Unemployment Rate in Interactive R code

Unemployment Rate

If the Unemployment rate were to rise above the expected value of 3.5%, surpassing the benchmark of 3.5%, it could have several implications for the U.S. economy:

  1. Increased Risk of Recession: A higher unemployment rate could be interpreted as a sign of an impending economic downturn. Rising unemployment might lead to reduced consumer spending and decreased business investment, potentially affecting economic activity.
  2. Decline in Consumer Spending: People who lose their jobs due to higher unemployment may cut back on their spending. This could impact sectors reliant on consumer spending, such as retail.
  3. Reduced Investment: Businesses might delay or cut back on investments in an uncertain economic environment, potentially slowing down economic growth.
  4. Policy Adjustments: Governments and central banks could adjust policies to respond to rising unemployment during an economic downturn. This might involve measures such as interest rate cuts or increased fiscal spending.
  5. Income Reduction: Reduced employment could lead to lower incomes for affected individuals, impacting household financial stability.
  6. Political Impact: Changes in economic conditions can influence the political landscape. Increased dissatisfaction or anxiety could lead to political instability.
  7. Financial Market Impact: Heightened uncertainty due to economic slowdown could impact stock markets and financial markets, potentially leading to stock price declines.

An increase in the unemployment rate can potentially influence the Federal Reserve’s (Fed) interest rate decisions. Generally, the Fed adjusts interest rates to stabilize or support the economy based on its conditions. Depending on the circumstances, the following scenarios could unfold:

  1. Possibility of Rate Cuts: Amid higher unemployment and economic uncertainty, the Fed might consider lowering interest rates to support economic activity. Reduced rates can stimulate borrowing, encouraging consumption and investment.
  2. Maintaining or Adjusting Policy: The Fed could opt to maintain its current policy stance or make minor adjustments. This decision would depend on the extent of unemployment fluctuations and the broader economic situation.
  3. Rate Hike in Anticipation of Economic Improvement: Conversely, if the rise in unemployment is temporary or other parts of the economy show signs of improvement, the Fed might consider raising rates to maintain price stability.

These decisions are made during meetings of the Federal Open Market Committee (FOMC), where the Fed considers various factors. Interest rate determinations are based on a range of indicators, including economic data, inflation rates, employment conditions, GDP growth, and future projections. The Fed is expected to take appropriate measures to support economic stability and growth.


Unemployment rate, August (3.5% expected, 3.5% previously)

The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces.

This rate is also defined as the U-3 measure of labor underutilization.
The series comes from the ‘Current Population Survey (Household Survey)’
The source code is: LNS14000000

Suggested Citation:
U.S. Bureau of Labor Statistics, Unemployment Rate [UNRATE], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/UNRATE, August 27, 2023.

# Load necessary libraries

# Create a data frame from the provided data
data <- data.frame(
  DATE = as.Date(c("2021-01-01", "2021-02-01", "2021-03-01", "2021-04-01", "2021-05-01",
                   "2021-06-01", "2021-07-01", "2021-08-01", "2021-09-01", "2021-10-01",
                   "2021-11-01", "2021-12-01", "2022-01-01", "2022-02-01", "2022-03-01",
                   "2022-04-01", "2022-05-01", "2022-06-01", "2022-07-01", "2022-08-01",
                   "2022-09-01", "2022-10-01", "2022-11-01", "2022-12-01", "2023-01-01",
                   "2023-02-01", "2023-03-01", "2023-04-01", "2023-05-01", "2023-06-01", "2023-07-01")),
  Unemployment_Rate = c(6.3, 6.2, 6.1, 6.1, 5.8, 5.9, 5.4, 5.2, 4.8, 4.5, 4.2, 3.9,
                        4.0, 3.8, 3.6, 3.6, 3.6, 3.6, 3.5, 3.7, 3.5, 3.7, 3.6, 3.5, 3.4,
                        3.6, 3.5, 3.4, 3.7, 3.6, 3.5)

# Create an interactive bar plot using Plotly
plot <- ggplot(data, aes(x = DATE, y = Unemployment_Rate, fill = Unemployment_Rate)) +
  geom_bar(stat = "identity") +
  labs(title = "Unemployment Rate") +
  theme_minimal() +
  theme(legend.position = "none") +
  scale_fill_viridis_c()  # You can choose other color palettes like scale_fill_brewer() or scale_fill_gradient()

# Convert the ggplot object to a Plotly object for interactivity
plotly_plot <- ggplotly(plot)

# Display the interactive plot


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