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June 01, 2016
Putting the Wage Growth Tracker to Work
The April pop in the Atlanta Fed's Wage Growth Tracker has attracted some attention in recent weeks, resulting in some interesting analysis. What is the tracker telling us about the tightness of the labor market and the risks to the inflation outlook?
We had earlier noted the strong correlation between the Wage Growth Tracker and the unemployment rate. Tim Duy took the correlation a step further and estimated a wage Phillips curve. Here's what he found:
The chart shows that lower unemployment generally coincides with higher wage growth (as measured by the Wage Growth Tracker), but wage growth varies a lot by unemployment rate. In the past, an unemployment rate around 5 percent has often been associated with higher wage growth than we currently have.
If the Wage Growth Tracker increased further, would that necessarily lead to an increase in inflation? Jared Bernstein suggests that there isn't much of an inflation signal coming from the Wage Growth Tracker. His primary evidence is the insignificant response of core personal consumption expenditure (PCE) inflation to an increase in the Wage Growth Tracker in a model that relates inflation to lags of inflation, wage growth, and the exchange rate.
However, I don't think the absence of a wage-push inflation connection using the Wage Growth Tracker is really that surprising. The Wage Growth Tracker better captures the wage dynamics associated with improving labor market conditions than rising labor cost pressures per se. For example, if firms are replacing departing workers with relatively low-wage hires, then the wages of incumbent workers could rise faster than do total wage costs (as this analysis by our colleagues at the San Francisco Fed shows). That said, as Bernstein also pointed out in the Washington Post, it's also pretty hard to find evidence of wage pass-through pushing up inflation in his model using more direct measures of labor costs.
I look forward to seeing more commentary about Atlanta Fed tools like the Wage Growth Tracker and how they can be part of the broader discussion of economic policy.
May 23, 2016
Can Two Wrongs Make a Right?
In a recent macroblog post, I showed that forecasts from the Atlanta Fed's real gross domestic product (GDP) nowcasting model—GDPNow—have been about as accurate a forecast of the U.S. Bureau of Economic Analysis's (BEA) first estimate of real GDP growth as the consensus from the Wall Street Journal Economic Forecasting Survey. Because GDPNow essentially uses a "bean-counting" approach that tallies the forecasts of the various main subcomponents of GDP, the total GDP forecast error can be broken up into the forecast errors coming from each piece of GDP. For most of the subcomponents of GDP, the contribution to total GDP growth is approximately its real growth rate multiplied by its expenditure share of nominal GDP (the exact formulas are in the working paper for GDPNow). The following chart shows the subcomponent contributions to the GDPNow forecast errors since the third quarter of 2011. (I want to note that the forecast errors are based on the final GDPNow forecasts formed before the BEA's first estimates of GDP are released.)
The forecast errors for the subcomponents can sometimes be quite large. For example, for the fourth quarter of 2013, GDPNow underestimated the combined contributions of net exports and inventory investment by nearly 2 percentage points. However, these misses were nearly offset by overestimates of the other contributions to growth (consumption, business and residential fixed investment, and government spending).
The pattern of large but largely offsetting GDP subcomponent errors has been attributed to the work of a fictional "Saint Offset," as former Fed Governor Laurence Meyer noted in a 1998 speech. Unfortunately, "Saint Offset" doesn't always come to the forecaster's aid. For example, in the fourth quarter of 2011, GDPNow predicted 5.2 percent growth—well above the BEA's first estimate of 2.8 percent—and the subcomponent errors were predominantly on the high side.
A closer look at the chart also reveals that GDPNow has had a tendency to overestimate the contribution of business fixed investment to growth and underestimate the growth contribution of inventory investment. Although these subcomponent biases have nearly offset one another on average, we really don't want to have to rely on "Saint Offset." We would like the subcomponent forecasts to be reasonably accurate because the subcomponents of GDP are of interest in their own right.Have the subcomponent biases been a unique feature of GDPNow forecasts? It appears not. Both the Survey of Professional of Forecasters (SPF), conducted about 11 weeks prior to the first GDP release, and Blue Chip Economic Indicators, conducted as close as three weeks prior to the first release, provide consensus forecasts for some GDP subcomponents. The following table provides an average forecast error (as a measure of bias) and average absolute forecast error (as a measure of accuracy) of the subcomponent growth contributions for the two surveys and comparably timed GDPNow forecasts.
We see that the biases in GDPNow's subcomponents have been fairly similar to those in the two surveys. For example, all three sources have underestimated the average inventory investment contribution to growth by fairly similar magnitudes.
The relative accuracy of GDPNow's subcomponent and overall GDP forecasts has also been similar to the accuracy of the two surveys. "Saint Offset" has helped all three forecasters; the standard errors of the real GDP forecasts are 20 percent to 40 percent lower than they would be if the forecast errors of the subcomponents did not cancel each other out.
Finally, notice that some GDP subcomponents appear to be much more difficult to forecast than others. For instance, the bias and accuracy metrics for consumer spending are smaller than they are for inventory investment. This differential is not really that surprising, because more monthly source data are available prior to the first GDP release for consumer spending than for inventory investment.
Can we take any comfort in knowing that private forecasters have mirrored the biases in GDPNow's subcomponent forecasts? An optimistic interpretation is that the string of one-sided misses are the result of bad luck—an atypical sequence of shocks that neither GDPNow nor private forecasters could account for. A more troubling interpretation is that there have been structural changes in the economy that neither GDPNow nor the consensus of private forecasters have identified. Irrespective of the reason, though, optimal forecasts should be unbiased. If biases in some of the subcomponents continue, then forecasters will need to look for a robust way to eliminate them.
May 19, 2016
Are People in Middle-Wage Jobs Getting Bigger Raises?
As observed in this Bloomberg article and elsewhere, the Atlanta Fed's Wage Growth Tracker (WGT) reached its highest postrecession level in April. This related piece from Yahoo Finance suggests that the uptick in the WGT represents good news for middle-wage workers. That might be so.
Technically, though, the WGT is the median change in the wages of all continuously employed workers, not the change in wages among middle-income earners. However, we can create versions of the WGT by occupation group that roughly correspond to low-, middle-, and high-wage jobs, which allows us to assess whether middle-wage workers really are experiencing better wage growth. Chart 1 shows median wage growth experienced by each group over time. (Note that the chart shows a 12-month moving average instead of a three-month average, as depicted in the overall WGT on our website.)
Wage growth for all three categories has risen during the past few years. However, the timing of the trough and the speed of recovery vary somewhat. For example, wage growth among low-wage earners stayed low for longer and then recovered relatively more quickly. Wage growth of those in high-wage jobs fell by less but also has recovered by relatively less. In fact, while the median wage growth of low-wage jobs is back to its 2003–07 average, wage growth for those in high-wage jobs sits at about 75 percent of its prerecession average.
Are middle-wage earners experiencing good wage growth? In a relative sense, yes. The 12-month WGT for high-wage earners was 3.1 percent in April compared with 3.2 percent and 3.0 percent for middle- and low-wage workers, respectively. So the typical wage growth of those in middle-wage jobs is trending slightly higher than for high-wage earners, a deviation from the historical picture.
Interestingly, this pattern of wage growth doesn't quite jibe with the relative tightness of the labor market for different types of jobs. As was shown here, the overall WGT appears to broadly reflect the tightness of the labor market (possibly with some lag).
In theory, as the pool of unemployed shrinks, employers will face pressure to increase wages to attract and retain talent. Chart 2 shows the 12-month average unemployment rates for people who were previously working in one of the three wage groups.
Like the relationship between overall WGT and the unemployment rate, wage growth and the unemployment rate within these wage groups are negatively correlated (in other words, when the unemployment rate is high, wage growth is sluggish). The correlation ranges from minus 0.81 for low-wage occupations to minus 0.88 for middle-wage occupations.
However, notice that although the current gap between unemployment rates across the wage spectrum is similar to prerecession averages, the current relative gap in median wage growth is different than in the past. In particular, the wage growth for those in higher-wage jobs has been sluggish compared to middle- and lower-wage occupations.
Nonetheless, it's clear that the labor market is getting tighter. Wage growth overall has moved higher over the past year, driven primarily by those working in low- and middle-wage jobs. Is firming wage growth starting to show up in price inflation? Perhaps.
The consumer price index inflation numbers moved higher again in April, and Atlanta Fed President Dennis Lockhart said on Tuesday that—from a monetary policy perspective—recent inflation readings and signs of better growth in economic activity during the second quarter (as indicated by the Atlanta Fed's GDPNow tracker) are encouraging signs.
May 16, 2016
GDPNow and Then
Real-time forecasts from the Atlanta Fed’s real gross domestic product (GDP) nowcasting model—GDPNow—have been regularly updated since August 2011 (the model was introduced online in July 2014). So we now have a nearly five-year history to allow us to evaluate the accuracy of the model’s forecasts. The chart below shows forecasts from GDPNow (red dots) alongside actual first estimates of real GDP growth (gray bars) from the U.S. Bureau of Economic Analysis (BEA). For comparison, the blue dots in the chart are the consensus (average) forecasts from the Wall Street Journal Economic Forecasting Survey (WSJ Survey).
The initial estimate of real GDP growth for a particular quarter is usually published at the end of the subsequent month. The WSJ Survey consensus forecasts plotted above were released about two weeks before these estimates. To maintain comparable timing with the WSJ Survey, the GDPNow forecasts shown in the chart are those constructed on or before the 12th day of the same month.
Occasionally, there has been relatively large disagreement between GDPNow and the WSJ consensus. For example, GDPNow predicted that GDP growth would be below 0.5 percent for five out of 19 quarters between 2011 and 2016, and the lowest WSJ Survey consensus forecast for any of those quarters was 1.3 percent. Nonetheless, the average accuracy of the GDPNow and WSJ Survey consensus forecasts has been similar: the average absolute forecast error (average error without regard to sign) for GDPNow was 0.56 versus 0.60 for the WSJ Survey consensus.
Studies have shown that the average or median of a set of professional forecasts tends to be more accurate than an individual forecaster (see, for example, here and here). Therefore, it’s surprising that GDPNow has been about as accurate on average as the WSJ Survey consensus. To see just how surprising this result is, I used the fact that the WSJ Survey provides both the names and forecasts of its respondents. From these, I constructed a panel dataset with each respondent’s absolute forecast errors and their absolute disagreement (difference) from the consensus forecast. Using a standard econometric technique (a two-way fixed-effects regression), we can then calculate each panelist’s average absolute GDP forecast error and their average absolute disagreement with the WSJ Survey consensus. These points are shown in the scatterplot below.
There is a clear inverse relationship between average forecast accuracy and average disagreement with the WSJ Survey consensus. However, GDPNow’s accuracy and disagreement statistics do not fit the general pattern. GDPNow (the orange diamond in the chart) was more accurate on average than all but six out of 49 WSJ panelists, though at the same time it differed from the consensus by more on average than all but four of the panelists.
What should one infer from all of this? Differences in forecasting method could be part of the explanation. GDPNow differs from many other approaches to nowcasting in that it is essentially a “bean counting” exercise. It doesn’t use historical correlations of GDP with other economic series in the way that commonly used dynamic factor models do, and it also doesn’t incorporate judgmental adjustments (see here for more discussion of these differences). During a period when the economy has been giving very mixed signals, perhaps it doesn’t come as a surprise that GDPNow’s forecasts occasionally deviate quite a bit from the WSJ Survey consensus. Time will tell if GDPNow continues to perform at least as well as the consensus.
May 04, 2016
What's behind the Recent Uptick in Labor Force Participation?
The labor force participation rate had been generally declining since around 2007. However, that trend has partially reversed in recent months. As noted in the minutes of the March meeting of the Federal Open Market Committee, this rise was interpreted as further strengthening of the labor market. But will the increase persist?
As shown in a previous macroblog post, the dominant contributor to the decline in participation during the last several years has been the aging of the population. To see what's behind the increase in participation during the last few months, the following chart breaks the participation rate change between the first quarters of 2015 and 2016 into a part that is the result of shifts in the age distribution (holding behavior within age groups fixed), and the parts that are the result of changes in behavior (holding the age distribution fixed).
During the last year, the negative effect on participation attributable to an aging population (0.22 percentage points) has been offset by a 0.23 percentage point decline in the share of people who want a job but are not counted as unemployed (including people who are marginally attached). This decline is an encouraging sign, and consistent with a tightening labor market.
How much more can the want-a-job category improve? We don't really know. But that category's share of the population is currently about 0.3 percentage points above the prerecession trough of 2.0 percent. So at the current pace we would be at prerecession levels in about a year.
Despite the recent uptick, projections over the next decade or so have the labor force participation rate moving lower, chiefly because of an aging population. But how much farther participation actually declines will also depend on the evolution of various behavioral factors. The employment report for April will be released this Friday by the U.S. Bureau of Labor Statistics, and it will be interesting to see whether the number of people on the margin of the labor force continues to shrink.
April 29, 2016
Is the Number of Stay-at-Home Dads Going Up or Down?
A recent Wall Street Journal post observed that most of the recession's "stay-at-home dads" are going back to work. Specifically, data from the U.S. Labor Department shows that the share of married men with children under 18 who are not employed (but their spouse is) rose during the recession and has since given back much of that increase, as the Journal's chart below indicates.
Of course, being a stay-at-home dad in the sense defined in the previous chart (that is, not employed) can be either involuntary because of unemployment, or it can be the result of a voluntary decision to not be in the workforce. Most of the variation in the previous chart is cyclical, suggesting that it is related to the rise and fall in unemployment. But it also looks like the share of stay-at-home dads is higher now than it was a decade or so ago. So perhaps there is also an increasing trend in the propensity to voluntarily be a stay-at-home dad.
To explore this possibility, the next chart shows the annual average share of married men ages 25–54 who have children and who say the main reason they do not currently want a job is because of family or household responsibilities. (This reason doesn't necessarily imply that they are looking after children, but it is likely to be the leading reason.) The fraction is very small—about 1.3 percent in 2015, or 285,000 men—but the share has more than doubled during the last 15 years and would account for about half of the elevated level of the stay-at-home rate in 2015 relative to 2000.
So although large numbers of unemployed stay-at-home dads have been going back to work, it also appears that there's a small but growing group of men who are choosing to take on household and family responsibilities instead.
April 15, 2016
Labor Force Participation: Aging Is Only Half of the Story
The labor force participation rate (LFPR) is an important ingredient in projecting employment growth and the unemployment rate. However, predicting the LFPR has proven difficult. For example, in 2011 the Congressional Budget Office (CBO) projected that the LFPR in 2015 would be about 64.3 percent. In reality, the LFPR turned out to be 62.6 percent. Based on the CBO projection, the economy would have needed to create about 4 million more jobs to reach the 2015 unemployment rate of 5.3 percent.
Why is the LFPR so hard to predict? Leaving aside the challenge of projecting the size of the population, movements in LFPR primarily reflect shifts in the age distribution of the population as well as a number of behavioral factors. Although the aging trends are largely baked in, the behavioral factors vary over time. According to our estimates, about half of the 3.4 percentage-point decline in the LFPR between 2007 and 2015 is the result of the aging of the population, while behavioral factors account for the rest.
The complication is that the specific behaviors can change. The following chart shows a decomposition of the change in LFPR from 2007 to 2011 and from 2011 to 2015. Though the aging of the population contributed about the same amount to the decline in LFPR in both periods, the contributions from other factors have varied a lot. (We delve into the changes in the factors following the chart.)
Aging: The single largest factor contributing to the decline in the overall LFPR has been the rising share of older Americans in the population. In 2007, about one in five Americans were over 60 years old. In 2015, almost one in four were over 60. Moreover, this demographic force will continue to suppress the overall LFPR as the share of older Americans increases further in coming years. (For an in-depth discussion of the economic implications of an aging population—including changes in the labor market—please read the Atlanta Fed's 2015 annual report.)
Later retirement: One countervailing factor to an aging population has been the rising LFPR of older individuals. The retirement rate of those over 60 declined between 2007 and 2011 by a similar amount as it had before the recession. However, the trend toward later retirement has slowed considerably in recent years. The reason for this slowing is a puzzle and has important implications for the future course of overall LFPR.
Schooling among the young: The enrollment in educational programs by American youth has been generally rising over several decades, and this trend has put downward pressure on the overall LFPR. During the 2007–11 period, the share of 16- to 24-year-olds who do not want a job because they were in school or college accelerated relative to the prerecession trend. However, enrollment rates have since flattened out. The slowing may reflect enrollment rates catching up to the longer-term trend or may be a result of changes in the opportunity cost of education.
Not in the labor force but want a job: The share of the population saying they want a job but are not classified as unemployed by the U.S. Bureau of Labor Statistics definition is countercyclical—it tends to go up during bad times and down during good times. The relative size of this group increased between 2007 and 2011 and has since retraced about half of that increase as the economy has strengthened. We expect that this category will continue to shrink some more as the economy continues to expand.
Health: The share of individuals who do not want a job because they were too ill or disabled to work has increased over time. The relative size of this group increased between 2007 and 2011. Since 2011, the rate of increase has slowed, and it actually declined in 2015. It is not clear what drove the larger increase during the 2007–11 period, but there is some literature linking weak labor market conditions to poor health outcomes.
Prime-age reasons for not wanting a job (other than health): During the recession, the share of prime-age (ages 25 to 54) women not wanting a job because of household or family responsibilities decreased. One explanation is that some women entered the labor force to help make ends meet. At the same time, there was an offsetting effect from a rise in educational enrollment. Since the recession, nonparticipation because of household or family responsibilities has returned to near prerecession levels, and educational enrollment has leveled off.
To summarize, we find that relative to the 2007–11 period there has been a:
- flattening in the rate of retirement by older individuals,
- flattening in the rate of educational program enrollment by younger individuals,
- declining share of the population saying they want a job but not officially counted as unemployed,
- smaller drag from nonparticipation because of health, and
- larger drag for reasons other than health among prime-age individuals.
Where will LFPR be by the end of 2016? What about five years from now?
During the first three months of 2016, there has been an increase in the overall LFPR. This was largely the result of a decline in the share of prime-age people citing health reasons for nonparticipation, with some contribution from a decline in the share who want a job but are not "unemployed."
Though these boosts to participation may offset the effect of an aging population in the short term, most forecasts have the LFPR declining over the next several years. How much participation will actually decline depends on the answers to several difficult questions, such as: Will older individuals push retirement out even farther? Will school enrollment rates rise more rapidly again? Will the health status of the population improve? The difficulty of answering these questions helps explain why making accurate labor force projections is challenging.
April 13, 2016
Putting the MetLife Decision into an Economic Context
In a recently released decision, a U.S. district court has ruled that the Financial Stability Oversight Council's (FSOC's) decision to designate MetLife as a potential threat to financial stability was "arbitrary and capricious" and rescinded that designation. This decision raises many questions, among them:
- Why did MetLife sue to end its status as a too-big-to-fail (TBTF) firm?
- How will this decision affect the Federal Reserve's regulation of nonbank financial firms?
- What else can be done to reduce the risk of crisis arising from nonbank financial firms?
Why does MetLife want to end its TBTF status?
An often-expressed concern is that market participants will consider FSOC-designated firms too big to fail, and investors will accord these firms lower risk premiums (see, for example, Peter J. Wallison). The result is that FSOC-designated firms will gain a competitive advantage. If so, why did MetLife sue to have the designation rescinded? And why did the announcement of the court's determination result in an immediate 5 percent increase in the MetLife's stock price?
One possible explanation is that the FSOC's designation guarantees the firm will be subject to higher regulatory costs, but it only marginally changes the likelihood it would receive a government bailout. The Dodd-Frank Act (DFA) requires that FSOC-designated firms be subject to consolidated prudential supervision by the Federal Reserve using standards that are more stringent than the requirements for other nonbank financial firms.
Moreover, the argument that such designation automatically conveys a competitive advantage has at least two weaknesses. First, although Title II of the DFA authorizes the Federal Deposit Insurance Corporation (FDIC) to resolve a failing nonbank firm in certain circumstances, DFA does not provide FDIC insurance for any of the nonbank firm's liabilities, nor does it provide the FDIC with funds to undertake a bailout. The FDIC is supposed to recover its costs from the failed firm's assets. Admittedly, DFA does allow for the possibility that the FDIC would need to assess other designated firms for part of the cost of a resolution. However, MetLife could as easily have been assessed to pay for another firm as it could have been the beneficiary of assessments on other systemically important firms.
A second potential weakness in the competitive advantage argument is that the U.S. Treasury Secretary decides to invoke FDIC resolution only after receiving a recommendation from the Federal Reserve Board and one other federal financial regulatory agency (depending upon the type of failing firm). Invocation of resolution is not automatic. Moreover, a part of any decision authorizing FDIC resolution are findings that at the time of authorization:
- the firm is in default or in danger of default,
- resolution under other applicable law (bankruptcy statutes) would have "serious adverse consequences" on financial stability, and
- those adverse effects could be avoided or mitigated by FDIC resolution.
Although it would seem logical that FSOC-designated firms are more likely to satisfy these criteria than other financial firms, the Title II criteria for FDIC resolution are the same for both types of firms.
How does this affect the Fed's regulation of nonbank firms?
Secretary of the Treasury Jack Lew has indicated his strong disagreement with the district court's decision, and the U.S. Treasury has said it will appeal. Suppose, however, that FSOC designation ultimately does become far more difficult. How significantly would that affect the Federal Reserve's regulatory power over nonbank financial firms?
Although the obvious answer would be that it would greatly reduce the Fed's regulatory power, recent experience casts some doubt on this view. Nonbank financial firms appear to regard FSOC designation as imposing costly burdens that substantially exceed any benefits they receive. Indeed, GE Capital viewed the costs as so significant that it had been selling large parts of its operations and recently petitioned the FSOC to rescind its designation. Unless systemically important activities are a core part of the firm's business model, nonbank financial firms may decide to avoid undertaking activities that would risk FSOC designation.
Thus, a plausible set of future scenarios is that the Federal Reserve would be supervising few, if any, nonbank financial firms regardless of the result of the MetLife case. Rather, ultimate resolution of the case may have more of an impact on whether large nonbank financial firms conduct systemically important activities (if designation becomes much harder) or the activities are conducted by some combination of smaller nonbank financial firms and by banks that are already subject to Fed regulation (if the ruling does not prevent future designations).
Regardless of how the courts and the FSOC respond to this recent judicial decision, the financial crisis should have taught us valuable lessons about the importance of the nonbank financial sector to financial stability. However, those lessons should go beyond merely the need to impose prudential supervision on any firms that are systemically important.
The cause of the financial crisis was not the failure of one or two large nonbank financial firms. Rather, the cause was that almost the entire financial system stood on the brink of collapse because almost all the major participants were heavily exposed to the weak credit standards that were pervasive in the residential real estate business. Yet if the real problem was the risk of multiple failures as a result of correlated exposures to a single large market, perhaps we ought to invest more effort in evaluating the riskiness of markets that could have systemic consequences.
In an article in Notes from the Vault and other forums, I have called for systematic end-to-end reviews of major financial markets starting with the origination of the risks and ending with the ultimate holder(s) of the risks. This analysis would involve both quantitative analysis of risk measures and qualitative analysis of the safeguards designed to reduce risk.
The primary goal would be to identify and try to correct weaknesses in the markets. A secondary goal would be to give the authorities a better sense of where problems are likely to arise if a market does encounter problems.
April 11, 2016
The Rise of Shadow Banking in China
China's banking system has suffered significant losses over the past two years, which has raised concerns about the health of China's financial industry. Such losses are perhaps not all that surprising. Commercial banks have been increasing their risk-taking activities in the form of shadow lending. See, for example, here, here, and here for some discussion of the evolution of China's shadow banking system.
The increase in risk taking by banks has occurred despite a rapid decline in money growth since 2009 and the People's Bank of China's efforts to limit credit expansions to real estate and other industries that appear to be over capacity.
One area of expanded activity has been investment in asset-backed "securities" by China's large non-state banks. This investment has created potentially significant risks to the balance sheets of these institutions (see the charts below). Using the micro-transaction-based data on shadow entrusted loans, Chen, Ren, and Zha (2016) have provided theoretical and empirical insights into this important issue (see also this Vox article that summarizes the paper).
Recent regulatory reforms in China have taken a positive step to try to limit such risk-taking behavior, although the success of these efforts remains to be seen. An even more challenging task lies ahead for designing a comprehensive and sustainable macroprudential framework to support the healthy functioning of China's traditional and shadow banking industries.
April 04, 2016
Which Wage Growth Measure Best Indicates Slack in the Labor Market?
The unemployment rate is close to what most economists think is the level consistent with full employment over the longer run. According to the Federal Open Market Committee's latest Summary of Economic Projections, the unemployment rate is currently only 15 basis points above the natural rate. Yet, average hourly earnings (AHE) for production and nonsupervisory workers in the private sector increased a paltry 2.3 percent in March from a year earlier (as did the AHE of all private workers), and is barely above its average course of 2.1 percent since 2009.In contrast, the Atlanta Fed's Wage Growth Tracker (WGT) suggests that wage growth has been increasing. The February WGT reading was 3.2 percent (the March data will be available later in April), considerably higher than its post-2009 average of 2.3 percent.
Why is there such a large difference between these measures of wage growth? Besides differences in data sources, the primary reason is that they measure fundamentally different things. The WGT is an estimate of the wage growth of continuously employed workers—the same worker's wage is measured in the current month and a year earlier.
In contrast, the AHE measure is an estimate of the change in the typical wage of everyone employed this month relative to everyone employed a year earlier. Most of these workers are continuously employed, but some of those employed in the current month were not employed the prior year, and vice versa. These changes in the composition of employment can have a significant effect.
A recent study by Mary C. Daly, Bart Hobijn, and Benjamin Pyle at the San Francisco Fed shows that while growth in wages tends to be pushed higher by the wage gains of continuously employed workers, the net effect of entry and exit into employment tends to put a drag on the growth in wages. Moreover, the magnitude of the entry/exit drag can be relatively large, varies over time, and differs by the type of entry and exit.
For example, older workers who have retired and left the workforce tend to come from the higher end of the wage distribution, and their absence from the current period wage pool exerts downward pressure on the typical wage. The greater number of baby boomers starting to retire is having an even larger depressing effect on growth in wages than in the past. Because the WGT looks only at continuously employed workers, it is not influenced by these net entry/exit effects.
To the extent that firms adjust the pay for incumbent workers in response to labor market pressures to attract and retain workers, the WGT should reasonably capture changes in the tightness of the labor market.
Economists at the Conference Board modeled the relationship between different wage growth series and measures of labor market slack. One of the slack measures they use is the unemployment gap—the difference between an estimate of the natural rate of unemployment and the actual unemployment rate.To illustrate their findings, the following chart shows the WGT and AHE measures along with the unemployment gap lagged six months (using the Congressional Budget Office estimate of the natural rate).
The WGT appears to move more closely with the lagged unemployment gap than does the growth in AHE, and a comparison of the correlation coefficients confirms the stronger relationship with the WGT. The correlation between the lagged unemployment gap and the change in average hourly earnings is 0.75.
In contrast, the correlation with the wage growth tracker is higher at 0.93. Moreover, the unemployment gap-AHE relationship appears to be particularly weak since the Great Recession. The correlation since 2009 falls to just 0.08 for the AHE, whereas the WGT correlation is still 0.93.
Our colleagues at the San Francisco Fed concluded their analysis of the effect of flows into and out of the employment on wage growth by suggesting that:
"... wage growth measures that focus on the continuously full-time employed are likely to do a better job of gauging labor market strength, since they are constructed to more clearly capture the wage dynamics associated with improving labor market conditions. The Federal Reserve Bank of Atlanta's Wage Growth Tracker is an example."
That assessment is consistent with the Conference Board study, and suggests that labor markets may be tighter than is commonly believed based on sluggish growth in measures of average wages such as AHE.
- Men at Work: Are We Seeing a Turnaround in Male Labor Force Participation?
- What’s Moving the Market’s Views on the Path of Short-Term Rates?
- Lockhart Casts a Line into the Murky Waters of Uncertainty
- How Will Employers Respond to New Overtime Regulations?
- How Good Is The Employment Trend? Decide for Yourself
- Is the Labor Market Tossing a Fair Coin?
- When It Rains, It Pours
- Pay As You Go: Yes or No?
- Was May's Drop in Labor Force Participation All Bad News?
- Wage Growth for Job Stayers and Switchers Added to the Atlanta Fed's Wage Growth Tracker
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