Policy Hub: Macroblog provides concise commentary and analysis on economic topics including monetary policy, macroeconomic developments, inflation, labor economics, and financial issues for a broad audience.
Authors for Policy Hub: Macroblog are Dave Altig, John Robertson, and other Atlanta Fed economists and researchers.
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February 14, 2023
Real Wage Growth: A View from the Wage Growth Tracker
The Atlanta Fed's Wage Growth Tracker (WGT) measure of year-over-year nominal wage growth has been elevated during the last couple of years. It was 6.1 percent in December 2022. Although this level is down from its high of 6.7 percent in June and July of last year, it is much higher than the 3.6 percent average seen in 2019, before the COVID-19 pandemic. At the same time, inflation has also been high. For example, the consumer price index (CPI) increased 6.5 percent from December 2021 to December 2022. As a result, the real or inflation-adjusted WGT for December 2022 was −0.4 percent.
To see how the WGT has performed relative to inflation over time, chart 1 shows the time series of the Wage Growth Tracker (the solid green line) and the 12-month rate of change in the headline CPI (the solid orange line) since 1998.
As you can see, the WGT, in terms of median wage growth, has been below the average rate of inflation for most of 2021 and 2022. Prior to that period, the last time the real WGT had been negative was during 2011—a short period when CPI inflation reached 4 percent while the WGT was hovering around 2 percent.
The fact that the real WGT is negative tells us that less than 50 percent of the people in the WGT sample had real wage increases, relative to the CPI. But as you can also see in chart 1, at any point in time the distribution of wage growth across individuals varies widely. For example, during 2022, around 25 percent of the sample reported nominal wage growth of more than 18 percent, while another 25 percent reported nominal wage growth at or below zero. As a result of this wide dispersion of wage growth across people, a significant minority of people experience real wage gains even when inflation is elevated, just as a significant minority experience real wage declines even when inflation is low. Chart 2 shows the percentage of the nominal wage growth sample that was above the prevailing rate of inflation in each quarter.
For example, about 57 percent of the WGT sample had positive real wage gains during 2019, whereas during 2022, only 45 percent of people had positive real wage growth. Put another way, despite higher median nominal wage growth, the share of people with positive real wage growth between 2019 and 2022 due to higher inflation fell by 12 percentage points.
As a simple counterfactual, if the rate of inflation had stayed at its 2019 average level of 1.8 percent, then close to 63 percent of people would have had positive real nominal wage growth in 2022. That is, a tight labor market without high inflation would have resulted in a 6 percentage point gain in the share of workers experiencing real wage growth relative to 2019, rather than a 12 percent decline.
One thing to keep in mind when interpreting chart 2 is that it implicitly assigns the same CPI inflation to everyone's wage growth within a period. Recall that the CPI is an index of price changes for a representative basket of goods and services, but such a basket is unlikely to fully capture the cost-of-living experience for any individual. (The Atlanta Fed's myCPI tool will give you an idea of how CPI inflation varies among broad groups of people. Unfortunately, users cannot map myCPI inflation data to the individual-level data used to construct the WGT.)
Not surprisingly, groups of workers with the highest (lowest) median wage growth are those with the highest (lowest) share of positive real wage growth. The table depicts these differences, comparing outcomes for 2019 with those for 2022 for a selection of worker types.
For example, in 2022, 60 percent of workers aged 16−24 had positive real wage growth versus 65 percent in 2019. However, the 5 percentage point decline in the share of people aged 16−24 with positive real wage growth is much smaller than the decline in the share of workers aged 25 and older who had positive real wage growth. In particular, the share of people 55 and older who saw positive real wage growth declined by 15 percentage points.
Some workers have seen their nominal hourly wage increase proportionately more than others because of the tight labor market during the last couple of years, which has blunted the impact of higher inflation on those workers. Older workers, as well as people staying in the same job, have seen the largest increase in the share of real wage losses in 2022 relative to before the COVID-19 pandemic. That said, nominal wage growth across individuals varies a lot, even within age and job mobility categories. Explaining that variation remains a challenge for economists who often attribute it factors such as differences in productivity growth at the individual and firm level. Your own wage growth experience might not look like that of your neighbors or your colleagues, and it might not resemble that of the person with median wage growth either. The median wage growth is a useful guide to shifts in the distribution of wage growth over time, but it doesn't fully capture the breadth of wage growth experiences across individuals.
January 9, 2023
The Wage Growth Tracker with Rounded Wage Data: The Final Plan
On December 15, 2022, the US Census Bureau released its final plan for improving disclosure avoidance procedures for the Current Population Survey Public Use Files (CPS PUF), and that plan is available here. As you may recall, we here at the Atlanta Fed have been keenly interested in the proposed changes because we actively use the public use files to produce statistics such as the Wage Growth Tracker.
Part of the plan to avoid disclosure of individuals in the CPS-PUF is to round the CPS PUF earnings data. Previously, I have written about how the initial proposed rounding rules for hourly and weekly wages would have harmed the reliability of the Wage Growth Tracker (see here and here). In this Policy Hub: Macroblog post, I take a look at how the final plan for rounding wages would affect the Wage Growth Tracker.
The following table summarizes the Census Bureau's final rounding rules for hourly and weekly wages in the CPS PUF, with the prior proposed values shown in parentheses if they differ from the final values:
As you can see in the table, the final rounding rules are less restrictive than the prior proposal released in July 2022. In particular, the Census Bureau modified its proposal by raising the upper boundaries of the rounding for hourly wages. It also updated the weekly rounding to better align with the hourly wage rounding rules, assuming a traditional 40-hour work week, along the lines I had suggested here.
The following chart shows the published Wage Growth Tracker based on unrounded data (orange line), and what it would have been if the final rounding plan had been in place (blue line).
If you have difficulty seeing any difference between the two lines, it's because they differ very little. The rounded wage data would have had very little impact on the Wage Growth Tracker statistic. I believe this outcome is a win for the collaborative process that the Census Bureau employed when developing this final plan, which included sharing information about the proposals and gathering suggestions for revision from the user community.
The Census Bureau plan includes one other change that will also directly affect the Wage Growth Tracker data. The Wage Growth Tracker excludes wage observations that have been topcoded. (Topcoding helps preserve the anonymity of the highest wage earners in the sample under study by replacing their actual wage with a topcode value.) The Census Bureau is introducing a dynamic topcode that will apply to the top 3 percent of earnings reported each month. This method will replace the current one, which applies fixed-dollar topcode thresholds to the wage data. For weekly earnings, the static threshold is currently $2,884.61 ($150,000 a year) and results in the potential exclusion of about 5.5 percent of the wage data that could have gone into the Wage Growth Tracker statistics. The new dynamic topcode will result in fewer cases being topcoded and thereby modestly expand the sample size used to compute the Wage Growth Tracker. However, if the highest wages are mostly people with relatively low wage growth (because, for example, they are late in their careers), then the calculated median wage growth could be a bit lower than it would have been. For that reason, at least initially, we plan to maintain a parallel set of Wage Growth Tracker data that continue to implement the static topcoding to see if we note any systematic differences arising from the dynamic topcode.
The changes to the CPS PUF will be implemented with the release of the January 2023 data in early February. I will report here on what we learn about the impact of the switch to dynamic topcoding, but users of the Wage Growth Tracker data can be confident that the switch to the rounding of the underlying wage data will have minimal impact.
October 21, 2022
Viewing the Wage Growth Tracker through the Lens of Wage Levels
One of the most popular features of the Atlanta Fed's Wage Growth Tracker is its depiction of median year-over-year wage growth of four different wage levels (wage quartiles). Unfortunately, the sample size of each quartile for a month is quite small, and thus the median wage growth for each quartile is noisy. For that reason, the Tracker shows changes by wage quartile only as a 12-month moving average. However, although the averaging smooths out a lot of the month-to-month noise in the series, it also means that the series have a substantial lag in showing wage growth changes across quartiles.
Instead, I have produced a cut of the wage growth data by wage level that can show a three-month moving average, which gives a better near-term picture of wage growth trends. The restriction, however, is that rather than using four wage groups, I put the average wage-level data (that is, the average of a person's reported wage in the current month and their reported wage a year earlier) into two groups: those whose average wage is above the median and those whose average wage is below the median. Essentially, I split the distribution of average wages in half.
Chart 1 plots the resulting three-month moving average of the two groups' median wage growth.
As you can see, median wage growth has been elevated since 2020 for workers across the wage distribution. But for workers in the bottom half of the wage distribution, median growth has been especially high during the last year. High wage growth for lower-paid workers aligns with numerous anecdotal reports suggesting that worker shortages since the pandemic have been especially acute in industries that pay below-average wages, such as leisure and hospitality.
Chart 1 allows another interesting observation: in the years leading up to the pandemic, the median wage growth of those in the lower half of the wage distribution was typically a bit above those in the upper part of the distribution. This was a period when the labor market was also tight, although much less so than it is today. Chart 2, which shows the sum of employment and job openings relative to the size of the available labor force, makes clear the divergence in the degree of overall labor market tightness today versus prior to the pandemic.
By this measure, though the gap has narrowed a bit in recent months, labor demand remains well above its supply, and this gap has been putting upward pressure on wages across the spectrum.
The Wage Growth Tracker series for the two wage groups is available now in the downloadable spreadsheet here and will be updated with October data after the Current Population Survey micro data for October is released, which usually occurs about a week after the US Bureau of Labor Statistics issues its labor report.
October 20, 2022
The Atlanta Fed's Early Career Visitor Program Workshop: A Synopsis
On September 9, 2022, the Federal Reserve Bank of Atlanta hosted the Early Career Visitor Program Workshop, organized by Salome Baslandze, Simon Fuchs, Indrajit Mitra, and Veronika Penciakova. The purpose of the program is to offer early- and mid-career researchers the opportunity to spend several months visiting the department. The program, an innovative addition to the existing landscape of offerings across the Federal Reserve System, provides a unique opportunity for researchers in the early part of their careers to spend some time at a regional Reserve Bank, and for Atlanta Fed's Research Department to strengthen ties with new generations of policy-oriented economists. The program also supports our policy-making process by keeping us in touch with new theoretical, quantitative, and empirical methods in the profession. The workshop brought together participants in the Atlanta Fed's 2021 Early Career Visitor Program with an aim to foster active exchange and discussion among economists on a wide range of topics. Tao Zha from the Atlanta Fed opened the conference by welcoming the participants. He talked about our unprecedented times and the challenges policymakers face in light of high inflation, government debt, and ongoing macroeconomic shocks. He also discussed the importance of high-quality research in informing policymakers.
Yuhei Miyauchi from Boston University presented his in-progress research (coauthored with with Elisa Giannone, Nuno Paixão, Xinle Pang, and Yuta Suzuki) titled "Living in a Ghost Town: The Geography of Depopulation and Aging." This project explores the dynamics of aging and depopulation across different regions within a country and how this process affects welfare across regions and generations. Using spatially disaggregated data from Japan for the last 40 years, he documents that depopulation and aging have progressed more rapidly in less populous areas. This empirical pattern is primarily driven by the youths' net outmigration. Motivated by this evidence, the author develops a dynamic life-cycle spatial equilibrium model of migration decisions. The model matches the historical spatial population changes in Japan and projects future spatial patterns of depopulation and aging. A key take-away from this project is that abstracting from endogenous migration decisions over the life cycle and their effects on local economies substantially biases the projected spatial patterns of demographic changes and welfare.
Wookun Kim from Southern Methodist University presented his joint work with Changsu Ko and Hwanoong Lee, "Heterogeneous Local Employment Multipliers: Evidence from Relocations of Public Entities in South Korea." The authors exploit a variation in public-sector employment from an episode of the relocations of public-sector entities and estimate local employment multipliers. The estimated multiplier is positive and persistent over time: an introduction of one public sector employment increases the private sector employment by one unit, with employment growth in the services sector driving this increase in private sector employment. The authors document that the effect of public employment on private employment is highly localized. In addition to changes in private employment, the relocations of public-sector employees led to a positive net inflow of residents into the treated neighborhood. Examining the variation in the extent of public employment shock across different relocations, the paper identifies heterogeneous local employment multipliers and provides evidence that the extent of public sector shocks and different types of relocation shape this heterogeneity. Their results imply that local employment multipliers tend to be higher in areas with predetermined characteristics that allow faster and larger general equilibrium responses to take place after the public sector shock.
Maya Eden from Brandeis University presented her work titled "The Cross-Sectional Implications of the Social Discount Rate." In her research, Eden asks, how should policy discount future returns? The standard approach to this normative question is to ask how much society should care about future generations. The author establishes an alternative approach, based on the social desirability of age-based redistribution. The social discount rate is below the market interest rate only if it is desirable to increase the consumption of the young at the expense of the old. Along the balanced growth path, small deviations of the social discount rate from the market interest rate imply large welfare gains from redistributing consumption across age groups.
Boyoung Seo from Indiana University presented her work, "Racial Differences in Prices Paid for Same Goods," coauthored with Andrew Butters and Daniel Sacks. The authors document that Black non-Hispanic households pay 2.0 percent higher prices than white non-Hispanic households, and Hispanic households pay 0.8 percent higher prices for physically identical products. This difference suggests that conventional measures of racial income differences understate real racial income inequality. Differences in income, demographics, or education do not explain the racial price gap. Instead, it is entirely explained by three factors: Black non-Hispanic and Hispanic households buy smaller packages with higher unit prices, benefit less from coupons, and live in places where prices tend to be high. The place-based price differences appear driven not by supermarket presence but by differences in carrying and transportation costs.
Abdoulaye Ndiaye from New York University presented "Bonus Question: How Does Incentive Pay Affect Wage Rigidity?," a paper coauthored with Meghana Gaur, John Grigsby, and Jonathan Hazell. Wage rigidity occupies a central role in models of macroeconomic fluctuations. However, recent work shows that wage rigidity is not sustained in equilibrium with appropriately calibrated idiosyncratic shocks. Indeed, individual wages frequently adjust in response to both idiosyncratic and aggregate shocks in the data. Many of these fluctuations result from movements in nonbase compensation such as bonuses, which most existing models are ill-equipped to study. The authors study whether and how flexible incentive pay affects macroeconomic fluctuations. They develop a general model of dynamic contracting, in which firms offer contracts to workers to give them incentives to supply costly effort that is otherwise unobservable by the firm. In this class of models, the first-order response of firm value to exogenous shocks is summarized by the direct effect of the shock on firms' objective function and constraints—the envelope theorem, which examines the effects of changes in certain variables, would hold that the indirect effects of the shock on wage payments and effort are not value-relevant. The authors consider the implications of this result both theoretically and quantitatively for the two fields that most commonly rely on wage rigidity to generate macroeconomic fluctuations: labor search and New Keynesian business cycle theory.
Yu Xu from the University of Delaware presented his work, titled "Ambiguity and Unemployment Fluctuations" and coauthored with Indrajit Mitra. The authors analyze the consequences of ambiguity aversion in the Diamond-Mortensen-Pissarides (DMP) search and matching model. Their model features a cross-section of workers whose productivity is the sum of an aggregate component and a match-specific component. Firms are ambiguity averse towards match-specific productivity. The model delivers two insights. First, ambiguity aversion substantially amplifies unemployment rate volatility. Second, a part of the high value of leisure required by the canonical DMP model to generate realistic unemployment rate volatility can arise from fitting a model missing ambiguity aversion to data generated in an environment where agents are ambiguity averse.
The workshop organizers hope that participants found the diverse array of presentations thought provoking as they progress in their careers as researchers, and that the discussions contributed to their professional and intellectual development.
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- This That and the Other
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