Federal Reserve Economic Data

The FRED Blog often uses data from the US Bureau of Labor Statistics (BLS): A few years ago, we used their Consumer Expenditures Survey to discuss the preferences for life insurance and other personal insurance services among different population groups. Today, we use data from the Producer Price Index program of the BLS to discuss the premiums charged for some of those services.

The two solid lines in the FRED graph above show the year-over-year percent growth rate in the premiums charged for insuring two types of assets: private automobiles (red line) and homes (green line). The dashed black line is the annual growth rate in the headline property and casualty producer price index, which includes, among others, commercial, medical, and worker’s compensation insurance.

Since 1999, when data are first available, cycles in the growth rate of insurance premiums are easily visible. For example, two distinct periods of fast growth in automobile insurance premiums in the early 2000s and mid-to-late 2010s were followed by periods of much slower growth and even decreases in premium values. So, what can help explain those cyclical fluctuations in value?

The 2023 annual report on the insurance industry by the Federal Insurance Office names several factors impacting the overall financial standing of insurers. Most recently, widespread natural disasters have resulted in large payouts and higher interest rates have decreased the value of fixed-income securities held in this sector’s investment portfolios. To compensate for those losses, insurers have raised their premiums at a pace not recorded in many years.

How this graph was created: Search FRED for and select “Producer Price Index by Industry: Premiums for Property and Casualty Insurance.” From the “Edit Graph” panel, use the “Add Line” tab to search for and select “Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Private Passenger Auto Insurance.” Click on “Add data series” and repeat the previous step to add “Producer Price Index by Industry: Premiums for Property and Casualty Insurance: Premiums for Homeowner’s Insurance” to the graph. Next, select the “Edit Lines” tab and use the “Units” dropdown menu to select “Percent Change from Year Ago.” Lastly, use the “Format” tab to customize the line styles.

Suggested by Diego Mendez-Carbajo.

The FRED graph above shows a data series that has been featured in recent FRED Blog posts: ”Dating a recession,” “Are we in a recession (yet)?” and “Assessing recession probabilities.”

Each data point represents the probability of the US economy being in a recession during the preceding month. In other words, these are backward-looking probabilities. These probabilities can range between 0 (complete confidence the economy is expanding) and 100 (complete confidence the economy is contracting).

As of October 2023, the latest observation available at the time of this writing, the data signaled a 2.2% probability the US economy was in recession during September 2023.

But what about forward-looking probabilities? Organizations of professional forecasters such as Consensus Economics synthesize available economic data to estimate the likelihood of an economic downturn occurring in the near future. Recent research from Christopher Neely at the St. Louis Fed investigates what variables that organization appears to use to predict the probability of recession occurring in the next 12 months.

Neely finds that although 10 economic variables are useful when forecasting recessions, they do not explain the Consensus Economics estimated probability of recession very well. Moreover, Treasury yield spreads are not among those best predictors, though they have a well-established record in predicting recessions. You can learn more about forecasting from yield spreads.

For more about this and other research, visit the website of the Research Division of the Federal Reserve Bank of St Louis, which offers an array of economic analysis and expertise provided by our staff.

How this graph wase created: Search FRED for and select “Smoothed U.S. Recession Probabilities.”

Suggested by Diego Mendez-Carbajo.

Background

The overall unemployment rate is a key macroeconomic indicator that economists, policymakers, and the media follow closely. The Bureau of Labor Statistics calculates it from a monthly survey of households that tallies the number of those not employed but looking for work divided by the total labor force, which itself is the sum of employed workers plus the number of those not employed but looking for work.

A separate unemployment measure that receives less attention is the insured unemployment rate: It is the number of workers currently claiming unemployment insurance benefits relative to the entire pool of workers covered by unemployment insurance. A worker is “covered” if they could claim unemployment insurance benefits after losing their job—and meeting other eligibility requirements.

The insured unemployment rate serves as a switch that activates state-level extended unemployment insurance benefits when it rises above a specified threshold. Some argue that extending benefits for unemployed workers can help stabilize the macroeconomy during downturns. While many states adopt additional criteria tied to the overall unemployment rate, the insured unemployment rate is the one criterion that all states use.

What the data show

The red line in the FRED graph above shows that the share of workers covered by unemployment insurance has grown since the Department of Labor began tracking these data in 1971: Back then, only 70% of workers were covered. Today, about 90% are.

Yet, over the same period, the insured unemployment rate (IUR) has declined relative to the overall unemployment rate, as shown by the blue line in the graph. For the third quarter of 2023, an IUR of 1.3% is divided by an overall unemployment rate of 3.9% to achieve the value of 0.3 shown in the graph—a big drop from the nearly 0.7 value back in 1971.

Note that in 2020, both values in the graph briefly rise above 100%: During the pandemic, the US government expanded unemployment benefits to individuals who would typically be ineligible and paused the requirement that individuals receiving benefits be actively looking for work.

Discussion and analysis

If more people than ever work in jobs covered by unemployment insurance, why aren’t more unemployed individuals claiming benefits?

Individuals separated from jobs covered by unemployment insurance may still be ineligible for benefits under additional state eligibility requirements: for example, enrolling in educational programs; picking up part-time temporary work; earning too low an income while employed; and working for too short a period in their last job. (See Nicholson and Needels, 2006.)

But eligibility alone cannot explain the trend, as many eligible workers don’t file for unemployment insurance. Birinci and See (2023) find that, on average, about 57% of unemployed individuals are eligible for unemployment benefits, but only 61% of the eligible unemployed workers claim these benefits.

Let’s consider several possible reasons why the “take-up” rate (the share of unemployed individuals receiving benefits) is relatively low, including worker characteristics, policy changes, and regional patterns.

Those eligible for unemployment benefits may be less likely to need them: Eligible unemployed individuals who choose not to take-up these benefits tend to have greater wealth and shorter unemployment durations. (See Birinci and See, 2023.) Current Population Survey data identify the most popular reason eligible unemployed workers do not apply for benefits: They do not expect to be unemployed for long. (See Wandner and Stettner, 2002.)

The national average take-up rate was mostly stable throughout the 1960s and 70s, but it declined throughout the 1980s. Blank and Card (1991) argue this sharp decline was driven by composition effects: Regions with persistently low and decreasing take-up rates also tended to have higher rates of unemployment due to sectoral and demographic shifts. At the same time, the share of covered jobs expanded to include more government employees, who are less likely to become unemployed.

Around the same time, unemployment benefits became taxable income, reducing the after-tax benefits filers received. By 1987, these benefits were “fully taxable for all recipients,” which Anderson and Meyer (1997, p. 915) argue was also an important reason for the decline in take-up. More-recent research points out that states and regions with larger benefits and longer maximum benefit durations tend to have higher take-up rates (Bell et al., 2023; Nicholson and Needels, 2006) and that individuals are less likely to apply for benefits when what they can receive is small relative to prior income. (See Birinci and See, 2023.)

Finally, the percent of lost income that can be recovered through benefits and eligibility requirements may be crude proxies for true accessibility. (See O’Leary et al., 2021.) Social institutions can also have an impact, albeit one difficult to quantify. For example, by educating their members about unemployment insurance and helping them file, unions may bolster take-up, leading to lower overall take-up rates as unionization has declined. (See Blank and Card, 1991 and Nicholson and Needels, 2006.) Similarly, difficulty navigating the benefits system due to limited technology or English proficiency may keep some populations from applying, although earlier analysis finds relatively little support for this being widespread.

How this graph was created: Search FRED for and select the insured unemployment rate series (IURSA). From the “Edit Graph” panel, use the “Customize data” option to search for and add the unemployment rate. In the “Formula” bar below, type a/b and click “Apply.” Next, use the “Add Line” option to search for and add the covered employment series. Use the “Edit Line” tab to select Line 2 and the “Customize Data” option again to add the employment level series and apply the formula a/(b*1000). (We multiply the denominator here by 1000 because the employment level is reported in thousands while covered employment is not.)

Suggested by Bill Dupor and Marie Hogan.