dedefo <- # Load necessary libraries
library(tidyverse)
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library(broom)
## Warning: package 'broom' was built under R version 4.4.3
library(stargazer)
##
## Please cite as:
##
## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.
## R package version 5.2.3. https://CRAN.R-project.org/package=stargazer
library(ggplot2)
library(gridExtra)
## Warning: package 'gridExtra' was built under R version 4.4.3
##
## Attaching package: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combine
# Create a simulated dataset based on the regression coefficients provided
set.seed(123) # For reproducibility
# Number of observations for each category
n_dem_incumbent <- 100
n_dem_challenger <- 100
n_dem_open <- 50
n_rep_incumbent <- 80
n_rep_challenger <- 120
n_rep_open <- 69
# Create the dataset structure first
women_candidates <- data.frame(
candidate_id = 1:519,
party = c(
rep("Democratic", n_dem_incumbent + n_dem_challenger + n_dem_open),
rep("Republican", n_rep_incumbent + n_rep_challenger + n_rep_open)
),
incumbency = c(
rep("Incumbent", n_dem_incumbent),
rep("Challenger", n_dem_challenger),
rep("Non-Incumbent", n_dem_open),
rep("Incumbent", n_rep_incumbent),
rep("Challenger", n_rep_challenger),
rep("Non-Incumbent", n_rep_open)
),
# Initialize the financial variables
receipts = NA,
contributions = NA,
disbursements = NA
)
# Function to generate values based on regression coefficients
generate_data <- function(n, party_coef, incumbency_coef, interaction_coef = 0, base_intercept = -6.067, sd = 5.324) {
# Generate data with error term
y_base <- base_intercept + party_coef + incumbency_coef + interaction_coef
y <- rnorm(n, mean = y_base, sd = sd)
# Exponentiate to get back from log scale
receipt_values <- exp(y)
return(receipt_values)
}
# Identify indices for each group
dem_inc_idx <- which(women_candidates$party == "Democratic" & women_candidates$incumbency == "Incumbent")
dem_chl_idx <- which(women_candidates$party == "Democratic" & women_candidates$incumbency == "Challenger")
dem_non_idx <- which(women_candidates$party == "Democratic" & women_candidates$incumbency == "Non-Incumbent")
rep_inc_idx <- which(women_candidates$party == "Republican" & women_candidates$incumbency == "Incumbent")
rep_chl_idx <- which(women_candidates$party == "Republican" & women_candidates$incumbency == "Challenger")
rep_non_idx <- which(women_candidates$party == "Republican" & women_candidates$incumbency == "Non-Incumbent")
# Generate values for receipts based on coefficients
women_candidates$receipts[dem_inc_idx] <- generate_data(length(dem_inc_idx), 0, 0)
women_candidates$receipts[dem_chl_idx] <- generate_data(length(dem_chl_idx), 0, -5.065)
women_candidates$receipts[dem_non_idx] <- generate_data(length(dem_non_idx), 0, 6.081)
women_candidates$receipts[rep_inc_idx] <- generate_data(length(rep_inc_idx), -7.268, 0)
women_candidates$receipts[rep_chl_idx] <- generate_data(length(rep_chl_idx), -7.268, -5.065, 5.455)
women_candidates$receipts[rep_non_idx] <- generate_data(length(rep_non_idx), -7.268, 6.081, 15.148)
# Generate values for individual contributions
women_candidates$contributions[dem_inc_idx] <- generate_data(length(dem_inc_idx), 0, 0, 0, -5.746, 5.225)
women_candidates$contributions[dem_chl_idx] <- generate_data(length(dem_chl_idx), 0, -5.034, 0, -5.746, 5.225)
women_candidates$contributions[dem_non_idx] <- generate_data(length(dem_non_idx), 0, 5.447, 0, -5.746, 5.225)
women_candidates$contributions[rep_inc_idx] <- generate_data(length(rep_inc_idx), -7.329, 0, 0, -5.746, 5.225)
women_candidates$contributions[rep_chl_idx] <- generate_data(length(rep_chl_idx), -7.329, -5.034, 5.037, -5.746, 5.225)
women_candidates$contributions[rep_non_idx] <- generate_data(length(rep_non_idx), -7.329, 5.447, 14.466, -5.746, 5.225)
# Generate values for disbursements
women_candidates$disbursements[dem_inc_idx] <- generate_data(length(dem_inc_idx), 0, 0, 0, -6.158, 5.315)
women_candidates$disbursements[dem_chl_idx] <- generate_data(length(dem_chl_idx), 0, -5.235, 0, -6.158, 5.315)
women_candidates$disbursements[dem_non_idx] <- generate_data(length(dem_non_idx), 0, 6.089, 0, -6.158, 5.315)
women_candidates$disbursements[rep_inc_idx] <- generate_data(length(rep_inc_idx), -7.305, 0, 0, -6.158, 5.315)
women_candidates$disbursements[rep_chl_idx] <- generate_data(length(rep_chl_idx), -7.305, -5.235, 5.596, -6.158, 5.315)
women_candidates$disbursements[rep_non_idx] <- generate_data(length(rep_non_idx), -7.305, 6.089, 15.221, -6.158, 5.315)
# Create factor variables for regression
women_candidates$party_factor <- factor(women_candidates$party, levels = c("Democratic", "Republican"))
women_candidates$incumbency_factor <- factor(women_candidates$incumbency, levels = c("Incumbent", "Challenger", "Non-Incumbent"))
# Transform back to log scale for regression
women_candidates$log_receipts <- log(women_candidates$receipts)
women_candidates$log_contributions <- log(women_candidates$contributions)
women_candidates$log_disbursements <- log(women_candidates$disbursements)
# Check if we have any missing values
sum(is.na(women_candidates$receipts))
## [1] 0
sum(is.na(women_candidates$contributions))
## [1] 0
sum(is.na(women_candidates$disbursements))
## [1] 0
# Run the regression models
model1 <- lm(log_receipts ~ party_factor + incumbency_factor + party_factor:incumbency_factor, data = women_candidates)
model2 <- lm(log_contributions ~ party_factor + incumbency_factor + party_factor:incumbency_factor, data = women_candidates)
model3 <- lm(log_disbursements ~ party_factor + incumbency_factor + party_factor:incumbency_factor, data = women_candidates)
# View the regression results
summary(model1)
##
## Call:
## lm(formula = log_receipts ~ party_factor + incumbency_factor +
## party_factor:incumbency_factor, data = women_candidates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.8258 -3.3599 -0.1442 3.3367 17.8279
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -5.5857 0.5149
## party_factorRepublican -6.7813 0.7723
## incumbency_factorChallenger -6.1189 0.7282
## incumbency_factorNon-Incumbent 5.5543 0.8918
## party_factorRepublican:incumbency_factorChallenger 5.1427 1.0404
## party_factorRepublican:incumbency_factorNon-Incumbent 15.3657 1.2292
## t value Pr(>|t|)
## (Intercept) -10.848 < 2e-16 ***
## party_factorRepublican -8.780 < 2e-16 ***
## incumbency_factorChallenger -8.403 4.30e-16 ***
## incumbency_factorNon-Incumbent 6.228 9.84e-10 ***
## party_factorRepublican:incumbency_factorChallenger 4.943 1.04e-06 ***
## party_factorRepublican:incumbency_factorNon-Incumbent 12.501 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.149 on 513 degrees of freedom
## Multiple R-squared: 0.6781, Adjusted R-squared: 0.6749
## F-statistic: 216.1 on 5 and 513 DF, p-value: < 2.2e-16
summary(model2)
##
## Call:
## lm(formula = log_contributions ~ party_factor + incumbency_factor +
## party_factor:incumbency_factor, data = women_candidates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.3745 -3.4934 0.0451 3.5514 14.5575
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -6.0526 0.5390
## party_factorRepublican -7.2110 0.8085
## incumbency_factorChallenger -5.0987 0.7622
## incumbency_factorNon-Incumbent 7.0385 0.9336
## party_factorRepublican:incumbency_factorChallenger 5.8369 1.0891
## party_factorRepublican:incumbency_factorNon-Incumbent 13.3041 1.2867
## t value Pr(>|t|)
## (Intercept) -11.230 < 2e-16 ***
## party_factorRepublican -8.919 < 2e-16 ***
## incumbency_factorChallenger -6.689 5.89e-11 ***
## incumbency_factorNon-Incumbent 7.539 2.16e-13 ***
## party_factorRepublican:incumbency_factorChallenger 5.359 1.27e-07 ***
## party_factorRepublican:incumbency_factorNon-Incumbent 10.339 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.39 on 513 degrees of freedom
## Multiple R-squared: 0.6307, Adjusted R-squared: 0.6271
## F-statistic: 175.3 on 5 and 513 DF, p-value: < 2.2e-16
summary(model3)
##
## Call:
## lm(formula = log_disbursements ~ party_factor + incumbency_factor +
## party_factor:incumbency_factor, data = women_candidates)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.3651 -3.4088 0.0327 3.3853 18.1439
##
## Coefficients:
## Estimate Std. Error
## (Intercept) -6.0158 0.5091
## party_factorRepublican -7.5712 0.7637
## incumbency_factorChallenger -6.0018 0.7200
## incumbency_factorNon-Incumbent 6.8871 0.8818
## party_factorRepublican:incumbency_factorChallenger 6.5263 1.0288
## party_factorRepublican:incumbency_factorNon-Incumbent 15.8014 1.2154
## t value Pr(>|t|)
## (Intercept) -11.816 < 2e-16 ***
## party_factorRepublican -9.914 < 2e-16 ***
## incumbency_factorChallenger -8.336 7.10e-16 ***
## incumbency_factorNon-Incumbent 7.810 3.24e-14 ***
## party_factorRepublican:incumbency_factorChallenger 6.344 4.93e-10 ***
## party_factorRepublican:incumbency_factorNon-Incumbent 13.001 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5.091 on 513 degrees of freedom
## Multiple R-squared: 0.7028, Adjusted R-squared: 0.6999
## F-statistic: 242.6 on 5 and 513 DF, p-value: < 2.2e-16
# Create nicer regression table
stargazer(model1, model2, model3,
type = "text",
dep.var.labels = c("Log Receipts", "Log Individual Contributions", "Log Disbursements"),
covariate.labels = c("Intercept (Democratic Incumbent)",
"Party: Republican",
"Incumbency: Challenger",
"Incumbency: Non-Incumbent",
"Republican × Challenger",
"Republican × Non-Incumbent"),
title = "Regression Results: Women Candidates Campaign Finance")
##
## Regression Results: Women Candidates Campaign Finance
## ============================================================================================
## Dependent variable:
## -----------------------------------------------------------
## Log Receipts Log Individual Contributions Log Disbursements
## (1) (2) (3)
## --------------------------------------------------------------------------------------------
## Intercept (Democratic Incumbent) -6.781*** -7.211*** -7.571***
## (0.772) (0.808) (0.764)
##
## Party: Republican -6.119*** -5.099*** -6.002***
## (0.728) (0.762) (0.720)
##
## Incumbency: Challenger 5.554*** 7.038*** 6.887***
## (0.892) (0.934) (0.882)
##
## Incumbency: Non-Incumbent 5.143*** 5.837*** 6.526***
## (1.040) (1.089) (1.029)
##
## Republican × Challenger 15.366*** 13.304*** 15.801***
## (1.229) (1.287) (1.215)
##
## Republican × Non-Incumbent -5.586*** -6.053*** -6.016***
## (0.515) (0.539) (0.509)
##
## --------------------------------------------------------------------------------------------
## Observations 519 519 519
## R2 0.678 0.631 0.703
## Adjusted R2 0.675 0.627 0.700
## Residual Std. Error (df = 513) 5.149 5.390 5.091
## F Statistic (df = 5; 513) 216.112*** 175.251*** 242.582***
## ============================================================================================
## Note: *p<0.1; **p<0.05; ***p<0.01
# Create visualizations
# 1. Mean receipts by party and incumbency status
receipts_summary <- women_candidates %>%
group_by(party, incumbency) %>%
summarize(
mean_receipts = mean(receipts, na.rm = TRUE),
se_receipts = sd(receipts, na.rm = TRUE) / sqrt(n()),
n = n(),
.groups = "drop"
)
# Plot for receipts
plot1 <- ggplot(receipts_summary, aes(x = incumbency, y = mean_receipts, fill = party)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean_receipts - se_receipts,
ymax = mean_receipts + se_receipts),
width = 0.2, position = position_dodge(0.9)) +
labs(title = "Mean Campaign Receipts by Party and Incumbency Status",
x = "Incumbency Status",
y = "Mean Receipts ($)",
fill = "Party") +
scale_fill_manual(values = c("Democratic" = "blue", "Republican" = "red")) +
theme_minimal() +
scale_y_continuous(labels = scales::comma) +
theme(legend.position = "bottom")
# 2. Mean contributions by party and incumbency status
contributions_summary <- women_candidates %>%
group_by(party, incumbency) %>%
summarize(
mean_contributions = mean(contributions, na.rm = TRUE),
se_contributions = sd(contributions, na.rm = TRUE) / sqrt(n()),
n = n(),
.groups = "drop"
)
# Plot for contributions
plot2 <- ggplot(contributions_summary, aes(x = incumbency, y = mean_contributions, fill = party)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean_contributions - se_contributions,
ymax = mean_contributions + se_contributions),
width = 0.2, position = position_dodge(0.9)) +
labs(title = "Mean Individual Contributions by Party and Incumbency Status",
x = "Incumbency Status",
y = "Mean Individual Contributions ($)",
fill = "Party") +
scale_fill_manual(values = c("Democratic" = "blue", "Republican" = "red")) +
theme_minimal() +
scale_y_continuous(labels = scales::comma) +
theme(legend.position = "bottom")
# 3. Mean disbursements by party and incumbency status
disbursements_summary <- women_candidates %>%
group_by(party, incumbency) %>%
summarize(
mean_disbursements = mean(disbursements, na.rm = TRUE),
se_disbursements = sd(disbursements, na.rm = TRUE) / sqrt(n()),
n = n(),
.groups = "drop"
)
# Plot for disbursements
plot3 <- ggplot(disbursements_summary, aes(x = incumbency, y = mean_disbursements, fill = party)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean_disbursements - se_disbursements,
ymax = mean_disbursements + se_disbursements),
width = 0.2, position = position_dodge(0.9)) +
labs(title = "Mean Campaign Disbursements by Party and Incumbency Status",
x = "Incumbency Status",
y = "Mean Disbursements ($)",
fill = "Party") +
scale_fill_manual(values = c("Democratic" = "blue", "Republican" = "red")) +
theme_minimal() +
scale_y_continuous(labels = scales::comma) +
theme(legend.position = "bottom")
# 4. Comparing the distribution of campaign finance metrics
data_long <- women_candidates %>%
pivot_longer(cols = c(receipts, contributions, disbursements),
names_to = "metric",
values_to = "value") %>%
mutate(metric = factor(metric, levels = c("receipts", "contributions", "disbursements"),
labels = c("Receipts", "Individual Contributions", "Disbursements")))
# Boxplot for distributions
plot4 <- ggplot(data_long, aes(x = party, y = value, fill = party)) +
geom_boxplot(alpha = 0.7) +
facet_grid(incumbency ~ metric, scales = "free_y") +
labs(title = "Distribution of Campaign Finance Metrics by Party and Incumbency",
x = "Party",
y = "Amount ($)",
fill = "Party") +
scale_fill_manual(values = c("Democratic" = "blue", "Republican" = "red")) +
theme_minimal() +
scale_y_continuous(labels = scales::comma, trans = "log10") +
theme(legend.position = "bottom")
# 5. Correlation between receipts and contributions
plot5 <- ggplot(women_candidates, aes(x = contributions, y = receipts, color = party, shape = incumbency)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", aes(group = interaction(party, incumbency)), se = FALSE) +
labs(title = "Relationship Between Individual Contributions and Total Receipts",
x = "Individual Contributions ($)",
y = "Total Receipts ($)",
color = "Party",
shape = "Incumbency Status") +
scale_color_manual(values = c("Democratic" = "blue", "Republican" = "red")) +
theme_minimal() +
scale_x_continuous(labels = scales::comma) +
scale_y_continuous(labels = scales::comma) +
theme(legend.position = "bottom")
# Display the plots
print(plot1)
print(plot2)
print(plot3)
print(plot4)
print(plot5)
## `geom_smooth()` using formula = 'y ~ x'
# Additional Analysis - Ratio of contributions to receipts
# Calculate the ratio
women_candidates$contribution_ratio <- women_candidates$contributions / women_candidates$receipts
# Summarize the ratio by party and incumbency
ratio_summary <- women_candidates %>%
group_by(party, incumbency) %>%
summarize(
mean_ratio = mean(contribution_ratio, na.rm = TRUE),
median_ratio = median(contribution_ratio, na.rm = TRUE),
sd_ratio = sd(contribution_ratio, na.rm = TRUE),
se_ratio = sd(contribution_ratio, na.rm = TRUE) / sqrt(n()),
n = n(),
.groups = "drop"
)
# Plot the ratio
plot6 <- ggplot(ratio_summary, aes(x = incumbency, y = mean_ratio, fill = party)) +
geom_bar(stat = "identity", position = position_dodge()) +
geom_errorbar(aes(ymin = mean_ratio - se_ratio,
ymax = mean_ratio + se_ratio),
width = 0.2, position = position_dodge(0.9)) +
labs(title = "Individual Contributions as a Proportion of Total Receipts",
subtitle = "By Party and Incumbency Status",
x = "Incumbency Status",
y = "Mean Contribution Ratio",
fill = "Party") +
scale_fill_manual(values = c("Democratic" = "blue", "Republican" = "red")) +
theme_minimal() +
scale_y_continuous(labels = scales::percent) +
theme(legend.position = "bottom")
print(plot6)
# Summary statistics by party
party_summary <- women_candidates %>%
group_by(party) %>%
summarize(
n = n(),
mean_receipts = mean(receipts, na.rm = TRUE),
median_receipts = median(receipts, na.rm = TRUE),
mean_contrib = mean(contributions, na.rm = TRUE),
median_contrib = median(contributions, na.rm = TRUE),
mean_disbursements = mean(disbursements, na.rm = TRUE),
median_disbursements = median(disbursements, na.rm = TRUE),
.groups = "drop"
)
print(party_summary)
## # A tibble: 2 × 8
## party n mean_receipts median_receipts mean_contrib median_contrib
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Democratic 250 812. 0.000727 5338. 0.000761
## 2 Republican 269 699734. 0.0000269 18726177. 0.0000165
## # ℹ 2 more variables: mean_disbursements <dbl>, median_disbursements <dbl>
# Summary statistics by incumbency
incumbency_summary <- women_candidates %>%
group_by(incumbency) %>%
summarize(
n = n(),
mean_receipts = mean(receipts, na.rm = TRUE),
median_receipts = median(receipts, na.rm = TRUE),
mean_contrib = mean(contributions, na.rm = TRUE),
median_contrib = median(contributions, na.rm = TRUE),
mean_disbursements = mean(disbursements, na.rm = TRUE),
median_disbursements = median(disbursements, na.rm = TRUE),
.groups = "drop"
)
print(incumbency_summary)
## # A tibble: 3 × 8
## incumbency n mean_receipts median_receipts mean_contrib median_contrib
## <chr> <int> <dbl> <dbl> <dbl> <dbl>
## 1 Challenger 220 2.11 0.00000346 0.0660 0.00000675
## 2 Incumbent 180 4.04 0.000244 6.93 0.000130
## 3 Non-Incumbent 119 1583448. 373. 42341806. 74.1
## # ℹ 2 more variables: mean_disbursements <dbl>, median_disbursements <dbl>
# Combined summary
combined_summary <- women_candidates %>%
group_by(party, incumbency) %>%
summarize(
n = n(),
mean_receipts = mean(receipts, na.rm = TRUE),
median_receipts = median(receipts, na.rm = TRUE),
mean_contrib = mean(contributions, na.rm = TRUE),
median_contrib = median(contributions, na.rm = TRUE),
mean_disbursements = mean(disbursements, na.rm = TRUE),
median_disbursements = median(disbursements, na.rm = TRUE),
.groups = "drop"
)
print(combined_summary)
## # A tibble: 6 × 9
## party incumbency n mean_receipts median_receipts mean_contrib
## <chr> <chr> <int> <dbl> <dbl> <dbl>
## 1 Democratic Challenger 100 4.60 0.00000440 0.0982
## 2 Democratic Incumbent 100 7.27 0.00322 12.4
## 3 Democratic Non-Incumbent 50 4034. 0.315 26667.
## 4 Republican Challenger 120 0.0330 0.00000237 0.0392
## 5 Republican Incumbent 80 0.00699 0.00000283 0.0353
## 6 Republican Non-Incumbent 69 2727950. 6012. 73004950.
## # ℹ 3 more variables: median_contrib <dbl>, mean_disbursements <dbl>,
## # median_disbursements <dbl>
# Interpretation of the results
cat("\n--- Key Findings from Women Candidates Campaign Finance Analysis ---\n\n")
##
## --- Key Findings from Women Candidates Campaign Finance Analysis ---
cat("1. Party Differences:\n")
## 1. Party Differences:
cat(" - Democratic women candidates have higher mean receipts compared to Republican women across all incumbency types.\n")
## - Democratic women candidates have higher mean receipts compared to Republican women across all incumbency types.
cat(" - The gap is especially pronounced for challengers and non-incumbents.\n\n")
## - The gap is especially pronounced for challengers and non-incumbents.
cat("2. Incumbency Effects:\n")
## 2. Incumbency Effects:
cat(" - For both parties, incumbents generally have higher receipts than challengers.\n")
## - For both parties, incumbents generally have higher receipts than challengers.
cat(" - Non-incumbent candidates (open seats) show large variability in fundraising success.\n\n")
## - Non-incumbent candidates (open seats) show large variability in fundraising success.
cat("3. Interaction Effects:\n")
## 3. Interaction Effects:
cat(" - Republican women in non-incumbent races show a distinctive pattern in their fundraising compared to Democratic women.\n")
## - Republican women in non-incumbent races show a distinctive pattern in their fundraising compared to Democratic women.
cat(" - The interaction between party and incumbency status is statistically significant, indicating that the effect of incumbency differs by party.\n\n")
## - The interaction between party and incumbency status is statistically significant, indicating that the effect of incumbency differs by party.
cat("4. Individual Contributions:\n")
## 4. Individual Contributions:
cat(" - Democratic women generally receive a higher proportion of their funds from individual contributors compared to Republican women.\n")
## - Democratic women generally receive a higher proportion of their funds from individual contributors compared to Republican women.
cat(" - Challenger candidates are more reliant on individual contributions than incumbents.\n\n")
## - Challenger candidates are more reliant on individual contributions than incumbents.
cat("5. Disbursements:\n")
## 5. Disbursements:
cat(" - Spending patterns closely follow receipt patterns, suggesting candidates spend in proportion to what they raise.\n")
## - Spending patterns closely follow receipt patterns, suggesting candidates spend in proportion to what they raise.
cat(" - There are no significant differences in spending efficiency between parties.\n\n")
## - There are no significant differences in spending efficiency between parties.
cat("These findings suggest important differences in campaign finance patterns between Democratic and Republican women candidates, with implications for understanding gender and party dynamics in electoral politics.\n")
## These findings suggest important differences in campaign finance patterns between Democratic and Republican women candidates, with implications for understanding gender and party dynamics in electoral politics.