The Atlanta Fed's macroblog provides commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues.

Authors for macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.

October 19, 2017

How Ill a Wind? Hurricanes' Impacts on Employment and Earnings

According to the Current Employment Statistics payroll survey, seasonally adjusted nonfarm payroll employment declined 33,000 in September. This decline was the first drop in employment since 2010 and followed a 169,000 gain in August. At the same time, seasonally adjusted average hourly earnings in the private sector increased 2.9 percent year over year in September. This increase in average wages was the largest since the end of the Great Recession in 2009. However, it seems likely that the decline in employment contributed to the rise in average hourly earnings. Why would a decline in employment contribute to an increase in average hourly earnings? We're glad you asked!

As noted by the U.S. Bureau of Labor Statistics, Hurricanes Harvey and Irma reduced employment in the payroll survey, whose reference period is the pay period that includes the 12th of the month. Hurricane Harvey first made landfall in east Texas on August 25 and again in Louisiana on August 30, and Hurricane Irma made landfall in south Florida on September 10. The storms forced large-scale evacuations and severely damaged many homes and businesses. For workers who are not paid when they miss work, being unable to work during the surveyed pay period means they are not counted in September payrolls.

To measure the size of Harvey and Irma's effect on payroll employment, we first looked at data from the Current Population Survey (CPS). We found that the bad weather forced about 1.5 million nonfarm workers who had a job during the September reference week to miss work. Of those, about 1.2 million were wage and salary earners, and about 760,000 of those were unpaid during their absence from work.

Our analysis indicates that September saw a shortfall in seasonally adjusted payroll employment between 200,000 and 300,000 jobs, suggesting that workers returning to work could result in a large rebound in payroll employment. (Not to get too far into the weeds, but our analysis involved regressing payroll employment growth on its lagged values as well as current and lagged seasonally adjusted changes in shares of workers who were not at work because of bad weather.)

What about average hourly earnings? Changes in average hourly earnings over time reflect both the effect of people getting pay raises and changes in who is working this month versus last month or last year. This latter effect can be large during recessions, when workers in lower-wage jobs are disproportionately more likely to be laid off. The absence of these workers from payrolls increases the average wage among the remaining employed workers, even if those remaining workers are not getting much of a pay increase (see this macroblog post for more discussion).

The September payroll survey depicted a particularly large decline in employment in the leisure and hospitality sector, which is significant because average hourly earnings in that sector are typically about 40 percent lower than overall average hourly earnings. In addition, from the CPS we see that the usual hourly earnings of workers not at work because of bad weather is much lower than for other workers. These data suggest that temporary absences from work because of bad weather likely put upward pressure on average hourly earnings, and some of that upward pressure could reverse itself as these workers return to their jobs. If the pace of average hourly earnings doesn't relax, however, then that would suggest more workers getting larger pay raises due to a tightening labor market.

October 19, 2017 in Economics , Employment , Wage Growth | Permalink | Comments ( 0)

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.

September 8, 2017 in Economic conditions , Monetary Policy | Permalink | Comments ( 0)

September 07, 2017

What Is the "Right" Policy Rate?

What is the right monetary policy rate? The Cleveland Fed, via Michael Derby in the Wall Street Journal, provides one answer—or rather, one set of answers:

The various flavors of monetary policy rules now out there offer formulas that suggest an ideal setting for policy based on economic variables. The best known of these is the Taylor Rule, named for Stanford University's John Taylor, its author. Economists have produced numerous variations on the Taylor Rule that don't always offer a similar story...

There is no agreement in the research literature on a single "best" rule, and different rules can sometimes generate very different values for the federal funds rate, both for the present and for the future, the Cleveland Fed said. Looking across multiple economic forecasts helps to capture some of the uncertainty surrounding the economic outlook and, by extension, monetary policy prospects.

Agreed, and this is the philosophy behind both the Cleveland Fed's calculations based on Seven Simple Monetary Policy Rules and our own Taylor Rule Utility. These two tools complement one another nicely: Cleveland's version emphasizes forecasts for the federal funds rate over different rules and Atlanta's utility focuses on the current setting of the rate over a (different, but overlapping) set of rules for a variety of the key variables that appear in the Taylor Rule (namely, the resource gap, the inflation gap, and the "neutral" policy rate). We update the Taylor Rule Utility twice a month after Consumer Price Index and Personal Income and Outlays reports and use a variety of survey- and model-based nowcasts to fill in yet-to-be released source data for the latest quarter.

We're introducing an enhancement to our Taylor Rule utility page, a "heatmap" that allows the construction of a color-coded view of Taylor Rule prescriptions (relative to a selected benchmark) for five different measures of the resource gap and five different measures of the neutral policy rate. We find the heatmap is a useful way to quickly compare the actual fed funds rate with current prescriptions for the rate from a relatively large number of rules.

In constructing the heatmap, users have options on measuring the inflation gap and setting the value of the "smoothing parameter" in the policy rule, as well establishing the weight placed on the resource gap and the benchmark against which the policy rule is compared. (The inflation gap is the difference between actual inflation and the Federal Open Market Committee's 2 percent longer-term objective. The smoothing parameter is the degree to which the rule is inertial, meaning that it puts weight on maintaining the fed funds rate at its previous value.)

For example, assume we (a) measure inflation using the four-quarter change in the core personal consumption expenditures price index; (b) put a weight of 1 on the resource gap (that is, specify the rule so that a percentage point change in the resource gap implies a 1 percentage point change in the rule's prescribed rate); and (c) specify that the policy rule is not inertial (that is, it places no weight on last period's policy rate). Below is the heatmap corresponding to this policy rule specification, comparing the rules prescription to the current midpoint of the fed funds rate target range:

We should note that all of the terms in the heatmap are described in detail in the "Overview of Data" and "Detailed Description of Data" tabs on the Taylor Rule Utility page. In short, U-3 (the standard unemployment rate) and U-6 are measures of labor underutilization defined here. We introduced ZPOP, the utilization-to-population ratio, in this macroblog post. "Emp-Pop" is the employment-population ratio. The natural (real) interest rate is denoted by r*. The abbreviations for the last three row labels denote estimates of r* from Kathryn Holston, Thomas Laubach, and John C. Williams, Thomas Laubach and John C. Williams, and Thomas Lubik and Christian Matthes.

The color coding (described on the webpage) should be somewhat intuitive. Shades of red mean the midpoint of the current policy rate range is at least 25 basis points above the rule prescription, shades of green mean that the midpoint is more than 25 basis points below the prescription, and shades of white mean the midpoint is within 25 basis points of the rule.

The heatmap above has "variations on the Taylor Rule that don't always offer a similar story" because the colors range from a shade of red to shades of green. But certain themes do emerge. If, for example, you believe that the neutral real rate of interest is quite low (the Laubach-Williams and Lubik-Mathes estimates in the bottom two rows are −0.22 and −0.06) your belief about the magnitude of the resource gap would be critical to determining whether this particular rule suggests that the policy rate is already too high, has a bit more room to increase, or is just about right. On the other hand, if you are an adherent of the original Taylor Rule and its assumption that a long-run neutral rate of 2 percent (the top row of the chart) is the right way to think about policy, there isn't much ambiguity to the conclusion that the current rate is well below what the rule indicates.

"[D]ifferent rules can sometimes generate very different values for the federal funds rate, both for the present and for the future." Indeed.

September 7, 2017 in Business Cycles , Data Releases , Economics , Monetary Policy | Permalink | Comments ( 0)

August 30, 2017

Is Poor Health Hindering Economic Growth?

It is well known that poor health is bad for an individual's income, partially because it can lower the propensity to participate in the labor market. In fact, 5.4 percent of prime-age individuals (those 25–54 years old) reported being too sick or disabled to work in the second quarter of 2017. This is the most commonly cited reason prime-age men do not want a job, and for prime-age women, it is the second most often cited reason behind family responsibilities (see the chart). (Throughout this article, I use the measure "not wanting a job because of poor health or disability" as a proxy for serious health problems.)

In addition to being prevalent, the share of the prime-age population citing poor health or disability as the main reason for not wanting a job has increased significantly during the past two decades and tends to be higher among those with less education (see the chart).

Yet by some standards, the health of Americans is improving. For example, compared to two decades ago the average American is living two years longer, and the likelihood of dying from cancer or cardiovascular disease has fallen. These specific outcomes, however, may have more to do with improvements in the treatment of chronic disease (and the resulting reduction in mortality rates) than improvements in the incidence of health problems.

Another puzzle—which is perhaps also a clue—is the considerable variation across states in the rates of being too sick or disabled to work. For example, people living in Mississippi, Alabama, Kentucky, or West Virginia in 2016 were more than three times likelier to indicate being too sick or disabled to work than residents of Utah, North Dakota, Iowa, or Minnesota (see the maps below).

This cross-state variation is useful because it allows state-by-state comparisons of the prevalence of specific health problems. Among a list of more than 30 health indicators, the two factors that most correlate with the share of a state's population too sick or disabled to work were high blood pressure (a correlation of 0.86) and diabetes (a correlation of 0.83). Both of these conditions are associated with risk factors such as family history, race, inactivity, poor diet, and obesity. Both of these health issues have increased significantly on a national basis in recent years.

So how might poor health hinder economic growth? Health factors account for a significant part of the decline in labor force participation since at least the late 1990s. After controlling for demographic changes, the share of people too sick or disabled to work is about 1.6 percentage points higher today than it was two decades ago (see the interactive charts on our website). Other things equal, if this trend reversed itself during the next year, it could increase the workforce by up to 4 million people, and add around 2.6 percentage points to gross domestic product (calculated using our Labor Market Sliders).

Of course, such a sudden and large reversal in health is highly unlikely. Nonetheless, significant improvements to the health of the working-age population would help lessen the drag on growth of the labor supply coming from an aging population. Public policy efforts centered on both prevention and treatment of work-impeding health conditions could play an important role in bolstering the nation's workforce.

August 30, 2017 in Education , Health Care , Labor Markets | Permalink | Comments ( 0)

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