{"id":390784,"date":"2025-02-23T22:31:50","date_gmt":"2025-02-24T04:31:50","guid":{"rendered":"https:\/\/policyanalysislab.com\/?p=13649"},"modified":"2025-02-23T22:31:50","modified_gmt":"2025-02-24T04:31:50","slug":"political-ideology-and-doge-layoffs","status":"publish","type":"post","link":"https:\/\/www.r-bloggers.com\/2025\/02\/political-ideology-and-doge-layoffs\/","title":{"rendered":"Political ideology and DOGE layoffs"},"content":{"rendered":"\r\n\r\n
\r\n[This article was first published on R \u2013 Policy Analysis Lab<\/a><\/strong>, and kindly contributed to R-bloggers<\/a>]. (You can report issue about the content on this page here<\/a>)\r\n
Want to share your content on R-bloggers? click here<\/a> if you have a blog, or here<\/a> if you don't.\r\n<\/div>\n\n

Summary: <\/strong>This post reproduces Dr. Adam Bonica\u2019s analysis into the relationship between the ideological alignment of government agencies and the targeting of layoffs by the Department of Government Efficiency (DOGE). <\/p>\n\n\n\n

Credit:<\/strong> Dr Adam Bonica is a Professor of Political Science at Stanford University (link<\/a>). He can be found on Bluesky at @adambonica.bsky.social\u202c<\/a>. His original analysis was posted on Bluesky on the 20 February 2025 and can be found here<\/a>. <\/p>\n\n\n\n

Note<\/strong>: This post reproduces research relevant to public policy analysis. It presents findings without endorsing or critiquing the implications of the original research. Errors and\/or omissions are the responsibility of the author.<\/p>\n\n\n\n

Packages: <\/strong>ddplyr, ggplot2, readr and stargazer<\/p>\n\n\n\n

Data:<\/strong> Data used in this post<\/a> was drawn from the Google Sheet<\/a> shared by Dr Bonica on the 22nd of February 2025. <\/p>\n\n\n\n

Background<\/h3>\n\n\n\n

The Department of Government Efficiency (DOGE) <\/a>is a temporary contracted organization<\/em> with the apparent purpose <\/a>to carry out Trump\u2019s agenda of federal spending cuts and deregulation and to \u201cmodernize federal technology and software to maximize governmental efficiency and productivity\u201d. Key initiatives include departmental spending audits, reducing diversity and inclusion programs (claiming $1 billion in savings), cutting foreign aid through USAID, offering federal workforce buyouts, and attempting to restructure the Consumer Financial Protection Bureau. <\/em><\/p>\n\n\n\n

While DOGE has reported achieving savings, the actual fiscal impact of its work remains unverified.<\/em> Critics, like former CBO director Douglas Holtz-Eakin, have also suggested the department\u2019s focus is ideologically driven, targeting agencies based on political disagreement rather than efficiency metrics (source<\/a>)<\/em>. A contention that appeared to have some support, based on analysis shared by Dr Adam Bonica on Bluesky<\/a> of the association between DOGE layoffs and an agencies perceived ideological leaning (below)<\/em>.<\/p>\n\n\n

\n
\"\"
Source: <\/strong>@adambonica.bsky.social<\/a>, Bluesky.<\/figcaption><\/figure><\/div>\n\n\n

Project setup <\/h3>\n\n\n\n

The code below loads the packages needed to reproduce the analysis and creates a number of simple functions to help us clean the agency names. <\/p>\n\n\n\n

Note: <\/strong>The prefixes used for object names are based on this style guide<\/a>. <\/em><\/p>\n\n\n\n

Code: project setup<\/strong><\/p>\n\n\n\n

#Project Setup: Install and load necessary packages etc ----\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(readr)\nlibrary(stargazer)\n\n#Create functions for cleaning labels ----\n#Extract text between brackets\n#(assumed to be parent agency of department)\nfnc_extract_brackets <- function(text) {\n  matches <- regexpr("\\\\(([^)]+)\\\\)", text)\n  has_brackets <- matches != -1\n  result <- ifelse(has_brackets,\n                   substring(text, matches + 1, matches + attr(matches, "match.length") - 2),\n                   NA_character_)\n  return(result)\n}\n\n#Remove bracketed text \nfnc_remove_brackets <- function(text) {\n  trimws(gsub("\\\\([^)]+\\\\)", "", text))\n}\n\n#acronym function\nfnc_create_acronym<- function(texts, ignore_words = c("of", "the", "and", "in", "on", "at", "to", "for")) {\n  sapply(texts, function(text) {\n    words <- strsplit(trimws(text), "\\\\s+")[[1]]\n    words <- words[!tolower(words) %in% tolower(ignore_words)]\n    abbrev <- toupper(substr(words, 1, 1))\n    paste(abbrev, collapse="")\n  })\n}<\/pre>\n\n\n\n
Taking a look at the data<\/h3>\n\n\n\n
Taking a look at the data, the variable \u2018agency\u2019 appears to provide the name of the government body, with the parent agency listed in brackets. For instance, the entry \u2018Department of the Army (DOD)\u2019 indicates the agency is the \u2018Department of the Army<\/a>\u2018 and the parent agency is DOD (the Department of Defence). The budget and staff numbers of each agency are then listed under the \u2018annual_budget_usd\u2019 and \u2018total_staff\u2019 respectively. <\/p>\n\n\n\n
Whether an agency has been targeted for layoffs or dismantling by DOGE is listed under the doge_layoffs and targeted_for_dismantling variables. <\/p>\n\n\n\n
The variable perceived_ideological_estimate is sourced from research that investigated the perceived <\/em>ideology of government agencies<\/a>. Ranging from -2 to +2, agencies perceived as \u2018liberal\u2019 tend to have scores below 0, while those perceived as more conservative have scores above 0. Although the distribution is close to normal, there are slightly more agencies in the data set with scores above zero (54%) than below zero (46%).<\/p>\n\n\n
\n
\"\"<\/figure><\/div>\n\n\n
Data cleaning<\/h3>\n\n\n\n
To make the data easier to work with, the code below makes a number of minor tweaks to the source data. Firstly, the variable names are converted to lowercase for the sake of consistency. Both the doge_layoffs and targeted_for_dismantling variables are also converted from numeric to logical to reflect them being binary. To make it easier to visualize the agency-level data the name of a government body is also abbreviated using the acronym function defined in the code above.<\/p>\n\n\n\n
Code: data cleaning<\/strong><\/p>\n\n\n\n
#Import data ----\ndta_doge<-read_csv(".\/Data\/250222 - Agency Ideology and DOGE Firings.csv")\n\n#Data Wrangling and Cleaning ---- \n#change variable names to lower case\nnames(dta_doge)<-names(dta_doge) |> tolower()\n\n#take a look at the data\nstr(dta_doge)\nsummary(dta_doge)\n\n#distribution of ideology scores\nprop.table(table(dta_doge$perceived_ideology_estimate>0)) |> round(2)\nhist(dta_doge$perceived_ideology_estimate)\n\n#change dummy variables to logical\ndta_doge<-dta_doge |> \n  mutate(doge_layoffs= as.logical(doge_layoffs),\n         targeted_for_dismantling= as.logical(targeted_for_dismantling))\n\n#Clean agency name labels\ndta_doge<-dta_doge |> \n  mutate(parent_agency= fnc_extract_brackets(agency),\n         agency_name =  fnc_remove_brackets(agency),\n         agency_initials=fnc_create_acronym(agency_name) )<\/pre>\n\n\n\n
Political ideology vs DOGE layoffs <\/h3>\n\n\n\n
For the sake of brevity, we won\u2019t precisely reproduce Adam\u2019s plot, but focuses on the most important features. Note that the agency size presented on the Y axis uses a logarithmic scale and only organizations with staff sizes between 500 and 1,000,000 employees are presented.<\/p>\n\n\n\n
If DOGE layoffs were unrelated to the ideology of an agency, we might expect as many layoffs on the right side of the dotted line to the left. However, this doesn\u2019t appear to be the case. Instead, agencies with more \u2018liberal\u2019 ideological scores appear to have been disproportionately targeted for layoffs compared to those with more \u2018conservative\u2019 ideological scores.  <\/p>\n\n\n\n
<\/p>\n\n\n
\n
\"\"<\/figure><\/div>\n\n\n
Visualizing layoffs vs. ideological leaning<\/strong>:<\/p>\n\n\n\n
#Explanatory Analysis ---- \n#Reproduce analysis completed by @adambonica.bsky.social's\n\n# Reproduction of @adambonica.bsky.social's plot\n#create filtered dataset for plot \ndta_plt_doge<-dta_doge |> \n  filter(total_staff>500,\n         total_staff<10^6)\n\n#create scatter plot with vertical line at zero perceived ideology  \nplt_doge<-ggplot(data=dta_plt_doge,\n       aes(x = perceived_ideology_estimate, y = total_staff)) +\n  # Add grid lines\n  geom_hline(yintercept = c(1000, 10000, 100000, 1000000), \n             color = "gray90", linetype = "dashed") +\n  geom_vline(xintercept = 0, color = "gray60", linetype = "dashed") +\n  # Add agency acronyms colored according to DOGE layoff variable\n  geom_text(aes(label = agency_initials,\n                color = doge_layoffs), \n            size=3)+ \n  # Scale transformations\n  scale_y_log10(breaks = c(1000, 10000, 100000, 1000000),\n                labels = scales::comma) +\n  scale_x_continuous(breaks = seq(-2, 2, 1)) +\n  theme_minimal()+\n  theme(\n      plot.title = element_text(face = "bold", size = 16),\n      plot.subtitle = element_text(size = 14),\n      plot.caption = element_text(size = 10, hjust = 0))+\n  # Custom colors\n  scale_color_manual(values = c("gray60", "red"),\n                     name = "Layoff Status",\n                     labels = c("No Layoffs", "Layoffs")) +\n\n  # Labels\n  labs(title = "Empirical Evidence of Ideological Targeting in Federal Layoffs",\n       subtitle = "Agencies seen as liberal are significantly more likely to face DOGE layoffs.",\n       x = "Perceived Ideological Leaning\\n(\u2190 More Liberal | More Conservative \u2192)",\n       y = "Agency Size (Number of Staff)",\n       caption = "Note: Analysis includes only agencies with 500+ staff members. Ideology estimates are based on survey responses from 1,500+ federal executives rating agencies\npolicy views as liberal to conservative across both Democratic and Republican administrations.\nSource: Richardson, Clinton, & Lewis (2018). Elite Perceptions of Agency Ideology and Workforce Skill. The Journal of Politics 80(1).") \n\nplt_doge<\/pre>\n\n\n\n
Does ideology predict DOGE layoffs?<\/h3>\n\n\n\n
\n
To investigate this relationship further, Dr Bonica uses a OLS linear probability model to examine the extent which DOGE\u2019s layoff decisions can be predicted by:<\/p>\n\n\n\n