The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.
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August 23, 2018
What Does the Current Slope of the Yield Curve Tell Us?
As I make the rounds throughout the Sixth District, one of the most common questions I get these days is how Federal Open Market Committee (FOMC) participants interpret the flattening of the yield curve. I, of course, do not speak for the FOMC, but as the minutes from recent meetings indicate, the Committee has indeed spent some time discussing various views on this topic. In this blog post, I'll share some of my thoughts on the framework I use for interpreting the yield curve and what I'll be watching. Of course, these are my views alone and do not reflect the views of any other Federal Reserve official.
Many observers see a downward-sloping, or "inverted," yield curve as a reliable predictor for a recession. Chart 1 shows the yield curve's slope—specifically, the difference between the interest rates paid on 10-year and 2-year Treasury securities—is currently around 20 basis points. This is lowest spread since the last recession.
The case for worrying about yield-curve flattening is apparent in the chart. The shaded bars represent recessionary periods. Both of the last two recessions were preceded by a flat (and, for a time, inverted) 10-year/2-year spread.
As we all know, however, correlation does not imply causality. This is a particularly important point to keep in mind when discussing the yield curve. As a set of market-determined interest rates, the yield curve not only reflects market participants' views about the evolution of the economy but also their views about the FOMC's likely reaction to that evolution and uncertainty around these and other relevant factors. In other words, the yield curve represents not one signal, but several. The big question is, can we pull these signals apart to help appropriately inform the calibration of policy?
We can begin to make sense of this question by noting that Treasury yields of any given maturity can be thought of as the sum of two fundamental components:
- An expected policy rate path over that maturity: the market's best guess about the FOMC's rate path over time and in response to the evolution of the economy.
- A term premium: an adjustment (relative to the path of the policy rate) that reflects additional compensation investors receive for bearing risk related to holding longer-term bonds.
Among other things, this premium may be related to two factors: (1) uncertainty about how the economy will evolve over that maturity and how the FOMC might respond to events as they unfold and (2) the influence of supply and demand factors for U.S. Treasuries in a global market.
Let's apply this framework to the current yield curve. As several of my colleagues (including Fed governor Lael Brainard) have noted, the term premium is currently quite low. All else equal, this would result in lower long-term rates and a flatter yield curve. The term premium bears watching, but it is unclear that movements in the premium reflect particular concerns about the course of the economy.
I tend to focus on the other component: the expected path of policy. When we ask whether a flattening yield curve is a cause for concern, what we are really asking is: does the market expect an economic slowdown that will require the FOMC to reverse course and lower rates in the near future?
The eurodollar futures market shows us one measure of the market's expectation for the policy rate path. These derivative contracts are quoted in terms of a three-month rate that closely follows the FOMC's policy rate, which makes them well-suited for this kind of analysis. (Some technical details regarding this market can be found in a 2016 issue of the Atlanta Fed's "Notes from the Vault.")
Chart 2 illustrates the current estimate of the market's expected policy rate path. Read simply, the market appears to be forecasting continuing policy rate increases through 2020, and there is no evidence of a market forecast that the FOMC will need to reverse course in the medium term. However, the level of the policy rate is lower than the median of the FOMC's June Summary of Economic Projections (SEP) for 2019 and 2020.
Once we get past 2020, the market's expected policy path flattens. I read this as evidence that market participants overall expect a very gradual pace of tightening as the most likely outcome over the next two years. Interestingly, the market appears to expect a slower pace of tightening than the pace that at least some members of the FOMC currently view as "appropriate" as represented in their SEP submissions.
For this measure, I find the short-term perspective most informative. As one looks further into the future, the range of possible outcomes widens, as many the factors that influence the economy can evolve and interact widely. Thus, the precision of any signal the market is providing about policy expectations—if indeed there is any signal at all—is likely to be quite low.
With this information in mind, I do not interpret that the yield curve indicates that the market believes the evolution of the economy will cause the FOMC to lower rates in the foreseeable future. This interpretation is consistent with my own economic forecast, gleaned from macroeconomic data and a robust set of conversations with businesses both large and small. My modal outlook is for expansion to continue at an above-trend pace for the next several quarters, and I see the risks to that projection as balanced. Yes, there are downside risks, chief among them the effects of (and uncertainty about) trade policy. But those risks are countered by the potential for recent fiscal stimulus to have a much more transformative impact on the economy than I've marked into my baseline outlook.
I believe the yield curve gives us important and useful information about market participants' forecasts. But it is only one signal among many that we use for the complex task of forecasting growth in the U.S. economy. As the economy evolves, I will be assessing the response of the yield curve to incoming data and policy decisions along the lines I've laid out here, incorporating market signals along with a constellation of other information to achieve the FOMC's dual objectives of price stability and maximum employment.
June 01, 2018
Part-Time Workers Are Less Likely to Get a Pay Raise
A recent FEDS Notes article summarized some interesting findings from the Board of Governors' 2017 Survey of Household Economics and Decisionmaking. One set of responses that caught my eye explored the connection between part-time employment and pay raises. The report estimates that about 70 percent of people working part-time did not get a pay increase over the past year (their pay stayed the same or went down). In contrast, only about 40 percent of full-time workers had no increase in pay.
This pattern is broadly consistent with what we see in the Atlanta Fed's Wage Growth Tracker data. As the following chart indicates, the population of part-time workers (who were also employed a year earlier) is generally less likely to get an increase in the hourly rate of pay than their full-time counterparts. Median wage growth for part-time workers has been lower than for full-time workers since 1998.
This wage growth premium for full-time work is partly accounted for by the fact that the typical part-time and full-time worker are different along several dimensions. For example, a part-time worker is more likely to have a relatively low-skilled job, and wage growth tends to be lower for workers in low-skilled jobs.
As the chart shows, the wage growth gap widened considerably in the wake of the Great Recession. The share of workers who are in part-time jobs because of slack business conditions increased across industries and occupation skill levels, and median part-time wage growth ground to a halt.
While part-time wage growth has improved since then, the wage growth gap is still larger than it used to be. This larger gap appears to be attributable to a rise in the share of part-time employment in low-skilled jobs since the recession. In particular, relative to 2007, the share of part-time workers in the Wage Growth Tracker data in low-skilled jobs has increased by about 3 percentage points, whereas the share of full-time workers in low-skilled jobs has remained essentially unchanged. Note that what is happening here is that more part-time jobs are low skilled than before, and not the other way around. Low-skilled jobs are about as likely to be part-time now as they were before the recession.
How does this shift affect an assessment of the overall tightness of today's labor market? Looking at the chart, the answer is probably “not much.” As measured by the Wage Growth Tracker, median wage growth for both full-time and part-time workers has not been accelerating recently. If the labor market were very tight, then this is not what we would expect to see. The modest rise in average hourly earnings in the June 1 labor report for May 2018 to 2.7 percent year over year, even as the unemployment rate declined to an 18-year low, seems consistent with that view. A reading on the Wage Growth Tracker for May should be available in about a week.
March 06, 2018
A First Look at Employment
One Friday morning each month at 8:30 is always an exciting time here at the Atlanta Fed. Why, you might ask? Because that's when the U.S. Bureau of Labor Statistics (BLS) issues the newest employment and labor force statistics from the Employment Situation Summary. Just after the release, Atlanta Fed analysts compile a "first look" report based on the latest numbers. We have found this initial view to be a very useful glimpse into the broad health of the national labor market.
Because we find this report useful, we thought you might also find it of interest. To that end, we have added the Labor Report First Look tool to our website, and we'll strive to post updated data soon after the release of the BLS's Employment Situation Report. Our Labor Report First Look includes key data for the month and changes over time from both the payroll and household surveys, presented as tables and charts.
Additionally, we will also use the bureau's data to create other indicators included in the Labor Report First Look. For example, one of these is a depiction of changes in payroll employment by industry, in which we rank industry employment changes by average hourly pay levels. This tool allows us to see if payrolls are gaining or losing higher- or lower-paying jobs, as the following chart shows.
But wait, there's more! We will also report information on the so-called job finding rate—an estimate of the share of unemployed last month who are employed this month—and a broad measure of labor underutilization. Our underutilization concept is related to another statistic we created called Z-Pop, computed as the share of the population who are either unemployed or underemployed (working part-time hours but wanting full-time work) or who say they currently want a job but are not actively looking. We have found this to be a useful supplement to the BLS's employment-to-population ratio (see the chart).
The Labor Report First Look tool also allows you to dig a bit deeper into Atlanta Fed labor market analysis via links to our Human Capital Data & Tools (which includes the Wage Growth Tracker and Labor Force Dynamics web pages) and links to some of our blog posts on labor market developments and related research. (In fact, it's easy to stay informed of all Labor Report First Look updates by subscribing to our RSS feed or following the Atlanta Fed on Twitter.
We hope you'll look for the inaugural Labor Report First Look next Friday morning...we know you'll be as excited as we will!
January 18, 2018
How Low Is the Unemployment Rate, Really?
In 2017, the unemployment rate averaged 4.4 percent. That's quite low on a historical basis. In fact, it's the lowest level since 2000, when unemployment averaged 4.0 percent. But does that mean that the labor market is only 0.4 percentage points away from being as strong as it was in 2000? Probably not. Let's talk about why.
As observed by economist George Perry in 1970, although movement in the aggregate unemployment rate is mostly the result of changes in unemployment rates within demographic groups, demographic shifts can also change the overall unemployment rate even if unemployment within demographic groups has not changed. Adjusting for demographic changes makes for a better apples-to-apples comparison of unemployment today with past rates.
Three large demographic shifts underway since the early 2000s are the rise in the average age and educational attainment of the labor force, and the decline in the share who are white and non-Hispanic. These changes are potentially important because older workers and those with more education have lower rates of unemployment across age and education groups respectively, and white non-Hispanics tend to have lower rates of unemployment than other ethnicities.
The following chart shows the results of a demographic adjustment that jointly controls for year-to-year changes in two sex, three education, four race/ethnicity, and six age labor force groups, (see here for more details). Relative to the year 2000, the unemployment rate in 2017 is about 0.6 percentage points lower than it would have been otherwise simply because the demographic composition of the labor force has changed (depicted by the blue line in the chart).
In other words, even though the 2017 unemployment rate is only 0.4 percentage points higher than in 2000, the demographically adjusted unemployment rate (the green line in the chart) is 1.0 percentage points higher. In terms of unemployment, after adjusting for changes in the composition of the labor force, we are not as close to the 2000 level as you might have thought.
The demographic discrepancy is even larger for the broader U6 measure of unemployment, which includes marginally attached and involuntarily part-time workers. The 2017 demographically adjusted U6 rate is 2.5 percentage points higher than in 2000, whereas the unadjusted U6 rate is only 1.5 percentage points higher. That is, on a demographically adjusted basis, the economy had an even larger share of marginally attached and involuntarily part-time workers in 2017 than in 2000.
The point here is that when comparing unemployment rates over long periods, it's advisable to use a measure that is reasonably insulated from demographic changes. However, you should also keep in mind that demographics are only one of several factors that can cause fluctuation. Changes in labor market and social policies, the mix of industries, as well as changes in the technology of how people find work can also result in changes to how labor markets function. This is one reason why estimates of the so-called natural rate of unemployment are quite uncertain and subject to revision. For example, participants at the December 2012 Federal Open Market Committee meeting had estimates for the unemployment rate that would prevail over the longer run ranging from 5.2 to 6.0 percent. At the December 2017 meeting, the range of estimates was almost a whole percentage point lower at 4.3 to 5.0 percent.
January 17, 2018
What Businesses Said about Tax Reform
Many folks are wondering what impact the Tax Cuts and Jobs Act—which was introduced in the House on November 2, 2017, and signed into law a few days before Christmas—will have on the U.S. economy. Well, in a recent speech, Atlanta Fed president Raphael Bostic had this to say: "I'm marking in a positive, but modest, boost to my near-term GDP [gross domestic product] growth profile for the coming year."
Why the measured approach? That might be our fault. As part of President Bostic's research team, we've been curious about the potential impact of this legislation for a while now, especially on how firms were responding to expected policy changes. Back in November 2016 (the week of the election, actually), we started asking firms in our Sixth District Business Inflation Expectations (BIE) survey how optimistic they were (on a 0–100 scale) about the prospects for the U.S. economy and their own firm's financial prospects. We've repeated this special question in three subsequent surveys. For a cleaner, apples-to-apples approach, the charts below show only the results for firms that responded in each survey (though the overall picture is very similar).
As the charts show, firms have become more optimistic about the prospects for the U.S. economy since November 2016, but not since February 2017, and we didn't detect much of a difference in December 2017, after the details of the tax plan became clearer. But optimism is a vague concept and may not necessarily translate into actions that firms could take that would boost overall GDP—namely, increasing capital investment and hiring.
In November, we had two surveys in the field—our BIE survey (undertaken at the beginning of the month) and a national survey conducted jointly by the Atlanta Fed, Nick Bloom of Stanford University, and Steven Davis of the University of Chicago. (That survey was in the field November 13–24.) In both of these surveys, we asked firms how the pending legislation would affect their capital expenditure plans for 2018. In the BIE survey, we also asked how tax reform would affect hiring plans.
The upshot? The typical firm isn't planning on a whole lot of additional capital spending or hiring.
In our national survey, roughly two-thirds of respondents indicated that the tax reform hasn't enticed them into changing their investment plans for 2018, as the following chart shows.
The chart below also makes apparent that small firms (fewer than 100 employees) are more likely to significantly ramp up capital investment in 2018 than midsize and larger firms.
For our regional BIE survey, the capital investment results were similar (you can see them here). And as for hiring, the typical firm doesn't appear to be changing its plans. Interestingly, here too, smaller firms were more likely to say they'd ramp up hiring. Among larger firms (more than 100 employees), nearly 70 percent indicated that they'd leave their hiring plans unchanged.
One interpretation of these survey results is that the potential for a sharp acceleration in GDP growth is limited. And that's also how President Bostic described things in his January 8 speech: "For now, I am treating a more substantial breakout of tax-reform-related growth as an upside risk to my outlook."
September 08, 2017
When Health Insurance and Its Financial Cushion Disappear
Personal health care costs can skyrocket with a new diagnosis or accident, often leading to catastrophic financial costs for people. Health insurance plays an important role in protecting individuals from unexpected large financial shocks as a result of adverse health events. Just as homeowner's insurance helps protect you from financial devastation if your house burns down, health insurance helps protects you from burning through your savings because of a heart attack. This 2008 report from the Commonwealth Fund shows that the uninsured are far more likely to have to use their savings and reduce other types of spending to pay medical bills.
Much research has been done on the impact of health insurance on financial and health outcomes. (This paper , for example, summarizes the history and impact of Medicaid.) However, most of the studies look at the case of individuals who are gaining health insurance. In a recent Atlanta Fed working paper and the related podcast episode , we measure the impact of losing public health insurance on measures of financial well-being such as credit scores, delinquent debt eligible to be sent to debt collectors, and bankruptcies. We performed these measurements by studying the case of Tennessee's Medicaid program, known as TennCare, in the mid-2000s. At that time, a large statewide Medicaid expansion that began in the 1990s ran into financial difficulties and was scaled back. As the following chart shows, some 170,000 individuals were removed from TennCare rolls between 2005 and 2006.
Our analysis of this episode, using data from the New York Fed's Consumer Credit Panel/Equifax, revealed some striking findings. Individuals who lost health insurance experienced lower credit scores, more debt eligible to be sent to collections, and a higher incidence of bankruptcy. Those who were already financially vulnerable suffered the worst. In particular, individuals who already had poor credit, as measured by Fannie Mae's lowest creditworthiness categories , and then lost Medicaid see their credit scores fall by close to 40 points on average and are almost 17 percent more likely to have their debt sent to collection agencies. Our analysis also finds that gaining or losing health insurance is not symmetric in its impact—losing insurance has larger negative financial effects than the positive financial impacts of gaining insurance.
Our results provide evidence that losing Medicaid coverage not only removes inexpensive access to health care but also eliminates an important layer of financial protection. A cost-benefit analysis of proposed cuts to Medicaid coverage (see here, here, and here for a discussion of recent legislative efforts in the U.S. Congress) would need to consider the negative financial consequences for individuals of the type that we have identified.
July 31, 2017
Behind the Increase in Prime-Age Labor Force Participation
Prime-age labor force participation has been on a tear recently. Over the last eight quarters, it is up by about 65 basis points (bps) and more than 40 bps in just the last year. When combined with declines in the rate of unemployment, this increase has helped lift the employment-to-population (EPOP) ratio for this key population group by around 120 bps during the last two years.
Placed in the context of an almost 260 bp decline in the prime-age EPOP ratio between 2007 and 2015, this development is significant. Although the unemployment rate is close to what most economists consider full employment, rising labor force participation can indicate that the labor market might still have some room to run before the employment gap is fully closed. (The Congressional Budget Office offers some analysis consistent with this idea.)
So what's behind the increase in prime-age (defined as people between 25 and 54) participation in the last year? Changes in the labor force participation rate (LFPR) either can be the result of changes in the mix of demographic groups in the population with different average rates of participation (for example, across education and race/ethnicity), or they can result from changes in average participation rates within demographic groups. It turns out that most of the increase in the prime-age LFPR has been because of increased LFPR within demographic groups—in particular, prime-age women and especially women without a college degree. Prime-age men have not contributed much to the rise in participation beyond the increased participation associated with a more educated population.
The following chart shows the contribution to the change in the prime-age LFPR over the last year as a result of changes in the relative mix of age-education-race groups (the blue bars) and changes in participation rates within age-education-race groups (the orange bars). It shows the contribution from both sexes combined and from prime-age women and men separately.
Note that the we computed the contributions using six five-year age groups, three education groups (less than high school, high school but no college degree, and college degree), three race/ethnicity groups (Hispanic, non-Hispanic black, and non-Hispanic white/other), and two sexes.
Of the total increase in the prime-age LFPR, most of that was the result of changes in labor force participation behavior within female demographic groups. In fact, changes in LFPR behavior from prime-age men served as a drag on the overall prime-age LFPR. The modestly positive demographic effect on the LFPR for both men and women reflects the higher LFPR for those with a college degree and the relative increase in the share of both prime-age men and women with a college degree.
This development stands in contrast to the drivers of the change in the prime-age LFPR between 2015 and 2016. Of the 24 bp increase in prime-age LFPR between the second quarters of 2015 and 2016, changes in the demographic composition of the population (primarily increased education levels) accounted for all of it rather than changes in average participation rates within demographic groups.
The next chart shows the contribution to the change in the prime-age LFPR between 2016 and 2017 due to changes in the LFPR behavior of women for specific education-race groups.
As the chart shows, the bulk of the demographically adjusted contribution from female labor force participation came from women without a college degree, and the largest contribution across female education-race groups was from Hispanics without a college degree. The increase in labor force participation among women with less education is consistent with evidence of recent improvement in the wage gains for relatively low-wage earners.
Although this simple decomposition doesn't explain why nondegreed women are increasingly finding the labor force to be an attractive option, we can infer some clues by looking at changes in the reasons people give for not participating. In particular, the largest contribution from changes in behavior among prime-age women over the last year came from a decrease in the propensity to be out of the labor force because of poor health or being in the shadow labor force (wanting a job but not looking).
Recently, former Minneapolis Fed President Narayana Kocherlakota has argued that macroeconomists should take more seriously the differences in behavior across demographic groups. The Atlanta Fed's Labor Force Dynamics web page contains more information on the behavioral trends in the reasons people give for not participating in the labor force across demographic groups, and the page was just updated to include data for the second quarter of 2017. Check it out, and we'll keep reporting here on the relative contributions to the labor force of behavioral versus demographic changes—and whether the winning streak for prime-age labor force participation continues.
July 12, 2017
An Update on Labor Force Participation
With the unemployment rate essentially back to prerecession levels, economists have been paying increased attention to the labor force participation rate (LFPR). Many economists, including those at the Congressional Budget Office , believe untapped resources remain on the sidelines of the labor market.
What exactly does "on the sidelines" entail? Discouraged workers are only a small part of the story. To help unravel the rest of the mystery behind the elevated share of people not participating, we at the Atlanta Fed use the microdata from the Current Population Survey to code the activities of persons not in the labor force. We then calculate how changes in each activity contribute to the total change in the LFPR.
The chart below depicts the drivers of the change in the LFPR from the first quarter of 2016 to the first quarter of 2017. (The interactive tool on our website allows you to make comparisons across gender, age group, and time.) The LFPR rose just slightly (about 0.06 percentage points). However, that small change was the net result of much larger countervailing forces. Other things equal, demographic changes during the year would have lowered the LFPR by around 0.14 percentage points. The aging of the population put significant downward pressure on the LFPR (pushing it down 0.24 percentage points), but a more educated workforce helped push up the LFPR (0.10 percentage points). If the age and education mix of the population had not changed, the LFP rate would have risen by about 0.19 percentage points (see the chart).
The following chart further breaks down the behavioral and cyclical components at work. After controlling for shifts in the demographic mix of the population during the year, the largest contributing factor was a decline in the rate of nonparticipation because of family responsibilities.
This is a particularly important explanation for prime-age women (defined as women between 25 and 54 years of age). A smaller share of prime-age women who say they are busy with home and/or family responsibilities accounts for about half of the 0.62 percentage point increase in LFPR that occurred between the first quarter of 2016 and the first quarter of 2017 (see the chart).
To examine factors affecting prime-age men's participation or to learn more about the cyclical and structural factors behind each reason, visit our website.
May 05, 2017
Slide into the Economic Driver's Seat with the Labor Market Sliders
The Atlanta Fed has just launched the Labor Market Sliders, a tool to help explore simple "what if" questions using actual data on employment, the unemployment rate, labor force participation, gross domestic product (GDP) growth, and labor productivity (GDP per worker).
We modeled the Labor Market Sliders after the popular Atlanta Fed Jobs Calculator. In particular, the sliders take the rate of labor productivity growth and the rate of labor force participation as given (not a function of GDP or employment growth) and then asks questions about GDP growth and labor market outcomes. Like the Jobs Calculator, the sliders require that things add up, a very useful feature for all those backyard economic prognosticators (we know you're out there).
Let's look at an example of using the sliders. The Congressional Budget Office (CBO) projects that the labor force participation rate (LFPR) will maintain roughly its current level of 62.9 percent during the next couple of years, as the downward pressure of retiring baby boomers and the upward pressure from robust hiring hold the rate stable. The CBO also projects that labor productivity growth will gradually increase to almost 1 percent over roughly the same period.
Suppose we want to know what GDP growth would be over the next couple of years (other things equal) if labor productivity, which has been sluggish lately, returned to 1 percent, as projected by the CBO. By moving the Labor Productivity slider in the tool to 1 percent and the Months slider to 24, you will see how productivity alone affects GDP growth: it increases to about 2 percent (see the image below). In this experiment, the unemployment rate, average job growth, and LFPR are constrained to current levels.
However, there's more than one way to achieve GDP growth of 2 percent over the next two years. Let's take a look.
Hit the reset button, and productivity, GDP growth, and months revert to their starting values. Then move the Months slider to 24 and the GDP Growth slider to 2 percent. You then see that—at current levels of labor force participation and labor productivity growth—achieving 2 percent GDP growth over the next two years would require the economy to create about 200,000 jobs per months (see the image below), which would push the unemployment rate down to 3.1 percent (a rate not seen since the early 1950s).
Hit the reset button again. Achieving 2 percent GDP growth over the next two years is also realistic with a higher LFPR, some other things equal. First, move the Months slider to 24, then move the Labor Force Participation Rate slider to 63.7 percent. The higher LFPR is consistent with about 2 percent growth in GDP and roughly 200,000 additional jobs added each month (see the image below). (This scenario constrains the unemployment rate and labor productivity growth rate to their current levels.) Of course, we haven't seen the LFPR at 63.7 percent since 2012, but that's another discussion.
What if we wanted something a bit more ambitious, such as averaging 3 percent GDP growth over the next couple of years? Hit the reset button again, and try this scenario. Keep Labor Force Participation Rate at its current level (consistent with the CBO's projection), set Labor Productivity growth to 1 percent (also using the CBO projection as a guide), move the Months slider to 24, and the GDP Growth slider to 3 percent. The Labor Market Sliders allow us to see that the economy would need to add an average of about 240,000 jobs each month for those two years. This scenario, the tight-labor-market method of achieving 3 percent GDP growth, would bring the unemployment rate down to 2.6 percent.
However, suppose the United States were somehow able to recapture productivity growth of around 2 percent, which we experienced in the late 1990s and early 2000s. In that case, 3 percent GDP could be achieved at the current employment growth and unemployment rate.
I encourage you to play around and devise your own "what if" scenarios—and use the Labor Market Sliders to make sure they add up.
March 30, 2017
Bad Debt Is Bad for Your Health
The amount of debt held by U.S. households grew steadily during the 2000s, with some leveling off after the recession. However, the level of debt remains elevated relative to the turn of the century, a fact easily seen by examining changes in debt held by individuals from 2000 to 2015 (the blue line in the chart below).
Not only is the amount of debt elevated for U.S. households, but the proportion of delinquent household debt has also fluctuated significantly, as the red line in the above chart depicts.
The amount of debt that is severely delinquent (90 days or more past due) peaked during the last recession and remains above prerecession levels. The Federal Reserve Bank of New York reports these measures of financial health quarterly.
In a recent working paper, we demonstrate a potential causal link between these fluctuations in delinquency and mortality. (A recent Atlanta Fed podcast episode also discussed our findings.) By isolating unanticipated variations in debt and delinquency not caused by worsening health, we show that carrying debt—and delinquent debt in particular—has an adverse effect on mortality rates.
Our results suggest that the decline in the quality of debt portfolios during the Great Recession was associated with an additional 5.7 deaths per 100,000 people, or just over 12,000 additional deaths each year during the worst part of the recession (a calculation based on census population estimates found here). To put this rate in perspective, in 2014 the death rate from homicides was 5.0 per 100,000 people, and motor vehicle accidents caused 10.7 deaths per 100,000 people.
It is well understood that an individual experiencing a large and unexpected decline in health can encounter financial difficulties, and that this sort of event is a major cause of personal bankruptcy. Our findings suggest that significant unexpected financial problems can themselves lead to worse health outcomes. This link between delinquent debt and health outcomes provides more reason for public policy discussions to take seriously the nexus between financial well-being and public health.
- What Does the Current Slope of the Yield Curve Tell Us?
- Does Loyalty Pay Off?
- Immigration and Hispanics' Educational Attainment
- Are Tariff Worries Cutting into Business Investment?
- Improving Labor Market Fortunes for Workers with the Least Schooling
- Part-Time Workers Are Less Likely to Get a Pay Raise
- Learning about an ML-Driven Economy
- Hitting a Cyclical High: The Wage Growth Premium from Changing Jobs
- Thoughts on a Long-Run Monetary Policy Framework, Part 4: Flexible Price-Level Targeting in the Big Picture
- Thoughts on a Long-Run Monetary Policy Framework, Part 3: An Example of Flexible Price-Level Targeting
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