropped during statistical transformation: label.
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?## Warning: Removed 6 rows containing missing values or values outside the scale range
## (`geom_point()`).
This graph also suggests that the allocations of funding for infrastructure were unlikely to be politically motivated. The majority of states are receiving similar amounts of funding regardless of how they voted in the previous election. The four states that seem to have the most disproportional benefit, none had a vote percentage for Biden over 43%. In fifth place, you had the District of Columbia which while it is the territory with the largest proportion of Biden voters, it also is a territory which lacks a state government capable of allocating funds to infrastructure projects. In total, the hypothesis that the allocation of funding from the IIJA was biased has little merit to it. The correlation between funding amounts and population was unquestionable close where the correlation between funding amounts and vote share for the Biden administration was weak and indeed negative.