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November 05, 2015
A Closer Look at Changes in the Labor Market
The Atlanta Fed's Center for Human Capital Studies hosted its annual employment conference on October 1–2, 2015, organized once again by Richard Rogerson (Princeton University), Robert Shimer (University of Chicago), and the Atlanta Fed's Melinda Pitts. This macroblog post provides a summary of the papers presented at the conference.
Many measures of labor market performance remain at relatively low levels compared with levels seen before the Great Recession. A key question for policymakers and academic researchers is the extent to which these changes reflect a slow recovery from a large cyclical shock—or do they simply represent the "new normal"? This conference brought together researchers studying several dimensions of these changes in labor-market outcomes. A common theme is that current labor market outcomes largely reflect the ongoing effect of secular trends that predated the Great Recession.
Recent empirical work has highlighted that the U.S. economy, and in particular the labor market, has seen a pronounced downward trend in several measures of "dynamism." Prominent among these measures are decreases in job and worker flows as well as in the entry rate of new establishments. A key challenge is to uncover the driving forces behind these trends and determine whether they reflect a worsening of U.S. economic performance.
Three papers addressed these changes. In "Changing in Business Dynamism: Volatility of vs. Responsiveness to Shocks?," Decker, Haltiwanger, Jarmin, and Miranda pose a key question for assessing whether these declines might reflect positive versus negative forces. Specifically, if lower volatility in firm-level outcomes reflects a change in the volatility in the economic environment in which firms operate, then it might well be a positive development. On the other hand, if the decreased volatility in firm-level measures reflects less responsiveness to changes in the economic environment, then the changes may constitute a negative development. The paper notes that elements of each may be present in different sectors of the economy, but their analysis suggests that lower responsiveness to shocks is an important factor.
In a second paper on the topic, "Dynamism Diminished: The Role of Credit Conditions," Davis and Haltiwanger focus on the decline in the business entry rate and consider one particular driving force: the role of housing wealth in facilitating start-up entrepreneurship. They ask whether cities that had the largest drops in housing wealth also had larger drops in entrepreneurial activity, holding other factors constant. Their analysis finds a strong correlation between the two, suggesting that the loss of housing wealth from the Great Recession has had a significant negative effect on the rate of business startups.
A third paper on the theme of diminished dynamics offered a somewhat different perspective. In their paper "Understanding the Thirty Year Decline in the Start-Up Rate: A General Equilibrium Approach," Karahan, Pugsley, and Sahin offer a more innocuous interpretation of the trend decline in the entry rate. They note that the growth of the U.S. labor force has slowed in the last 30 years, because of the aging of the baby boomers as well as the slowdown in the growth rate of women in the labor force. Standard models of industry equilibrium imply that this will require a slowdown in the rate of growth of firms, achieved through a decrease in the rate of entry. They also note that standard models imply that substantial differences in cohort dynamics in response to such a change will not be evident, and they depict this in the data.
Secular changes in inequality have received much attention in recent years. Two papers examined the nature of these changes. In "Firming Up Inequality," Bloom, Guvenen, Price, Song, and von Wachter use tax return data from the Social Security Administration to examine the underlying sources of increased income inequality since 1978. A key feature of this analysis is that it is based on tax return data for the universe of individuals, making it much more extensive and reliable than estimates based on smaller samples and self-reported measures of income. The authors find that the rise in income inequality is dominated by an increase in income dispersion across firms rather than within firms, which seems to result from an increase in the extent of sorting of workers across firms. The authors suggest that this increase reflects a change in the way firms are organized. The authors also show that executive pay plays essentially no role in the overall rise of inequality.
Lochner and Shin also examine the dynamics of inequality in "Understanding Earnings Dynamics: Identifying and Estimating the Changing Roles of Unobserved Ability, Permanent and Temporary Shocks." This paper focuses on changes in labor earnings among males from 1970 to 2008. Unlike the previous paper that focused on dispersion between and within firms, this paper focuses on permanent versus transitory components of inequality and the extent to which changes in inequality reflect changes in the price of unobserved skill. The paper provides a detailed decomposition of the evolution of these various components over a 40-year period. The decomposition between permanent and transitory components is of central concern since higher transitory variance averages out over time at the individual level. One key finding is that since 1990, the dispersion of permanent shocks has increased, especially for low-income workers.
Hall and Schulhofer-Wohl analyze changes in match efficiency in the U.S. economy since 2001 in their paper "Measuring Job Finding Rates and Matching Efficiency with Heterogeneous Job Seekers." Standard estimates based on an aggregate matching function that treats all workers as identical imply that matching efficiency has deteriorated dramatically during the Great Recession and its aftermath. The authors show that if one takes into account heterogeneity in matching rates for workers with different observable characteristics, a very different picture emerges. In particular, although a decrease is still evident in the aftermath of the Great Recession, this decrease reflects a continuation of an existing downward trend. The key implication is that lower matching rates currently found in the data reflect a secular trend.
In "The Great Reversal in the Demand for Skill and Cognitive Tasks," Beaudry, Green, and Sand offer a new perspective on secular trends in the labor market. Key to their explanation is that the boom prior to 2000 is associated with investment in the new general-purpose technology associated with information technology. Their theory holds that this technology is put in place during a period of high investment demand and high demand for skilled labor. But once the new technology is in place, it requires much less high-skilled labor to maintain or operate it. In this "de-skilling" phase, high-skilled individuals will move to jobs that are lower in the skill spectrum, thereby displacing individuals with lower skill levels to either move farther down ladder or even out of the labor force. The authors argue that this de-skilling phase began sometime around 2000 and was somewhat obscured prior to the Great Recession. The paper presents a stylized model of this process and presents several pieces of empirical evidence consistent with this dynamic. The key implication is that recent developments in the labor market indicate secular trends.
Autor, Figlio, Karbownik, Roth and Wasserman examine a different trend in U.S. labor market outcomes. In "Family Disadvantage and the Gender Gap in Behavioral and Education Outcomes," the authors examine the growing gap between male and female educational attainment. This gap is particularly large for children from disadvantaged backgrounds. The authors evaluate the hypothesis that the gap reflects differences in the sensitivity of boys and girls to adverse environments. They use data from Florida that allow them to study brother-sister pairs, allowing them to control for family environment. The key finding is that their study supports this hypothesis, though they are unable to identify which specific factors might be at work.
Full papers for most of these presentations are available on the Atlanta Fed's Center for Human Capital Studies website.
October 19, 2015
Should We Be Concerned about Declines in Labor Force Growth?
For the second month in a row, the October jobs report from the U.S. Bureau of Labor Statistics (BLS) has revealed a decline in the labor force. From August to September, the labor force lost a seasonally adjusted 350,000 participants. And the August number of participants was a seasonally adjusted 41,000 below July's level. Although two months don't necessarily make a trend, observers have noticed the declines in the labor force (here and here, for example), and they deserve some attention.
Economists might be concerned about these labor force declines for two reasons. First, these losses might indicate that the current unemployment rate doesn't accurately reflect a strong labor market. Second, our economy needs labor to make things, perform services, and continue to grow. Let's take a look at the evidence supporting these two concerns.
Concerns about a shadow weak labor market
Two pieces of evidence suggest that the declines in the labor force don't indicate a weak labor market: employment growth and the reasons people cite for being out of the labor force. Employment growth is robust. According to the Atlanta Fed's Jobs Calculator, the labor market needs to create an average of only 112,000 jobs per month to maintain its relatively low unemployment rate of 5.1 percent. During 2015, the economy has created, on average, 198,000 jobs per month.
But we might be concerned if the workers leaving the labor market were entering into the no-man's land of the marginally attached, a term describing those who want a job, are available to work, have looked for work in the previous year, but recently have stopped looking. Some of these people have stopped looking explicitly because they think jobs prospects are poor (called "discouraged workers"). Others have stopped looking for other reasons such as attending school or taking care of family members. If these categories of nonparticipants were absorbing a large share of those leaving the labor force, we could be concerned that they would, at any moment, reenter the labor market and push that unemployment rate right back up again. The chart below tells us that this possibility is unlikely.
The chart decomposes the year-over-year changes in the total number of labor force participants into changes in the population and the negative changes among reasons given for nonparticipation in the labor force. (I use year-over-year changes because the reasons given for not being in the labor force are not seasonally adjusted.) Year-over-year changes in the population have been consistent in their contributions to changes in the labor force, propping it up. The growth in the contribution of those not wanting a job (pulling down labor force growth) has been fairly striking.
The share of people giving other reasons for not being in the labor force (discouraged, not available, etc.)—in addition to making relatively small contributions to changes to the labor force—has mostly been shrinking since April, meaning that they cannot explain the recent slowing of labor force growth. In other words, only a very small part of the growth in nonparticipants has come from those marginal workers who are most likely to reenter the labor force. So the first fear—that this declining labor force growth is producing a false sense of security in a relatively strong labor market—appears unfounded.
Threats to economic growth
Labor is an important component in the production process. Short of dramatic technological advancements, both the manufacturing and service sectors need a consistent source of labor to fuel output. Even though the economy appears to be on the right track with respect to job creation, ongoing declines in labor force growth could pose a challenge to economic growth. Additionally, as employers compete for fewer workers, we would expect wages to be bid up. Keep an eye on the Atlanta Fed's wage tracker to see how slowing labor force growth plays out in wages.
October 05, 2015
Labor Report Silver Lining? ZPOP Ratio Continued to Rise in September
We have received several requests for an update of our ZPOP ratio statistic to incorporate September's data. We have also been asked whether the ZPOP ratio can be constructed from labor force data from the U.S. Bureau of Labor Statistics (BLS).
The ZPOP ratio is an estimate of the share of the civilian population aged 16 years and over whose labor market status is what they say they currently want (assuming that people who work full-time want to do so). A rising ZPOP ratio is consistent with a strengthening labor market. We constructed the ZPOP ratio from the microdata in the BLS's Current Population Survey, but we can also construct a very close approximation from the BLS's Labor Force Statistics data. Here's how (using data that are not seasonally adjusted):
- Take total employment, and add those not in the labor force who do not currently want a job. Then subtract those who were at work from one to 34 hours for economic reasons. The ZPOP ratio is this figure as a percentage of the civilian population 16 years and over.
The following chart shows the history of the resulting ZPOP ratio over 20 years, seasonally adjusted.
Unlike the headline U-3 unemployment rate, which remained unchanged from August to September, the seasonally adjusted ZPOP ratio improved slightly (from 92.0 to 92.1 percent). Relative to an estimated 230,000 increase in the population over the month, the improvement in the ZPOP ratio was the result of an increase in the number of people who said they do not currently want a job and a decline in involuntary part-time employment in excess of the decline in total employment.
Finally, the chart below shows the performance of the seasonally adjusted ZPOP ratio relative to the comparable employment-to-population (EPOP ratio) and the EPOP ratio for those aged 25–54. The relatively greater recovery in the ZPOP ratio since 2009 is primarily because the EPOP ratios do not adjust for the share of the population who say they do not currently want a job.
September 22, 2015
The ZPOP Ratio: A Simple Take on a Complicated Labor Market
In her press conference following the latest FOMC meeting, Federal Open Market Committee (FOMC) Chair Janet Yellen emphasized that she still sees cyclical weakness in the labor market, even as the headline unemployment rate has moved close to FOMC participants' median estimate of its longer-run normal level.
She also noted that FOMC participants look at many different indicators of labor utilization, because the headline unemployment rate (commonly known as the U-3 rate) is overstating the health of the labor market. One alternative measure that has received some attention is the employment-to-population (EPOP) ratio. However, a well-recognized problem with the EPOP ratio is that because it defines utilization as employment, trends in demographic and behavioral labor force participation can affect it.
This problem is partially addressed by looking at the EPOP ratio for the prime-age population, or by making adjustments for demographic changes as suggested by Kapon and Tracy at the New York Fed and further analyzed by our Atlanta Fed colleague Pat Higgins. Here, we propose an alternative approach that uses a broader definition of utilization that makes it less affected by labor supply trends.
The Current Population Survey does not ask the question "are your labor services being fully utilized?" Therefore, we have to use our judgment to classify someone as fully utilized. The figure below shows the choices we make. We assume that everyone who says they are working fewer hours than they want is underutilized (the red boxes). This includes those in the labor force but unemployed, those not in the labor force but wanting a job, and those working part-time but wanting full-time hours (similar to the treatment of underutilization in the broad U-6 unemployment rate measure).
Everyone working full-time, working part-time for a noneconomic reason, and those who say they don't want a job are considered fully utilized (the green boxes). Of course, this takes the "don't want a job" classification at face value. For example, someone who is retired is counted as fully utilized, irrespective of the (unknown) reason they chose to retire.
As shown in the Chart 1 below, the share of the population 16 years or older that is fully utilized—what we call the utilization-to-population (ZPOP) ratio—is currently about 1.5 percentage points below its prerecession level, after having fallen by 6 percentage points during the recession.
Notice that because the ZPOP ratio treats those who are not employed and don't want a job as fully utilized, it is less affected by demographic and behavioral trends in labor force participation than the EPOP ratio. (You can learn more on our website about how demographic and behavioral trends are affecting labor force participation.) When compared with the EPOP ratio, the ZPOP ratio paints a somewhat rosier picture of labor market conditions (see chart 2).
In sum, the utilization-to-population (ZPOP) ratio is the share of the working-age population that is working full time, is voluntarily working part-time, or doesn't want to work any hours. According to this measure, about 91 percent of the working-age population is considered fully utilized. The remaining 9 percent are "underutilized" and are a roughly even mixture of the unemployed, those not in the labor force but wanting to work, and those working part-time but wanting full-time hours.
The headline U-3 unemployment rate is very close to its prerecession level but is thought to overstate the health of the labor market. At the same time, we think that the EPOP ratio overstates the amount of remaining labor market slack. The ZPOP ratio is in the middle; approaching its prerecession level but still with some way to go.
September 21, 2015
What Do U.S. Businesses Know that New Zealand Businesses Don't? A Lot (Apparently).
A recent paper presented at the Brookings Institute, picked up by the Financial Times and the Washington Post, suggests that when it comes to communicating their inflation objective, central banks have a lot of work to do. This conclusion is based primarily on two pieces of evidence.
The first piece is that when businesses in New Zealand are asked about their expectations for changes in "overall prices"—which presumably corresponds with their inflation expectation—the responses, on average, appear to be much too high relative to observed inflation trends. And the responses vary widely from business to business. According to this survey, the average firm in New Zealand expects 4 to 5 percent inflation on a year-ahead basis, and 3.5 percent inflation over the next five to 10 years. Those expectations are for the average firm. Apparently, about one in four firms in New Zealand think inflation in the year ahead will be more than 5 percent, and about one in six firms believe inflation will top 5 percent during the next five to 10 years. Certainly, these aren't the responses one would expect from businesses operating in an economy (like New Zealand) where the central bank has been targeting 2 percent inflation for the past 13 years, over which time inflation has averaged only 2.2 percent (and a mere 0.9 percent during the past four years).
But count us skeptical of this evidence. In this paper from last year, we challenge the assumption that asking firms (or households, for that matter) about expected changes in "overall prices" corresponds to an inflation prediction.
The second piece of evidence regarding the ineffectiveness of inflation targeting is more direct—the authors of this paper actually asked New Zealand businesses a few questions about the central bank and its policies, including this one:
What annual percentage rate of change in overall prices do you think the Reserve Bank of New Zealand is trying to achieve? (Answer: ______%)
The distribution of answers by New Zealand firms is shown in the chart below. According to the survey, the median New Zealand firm appears to think the central bank's inflation target is 5 percent. Indeed, more than a third of firms in New Zealand reported that they think the central bank is targeting an inflation rate greater than 5 percent. Only about 12 percent of the firms were able to correctly identify their central bank's actual inflation target of 2 percent (actually, the New Zealand inflation target is a range of between 1 and 3 percent, centered on 2 percent).
If this weren't embarrassing enough for central bankers, the study also reports that New Zealand households (like U.S. households) don't seem to know who the head of the central bank is. In fact, the authors show that there are more online searches for "puppies" than for information about macroeconomic variables.
OK, to be honest, we don't find that last result very surprising. Puppies are adorable. Central bankers? Not so much. But we were very surprised to see just how high and wide-ranging businesses in New Zealand perceived their central bank's inflation target to be. We're surprised because that bit of information doesn't fit with our understanding of U.S. firms.
In December 2011, the month before the Fed officially announced an explicit numerical target for inflation, we wanted to know whether firms had already formed an opinion about the Fed's inflation objective. So we asked a panel of Southeast businesses the following question:
What we learned was that 16 percent of the 151 firms who responded to our survey had no opinion regarding what rate of inflation the Federal Reserve was aiming for. But of the firms that had an opinion, 58 percent identified a 2 percent inflation target.
But perhaps this isn't a fair comparison to the recent survey of New Zealand businesses. In our 2011 survey, firms had only six options to choose from (including "no opinion"). It could be that our choice of options biased the responses away from high inflation values. So last week, we convened another panel of firms and asked the question in the same open-ended format given to New Zealanders:
What annual rate of inflation do you think the Federal Reserve is aiming for over the long run? (Answer: ______%)
The only material distinction between their question and ours is that we substituted the word "inflation" for the phrase "changes in overall prices." (For this special survey, we polled a national sample of firms that had never before answered one of our survey questions.) The chart below shows what we found relative to the results recently reported for New Zealand firms.
Our survey results look very similar to our results of four years ago. About one in five of the 102 firms that answered our survey was unsure about the Fed's inflation target. But almost 53 percent of the firms that responded answered 2 percent. (On average, U.S. firms judged the central bank's inflation target to be 2.2 percent, just a shade higher than our actual target.)
Furthermore, the distribution of responses to our survey was very tightly centered on 2 percent. The highest estimate of the Fed's inflation target (from only one firm) was 5 percent. So again, our results don't at all resemble what has been reported for the firms down under.
Why is there a glaring difference between what the survey of New Zealand firms found and what we're finding? Well, as noted earlier, we've got our suspicions, but we'll keep studying the issue. And in the meantime, have you seen this?
Editor's note: Learn more about inflation and the consumer price index in an ECONversations webcast featuring Atlanta Fed economist Brent Meyer.
September 04, 2015
5-Year Deflation Probability Moves Off Zero
Since 2010, our Bank has regularly posted 5-year deflation probabilities derived from prices of Treasury Inflation-Protected Securities (TIPS) on our Deflation Probabilities web page. Each deflation probability, which measures the likelihood of a decline in the Consumer Price Index over a fixed five-year window, is estimated by comparing the price of a recently issued 5-year TIPS with a 10-year TIPS issued about five years earlier. Because the 5-year TIPS has more "deflation protection" than the 10-year TIPS, the implied deflation probability rises when the 5-year TIPS becomes more valuable relative to the 10-year TIPS. (See this macroblog post for a more detailed explanation, or this appendix with the mathematical details.)
From early September 2013 to the first week of August 2015, the five-year deflation probability estimated with the most recently issued 5-year TIPS was identically 0 as the chart shows.
Of course, we should not interpret this long period of zero probability of deflation too literally. It could easily be the case that the "true" deflation probability was slightly above zero but that confounding factors—such as differences in the coupon rates, maturity dates, or liquidity of the TIPS issues—prevented the model from detecting it.
Since August 11, however, the deflation probability has had its own "liftoff" of sorts, fluctuating between 0.0 and 1.3 percent over the 16-day period ending August 26 before rising steadily to 4.1 percent on September 2. Of course, this rise off zero could be temporary, as it proved to be in the summer of 2013.
How seriously should we take this recent liftoff? We can look at options prices on Consumer Price Index inflation (inflation caps and floors) to get a full probability distribution for future inflation; see this published article by economists Yuriy Kitsul and Jonathan Wright or a nontechnical summary in this New York Times article. An alternative is simply to ask professional forecasters for their subjective probabilities of inflation falling within various ranges like "1.0 to 1.4 percent," "1.5 to 1.9 percent," and so forth. The Philly Fed's Survey of Professional Forecasters does just this, with the chart below showing probabilities of low inflation for the Consumer Price Index excluding food and energy (core CPI) from each of the August surveys since 2007.
Although the price index, and the horizon for the inflation outcome, differs from the TIPS-based deflation probability, we see that the shape of the curves is broadly similar to the one shown in the first chart. In the most recent survey, the probability that next year's core CPI inflation rate will be low was small and not particularly elevated relative to recent history. However, the deadline date for this survey was August 11, before liftoff in either the TIPS-based deflation probability or the recent volatility in global financial markets. So stay tuned.
September 01, 2015
Should I Stay or Should I Go Now?
A recent article by Jason Faberman and Alejandro Justiniano at the Chicago Fed shows that there is a strong relationship between quit rates—as a proxy for the pace of job switching—and wage growth. Movements in the quit rate and wage growth are both procyclical. A tighter (weaker) labor market implies workers are more (less) likely to find better employment matches, and employers are more (less) willing to offer higher wages to attract new workers and retain existing workers.
To get some idea of the different wage outcomes of job switching versus job staying, we can use microdata underlying the Atlanta Fed's Wage Growth Tracker from the Current Population Survey. The following chart plots the quarterly private-sector quit rate (orange line) from the Job Openings and Labor Turnover Survey using Davis, Faberman, and Haltiwanger (published in 2012 in the Journal of Monetary Economics) estimates before 2001. Also shown is the median year-over-year wage growth of private-sector wage and salary earners who switched jobs (blue line) or stayed in the same job (green line). Job stayers are approximated by the restriction that they are in the same broad industry and occupation as 12 months earlier and have been with the same employer for each of the last four months. Job switchers do not satisfy these restrictions but were employed in the current month and 12 months earlier.
The correlation between the quit rate and median wage growth is strongly positive and is slightly higher for job switchers (0.91) than for job stayers (0.88). In most periods, the median wage growth of job switchers is higher than for job stayers. This difference is consistent with the notion that job switching tends to involve moving to a better-paying job. However, during periods when the quit rate is slowing, median wage growth slows for both job stayers and switchers (reflecting the correlation between quits and wages), and the wage-growth premium from job switching tends to vanish.
Since the end of the last recession, the quit rate has been rising and a wage-growth premium for job switching has emerged again. Interestingly, during the last year, the wage growth of job stayers appears to have strengthened as well, consistent with a general tightening of the labor market.
August 21, 2015
No Wage Change?
Even when prevailing market wages are lower, businesses can find it difficult to reduce wages for their current employees. This phenomenon, often referred to as "downward nominal wage rigidity," can result in rising average wages for incumbent workers despite high unemployment levels. Some economic models predict that a period of subdued wage growth can follow, even as the labor market recovers—a kind of delayed wage-adjustment effect.
In her 2014 Jackson Hole speech, Fed Chair Janet Yellen suggested this effect may explain sluggish growth in average wages in recent years, despite significant declines in the rate of unemployment.
This macroblog post looks at evidence of wage rigidity, particularly a spike in the frequency of zero wage changes relative to wage declines. A comparison is made between hourly and weekly wages and between incumbent workers (job stayers) and those who have changed employers (job switchers).
Chart 1 shows the fractions of job stayers reporting the same or a lower hourly or weekly wage than 12 months earlier. These measures are constructed from the Current Population Survey microdata in the Atlanta Fed's Wage Growth Tracker. They include workers who are paid hourly (accounting for about 60 percent of all wage and salary earners). The measures exclude those who usually receive overtime and other supplemental pay and those with imputed or top-coded (redacted) wages. Weekly wage is defined as the hourly wage times the usual number of hours per week worked at that rate. The data are aggregated to an annual frequency (except for 2015, where the first six months of the year are covered).
Job stayers cannot be exactly identified in the data and are approximated by those who are in the same occupation and industry as they were 12 months earlier and the same job as they were in the prior month. Consistent with other studies (see, for example, the work of our colleagues at the San Francisco Fed), we find that the incidence of unchanged hourly wages among job stayers is substantial (although some of this is probably the result of rounding errors in self-reported wages). The measured share of unchanged hourly wages rose disproportionately between 2008 and 2010, and it has remained elevated since. Zero hourly wage changes (the green line in chart 1) have become almost as common as declines in hourly wages (the blue line in chart 1).
Chart 1 also suggests that weekly wages for job stayers show a pattern over time broadly similar to hourly wages. But the fraction of unchanged weekly wages (the purple line in chart 1) is lower. Each year, about 60 percent of those with no change in their hourly wage had no change in their weekly wage (or hours) either. Also, there are relatively more declines in weekly wages (the orange line in chart 1) than in hourly wages—mostly the result of reduced hours worked. On average, a reduction in weekly wages is associated with a four-hour decline in hours worked per week. About 90 percent of those with lower hourly wages also had lower weekly wages, and 20 percent of those with no change in their hourly wage had a lower weekly wage (working fewer hours).
If job stayers show a relatively high incidence of no wage change, we might expect a different story for job switchers, since they are establishing a new wage contract with a new employer. Chart 2 shows the fraction of job switchers reporting the same or a lower hourly or weekly wage than 12 months earlier. Job switchers are approximated by workers who are in a different industry than a year earlier.
Not surprisingly, a smaller share of workers experience no change in their hourly or weekly wage when switching jobs. But the pattern of zero wage change for job switchers over time is generally similar to that of job stayers. It is also true that a decline in hourly and weekly wages is more likely for job switchers than for job stayers, with a significant temporary spike in the relative frequency of wage declines for job switchers during the last recession.
Taken at face value, this analysis suggests the presence of some amount of wage rigidity. Also, rigidity increases during recessions and has remained quite elevated since the end of the last recession—especially for job stayers. The question then becomes whether this phenomenon has important macroeconomic consequences. A prediction of most models in which wage stickiness has allocative effects is that it causes firms to increase layoffs when faced with a decline in aggregate demand. Interestingly, during the last recession—when wage stickiness appears to have increased substantially—the rate of layoffs was not unusually high relative to earlier recessions. What was atypical was the size of the decline in the rate of job creation, and this decline contributed to unusually long unemployment spells. As noted by Elsby, Shin, and Solon (2014), it is not clear that an increase in wage rigidity would constrain the hiring of new workers more than it constrains the retention of existing workers.
On the other hand, persistently high wage rigidity in the wake of the Great Recession is consistent with the relatively sluggish pace of wage increases seen in most measures of aggregate wage growth via the "bending" of the short-run Phillips curve (as described by Daly and Hobijn (2014)). Interestingly, the Atlanta Fed's Wage Growth Tracker is an exception. It has indicated somewhat stronger wage growth during the last year than other measures. It will be interesting to see if that trend continues in coming months.
July 17, 2015
Getting to the Core of Goods and Services Prices
In yesterday's macroblog post, I highlighted an aspect of a recent Wall Street Journal article that concerns how households perceive inflation. Today, I'm going back to the same well to comment on another aspect of that story, which correctly notes that service-sector prices are rising at a faster clip than the price of goods.
Of course, this isn't just a recent event. Core services prices have outpaced core goods prices over the past 50 years, save a few short-lived deviations. What's unusual about the current recovery, as the chart below shows, is how low services inflation has been.
In the nearly six years since the end of the 2007–09 recession, core services prices have risen at an annualized pace of 2.1 percent, a full percentage point below their average during the last expansion. Conversely, the annualized growth rate in core goods prices during the recovery has been 0.5 percent, compared to a decline of 0.6 percent during the last expansion (see the chart below).
To see how broad-based the slowdown across core services prices has been relative to that of core goods prices, let's take a deeper dive into the components. The chart below compares the difference between a particular component's annualized growth rate during the current expansion and its growth rate during the previous expansion. A negative number here means that a component's price is growing more slowly now than it did prior to the recession.
It's evident that the slowdown in core services prices is fairly broad-based (17 of 22 components are exhibiting disinflation relative to their growth rate over the previous expansion). For core goods components, that number is just five of 15 components. So, if we accept the premise of the WSJ article—that trends in services prices more closely reflect "unused domestic capacity"—then it's possible we could be farther away than we think.
July 16, 2015
Different Strokes for Different Folks
A recent Wall Street Journal article offered an interesting conjecture. The author stated,"[b]ecause consumers pay service bills more often than they buy most goods other than food and gasoline, perceptions of inflation skew on the high side."
Research supports the idea that inflation perceptions are unusually influenced by particular prices. For example, some authors have noted that inflation expectations appear to be unusually influenced by movements in gasoline prices.
This research by Georganas, Healy, and Li shows that inflation perceptions are affected by how frequently people buy a particular good—so that nondurable goods prices like gasoline affect inflation perceptions more than durable goods.
And recent work by Johannsen at the Federal Reserve Board shows that demographic groups who have a more disperse set of inflation experiences also tend to hold more disperse inflation expectations. One thing I think we can say is that different demographic groups appear to have different inflation experiences, as this research by Hobjin, Mayer, Stennis, and Topa indicates.For example, let's take a look at the difference between the inflation experiences of two households. The first is a single older female (over 55 years of age) who rents her home and has a relatively low income (less than $30,000 a year). The second is a young couple (younger than 35 years old) who own their home and have a high income (over $70,000 annually). Both households have high school educations. Recently, the difference between the inflation experiences of these two demographic groups has opened up to a sizable 2.0 percentage points (see the chart). Why?
Well, the spending habits of these two groups contain a few striking differences. For example, the older female spends a lot more of her household income on food at home, rent, and medical care than the young couple does (see the table). Also, the young couple appears to spend a larger fraction of their income on transportation (a large portion of which is gasoline).
Comparison of myCPI Weights
Average of the previous five years (through December 2014)
A young couple, homeowner, high income, high school education
Older female, renter, low income, high school education
Food at home
Food away from home
Household furnishings and equipment
Note: "Other" includes personal care, alcohol, tobacco, reading, and miscellaneous goods and services
What's the inflation experience for someone in your particular demographic group? Let's find out. We've developed a tool called myCPI. It allows users to track a measure of the cost of living that captures some of the variation that occurs between demographic groups. In less than a minute, you can answer a few questions about your demographic category, and we'll show you the cost-of-living trends for "your" group. And if you want, we'll send you updates of your demographic group's inflation with every consumer price index (CPI) report.
Why not get your myCPI report? And when tomorrow's CPI report is released, we'll send you a note telling your how your group's cost-of-living adjustment compares to the average urban consumer in the headline CPI.
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