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

Farming Electrons

Many Midwestern states have become energy as well as food producers. North Dakota and Oklahoma produce lots of the non-renewable kind, but for the other Midwestern states, the energy comes primarily from ethanol and wind. The U.S. Energy Information Administration (EIA) reported that during 2018, 92% of the 383.1 million barrels of ethanol produced in the United States came from the Midwest, as did 47% of the total USA production, 275 TeraWatt-hours, of wind-generated electricity. In Iowa, wind turbines occupy about 7 thousand acres, which round up to 0.03% of the 23 million acres of cropland, and generate 42% of electricity produced in the state.

But first: Mike Lipsman is an economist, who is President and Chief Economist for the Strategic Economics Group (SEG). During both his years with state government and as a private consultant he has completed numerous agricultural and energy sector studies. We are happy to publish the meticulous work he did on this article, which presents high-level views of the gross and net energy yields from ethanol and wind technologies in the United States, and then uses Iowa as a case study to illustrate the tradeoffs between the two technologies.

The Energy Balance: The first contender

In the United States corn serves as the primary feedstock for the production in ethanol. In other countries, such as Brazil, ethanol is made from sugar cane or other crops. The average energy content of a gallon of ethanol equals 76,100 BTUs (British thermal units). About 2.8 gallons of ethanol can be produced from one bushel of corn.

But the important number is the energy balance or Energy Returned on Energy Invested (EROEI), the ratio of the amount of energy contained in a gallon of ethanol and the amount required to produce that gallon. Here there is room for disagreement: How carefully should inputs be analyzed, and which assumptions are most useful? Not for us to say, and we’ll stay out of those weeds.

In their January 2016 article, Bentsen, Felby, and Ipsen summarize energy balance findings from 27 prior studies, twelve of which pertain to corn-based ethanol produced in the United States. Looking at the US studies, energy balances are somewhat higher if credits for by-products, like distillers grain used for animal feed, are allowed, and range from 0.58 to 1.14 without such credits, and 0.69 to 1.9 with. Of course, the technology used to produce ethanol has itself become more energy efficient and, indeed, the lowest values come from studies produced in 1991, the highest from a 2005 study, so we’ll use 1.9. That energy balance means a gallon of ethanol yields net energy equal to 36,047 BTUs, and a bushel of corn used to produce ethanol produces net energy of 100,932 BTUs.

As an aside, commercial-scale ethanol production got underway during the oil embargoes of the 1970s, and as a replacement for lead used to lift octave levels. The less-than-1 ratios above mean that ethanol production was consuming more energy than it produced, but was achieving other objectives.

The Energy Balance: Enter the Turbine….

The National Renewable Energy Laboratory’s survey of 172 wind firms shows that including the concrete tower pad, power substations, and access roads, a utility-scale wind turbine with a nameplate capacity of about 2MW directly occupies about 1.5 acres. To minimize the effects of turbulence, the optimal density of wind turbines is about five per square mile, and most of the land in a wind farm remains available for crop production and pasture.

No matter what you often hear, it takes 0.27 years for a 2MW wind turbine to produce the amount of energy consumed to install it, and the average life-span of the turbine is 20 years. That means about 74.1 times more energy comes out than goes in. That compares to ethanol’s highest efficiency measure, just 1.9 times as much energy as was consumed in production. For those of you who like to think in gross margins, a wind turbine’s net yearly output is 98.7%.

Details: A 2MW wind turbine with an efficiency factor of 30%, meaning it’s producing energy about 30% of the time, will generate 17,933.5 billion BTUs of energy per year. Spread over its 20-year life, the annual amount of energy consumed in manufacturing and installing a 2MW wind turbine is 242.1 billion BTUs, and the net amount of energy produced by a 2MW wind turbine equals 17,691.4 billion BTUs/year.

How’s it going in Iowa?

During 2018, Iowa produced 2.5 billion bushels of corn, and Iowa’s ethanol plants produced 4.4 billion gallons of fuel ethanol, about 27% of total U.S. production. This implies that about 1.6 billion bushels of corn were used to produce ethanol, equating to a net energy yield of 156.8 trillion BTUs. Iowa’s wind turbines generated 21,685.1 GWh of electricity during 2018, or 73.0 trillion net BTUs. That means ethanol yielded about 2.15 times as much net energy as did wind turbines, but the amount of corn used to produce the ethanol spread over 7,800,000 acres, while the land occupied by the 4,859 utility-scale Iowa wind turbines was just a bit more than 7,000 acres. Problem number one.

But let’s look at net revenue. The past couple of years have not been particularly good financially for corn farmers in Iowa. According to Iowa State University Extension, the average cash price of corn in 2019 was $3.71 a bushel, while the fully allocated cost of growing that bushel ranged from $3.23 to $3.76, depending on whether the field was planted in soybeans or corn the prior year, if you must know. So, on a fully allocated cost basis the return for planting corn during 2019 was either a loss of 5-cents per bushel or a gain of 48-cents. On a per-acre basis, assuming a 200-bushels per acre yield, the return ranged between -$10 and $96. And even if only variable costs are taken into consideration the gross margin from planting corn averaged only between $350 and $420 per acre.

Property owners who lease their land for siting wind turbines generally receive annual payments of 2% to 4% of the gross revenue yielded by the wind turbine. Working with the nameplate capacity of Iowa’s utility-scale wind turbines, averaging 1.65MW with a 33% efficiency, or 4,813.2 MWh per turbine a year, and the retail price of electricity in the state, $89.2/MW, shows that the average return on a turbine is $429,377, or $286,224 per acre. At 3%, that’s a $8,600 payment to the landowner per acre, which compares to the highest margin on ethanol inputs of $420.00. ($8,600 is higher than that coming from the older turbines in place that have lower capacity.)

Wind farming can be a lucrative, reliable, and stable source of supplementary income for rural landowners. Iowa ranks second in terms of wind capacity and first in terms of share of electricity produced from wind in the nation, and has real potential to expand. The National Renewable Energy laboratory estimates that Iowa has a wind energy potential of 570,714 MW, so Iowa’s existing capacity of 10,190MW barely scratches the surface. It’s kind of “fun with statistics” to put that in percentage terms, but that’s an eye-popping growth rate of 5,600%. Consider the effects of that kind of sustainable growth around the country.

The Farm Bureau is not on board, and there’s a lot of pushback, often supported by claims that don’t hold up. And many complaints that have merit can be mitigated. But straight-up economics suggest that farming electrons is far more lucrative than farming ethanol, and would go a lot further toward bridging the rural divide.

Side note:
Birds, and don’t forget bats, do get killed flying into turbines, and about one third of those deaths are large, scarce raptors, including our beautiful arctic visitors who wing down every winter to course our fields. Sound conservation practices & wise siting can offset some of this, while providing local employment BTW, and slower blades are already lowering the counts.

Nevertheless, when we think about the raw numbers of bird deaths from different sources, we have to consider the status of each species involved, not easy to do. Estimates vary widely, and are changing as more solar and wind-generating facilities are built. There are also real concerns about differential detectability, as in, is it easier to find evidence of bird kills near urban buildings than by rural turbines? In any case, some of the highest current estimates for wind turbines are about 330,000 a year. Too many, yes, but dwarfed by annual deaths from building strikes, with current estimates ranging from hundreds of millions to a billion, fatalities from agricultural chemicals used in corn and soybean production, or feral and free-range cats, whose kills are in the billions.

Or take a page from Benjamin Sovacool who suggests we look at death rates in terms of energy produced. His preliminary findings
show wind farms responsible for 0.3 fatalities per GWh, with fossil-fuel powered stations responsible for 5.2 fatalities, a figure that, of course, includes the effects of climate change.

Not the last word, but a context. In the meantime, please keep your cats in, and support lights out programs to reduce building strikes.