Tag: USA

Tightening Is Toxic

From Moody’s.

The FOMC is expected to announce a 25 bp hike in the federal funds rate’s midpoint to 1.125% on Wednesday, June 14. Despite March 14’s 25 bp hiking of fed funds to a 0.875% midpoint, the 10-year Treasury yield fell from March 13’s 2.62% to a recent 2.20%. If the 10-year Treasury yield does not climb higher following June 14’s likely rate hike, the scope for future rate hikes should narrow.

At each of its end-of-quarter meetings, the FOMC updates its median projections for economic activity, inflation, and the federal funds rate. At the March 2017 meeting, the FOMC’s median projections for the year-end federal funds rate were 1.375% for 2017, 2.125% for 2018, and 3.0% for 2019 and beyond. However, the recent 10-year Treasury yield of 2.20% implicitly reflects doubts concerning whether the fed funds rate’s long-run equilibrium will be as high as 3.0%.

Perhaps, the FOMC will supply a lower long-run projection for fed funds. Nevertheless, in order to ward off speculative excess in the equity and corporate credit markets, the FOMC may wisely decide to overestimate the likely path of fed funds. The last thing the FOMC wants to do is help further inflate an already overvalued equity market.

Moreover, equity market overvaluation has pumped up systemic liquidity by enough to narrow high-yield bond spreads to widths that now under-compensate creditors for default risk. According to a multi-variable regression model that explains the high-yield bond spread in terms of (1) the VIX index, (2) the average EDF (expected default frequency) metric of non-investment grade companies, (3) the Chicago Fed’s national activity index, and (4) the three-month trend of nonfarm payrolls, the high-yield spread’s recent projected midpoint of 410 bp exceeds the actual spread of 380 bp. Moreover, after excluding the VIX index from the model, the predicted midpoint widens to 500 bp. The 90 bp jump by the predicted spread after excluding the VIX index is the biggest such difference for a sample that commences in 1996. The considerable downward bias imparted to the predicted high-yield spread by the recent ultra-low VIX of 10.2 points highlights the degree to which a richly priced and highly confident equity market has narrowed the high-yield bond spread. (Figure 1.)

High-yield spreads can narrow amid Fed rate hikes

There is absolutely nothing unusual about financial market conditions easing amid Fed rate hikes. When the fed funds’ midpoint was hiked from 0.125% to 0.375% in December 2015, the high-yield bond spread quickly swelled from a November 2015 average of 697 bp to February 2016’s 839 bp. However, though the midpoint is likely to reach 1.125% at the FOMC’s upcoming meeting of June 14, the high-yield spread has since narrowed to a recent 380 bp. (Figure 2.)

Early on, Fed rate hikes often were followed by thinner corporate bond yield spreads. For example at the start of the first tightening cycle of 1991-2000’s economic upturn, fed funds was hiked from year-end 1993’s 3.0% to 5.5% by year-end 1994. Despite that 2.5 percentage point hiking of fed funds, the high-yield bond spread managed to narrow from Q4-1993’s 439 bp to Q4-1994’s 350 bp. Not until the 10-year Treasury yield dipped under August 1998’s 5.5% fed funds rate did the high-yield spread widen beyond 600 bp.

It’s also worth recalling how the market value of US common stock soared higher by 19.4% annualized, on average, from January 1994 through March 2000 despite a hiking of fed funds from 3.00% to 5.75%. However, once fed funds reached 6.00% in March 2000, a grossly overvalued equity market finally crested and began a descent that would slash the market value of US common stock by a cumulative -43% from March 2000’s top to October 2002’s bottom. (Figure 3.)

The series of Fed rate hikes that occurred during 2002-2007’s recovery told a similar story. Notwithstanding a steep and rapid ascent by fed funds from the 1% of June 2004 to 5.25% by June 2006, the high-yield bond spread averaged an extraordinarily thin 340 bp from July 2004 through July 2007. At the same time, the VIX index averaged a very low 13.2 points despite the span’s 425 bp hiking of fed funds. Moreover, from June 2004 through October 2007, the market value of US common stock advanced by nearly 11% annualized, on average.

 

US Unemployment Rate Was 4.3 Percent in May 2017

From The US Bureau of Labor Statistics.

The US unemployment rate was 4.3 percent in May 2017, down from 4.8 percent in January. Among the unemployed, the number of job losers and persons who completed temporary jobs declined by 211,000 to 3.3 million in May, or 2.1 percent of the total labor force. In comparison, job leavers made up 0.5 percent of the labor force. These are people who quit or voluntarily ended their jobs and began searching for a new job.

Unemployed reentrants to the labor force made up 1.3 percent of the labor force in May 2017. Reentrants are people who previously worked but were out of the labor force before they began their job search. Unemployed new entrants made up 0.4 percent of the labor force in May.

These data are from the Current Population Survey. For more information, see “The Employment Situation — May 2017″

Why You Still Can’t Trust Your Financial Adviser

From Bloomberg.

Your new financial adviser has a well-decorated office, a firm handshake, and a bright smile. After an hourlong meeting, you leave with what you think is a state-of-the-art investment portfolio. You feel financially secure, taken care of.

It’s also possible you’ve made a huge mistake. The White House under President Barack Obama estimated that Americans lose $17 billion a year to conflicts of interest among financial advisers. Wall Street lobbying groups dispute that math—and they’re right to do so. The actual dollar amount is probably much higher.

The Fiduciary Rule, finalized under Obama and originally set to take effect earlier this year, seeks to cure this disconnect. All advisers were to be required to put clients first when handling retirement accounts, where the bulk of everyday Americans’ savings reside. But then Donald Trump won the election, and on his 15th day in office, the Republican president ordered the Department of Labor to reconsider the rule. His advisers echoed Wall Street arguments that tying the hands of advisers would limit investor choices, raise the cost of financial advice, and trigger a wave of litigation.

This Friday, the rule will take partial effect. Its future, though, remains deep in doubt. Many Republicans in Congress oppose it, and Labor Secretary Alexander Acosta has suggested that at the very least it be revised. Then last week, Trump’s newly appointed chairman of the Securities and Exchange Commission, Wall Street lawyer Jay Clayton, announced his agency would also seek comment on the topic, a process that could further threaten the rule’s survival.

While Washington wrestles with the fate of the Fiduciary Rule, the financial advice landscape remains supremely dangerous. Three professors recently analyzed a decade of disciplinary data on 1.2 million financial advisers. What they found is decidedly unpleasant:

  • At the average firm, 8 percent of advisers have a record of serious misconduct.
  • Nearly half of those 8 percent held on to their jobs after being caught. About half of the rest got jobs at other financial firms. In other words, a year after serious misconduct, about three-quarters of advisers found to have wronged clients are still working.
  • It gets worse: Some 38 percent of those misbehaving advisers later go on to hurt even more clients.
  • You might think bigger firms would be more diligent, but you’d be wrong. At some large firms, more than 15 percent of advisers have records of serious misconduct. The highest was Oppenheimer & Co., where 20 percent had such black marks. Oppenheimer responded to the study, first published a year ago, by saying it replaced managers and made changes to hiring, technology, and compliance procedures.
  • Predators typically seek out the weak, and financial advisers are no different: The study shows that those with misconduct records are concentrated in counties with fewer college graduates and more retirees.

Offering financial advice is enormously profitable, with U.S. investment firms achieving operating profit margins as high as 39 percent, according to the CFA Institute. And once advisers collect enough client assets, they can get huge bonuses for switching firms (and bringing their customers with them). Until recently, the going rate was a bonus of more than three times the annual fees and commissions the adviser brings in the door; an adviser with $200 million under management could expect a bonus of $6.6 million. (The threat of the Fiduciary Rule, however, caused bonus offers to plunge.)

Meanwhile, the total cost of bad advice to consumers—in higher fees and lower performance—is probably much higher than the $17 billion estimated by Obama’s Council of Economic Advisers. The CEA figured investors are losing an extra 1 percent annually on $1.7 trillion in individual retirement accounts controlled by conflicted advisers. But IRAs represent just an eighth of the $56 trillion in financial wealth Americans control, according to Boston Consulting Group.

Understanding the labor productivity and compensation gap

From The US Bureau of Statistics.

Increases in productivity have long been associated with increases in compensation for employees. For several decades beginning in the 1940s, productivity had risen in tandem with employees’ compensation. However, since the 1970s, productivity and compensation have steadily diverged.1 This trend, which will be referred to as the “productivity–compensation gap,” has received much scrutiny from both academics and policymakers alike.

Although research on the productivity–compensation gap has existed for some time, most work in this field has been conducted at the total nonfarm business sector or similar aggregate level.2 However, the Bureau of Labor Statistics (BLS) publishes a wealth of detailed industry-level labor productivity and compensation data. Industry data can be used to look at this topic from a fresh perspective in order to see what is driving trends in the broader economy. This Beyond the Numbers article studies underlying trends over the 1987–2015 period in 183 industries that are driving some of the widening gap between labor productivity and compensation observed in the nonfarm business sector.3 Most of the industries studied had increases in both labor productivity and compensation over the period studied; however, compensation lagged behind productivity in most cases.

Labor productivity, defined as real output per hour worked, is a measure of how efficiently labor is used in producing goods and services. There are many possible factors affecting labor productivity growth, including changes in technology, capital investment, capacity utilization, use of intermediate inputs, improved managerial skills or organization of production, and improved skills of the workforce. In this article, all references to labor productivity are labeled as productivity for ease of reference. In addition, labor compensation, a measure of the cost to the employer for securing the services of labor, is defined as an employee’s base wage and salary plus benefits. All references to labor compensation are on a per-hour basis and are adjusted for price change but are labeled as compensation for ease of reference.4 Measures of hours worked and compensation cover all workers including production, supervisory, self-employed, and unpaid family workers.

The productivity–compensation gap by sector and industry

To understand the productivity–compensation gap at an industry-level, it is helpful to first consider this relationship in different sectors of the economy. Each sector referenced below in chart 1 represents the combined activity of many individual industries that perform a similar type of activity.5

Productivity outpaced compensation for the 1987–2015 period in all sectors with significant industry coverage except for the mining sector. (See chart 1.) Some sectors including information, manufacturing, and retail trade exhibited major gaps between productivity and compensation, while other sectors such as accommodation and food services and other services showed slight differences. Compensation in chart 1 has been adjusted for inflation with the BLS Consumer Price Index (CPI).

As mentioned earlier, there have not been many studies of the productivity–compensation gap at the industry level. BLS industry productivity data allow for a deeper analysis by providing information on industries that make up each sector in the panels of chart 1. When examined at a detailed industry level, the average annual percent change in productivity outpaced compensation in 83 percent of 183 industries studied. (See chart 2.) The distance of each industry (represented by a dot) to the equal growth rates line indicates the size of the productivity–compensation gap. Industries above the equal growth rates line saw productivity outpace compensation and those below saw compensation outpace productivity. The largest differences between productivity and compensation occur in Information Technology- (IT) related industries such as computer and peripheral equipment manufacturing, and semiconductor and other electronic component manufacturing.

Does the type of price adjustment matter?

As mentioned above, compensation is calculated in real terms by adjusting nominal values to exclude changes in prices over time. The price indexes that are used to adjust dollar amounts for changes in prices are referred to as “deflators.”

The Consumer Price Index (CPI) is typically used to adjust compensation as it measures how the prices of a basket of consumer goods change over time. Thus, using the CPI shows how changes in workers’ purchasing power compare to productivity within their respective industries. In most cases, productivity gains did not equate to a proportional rise in workers’ purchasing power of goods and services. (See chart 2.)

However, the CPI might not be the most appropriate deflator to use when comparing compensation to productivity. Workers are compensated based on the value of goods and services produced, not on what they consume. Using an output price deflator, a measure of changes in prices for producers, instead of the CPI is an alternative that better aligns what is produced to the compensation that workers receive. Each industry has its own unique output deflator that matches the goods and services that are produced in that industry.6

If the output deflator is used to adjust compensation, a different story emerges. Chart 3 shows that the compensation workers are receiving is rising more in line with productivity than when CPI deflators are used to adjust compensation. The largest gaps from chart 2 shrink considerably once this adjustment is made. In fact, the size of the gap decreased in 87 percent of industries that previously showed productivity rising faster than compensation.

Charts 2 and 3 show an interesting contrast in employee compensation—employees are both consumers and producers. Using the CPI as a deflator is appropriate for analyzing the purchasing power of employees. However, from a producer perspective, using the output deflator is more appropriate for comparing the compensation workers receive for the goods and services they produce in their industry.

Components of the productivity–compensation gap

The gap between productivity and compensation can be divided into two components: (1) the difference between compensation adjusted by the CPI and by the output deflator, as detailed in the previous section and (2) the change in the labor share of income.7 The labor share of income measures how much revenue is going to workers as opposed to the other components of production—intermediate purchases and capital.8

Using the power generation and supply industry as an illustrative example, chart 4 shows how the overall gap in labor productivity and compensation within an industry can be divided into these two components. In this case, the decline in labor share and the difference in deflators contributed equally to productivity rising faster than compensation over the period studied. The composition of the gap, however, varies by sector and industry. For example, the software publishing industry posted a 42-percent decline in its labor share while the newspaper, periodical, book, and directory publishers industry experienced a 22-percent increase in its labor share. All 183 industries are affected differently by current economic trends, which would explain why the labor share and difference in deflators vary by industry.

Chart 5 shows the composition of the productivity–compensation gap at the sector level, which varied significantly. The difference in deflators contributed to the gap in seven of the sectors and was particularly large in the information, wholesale trade, and retail trade sectors. The change in labor’s share of income also contributed to the gap in seven of the sectors and was most important in explaining the gap in manufacturing. In the mining sector, an increase in the labor share led to hourly compensation growing faster than productivity. Both of these components are important in explaining the widespread existence of productivity–compensation gaps among U.S. industries.

The composition of the productivity—compensation gap at the detailed industry level shows 79 percent of the 183 detailed industries had an output deflator that increased slower than the CPI. This means that the rate of change in the productivity–-compensation gap grew faster when adjusted by the CPI than by an output deflator. This difference in deflators contributed to the overall gap between productivity and compensation. The median difference in growth rates between the output deflator and CPI was -0.6 percent per year.

The labor share of income declined in 77 percent of industries studied. This means that a growing share of income was going to factors of production other than employee compensation over the period studied. Factors of production include labor, capital (e.g. machinery, computers, and software), and intermediate purchases (purchased materials, services and energy that go into producing a final product). The median growth rate in the labor share of income was -0.6 percent per year. The median effect of the change in labor share was the same as that of the difference in deflators.

High productivity—wide compensation gaps

Industries with the largest productivity gains experienced the largest productivity–compensation gaps. (See chart 6.) This group of high productivity industries experienced huge technological advances during the IT boom. All of these industries saw compensation rise much more slowly than productivity over time. This was mainly due to the difference in deflators. The prices of the electronic components used in production for these industries fell substantially over time. This is in contrast to the CPI, which rose steadily over the same period. The change in labor’s share of income was a much smaller contributor to the gap for these industries but still declined in each one.

The strong correlation between productivity and the productivity–compensation gap was primarily due to the difference in deflators. The relationship between productivity and the change in labor share was much weaker, yet it still existed. The difference in deflators was the stronger effect among high productivity industries while the change in labor’s share of income was the stronger effect among most other industries.

What about the 17 percent of industries that saw compensation rise at least as fast as productivity?  These tended to be industries with low productivity growth or even productivity declines. (See chart 7.) The median change in productivity of these industries since 1987 was 0.4 percent per year. In contrast, the median change in productivity of industries that saw compensation rise slower than productivity was 1.9 percent.

Industries in which compensation grew the fastest relative to productivity include the water, sewage, and other systems industry; the golf courses and country clubs industry; and the newspaper, periodical, book, and directory publishers industry. The first industry had a large difference in deflators, the second industry saw a large increase in the labor share, and the third industry had a combination of these two components affecting the gap. All three of these industries had productivity declines over the period.

Why the decline in labor share?

Although the difference in deflators explains much of the gap, as mentioned earlier, the share of income going to workers has declined in 77 percent of industries since 1987.

This raises the question: if not for labor compensation, what were the revenues used for?9 Industries divide their income amongst three broad groups: intermediate purchases, capital, and labor compensation. Relative changes to both intermediate purchases and capital can affect labor compensation. It is likely that numerous factors are responsible for recent changes in the labor share.

Using the information sector as an example, we can see in chart 8 that some industries had significant declines in labor’s share of income while others had modest declines or even increases from 1987 to 2015. The largest declines in labor share were in newer, information technology-related industries such as software publishing and wireless telecommunications carriers, where labor share declined by 23 and 16 percentage points respectively. These industries also saw a large rise in output and productivity in this period. In contrast, labor share increased by 7 percentage points in the more established newspaper, periodical, book, and directory publishing industry, which declined in output and productivity.

It is important to note that the reason for declining labor share will likely vary significantly by industry. Here are some plausible explanations:

Globalization – Some of the income that might have gone to domestic workers is now going to foreign workers due to increased offshoring (i.e. the outsourcing of production and service activities to workers in other countries). This could have caused intermediate purchases to increase and labor compensation to decrease.10

Increased automation – It is possible that increased automation has been leading to an overall drop in the need for labor input. This would cause capital share to increase, relative to labor share as machines replace some workers.11

Faster capital depreciation – It is possible that the capital used by industries is depreciating at a faster rate in recent years than in the past. These assets include items such as computer hardware and software that are upgraded or replaced more frequently than machinery used in prior decades. This faster depreciation could require a higher capital share to cover upgrade and replacement costs.12

Change over time

The American economy is dynamic and changes over time. These changes appear in the productivity–compensation gap and its components. Chart 9 shows the components of the gap in each sector for the 1987–2000 and 2000–2015 periods. These periods roughly divide the data in half and use an important point in the business cycle as a breakpoint. Several observations can be made based on this chart.

First, the average productivity–compensation gap among the sectors grew faster in the first period than in the second. This was mainly due to changes in the utilities and wholesale trade sectors.

Second, the difference in deflators accounted for most of the gap on average in the first period, but had a smaller effect on average in the second period. This was particularly true in the utilities, manufacturing, wholesale trade, and transportation and warehousing sectors.

Third, there are large changes in labor’s share of income happening in the mining and manufacturing sectors during the two periods. The manufacturing sector’s drop in labor share during the 2000–2015 period was the largest decline observed in any sector and time period. Conversely, mining experienced the largest increase in labor share during the 2000–2015 period.

What about changes over time in detailed industries? Chart 10 shows how the components of the gap changed over time in industries with the highest employment in 2015. These 10 industries, ordered by employment, made up about 39 percent of the total employment of the 183 industries studied. The first three industries in the chart had component effects that flipped direction from one period to the next. Other general merchandise stores industry, which includes warehouse clubs and supercenters, had a very large drop in labor’s share of income in the first period and a much more modest drop in the second. Charts 9 and 10 show that the productivity and compensation dynamics of sectors, and the industries within them, are changing over time and will likely continue to do so as the economy evolves.

Choosing the right tools, focusing on industries

Studying the productivity and compensation trends of industries can help us better understand the productivity–compensation gap observed in the broader economy. It can show which industries have the largest gaps and the extent to which gaps are widespread. It is important to choose an appropriate deflator for compensation when comparing to productivity. Failing to do so can exaggerate the gap, especially for high productivity industries. A full 83 percent of industries studied here had productivity–compensation gaps when the same deflator was used for output and compensation. These gaps came from a declining labor share of income. Sectors with the strongest declines in labor share included manufacturing, information, retail trade, and transportation and warehousing. Although the causes of the decline in labor share are still unclear, focusing on industries may help to isolate and understand the causes unique to each industry.

1 See Susan Fleck, John Glaser, and Shawn Sprague, “The compensation–productivity gap: a visual essay,” Monthly Labor Review, January 2011, https://www.bls.gov/opub/mlr/2011/01/art3full.pdf.

2 For example, see Barry Bosworth and George L. Perry, “Productivity and Real Wages: Is There a Puzzle?” Brookings Papers on Economic Activity, 1:1994, https://www.brookings.edu/bpea-articles/productivity-and-real-wages-is-there-a-puzzle/.

3 The detailed industries in this article include all published industries at the 4-digit NAICS level as well as some industries at the 3-, 5-, and 6 digit level for cases where the 4 digit is not published. There is an exception for NAICS industry 71311, which is used in place of NAICS 7131. This was done because NAICS 71311 is published back to 1987 while NAICS 7131 is only published back to 2007 and the more detailed industry makes up most of the 4-digit industry.

4 The measure of real hourly compensation used in this article differs from the labor compensation measure typically published for the industries examined. Measures of labor compensation typically published are not adjusted for inflation or on a per-hour basis. The measures of real hourly compensation calculated here are available upon request.

5 The sectors in this article are 2-digit NAICS sectors. The detailed industries, defined in the third endnote, are components of these sectors.

6 Industry output deflators are mostly based on Producer Price Indexes (PPIs) unique to each industry. PPIs measure price change from the perspective of the seller. Consumer Price Indexes (CPIs) for individual products are used to deflate output in some industries (e.g. industries in retail trade).

7 The rates of change calculated in this article are compound annual growth rates. One must use logarithmic changes for the components of the gap between productivity and real hourly compensation to equal the total gap in all cases. For most industries, the components sum up to the total gap using either method but may differ by 0.1 percent due to rounding.

8 Intermediate purchases include all of the purchased materials, services, and energy that go into producing a final product. Measures of the labor share included in this analysis are not directly comparable with the labor share measures of the nonfarm business sector, business sector, or nonfinancial corporate sector. The difference has to do with how output is measured at the industry and major sector levels. Measures at the industry level exclude intra-industry transactions but include all other intermediate purchases. Output at the major sector level is constructed using a value-added concept and subtracts out all intermediate purchases. Thus, industry output can be divided between labor, capital, and intermediate purchases, whereas major sector output can only be divided between labor and capital.

9 For another BLS discussion of the labor share of income, see Michael D. Giandrea and Shawn A. Sprague, “Estimating the U.S. Labor Share,” Monthly Labor Review, February 2017, https://www.bls.gov/opub/mlr/2017/article/estimating-the-us-labor-share.htm.

10 See Michael W. L. Elsby, Bart Hobijn, and Aysegul Sahin, “The Decline of the U.S. Labor Share,” Brookings Papers on Economic Activity, Fall 2013, https://www.brookings.edu/wp-content/uploads/2016/07/2013b_elsby_labor_share.pdf. A number of possible explanations for the declining labor share were examined. Analysis showed that offshoring of labor-intensive work is a leading potential explanation.

11 See Maya Eden and Paul Gaggl, “On the Welfare Implications of Automation,” Policy Research Working Paper, No. 7487, World Bank Group, November 2015, http://documents.worldbank.org/curated/en/2015/11/25380579/welfare-implications-automation. Some of the decline in labor’s share of income can be linked to an increase in the income share of information and communication technology (ICT). ICT effects may have had a larger impact on the distribution of income among workers.

12 See Dean Baker, “The Productivity to Paycheck Gap: What the Data Show,” Center for Economic and Policy Research, April 2007, http://cepr.net/publications/reports/the-productivity-to-paycheck-gap-what-the-data-show This is one of many articles that documents the fact that a rising share of GDP goes to replace worn-out capital goods. Income going towards replacing these goods should not be expected to raise living standards.

The Financial Challenges of Small Businesses

From “On The Economy Blog”

More than 60 percent of small businesses faced financial challenges in the past year, according to the USA 2016 Small Business Credit Survey.

The survey, which was a collaboration of all 12 Federal Reserve banks, provides an in-depth look at small business performance and debt. This report focuses on employer firms, or those with at least one full- or part-time employee.1 When looking at the financial challenges of small businesses, the report covered the second half of 2015 through the second half of 2016.

Financial Challenges and How They Were Addressed

Among all firms, 61 percent reported facing financial challenges over this time period. Financial challenges included:

  • Credit availability or securing funds for expansion
  • Paying operating expenses
  • Making payments on debt
  • Purchasing inventory or supplies to fulfill contracts

Firms with smaller annual revenue were more likely to experience financial challenges. Of firms with $1 million or less, 67 percent reported facing financial challenges, compared to only 47 percent of firms with more than $1 million.

The figure below shows the breakdown of which financial challenges were most prevalent among small businesses.

financial challenges

The survey also asked small businesses how they addressed these issues. Their responses are captured in the figure below. (It should be noted that respondents could also answer “unsure” and “other,” and those responses are not captured below.)

small business actions

Notes and References

1 This does not include self-employed or firms where the owner is the only employee.

US Employment Data Mixed In May

According to the US Bureau of Labor Statistics, total nonfarm payroll employment increased by 138,000 in May, and the unemployment rate was little changed at 4.3 percent. Job gains occurred in health care and mining. The labor force participation rate declined by 0.2 percentage point to 62.7 percent.

The unemployment rate, at 4.3 percent, and the number of unemployed persons, at 6.9 million, changed little in May. Since January, the unemployment rate has declined by 0.5 percentage point, and the number of unemployed has decreased by 774,000.

Among the unemployed, the number of job losers and persons who completed temporary jobs declined by 211,000 to 3.3 million in May. The number of long-term unemployed (those jobless for 27 weeks or more) was essentially unchanged over the month at 1.7 million and accounted for 24.0 percent of the unemployed.

The labor force participation rate declined by 0.2 percentage point to 62.7 percent in May but has shown no clear trend over the past 12 months. The employment-population ratio edged down to 60.0 percent in May.

The number of persons employed part time for economic reasons (sometimes referred to as involuntary part-time workers) was little changed at 5.2 million in May. These individuals, who would have preferred full-time employment, were working part time because their hours had been cut back or because they were unable to find a full-time job.

In May, 1.5 million persons were marginally attached to the labor force, down by 238,000 from a year earlier. (The data are not seasonally adjusted.) These individuals were not in the labor force, wanted and were available for work, and had looked for a job sometime in the prior 12 months. They were not counted as unemployed because they had not searched for work in the 4 weeks preceding the survey.

Among the marginally attached, there were 355,000 discouraged workers in May, down by 183,000 from a year earlier. (The data are not seasonally adjusted.) Discouraged workers are persons not currently looking for work because they believe no jobs are available for them. The remaining  1.1 million persons marginally attached to the labor force in May had not searched for work for reasons such as school attendance or family responsibilities.

Establishment Survey Data

Total nonfarm payroll employment increased by 138,000 in May, compared with an average monthly gain of 181,000 over the prior 12 months. In May, job gains occurred in health care and mining.

Employment in health care rose by 24,000 in May. Hospitals added 7,000 jobs over the month, and employment in ambulatory health care services continued to trend up (+13,000). Job growth in health care has averaged 22,000 per month thus far in 2017, compared with an average monthly gain of 32,000 in 2016.

Mining added 7,000 jobs in May. Employment in mining has risen by 47,000 since reaching a recent low point in October 2016, with most of the gain in support activities for mining.

In May, employment in professional and business services continued to trend up (+38,000). The industry has added an average of 46,000 jobs per month thus far this year, in line with the average monthly job gain in 2016.

Employment in food services and drinking places also continued to trend up in May (+30,000) and has grown by 267,000 over the past 12 months.

Employment in other major industries, including construction, manufacturing, wholesale trade, retail trade, transportation and warehousing, information, financial activities, and government, showed little change over the month.

The average workweek for all employees on private nonfarm payrolls was unchanged at 34.4 hours in May. In manufacturing, the workweek also was unchanged at 40.7 hours, while overtime edged up by 0.1 hour to 3.3 hours. The average workweek for production and nonsupervisory employees on private nonfarm payrolls edged down by 0.1 hour to 33.6 hours.

In May, average hourly earnings for all employees on private nonfarm payrolls rose by 4 cents to $26.22. Over the year, average hourly earnings have risen by 63 cents, or 2.5 percent. In May, average hourly earnings of private-sector production and nonsupervisory employees increased by 3 cents to $22.00.

The change in total nonfarm payroll employment for March was revised down from +79,000 to +50,000, and the change for April was revised down from +211,000 to +174,000. With these revisions, employment gains in March and April combined were 66,000 less than previously reported. Monthly revisions result from additional reports received from businesses and government agencies since the last published estimates and from the recalculation of seasonal factors. Over the past 3 months, job gains have averaged 121,000 per month.

Biggest Threats to Dollar’s Global Supremacy are at Home

From FitchRatings.

The US dollar will almost certainly remain the world’s most important reserve currency for the foreseeable future, as no other offers the same set of advantages to money managers, including central banks, or is as deeply embedded in the global financial system. The primary cost to the US is surrendered competitiveness due to dollar appreciation, but lower interest rates and unrivalled government access to funding bestow considerable benefits, ultimately supporting the sovereign’s ‘AAA’ rating.

 

The dollar dominates global bond markets, central bank foreign reserve holdings, international trade invoicing and cross-border lending. It is the standard currency used for commodity and other prices, and is the preeminent safe-haven asset and preferred store of value in times of financial turmoil. Crucially, the dollar is underpinned by the fact that the US Treasury market is the world’s largest and most liquid for risk-free assets, and the Federal Reserve operates independently of government with respect to the market, and in implementing policy more broadly.

The dollar’s role is so widespread that its supremacy is self-reinforcing. The additional costs and/or inconvenience of switching to another currency for transactions normally conducted in dollars create a high degree of inertia, making it difficult for other currencies to gain traction.

Calls for the dollar’s displacement were relatively infrequent — though not entirely absent — when US monetary policy was exceptionally accommodative in the aftermath of the global financial crisis. That changed in mid-2013 when the Federal Reserve announced it would begin to slow its asset purchases, causing considerable turmoil in emerging markets (the “taper tantrum”) and appeals to the Fed for greater consideration to be given to the international implications of its policy decisions.

The Fed now appears poised not only to continue with policy interest rate hikes that began in December 2015, but also to consider the pace and magnitude of eventual balance-sheet reductions. Dollar funding is already costlier in markets outside the US, and has been for several years, as reflected in elevated cross-currency basis spreads for several currencies versus the dollar. If they rise further, as they may when Fed balance-sheet reduction draws nearer, there will again be concerns about global stresses associated with Fed tightening and inevitable suggestions that the dollar’s hegemony be somehow curtailed.

Realistic, immediately available alternatives to the dollar are limited. It is important to note, however, that the dollar is not alone either as a reserve currency or in many of its other global roles; it is just the biggest player. Other recognised reserve currencies (tracked by IMF data) are the euro, Japanese yen, pound sterling, Swiss franc, Australian and Canadian dollars
and Chinese renminbi.

In most instances, financial markets in countries that have reserve currencies are far too small to pose a threat to the dominance of the dollar. The most obvious candidate to replace the dollar is the euro, given the size and depth of euro-denominated capital markets as well as the credible focus of the European Central Bank on controlling inflation. However, for at least as long as the currency zone is plagued by lingering existential risks amid questions over possible member withdrawals, it will not be in a position to overtake the dollar. The renminbi is growing rapidly in trade settlement, but neither it nor the yen offer truly risk-free assets given their sovereign ratings, and China seems some distance from having an open capital account and fully internationalised currency even if it were rated higher.

The lack of a ready substitute, however, does not mean the dollar’s current position is entirely assured. Perhaps the most plausible scenario for the dollar being meaningfully displaced does not begin with the emergence of a viable alternative, but rather it being undermined at home.

Two pieces of legislation currently working their way through Congress are the Federal Reserve Transparency Act (FRTA) and the Financial Choice Act (FCA). The first would allow the Government Accountability Office to audit the monetary policy decisions of the Fed and make subsequent recommendations for administrative or legislative actions. The second would restrict the Fed’s ability to provide financial sector support to avert or address a crisis, and empower a commission to review and recommend changes to the Fed’s operations, as well as to consider a rules-based rather than discretionary monetary policy framework.

It is the unambiguous intention of these legislative initiatives to curtail the independence of the Fed and allow for greater congressional oversight of monetary policy as well as the Fed’s regulatory decisions and interventions related to financial stability. If implemented, the proposals would diminish the appeal of the dollar as a reserve currency over time. Investors
considering dollar assets and other dollar exposures would weigh the risk of political interference in monetary policy decisions and the possibility of the Fed’s remit being broadened to include congressional priorities such as indirect funding of infrastructure investment. There may also be concerns about episodes of financial sector stress being deeper and more prolonged if the Fed’s policy response options were explicitly limited.

Parties in favour of the FRTA and FCA might argue that the risks identified by those concerned about the Fed’s independence — and, incidentally, the dollar’s global role — are, in fact, the purpose of the proposed legislation, and that the overall economic interests of the US would be better served by their implementation. The debate is unlikely to end soon no matter the fate of the FRTA and FCA. Either way, the dollar is set to remain the world’s most important reserve currency, a position it is likely to hold for some time.

That Other Bubble

From Bloomberg Technology

The financial world has been obsessed lately with debating whether we’re in a different sort of tech bubble, this time among public companies. One stock market strategist recently warned of “tech mania.”

The talk about tech stock froth is based on three interrelated facts: The performance of the U.S. stock market is more dependent on technology companies than any time in more than 15 years. Investors are willing to pay more to own these shares. And they’re crowded mostly into the same handful of big tech companies such as Amazon and Google parent company Alphabet.

Putting those data points together, some market watchers are worried that what has gone up in tech must inevitably come down — and take the whole ebullient stock market down with it.

It’s easy to understand why the finance world can’t stop talking about technology stocks. In the S&P 500 index, the sector accounts for about one quarter of the total market value of the equity benchmark. That is the largest share since 2001, according to Bloomberg data. (It’s worth noting that the S&P 500 doesn’t classify Amazon as a tech company, which is nuts. If the e-commerce giant took its rightful place, even more of the index would be tied to technology.)

Plus, money is pouring into the sector at a rate not seen in 15 years, according to research from Bank of America Merrill Lynch. And while investors aren’t paying stratospheric prices, as they did in the late 90s dot-com bubble, values of a broad collection of tech companies relative to their profits are higher than they have been since early 2004, Pavilion Global Markets calculated last week.

When you start mentioning things that haven’t happened to tech stocks since the early 2000s, you know we are living in odd times.

Every time there is tech froth, people will argue why this is or isn’t different than 1999. This isn’t 1999. But that doesn’t necessarily mean the exploding value of companies such as Apple, Netflix, Nvidia and Amazon is sustainable. I won’t try to predict the future, but the debate surely shows the outsized power of tech firms to drive global growth and equity markets.

Bubble talk isn’t likely to go away. Apple in May became the first U.S. company to top $800 billion in the total value of its stock. Now there’s a race to become the first company to sustain $1 trillion or more in market capitalization. Will it be Apple, or maybe Alphabet or Amazon? No non-technology companies, apart from Saudi Arabia’s mega government oil company, have a shot at the moment.

A Case for Shrinking the Fed’s Balance Sheet

Time for the FED to shrink it’s balance sheets which has grown from US$800 billion in 2006 to about US$4.5 trillion now, according to Federal Reserve Bank of St. Louis President James Bullard, in an article in the second quarter 2017 issue of The Regional Economist.

As a consequence of the financial crisis, Great Recession of 2007-09 and sluggish economy that persisted for several years beyond that, the Federal Open Market Committee (FOMC) took extraordinary actions to stimulate the economy and promote the recovery. By December 2008, for instance, the FOMC had reduced the federal funds rate target (i.e., the policy rate) to near zero—exhausting its conventional monetary policy tool. With the economy still weak and to guard against deflation, the FOMC turned to unconventional monetary policy, including three rounds of large-scale asset purchases from late 2008 to late 2014. The purchases were primarily of longer-term Treasuries and mortgage-backed securities. This policy, better known as quantitative easing (QE), led to an expansion of the Fed’s balance sheet.

Fast forward to today. The Fed’s goals for employment and inflation have essentially now been met. The FOMC’s focus has shifted to monetary policy normalization, including increasing the policy rate, which it has done three times since December 2015. With this return to more conventional monetary policy now underway, the question of how and when to begin normalizing the Fed’s balance sheet is timely.

As a result of the three QE programs, the Fed’s balance sheet increased from about $800 billion in 2006 to about $4.5 trillion today. The FOMC’s reinvestment policy, which includes replacing maturing securities with new securities, is keeping the balance sheet at its current size. If the FOMC wanted to begin shrinking the balance sheet, the most natural step would be to end the reinvestment policy. Ending reinvestments would lead to a gradual reduction in the size of the balance sheet over several years.

In recent months, I have been an advocate of ending reinvestments for two main reasons. One is that current monetary policy is distorting the yield curve. While actual and projected increases in the policy rate are putting upward pressure on short-term interest rates, maintaining a large balance sheet is putting downward pressure on medium- and long-term interest rates. Of course, interest rates are volatile and are affected by many factors, but raising the policy rate would normally tend to raise interest rates all along the yield curve. Therefore, a more natural way to normalize interest rates would be to allow all of them to increase together.

My second argument for ending reinvestments is to allow for more balance-sheet “policy space” in the future. In other words, the FOMC should begin reducing the balance sheet now in case it needs to add to the balance sheet during a future recession. If, at that time, the policy rate is once again reduced to zero, the FOMC may want to consider using QE again. By having a smaller balance sheet in that situation, the FOMC would have more “policy space” to buy assets, if necessary.

Although I am in favor of ending reinvestments, some may argue that the “taper tantrum” of the summer of 2013 calls for caution in doing so. The FOMC’s QE3 program was ongoing at that time, and the taper tantrum was related to communications about the pace of asset purchases. In May of that year, then-Chairman Ben Bernanke commented to a congressional committee that he thought the pace of asset purchases might be slowed at future meetings. That message was reinforced by the results of the June meeting, when the FOMC authorized Bernanke to announce a road map for a possible decision to begin tapering later in the year. Financial markets viewed this announcement as relatively hawkish and reacted accordingly. (For example, longer-term U.S. interest rates increased.) At the September meeting, the FOMC postponed the decision, which financial markets viewed as relatively dovish. When the FOMC finally decided in December to begin tapering the pace of asset purchases, global financial markets did not react very much.

In my view, the taper tantrum was a communications issue—not an issue about actual changes in the size of the balance sheet. Similarly, communication will be important in the current situation. If the FOMC properly communicates the end of the reinvestment policy, I would expect the experience to be similar to December 2013, when there was no appreciable impact on global financial markets because they had already anticipated the changes in the Fed’s policy.

Some have suggested waiting to end the reinvestment policy until the FOMC has decided on the final size of the balance sheet. But few would argue that today’s $4.5 trillion is appropriate in the long run.2 Given that balance sheet normalization will take years, the FOMC could continue to debate the final size after reinvestment ends. In my view, it would be prudent to begin shrinking the balance sheet and making progress toward the eventual goal. The balance sheet policy was designed to cope with a near-zero policy rate, but now that the policy rate has increased, having such a large balance sheet is less critical.

Bubbles and the US Market In A Time Of Easy Money

From The Daily Reckoning.

The key to bubble analysis is to look at what’s causing the bubble. Based on data going back to the 1929 crash, this current bubble looks like a particular kind that can produce large, sudden losses for investors.

This chart shows the Shiller Cyclically Adjusted PE Ratio (CAPE) from 1880-2017. Over this 137-year period, the mean ratio is 16.75, media ratio is 16.12, low is 4.78 (Dec 1920) and high is 44.19 (Dec 1999). Right now the 29.45 ratio is above the level of the Panic of 2008, and about equal to the level of the market crash that started the Great Depression.

My preferred metric is the Shiller Cyclically Adjusted PE Ratio or CAPE. This particular PE ratio was invented by Nobel Prize-winning economist Robert Shiller of Yale University.

CAPE has several design features that set it apart from the PE ratios touted on Wall Street. The first is that it uses a rolling ten-year earnings period. This smooths out fluctuations based on temporary psychological, geopolitical, and commodity-linked factors that should not bear on fundamental valuation.The second feature is that it is backward-looking only. This eliminates the rosy scenario forward-looking earnings projections favored by Wall Street.

The third feature is that that relevant data is available back to 1870, which allows for robust historical comparisons.

The chart below shows the CAPE from 1870 to 2017. Two conclusions emerge immediately. The CAPE today is at the same level as in 1929 just before the crash that started the Great Depression. The second is that the CAPE is higher today than it was just before the Panic of 2008.

Neither data point is definitive proof of a bubble. CAPE was much higher in 2000 when the dot.com bubble burst. Neither data point means that the market will crash tomorrow.

But today’s CAPE ratio is 182% of the median ratio of the past 137-years.

Given the mean-reverting nature of stock prices, the ratio is sending up storm warnings even if we cannot be sure exactly where and when the hurricane will come ashore.

With the likelihood of a bubble clear, we can now turn to bubble dynamics. The analysis begins with the fact that there are two distinct types of bubbles.

Some bubbles are driven by narrative, and others by cheap credit. Narrative bubbles and credit bubbles burst for different reasons at different times. The difference is critical in knowing what to look for when you time bubbles, and for understanding who gets hurt when they burst.

A narrative-driven bubble is based on a story, or new paradigm, that justifies abandoning traditional valuation metrics. The most famous case of a narrative bubble is the late 1960s, early 1970s “Nifty Fifty” list of fifty stocks that were considered high growth with nowhere to go but up.

The Nifty Fifty were often referred to as “one decision” stocks because you would just buy them and never sell. No further thought was required. Of course, the Nifty Fifty crashed with the overall market in 1974 and remained in an eight-year bear market until a new bull market began in 1982.

The dot.com bubble of the late 1990s is another famous example of a narrative bubble. Investors bid up stock prices without regard to earnings, PE ratios, profits, discounted cash flow or healthy balance sheets.

All that mattered were “eyeballs,” “clicks,” and other superficial internet metrics. The dot.com bubble crashed and burned in 2000. The NASDAQ fell from over 5,000 to around 2,000, then took sixteen years to regain that lost ground before recently making new highs. Of course, many dot.com companies did not recover their bubble valuations because they went bankrupt, never to be heard from again.

The credit-driven bubble has a different dynamic than a narrative-bubble. If professional investors and brokers can borrow money at 3%, invest in stocks earning 5%, and leverage 3-to-1, they can earn 6% returns on equity plus healthy capital gains that can boost the total return to 10% or higher. Even greater returns are possible using off-balance sheet derivatives.

Credit bubbles don’t need a narrative or a good story. They just need easy money.

A narrative bubble bursts when the story changes. It’s exactly like The Emperor’s New Clothes where loyal subjects go along with the pretense that the emperor is finely dressed until a little boy shouts out that the emperor is actually naked.

Psychology and behavior change in an instant.

When investors realized in 2000 that Pets.com was not the next Amazon but just a sock-puppet mascot with negative cash flow, the stock crashed 98% in 9 months from IPO to bankruptcy. The sock-puppet had no clothes.

A credit bubble bursts when the credit dries up. The Fed won’t raise interest rates just to pop a bubble — they would rather clean up the mess afterwards that try to guess when a bubble exists in the first place.

But the Fed will raise rates for other reasons, including the illusory Phillips Curve that assumes a tradeoff between low unemployment and high inflation, currency wars, inflation or to move away from the zero bound before the next recession. It doesn’t matter.

Higher rates are a case of “taking away the punch bowl” and can cause a credit bubble to burst.

The other leading cause of bursting credit bubbles is rising credit losses. Higher credit losses can emerge in junk bonds (1989), emerging markets (1998), or commercial real estate (2008).

Credit crack-ups in one sector lead to tightening credit conditions in all sectors and lead in turn to recessions and stock market corrections.

What type of bubble are we in now? What signs should investors look for to gauge when this bubble will burst?

My starting hypothesis is that we are in a credit bubble, not a narrative bubble. There is no dominant story similar to the Nifty Fifty or dot.com days. Investors do look at traditional valuation metrics rather than invented substitutes contained in corporate press releases and Wall Street research. But even traditional valuation metrics can turn on a dime when the credit spigot is turned off.

Milton Friedman famously said the monetary policy acts with a lag. The Fed has force-fed the economy easy money with zero rates from 2008 to 2015 and abnormally low rates ever since. Now the effects have emerged.

On top of zero or low rates, the Fed printed almost $4 trillion of new money under its QE programs. Inflation has not appeared in consumer prices, but it has appeared in asset prices. Stocks, bonds, commodities and real estate are all levitating above an ocean of margin loans, student loans, auto loans, credit cards, mortgages, and their derivatives.

Now the Fed is throwing the gears in reverse. They are taking away the punchbowl.

The Fed has raised rates three times in the past sixteen months and is on track to raise them three more times in the next seven months. In addition, the Fed is preparing to do QE in reverse by reducing its balance sheet and contracting the base money supply. This is called quantitative tightening or QT, which I’ve discussed recently.

Credit conditions are already starting to affect the real economy. Student loan losses are skyrocketing, which stands in the way of household formation and geographic mobility for recent graduates. Losses are also soaring on subprime auto loans, which has put a lid on new car sales. As these losses ripple through the economy, mortgages and credit cards will be the next to feel the pinch.