US Banks Now Significantly More Capitalised

The US Federal Reserve Board has announced it has completed its review of the capital planning practices of the nation’s largest banks and reports that their ratios have more than doubled from 5.5 percent in the first quarter of 2009 to 12.5 per-cent in the fourth quarter of 2016.

Each US bank is listed, so we can make comparisons, unlike the “behind closed door” arrangements in Australia, where regulatory disclosure is so poor.

CCAR, in its seventh year, evaluates the capital planning processes and capital adequacy of the largest U.S.-based bank holding companies, including the firms’ planned capital actions such as dividend payments and share buybacks. Strong capital levels act as a cushion to absorb losses and help ensure that banking organizations have the ability to lend to households and businesses even in times of stress.

“I’m pleased that the CCAR process has motivated all of the largest banks to achieve healthy capital levels and most to substantially improve their capital planning processes,” said Governor Jerome H. Powell.

Figure A provides the aggregate ratio of common equity capital to risk-weighted assets for the firms in CCAR from 2009 through the fourth quarter of 2016. This ratio has more than doubled from 5.5 percent in the first quarter of 2009 to 12.5 per-cent in the fourth quarter of 2016. That gain reflects a total increase of more than $750 billion in common equity capital from the beginning of 2009 among these firms, bringing their total common equity capital to over $1.2 trillion in the fourth quarter of 2016.

The decline in the common equity ratio in the first quarter of 2015 resulted from the incorporation of risk-weighted assets calculated under the standardized approach under the capital rules that the Board adopted in 2013, which had a one-time effect of reducing all risk-based capital ratios. However, the aggregate common equity capital ratio of the 34 firms increased by around 65 basis points between the first quarter of 2015 and the fourth quarter of 2015. Previously, risk-weighted assets were calculated under a prior version of the capital rules.

In the aggregate, the 34 firms participating in CCAR 2017 have estimated that their common equity will remain near current levels between the third quarter of 2017 and the second quarter of 2018, based on their planned capital actions and net income projections under their baseline scenario.

When considering a firm’s capital plan, the Federal Reserve considers both quantitative and qualitative factors. Quantitative factors include a firm’s projected capital ratios under a hypothetical scenario of severe economic and financial market stress. Qualitative factors include the strength of the firm’s capital planning process, which incorporate risk management, internal controls, and governance practices that support the process.

This year, 13 of the largest and most complex banks were subject to both the quantitative and qualitative assessments. The 21 other firms in CCAR were subject only to the quantitative assessment. The Federal Reserve may object to a capital plan based on quantitative or qualitative concerns, and if it does, a firm may not make any capital distribution unless authorized by the Federal Reserve.

The Federal Reserve did not object to the capital plans of Ally Financial, Inc.; American Express Company; BancWest Corporation; Bank of America Corporation; The Bank of New York Mellon Corporation; BB&T Corporation; BBVA Compass Bancshares, Inc.; BMO Financial Corp.; CIT Group Inc.; Citigroup, Inc.; Citizens Financial Group; Comerica Incorporated; Deutsche Bank Trust Corporation; Discover Financial Services; Fifth Third Bancorp; Goldman Sachs Group, Inc.; HSBC North America Holdings, Inc.; Huntington Bancshares, Inc.; JP Morgan Chase & Co.; Keycorp; M&T Bank Corporation; Morgan Stanley; MUFG Americas Holdings Corporation; Northern Trust Corp.; The PNC Financial Services Group, Inc.; Regions Financial Corporation; Santander Holdings USA, Inc.; State Street Corporation; SunTrust Banks, Inc.; TD Group US Holdings LLC; U.S. Bancorp; Wells Fargo & Company; and Zions Bancorporation.

The Federal Reserve did not object to the capital plan of Capital One Financial Corporation, but is requiring the firm to submit a new capital plan within six months that addresses identified weaknesses in its capital planning process.

 

US Banks Pass Federal Reserve’s Stress Test

From Moody’s

Last Thursday, the US Federal Reserve published the results of the 2017 Dodd-Frank Act stress test (DFAST) for 34 of the largest US bank holding companies (BHCs), all of which exceeded the 4.5% minimum required common equity Tier 1 (CET1) capital ratio under the Fed’s severely adverse stress scenario, a credit positive.

This is the third consecutive year that all tested BHCs exceeded the Fed’s minimum requirement, and the median margin above the minimum also increased. However, for the first time, this year’s test incorporated the supplementary leverage ratio (SLR) for advanced-approach banks, which was more constraining for some of the banks.

DFAST considers how well banks withstand a severely adverse economic scenario, which is characterized as a severe global recession. The 2017 test scenario used modestly more favorable interest rates than in 2016 with a greater increase in rates and no negative short-term rates. The test incorporated a 6.5% peak-to-trough decline in US real gross domestic product, an increase in the unemployment rate to 10%, a 50% decline in equity prices through year-end 2017, and a 25% drop in home prices and a 35% decline in commercial real estate prices by 2019.

All 34 BHCs were subjected to this scenario, including new participant CIT Group Inc. In addition, the stress tests for eight of the 34 BHCs with substantial trading or processing operations were required to incorporate the sudden default of their largest loss-generating counterparty. The eight BHCs subject to the counterparty default component were Bank of America Corporation, The Bank of New York Mellon Corporation, Citigroup Inc., The Goldman Sachs Group, JPMorgan Chase & Co. Morgan Stanley, State Street Corporation, and Wells Fargo & Company. Finally, six of these eight BHCs with significant trading operations were also required to include a global market shock (Bank of New York Mellon Corporation and State Street Corporation were excluded from this global market shock scenario.)

On 28 June, the Fed will release the results of the Comprehensive Capital Analysis and Review (CCAR), which evaluates the BHCs’ capital plans, including dividends and stock repurchases, incorporating their DFAST results. The capital-planning processes of the large complex banks will also be publicly evaluated. Prior to the CCAR release, BHCs can reduce their planned capital distributions, commonly known as taking a “mulligan.” Our analysis of pre-provision net revenue declines and loan losses under the severely adverse scenario highlights still significant tail risks for DFAST participants. Nonetheless, we expect banks’ capital distribution requests to be more aggressive than in prior years, which will limit or negate improvement in their capital ratios.

ALL BANKS EXCEED MINIMUM REQUIRED CAPITAL IN THE SEVERELY ADVERSE SCENARIO

Exhibit 1 compares the minimum CET1 ratios of 34 participating BHCs under the Fed’s severely adverse scenario with their actual CET1 ratios reported at year-end 2016. The exhibit segments the BHCs into two groups: the 26 BHCs subject only to the severely adverse economic scenario (on the right), and the eight BHCs also subject to the additional global market shock and counterparty default components noted above (on the left). The minimum CET1 ratios of the eight large BHCs are all comfortably above the Fed’s 4.5% requirement despite being subjected to the additional stress components. The other 26 BHCs are also above the 4.5% requirement, although for many the margin is smaller than for the largest BHCs. The lowest minimum ratios were for Ally Financial Inc. at 6.6%, up from 6.1% in the 2016 test; and KeyCorp at 6.8%, up from 6.4% in 2016.

Even though all of the BHCs passed the 4.5% minimum threshold, many would still take sizeable capital hits under the Fed’s severely adverse scenario (Exhibit 2). The estimated declines in the BHCs’ CET1 ratios range from a high of 840 basis points (bp) for Morgan Stanley to a low of 210 bp for Santander Holdings USA, Inc.. Positively, the median of the 34 banks was narrower at 280 bp compared with 350 bp last year, indicating greater overall resilience to an economic shock. In its report, the Fed partly attributed this to lower losses from changes in the banks’ portfolio composition and risk characteristics.

SUPPLEMENTARY LEVERAGE RATIO IS A GREATER CONSTRAINT FOR SOME BANKS

The BHCs’ generally good results for stressed CET1 ratios in DFAST suggests that increased capital distributions are likely for the vast majority of institutions. However, CET1 is not the most constraining ratio for all banks. In particular, this year’s test for the first time incorporated the supplementary leverage ratio (SLR) for the advanced approach banks (Exhibit 3). Because the denominator of the SLR comprises average assets and off-balance sheet exposures, it tends to be much larger than the risk-weighted asset denominator of CET1, with the result that the banks’ margin above the 3% minimum SLR is smaller. Morgan Stanley had the lowest minimum SLR of 3.8%, which is likely to constrain its efforts to return more capital to shareholders. State Street and Goldman Sachs also had comparatively low minimum SLRs.

US Fannie Mae to increase its debt-to-income (DTI) ceiling

From Moody’s

On 9 June, Fannie Mae announced that it would increase its debt-to-income (DTI) ceiling for mortgage borrowers to 50% from 45%, effective on 29 July. The increase is credit positive for US state housing finance agencies (HFAs) because it will make mortgage loans more attainable for first-time homebuyers, thereby supporting HFA loan originations, which have been driving HFAs’ profitability margin growth.

HFAs are charged with providing and increasing the supply of affordable housing in their respective states, specifically for first-time homebuyers. The DTI ratio is often the barrier to home ownership for first-time borrowers, so increasing the DTI ratio ceiling will increase mortgage approvals, thereby increasing the pool of borrowers who may opt for HFA loans.

Over the past five years, HFAs have more than tripled their single-family loan originations to $20.6 billion in 2016 from $6.5 billion in 2012. This has been one of the primary drivers of HFA profit margin growth, which reached an all-time high of 17% in fiscal 2015 (see exhibit).

One of the challenges that HFAs face is a shrinking supply of single-family affordable housing inventory, which hinders first-time homebuyers and hampers HFA loan originations. The increase in the DTI ratio limits will help offset these challenges by expanding the pool of borrowers eligible for mortgages as well as allowing some borrowers to buy somewhat more expensive homes. Additionally, we expect HFAs to continue to maintain their high level of originations, which will support their strong margins.

Although Fannie Mae’s increase in the DTI ratio will ease financial standards for potential first-time homebuyers by allowing applicants to carry additional debt, the HFAs will not bear the credit risk of these lower credit quality borrowers. Loans approved by Fannie Mae are either securitized or sold to Fannie Mae and loan payments are guaranteed by Fannie Mae regardless of the underlying performance of the mortgage.

Fed Heading for Faster-than-Expected Normalisation

The Federal Reserve hiked its benchmark rate hike this week. Judging by their associated comments, Fitch Ratings says this reinforces the view that U.S. interest rates will normalise faster than financial markets expect.

The Fed on Wednesday raised the fed funds target rate for the third time in seven months, to 1.00%-1.25%. The Fed also announced that it expects to start phasing out full balance sheet reinvestment in 2017 and provided details on the modalities of doing so.

The rate increase and accompanying comments bolster our view that the fed funds rate is likely to normalise at 3.5% by 2020, and U.S. 10-year bond yields will rise back above 4%. These developments would mark a significant shift in the global interest rate environment.

Fitch believes the Fed is increasingly comfortable with its normalisation process and less data-dependent following recent inflation readings that have been slightly lower than consensus expectations (although they remain close to target). The interest rate hike showed the Fed was prepared to look through weak first quarter consumption and GDP and underlines Fed concerns about unemployment falling too far below its equilibrium rate. .

Our fed funds rate forecasts also reflect scepticism regarding the idea that the equilibrium (or “natural”) U.S. real interest rate has fallen close to zero. We think the fall in actual real rates is explained by the slowdown in potential GDP growth driven by demographics and weaker productivity growth, and by an elongated credit and monetary policy cycle. As this extended credit cycle comes to an end, Fitch believes the Fed will set rates according to its view of the U.S.’s long-term potential growth rate and its inflation target. This suggests the equilibrium nominal fed funds rate would be 3.5%-4% if real rates normalise in line with our estimate of U.S. potential growth at slightly below 2%.

The impact on bond yields will also be determined by how far the term premium rises from the current historically low level partly caused by the Fed’s Quantitative Easing (QE) programme. The Fed’s approach to balance sheet normalisation sees reinvestment only to the extent that maturities exceed pre-set caps. The caps will initially be set at low levels but will rise to maximum levels of USD30bn per month for Treasuries and USD20bn per month for agency debt and mortgage-backed securities. A return to a positive term premium of 50bp-100bp as the QE programme is unwound would see long-term U.S. bond yields normalise at 4%-5% given our estimates of the equilibrium Fed Funds rate.

Fed Hikes Benchmark Rate

The decision to raise the target range for the federal funds rate to 1 to 1-1/4 percent will put further upward pressure on interest rates in the capital markets. Further gradual increases should be expected, but there was no concrete news on the Fed’s strategy for reducing its bloated balance sheet, other than signalling an intention to throttle this back later in the year.

Information received since the Federal Open Market Committee met in May indicates that the labor market has continued to strengthen and that economic activity has been rising moderately so far this year. Job gains have moderated but have been solid, on average, since the beginning of the year, and the unemployment rate has declined. Household spending has picked up in recent months, and business fixed investment has continued to expand. On a 12-month basis, inflation has declined recently and, like the measure excluding food and energy prices, is running somewhat below 2 percent. Market-based measures of inflation compensation remain low; survey-based measures of longer-term inflation expectations are little changed, on balance.

Consistent with its statutory mandate, the Committee seeks to foster maximum employment and price stability. The Committee continues to expect that, with gradual adjustments in the stance of monetary policy, economic activity will expand at a moderate pace, and labor market conditions will strengthen somewhat further. Inflation on a 12-month basis is expected to remain somewhat below 2 percent in the near term but to stabilize around the Committee’s 2 percent objective over the medium term. Near term risks to the economic outlook appear roughly balanced, but the Committee is monitoring inflation developments closely.

In view of realized and expected labor market conditions and inflation, the Committee decided to raise the target range for the federal funds rate to 1 to 1-1/4 percent. The stance of monetary policy remains accommodative, thereby supporting some further strengthening in labor market conditions and a sustained return to 2 percent inflation.

In determining the timing and size of future adjustments to the target range for the federal funds rate, the Committee will assess realized and expected economic conditions relative to its objectives of maximum employment and 2 percent inflation. This assessment will take into account a wide range of information, including measures of labor market conditions, indicators of inflation pressures and inflation expectations, and readings on financial and international developments. The Committee will carefully monitor actual and expected inflation developments relative to its symmetric inflation goal. The Committee expects that economic conditions will evolve in a manner that will warrant gradual increases in the federal funds rate; the federal funds rate is likely to remain, for some time, below levels that are expected to prevail in the longer run. However, the actual path of the federal funds rate will depend on the economic outlook as informed by incoming data.

The Committee is maintaining its existing policy of reinvesting principal payments from its holdings of agency debt and agency mortgage-backed securities in agency mortgage-backed securities and of rolling over maturing Treasury securities at auction. The Committee currently expects to begin implementing a balance sheet normalization program this year, provided that the economy evolves broadly as anticipated. This program, which would gradually reduce the Federal Reserve’s securities holdings by decreasing reinvestment of principal payments from those securities, is described in the accompanying addendum to the Committee’s Policy Normalization Principles and Plans.

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.