Job Openings and Labor Turnover Survey: Job openings and quits edged up to series highs for March
Below, EPI senior economist Elise Gould offers her initial insights on today’s release of the Job Openings and Labor Turnover Survey (JOLTS) for March. Read the full Twitter thread here.
While there was “little changed” overall in the latest #JOLTS data released this morning from the Bureau of Labor Statistics, job openings and quits edged up to series highs for March. Hiring remains strong and hires still significantly outpace quits.https://t.co/djGEusO8gy
— Elise Gould (@eliselgould) May 3, 2022
Much has changed since the first May Day, but building worker power and combating racism and xenophobia remain just as important
May 1 is International Workers’ Day, a day workers around the world mark as Labor Day with marches, demonstrations, and renewed calls for workers’ rights. “May Day” got its start in 1886, when U.S. workers rallied in support of ongoing campaigns for an eight-hour day, setting May 1 as a deadline to begin mass strikes if employers failed to adopt shorter hours.
In 1886 Chicago, where tens of thousands joined May Day actions and thousands went on strike, subsequent police shootings of striking workers escalated into the well-known Haymarket Tragedy. Months of state-sanctioned, anti-immigrant repression of labor organizing followed. Police raids of union halls and arrests of organizers culminated in a sham trial, eight guilty verdicts, and public hanging of four prominent immigrant, working-class movement leaders (a fifth died by suicide prior to the execution date). The trial and executions were followed closely by workers across the country and around the world. In memory of the Haymarket Martyrs, labor and socialist organizations declared May Day International Workers’ Day, now an official public holiday in many countries.
Over 100 years later, our May Day 2022 economy has much in common with that of May Day 1886—rising inequality, economic upheavals affecting those with the least financial security, xenophobia, market concentration, and an upsurge in workers taking matters into their own hands while facing intense employer resistance. U.S. factory workers and railroad workers are still campaigning for shorter hours, in some cases striking (or threatening strikes) to challenge inhumane 12-to-14-hour shifts and unpredictable forced overtime. New generations of workers, including many immigrants, are breaking through barriers of employer union-busting to organize unions in warehouses, hospitals, nursing homes, coffee shops, retail stores, media outlets, universities, and beyond.
This Workers Memorial Day, honor lives lost by joining workers’ fight for a future that includes safe work
“Our health is just as essential,” read the homemade sign Chris Smalls carried in front of the Amazon JFK8 Staten Island warehouse on March 30, 2020, a moment when it had become clear that exposure to coronavirus could be deadly. After a week of appealing to management for masks, gloves, and a temporary shutdown to sanitize exposed areas, several JFK8 workers walked out and called on Amazon to take steps to protect those inside the warehouse where positive cases were known but not always being reported to employees.
This Workers Memorial Day, policymakers should listen to and follow the lead of workers at Amazon and elsewhere who are organizing to build the power necessary to demand safe work, often in the face of long odds and intense employer union-busting. With pandemic losses still mounting and untold numbers of worker deaths to mourn, it’s past time to ensure effective regulation of workplace safety and the protection of all workers’ rights to form a union.
Stagnant top-code thresholds threaten data reliability for the highest earners and make inequality difficult to accurately measure
Measuring wage growth, particularly at high wage levels, has become a difficult task. The most useful, publicly-available data for measuring trends in hourly wages is the Current Population Survey (CPS). Analyses from CPS, for example, (like this one here) are a key reason why we know that except for brief periods of decent wage growth for middle- or low-wage workers between 1979 and today, wage growth for most workers has been slow while wage growth for top earners has been far more rapid.
Unfortunately, it is increasingly difficult to report accurate data on top-end wages or wage changes using the CPS because of growing inequality and measurement issues related to top-coding, an earnings reporting method that hasn’t kept up with rising wages for top earners and thus limits measuring of earnings above a certain threshold (and therefore makes it difficult to accurately measure average wages as well). “Top codes” used in the CPS assign observations that report wages over some threshold the identical “top-coded” value in the data (the current top code is $2,884.61 in weekly wages). The top code is not updated annually for inflation or anything else. Consequently, it suppresses a larger and larger fraction of the CPS data over time. If wage-growth for workers who make more than the top code has been systematically faster than for workers beneath the top code, we will miss out on just how much wage inequality has risen.
This creates a real problem for assessing overall trends in inequality, because so much income (including wage income) has been concentrated at the very top of distributions. For example, data on annual wage growth from the Social Security Administration that is not top coded show substantially more dramatic growth in inequality than what is apparent in CPS data (see an analysis of this SSA data here). Given that the overwhelming majority of high-wage workers work full-time, it is surely the case that the SSA data for the highest-paid workers is mostly representative of very rapid growth in hourly wages, not hours worked. Yet we cannot really validate this with direct measures of hourly wages available to use in the CPS.
Aside from the problems it presents for interpreting overall trends in inequality, the CPS top code has radically different implications for analyses of smaller demographic groups. At the Economic Policy Institute (EPI), we provide labor market and wage analysis on our State of Working America Data Library page based on the best available data. When we attempt to analyze wages among demographic groups, by gender, race/ethnicity, and/or education, the parts of the wage distribution we are able to measure is even smaller because more and more observations’ data are suppressed by the top code.
The failure to adjust the top code to better capture wages at the high end isn’t a new phenomenon. Between 1973 and 1988, the top code for weekly earnings was constant in nominal dollars at $999 per week, even as inflation ran in double-digits over some of the intervening years. Then, it stayed at $1,923 per week between 1989 and 1997. The latest change in the top code was made back in 1998; the current top code has now sat at $2,884.61 per week in nominal value for the last twenty four years. Even if wages did not grow in real terms and only kept up with inflation, the ability to measure high end wages would be compromised. But, in most years, high end wages grew far faster than inflation, increasing the pace at which wages could not be reliably measured.
The Census Bureau has announced upcoming changes to the top-coding procedure for usual weekly earnings and usual hourly earnings data, which will certainly improve data analysis moving forward. But, there has been no indication that data will be changed historically, which does not solve the problem of trying to uncover high end earnings trends over the last few decades.
We start our display of the top-code issue with an examination of the shares of each demographic group that have been top-coded over time. Figure A displays top-coding shares overall as well as by gender from 1979 to 2021. Figure B displays the same for White, Black, Hispanic, and Asian American and Pacific Islander (AAPI) workers. And, Figure C displays the same trends by educational attainment.
Figure A illustrates the difficulties in measuring between and within group inequality at the high end of the wage distribution resulting from a significant portion of men being top-coded. This is particularly noticeable during 1985–1988 and 2018–2021. The share of men who were top-coded increased sharply through the 1980s, exceeding 5% of workers between 1986 and 1988, and then reached an all-time high of 7.7% in 2021. The higher shares of men top-coded compared with women is not surprising given the fact that they are far more likely to be found in higher paying jobs in the U.S. economy while women continue to face a significant gender pay gap, particularly at the middle and upper portion of the wage distribution, even among women with higher levels of educational attainment.
The top 5% of men’s wages have been topcoded in 8 of the last 43 years: Topcode shares by overall and gender, 1979–2021
| Year | All | Men | Women |
|---|---|---|---|
| 1979 | 0.6% | 1.0% | 0.1% |
| 1980 | 0.7% | 1.2% | 0.1% |
| 1981 | 1.0% | 1.8% | 0.1% |
| 1982 | 1.4% | 2.5% | 0.2% |
| 1983 | 1.8% | 3.3% | 0.3% |
| 1984 | 2.3% | 3.9% | 0.4% |
| 1985 | 2.7% | 4.7% | 0.6% |
| 1986 | 3.2% | 5.5% | 0.7% |
| 1987 | 3.7% | 6.4% | 0.9% |
| 1988 | 4.5% | 7.4% | 1.1% |
| 1989 | 0.5% | 0.8% | 0.1% |
| 1990 | 0.6% | 1.0% | 0.1% |
| 1991 | 0.7% | 1.2% | 0.2% |
| 1992 | 0.7% | 1.3% | 0.2% |
| 1993 | 0.8% | 1.4% | 0.2% |
| 1994 | 1.2% | 2.0% | 0.4% |
| 1995 | 1.3% | 2.2% | 0.4% |
| 1996 | 1.4% | 2.3% | 0.4% |
| 1997 | 1.7% | 2.6% | 0.6% |
| 1998 | 0.6% | 1.0% | 0.2% |
| 1999 | 0.7% | 1.1% | 0.2% |
| 2000 | 0.8% | 1.3% | 0.3% |
| 2001 | 0.9% | 1.4% | 0.3% |
| 2002 | 1.0% | 1.6% | 0.4% |
| 2003 | 1.1% | 1.6% | 0.4% |
| 2004 | 1.2% | 1.9% | 0.4% |
| 2005 | 1.3% | 2.0% | 0.5% |
| 2006 | 1.5% | 2.3% | 0.6% |
| 2007 | 1.7% | 2.6% | 0.7% |
| 2008 | 1.9% | 3.0% | 0.9% |
| 2009 | 2.1% | 3.2% | 0.9% |
| 2010 | 2.3% | 3.4% | 1.0% |
| 2011 | 2.3% | 3.4% | 1.1% |
| 2012 | 2.6% | 3.9% | 1.2% |
| 2013 | 2.8% | 4.2% | 1.4% |
| 2014 | 2.9% | 4.2% | 1.5% |
| 2015 | 3.2% | 4.6% | 1.6% |
| 2016 | 3.5% | 4.9% | 1.7% |
| 2017 | 3.7% | 5.1% | 2.0% |
| 2018 | 4.2% | 6.0% | 2.3% |
| 2019 | 4.6% | 6.3% | 2.6% |
| 2020 | 5.4% | 7.4% | 3.2% |
| 2021 | 5.8% | 7.7% | 3.5% |
Notes: The topcode nominal dollar thresholds for weekly earnings have been updated three times since 1973. The topcode value was $999 from 1973–1988, $1923 from 1989–1997, and has currently sat at $2,884.61 since 1998.
Source: Authors’ analysis of EPI Microdata Extracts.
Figure B makes apparent how the historical and current discrimination against Black and Hispanic workers has meant that they are less likely to be in higher paying jobs, thus leading them to have significantly lower shares of top-coded workers than their AAPI and white counterparts. Between 1979 and 1997, the share of Black and Hispanic workers who were top-coded did not exceed 1.6% for either group, while “other” (largely AAPI) workers and white workers’ shares hit 4.8% and 5.1%, respectively. After the top-code threshold was reset in 1998, Black and Hispanic shares remained low until the last couple of years, hitting 2.5% and 2.6%, respectively. At the same time, AAPI and white workers experienced significant increases in the shares of their workforce hitting the top code, 10.9% and 6.8%, respectively. Ultimately, the significant increase in top-code shares among AAPI and white workers makes measuring the true high-end wage inequality within and across racial and ethnic groups nearly impossible; in fact, it also makes calculating average wages more challenging—relying more heavily on imputation assumptions—if the upper end is increasingly top-coded.
Earnings at the top for AAPI and white workers have been impossible to accurately measure since the mid 2010s: Topcode shares by race/ethnicity, 1979–2021
| Year | White | Black | Hispanic | AAPI | Other |
|---|---|---|---|---|---|
| 1979 | 0.6% | 0.1% | 0.1% | 0.4% | |
| 1980 | 0.8% | 0.1% | 0.3% | 0.7% | |
| 1981 | 1.2% | 0.2% | 0.4% | 1.0% | |
| 1982 | 1.6% | 0.3% | 0.4% | 1.9% | |
| 1983 | 2.1% | 0.5% | 0.6% | 2.0% | |
| 1984 | 2.6% | 0.7% | 0.7% | 2.0% | |
| 1985 | 3.1% | 0.8% | 0.9% | 2.5% | |
| 1986 | 3.7% | 0.8% | 1.1% | 3.8% | |
| 1987 | 4.3% | 1.2% | 1.2% | 4.3% | |
| 1988 | 5.1% | 1.6% | 1.4% | 4.8% | |
| 1989 | 0.6% | 0.0% | 0.2% | 0.4% | 0.4% |
| 1990 | 0.7% | 0.1% | 0.2% | 0.8% | |
| 1991 | 0.8% | 0.1% | 0.2% | 0.9% | |
| 1992 | 0.9% | 0.1% | 0.1% | 1.1% | |
| 1993 | 1.0% | 0.2% | 0.2% | 0.9% | |
| 1994 | 1.4% | 0.5% | 0.4% | 1.4% | |
| 1995 | 1.6% | 0.3% | 0.4% | 1.6% | |
| 1996 | 1.7% | 0.4% | 0.5% | 1.8% | |
| 1997 | 2.0% | 0.5% | 0.5% | 2.4% | |
| 1998 | 0.8% | 0.2% | 0.2% | 0.6% | |
| 1999 | 0.9% | 0.2% | 0.2% | 0.7% | |
| 2000 | 1.0% | 0.3% | 0.3% | 1.1% | |
| 2001 | 1.1% | 0.3% | 0.2% | 1.3% | |
| 2002 | 1.3% | 0.2% | 0.3% | 1.4% | |
| 2003 | 1.3% | 0.3% | 0.3% | 1.3% | |
| 2004 | 1.4% | 0.3% | 0.5% | 1.7% | |
| 2005 | 1.6% | 0.3% | 0.4% | 1.6% | |
| 2006 | 1.9% | 0.4% | 0.4% | 2.1% | |
| 2007 | 2.1% | 0.6% | 0.5% | 2.2% | |
| 2008 | 2.4% | 0.7% | 0.7% | 2.8% | |
| 2009 | 2.5% | 0.8% | 0.8% | 2.6% | |
| 2010 | 2.8% | 0.8% | 0.9% | 3.0% | |
| 2011 | 2.8% | 1.0% | 0.8% | 3.2% | |
| 2012 | 3.2% | 1.1% | 0.9% | 3.9% | |
| 2013 | 3.4% | 1.1% | 1.0% | 4.1% | |
| 2014 | 3.5% | 1.2% | 1.0% | 4.6% | |
| 2015 | 3.9% | 1.4% | 1.3% | 5.3% | |
| 2016 | 4.2% | 1.6% | 1.3% | 5.9% | |
| 2017 | 4.5% | 1.6% | 1.4% | 6.2% | |
| 2018 | 5.2% | 1.8% | 1.6% | 6.6% | |
| 2019 | 5.5% | 2.1% | 1.9% | 8.3% | |
| 2020 | 6.4% | 2.4% | 2.3% | 10.3% | |
| 2021 | 6.8% | 2.6% | 2.5% | 10.9% | |
Notes: The topcode nominal dollar thresholds for weekly earnings have been updated three times since 1973. The topcode value was $999 from 1973–1988, $1923 from 1989–1997, and has currently sat at $2,884.61 since 1998. AAPI refers to Asian American and Pacific Islander. Race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, Black non-Hispanic, AAPI non-Hispanic, and Hispanic any race). Prior to 1989, AAPI was not reported separately and included in “Other.”
Source: Authors’ analysis of EPI Microdata Extracts.
Figure C demonstrates how higher levels of educational attainment are related to higher earnings. There is a consistent and significant gap between the top-code share of workers with an advanced or college degree and those with the next highest level of educational attainment (some college experience). In 1988, 20.1% of workers with an advanced degree were top-coded, while the same was true for 11.0% of workers with a college degree. The share of workers with some college experience who were top-coded was only 2.9%. This large gap persisted in 2021, with the top-code shares being 17.0% and 9.3% for workers with an advanced and college degree respectively while workers with some college experience had only 1.9% top-coded. The significant shares of top-coded workers within the advanced degree and college educational attainment groups increases the difficulty of measuring their wages both alone, and in comparison, to other groups.
Advanced degree holders' wages exceed 5% topcode share in all but 10 years since 1979: Topcode shares by education, 1979–2021
| Year | Less than high school | High school | Some college | College | Advanced degree |
|---|---|---|---|---|---|
| 1979 | 0.1% | 0.2% | 0.4% | 1.6% | 3.3% |
| 1980 | 0.1% | 0.2% | 0.5% | 2.1% | 3.8% |
| 1981 | 0.2% | 0.4% | 0.7% | 3.1% | 5.1% |
| 1982 | 0.2% | 0.4% | 0.8% | 4.0% | 7.3% |
| 1983 | 0.3% | 0.5% | 1.1% | 5.1% | 9.1% |
| 1984 | 0.3% | 0.7% | 1.5% | 6.1% | 11.2% |
| 1985 | 0.4% | 0.9% | 1.9% | 6.8% | 13.0% |
| 1986 | 0.3% | 1.0% | 2.2% | 8.3% | 15.3% |
| 1987 | 0.4% | 1.1% | 2.6% | 9.6% | 17.6% |
| 1988 | 0.6% | 1.4% | 2.9% | 11.0% | 20.1% |
| 1989 | 0.0% | 0.1% | 0.3% | 1.1% | 2.7% |
| 1990 | 0.1% | 0.1% | 0.3% | 1.3% | 3.4% |
| 1991 | 0.0% | 0.1% | 0.3% | 1.5% | 4.3% |
| 1992 | 0.0% | 0.1% | 0.3% | 1.7% | 4.5% |
| 1993 | 0.0% | 0.1% | 0.3% | 1.8% | 5.1% |
| 1994 | 0.2% | 0.3% | 0.5% | 2.7% | 6.8% |
| 1995 | 0.1% | 0.2% | 0.5% | 2.9% | 7.7% |
| 1996 | 0.1% | 0.2% | 0.6% | 3.0% | 7.8% |
| 1997 | 0.1% | 0.4% | 0.7% | 3.6% | 8.9% |
| 1998 | 0.1% | 0.1% | 0.3% | 1.3% | 3.3% |
| 1999 | 0.1% | 0.1% | 0.2% | 1.6% | 3.7% |
| 2000 | 0.0% | 0.1% | 0.3% | 1.8% | 4.1% |
| 2001 | 0.1% | 0.1% | 0.3% | 2.1% | 4.2% |
| 2002 | 0.1% | 0.2% | 0.4% | 2.2% | 4.9% |
| 2003 | 0.1% | 0.2% | 0.4% | 2.3% | 4.8% |
| 2004 | 0.1% | 0.3% | 0.4% | 2.3% | 5.7% |
| 2005 | 0.1% | 0.2% | 0.5% | 2.6% | 5.9% |
| 2006 | 0.1% | 0.3% | 0.5% | 2.9% | 6.9% |
| 2007 | 0.1% | 0.3% | 0.6% | 3.3% | 7.3% |
| 2008 | 0.1% | 0.5% | 0.7% | 3.6% | 8.0% |
| 2009 | 0.2% | 0.4% | 0.7% | 3.7% | 8.9% |
| 2010 | 0.2% | 0.5% | 0.7% | 4.2% | 8.9% |
| 2011 | 0.2% | 0.5% | 0.8% | 4.1% | 8.9% |
| 2012 | 0.2% | 0.5% | 0.7% | 4.6% | 10.5% |
| 2013 | 0.2% | 0.5% | 0.8% | 5.0% | 10.6% |
| 2014 | 0.2% | 0.6% | 0.9% | 5.0% | 10.6% |
| 2015 | 0.3% | 0.8% | 1.2% | 5.5% | 10.9% |
| 2016 | 0.2% | 0.7% | 1.0% | 6.1% | 11.9% |
| 2017 | 0.4% | 0.8% | 1.0% | 6.4% | 12.3% |
| 2018 | 0.4% | 1.0% | 1.3% | 6.8% | 14.1% |
| 2019 | 0.4% | 1.0% | 1.4% | 7.5% | 14.8% |
| 2020 | 0.4% | 1.2% | 1.8% | 8.4% | 16.3% |
| 2021 | 0.5% | 1.2% | 1.9% | 9.3% | 17.0% |
Notes: The topcode nominal dollar thresholds for weekly earnings have been updated three times since 1973. The topcode value was $999 from 1973–1988, $1923 from 1989–1997, and has currently sat at $2,884.61 since 1998.
Source: Authors’ analysis of EPI Microdata Extracts.
Of course, the difficulties of measuring high end earnings are even more acute when we look at demographic groups that cut across gender, race/ethnicity, and education.
Figure D makes the power of intersectionality evident. Workers who identify with the groups with the largest top-code shares across gender, race, and education have earnings which are by far the most difficult to measure. Men (7.7%), AAPI workers (10.9%), and those with advanced degrees (17.0%) have the largest top-code shares of their respective demographic groups. However, when you examine these demographics in combination, AAPI men with advanced degrees, the share of workers who are top-coded is even more significant (29.2%). The group with the second largest top-code shares across all combinations of gender, race, and education are white men with advanced degrees (25.6%). For these groups, and all those displayed in Figure D, high-end earnings are exceedingly difficult to measure due to large top-code shares.
For some demographic groups, even 85th percentile wages are not measurable: Topcode shares for specific groups, 2021
| Share for specific groups | |
|---|---|
| All | 5.8% |
| White women, advanced degree | 10.1% |
| AAPI men, college degree | 13.1% |
| White men, college degree | 14.9% |
| AAPI women, advanced degree | 15.1% |
| Black men, advanced degree | 15.6% |
| Hispanic men, advanced degree | 18.8% |
| White men, advanced degree | 25.6% |
| AAPI men, advanced degree | 29.2% |
Notes: The topcode nominal dollar thresholds for weekly earnings have been updated three times since 1973. The topcode value was $999 from 1973–1988, $1923 from 1989–1997, and has currently sat at $2,884.61 since 1998. AAPI refers to Asian American and Pacific Islander. Race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, Black non-Hispanic, AAPI non-Hispanic, and Hispanic any race).
Source: Authors’ analysis of EPI Microdata Extracts.
See our State of Working America Data Library page for wages by percentile, including NA’s for percentile values that can’t be reliably measured.
It’s unfortunate that policy has not made a significant contribution to reining in rising wage inequality over these years. But allowing rising inequality to mechanically obscure even its own measurement seems truly absurd, and yet extremely easy to fix. The BLS should commit to higher and far more regularly updated top codes, or find some other way to allow researchers to get a clearer sense of what is happening to wage inequality than what the CPS currently allows.
Corporate profits have contributed disproportionately to inflation. How should policymakers respond?
The inflation spike of 2021 and 2022 has presented real policy challenges. In order to better understand this policy debate, it is imperative to look at prices and how they are being affected.
The price of just about everything in the U.S. economy can be broken down into the three main components of cost. These include labor costs, nonlabor inputs, and the “mark-up” of profits over the first two components. Good data on these separate cost components exist for the nonfinancial corporate (NFC) sector—those companies that produce goods and services—of the economy, which makes up roughly 75% of the entire private sector.
Since the trough of the COVID-19 recession in the second quarter of 2020, overall prices in the NFC sector have risen at an annualized rate of 6.1%—a pronounced acceleration over the 1.8% price growth that characterized the pre-pandemic business cycle of 2007–2019. Strikingly, over half of this increase (53.9%) can be attributed to fatter profit margins, with labor costs contributing less than 8% of this increase. This is not normal. From 1979 to 2019, profits only contributed about 11% to price growth and labor costs over 60%, as shown in Figure A below. Nonlabor inputs—a decent indicator for supply-chain snarls—are also driving up prices more than usual in the current economic recovery.
Child care and elder care investments are a tool for reducing inflationary expectations without pain
Inflation is by far the biggest economic concern facing the U.S. economy today. While job growth is historically rapid and survey evidence indicates that workers think now is the best time in years to find a good job, the inflation surge has kept this labor market strength from translating into higher wages and incomes for most households. The most well-known tool to restrain inflation—higher interest rates engineered by the Federal Reserve—is potentially very costly if it leads to higher unemployment and a weaker labor market.
Given all of this, policymakers should look for any tool that can help restrain inflationary pressures without causing significant collateral damage. One such tool could be investments in child care and elder care. By subsidizing families’ use of child care and elder care and providing direct investments to providers, such investments could boost future labor supply by allowing working-age parents and children who want to look for paid employment to do so while remaining confident their family members are receiving care. Further, these investments can help dampen inflationary pressures—that rising wages could in theory contribute to—even well before they fully take effect.
To understand why, one must realize that developments in the labor market will likely determine just how easily (or not) inflationary pressures can be lowered in the next year or so. The inflationary spike that began in 2021 didn’t start in the labor market—it started in commodities and in supply-chain-snarled durable goods sectors where wage growth was actually slower than in other parts of the economy. But going forward, whether or not the Federal Reserve needs to start applying ever-stronger medicine (with deeply damaging side effects) to slow inflation depends on what happens in labor markets. Specifically, it depends on whether or not the initial inflationary shock leads to unsustainably large wage increases that push up inflation even further, leading to wage-price spirals of the sort that characterized the 1970s.
March jobs report shows strong growth as the labor market continues to recover at a rapid pace
Below, EPI economists offer their initial insights on the jobs report released this morning, which showed 431,000 jobs added in March.
Job Openings and Labor Turnover Survey: Job openings were little changed while hires edged up
Below, EPI senior economist Elise Gould offers her initial insights on today’s release of the Job Openings and Labor Turnover Survey (JOLTS) for February. Read the full Twitter thread here.
The hires and quits rates have been moving in the same general direction for months. They both ticked up slightly in February. Workers continue to quit and get hired at fast rates in today’s economy, including the hires of workers returning/entering the labor market. pic.twitter.com/erHPkXA7aV
— Elise Gould (@eliselgould) March 29, 2022
Biden can fix the anti-worker H-1B immigration visa scam
This is an excerpt from an op-ed in Jacobin. Read the full op-ed here.
Every April 1, the government decides, via lottery of all methods, which employers will get new visas for the H-1B, a temporary work program that has inflicted serious harm on millions of workers over the past three decades.
Enforcers take action to protect building superintendents and grocery and construction workers: A snapshot of state and local enforcement actions across the country
Series: The New Labor Law Enforcers
State attorneys general, district attorneys, and localities like cities are increasingly key players in protecting workers’ rights. This new series by Terri Gerstein provides snapshots of enforcement and other actions to protect workers’ rights by these new and emerging labor law enforcers at the state and local level. Gerstein is an EPI senior fellow and director of the state and local enforcement project at the Harvard Labor and Worklife Program, who has chronicled the growing influence of these new enforcers.
Recent cases brought by state and local enforcers include the recovery of $130,000 for New York City building superintendents, who were paid no wages at all, and a recovery of nearly $220,000 for workers in a Seattle specialty bar and grocery store based on minimum wage and paid sick leave violations. In addition, prosecutors on both sides of the country took action against contractors in the construction industry: The King County (WA) prosecuting attorney concluded a case in which a worker was killed in a preventable trench collapse, while the Manhattan district attorney indicted several interior construction companies and their owners for a conspiracy to evade more than $1.7 million in workers’ compensation premiums.
Here’s a snapshot of some enforcement actions in February and March 2022.
The New York Attorney General (AG) announced the settlement and recovery of $130,000 in a case involving building employers that failed to pay live-in superintendents any wages at all, and compensated them only through providing lodging (which was needed to perform the job).