Employment & Productivity

Racial & Ethnic Disparities in EPOP Recoveries

Employment-population ratios have been recovering: the overall EPOP had regained 54.1% of its February-April loss by September, and 62.2% through December. However, in the last two months only half the major demographic groups retained traction, so November and December overall were flat.

That’s graphed below, and here are some highlights: Men overall have gained 5.2 points of their loss, about half women’s 10-point recovery. White & Hispanic women have turned in stronger performances than men, contradicting a popular meme that played off women’s weakness in December.

Women’s EPOP fell by 10 points between February and April, and has regained 6.4%. Men lost 9.6 points and regained 5.7. But since women started with a lower EPOP than men, these translate to larger percentages. However, in December the EPOP for women was 83% of that of men, close to February’s share, after having fallen to 80% at the April low.

Racial and ethnic disparities are far greater than those of gender. Black and Latino EPOPs fell harder than whites’ and have recovered less, with black women showing the weakest recovery of any of the demographic groups shown. One thing dragging down the recovery among black workers is almost certainly the continuing decline in government employment, where they’re heavily overrepresented, with many jobs earning good pay.

by admin· · 0 comments · Employment & Productivity

May state employment: At what cost?

The Bureau of Labor Statistics released state-level employment data for May this morning. Looking over the last two months, where things stand since March, largest job losses are in Hawaii, Michigan, New York, and Nevada, all around 20%. There’s a big cluster of states that lost about 15% of their employment over the two months, including the Eastern states not listed above, and Kentucky. Clustered just above 10% are many large Western and Midwestern states, West Virginia, North Carolina, and New Mexico. Those down 10% and less include more of the Southern states, and the Plains and Mountain states. Smallest two-month declines were in Oklahoma and Utah, -6%, followed by Arkansas, Arizona, Idaho, Mississippi, and Nebraska, all -7%.

Our diffusions indexes, which were all 0 in April, except one case of construction hiring, rose to a broad 49 overall, with Leisure & Hospitality, 49, leading the way, followed by education and health, 47, trade/transport & construction, 46, and professional/business services 38. Government work was up in only 2 states, Wisconsin and New Mexico, and DC.

Looking at Leisure & Hospitality employment, only the District of Columbia and Hawaii added to April losses in May, now down 63% for the two months combined. In Oklahoma and Montana L&H work is down only 10% over the two months, and New York, -56%, Massachusetts, -52%, with Delaware & Michigan, 49%, close behind. The largest losses are heavily concentrated in the Northeast and Midwest, with L&H employment off between 40 and 63% in fifteen states. California, Oregon and Connecticut are at the bottom of that column, within striking distance of -40%.

Losses of, very roughly, 10%, in Oklahoma and Montana, to 35%, Maine, Ohio, Wisconsin, are concentrated in the Mid- and South-West, the mid-Atlantic, and the Southeast. Losses in the Plains states all round to about 20%, and in the Mountain West and across the Southwest generally to the high teens. In Utah, Idaho and Tennessee L&H losses total about -15%. None of this is surprising, and there does seem to be a relaxation between more lax state policies, for now. L&H work in Alaska is down 25%.

This is a noisy series with small samples. Largest declines in unemployment rates occurred in Mississippi & Kentucky, around -5.6 pps; in Indiana, Nevada & Arizona, around 5pps; in Vermont, Ohio, Alabama, and Tennessee, around -4pps. Unemployment rates rose in Minnesota, Connecticut & Florida, and lost less than 1 percentage point in Texas, Wyoming, New York, and Alaska.

We’d be more encouraged by all of this of 8 more states hadn’t crossed over the line into the “spreading quickly” red zone this morning in the tracker we sent around on Monday.

by admin· · 0 comments · Employment & Productivity

Diminishing Dynamism

The BLS is just out with the Business Employment Dynamics (BED) release for the first quarter of 2019—not exactly breaking news, but of longer-term interest. First quarter gross job gains fell to 5.9% of employment from 6.3% of employment in 2018Q4, while gross job losses slipped only from 5.6% to 5.5%. A year earlier the numbers were 6.1% (gains) and 5.5% (losses). Not only were net gains weaker in 2019Q1 than 2018Q1, job turnover, the combined total of gross gains and losses, 11.4% vs. 11.6%, is down, further evidence of the eroding dynamism of the US economy. From 1992 to 2000, turnover averaged 15.5% of employment; from 2001–2005, 14.2%; since 2010, 12.1%.

BED also includes stats on establishment openings and closings, which is important because employment growth is driven by young (but not newborn) firms. New establishments grew 3.1% in the first quarter, pretty much where they’ve been since 2010. That compares with 3.4% growth in the 1990s and 3.3% in the early 2000s. Despite the unchanged birth rate, the number of jobs produced by these newborn establishments fell to 0.6% of employment, tying the all-time low for the series. Establishment deaths are only reported with a three-quarter delay (gotta make sure they’re dead and not just asleep): closings were 2.9% of the total, up two ticks from the previous quarter, and also 0.2 above the average since 2010. The gusher of entrepreneurship that was supposed to flourish under a regime of tax cuts and deregulation has yet to materialize.

Further evidence of that comes from the Census Bureau’s business application series, which is derived from applications for new employer ID numbers. From those, Census derives a subset with a high propensity of producing a payroll. Third quarter figures were released on October 16. They showed an 0.5% overall decline in applications from the second quarter, an 0.7% decline in high-propensity formations, and a 2.1% decline in those with planned wages. For the year, new applications were off 1.5%, and off 0.8% for high-propensity ones. As this graph shows, there was a brief surge in formations in 2017 and 2018, but that burst of animal spirits looks to have run its course.

Where the Money Goes

We happened to come across some graphs from several years ago by Pavlina Tchernova of Bard College showing the growth in income by expansion for the top 10% and bottom 90% and wondered what an update would look like. We didn’t do exactly what she did, but we did want to give her credit for the general idea.

The striking graphs below tell the story by themselves, using numbers assembled by Thomas Piketty and Emmanuel Saez. In the expansions from the end of the World War II to 1973, the further up the income distribution you went, the lower the income gains. For the first five cycles, the bottom 90% saw an average of unweighted average of the growth for the bottom 90% first five cycles of 17%; for the second five cycles (the current one only through 2018), of 6%. For the top 1%, the figures are 7% and 52%. The first is less than half the bottom 90%’s performance; the second, almost eight times as much.

Within the top 1%, similar patterns prevail. For the 99.0–99.5% set, the gains were 10% and 29% for the two sets of cycles; for the 99.9–99.99% fellowship, the figures are 4% and 64%.

We live in very separate economic universes. Your opinion of that depends heavily on your percentile, but whether it’s sustainable is open to question.

by admin· · 0 comments · Employment & Productivity

Where the Assets Are

The Federal Reserve’s distributional accounts are a combined product of two Fed projects, the quarterly financial accounts, which we review soon after their publication, and the triennial Survey of Consumer Finances, a detailed look at household income and balance sheets. Often when we do our quarterly reviews of the financial accounts, we lament that the aggregates (which behave pretty much like means) don’t tell us anything about distribution. Middle-class wealth, such as it is, is largely in housing, while high-end wealth is largely in financial assets. The movements in those assets are proxies for a distributional analysis, and now we have the real thing.

We graphed dollar levels, not shares, but a few words about them first. In 2019Q2, the top 1% of the distribution owned 29.0% of all assets, up from 21.2% in 1989; the bottom 50% went from 7.3% to 6.1%. Percentiles 90-99 saw little change in their share, but the next 40% lost 7 points of share, 37.2% to 30.1%. Holdings of stocks and mutual funds also become more concentrated, with the top 1% going from holding 47.8% in 1989Q3 to 52.0% in 2019Q2. The bottom 1% saw their share rise from 1.0% to 2.2%. Between the extremes, the most interesting development was how those between percentiles 50 and 90 rode the 1990s stock boom, going from owning 14% of stock in 1989 to a peak of 19.1% in 2001; they now account for just 11.4%. The top 1% went from holding 23.9% of net worth in 1989 to 32.4% this year; the next 9% were unchanged at 37.0%; the next 40% went from 35.4% to 28.7%; and the bottom half from 3.7% to 1.9%. In 1989, the top 1% had 6.5 times the share of the bottom half; in 2019Q2 it was 17.1 times—though that is down from a 2011 peak of 150 times, when the bottom half, hammered by the housing bust, was barely above water.

On to the graphs, which show residential and nonresidential net worth by fractile (slice of the distribution), first in the aggregate and then by household. (We estimate the household numbers by dividing the aggregate by the fractile’s share of total households in the quarter.) “Nonresidential” subtracts the value of real estate from assets and adds back mortgages from assets. It also excludes consumer durables, which are illiquid and depreciate rapidly, which the Fed counts as a household asset. We also deflate by the PCE index; the Fed presents nominal figures.

Although the 1% gets all the press, when it comes to residential net worth they don’t hold as much as the next 9% or the next 50%, though of course those populations are far more numerous. So, movements in the balance sheets of the 90-99%, what in economic slang is oddly called the upper middle class (as it is in the new book by Saez and Gabriel Zucman) and the next 40%, the better off segments of the “middle class,” matter for consumption. The bottom 50% has close to nothing in the aggregate.

The picture changes when you take out the residential parts: there, the 1% have more than percentiles 50–89, and is within hailing distance of 90–99. The bottom 50% have a little more than nothing; they’re disproportionately renters (though they took a hit on residential wealth in the crash), and might have a small savings or retirement account, but not much else.

Looking at households, the bottom 50% meanders around the zero line; they had a net worth of $15,280 in 2019Q2. The 40% above them had a net worth of just under $160,000. You can’t see it on the graph, but that’s up 42% since 1989, though there was an intervening hit of -37% during the housing collapse. The top 1%, who don’t have most of their wealth in housing, still have a lot: $2.9 million, up 188% from 1989. But again, the 1% star in nonresidential wealth: $25.3 million in 2019Q2, up 214% from thirty years earlier. The next 9% were not begging: $3.2 million. The bottom 50% have a net of $84,189 to their name, up 90% from 30 years ago, well under half the gain clocked by the top 1%.

Again, people are living in very different universes.

by admin· · 0 comments · Employment & Productivity