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How are we transporting ourselves? Part 2

Public transportation before, during, and after the pandemic

In early 2020, the COVID-19 pandemic severely reduced the number of miles Americans drove their cars, as we described in a previous blog post. The pandemic also decreased public transit ridership in a similar way: Many people started working from home, and others took the safety precaution of avoiding travel that would put them in close proximity to others.

However, the use of public transit has not rebounded from the pandemic as quickly as the number of vehicle miles traveled has. In fact, public transit ridership appears to be leveling off at 30% below pre-pandemic levels.

The reasons for the quick rebound in total vehicle miles driven can explain some of the lag in public transit ridership: A study by the Cleveland Fed showed that large numbers of residents in large metro areas migrated to small and midsize metro areas. These residents must now drive farther to reach those large metro areas, which has boosted vehicle miles driven.

But large metro areas also include well-established public transit systems—think, New York City, Chicago, and Washington D.C. Fewer residents in these large metro areas means fewer riders that could use those public transit systems. (Many midsize and small metro areas are less densely populated and have less-extensive public transit systems, so their populations rely more on motor vehicles.)

Fewer people available to use public transit also means less money for it, since there are fewer paying customers. A decline in public transit revenue can reduce hours and available routes, which can further reduce the opportunities for ridership.

There also has been an increase in remote work, which further reduces demand for daily trips. This movement of people and where they can work helps explain why, as of December 2023, public transit ridership numbers have not recovered to levels seen before the pandemic as total vehicle miles has.

How this graph was created: In FRED, search for and select the seasonally adjusted “public transit ridership” series. Select the timeline of 2010 to the latest data for 2023 to focus on the decline of ridership below 2019 levels.

Suggested by John Fuller and Charles Gascon.

The insured unemployment rate

Those receiving vs. those eligible for unemployment insurance benefits

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.

How are we transporting ourselves? Part 1

Vehicle miles before, during, and after the pandemic

When the COVID-19 pandemic hit in March 2020, many Americans stopped driving due to stay-at-home orders across the nation. And our FRED graph above shows that total vehicle miles traveled plummeted.

But cars weren’t parked for long: As the US began to emerge from the initial shutdowns, vehicle miles quickly returned to where they were in 2019.

How can we reconcile the rise in remote work since the pandemic with this car-driving rebound? Arbogast, Gascon, and Spewak researched this question in late 2019 and found that people who worked from home actually tended to drive more miles than people who commuted to work. And this trend seems to have continued since the pandemic.

A myriad of reasons could explain this. For example, people could choose to live in cheaper areas that are further away from urban centers, but still value the urban amenities that require them to drive into town. As 2024 nears, the US is seeing more people move toward the suburbs and other car-dependent areas, which could help explain why vehicle miles rebounded more quickly than other modes of transportation, such as public transit. In part 2 of this post, we’ll examine public transit ridership more closely.

How this graph was created: Search FRED for “Vehicle Miles Traveled” and click on the result that is seasonally adjusted.

Suggested by Charles Gascon and Jack Fuller.



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