Comparing Unemployment Rates Across U.S. States During Economic Recessions

GEO511

Author

Swetha

Published

December 6, 2024

Introduction

This project analyzes unemployment rate fluctuations across U.S. states during two major economic downturns: the 2008 financial crisis and the COVID-19 pandemic. By leveraging spatial and temporal data, we map and compare unemployment rate changes before, during, and after these recessions. The insights from this analysis may highlight patterns in economic impact and recovery across states, informing potential strategies for future recession management.

Materials and Methods

Data Collection and Preparation

Leveraged datasets from the U.S. Bureau of Labor Statistics and supplementary geospatial sources. Utilized read.csv() and dplyr for comprehensive data cleaning, focusing on precise filtering and formatting of columns including State, Year, and Unemployment_rate.

Visualization and Analysis

Developed comprehensive visualization strategies using:

ggplot2: For generating detailed static maps.
gganimate: For creating temporal trend animations.
maps package: For foundational U.S. base map layers.
gifski: For high-quality animated graphic exports.

The methodology transforms complex unemployment data into clear, engaging visual narratives that reveal nuanced economic patterns across geographic and temporal dimensions.

Data Sources

U.S. Bureau of Labor Statistics (BLS): Monthly state-level unemployment data.
U.S. Census Bureau: State population estimates.

Data Cleaning and Preparation

The following code loads, cleans, and aggregates the data. ### Load libraries

Let’s install the following required libraries.

Code

if (!requireNamespace("gganimate", quietly = TRUE)) install.packages("gganimate")
if (!requireNamespace("dplyr", quietly = TRUE)) install.packages("dplyr")
if (!requireNamespace("sf", quietly = TRUE)) install.packages("sf")
if (!requireNamespace("maps", quietly = TRUE)) install.packages("maps")
if (!requireNamespace("ggplot2", quietly = TRUE)) install.packages("ggplot2")
if (!requireNamespace("tidyverse", quietly = TRUE)) install.packages("tidyverse")
if (!requireNamespace("gifski", quietly = TRUE)) install.packages("gifski", type = "binary")
if (!requireNamespace("knitr", quietly = TRUE)) install.packages("knitr")
if (!requireNamespace("geofacet", quietly = TRUE)) install.packages("geofacet")
if (!requireNamespace("plotly", quietly = TRUE)) install.packages("plotly")

library(dplyr)
library(ggplot2)
library(gganimate)
library(sf)
library(maps)
library(tidyverse)
library(gifski)
library(knitr)
library(geofacet)
library(plotly)

Load the monthly dataset

Loading the States Unemployment Dataset

Code

data <- read.csv("data/States_Unemploment_Data.csv")

Data Cleaning Steps

The following code has the data cleaning code and getting the summary of the data.

Code

# 1. Drop duplicate rows
data <- data %>% distinct()

# 2. Remove rows with any missing values
data <- data %>% drop_na()

# 3. Replace placeholder values ("NA", "unknown") with NA
data[data == "NA"] <- NA
data[data == "unknown"] <- NA

# 4. Identify and clean inconsistent values (e.g., if certain columns should only contain specific ranges or categories)
# Example: Ensure `Unemployment_rate` is within a realistic range (0 - 100)
data <- data %>%
  filter(Unemployment_rate >= 0 & Unemployment_rate <= 100)

# Check for remaining missing values after cleaning
summary(data)

Aggregate data by State and Year to calculate the annual averages and displaying first ten rows.

Code

annual_data <- data %>%
  group_by(State, Year) %>%
  summarise(
    Labor_Force_Participation_Ratio = mean(Labor_Force_Participation_Ratio, na.rm = TRUE),
    Employment_Participation_Ratio = mean(Employment_Participation_Ratio, na.rm = TRUE),
    Labor_Force = mean(Labor_Force, na.rm = TRUE),
    Employment = mean(Employment, na.rm = TRUE),
    Unemployment = mean(Unemployment, na.rm = TRUE),
    Unemployment_rate = mean(Unemployment_rate, na.rm = TRUE),
    Latitude = first(Latitude),
    Longitude = first(Longitude)
  ) %>%
  ungroup()


# Display the first few rows of the aggregated data
annual_data %>%
  head(10) %>%
  kable(
    caption = "Summary of Annual Data by State and Year",
    col.names = c("State", "Year", "Labor Force Participation Ratio", "Employment Participation Ratio",
                  "Labor Force", "Employment", "Unemployment", "Unemployment Rate", "Latitude", "Longitude"),
    align = "c"
  )

Summary of Annual Data by State and Year
State	Year	Labor Force Participation Ratio	Employment Participation Ratio	Labor Force	Employment	Unemployment	Unemployment Rate	Latitude	Longitude
Alabama	2006	61.60833	59.13333	2169933	2083033	86899.50	4.000000	32.31823	-86.9023
Alabama	2007	61.28333	58.81667	2180868	2092450	88418.08	4.041667	32.31823	-86.9023
Alabama	2008	60.61667	57.05000	2177201	2049357	127843.58	5.883333	32.31823	-86.9023
Alabama	2009	59.56667	53.56667	2157016	1939467	217549.33	10.083333	32.31823	-86.9023
Alabama	2010	59.50833	53.34167	2196694	1968757	227937.67	10.375000	32.31823	-86.9023
Alabama	2011	59.11667	53.45833	2201954	1991027	210927.92	9.583333	32.31823	-86.9023
Alabama	2012	58.16667	53.42500	2179075	2001118	177957.33	8.158333	32.31823	-86.9023
Alabama	2018	57.25833	55.00000	2239818	2151843	87974.50	3.941667	32.31823	-86.9023
Alabama	2019	57.69167	55.86667	2271412	2199088	72324.17	3.183333	32.31823	-86.9023
Alabama	2020	57.23333	53.58333	2263014	2118626	144388.67	6.408333	32.31823	-86.9023

Visualization Techniques

State-Level Map Visualization
Time-Series Trend Charts
Bar Chart Race

Results

Map Visualization

The following animated map shows the unemployment rate across states over time. The color scale represents unemployment rate levels.

Code

us_map <- map_data("state")
annual_data$region <- tolower(annual_data$State)

# Merge map data with your dataset
map_data <- inner_join(us_map, annual_data, by = c("region" = "region"))

# Animated Map Code
ggplot(map_data, aes(long, lat, group = group, fill = Unemployment_rate)) +
  geom_polygon(color = "white") +
  scale_fill_gradientn(colors = c("#2D004B", "#4144B3", "#1EA8C1", "#A8E200", "#FFDD00", "#FF6600"),
                       values = scales::rescale(c(2, 4, 6, 8, 10, 16)),
                       name = "Unemployment Rate (%)",
                       limits = c(2, 16)) +
  labs(title = "Unemployment Rate by State", subtitle = "Year: {frame_time}") +
  coord_fixed(1.3) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold"),
    plot.subtitle = element_text(size = 14)
  ) +
  transition_time(Year) +
  ease_aes('linear')

Line Plot of Unemployment Trends

This line plot illustrates unemployment rate trends by state over time.

Code

ggplot(annual_data, aes(x = Year, y = Unemployment_rate, color = State, group = State)) +
  geom_line(size = 1) +
  labs(title = "Unemployment Rate Over Time by State", x = "Year", y = "Unemployment Rate (%)") +
  theme_minimal() +
  transition_reveal(Year)

Interactive Line Plot to know about the Employment rates

Code

plot_ly(annual_data, x = ~Year, y = ~Unemployment_rate, color = ~State, type = 'scatter', mode = 'lines') %>%
  layout(
    title = "Unemployment Rate Trends",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Unemployment Rate (%)")
  )

Code

#animate(line_plot, nframes = 100, fps = 10,renderer = gifski_renderer())

Bar Chart Race of Top 10 States by Unemployment Rate

A bar chart race visualizes the top 10 states with the highest unemployment rate each year.

Code

top_states <- annual_data %>%
  group_by(Year) %>%
  top_n(10, Unemployment_rate) %>%
  arrange(Year, -Unemployment_rate)

# Bar Chart Race Code
ggplot(top_states, aes(x = reorder(State, -Unemployment_rate), y = Unemployment_rate, fill = State)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  labs(
    title = "Top 10 States by Unemployment Rate",
    subtitle = "Year: {frame_time}",
    x = "State",
    y = "Unemployment Rate (%)"
  ) +
  theme_minimal() +
  transition_time(Year) +
  ease_aes('linear')

This visualization provides a comparative analysis of key labor market metrics—Unemployment Rate, Labor Force Participation Ratio, and Employment Participation Ratio—across U.S. states. By organizing states geographically using the geofacet package, this plot highlights the variation in these metrics in a spatially intuitive manner.

Code

geofacet_comparison_data <- annual_data %>%
  select(State, Year, Unemployment_rate, Labor_Force_Participation_Ratio, Employment_Participation_Ratio) %>%
  filter(!is.na(Unemployment_rate), 
         !is.na(Labor_Force_Participation_Ratio),
         !is.na(Employment_Participation_Ratio)) %>%
  pivot_longer(
    cols = c(Unemployment_rate, Labor_Force_Participation_Ratio, Employment_Participation_Ratio),
    names_to = "Metric",
    values_to = "Value"
  )

ggplot(geofacet_comparison_data, aes(y = Metric, x = Value, fill = Metric)) +
  geom_bar(stat = "identity", position = "dodge") +  
  facet_geo(~ State, grid = "us_state_grid1") +     
  scale_fill_manual(
    values = c(
      "Unemployment_rate" = "red",
      "Labor_Force_Participation_Ratio" = "green",
      "Employment_Participation_Ratio" = "blue"
    ),
    name = "Metric"
  ) +
  labs(
    title = "Comparison of Metrics Across U.S. States",
    x = "Value",
    y = "Metric",
    fill = "Metric"
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(size = 10, face = "bold"),
    legend.position = "bottom"
  )

Conclusion

The graphical analysis of U.S. unemployment rates during the 2008 financial crisis and the COVID-19 pandemic highlights significant trends and disparities in economic resilience across states.

From the animated map visualization, we observe that states with diverse, service-based economies (such as California and New York) experienced sharp rises in unemployment during both crises. However, these states tended to recover faster due to a shift toward remote work and flexible job structures, especially during COVID-19.
States heavily reliant on specific sectors like tourism and manufacturing (e.g., Nevada and Michigan) faced prolonged high unemployment rates. This suggests that sector-specific economic dependencies can heighten vulnerability during global economic disruptions.
The line plot of unemployment trends over time reveals that while the 2008 financial crisis caused a relatively uniform spike in unemployment across states, the COVID-19 pandemic led to more varied impacts, with certain states being hit much harder than others. This difference may be attributed to varying public health restrictions, digital adaptability, and government support measures across states.
The bar chart race visualization, showing the top 10 states with the highest unemployment rates over time, highlights that certain states consistently appeared in the high-unemployment category across crises. This indicates persistent structural challenges that may require targeted policy interventions.

References

Li, J. (2013). Analysis of the High Unemployment Rate in the USA. World Review of Political Economy, 4(2), 218-229.
Athar, H. M., Chang, M. H., Hahn, R. A., Walker, E., & Yoon, P. (2013). Unemployment–United States, 2006 and 2010. MMWR: Morbidity & Mortality Weekly Report, 62.
Hagen, D., Lai, A. Y., & Goldmann, E. (2022). State-level unemployment and negative emotions throughout the Covid-19 pandemic in the United States. Preventive Medicine, 164, 107239.
Yilmazkuday, H. (2020). Unequal unemployment effects of COVID-19 and monetary policy across US States. Journal of Behavioral Economics for Policy, Forthcoming.