Unconventional Monetary Policy and Valuations of Risk Financial Assets, Recovery without Hiring, Ten Million Fewer Full-time Jobs, Youth and Middle Age Unemployment, United States International Trade, United States Producer Prices, Collapse of United States Dynamism of Income Growth and Employment Creation, World Cyclical Slow Growth and Global Recession Risk: Part I

 

Unconventional Monetary Policy and Valuations of Risk Financial Assets, Recovery without Hiring, Ten Million Fewer Full-time Jobs, Youth and Middle Age Unemployment, United States International Trade, United States Producer Prices, Collapse of United States Dynamism of Income Growth and Employment Creation, World Cyclical Slow Growth and Global Recession Risk

Carlos M. Pelaez

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017

IA Unconventional Monetary Policy and Valuations of Risk Financial Assets

I Recovery without Hiring

IA1 Hiring Collapse

IA2 Labor Underutilization

ICA3 Ten Million Fewer Full-time Jobs

IA4 Theory and Reality of Cyclical Slow Growth Not Secular Stagnation: Youth and Middle-Age Unemployment

II United States International Trade

IIB United States Producer Prices

II IB Collapse of United States Dynamism of Income Growth and Employment Creation

III World Financial Turbulence

IIIA Financial Risks

IIIE Appendix Euro Zone Survival Risk

IIIF Appendix on Sovereign Bond Valuation

IV Global Inflation

V World Economic Slowdown

VA United States

VB Japan

VC China

VD Euro Area

VE Germany

VF France

VG Italy

VH United Kingdom

VI Valuation of Risk Financial Assets

VII Economic Indicators

VIII Interest Rates

IX Conclusion

References

Appendixes

Appendix I The Great Inflation

IIIB Appendix on Safe Haven Currencies

IIIC Appendix on Fiscal Compact

IIID Appendix on European Central Bank Large Scale Lender of Last Resort

IIIG Appendix on Deficit Financing of Growth and the Debt Crisis

IIIGA Monetary Policy with Deficit Financing of Economic Growth

IIIGB Adjustment during the Debt Crisis of the 1980s

Executive Summary

Contents of Executive Summary

ESI Financial “Irrational Exuberance,” Increasing Interest Rate and Exchange Rate Risk, Duration Dumping, Competitive Devaluations, Steepening Yield Curve and Global Financial and Economic Risk

ESII Valuations of Risk Financial Assets

ESIII Unconventional Monetary Policy and Valuations of Risk Financial Assets

ESIV Recovery without Hiring

ESV Ten Million Fewer Full-time Jobs

ESVI Youth and Middle Age Unemployment

ESVII United States International Trade

ESVIII Collapse of United States Dynamism of Income Growth and Employment Creation

ESI “Financial “Irrational Exuberance,” Increasing Interest Rate and Exchange Rate Risk, Duration Dumping, Competitive Devaluations, Steepening Yield Curve and Global Financial and Economic Risk. The International Monetary Fund (IMF) provides an international safety net for prevention and resolution of international financial crises. The IMF’s Financial Sector Assessment Program (FSAP) provides analysis of the economic and financial sectors of countries (see Pelaez and Pelaez, International Financial Architecture (2005), 101-62, Globalization and the State, Vol. II (2008), 114-23). Relating economic and financial sectors is a challenging task for both theory and measurement. The IMF provides surveillance of the world economy with its Global Economic Outlook (WEO) (http://www.imf.org/external/ns/cs.aspx?id=29), of the world financial system with its Global Financial Stability Report (GFSR) (http://www.imf.org/external/pubs/ft/gfsr/index.htm) and of fiscal affairs with the Fiscal Monitor (http://www.imf.org/external/ns/cs.aspx?id=262). There appears to be a moment of transition in global economic and financial variables that may prove of difficult analysis and measurement. It is useful to consider a summary of global economic and financial risks, which are analyzed in detail in the comments of this blog in Section VI Valuation of Risk Financial Assets, Table VI-4.

Economic risks include the following:

1. China’s Economic Growth. The National People’s Congress of China in Mar 2016 is reducing the GDP growth target to the range of 6.5 percent to 7.0 percent in guiding stable market expectations (http://news.xinhuanet.com/english/photo/2016-03/05/c_135157171.htm). President Xi Jinping announced on Nov 3, 2015 that “For China to double 2010 GDP and the per capita income of both urban and rural residents by 2010, annual growth for the 2016-2020 period must be at least 6.5 percent,” as quoted by Xinhuanet (http://news.xinhuanet.com/english/2015-11/03/c_134780377.htm). China lowered the growth target to approximately 7.0 percent in 2015, as analyzed by Xiang Bo, writing on “China lowers 2015 economic growth target to around 7 percent,” published on Xinhuanet on Mar 5, 2015 (http://news.xinhuanet.com/english/2015-03/05/c_134039341.htm). China had lowered its growth target to 7.5 percent per year. Lu Hui, writing on “China lowers GDP target to achieve quality economic growth, on Mar 12, 2012, published in Beijing by Xinhuanet (http://news.xinhuanet.com/english/china/2012-03/12/c_131461668.htm), informs that Premier Jiabao wrote in a government work report that the GDP growth target will be lowered to 7.5 percent to enhance the quality and level of development of China over the long term. The Third Plenary Session of the 18^th Central Committee of the Communist Party of China adopted unanimously on Nov 15, 2013, a new round of reforms with 300 measures (Xinhuanet, “China details reform decision-making process,” Nov 19, 2013 http://news.xinhuanet.com/english/china/2013-11/19/c_125722517.htm). Growth rates of GDP of China in a quarter relative to the same quarter a year earlier have been declining from 2011 to 2016. China’s GDP grew 1.9 percent in IQ2012, annualizing to 7.8 percent, and 8.1 percent relative to a year earlier. The GDP of China grew at 2.2 percent in IIQ2012, which annualizes to 9.1 percent, and 7.6 percent relative to a year earlier. China grew at 1.8 percent in IIIQ2012, which annualizes at 7.4 percent, and 7.5 percent relative to a year earlier. In IVQ2012, China grew at 1.9 percent, which annualizes at 7.8 percent, and 8.1 percent in IVQ2012 relative to IVQ2011. In IQ2013, China grew at 1.9 percent, which annualizes at 7.8 percent, and 7.9 percent relative to a year earlier. In IIQ2013, China grew at 1.7 percent, which annualizes at 7.0 percent, and 7.6 percent relative to a year earlier. China grew at 2.1 percent in IIIQ2013, which annualizes at 8.7 percent, and increased 7.9 percent relative to a year earlier. China grew at 1.6 percent in IVQ2013, which annualized to 6.6 percent, and 7.7 percent relative to a year earlier. China’s GDP grew 1.7 percent in IQ2014, which annualizes to 7.0 percent, and 7.4 percent relative to a year earlier. China’s GDP grew 1.8 percent in IIQ2014, which annualizes at 7.4 percent, and 7.5 percent relative to a year earlier. China’s GDP grew 1.8 percent in IIIQ2014, which is equivalent to 7.4 percent in a year, and 7.1 percent relative to a year earlier. The GDP of China grew 1.8 percent in IVQ2014, which annualizes at 7.4 percent, and 7.2 percent relative to a year earlier. The GDP of China grew at 1.6 percent in IQ2015, which annualizes at 6.6 percent, and 7.0 percent relative to a year earlier. The GDP of China grew 1.9 percent in IIQ2015, which annualizes at 7.8 percent, and increased 7.0 percent relative to a year earlier. In IIIQ2015, China’s GDP grew at 1.7 percent, which annualizes at 7.0 percent, and increased 6.9 percent relative to a year earlier. The GDP of China grew at 1.6 percent in IVQ2015, which annualizes at 6.6 percent, and increased 6.8 percent relative to a year earlier. The GDP of China grew 1.2 percent in IQ2016, which annualizes at 4.9 percent, and increased 6.7 percent relative to a year earlier. In IIQ2016, the GDP of China increased 1.9 percent, which annualizes to 7.8 percent, and increased 6.7 percent relative to a year earlier. The GDP of China increased at 1.8 percent in IIIQ2016, which annualizes at 7.4 percent, and increased 6.7 percent relative to a year earlier. There is decennial change in leadership in China (http://www.xinhuanet.com/english/special/18cpcnc/index.htm). (http://cmpassocregulationblog.blogspot.com/2016/11/the-case-for-increase-in-federal-funds.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/unresolved-us-balance-of-payments.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/imf-view-of-world-economy-and-finance.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/uncertainty-of-valuations-of-risk.html and earlier http://cmpassocregulationblog.blogspot.com/2015/07/valuation-of-risk-financial-assets.html and earlier (http://cmpassocregulationblog.blogspot.com/2016/04/imf-view-of-world-economy-and-finance.html and earlier (http://cmpassocregulationblog.blogspot.com/2015/04/imf-view-of-economy-and-finance-united.html and earlier http://cmpassocregulationblog.blogspot.com/2015/01/competitive-currency-conflicts-world.html and earlier http://cmpassocregulationblog.blogspot.com/2014/10/financial-oscillations-world-inflation.html and earlier http://cmpassocregulationblog.blogspot.com/2014/07/financial-irrational-exuberance.html and earlier http://cmpassocregulationblog.blogspot.com/2014/04/imf-view-world-inflation-waves-squeeze.html and earlier http://cmpassocregulationblog.blogspot.com/2014/01/capital-flows-exchange-rates-and.html). There is also ongoing political development in China during a decennial political reorganization with new leadership (http://www.xinhuanet.com/english/special/18cpcnc/index.htm). Xinhuanet informs that Premier Wen Jiabao considers the need for macroeconomic stimulus, arguing that “we should continue to implement proactive fiscal policy and a prudent monetary policy, while giving more priority to maintaining growth” (http://news.xinhuanet.com/english/china/2012-05/20/c_131599662.htm). Premier Wen elaborates that “the country should properly handle the relationship between maintaining growth, adjusting economic structures and managing inflationary expectations” (http://news.xinhuanet.com/english/china/2012-05/20/c_131599662.htm). Bob Davis, writing on “At China’s NPC, Proposed Changes,” on Mar 5, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304732804579420743344553328?KEYWORDS=%22china%22&mg=reno64-wsj), analyzes the wide ranging policy changes in the annual work report by Prime Minister Li Keqiang to China’s NPC (National People’s Congress of the People’s Republic of China http://www.npc.gov.cn/englishnpc/news/). There are about sixty different fiscal and regulatory measures.
2. United States Economic Growth, Labor Markets and Budget/Debt Quagmire. The US is growing slowly with 24.2 million in job stress, fewer 10 million full-time jobs, high youth unemployment, historically low hiring and declining/stagnating real wages. Actual GDP is about two trillion dollars lower than trend GDP.
3. Economic Growth and Labor Markets in Advanced Economies. Advanced economies are growing slowly. There is still high unemployment in advanced economies.
4. World Inflation Waves. Inflation continues in repetitive waves globally (http://cmpassocregulationblog.blogspot.com/2016/12/of-course-economic-outlook-is-highly.html and earlier http://cmpassocregulationblog.blogspot.com/2016/11/interest-rate-increase-could-well.html). There is growing concern on capital outflows and currency depreciation of emerging markets.

A list of financial uncertainties includes:

1. Euro Area Survival Risk. The resilience of the euro to fiscal and financial doubts on larger member countries is still an unknown risk. There are complex economic, financial and political effects of the withdrawal of the UK from the European Union or BREXIT after the referendum on Jun 23, 2016 (https://next.ft.com/eu-referendum for extensive coverage by the Financial Times).
2. Competitive Devaluations. Exchange rate struggles continue as zero interest rates and negative interest rates in advanced economies induce devaluation of their currencies with alternating episodes of revaluation. The dollar is revaluing sharply.
3. Valuation and Volatility of Risk Financial Assets. Valuations of risk financial assets have reached extremely high levels in markets with oscillating volumes. The President of the European Central Bank (ECB), Mario Draghi, warned on Jun 3, 2015 that (http://www.ecb.europa.eu/press/pressconf/2015/html/is150603.en.html):

“But certainly one lesson is that we should get used to periods of higher volatility. At very low levels of interest rates, asset prices tend to show higher volatility…the Governing Council was unanimous in its assessment that we should look through these developments and maintain a steady monetary policy stance.”

1. Duration Trap of the Zero Bound. The yield of the US 10-year Treasury rose from 2.031 percent on Mar 9, 2012, to 2.294 percent on Mar 16, 2012. Considering a 10-year Treasury with coupon of 2.625 percent and maturity in exactly 10 years, the price would fall from 105.3512 corresponding to yield of 2.031 percent to 102.9428 corresponding to yield of 2.294 percent, for loss in a week of 2.3 percent but far more in a position with leverage of 10:1. Min Zeng, writing on “Treasurys fall, ending brutal quarter,” published on Mar 30, 2012, in the Wall Street Journal (http://professional.wsj.com/article/SB10001424052702303816504577313400029412564.html?mod=WSJ_hps_sections_markets), informs that Treasury bonds maturing in more than 20 years lost 5.52 percent in the first quarter of 2012.
2. Credibility and Commitment of Central Bank Policy. There is a credibility issue of the commitment of monetary policy (Sargent and Silber 2012Mar20)
3. Carry Trades. Commodity prices driven by zero interest rates have resumed their increasing path with fluctuations caused by intermittent risk aversion mixed with reallocations of portfolios of risk financial

There are collateral effects of unconventional monetary policy. Chart VIII-1 of the Board of Governors of the Federal Reserve System provides the rate on the overnight fed funds rate and the yields of the 10-year constant maturity Treasury and the Baa seasoned corporate bond. Table VIII-3 provides the data for selected points in Chart VIII-1. There are two important economic and financial events, illustrating the ease of inducing carry trade with extremely low interest rates and the resulting financial crash and recession of abandoning extremely low interest rates.

• The Federal Open Market Committee (FOMC) lowered the target of the fed funds rate from 7.03 percent on Jul 3, 2000, to 1.00 percent on Jun 22, 2004, in pursuit of non-existing deflation (Pelaez and Pelaez, International Financial Architecture (2005), 18-28, The Global Recession Risk (2007), 83-85). Central bank commitment to maintain the fed funds rate at 1.00 percent induced adjustable-rate mortgages (ARMS) linked to the fed funds rate. Lowering the interest rate near the zero bound in 2003-2004 caused the illusion of permanent increases in wealth or net worth in the balance sheets of borrowers and also of lending institutions, securitized banking and every financial institution and investor in the world. The discipline of calculating risks and returns was seriously impaired. The objective of monetary policy was to encourage borrowing, consumption and investment. The exaggerated stimulus resulted in a financial crisis of major proportions as the securitization that had worked for a long period was shocked with policy-induced excessive risk, imprudent credit, high leverage and low liquidity by the incentive to finance everything overnight at interest rates close to zero, from adjustable rate mortgages (ARMS) to asset-backed commercial paper of structured investment vehicles (SIV). The consequences of inflating liquidity and net worth of borrowers were a global hunt for yields to protect own investments and money under management from the zero interest rates and unattractive long-term yields of Treasuries and other securities. Monetary policy distorted the calculations of risks and returns by households, business and government by providing central bank cheap money. Short-term zero interest rates encourage financing of everything with short-dated funds, explaining the SIVs created off-balance sheet to issue short-term commercial paper with the objective of purchasing default-prone mortgages that were financed in overnight or short-dated sale and repurchase agreements (Pelaez and Pelaez, Financial Regulation after the Global Recession, 50-1, Regulation of Banks and Finance, 59-60, Globalization and the State Vol. I, 89-92, Globalization and the State Vol. II, 198-9, Government Intervention in Globalization, 62-3, International Financial Architecture, 144-9). ARMS were created to lower monthly mortgage payments by benefitting from lower short-dated reference rates. Financial institutions economized in liquidity that was penalized with near zero interest rates. There was no perception of risk because the monetary authority guaranteed a minimum or floor price of all assets by maintaining low interest rates forever or equivalent to writing an illusory put option on wealth. Subprime mortgages were part of the put on wealth by an illusory put on house prices. The housing subsidy of $221 billion per year created the impression of ever-increasing house prices. The suspension of auctions of 30-year Treasuries was designed to increase demand for mortgage-backed securities, lowering their yield, which was equivalent to lowering the costs of housing finance and refinancing. Fannie and Freddie purchased or guaranteed $1.6 trillion of nonprime mortgages and worked with leverage of 75:1 under Congress-provided charters and lax oversight. The combination of these policies resulted in high risks because of the put option on wealth by near zero interest rates, excessive leverage because of cheap rates, low liquidity by the penalty in the form of low interest rates and unsound credit decisions. The put option on wealth by monetary policy created the illusion that nothing could ever go wrong, causing the credit/dollar crisis and global recession (Pelaez and Pelaez, Financial Regulation after the Global Recession, 157-66, Regulation of Banks, and Finance, 217-27, International Financial Architecture, 15-18, The Global Recession Risk, 221-5, Globalization and the State Vol. II, 197-213, Government Intervention in Globalization, 182-4). The FOMC implemented increments of 25 basis points of the fed funds target from Jun 2004 to Jun 2006, raising the fed funds rate to 5.25 percent on Jul 3, 2006, as shown in Chart VIII-1. The gradual exit from the first round of unconventional monetary policy from 1.00 percent in Jun 2004 (http://www.federalreserve.gov/boarddocs/press/monetary/2004/20040630/default.htm) to 5.25 percent in Jun 2006 (http://www.federalreserve.gov/newsevents/press/monetary/20060629a.htm) caused the financial crisis and global recession.
• On Dec 16, 2008, the policy determining committee of the Fed decided (http://www.federalreserve.gov/newsevents/press/monetary/20081216b.htm): “The Federal Open Market Committee decided today to establish a target range for the federal funds rate of 0 to 1/4 percent.” Policymakers emphasize frequently that there are tools to exit unconventional monetary policy at the right time. At the confirmation hearing on nomination for Chair of the Board of Governors of the Federal Reserve System, Vice Chair Yellen (2013Nov14 http://www.federalreserve.gov/newsevents/testimony/yellen20131114a.htm), states that: “The Federal Reserve is using its monetary policy tools to promote a more robust recovery. A strong recovery will ultimately enable the Fed to reduce its monetary accommodation and reliance on unconventional policy tools such as asset purchases. I believe that supporting the recovery today is the surest path to returning to a more normal approach to monetary policy.” Perception of withdrawal of $2671 billion, or $2.7 trillion, of bank reserves (http://www.federalreserve.gov/releases/h41/current/h41.htm#h41tab1), would cause Himalayan increase in interest rates that would provoke another recession. There is no painless gradual or sudden exit from zero interest rates because reversal of exposures created on the commitment of zero interest rates forever.

In his classic restatement of the Keynesian demand function in terms of “liquidity preference as behavior toward risk,” James Tobin (http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1981/tobin-bio.html) identifies the risks of low interest rates in terms of portfolio allocation (Tobin 1958, 86):

“The assumption that investors expect on balance no change in the rate of interest has been adopted for the theoretical reasons explained in section 2.6 rather than for reasons of realism. Clearly investors do form expectations of changes in interest rates and differ from each other in their expectations. For the purposes of dynamic theory and of analysis of specific market situations, the theories of sections 2 and 3 are complementary rather than competitive. The formal apparatus of section 3 will serve just as well for a non-zero expected capital gain or loss as for a zero expected value of g. Stickiness of interest rate expectations would mean that the expected value of g is a function of the rate of interest r, going down when r goes down and rising when r goes up. In addition to the rotation of the opportunity locus due to a change in r itself, there would be a further rotation in the same direction due to the accompanying change in the expected capital gain or loss. At low interest rates expectation of capital loss may push the opportunity locus into the negative quadrant, so that the optimal position is clearly no consols, all cash. At the other extreme, expectation of capital gain at high interest rates would increase sharply the slope of the opportunity locus and the frequency of no cash, all consols positions, like that of Figure 3.3. The stickier the investor's expectations, the more sensitive his demand for cash will be to changes in the rate of interest (emphasis added).”

Tobin (1969) provides more elegant, complete analysis of portfolio allocation in a general equilibrium model. The major point is equally clear in a portfolio consisting of only cash balances and a perpetuity or consol. Let g be the capital gain, r the rate of interest on the consol and r^e the expected rate of interest. The rates are expressed as proportions. The price of the consol is the inverse of the interest rate, (1+r^e). Thus, g = [(r/r^e) – 1]. The critical analysis of Tobin is that at extremely low interest rates there is only expectation of interest rate increases, that is, dr^e>0, such that there is expectation of capital losses on the consol, dg<0. Investors move into positions combining only cash and no consols. Valuations of risk financial assets would collapse in reversal of long positions in carry trades with short exposures in a flight to cash. There is no exit from a central bank created liquidity trap without risks of financial crash and another global recession. The net worth of the economy depends on interest rates. In theory, “income is generally defined as the amount a consumer unit could consume (or believe that it could) while maintaining its wealth intact” (Friedman 1957, 10). Income, Y, is a flow that is obtained by applying a rate of return, r, to a stock of wealth, W, or Y = rW (Friedman 1957). According to a subsequent statement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption is the long-run expected yield from wealth r*W. This yield was named permanent income: Y* = r*W” (Darby 1974, 229), where * denotes permanent. The simplified relation of income and wealth can be restated as:

W = Y/r (1)

Equation (1) shows that as r goes to zero, r→0, W grows without bound, W→∞. Unconventional monetary policy lowers interest rates to increase the present value of cash flows derived from projects of firms, creating the impression of long-term increase in net worth. An attempt to reverse unconventional monetary policy necessarily causes increases in interest rates, creating the opposite perception of declining net worth. As r→∞, W = Y/r →0. There is no exit from unconventional monetary policy without increasing interest rates with resulting pain of financial crisis and adverse effects on production, investment and employment.

Dan Strumpf and Pedro Nicolaci da Costa, writing on “Fed’s Yellen: Stock Valuations ‘Generally are Quite High,’” on May 6, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/feds-yellen-cites-progress-on-bank-regulation-1430918155?tesla=y ), quote Chair Yellen at open conversation with Christine Lagarde, Managing Director of the IMF, finding “equity-market valuations” as “quite high” with “potential dangers” in bond valuations. The DJIA fell 0.5 percent on May 6, 2015, after the comments and then increased 0.5 percent on May 7, 2015 and 1.5 percent on May 8, 2015.

Fri May 1	Mon 4	Tue 5	Wed 6	Thu 7	Fri 8
DJIA 18024.06 -0.3% 1.0%	18070.40 0.3% 0.3%	17928.20 -0.5% -0.8%	17841.98 -1.0% -0.5%	17924.06 -0.6% 0.5%	18191.11 0.9% 1.5%

There are two approaches in theory considered by Bordo (2012Nov20) and Bordo and Lane (2013). The first approach is in the classical works of Milton Friedman and Anna Jacobson Schwartz (1963a, 1987) and Karl Brunner and Allan H. Meltzer (1973). There is a similar approach in Tobin (1969). Friedman and Schwartz (1963a, 66) trace the effects of expansionary monetary policy into increasing initially financial asset prices: “It seems plausible that both nonbank and bank holders of redundant balances will turn first to securities comparable to those they have sold, say, fixed-interest coupon, low-risk obligations. But as they seek to purchase these they will tend to bid up the prices of those issues. Hence they, and also other holders not involved in the initial central bank open-market transactions, will look farther afield: the banks, to their loans; the nonbank holders, to other categories of securities-higher risk fixed-coupon obligations, equities, real property, and so forth.”

The second approach is by the Austrian School arguing that increases in asset prices can become bubbles if monetary policy allows their financing with bank credit. Professor Michael D. Bordo provides clear thought and empirical evidence on the role of “expansionary monetary policy” in inflating asset prices (Bordo2012Nov20, Bordo and Lane 2013). Bordo and Lane (2013) provide revealing narrative of historical episodes of expansionary monetary policy. Bordo and Lane (2013) conclude that policies of depressing interest rates below the target rate or growth of money above the target influences higher asset prices, using a panel of 18 OECD countries from 1920 to 2011. Bordo (2012Nov20) concludes: “that expansionary money is a significant trigger” and “central banks should follow stable monetary policies…based on well understood and credible monetary rules.” Taylor (2007, 2009) explains the housing boom and financial crisis in terms of expansionary monetary policy. Professor Martin Feldstein (2016), at Harvard University, writing on “A Federal Reserve oblivious to its effects on financial markets,” on Jan 13, 2016, published in the Wall Street Journal (http://www.wsj.com/articles/a-federal-reserve-oblivious-to-its-effect-on-financial-markets-1452729166), analyzes how unconventional monetary policy drove values of risk financial assets to high levels. Quantitative easing and zero interest rates distorted calculation of risks with resulting vulnerabilities in financial markets.

Another hurdle of exit from zero interest rates is “competitive easing” that Professor Raghuram Rajan, governor of the Reserve Bank of India, characterizes as disguised “competitive devaluation” (http://www.centralbanking.com/central-banking-journal/interview/2358995/raghuram-rajan-on-the-dangers-of-asset-prices-policy-spillovers-and-finance-in-india). The fed has been considering increasing interest rates. The European Central Bank (ECB) announced, on Mar 5, 2015, the beginning on Mar 9, 2015 of its quantitative easing program denominated as Public Sector Purchase Program (PSPP), consisting of “combined monthly purchases of EUR 60 bn [billion] in public and private sector securities” (http://www.ecb.europa.eu/mopo/liq/html/pspp.en.html). Expectation of increasing interest rates in the US together with euro rates close to zero or negative cause revaluation of the dollar (or devaluation of the euro and of most currencies worldwide). US corporations suffer currency translation losses of their foreign transactions and investments (http://www.fasb.org/jsp/FASB/Pronouncement_C/SummaryPage&cid=900000010318) while the US becomes less competitive in world trade (Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), Government Intervention in Globalization (2008c)). The DJIA fell 1.5 percent on Mar 6, 2015 and the dollar revalued 2.2 percent from Mar 5 to Mar 6, 2015. The euro has devalued 49.4 percent relative to the dollar from the high on Jul 15, 2008 to Jan 13, 2017.

Fri 27 Feb	Mon 3/2	Tue 3/3	Wed 3/4	Thu 3/5	Fri 3/6
USD/ EUR 1.1197 1.6% 0.0%	1.1185 0.1% 0.1%	1.1176 0.2% 0.1%	1.1081 1.0% 0.9%	1.1030 1.5% 0.5%	1.0843 3.2% 1.7%

Chair Yellen explained the removal of the word “patience” from the advanced guidance at the press conference following the FOMC meeting on Mar 18, 2015 (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20150318.pdf):

“In other words, just because we removed the word “patient” from the statement doesn’t mean we are going to be impatient. Moreover, even after the initial increase in the target funds rate, our policy is likely to remain highly accommodative to support continued progress toward our objectives of maximum employment and 2 percent inflation.”

Exchange rate volatility is increasing in response of “impatience” in financial markets with monetary policy guidance and measures:

Fri Mar 6	Mon 9	Tue 10	Wed 11	Thu 12	Fri 13
USD/ EUR 1.0843 3.2% 1.7%	1.0853 -0.1% -0.1%	1.0700 1.3% 1.4%	1.0548 2.7% 1.4%	1.0637 1.9% -0.8%	1.0497 3.2% 1.3%

Fri Mar 13	Mon 16	Tue 17	Wed 18	Thu 19	Fri 20
USD/ EUR 1.0497 3.2% 1.3%	1.0570 -0.7% -0.7%	1.0598 -1.0% -0.3%	1.0864 -3.5% -2.5%	1.0661 -1.6% 1.9%	1.0821 -3.1% -1.5%

Fri Apr 24	Mon 27	Tue 28	Wed 29	Thu 30	May Fri 1
USD/ EUR 1.0874 -0.6% -0.4%	1.0891 -0.2% -0.2%	1.0983 -1.0% -0.8%	1.1130 -2.4% -1.3%	1.1223 -3.2% -0.8%	1.1199 -3.0% 0.2%

In a speech at Brown University on May 22, 2015, Chair Yellen stated (http://www.federalreserve.gov/newsevents/speech/yellen20150522a.htm):

“For this reason, if the economy continues to improve as I expect, I think it will be appropriate at some point this year to take the initial step to raise the federal funds rate target and begin the process of normalizing monetary policy. To support taking this step, however, I will need to see continued improvement in labor market conditions, and I will need to be reasonably confident that inflation will move back to 2 percent over the medium term. After we begin raising the federal funds rate, I anticipate that the pace of normalization is likely to be gradual. The various headwinds that are still restraining the economy, as I said, will likely take some time to fully abate, and the pace of that improvement is highly uncertain.”

The US dollar appreciated 3.8 percent relative to the euro in the week of May 22, 2015:

Fri May 15	Mon 18	Tue 19	Wed 20	Thu 21	Fri 22
USD/ EUR 1.1449 -2.2% -0.3%	1.1317 1.2% 1.2%	1.1150 2.6% 1.5%	1.1096 3.1% 0.5%	1.1113 2.9% -0.2%	1.1015 3.8% 0.9%

The Managing Director of the International Monetary Fund (IMF), Christine Lagarde, warned on Jun 4, 2015, that: (http://blog-imfdirect.imf.org/2015/06/04/u-s-economy-returning-to-growth-but-pockets-of-vulnerability/):

“The Fed’s first rate increase in almost 9 years is being carefully prepared and telegraphed. Nevertheless, regardless of the timing, higher US policy rates could still result in significant market volatility with financial stability consequences that go well beyond US borders. I weighing these risks, we think there is a case for waiting to raise rates until there are more tangible signs of wage or price inflation than are currently evident. Even after the first rate increase, a gradual rise in the federal fund rates will likely be appropriate.”

The President of the European Central Bank (ECB), Mario Draghi, warned on Jun 3, 2015 that (http://www.ecb.europa.eu/press/pressconf/2015/html/is150603.en.html):

“But certainly one lesson is that we should get used to periods of higher volatility. At very low levels of interest rates, asset prices tend to show higher volatility…the Governing Council was unanimous in its assessment that we should look through these developments and maintain a steady monetary policy stance.”

The Chair of the Board of Governors of the Federal Reserve System, Janet L. Yellen, stated on Jul 10, 2015 that (http://www.federalreserve.gov/newsevents/speech/yellen20150710a.htm):

“Based on my outlook, I expect that it will be appropriate at some point later this year to take the first step to raise the federal funds rate and thus begin normalizing monetary policy. But I want to emphasize that the course of the economy and inflation remains highly uncertain, and unanticipated developments could delay or accelerate this first step. I currently anticipate that the appropriate pace of normalization will be gradual, and that monetary policy will need to be highly supportive of economic activity for quite some time. The projections of most of my FOMC colleagues indicate that they have similar expectations for the likely path of the federal funds rate. But, again, both the course of the economy and inflation are uncertain. If progress toward our employment and inflation goals is more rapid than expected, it may be appropriate to remove monetary policy accommodation more quickly. However, if progress toward our goals is slower than anticipated, then the Committee may move more slowly in normalizing policy.”

There is essentially the same view in the Testimony of Chair Yellen in delivering the Semiannual Monetary Policy Report to the Congress on Jul 15, 2015 (http://www.federalreserve.gov/newsevents/testimony/yellen20150715a.htm).

At the press conference after the meeting of the FOMC on Sep 17, 2015, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20150917.pdf 4):

“The outlook abroad appears to have become more uncertain of late, and heightened concerns about growth in China and other emerging market economies have led to notable volatility in financial markets. Developments since our July meeting, including the drop in equity prices, the further appreciation of the dollar, and a widening in risk spreads, have tightened overall financial conditions to some extent. These developments may restrain U.S. economic activity somewhat and are likely to put further downward pressure on inflation in the near term. Given the significant economic and financial interconnections between the United States and the rest of the world, the situation abroad bears close watching.”

Some equity markets fell on Fri Sep 18, 2015:

Fri Sep 11	Mon 14	Tue 15	Wed 16	Thu 17	Fri 18
DJIA 16433.09 2.1% 0.6%	16370.96 -0.4% -0.4%	16599.85 1.0% 1.4%	16739.95 1.9% 0.8%	16674.74 1.5% -0.4%	16384.58 -0.3% -1.7%
Nikkei 225 18264.22 2.7% -0.2%	17965.70 -1.6% -1.6%	18026.48 -1.3% 0.3%	18171.60 -0.5% 0.8%	18432.27 0.9% 1.4%	18070.21 -1.1% -2.0%
DAX 10123.56 0.9% -0.9%	10131.74 0.1% 0.1%	10188.13 0.6% 0.6%	10227.21 1.0% 0.4%	10229.58 1.0% 0.0%	9916.16 -2.0% -3.1%

Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. Chair Yellen, in a lecture on “Inflation dynamics and monetary policy,” on Sep 24, 2015 (http://www.federalreserve.gov/newsevents/speech/yellen20150924a.htm), states that (emphasis added):

· “The economic outlook, of course, is highly uncertain”

· “Considerable uncertainties also surround the outlook for economic activity”

· “Given the highly uncertain nature of the outlook…”

Is there a “science” or even “art” of central banking under this extreme uncertainty in which policy does not generate higher volatility of money, income, prices and values of financial assets?

Lingling Wei, writing on Oct 23, 2015, on China’s central bank moves to spur economic growth,” published in the Wall Street Journal (http://www.wsj.com/articles/chinas-central-bank-cuts-rates-1445601495), analyzes the reduction by the People’s Bank of China (http://www.pbc.gov.cn/ http://www.pbc.gov.cn/english/130437/index.html) of borrowing and lending rates of banks by 50 basis points and reserve requirements of banks by 50 basis points. Paul Vigna, writing on Oct 23, 2015, on “Stocks rally out of correction territory on latest central bank boost,” published in the Wall Street Journal (http://blogs.wsj.com/moneybeat/2015/10/23/stocks-rally-out-of-correction-territory-on-latest-central-bank-boost/), analyzes the rally in financial markets following the statement on Oct 22, 2015, by the President of the European Central Bank (ECB) Mario Draghi of consideration of new quantitative measures in Dec 2015 (https://www.youtube.com/watch?v=0814riKW25k&rel=0) and the reduction of bank lending/deposit rates and reserve requirements of banks by the People’s Bank of China on Oct 23, 2015. The dollar revalued 2.8 percent from Oct 21 to Oct 23, 2015, following the intended easing of the European Central Bank. The DJIA rose 2.8 percent from Oct 21 to Oct 23 and the DAX index of German equities rose 5.4 percent from Oct 21 to Oct 23, 2015.

Fri Oct 16	Mon 19	Tue 20	Wed 21	Thu 22	Fri 23
USD/ EUR 1.1350 0.1% 0.3%	1.1327 0.2% 0.2%	1.1348 0.0% -0.2%	1.1340 0.1% 0.1%	1.1110 2.1% 2.0%	1.1018 2.9% 0.8%
DJIA 17215.97 0.8% 0.4%	17230.54 0.1% 0.1%	17217.11 0.0% -0.1%	17168.61 -0.3% -0.3%	17489.16 1.6% 1.9%	17646.70 2.5% 0.9%
Dow Global 2421.58 0.3% 0.6%	2414.33 -0.3% -0.3%	2411.03 -0.4% -0.1%	2411.27 -0.4% 0.0%	2434.79 0.5% 1.0%	2458.13 1.5% 1.0%
DJ Asia Pacific 1402.31 1.1% 0.3%	1398.80 -0.3% -0.3%	1395.06 -0.5% -0.3%	1402.68 0.0% 0.5%	1396.03 -0.4% -0.5%	1415.50 0.9% 1.4%
Nikkei 225 18291.80 -0.8% 1.1%	18131.23 -0.9% -0.9%	18207.15 -0.5% 0.4%	18554.28 1.4% 1.9%	18435.87 0.8% -0.6%	18825.30 2.9% 2.1%
Shanghai 3391.35 6.5% 1.6%	3386.70 -0.1% -0.1%	3425.33 1.0% 1.1%	3320.68 -2.1% -3.1%	3368.74 -0.7% 1.4%	3412.43 0.6% 1.3%
DAX 10104.43 0.1% 0.4%	10164.31 0.6% 0.6%	10147.68 0.4% -0.2%	10238.10 1.3% 0.9%	10491.97 3.8% 2.5%	10794.54 6.8% 2.9%

Ben Leubsdorf, writing on “Fed’s Yellen: December is “Live Possibility” for First Rate Increase,” on Nov 4, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/feds-yellen-december-is-live-possibility-for-first-rate-increase-1446654282) quotes Chair Yellen that a rate increase in “December would be a live possibility.” The remark of Chair Yellen was during a hearing on supervision and regulation before the Committee on Financial Services, US House of Representatives (http://www.federalreserve.gov/newsevents/testimony/yellen20151104a.htm) and a day before the release of the employment situation report for Oct 2015 (Section I). The dollar revalued 2.4 percent during the week. The euro has devalued 49.4 percent relative to the dollar from the high on Jul 15, 2008 to Jan 13, 2016.

Fri Oct 30	Mon 2	Tue 3	Wed 4	Thu 5	Fri 6
USD/ EUR 1.1007 0.1% -0.3%	1.1016 -0.1% -0.1%	1.0965 0.4% 0.5%	1.0867 1.3% 0.9%	1.0884 1.1% -0.2%	1.0742 2.4% 1.3%

The release on Nov 18, 2015 of the minutes of the FOMC (Federal Open Market Committee) meeting held on Oct 28, 2015 (http://www.federalreserve.gov/monetarypolicy/fomcminutes20151028.htm) states:

“Most participants anticipated that, based on their assessment of the current economic situation and their outlook for economic activity, the labor market, and inflation, these conditions [for interest rate increase] could well be met by the time of the next meeting. Nonetheless, they emphasized that the actual decision would depend on the implications for the medium-term economic outlook of the data received over the upcoming intermeeting period… It was noted that beginning the normalization process relatively soon would make it more likely that the policy trajectory after liftoff could be shallow.”

Markets could have interpreted a symbolic increase in the fed funds rate at the meeting of the FOMC on Dec 15-16, 2015 (http://www.federalreserve.gov/monetarypolicy/fomccalendars.htm) followed by “shallow” increases, explaining the sharp increase in stock market values and appreciation of the dollar after the release of the minutes on Nov 18, 2015:

Fri Nov 13	Mon 16	Tue 17	Wed 18	Thu 19	Fri 20
USD/ EUR 1.0774 -0.3% 0.4%	1.0686 0.8% 0.8%	1.0644 1.2% 0.4%	1.0660 1.1% -0.2%	1.0735 0.4% -0.7%	1.0647 1.2% 0.8%
DJIA 17245.24 -3.7% -1.2%	17483.01 1.4% 1.4%	17489.50 1.4% 0.0%	17737.16 2.9% 1.4%	17732.75 2.8% 0.0%	17823.81 3.4% 0.5%
DAX 10708.40 -2.5% -0.7%	10713.23 0.0% 0.0%	10971.04 2.5% 2.4%	10959.95 2.3% -0.1%	11085.44 3.5% 1.1%	11119.83 3.8% 0.3%

In testimony before The Joint Economic Committee of Congress on Dec 3, 2015 (http://www.federalreserve.gov/newsevents/testimony/yellen20151203a.htm), Chair Yellen reiterated that the FOMC (Federal Open Market Committee) “anticipates that even after employment and inflation are near mandate-consistent levels, economic condition may, for some time, warrant keeping the target federal funds rate below the Committee views as normal in the longer run.” Todd Buell and Katy Burne, writing on “Draghi says ECB could step up stimulus efforts if necessary,” on Dec 4, 2015, published in the Wall Street Journal (http://www.wsj.com/articles/draghi-says-ecb-could-step-up-stimulus-efforts-if-necessary-1449252934), analyze that the President of the European Central Bank (ECB), Mario Draghi, reassured financial markets that the ECB will increase stimulus if required to raise inflation the euro area to targets. The USD depreciated 3.1 percent on Thu Dec 3, 2015 after weaker than expected measures by the European Central Bank. DJIA fell 1.4 percent on Dec 3 and increased 2.1 percent on Dec 4. DAX fell 3.6 percent on Dec 3.

Fri Nov 27	Mon 30	Tue 1	Wed 2	Thu 3	Fri 4
USD/ EUR 1.0594 0.5% 0.2%	1.0565 0.3% 0.3%	1.0634 -0.4% -0.7%	1.0616 -0.2% 0.2%	1.0941 -3.3% -3.1%	1.0885 -2.7% 0.5%
DJIA 17798.49 -0.1% -0.1%	17719.92 -0.4% -0.4%	17888.35 0.5% 1.0%	17729.68 -0.4% -0.9%	17477.67 -1.8% -1.4%	17847.63 0.3% 2.1%
DAX 11293.76 1.6% -0.2%	11382.23 0.8% 0.8%	11261.24 -0.3% -1.1%	11190.02 -0.9% -0.6%	10789.24 -4.5% -3.6%	10752.10 -4.8% -0.3%

At the press conference following the meeting of the FOMC on Dec 16, 2015, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20151216.pdf page 8):

“And we recognize that monetary policy operates with lags. We would like to be able to move in a prudent, and as we've emphasized, gradual manner. It's been a long time since the Federal Reserve has raised interest rates, and I think it's prudent to be able to watch what the impact is on financial conditions and spending in the economy and moving in a timely fashion enables us to do this.”

The implication of this statement is that the state of the art is not accurate in analyzing the effects of monetary policy on financial markets and economic activity. The US dollar appreciated and equities fluctuated:

Fri Dec 11	Mon 14	Tue 15	Wed 16	Thu 17	Fri 18
USD/ EUR 1.0991 -1.0% -0.4%	1.0993 0.0% 0.0%	1.0932 0.5% 0.6%	1.0913 0.7% 0.2%	1.0827 1.5% 0.8%	1.0868 1.1% -0.4%
DJIA 17265.21 -3.3% -1.8%	17368.50 0.6% 0.6%	17524.91 1.5% 0.9%	17749.09 2.8% 1.3%	17495.84 1.3% -1.4%	17128.55 -0.8% -2.1%
DAX 10340.06 -3.8% -2.4%	10139.34 -1.9% -1.9%	10450.38 -1.1% 3.1%	10469.26 1.2% 0.2%	10738.12 3.8% 2.6%	10608.19 2.6% -1.2%

On January 29, 2016, the Policy Board of the Bank of Japan introduced a new policy to attain the “price stability target of 2 percent at the earliest possible time” (https://www.boj.or.jp/en/announcements/release_2016/k160129a.pdf). The new framework consists of three dimensions: quantity, quality and interest rate. The interest rate dimension consists of rates paid to current accounts that financial institutions hold at the Bank of Japan of three tiers zero, positive and minus 0.1 percent. The quantitative dimension consists of increasing the monetary base at the annual rate of 80 trillion yen. The qualitative dimension consists of purchases by the Bank of Japan of Japanese government bonds (JGBs), exchange traded funds (ETFs) and Japan real estate investment trusts (J-REITS). The yen devalued sharply relative to the dollar and world equity markets soared after the new policy announced on Jan 29, 2016:

Fri 22	Mon 25	Tue 26	Wed 27	Thu 28	Fri 29
JPY/ USD 118.77 -1.5% -0.9%	118.30 0.4% 0.4%	118.42 0.3% -0.1%	118.68 0.1% -0.2%	118.82 0.0% -0.1%	121.13 -2.0% -1.9%
DJIA 16093.51 0.7% 1.3%	15885.22 -1.3% -1.3%	16167.23 0.5% 1.8%	15944.46 -0.9% -1.4%	16069.64 -0.1% 0.8%	16466.30 2.3% 2.5%
Nikkei 16958.53 -1.1% 5.9%	17110.91 0.9% 0.9%	16708.90 -1.5% -2.3%	17163.92 1.2% 2.7%	17041.45 0.5% -0.7%	17518.30 3.3% 2.8%
Shanghai 2916.56 0.5% 1.3	2938.51 0.8% 0.8%	2749.79 -5.7% -6.4%	2735.56 -6.2% -0.5%	2655.66 -8.9% -2.9%	2737.60 -6.1% 3.1%
DAX 9764.88 2.3% 2.0%	9736.15 -0.3% -0.3%	9822.75 0.6% 0.9%	9880.82 1.2% 0.6%	9639.59 -1.3% -2.4%	9798.11 0.3% 1.6%

In testimony on the Semiannual Monetary Policy Report to the Congress on Feb 10-11, 2016, Chair Yellen (http://www.federalreserve.gov/newsevents/testimony/yellen20160210a.htm) states: “U.S. real gross domestic product is estimated to have increased about 1-3/4 percent in 2015. Over the course of the year, subdued foreign growth and the appreciation of the dollar restrained net exports. In the fourth quarter of last year, growth in the gross domestic product is reported to have slowed more sharply, to an annual rate of just 3/4 percent; again, growth was held back by weak net exports as well as by a negative contribution from inventory investment.”

Jon Hilsenrath, writing on “Yellen Says Fed Should Be Prepared to Use Negative Rates if Needed,” on Feb 11, 2016, published in the Wall Street Journal (http://www.wsj.com/articles/yellen-reiterates-concerns-about-risks-to-economy-in-senate-testimony-1455203865), analyzes the statement of Chair Yellen in Congress that the FOMC (Federal Open Market Committee) is considering negative interest rates on bank reserves. The Wall Street Journal provides yields of two and ten-year sovereign bonds with negative interest rates on shorter maturities where central banks pay negative interest rates on excess bank reserves:

Sovereign Yields 2/12/16	Japan	Germany	USA
2 Year	-0.168	-0.498	0.694
10 Year	0.076	0.262	1.744

On Mar 10, 2016, the European Central Bank (ECB) announced (1) reduction of the refinancing rate by 5 basis points to 0.00 percent; decrease the marginal lending rate to 0.25 percent; reduction of the deposit facility rate to 0,40 percent; increase of the monthly purchase of assets to €80 billion; include nonbank corporate bonds in assets eligible for purchases; and new long-term refinancing operations (https://www.ecb.europa.eu/press/pr/date/2016/html/pr160310.en.html). The President of the ECB, Mario Draghi, stated in the press conference (https://www.ecb.europa.eu/press/pressconf/2016/html/is160310.en.html): “How low can we go? Let me say that rates will stay low, very low, for a long period of time, and well past the horizon of our purchases…We don’t anticipate that it will be necessary to reduce rates further. Of course, new facts can change the situation and the outlook.”

The dollar devalued relative to the euro and open stock markets traded lower after the announcement on Mar 10, 2016, but stocks rebounded on Mar 11:

Fri 4	Mon 7	Tue 8	Wed 9	Thu10	Fri 11
USD/ EUR 1.1006 -0.7% -0.4%	1.1012 -0.1% -0.1%	1.1013 -0.1% 0.0%	1.0999 0.1% 0.1%	1.1182 -1.6% -1.7%	1.1151 -1.3% 0.3%
DJIA 17006.77 2.2% 0.4%	17073.95 0.4% 0.4%	16964.10 -0.3% -0.6%	17000.36 0.0% 0.2%	16995.13 -0.1% 0.0%	17213.31 1.2% 1.3%
DAX 9824.17 3.3% 0.7%	9778.93 -0.5% 0.5%	9692.82 -1.3% -0.9%	9723.09 -1.0% 0.3%	9498.15 -3.3% -2.3%	9831.13 0.1% 3.5%

At the press conference after the FOMC meeting on Sep 21, 2016, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20160921.pdf ): “However, the economic outlook is inherently uncertain.” In the address to the Jackson Hole symposium on Aug 26, 2016, Chair Yellen states: “I believe the case for an increase in in federal funds rate has strengthened in recent months…And, as ever, the economic outlook is uncertain, and so monetary policy is not on a preset course” (http://www.federalreserve.gov/newsevents/speech/yellen20160826a.htm). In a speech at the World Affairs Council of Philadelphia, on Jun 6, 2016 (http://www.federalreserve.gov/newsevents/speech/yellen20160606a.htm), Chair Yellen finds that “there is considerable uncertainty about the economic outlook.” There are fifteen references to this uncertainty in the text of 18 pages double-spaced. In the Semiannual Monetary Policy Report to the Congress on Jun 21, 2016, Chair Yellen states (http://www.federalreserve.gov/newsevents/testimony/yellen20160621a.htm), “Of course, considerable uncertainty about the economic outlook remains.” Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. Is there a “science” or even “art” of central banking under this extreme uncertainty in which policy does not generate higher volatility of money, income, prices and values of financial assets?

Chart VIII-1, Fed Funds Rate and Yields of  Ten-year Treasury Constant Maturity and Baa Seasoned Corporate Bond, Jan 2, 2001 to Oct 6, 2016 

Source: Board of Governors of the Federal Reserve System

http://www.federalreserve.gov/releases/h15/

Chart VIII-1A, Fed Funds Rate and Yields of Ten-year Treasury Constant Maturity, Jan 2, 2001 to Jan 12, 2016

Source: Board of Governors of the Federal Reserve System

http://www.federalreserve.gov/releases/h15/

Table VIII-3, Selected Data Points in Chart VIII-1, % per Year

	Fed Funds Overnight Rate	10-Year Treasury Constant Maturity	Seasoned Baa Corporate Bond
1/2/2001	6.67	4.92	7.91
10/1/2002	1.85	3.72	7.46
7/3/2003	0.96	3.67	6.39
6/22/2004	1.00	4.72	6.77
6/28/2006	5.06	5.25	6.94
9/17/2008	2.80	3.41	7.25
10/26/2008	0.09	2.16	8.00
10/31/2008	0.22	4.01	9.54
4/6/2009	0.14	2.95	8.63
4/5/2010	0.20	4.01	6.44
2/4/2011	0.17	3.68	6.25
7/25/2012	0.15	1.43	4.73
5/1/13	0.14	1.66	4.48
9/5/13	0.089	2.98	5.53
11/21/2013	0.09	2.79	5.44
11/26/13	0.09	2.74	5.34 (11/26/13)
12/5/13	0.09	2.88	5.47
12/11/13	0.09	2.89	5.42
12/18/13	0.09	2.94	5.36
12/26/13	0.08	3.00	5.37
1/1/2014	0.08	3.00	5.34
1/8/2014	0.07	2.97	5.28
1/15/2014	0.07	2.86	5.18
1/22/2014	0.07	2.79	5.11
1/30/2014	0.07	2.72	5.08
2/6/2014	0.07	2.73	5.13
2/13/2014	0.06	2.73	5.12
2/20/14	0.07	2.76	5.15
2/27/14	0.07	2.65	5.01
3/6/14	0.08	2.74	5.11
3/13/14	0.08	2.66	5.05
3/20/14	0.08	2.79	5.13
3/27/14	0.08	2.69	4.95
4/3/14	0.08	2.80	5.04
4/10/14	0.08	2.65	4.89
4/17/14	0.09	2.73	4.89
4/24/14	0.10	2.70	4.84
5/1/14	0.09	2.63	4.77
5/8/14	0.08	2.61	4.79
5/15/14	0.09	2.50	4.72
5/22/14	0.09	2.56	4.81
5/29/14	0.09	2.45	4.69
6/05/14	0.09	2.59	4.83
6/12/14	0.09	2.58	4.79
6/19/14	0.10	2.64	4.83
6/26/14	0.10	2.53	4.71
7/2/14	0.10	2.64	4.84
7/10/14	0.09	2.55	4.75
7/17/14	0.09	2.47	4.69
7/24/14	0.09	2.52	4.72
7/31/14	0.08	2.58	4.75
8/7/14	0.09	2.43	4.71
8/14/14	0.09	2.40	4.69
8/21/14	0.09	2.41	4.69
8/28/14	0.09	2.34	4.57
9/04/14	0.09	2.45	4.70
9/11/14	0.09	2.54	4.79
9/18/14	0.09	2.63	4.91
9/25/14	0.09	2.52	4.79
10/02/14	0.09	2.44	4.76
10/09/14	0.08	2.34	4.68
10/16/14	0.09	2.17	4.64
10/23/14	0.09	2.29	4.71
11/13/14	0.09	2.35	4.82
11/20/14	0.10	2.34	4.86
11/26/14	0.10	2.24	4.73
12/04/14	0.12	2.25	4.78
12/11/14	0.12	2.19	4.72
12/18/14	0.13	2.22	4.78
12/23/14	0.13	2.26	4.79
12/30/14	0.06	2.20	4.69
1/8/15	0.12	2.03	4.57
1/15/15	0.12	1.77	4.42
1/22/15	0.12	1.90	4.49
1/29/15	0.11	1.77	4.35
2/05/15	0.12	1.83	4.43
2/12/15	0.12	1.99	4.53
2/19/15	0.12	2.11	4.64
2/26/15	0.11	2.03	4.47
3/5/215	0.11	2.11	4.58
3/12/15	0.11	2.10	4.56
3/19/15	0.12	1.98	4.48
3/26/15	0.11	2.01	4.56
4/03/15	0.12	1.92	4.47
4/9/15	0.12	1.97	4.50
4/16/15	0.13	1.90	4.45
4/23/15	0.13	1.96	4.50
5/1/15	0.08	2.05	4.65
5/7/15	0.13	2.18	4.82
5/14/15	0.13	2.23	4.97
5/21/15	0.12	2.19	4.94
5/28/15	0.12	2.13	4.88
6/04/15	0.13	2.31	5.03
6/11/15	0.13	2.39	5.10
6/18/15	0.14	2.35	5.17
6/25/15	0.13	2.40	5.20
7/1/15	0.13	2.43	5.26
7/9/15	0.13	2.32	5.20
7/16/15	0.14	2.36	5.24
7/23/15	0.13	2.28	5.13
7/30/15	0.14	2.28	5.16
8/06/15	0.14	2.23	5.15
8/20/15	0.15	2.09	5.13
8/27/15	0.14	2.18	5.33
9/03/15	0.14	2.18	5.35
9/10/15	0.14	2.23	5.35
9/17/15	0.14	2.21	5.39
9/25/15	0.14	2.13	5.29
10/01/15	0.13	2.05	5.36
10/08/15	0.13	2.12	5.40
10/15/15	0.13	2.04	5.33
10/22/15	0.12	2.04	5.30
10/29/15	0.12	2.19	5.40
11/05/15	0.12	2.26	5.44
11/12/15	0.12	2.32	5.51
11/19/15	0.12	2.24	5.44
11/25/15	0.12	2.23	5.44
12/03/15	0.13	2.33	5.51
12/10/15	0.14	2.24	5.43
12/17/15	0.37	2.24	5.45
12/23/15	0.36	2.27	5.53
12/30/15	0.35	2.31	5.54
1/07/2016	0.36	2.16	5.44
01/14/16	0.36	2.10	5.46
01/20/16	0.37	2.01	5.41
01/29/16	0.38	2.00	5.48
02/04/16	0.38	1.87	5.40
02/11/16	0.38	1.63	5.26
02/18/16	0.38	1.75	5.37
02/25/16	0.37	1.71	5.27
03/03/16	0.37	1.83	5.30
03/10/16	0.36	1.93	5.23
03/17/16	0.37	1.91	5.11
03/24/16	0.37	1.91	4.97
03/31/16	0.25	1.78	4.90
04/07/16	0.37	1.70	4.76
04/14/16	0.37	1.80	4.79
04/21/16	0.37	1.88	4.79
04/28/16	0.37	1.84	4.73
05/05/16	0.37	1.76	4.62
05/12/16	0.37	1.75	4.66
05/19/16	0.37	1.85	4.70
05/26/16	0.37	1.83	4.69
06/02/16	0.37	1.81	4.64
06/09/16	0.37	1.68	4.53
06/16/16	0.38	1.57	4.47
06/23/16	0.39	1.74	4.60
06/30/16	0.36	1.49	4.41
07/07/16	0.40	1.40	4.19
07/14/16	0.40	1.53	4.23
07/21/16	0.40	1.57	4.25
07/28/16	0.40	1.52	4.20
08/04/16	0.40	1.51	4.27
08/11/16	0.40	1.57	4.27
08/18/16	0.40	1.53	4.23
08/25/16	0.40	1.58	4.21
09/01/16	0.40	1.57	4.19
09/08/16	0.40	1.61	4.28
09/15/16	0.40	1.71	4.43
09/22/16	0.40	1.63	4.32
09/29/16	0.40	1.56	4.23
10/06/16	0.40	1.75	4.36
10/13/16	0.40	1.75	NA*
10/20/16	0.41	1.76	NA*
10/27/16	0.41	1.85	NA*
11/03/16	0.41	1.82	NA*
11/09/16	0.41	2.07	NA*
11/17/16	0.41	2.29	NA*
11/23/16	0.40	2.36	NA*
12/01/16	0.40	2.45	NA*
12/08/16	0.41	2.40	NA*
12/15/16	0.66	2.60	NA*
12/22/16	0.66	2.55	NA*
12/29/16	0.66	2.49	NA*
01/05/17	0.66	2.37	NA*
01/12/17	0.66	2.36	NA*

*Note: the Board of Governors of the Federal Reserve System discontinued the publication of the BAA bond yield.

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15/

Chart VIII-2 of the Board of Governors of the Federal Reserve System provides the rate of US dollars (USD) per euro (EUR), USD/EUR. The rate appreciated from USD 1.0860/EUR on Jan 6, 2016 to USD 1.0560/EUR on Jan 6, 2017 or 2.8 percent. The euro has devalued 49.4 percent relative to the dollar from the high on Jul 15, 2008 to Jan 13, 2017. US corporations with foreign transactions and net worth experience losses in their balance sheets in converting revenues from depreciated currencies to the dollar. Corporate profits with IVA and CCA fell at $127.9 billion in IVQ2015 with decrease of domestic industries at $149.8 billion, mostly because of decrease of nonfinancial business at $131.7 billion, and increase of profits from operations in the rest of the world at $22.0 billion. Receipts from the rest of the world fell at $19.9 billion. Corporate profits with IVA and CCA increased at $66.0 billion in IQ2016 with increase of domestic industries at $92.9 billion. Profits from operations from the rest of the world fell at $26.9 billion and payments to the rest of the world increased at $35.6 billion. Corporate profits with IVA and CCA decreased at $12.5 billion in IIQ2016. Profits from domestic industries fell at $50.5 billion and profits from nonfinancial business fell at $56.1 billion. Profits from the rest of the world increased at $38.0 billion. Corporate profits with IVA and CCA increased at $117.8 billion in IIIQ2016. Profits from domestic industries increased at $116.5 billion and profits from nonfinancial business increased at $66.4 billion. Profits from the rest of the world increased at $1.3 billion. Total corporate profits with IVA and CCA were $2138.8 billion in IIIQ2016 of which $1729.9 billion from domestic industries, or 80.9 percent of the total, and $408.9 billion, or 19.1 percent, from the rest of the world. Nonfinancial corporate profits of $1236.9 billion account for 57.8 percent of the total. Uncertainty originating in fiscal, regulatory and monetary policy causes wide swings in expectations and decisions by the private sector with adverse effects on investment, real economic activity and employment. There is increase in corporate profits from devaluing the dollar with unconventional monetary policy of zero interest rates and decrease of corporate profits in revaluing the dollar with attempts at “normalization” or increases in interest rates. Conflicts arise while other central banks differ in their adjustment process. The current account deficit seasonally adjusted decreases from 2.7 percent of GDP in IIIQ2015 to 2.5 percent in IVQ2015. The current account deficit increases to 2.9 percent of GDP in IQ2016. The deficit decreases to 2.6 percent in IIQ2016 and decreases to 2.4 percent in IIIQ2016. The net international investment position increases from minus $7.2 trillion in IIIQ2015 to minus $7.3 trillion in IVQ2015, increasing at minus $7.6 trillion in IQ2016. The net international investment position increases to minus $8.0 trillion in IIQ2016 and decreases to minus $7.8 trillion in IIIQ2016. The BEA explains as follows (https://www.bea.gov/newsreleases/international/intinv/2016/pdf/intinv316.pdf):

“The U.S. net international investment position increased to −$7,781.1 billion (preliminary) at the end of the third quarter of 2016 from −$8,026.9 billion (revised) at the end of the second quarter, according to statistics released today by the Bureau of Economic Analysis (BEA). The $245.8 billion increase in the net investment position reflected a $346.2 billion increase in U.S. assets and a $100.5 billion increase in U.S. liabilities. The net investment position increased 3.1 percent in the third quarter, compared with a decrease of 5.9 percent in the second quarter and an average quarterly decrease of 6.0 percent from the first quarter of 2011 through the first quarter of 2016.”

The BEA explains further (https://www.bea.gov/newsreleases/international/intinv/2016/pdf/intinv316.pdf): “U.S. assets increased $346.2 billion to $24,861.2 billion at the end of the third quarter, reflecting an increase in assets excluding financial derivatives that was partly offset by a decrease in financial derivatives. Assets excluding financial derivatives increased $794.9 billion to $22,086.1 billion, mostly reflecting increases in portfolio investment and direct investment assets due to increases in foreign equity prices. Financial derivatives decreased $448.7 billion to $2,775.1 billion, mostly in single-currency interest rate contracts and in foreign exchange contracts. U.S. liabilities increased $100.5 billion to $32,642.3 billion at the end of the third quarter, reflecting an increase in liabilities excluding financial derivatives that was partly offset by a decrease in financial derivatives. Liabilities excluding financial derivatives increased $546.3 billion to $29,922.5 billion, reflecting increases in portfolio investment and direct investment liabilities due to financial transactions and increases in U.S. equity prices. Financial derivatives decreased $445.8 billion to $2,719.9 billion, mostly in single-currency interest rate contracts and in foreign exchange contracts.”

Chart VIII-2, Exchange Rate of US Dollars (USD) per Euro (EUR), Jan 6, 2016 to Jan 6, 2017

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VIII-3 of the Board of Governors of the Federal Reserve System provides the yield of the 10-year Treasury constant maturity note from 1.79 percent on Oct 12, 2016 to 2.36 percent on Jan 12, 2017. There is turbulence in financial markets originating in a combination of intentions of normalizing or increasing US policy fed funds rate, quantitative easing in Europe and Japan and increasing perception of financial/economic risks.

Chart VIII-3, Yield of Ten-year Constant Maturity Treasury, Oct 12, 2016 to Jan 12, 2017

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H15/default.htm

Chart S provides the yield of the two-year Treasury constant maturity from Mar 17, 2014, two days before the guidance of Chair Yellen on Mar 19, 2014, to Jan 12, 2017. Chart SA provides the yields of the seven-, ten- and thirty-year Treasury constant maturity in the same dates. Yields increased right after the guidance of Chair Yellen. The two-year yield remain at a higher level than before while the ten-year yield fell and increased again. There could be more immediate impact on two-year yields of an increase in the fed funds rates but the effects would spread throughout the term structure of interest rates (Cox, Ingersoll and Ross 1981, 1985, Ingersoll 1987). Yields converged toward slightly lower earlier levels in the week of Apr 24, 2014 with reallocation of portfolios of risk financial assets away from equities and into bonds and commodities. There is ongoing reshuffling of portfolios to hedge against geopolitical events and world/regional economic performance.

Chart S, US, Yield of Two-Year Treasury Constant Maturity, Mar 17, 2014 to Jan 12, 2017 

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15/

Chart SA, US, Yield of Seven-Year, Ten-Year and Thirty-Year Treasury Constant Maturity, Mar 17, 2014 to Jan 12, 2017 

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15/

Chair Yellen states (http://www.federalreserve.gov/newsevents/speech/yellen20140331a.htm):

“And based on the evidence available, it is clear to me that the U.S. economy is still considerably short of the two goals assigned to the Federal Reserve by the Congress. The first of those goals is maximum sustainable employment, the highest level of employment that can be sustained while maintaining a stable inflation rate. Most of my colleagues on the Federal Open Market Committee and I estimate that the unemployment rate consistent with maximum sustainable employment is now between 5.2 percent and 5.6 percent, well below the 6.7 percent rate in February.

Let me explain what I mean by that word "slack" and why it is so important.

Slack means that there are significantly more people willing and capable of filling a job than there are jobs for them to fill. During a period of little or no slack, there still may be vacant jobs and people who want to work, but a large share of those willing to work lack the skills or are otherwise not well suited for the jobs that are available. With 6.7 percent unemployment, it might seem that there must be a lot of slack in the U.S. economy, but there are reasons why that may not be true.”

Inflation and unemployment in the period 1966 to 1985 is analyzed by Cochrane (2011Jan, 23) by means of a Phillips circuit joining points of inflation and unemployment. Chart VI-1B for Brazil in Pelaez (1986, 94-5) was reprinted in The Economist in the issue of Jan 17-23, 1987 as updated by the author. Cochrane (2011Jan, 23) argues that the Phillips circuit shows the weakness in Phillips curve correlation. The explanation is by a shift in aggregate supply, rise in inflation expectations or loss of anchoring. The case of Brazil in Chart VI-1B cannot be explained without taking into account the increase in the fed funds rate that reached 22.36 percent on Jul 22, 1981 (http://www.federalreserve.gov/releases/h15/data.htm) in the Volcker Fed that precipitated the stress on a foreign debt bloated by financing balance of payments deficits with bank loans in the 1970s. The loans were used in projects, many of state-owned enterprises with low present value in long gestation. The combination of the insolvency of the country because of debt higher than its ability of repayment and the huge government deficit with declining revenue as the economy contracted caused adverse expectations on inflation and the economy.  This interpretation is consistent with the case of the 24 emerging market economies analyzed by Reinhart and Rogoff (2010GTD, 4), concluding that “higher debt levels are associated with significantly higher levels of inflation in emerging markets. Median inflation more than doubles (from less than seven percent to 16 percent) as debt rises from the low (0 to 30 percent) range to above 90 percent. Fiscal dominance is a plausible interpretation of this pattern.”

The reading of the Phillips circuits of the 1970s by Cochrane (2011Jan, 25) is doubtful about the output gap and inflation expectations:

“So, inflation is caused by ‘tightness’ and deflation by ‘slack’ in the economy. This is not just a cause and forecasting variable, it is the cause, because given ‘slack’ we apparently do not have to worry about inflation from other sources, notwithstanding the weak correlation of [Phillips circuits]. These statements [by the Fed] do mention ‘stable inflation expectations. How does the Fed know expectations are ‘stable’ and would not come unglued once people look at deficit numbers? As I read Fed statements, almost all confidence in ‘stable’ or ‘anchored’ expectations comes from the fact that we have experienced a long period of low inflation (adaptive expectations). All these analyses ignore the stagflation experience in the 1970s, in which inflation was high even with ‘slack’ markets and little ‘demand, and ‘expectations’ moved quickly. They ignore the experience of hyperinflations and currency collapses, which happen in economies well below potential.”

Yellen (2014Aug22) states that “Historically, slack has accounted for only a small portion of the fluctuations in inflation. Indeed, unusual aspects of the current recovery may have shifted the lead-lag relationship between a tightening labor market and rising inflation pressures in either direction.”

Chart VI-1B provides the tortuous Phillips Circuit of Brazil from 1963 to 1987. There were no reliable consumer price index and unemployment data in Brazil for that period. Chart VI-1B used the more reliable indicator of inflation, the wholesale price index, and idle capacity of manufacturing as a proxy of unemployment in large urban centers.

ChVI1-B, Brazil, Phillips Circuit, 1963-1987

Source:

©Carlos Manuel Pelaez, O Cruzado e o Austral: Análise das Reformas Monetárias do Brasil e da Argentina. São Paulo: Editora Atlas, 1986, pages 94-5. Reprinted in: Brazil. Tomorrow’s Italy, The Economist, 17-23 January 1987, page 25.

The minutes of the meeting of the Federal Open Market Committee (FOMC) on Sep 16-17, 2014, reveal concern with global economic conditions (http://www.federalreserve.gov/monetarypolicy/fomcminutes20140917.htm):

“Most viewed the risks to the outlook for economic activity and the labor market as broadly balanced. However, a number of participants noted that economic growth over the medium term might be slower than they expected if foreign economic growth came in weaker than anticipated, structural productivity continued to increase only slowly, or the recovery in residential construction continued to lag.”

There is similar concern in the minutes of the meeting of the FOMC on Dec 16-17, 2014 (http://www.federalreserve.gov/monetarypolicy/fomcminutes20141217.htm):

“In their discussion of the foreign economic outlook, participants noted that the implications of the drop in crude oil prices would differ across regions, especially if the price declines affected inflation expectations and financial markets; a few participants said that the effect on overseas employment and output as a whole was likely to be positive. While some participants had lowered their assessments of the prospects for global economic growth, several noted that the likelihood of further responses by policymakers abroad had increased. Several participants indicated that they expected slower economic growth abroad to negatively affect the U.S. economy, principally through lower net exports, but the net effect of lower oil prices on U.S. economic activity was anticipated to be positive.”

There is concern at the Federal Open Market Committee (FOMC) with the world economy and financial markets (http://www.federalreserve.gov/newsevents/press/monetary/20160127a.htm): “The Committee is closely monitoring global economic and financial developments and is assessing their implications for the labor market and inflation, and for the balance of risks to the outlook” (emphasis added). This concern should include the effects on dollar revaluation of competitive easing by other central banks such as quantitative and qualitative easing with negative nominal interest rates.”

It is quite difficult to measure inflationary expectations because they tend to break abruptly from past inflation. There could still be an influence of past and current inflation in the calculation of future inflation by economic agents. Table VIII-1 provides inflation of the CPI. In the three months from Sep 2016 to Nov 2016, CPI inflation for all items seasonally adjusted was 3.7 percent in annual equivalent, obtained by calculating accumulated inflation from Sep 2016 to Nov 2016 and compounding for a full year. In the 12 months ending in Nov 2016, CPI inflation of all items not seasonally adjusted was 1.7 percent. Inflation in Nov 2016 seasonally adjusted was 0.2 percent relative to Oct 2016, or 2.4 percent annual equivalent (http://www.bls.gov/cpi/). The second row provides the same measurements for the CPI of all items excluding food and energy: 2.1 percent in 12 months, 1.6 percent in annual equivalent Sep 2016-Nov 2016 and 0.2 percent in Nov 2016 or 2.4 percent in annual equivalent. The Wall Street Journal provides the yield curve of US Treasury securities (http://professional.wsj.com/mdc/public/page/mdc_bonds.html?mod=mdc_topnav_2_3000). The shortest term is 0.520 percent for one month, 0.533 percent for three months, 0.608 percent for six months, 0.802 percent for one year, 1.197 percent for two years, 1.482 percent for three years, 1.898 percent for five years, 2.205 percent for seven years, 2.397 percent for ten years and 2.991 percent for 30 years. The Irving Fisher (1930) definition of real interest rates is approximately the difference between nominal interest rates, which are those estimated by the Wall Street Journal, and the rate of inflation expected in the term of the security, which could behave as in Table VIII-1. Inflation in Jan 2016 is low in 12 months because of the unwinding of carry trades from zero interest rates to commodity futures prices but could ignite again with subdued risk aversion. Real interest rates in the US have been negative during substantial periods in the past decade while monetary policy pursues a policy of attaining its “dual mandate” of (http://www.federalreserve.gov/aboutthefed/mission.htm):

“Conducting the nation's monetary policy by influencing the monetary and credit conditions in the economy in pursuit of maximum employment, stable prices, and moderate long-term interest rates”

Negative real rates of interest distort calculations of risk and returns from capital budgeting by firms, through lending by financial intermediaries to decisions on savings, housing and purchases of households. Inflation on near zero interest rates misallocates resources away from their most productive uses and creates uncertainty of the future path of adjustment to higher interest rates that inhibit sound decisions.

Table I-3, US, Consumer Price Index Percentage Changes 12 months NSA and Annual Equivalent ∆%

	% RI	∆% 12 Months Nov 2016/Nov 2015 NSA	∆% Annual Equivalent Sep 2016 to Nov 2016 SA	∆% Nov 2016/Oct 2016 SA
CPI All Items	100.000	1.7	3.7	0.2
CPI ex Food and Energy	79.193	2.1	1.6	0.2

% RI: Percent Relative Importance

Source: US Bureau of Labor Statistics http://www.bls.gov/cpi/

Professionals use a variety of techniques in measuring interest rate risk (Fabozzi, Buestow and Johnson, 2006, Chapter Nine, 183-226):

• Full valuation approach in which securities and portfolios are shocked by 50, 100, 200 and 300 basis points to measure their impact on asset values
• Stress tests requiring more complex analysis and translation of possible events with high impact even if with low probability of occurrence into effects on actual positions and capital
• Value at Risk (VaR) analysis of maximum losses that are likely in a time horizon
• Duration and convexity that are short-hand convenient measurement of changes in prices resulting from changes in yield captured by duration and convexity
• Yield volatility

Analysis of these methods is in Pelaez and Pelaez (International Financial Architecture (2005), 101-162) and Pelaez and Pelaez, Globalization and the State, Vol. (I) (2008a), 78-100). Frederick R. Macaulay (1938) introduced the concept of duration in contrast with maturity for analyzing bonds. Duration is the sensitivity of bond prices to changes in yields. In economic jargon, duration is the yield elasticity of bond price to changes in yield, or the percentage change in price after a percentage change in yield, typically expressed as the change in price resulting from change of 100 basis points in yield. The mathematical formula is the negative of the yield elasticity of the bond price or –[dB/d(1+y)]((1+y)/B), where d is the derivative operator of calculus, B the bond price, y the yield and the elasticity does not have dimension (Hallerbach 2001). The duration trap of unconventional monetary policy is that duration is higher the lower the coupon and higher the lower the yield, other things being constant. Coupons and yields are historically low because of unconventional monetary policy. Duration dumping during a rate increase may trigger the same crossfire selling of high duration positions that magnified the credit crisis. Traders reduced positions because capital losses in one segment, such as mortgage-backed securities, triggered haircuts and margin increases that reduced capital available for positioning in all segments, causing fire sales in multiple segments (Brunnermeier and Pedersen 2009; see Pelaez and Pelaez, Regulation of Banks and Finance (2008b), 217-24). Financial markets are currently experiencing fear of duration and riskier asset classes resulting from the debate within and outside the Fed on increasing interest rates. Table VIII-2 provides the yield curve of Treasury securities on Jan 13, 2017, Dec 31, 2013, May 1, 2013, Jan 13, 2016 and Jan 13, 2006. There is oscillating steepening of the yield curve for longer maturities, which are also the ones with highest duration. The 10-year yield increased from 1.45 percent on Jul 26, 2012 to 3.04 percent on Dec 31, 2013 and 2.40 percent on Jan 13, 2017, as measured by the United States Treasury. Assume that a bond with maturity in 10 years were issued on Dec 31, 2013, at par or price of 100 with coupon of 1.45 percent. The price of that bond would be 86.3778 with instantaneous increase of the yield to 3.04 percent for loss of 13.6 percent and far more with leverage. Assume that the yield of a bond with exactly ten years to maturity and coupon of 2.40 percent would jump instantaneously from yield of 2.40 percent on Jan 13, 2017 to 4.36 percent as occurred on Jan 13, 2006 when the economy was closer to full employment. The price of the hypothetical bond issued with coupon of 2.40 percent would drop from 100 to 84.2505 after an instantaneous increase of the yield to 4.36 percent. The price loss would be 15.7 percent. Losses absorb capital available for positioning, triggering crossfire sales in multiple asset classes (Brunnermeier and Pedersen 2009). What is the path of adjustment of zero interest rates on fed funds and artificially low bond yields? There is no painless exit from unconventional monetary policy. Chris Dieterich, writing on “Bond investors turn to cash,” on Jul 25, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323971204578625900935618178.html), uses data of the Investment Company Institute (http://www.ici.org/) in showing withdrawals of $43 billion in taxable mutual funds in Jun, which is the largest in history, with flows into cash investments such as $8.5 billion in the week of Jul 17 into money-market funds.

Table VIII-2, United States, Treasury Yields

	01/13/17	12/31/13	5/01/13	01/13/16	01/06/06
1 M	0.52	0.00	0.03	0.22	4.13
3 M	0.53	0.01	0.06	0.22	4.33
6 M	0.61	0.07	0.08	0.46	4.43
1 Y	0.82	0.25	0.11	0.60	4.40
2 Y	1.21	0.56	0.20	0.91	4.34
3 Y	1.48	0.91	0.30	1.15	4.29
5 Y	1.90	1.43	0.65	1.51	4.28
7 Y	2.20	1.80	1.07	1.85	4.30
10 Y	2.40	3.04	1.66	2.08	4.36
20 Y	2.71	3.72	2.44	2.47	4.59
30 Y	2.99	3.96	2.83	2.85	NA

M: Months; Y: Years

Source: United States Treasury

http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield

Interest rate risk is increasing in the US with amplifying fluctuations. Chart VI-13 of the Board of Governors provides the conventional mortgage rate for a fixed-rate 30-year mortgage. The rate stood at 5.87 percent on Jan 8, 2004, increasing to 6.79 percent on Jul 6, 2006. The rate bottomed at 3.35 percent on May 2, 2013. Fear of duration risk in longer maturities such as mortgage-backed securities caused continuing increases in the conventional mortgage rate that rose to 4.51 percent on Jul 11, 2013, 4.58 percent on Aug 22, 2013 and 3.42 percent on Oct 6, 2016, which is the last data point in Chart VI-13. The thirty-year mortgage rate was 4.12 percent on Jan 12, 2017 (http://www.freddiemac.com/finance/ http://www.freddiemac.com/pmms/index.html). The current decline of yields is encouraging a surge in mortgage applications that could be reversed in a new increase. Shayndi Raice and Nick Timiraos, writing on “Banks cut as mortgage boom ends,” on Jan 9, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303754404579310940019239208), analyze the drop in mortgage applications to a 13-year low, as measured by the Mortgage Bankers Association. Nick Timiraos, writing on “Demand for home loans plunges,” on Apr 24, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304788404579522051733228402?mg=reno64-wsj), analyzes data in Inside Mortgage Finance that mortgage lending of $235 billion in IQ2014 is 58 percent lower than a year earlier and 23 percent below IVQ2013. Mortgage lending collapsed to the lowest level in 14 years. In testimony before the Committee on the Budget of the US Senate on May 8, 2004, Chair Yellen provides analysis of the current economic situation and outlook (http://www.federalreserve.gov/newsevents/testimony/yellen20140507a.htm): “One cautionary note, though, is that readings on housing activity--a sector that has been recovering since 2011--have remained disappointing so far this year and will bear watching.”

Chart VI-13, US, Conventional Mortgage Rate, Jan 8, 2004 to Oct 6, 2016

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15/

Table IIB-8, US, Fed Funds Rate, Thirty Year Treasury Bond and Conventional Mortgage Rate, Monthly, Percent per Year, Dec 2012 to Aug 2016

	Fed Funds Rate	Yield of Thirty Year Constant Maturity	Conventional Mortgage Rate
2012-12	0.16	2.88	3.35
2013-01	0.14	3.08	3.41
2013-02	0.15	3.17	3.53
2013-03	0.14	3.16	3.57
2013-04	0.15	2.93	3.45
2013-05	0.11	3.11	3.54
2013-06	0.09	3.40	4.07
2013-07	0.09	3.61	4.37
2013-08	0.08	3.76	4.46
2013-09	0.08	3.79	4.49
2013-10	0.09	3.68	4.19
2013-11	0.08	3.80	4.26
2013-12	0.09	3.89	4.46
2014-01	0.07	3.77	4.43
2014-02	0.07	3.66	4.30
2014-03	0.08	3.62	4.34
2014-04	0.09	3.52	4.34
2014-05	0.09	3.39	4.19
2014-06	0.1	3.42	4.16
2014-07	0.09	3.33	4.13
2014-08	0.09	3.2	4.12
2014-09	0.09	3.26	4.16
2014-10	0.09	3.04	4.04
2014-11	0.09	3.04	4.00
2014-12	0.12	2.83	3.86
2015-01	0.11	2.46	3.67
2015-02	0.11	2.57	3.71
2015-03	0.11	2.63	3.77
2015-04	0.12	2.59	3.67
2015-05	0.12	2.96	3.84
2015-06	0.13	3.11	3.98
2015-07	0.13	3.07	4.05
2015-08	0.14	2.86	3.91
2015-09	0.14	2.95	3.89
2015-10	0.12	2.89	3.80
2015-11	0.12	3.03	3.94
2015-12	0.24	2.97	3.96
2016-01	0.34	2.86	3.87
2016-02	0.38	2.62	3.66
2016-03	0.36	2.68	3.69
2016-04	0.37	2.62	3.61
2016-05	0.37	2.63	3.60
2016-06	0.38	2.45	3.57
2016-07	0.39	2.23	3.44
2016-08	0.40	2.26	3.44
2016-09	0.40	2.35	3.46
2016-10	0.40	2.50	3.47
2016-11	0.41	2.86	3.77
2016-12	0.54	3.11	4.20

Source: Board of Governors of the Federal Reserve System

http://www.federalreserve.gov/releases/H15/default.htm

http://www.freddiemac.com/pmms/pmms30.htm

There is a false impression of the existence of a monetary policy “science,” measurements and forecasting with which to steer the economy into “prosperity without inflation.” Market participants are remembering the Great Bond Crash of 1994 shown in Table VI-7G when monetary policy pursued nonexistent inflation, causing trillions of dollars of losses in fixed income worldwide while increasing the fed funds rate from 3 percent in Jan 1994 to 6 percent in Dec. The exercise in Table VI-7G shows a drop of the price of the 30-year bond by 18.1 percent and of the 10-year bond by 14.1 percent. CPI inflation remained almost the same and there is no valid counterfactual that inflation would have been higher without monetary policy tightening because of the long lag in effect of monetary policy on inflation (see Culbertson 1960, 1961, Friedman 1961, Batini and Nelson 2002, Romer and Romer 2004). The pursuit of nonexistent deflation during the past ten years has resulted in the largest monetary policy accommodation in history that created the 2007 financial market crash and global recession and is currently preventing smoother recovery while creating another financial crash in the future. The issue is not whether there should be a central bank and monetary policy but rather whether policy accommodation in doses from zero interest rates to trillions of dollars in the fed balance sheet endangers economic stability.

Table VI-7G, Fed Funds Rates, Thirty and Ten Year Treasury Yields and Prices, 30-Year Mortgage Rates and 12-month CPI Inflation 1994

1994	FF	30Y	30P	10Y	10P	MOR	CPI
Jan	3.00	6.29	100	5.75	100	7.06	2.52
Feb	3.25	6.49	97.37	5.97	98.36	7.15	2.51
Mar	3.50	6.91	92.19	6.48	94.69	7.68	2.51
Apr	3.75	7.27	88.10	6.97	91.32	8.32	2.36
May	4.25	7.41	86.59	7.18	88.93	8.60	2.29
Jun	4.25	7.40	86.69	7.10	90.45	8.40	2.49
Jul	4.25	7.58	84.81	7.30	89.14	8.61	2.77
Aug	4.75	7.49	85.74	7.24	89.53	8.51	2.69
Sep	4.75	7.71	83.49	7.46	88.10	8.64	2.96
Oct	4.75	7.94	81.23	7.74	86.33	8.93	2.61
Nov	5.50	8.08	79.90	7.96	84.96	9.17	2.67
Dec	6.00	7.87	81.91	7.81	85.89	9.20	2.67

Notes: FF: fed funds rate; 30Y: yield of 30-year Treasury; 30P: price of 30-year Treasury assuming coupon equal to 6.29 percent and maturity in exactly 30 years; 10Y: yield of 10-year Treasury; 10P: price of 10-year Treasury assuming coupon equal to 5.75 percent and maturity in exactly 10 years; MOR: 30-year mortgage; CPI: percent change of CPI in 12 months

Sources: yields and mortgage rates http://www.federalreserve.gov/releases/h15/data.htm CPI ftp://ftp.bls.gov/pub/special.requests/cpi/cpiai.t

Chart VI-14 provides the overnight fed funds rate, the yield of the 10-year Treasury constant maturity bond, the yield of the 30-year constant maturity bond and the conventional mortgage rate from Jan 1991 to Dec 1996. In Jan 1991, the fed funds rate was 6.91 percent, the 10-year Treasury yield 8.09 percent, the 30-year Treasury yield 8.27 percent and the conventional mortgage rate 9.64 percent. Before monetary policy tightening in Oct 1993, the rates and yields were 2.99 percent for the fed funds, 5.33 percent for the 10-year Treasury, 5.94 for the 30-year Treasury and 6.83 percent for the conventional mortgage rate. After tightening in Nov 1994, the rates and yields were 5.29 percent for the fed funds rate, 7.96 percent for the 10-year Treasury, 8.08 percent for the 30-year Treasury and 9.17 percent for the conventional mortgage rate.

Chart VI-14, US, Overnight Fed Funds Rate, 10-Year Treasury Constant Maturity, 30-Year Treasury Constant Maturity and Conventional Mortgage Rate, Monthly, Jan 1991 to Dec 1996

Source: Board of Governors of the Federal Reserve System

http://www.federalreserve.gov/releases/h15/update/

Chart VI-15 of the Bureau of Labor Statistics provides the all items consumer price index from Jan 1991 to Dec 1996. There does not appear acceleration of consumer prices requiring aggressive tightening.

Chart VI-15, US, Consumer Price Index All Items, Jan 1991 to Dec 1996

Source: Bureau of Labor Statistics

http://www.bls.gov/cpi/data.htm

Chart IV-16 of the Bureau of Labor Statistics provides 12-month percentage changes of the all items consumer price index from Jan 1991 to Dec 1996. Inflation collapsed during the recession from Jul 1990 (III) and Mar 1991 (I) and the end of the Kuwait War on Feb 25, 1991 that stabilized world oil markets. CPI inflation remained almost the same and there is no valid counterfactual that inflation would have been higher without monetary policy tightening because of the long lag in effect of monetary policy on inflation (see Culbertson 1960, 1961, Friedman 1961, Batini and Nelson 2002, Romer and Romer 2004). Policy tightening had adverse collateral effects in the form of emerging market crises in Mexico and Argentina and fixed income markets worldwide.

Chart VI-16, US, Consumer Price Index All Items, Twelve-Month Percentage Change, Jan 1991 to Dec 1996

Source: Bureau of Labor Statistics

http://www.bls.gov/cpi/data.htm

Table VI-2 provides the Euro/Dollar (EUR/USD) exchange rate and Chinese Yuan/Dollar (CNY/USD) exchange rate that reveal pursuit of exchange rate policies resulting from monetary policy in the US and capital control/exchange rate policy in China. The ultimate intentions are the same: promoting internal economic activity at the expense of the rest of the world. The easy money policy of the US was deliberately or not but effectively to devalue the dollar from USD 1.1423/EUR on Jun 26, 2003 to USD 1.5914/EUR on Jul 14, 2008, or by 39.3 percent. The flight into dollar assets after the global recession caused revaluation to USD 1.192/EUR on Jun 7, 2010, or by 25.1 percent. After the temporary interruption of the sovereign risk issues in Europe from Apr to Jul, 2010, shown in Table VI-4 below, the dollar has revalued to USD 1.0645 EUR on Jan 13, 2017 or by 10.7 percent {[(1.0645/1.192)-1]100 = -10.7%}. Yellen (2011AS, 6) admits that Fed monetary policy results in dollar devaluation with the objective of increasing net exports, which was the policy that Joan Robinson (1947) labeled as “beggar-my-neighbor” remedies for unemployment. Risk aversion erodes devaluation of the dollar. China fixed the CNY to the dollar for a long period at a highly undervalued level of around CNY 8.2765/USD subsequently revaluing to CNY 6.8211/USD until Jun 7, 2010, or by 17.6 percent. After fixing again the CNY to the dollar, China devalued to CNY 6.8998/USD on Fri Jan 13, 2017, or by 1.2 percent, for cumulative revaluation of 16.6 percent. The final row of Table VI-2 shows: revaluation of 0.2 percent in the week of Dec 23, 2016; change of 0.0 percent in the week of Dec 30, 2016; revaluation of 0.4 percent in the week of Jan 6, 2017; and revaluation of 0.3 percent in the week of Jan 13, 2017. There could be reversal of revaluation to devalue the Yuan.

Table VI-2, Dollar/Euro (USD/EUR) Exchange Rate and Chinese Yuan/Dollar (CNY/USD) Exchange Rate

USD/EUR	12/26/03	7/14/08	6/07/10	01/13/17
Rate	1.1423	1.5914	1.192	1.0645
CNY/USD	01/03 2000	07/21 2005	7/15 2008	01/13/17
Rate	8.2765	8.2765	6.8211	6.8998
Weekly Rates	12/23/2016	12/30/2016	01/06/2017	01/13/17
CNY/USD	6.9463	6.9448	6.9185	6.8998
∆% from Earlier Week*	0.2	0.0	0.4	0.3

*Negative sign is depreciation; positive sign is appreciation

Source: http://professional.wsj.com/mdc/public/page/mdc_currencies.html?mod=mdc_topnav_2_3000

Professor Edward P Lazear (2013Jan7), writing on “Chinese ‘currency manipulation’ is not the problem,” on Jan 7, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887323320404578213203581231448.html), provides clear thought on the role of the yuan in trade between China and the United States and trade between China and Europe. There is conventional wisdom that Chinese exchange rate policy causes the loss of manufacturing jobs in the United States, which is shown by Lazear (2013Jan7) to be erroneous. The fact is that manipulation of the CNY/USD rate by China has only minor effects on US employment. Lazear (2013Jan7) shows that the movement of monthly exports of China to its major trading partners, United States and Europe, since 1995 cannot be explained by the fixing of the CNY/USD rate by China. The period is quite useful because it includes rapid growth before 2007, contraction until 2009 and weak subsequent expansion. Chart VI-1 of the Board of Governors of the Federal Reserve System provides the CNY/USD exchange rate from Jan 3, 1995 to Jan 6, 2017 together with US recession dates in shaded areas. China fixed the CNY/USD rate for a long period as shown in the horizontal segment from 1995 to 2005. There was systematic revaluation of 17.6 percent from CNY 8.2765 on Jul 21, 2005 to CNY 6.8211 on Jul 15, 2008. China fixed the CNY/USD rate until Jun 7, 2010, to avoid adverse effects on its economy from the global recession, which is shown as a horizontal segment from 2009 until mid 2010. China then continued the policy of appreciation of the CNY relative to the USD with oscillations until the beginning of 2012 when the rate began to move sideways followed by a final upward slope of devaluation that is measured in Table VI-2A but virtually disappeared in the rate of CNY 6.3589/USD on Aug 17, 2012 and was nearly unchanged at CNY 6.3558/USD on Aug 24, 2012. China then appreciated 0.2 percent in the week of Dec 21, 2012, to CNY 6.2352/USD for cumulative 1.9 percent revaluation from Oct 28, 2011 and left the rate virtually unchanged at CNY 6.2316/USD on Jan 11, 2013, appreciating to CNY 6.9176/USD on Jan 6, 2017, which is the last data point in Chart VI-1. Revaluation of the CNY relative to the USD of 16.6 percent by Jan 13, 2017 has not reduced the trade surplus of China but reversal of the policy of revaluation could result in international confrontation. The interruption with upward slope in the final segment on the right of Chart VI-I is measured as virtually stability in Table VI-2A followed with decrease or revaluation and subsequent increase or devaluation. The final segment shows decline or revaluation with another upward move or devaluation. Linglin Wei, writing on “China intervenes to lower yuan,” on Feb 26, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304071004579406810684766716?KEYWORDS=china+yuan&mg=reno64-wsj), finds from informed sources that the central bank of China conducted the ongoing devaluation of the yuan with the objective of driving out arbitrageurs to widen the band of fluctuation. There is concern if the policy of revaluation is changing to devaluation.

Chart VI-1, Chinese Yuan (CNY) per US Dollar (USD), Business Days, Jan 3, 1995-Jan 6, 2017

Note: US Recessions in Shaded Areas

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VI-1A provides the daily CNY/USD rate from Jan 5, 1981 to Jan 6, 2017. The exchange rate was CNY 1.5418/USD on Jan 5, 1981. There is sharp cumulative depreciation of 107.8 percent to CNY 3.2031 by Jul 2, 1986, continuing to CNY 5.8145/USD on Dec 29, 1993 for cumulative 277.1 percent since Jan 5, 1981. China then devalued sharply to CNY 8.7117/USD on Jan 7, 1994 for 49.8 percent relative to Dec 29, 1993 and cumulative 465.0 percent relative to Jan 5, 1981. China then fixed the rate at CNY 8.2765/USD until Jul 21, 2005 and revalued as analyzed in Chart VI-1. The final data point in Chart VI-1A is CNY 6.9176/USD on Jan 6, 2017. To be sure, China fixed the exchange rate after substantial prior devaluation. It is unlikely that the devaluation could have been effective after many years of fixing the exchange rate with high inflation and multiple changes in the world economy. The argument of Lazear (2013Jan7) is still valid in view of the lack of association between monthly exports of China to the US and Europe since 1995 and the exchange rate of China.

Chart VI-1A, Chinese Yuan (CNY) per US Dollar (USD), Business Days, Jan 5, 1981-Jan 6, 2017

Note: US Recessions in Shaded Areas

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VI-1B provides finer details with the rate of Chinese Yuan (CNY) to the US Dollar (USD) from Oct 28, 2011 to Jan 6, 2017. There have been alternations of revaluation and devaluation. The initial data point is CNY 6.5370 on Oct 28, 2011. There is an episode of devaluation from CNY 6.2790 on Apr 30, 2012 to CNY 6.3879 on Jul 25, 2012, or devaluation of 1.4 percent. Another devaluation is from CNY 6.0402/USD on Jan 21, 2014 to CNY 6.9176/USD on Jan 6, 2017, or devaluation of 14.5 percent. The United States Treasury estimates US government debt held by private investors at $11,184 billion in Sep 2016. China’s holding of US Treasury securities represent 10.0 percent of US government marketable interest-bearing debt held by private investors (http://www.fms.treas.gov/bulletin/index.html). Min Zeng, writing on “China plays a big role as US Treasury yields fall,” on Jul 16, 2014, published in the Wall Street Journal (http://online.wsj.com/articles/china-plays-a-big-role-as-u-s-treasury-yields-fall-1405545034?tesla=y&mg=reno64-wsj), finds that acceleration in purchases of US Treasury securities by China has been an important factor in the decline of Treasury yields in 2014. Japan decreased its holdings from $1149.2 billion in Oct 2015 to $1131.9 billion in Oct 2016 or 1.5 percent. The combined holdings of China and Japan in Oct 2016 add to $2247.6 billion, which is equivalent to 20.1 percent of US government marketable interest-bearing securities held by investors of $11,184 billion in Jun 2016 (http://www.fms.treas.gov/bulletin/index.html). Total foreign holdings of Treasury securities decreased from $6047.2 billion in Oct 2015 to $6038.9 billion in Oct 2016, or 0.1 percent. The US continues to finance its fiscal and balance of payments deficits with foreign savings (see Pelaez and Pelaez, The Global Recession Risk (2007)). A point of saturation of holdings of US Treasury debt may be reached as foreign holders evaluate the threat of reduction of principal by dollar devaluation and reduction of prices by increases in yield, including possibly risk premium. Shultz et al (2012) find that the Fed financed three-quarters of the US deficit in fiscal year 2011, with foreign governments financing significant part of the remainder of the US deficit while the Fed owns one in six dollars of US national debt. Concentrations of debt in few holders are perilous because of sudden exodus in fear of devaluation and yield increases and the limit of refinancing old debt and placing new debt. In their classic work on “unpleasant monetarist arithmetic,” Sargent and Wallace (1981, 2) consider a regime of domination of monetary policy by fiscal policy (emphasis added):

“Imagine that fiscal policy dominates monetary policy. The fiscal authority independently sets its budgets, announcing all current and future deficits and surpluses and thus determining the amount of revenue that must be raised through bond sales and seignorage. Under this second coordination scheme, the monetary authority faces the constraints imposed by the demand for government bonds, for it must try to finance with seignorage any discrepancy between the revenue demanded by the fiscal authority and the amount of bonds that can be sold to the public. Suppose that the demand for government bonds implies an interest rate on bonds greater than the economy’s rate of growth. Then if the fiscal authority runs deficits, the monetary authority is unable to control either the growth rate of the monetary base or inflation forever. If the principal and interest due on these additional bonds are raised by selling still more bonds, so as to continue to hold down the growth of base money, then, because the interest rate on bonds is greater than the economy’s growth rate, the real stock of bonds will growth faster than the size of the economy. This cannot go on forever, since the demand for bonds places an upper limit on the stock of bonds relative to the size of the economy. Once that limit is reached, the principal and interest due on the bonds already sold to fight inflation must be financed, at least in part, by seignorage, requiring the creation of additional base money.”

Chart VI-1B, Chinese Yuan (CNY) per US Dollar (US), Business Days, Oct 28, 2011-Jan 6, 2017

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

There are major ongoing and unresolved realignments of exchange rates in the international financial system as countries and regions seek parities that can optimize their productive structures. Seeking exchange rate parity or exchange rate optimizing internal economic activities is complex in a world of unconventional monetary policy of zero interest rates and even negative nominal interest rates of government obligations such as negative yields for the two-year government bond of Germany. Regulation, trade and devaluation conflicts should have been expected from a global recession (Pelaez and Pelaez (2007), The Global Recession Risk, Pelaez and Pelaez, Government Intervention in Globalization: Regulation, Trade and Devaluation Wars (2008a)): “There are significant grounds for concern on the basis of this experience. International economic cooperation and the international financial framework can collapse during extreme events. It is unlikely that there will be a repetition of the disaster of the Great Depression. However, a milder contraction can trigger regulatory, trade and exchange wars” (Pelaez and Pelaez, Government Intervention in Globalization: Regulation, Trade and Devaluation Wars (2008c), 181). Chart VI-2 of the Board of Governors of the Federal Reserve System provides the key exchange rate of US dollars (USD) per euro (EUR) from Jan 4, 1999 to Jan 6, 2017. US recession dates are in shaded areas. The rate on Jan 4, 1999 was USD 1.1812/EUR, declining to USD 0.8279/EUR on Oct 25, 2000, or appreciation of the USD by 29.9 percent. The rate depreciated 21.9 percent to USD 1.0098/EUR on Jul 22, 2002. There was sharp devaluation of the USD of 34.9 percent to USD 1.3625/EUR on Dec 27, 2004 largely because of the 1 percent interest rate between Jun 2003 and Jun 2004 together with a form of quantitative easing by suspension of auctions of the 30-year Treasury, which was equivalent to withdrawing supply from markets. Another depreciation of 17.5 percent took the rate to USD 1.6010/EUR on Apr 22, 2008, already inside the shaded area of the global recession. The flight to the USD and obligations of the US Treasury appreciated the dollar by 22.3 percent to USD 1.2446/EUR on Oct 27, 2008. In the return of the carry trade after stress tests showed sound US bank balance sheets, the rate depreciated 21.2 percent to USD 1.5085/EUR on Nov 25, 2009. The sovereign debt crisis of Europe in the spring of 2010 caused sharp appreciation of 20.7 percent to USD 1.1959/EUR on Jun 6, 2010. Renewed risk appetite depreciated the rate 24.4 percent to USD 1.4875/EUR on May 3, 2011. The rate appreciated 11.7 percent to USD 1.0560/EUR on Jan 6, 2016, which is the last point in Chart VI-2. The data in Table VI-6 is obtained from closing dates in New York published by the Wall Street Journal (http://professional.wsj.com/mdc/public/page/marketsdata.html?mod=WSJ_PRO_hps_marketdata).

Chart VI-2, US Dollars (USD) per Euro (EUR), Jan 4, 1999 to Jan 6, 2017

Note: US Recessions in Shaded Areas

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VI-3 provides three indexes of the US Dollars (USD) from Jan 4, 1995 to Jan 6, 2017.

Chart VI-3A provides the overnight fed funds rate and yields of the three-month constant maturity Treasury bill, the ten-year constant maturity Treasury note and Moody’s Baa bond from Jan 4, 1995 to Jul 7, 2016. Chart VI-3B provides the overnight fed funds rate and yields of the three-month constant maturity Treasury bill and the ten-year constant maturity Treasury from Jan 5, 2001 to Jan 12, 2017. The first phase from 1995 to 2001 shows sharp trend of appreciation of the USD while interest rates remained at relatively high levels. The dollar revalued partly because of the emerging market crises that provoked inflows of financial investment into the US and partly because of a deliberate strong dollar policy. DeLong and Eichengreen (2001, 4-5) argue:

“That context was an economic and political strategy that emphasized private investment as the engine for U.S. economic growth. Both components of this term, "private" and "investment," had implications for the administration’s international economic strategy. From the point of view of investment, it was important that international events not pressure on the Federal Reserve to raise interest rates, since this would have curtailed capital formation and vitiated the effects of the administration’s signature achievement: deficit reduction. A strong dollar -- or rather a dollar that was not expected to weaken -- was a key component of a policy which aimed at keeping the Fed comfortable with low interest rates. In addition, it was important to create a demand for the goods and services generated by this additional productive capacity. To the extent that this demand resided abroad, administration officials saw it as important that the process of increasing international integration, of both trade and finance, move forward for the interest of economic development in emerging markets and therefore in support of U.S. economic growth.”

The process of integration consisted of restructuring “international financial architecture” (Pelaez and Pelaez, International Financial Architecture: G7, IMF, BIS, Debtors and Creditors (2005)). Policy concerns subsequently shifted to the external imbalances, or current account deficits, and internal imbalances, or government deficits (Pelaez and Pelaez, The Global Recession Risk: Dollar Devaluation and the World Economy (2007)). Fed policy consisted of lowering the policy rate or fed funds rate, which is close to the marginal cost of funding of banks, toward zero during the past decade. Near zero interest rates induce carry trades of selling dollar debt (borrowing), shorting the USD and investing in risk financial assets. Without risk aversion, near zero interest rates cause devaluation of the dollar. Chart VI-3 shows the weakening USD between the recession of 2001 and the contraction after IVQ2007. There was a flight to dollar assets and especially obligations of the US government after Sep 2008. Cochrane and Zingales (2009) show that flight was coincident with proposals of TARP (Troubled Asset Relief Program) to withdraw “toxic assets” in US banks (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a) and Regulation of Banks and Finance (2009b)). There are shocks to globalization in the form of regulation, trade and devaluation wars and breakdown of international cooperation (Pelaez and Pelaez, Globalization and the State: Vol. I (2008a), Globalization and the State: Vol. II (2008b) and Government Intervention in Globalization: Regulation, Trade and Devaluation Wars (2008c)). As evident in Chart VI-3A, there is no exit from near zero interest rates without a financial crisis and economic contraction, verified by the increase of interest rates from 1 percent in Jun 2004 to 5.25 percent in Jun 2006. The Federal Open Market Committee (FOMC) lowered the target of the fed funds rate from 7.03 percent on Jul 3, 2000, to 1.00 percent on Jun 22, 2004, in pursuit of non-existing deflation (Pelaez and Pelaez, International Financial Architecture (2005), 18-28, The Global Recession Risk (2007), 83-85). The FOMC implemented increments of 25 basis points of the fed funds target from Jun 2004 to Jun 2006, raising the fed funds rate to 5.25 percent on Jul 3, 2006, as shown in Chart VI-3A. The gradual exit from the first round of unconventional monetary policy from 1.00 percent in Jun 2004 (http://www.federalreserve.gov/boarddocs/press/monetary/2004/20040630/default.htm) to 5.25 percent in Jun 2006 (http://www.federalreserve.gov/newsevents/press/monetary/20060629a.htm) caused the financial crisis and global recession. There are conflicts on exchange rate movements among central banks.

At the press conference after the FOMC meeting on Sep 21, 2016, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20160921.pdf ): “However, the economic outlook is inherently uncertain.” In the address to the Jackson Hole symposium on Aug 26, 2016, Chair Yellen states: “I believe the case for an increase in in federal funds rate has strengthened in recent months…And, as ever, the economic outlook is uncertain, and so monetary policy is not on a preset course” (http://www.federalreserve.gov/newsevents/speech/yellen20160826a.htm). In a speech at the World Affairs Council of Philadelphia, on Jun 6, 2016 (http://www.federalreserve.gov/newsevents/speech/yellen20160606a.htm), Chair Yellen finds that “there is considerable uncertainty about the economic outlook.” There are fifteen references to this uncertainty in the text of 18 pages double-spaced. In the Semiannual Monetary Policy Report to the Congress on Jun 21, 2016, Chair Yellen states (http://www.federalreserve.gov/newsevents/testimony/yellen20160621a.htm), “Of course, considerable uncertainty about the economic outlook remains.” Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. Is there a “science” or even “art” of central banking under this extreme uncertainty in which policy does not generate higher volatility of money, income, prices and values of financial assets?

Chart VI-3, US Dollar Currency Indexes, Jan 4, 1995-Jan 6, 2017

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VI-3A, US, Overnight Fed Funds Rate, Yield of  Three-Month Treasury Constant Maturity, Yield of  Ten-Year Treasury Constant Maturity and Yield of Moody’s Baa Bond, Jan 4, 1995 to Jul 7, 2016

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15/

Chart VI-3B, US, Overnight Fed Funds Rate, Yield of Three-Month Treasury Constant Maturity and Yield of Ten-Year Treasury Constant Maturity, Jan 5, 2001 to Jan 12, 2017

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/h15/

Carry trades induced by zero interest rates increase capital flows into emerging markets that appreciate exchange rates. Portfolio reallocations away from emerging markets depreciate their exchange rates in reversals of capital flows. Chart VI-4A provides the exchange rate of the Mexican peso (MXN) per US dollar from Nov 8, 1993 to Jan 6, 2017. The first data point in Chart VI-4A is MXN 3.1520 on Nov 8, 1993. The rate devalued to 11.9760 on Nov 14, 1995 during emerging market crises in the 1990s and the increase of interest rates in the US in 1994 that stressed world financial markets (Pelaez and Pelaez, International Financial Architecture 2005, The Global Recession Risk 2007, 147-77). The MXN depreciated sharply to MXN 15.4060/USD on Mar 2, 2009, during the global recession. The rate moved to MXN 11.5050/USD on May 2, 2011, during the sovereign debt crisis in the euro area. The rate depreciated to 11.9760 on May 9, 2013. The final data point is MXN 21.2960/USD on Jan 6, 2017.

Chart VI-4A, Mexican Peso (MXN) per US Dollar (USD), Nov 8, 1993 to Jan 6, 2017

Note: US Recessions in Shaded Areas

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

anticipation in 2005, Professor Raghuram G. Rajan (2005) warned of low liquidity and high risks of central bank policy rates approaching the zero bound (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 218-9). Professor Rajan excelled in a distinguished career as an academic economist in finance and was chief economist of the International Monetary Fund (IMF). Shefali Anand and Jon Hilsenrath, writing on Oct 13, 2013, on “India’s central banker lobbies Fed,” published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304330904579133530766149484?KEYWORDS=Rajan), interviewed Raghuram G Rajan, who is the former Governor of the Reserve Bank of India, which is India’s central bank (http://www.rbi.org.in/scripts/AboutusDisplay.aspx). In this interview, Rajan argues that central banks should avoid unintended consequences on emerging market economies of inflows and outflows of capital triggered by monetary policy. Professor Rajan, in an interview with Kartik Goyal of Bloomberg (http://www.bloomberg.com/news/2014-01-30/rajan-warns-of-global-policy-breakdown-as-emerging-markets-slide.html), warns of breakdown of global policy coordination. Professor Willem Buiter (2014Feb4), a distinguished economist currently Global Chief Economist at Citigroup (http://www.willembuiter.com/resume.pdf), writing on “The Fed’s bad manners risk offending foreigners,” on Feb 4, 2014, published in the Financial Times (http://www.ft.com/intl/cms/s/0/fbb09572-8d8d-11e3-9dbb-00144feab7de.html#axzz2suwrwkFs), concurs with Raghuram Rajan. Buiter (2014Feb4) argues that international policy cooperation in monetary policy is both in the interest of the world and the United States. Portfolio reallocations induced by combination of zero interest rates and risk events stimulate carry trades that generate wide swings in world capital flows. In a speech at the Brookings Institution on Apr 10, 2014, Raghuram G. Rajan (2014Apr10, 1, 10) argues:

“As the world seems to be struggling back to its feet after the great financial crisis, I want to draw attention to an area we need to be concerned about: the conduct of monetary policy in this integrated world. A good way to describe the current environment is one of extreme monetary easing through unconventional policies. In a world where debt overhangs and the need for structural change constrain domestic demand, a sizeable portion of the effects of such policies spillover across borders, sometimes through a weaker exchange rate. More worryingly, it prompts a reaction. Such competitive easing occurs both simultaneously and sequentially, as I will argue, and both advanced economies and emerging economies engage in it. Aggregate world demand may be weaker and more distorted than it should be, and financial risks higher. To ensure stable and sustainable growth, the international rules of the game need to be revisited. Both advanced economies and emerging economies need to adapt, else I fear we are about to embark on the next leg of a wearisome cycle. A first step to prescribing the right medicine is to recognize the cause of the sickness. Extreme monetary easing, in my view, is more cause than medicine. The sooner we recognize that, the more sustainable world growth we will have.”

Professor Raguram G Rajan, former governor of the Reserve Bank of India, which is India’s central bank, warned about risks in high valuations of asset prices in an interview with Christopher Jeffery of Central Banking Journal on Aug 6, 2014 (http://www.centralbanking.com/central-banking-journal/interview/2358995/raghuram-rajan-on-the-dangers-of-asset-prices-policy-spillovers-and-finance-in-india). Professor Rajan demystifies in the interview “competitive easing” by major central banks as equivalent to competitive devaluation.

Chart VI-4B provides the rate of the Indian rupee (INR) per US dollar (USD) from Jan 2, 1973 to Jan 6, 2017. The first data point is INR 8.0200 on Jan 2, 1973. The rate depreciated sharply to INR 51.9600 on Mar 3, 2009, during the global recession. The rate appreciated to INR 44.0300/USD on Jul 28, 2011 in the midst of the sovereign debt event in the euro area. The rate overshot to INR 68.8000 on Aug 28, 2013. The final data point is INR 68.1200/USD on Jan 6, 2017.

Chart VI-4B, Indian Rupee (INR) per US Dollar (USD), Jan 2, 1973 to Jan 6, 2017

Note: US Recessions in Shaded Areas

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VI-5 provides the exchange rate of JPY (Japan yen) per USD (US dollars). The first data point on the extreme left is JPY 357.7300/USD for Jan 4, 1971. The JPY has appreciated over the long term relative to the USD with fluctuations along an evident long-term appreciation. Before the global recession, the JPY stood at JPY 124.0900/USD on Jun 22, 2007. The use of the JPY as safe haven is evident by sharp appreciation during the global recession to JPY 110.48/USD on Aug 15, 2008, and to JPY 87.8000/USD on Jan 21, 2009. The final data point in Chart VI-5 is JPY 116.8500/USD on Jan 6, 2017 for appreciation of 5.8 percent relative to JPY 124.0900/USD on Jun 22, 2007 before the global recession and expansion characterized by recurring bouts of risk aversion. Takashi Nakamichi and Eleanor Warnock, writing on “Japan lashes out over dollar, euro,” on Dec 29, 2012, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887323530404578207440474874604.html?mod=WSJ_markets_liveupdate&mg=reno64-wsj), analyze the “war of words” launched by Japan’s new Prime Minister Shinzo Abe and his finance minister Taro Aso, arguing of deliberate devaluations of the USD and EUR relative to the JPY, which are hurting Japan’s economic activity. Gerard Baker and Jacob M. Shlesinger, writing on “Bank of Japan’s Kuroda signals impatience with Abe government,” on May 23, 2014, published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702303480304579579311491068756?KEYWORDS=bank+of+japan+kuroda&mg=reno64-wsj), analyze concerns of the Governor of the Bank of Japan Haruhiko Kuroda that the JPY has strengthened relative to the USD, partly eroding earlier depreciation. The data in Table VI-6 is obtained from closing dates in New York published by the Wall Street Journal (http://professional.wsj.com/mdc/public/page/marketsdata.html?mod=WSJ_PRO_hps_marketdata).

Chart VI-5, Japanese Yen JPY per US Dollars USD, Monthly, Jan 4, 1971-Jan 6, 2017

Note: US Recessions in Shaded Areas 

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

The causes of the financial crisis and global recession were interest rate and housing subsidies and affordability policies that encouraged high leverage and risks, low liquidity and unsound credit (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 157-66, Regulation of Banks and Finance (2009b), 217-27, International Financial Architecture (2005), 15-18, The Global Recession Risk (2007), 221-5, Globalization and the State Vol. II (2008b), 197-213, Government Intervention in Globalization (2008c), 182-4). Several past comments of this blog elaborate on these arguments, among which: http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html http://cmpassocregulationblog.blogspot.com/2011/01/professor-mckinnons-bubble-economy.html http://cmpassocregulationblog.blogspot.com/2011/01/world-inflation-quantitative-easing.html http://cmpassocregulationblog.blogspot.com/2011/01/treasury-yields-valuation-of-risk.html http://cmpassocregulationblog.blogspot.com/2010/11/quantitative-easing-theory-evidence-and.html http://cmpassocregulationblog.blogspot.com/2010/12/is-fed-printing-money-what-are.html

Zero interest rates in the United States forever tend to depreciate the dollar against every other currency if there is no risk aversion preventing portfolio rebalancing toward risk financial assets, which include the capital markets and exchange rates of emerging-market economies. The objective of unconventional monetary policy as argued by Yellen 2011AS) is to devalue the dollar to increase net exports that increase US economic growth. Increasing net exports and internal economic activity in the US is equivalent to decreasing net exports and internal economic activity in other countries.

Continental territory, rich endowment of natural resources, investment in human capital, teaching and research universities, motivated labor force and entrepreneurial initiative provide Brazil with comparative advantages in multiple economic opportunities. Exchange rate parity is critical in achieving Brazil’s potential but is difficult in a world of zero interest rates. Chart IV-6 of the Board of Governors of the Federal Reserve System provides the rate of Brazilian real (BRL) per US dollar (USD) from BRL 1.2074/USD on Jan 4, 1999 to BRL 3.2113/USD on Jan 6, 2017. The rate reached BRL 3.9450/USD on Oct 10, 2002 appreciating 60.5 percent to BRL 1.5580/USD on Aug 1, 2008. The rate depreciated 68.1 percent to BRL 2.6187/USD on Dec 5, 2008 during worldwide flight from risk. The rate appreciated again by 41.3 percent to BRL 1.5375/USD on Jul 26, 2011. The final data point in Chart VI-6 is BRL 3.2113/USD on Jan 6, 2017 for depreciation of 108.9 percent. The data in Table VI-6 is obtained from closing dates in New York published by the Wall Street Journal (http://professional.wsj.com/mdc/public/page/marketsdata.html?mod=WSJ_PRO_hps_marketdata).

Chart VI-6, Brazilian Real (BRL) per US Dollar (USD) Jan 4, 1999 to Jan 6, 2017

Note: US Recessions in Shaded Areas 

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

Chart VI-7 of the Board of Governors of the Federal Reserve System provides the history of the BRL beginning with the first data point of BRL 0.8440/USD on Jan 2, 1995. The rate jumped to BRL 2.0700/USD on Jan 29, 1999 after changes in exchange rate policy and then to BRL 2.2000/USD on Mar 3, 1999. The rate depreciated 26.7 percent to BRL 2.7880 on Sep 21, 2001 relative to Mar 3, 1999.

Chart VI-7, Brazilian Real (BRL) per US Dollar (USD), Jan 2, 1995 to Jan 6, 2017

Note: US Recessions in Shaded Areas 

Source: Board of Governors of the Federal Reserve System

https://www.federalreserve.gov/releases/H10/default.htm

The major reason and channel of transmission of unconventional monetary policy is through expectations of inflation. Fisher (1930) provided theoretical and historical relation of interest rates and inflation. Let i_n be the nominal interest rate, i_r the real or inflation-adjusted interest rate and π^e the expectation of inflation in the time term of the interest rate, which are all expressed as proportions. The following expression provides the relation of real and nominal interest rates and the expectation of inflation:

(1 + i_r) = (1 + i_n)/(1 + π^e) (1)

That is, the real interest rate equals the nominal interest rate discounted by the expectation of inflation in time term of the interest rate. Fisher (1933) analyzed the devastating effect of deflation on debts. Nominal debt contracts remained at original principal interest but net worth and income of debtors contracted during deflation. Real interest rates increase during declining inflation. For example, if the interest rate is 3 percent and prices decline 0.2 percent, equation (1) calculates the real interest rate as:

(1 +0.03)/(1 – 0.02) = 1.03/(0.998) = 1.032

That is, the real rate of interest is (1.032 – 1)100 or 3.2 percent. If inflation were 2 percent, the real rate of interest would be 0.98 percent, or about 1.0 percent {[(1.03/1.02) -1]100 = 0.98%}.

The yield of the one-year Treasury security was quoted in the Wall Street Journal at 0.114 percent on Fri May 17, 2013 (http://online.wsj.com/mdc/page/marketsdata.html?mod=WSJ_topnav_marketdata_main). The expected rate of inflation π^e in the next twelve months is not observed. Assume that it would be equal to the rate of inflation in the past twelve months estimated by the Bureau of Economic Analysis (BLS) at 1.1 percent (http://www.bls.gov/cpi/). The real rate of interest would be obtained as follows:

(1 + 0.00114)/(1 + 0.011) = (1 + r_r) = 0.9902

That is, i_r is equal to 1 – 0.9902 or minus 0.98 percent. Investing in a one-year Treasury security results in a loss of 0.98 percent relative to inflation. The objective of unconventional monetary policy of zero interest rates is to induce consumption and investment because of the loss to inflation of riskless financial assets. Policy would be truly irresponsible if it intended to increase inflationary expectations or π^e. The result could be the same rate of unemployment with higher inflation (Kydland and Prescott 1977).

At the press conference after the FOMC meeting on Sep 21, 2016, Chair Yellen states (http://www.federalreserve.gov/mediacenter/files/FOMCpresconf20160921.pdf ): “However, the economic outlook is inherently uncertain.” In the address to the Jackson Hole symposium on Aug 26, 2016, Chair Yellen states: “I believe the case for an increase in in federal funds rate has strengthened in recent months…And, as ever, the economic outlook is uncertain, and so monetary policy is not on a preset course” (http://www.federalreserve.gov/newsevents/speech/yellen20160826a.htm). In a speech at the World Affairs Council of Philadelphia, on Jun 6, 2016 (http://www.federalreserve.gov/newsevents/speech/yellen20160606a.htm), Chair Yellen finds that “there is considerable uncertainty about the economic outlook.” There are fifteen references to this uncertainty in the text of 18 pages double-spaced. In the Semiannual Monetary Policy Report to the Congress on Jun 21, 2016, Chair Yellen states (http://www.federalreserve.gov/newsevents/testimony/yellen20160621a.htm), “Of course, considerable uncertainty about the economic outlook remains.” Frank H. Knight (1963, 233), in Risk, uncertainty and profit, distinguishes between measurable risk and unmeasurable uncertainty. Is there a “science” or even “art” of central banking under this extreme uncertainty in which policy does not generate higher volatility of money, income, prices and values of financial assets?

The carry trade from zero interest rates to leveraged positions in risk financial assets had proved strongest for commodity exposures but US equities have regained leadership. The DJIA has increased 105.3 percent since the trough of the sovereign debt crisis in Europe on Jul 16, 2010 to Jan 13, 2017; S&P 500 has gained 122.4 percent and DAX 105.1 percent. Before the current round of risk aversion, almost all assets in the column “∆% Trough to 1/13/17” in Table VI-4 had double digit gains relative to the trough around Jul 2, 2010 followed by negative performance but now some valuations of equity indexes show varying behavior. China’s Shanghai Composite is 30.6 percent above the trough. Japan’s Nikkei Average is 118.6 percent above the trough. DJ Asia Pacific TSM is 28.9 percent above the trough. Dow Global is 52.6 percent above the trough. STOXX 50 of 50 blue-chip European equities (http://www.stoxx.com/indices/index_information.html?symbol=sx5E) is 32.7 percent above the trough. NYSE Financial Index is 66.7 percent above the trough. DAX index of German equities (http://www.bloomberg.com/quote/DAX:IND) is 105.1 percent above the trough. Japan’s Nikkei Average is 118.6 percent above the trough on Aug 31, 2010 and 69.3 percent above the peak on Apr 5, 2010. The Nikkei Average closed at 19,287.28 on Jan 13, 2017 (http://professional.wsj.com/mdc/public/page/marketsdata.html?mod=WSJ_PRO_hps_marketdata), which is 88.1 percent higher than 10,254.43 on Mar 11, 2011, on the date of the Tōhoku or Great East Japan Earthquake/tsunami. Global risk aversion erased the earlier gains of the Nikkei. The dollar appreciated 10.7 percent relative to the euro. The dollar devalued before the new bout of sovereign risk issues in Europe. The column “∆% week to 1/13/17” in Table VI-4 shows

decrease of 1.3 percent in the week for China’s Shanghai Composite. The Nikkei decreased 0.9 percent. DJ Asia Pacific increased 1.3 percent. NYSE Financial decreased 0.2 percent in the week. Dow Global increased 0.6 percent in the week of Jan 13, 2017. The DJIA decreased 0.4 percent and S&P 500 decreased 0.1 percent. DAX of Germany increased 0.3 percent. STOXX 50 decreased 0.1 percent. The USD depreciated 1.1 percent. There are still high uncertainties on European sovereign risks and banking soundness, US and world growth slowdown and China’s growth tradeoffs. Sovereign problems in the “periphery” of Europe and fears of slower growth in Asia and the US cause risk aversion with trading caution instead of more aggressive risk exposures. There is a fundamental change in Table VI-4 from the relatively upward trend with oscillations since the sovereign risk event of Apr-Jul 2010. Performance is best assessed in the column “∆% Peak to 1/13/17” that provides the percentage change from the peak in Apr 2010 before the sovereign risk event to Jan 13, 2017. Most risk financial assets had gained not only relative to the trough as shown in column “∆% Trough to 1/13/17” but also relative to the peak in column “∆% Peak to 1/13/17.” There are now several equity indexes above the peak in Table VI-4: DJIA 77.5 percent, S&P 500 86.9 percent, DAX 83.7 percent, Dow Global 24.5 percent, NYSE Financial Index (http://www.nyse.com/about/listed/nykid.shtml) 32.8 percent, Nikkei Average 69.3 percent, STOXX 50 12.4 percent. Shanghai Composite is 1.6 percent below the peak and DJ Asia Pacific TSM is 12.8 percent above the peak. The Shanghai Composite increased 57.7 percent from March 12, 2014, to Jan 13, 2017. The US dollar strengthened 29.6 percent relative to the peak. The factors of risk aversion have adversely affected the performance of risk financial assets. The performance relative to the peak in Apr 2010 is more important than the performance relative to the trough around early Jul 2010 because improvement could signal that conditions have returned to normal levels before European sovereign doubts in Apr 2010. Sharp and continuing strengthening of the dollar is affecting balance sheets of US corporations with foreign operations (http://www.fasb.org/jsp/FASB/Pronouncement_C/SummaryPage&cid=900000010318). The Federal Open Market Committee (FOMC) is following “financial and international developments” as part of the process of framing interest rate policy (http://www.federalreserve.gov/newsevents/press/monetary/20150128a.htm). Kate Linebaugh, writing on “Corporate profits set to shrink for fourth consecutive quarter,” on Jul 17, 2016, published in the Wall Street Journal (http://www.wsj.com/articles/corporate-profits-set-to-shrink-for-fourth-consecutive-quarter-1468799278), quotes forecasts of Thomson Reuters of 4.7 decline of adjusted earnings per share in the S&P 500 index in IIQ2016 relative to a year earlier. That would be the fourth consecutive quarterly decline. Theo Francis and Kate Linebaugh, writing on “US corporate profits on pace for third straight decline,” on Apr 28, 2016, published in the Wall Street Journal (http://www.wsj.com/articles/u-s-corporate-profits-on-pace-for-third-straight-decline-1461872242), analyze three consecutive quarters of decline of corporate earnings and revenue in companies in S&P 500. They quote Thomson Reuters on expected decline of earnings of 6.1 percent in IQ2016 based on 55 percent of reporting companies. Weakness of economic activity shows in decline of revenues in IQ2016 of 1.4 percent, increasing 1.7 percent excluding energy, and contraction of profits of 0.5 percent. Justin Lahart, writing on “S&P 500 Earnings: far worse than advertised,” on Feb 24, 2016, published in the Wall Street Journal (http://www.wsj.com/articles/s-p-500-earnings-far-worse-than-advertised-1456344483), analyzes S&P 500 earnings in 2015. Under data provided by companies, earnings increased 0.4 percent in 2015 relative to 2014 but under GAAP (Generally Accepted Accounting Principles), earnings fell 12.7 percent, which is the worst decrease since 2008. Theo Francis e Kate Linebaugh, writing on Oct 25, 2015, on “US Companies Warn of Slowing Economy, published in the Wall Street Journal (http://www.wsj.com/articles/u-s-companies-warn-of-slowing-economy-1445818298) analyze the first contraction of earnings and revenue of big US companies. Production, sales and employment are slowing in a large variety of companies with some contracting. Corporate profits also suffer from revaluation of the dollar that constrains translation of foreign profits into dollar balance sheets. Francis and Linebaugh quote Thomson Reuters that analysts expect decline of earnings per share of 2.8 percent in IIIQ2015 relative to IIIQ2014 based on reports by one third of companies in the S&P 500. Sales would decline 4.0% in a third quarter for the first joint decline of earnings per share and revenue in the same quarter since IIIQ2009. Dollar revaluation also constrains corporate results.

Inyoung Hwang, writing on “Fed optimism spurs record bets against stock volatility,” on Aug 21, 2014, published in Bloomberg.com (http://www.bloomberg.com/news/2014-08-21/fed-optimism-spurs-record-bets-against-stock-voalitlity.html), informs that the S&P 500 is trading at 16.6 times estimated earnings, which is higher than the five-year average of 14.3 Tom Lauricella, writing on Mar 31, 2014, on “Stock investors see hints of a stronger quarter,” published in the Wall Street Journal (http://online.wsj.com/news/articles/SB10001424052702304157204579473513864900656?mod=WSJ_smq0314_LeadStory&mg=reno64-wsj), finds views of stronger earnings among many money managers with positive factors for equity markets in continuing low interest rates and US economic growth. There is important information in the Quarterly Markets review of the Wall Street Journal (http://online.wsj.com/public/page/quarterly-markets-review-03312014.html) for IQ2014. Alexandra Scaggs, writing on “Tepid profits, roaring stocks,” on May 16, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323398204578487460105747412.html), analyzes stabilization of earnings growth: 70 percent of 458 reporting companies in the S&P 500 stock index reported earnings above forecasts but sales fell 0.2 percent relative to forecasts of increase of 0.5 percent. Paul Vigna, writing on “Earnings are a margin story but for how long,” on May 17, 2013, published in the Wall Street Journal (http://blogs.wsj.com/moneybeat/2013/05/17/earnings-are-a-margin-story-but-for-how-long/), analyzes that corporate profits increase with stagnating sales while companies manage costs tightly. More than 90 percent of S&P components reported moderate increase of earnings of 3.7 percent in IQ2013 relative to IQ2012 with decline of sales of 0.2 percent. Earnings and sales have been in declining trend. In IVQ2009, growth of earnings reached 104 percent and sales jumped 13 percent. Net margins reached 8.92 percent in IQ2013, which is almost the same at 8.95 percent in IIIQ2006. Operating margins are 9.58 percent. There is concern by market participants that reversion of margins to the mean could exert pressure on earnings unless there is more accelerated growth of sales. Vigna (op. cit.) finds sales growth limited by weak economic growth. Kate Linebaugh, writing on “Falling revenue dings stocks,” on Oct 20, 2012, published in the Wall Street Journal (http://professional.wsj.com/article/SB10000872396390444592704578066933466076070.html?mod=WSJPRO_hpp_LEFTTopStories), identifies a key financial vulnerability: falling revenues across markets for United States reporting companies. Global economic slowdown is reducing corporate sales and squeezing corporate strategies. Linebaugh quotes data from Thomson Reuters that 100 companies of the S&P 500 index have reported declining revenue only 1 percent higher in Jun-Sep 2012 relative to Jun-Sep 2011 but about 60 percent of the companies are reporting lower sales than expected by analysts with expectation that revenue for the S&P 500 will be lower in Jun-Sep 2012 for the entities represented in the index. Results of US companies are likely repeated worldwide. Future company cash flows derive from investment projects. Future company cash flows derive from investment projects. In IQ1980, real gross private domestic investment in the US was $951.6 billion of chained 2009 dollars, growing to $1,270.0 billion in IQ1990 or 33.5 percent. Real gross private domestic investment in the US increased 7.7 percent from $2605.2 billion in IVQ2007 to $2,804.7 billion in IIIQ2016. As shown in Table IAI-2, real private fixed investment increased 7.5 percent from $2,586.3 billion of chained 2009 dollars in IVQ2007 to $2,779.3 billion in IIIQ2016. Private fixed investment fell relative to IVQ2007 in all quarters preceding IQ2014 and changed 0.0 percent in IIIQ2016, declining 0.3 percent in IIQ2016 and falling 0.2 percent in IQ2016. Growth of real private investment in Table IA1-2 is mediocre for all but four quarters from IIQ2011 to IQ2012. The investment decision of United States corporations is fractured in the current economic cycle in preference of cash. There are three aspects. First, there is fluctuation in corporate profits. Corporate profits with IVA and CCA decreased at $127.9 billion in IVQ2015 and increased at $66.0 billion in IQ2016. Corporate profits with IVA and CCA fell at $12.5 billion in IIQ2016 and increased at $117.8 billion in IIIQ2016. Profits after tax with IVA and CCA fell at $172.7 billion in IVQ2015 and increased at $113.4 billion in IQ2016. Profits after tax with IVA and CCA fell at $28.9 billion in IIQ2016 and increased at $98.3 billion in IIIQ2016. Net dividends fell at $20.8 billion in IVQ2015 and increased at $7.3 billion in IQ2016. Net dividends fell at $9.3 billion in IIQ2016. Net dividends increased at $18.5 billion in IIIQ2016. Undistributed corporate profits with IVA and CCA fell at $152.0 billion in IVQ2015. Undistributed profits with IVA and CCA increased at $106.1 billion in IQ2016. Undistributed corporate profits fell at $19.6 billion in IIQ2016. Undistributed corporate profits increased at $79.8 billion in IIIQ2016. Undistributed corporate profits swelled 238.8 percent from $107.7 billion in IQ2007 to $364.9 billion in IIIQ2016 and changed signs from minus $55.9 billion in current dollars in IVQ2007. Uncertainty originating in fiscal, regulatory and monetary policy causes wide swings in expectations and decisions by the private sector with adverse effects on investment, real economic activity and employment. Second, sharp and continuing strengthening of the dollar is affecting balance sheets of US corporations with foreign operations (http://www.fasb.org/jsp/FASB/Pronouncement_C/SummaryPage&cid=900000010318) and the overall US economy. The bottom part of Table IA1-9 provides the breakdown of corporate profits with IVA and CCA in domestic industries and the rest of the world. Corporate profits with IVA and CCA fell at $127.9 billion in IVQ2015 with decrease of domestic industries at $149.8 billion, mostly because of decrease of nonfinancial business at $131.7 billion, and increase of profits from operations in the rest of the world at $22.0 billion. Receipts from the rest of the world fell at $19.9 billion. Corporate profits with IVA and CCA increased at $66.0 billion in IQ2016 with increase of domestic industries at $92.9 billion. Profits from operations from the rest of the world fell at $26.9 billion and payments to the rest of the world increased at $35.6 billion. Corporate profits with IVA and CCA decreased at $12.5 billion in IIQ2016. Profits from domestic industries fell at $50.5 billion and profits from nonfinancial business fell at $56.1 billion. Profits from the rest of the world increased at $38.0 billion. Corporate profits with IVA and CCA increased at $117.8 billion in IIIQ2016. Profits from domestic industries increased at $116.5 billion and profits from nonfinancial business increased at $66.4 billion. Profits from the rest of the world increased at $1.3 billion. Total corporate profits with IVA and CCA were $2138.8 billion in IIIQ2016 of which $1729.9 billion from domestic industries, or 80.9 percent of the total, and $408.9 billion, or 19.1 percent, from the rest of the world. Nonfinancial corporate profits of $1236.9 billion account for 57.8 percent of the total. Third, there is reduction in the use of corporate cash for investment. Vipal Monga, David Benoit and Theo Francis, writing on “Companies send more cash back to shareholders,” published on May 26, 2015 in the Wall Street Journal (http://www.wsj.com/articles/companies-send-more-cash-back-to-shareholders-1432693805?tesla=y), use data of a study by Capital IQ conducted for the Wall Street Journal. This study shows that companies in the S&P 500 reduced investment in plant and equipment to median 29 percent of operating cash flow in 2013 from 33 percent in 2003 while increasing dividends and buybacks to median 36 percent in 2013 from 18 percent in 2003.

The basic valuation equation that is also used in capital budgeting postulates that the value of stocks or of an investment project is given by:

Where R_τ is expected revenue in the time horizon from τ =1 to T; C_τ denotes costs; and ρ is an appropriate rate of discount. In words, the value today of a stock or investment project is the net revenue, or revenue less costs, in the investment period from τ =1 to T discounted to the present by an appropriate rate of discount. In the current weak economy, revenues have been increasing more slowly than anticipated in investment plans. An increase in interest rates would affect discount rates used in calculations of present value, resulting in frustration of investment decisions. If V represents value of the stock or investment project, as ρ → ∞, meaning that interest rates increase without bound, then V → 0, or

declines. Equally, decline in expected revenue from the stock or project, R_τ, causes decline in valuation.

An intriguing issue is the difference in performance of valuations of risk financial assets and economic growth and employment. Paul A. Samuelson (http://www.nobelprize.org/nobel_prizes/economics/laureates/1970/samuelson-bio.html) popularized the view of the elusive relation between stock markets and economic activity in an often-quoted phrase “the stock market has predicted nine of the last five recessions.” In the presence of zero interest rates forever, valuations of risk financial assets are likely to differ from the performance of the overall economy. The interrelations of financial and economic variables prove difficult to analyze and measure.

Table VI-4, Stock Indexes, Commodities, Dollar and 10-Year Treasury  

	Peak	Trough	∆% to Trough	∆% Peak to 01/13/ /17	∆% Week 01/13/17	∆% Trough to 01/13/ 17
DJIA	4/26/ 10	7/2/10	-13.6	77.5	-0.4	105.3
S&P 500	4/23/ 10	7/20/ 10	-16.0	86.9	-0.1	122.4
NYSE Finance	4/15/ 10	7/2/10	-20.3	32.8	-0.2	66.7
Dow Global	4/15/ 10	7/2/10	-18.4	24.5	0.6	52.6
Asia Pacific	4/15/ 10	7/2/10	-12.5	12.8	1.3	28.9
Japan Nikkei Aver.	4/05/ 10	8/31/ 10	-22.5	69.3	-0.9	118.6
China Shang.	4/15/ 10	7/02 /10	-24.7	-1.6	-1.3	30.6
STOXX 50	4/15/10	7/2/10	-15.3	12.4	-0.1	32.7
DAX	4/26/ 10	5/25/ 10	-10.5	83.7	0.3	105.1
Dollar Euro	11/25 2009	6/7 2010	21.2	29.6	-1.1	10.7
DJ UBS Comm.	1/6/ 10	7/2/10	-14.5	NA	NA	NA
10-Year T Note	4/5/ 10	4/6/10	3.986	2.784	2.381

T: trough; Dollar: positive sign appreciation relative to euro (less dollars paid per euro), negative sign depreciation relative to euro (more dollars paid per euro)

Source: http://professional.wsj.com/mdc/page/marketsdata.html?mod=WSJ_hps_marketdata

ESII Valuations of Risk Financial Assets. There are complex economic, financial and political effects of the withdrawal of the UK from the European Union or BREXIT after the referendum on Jun 23, 2016 (https://next.ft.com/eu-referendum for extensive coverage by the Financial Times).The most important source of financial turbulence is shifting toward fluctuating interest rates. The dollar/euro rate is quoted as number of US dollars USD per one euro EUR, USD 1.0533/EUR in the first row, first column in the block for currencies in Table III-1 for Fri Jan 6, depreciating to USD 1.0573/EUR on Mon Jan 9, 2017, or by 0.4 percent. The dollar depreciated because more dollars, 1.0573, were required on Mon Jan 9 to buy one euro than $1.0533 on Fri Jan 6. Table III-1 defines a country’s exchange rate as number of units of domestic currency per unit of foreign currency. USD/EUR would be the definition of the exchange rate of the US and the inverse [1/(USD/EUR)] is the definition in this convention of the rate of exchange of the euro zone, EUR/USD. A convention used throughout this blog is required to maintain consistency in characterizing movements of the exchange rate such as in Table III-1 as appreciation and depreciation. The first row for each of the currencies shows the exchange rate at 5 PM New York time, such as USD 1.0533/EUR on Jan 6. The second row provides the cumulative percentage appreciation or depreciation of the exchange rate from the rate on the last business day of the prior week, in this case Fri Jan 6, to the last business day of the current week, in this case Jan 13, such as depreciation of 1.1 percent to USD 1.0645/EUR by Jan 13. The third row provides the percentage change from the prior business day to the current business day. For example, the USD depreciated (denoted by negative sign) by 1.1 percent from the rate of USD 1.0533/EUR on Fri Jan 6 to the rate of USD 1.0645/EUR on Jan 13 {[(1.0645/1.0533) - 1]100 = 1.1%}. The dollar depreciated (denoted by negative sign) by 0.3 percent from the rate of USD 1.0612 on

Thu Jan 12 to USD 1.0645/EUR on Fri Jan 13 {[(1.0645/1.0612) -1]100 = 0.3%}. Other factors constant, increasing risk aversion causes appreciation of the dollar relative to the euro, with rising uncertainty on European and global sovereign risks increasing dollar-denominated assets with sales of risk financial investments. Funds move away from higher yielding risk assets to the safety of dollar-denominated assets.

There is mixed performance in equity indexes with several indexes in Table III-1 increasing in the week ending on Jan 6, 2017, after wide swings caused by reallocations of investment portfolios worldwide. Stagnating revenues, corporate cash hoarding, effects of currency oscillations on corporate earnings and declining investment are causing reevaluation of discounted net earnings with deteriorating views on the world economy and United States fiscal sustainability but investors have been driving indexes higher. There are complex economic, financial and political effects of the withdrawal of the UK from the European Union or BREXIT after the referendum on Jun 23, 2016 (https://next.ft.com/eu-referendum for extensive coverage by the Financial Times). DJIA changed 0.0 percent on Jan 13, decreasing 0.4 percent in the week. Germany’s DAX increased 0.9 percent on Jan 13 and increased 0.3 percent in the week. Dow Global increased 0.3 percent on Jan 13 and increased 0.6 percent in the week. Japan’s Nikkei Average increased 0.8 percent on Jan 13 and decreased 0.9 percent in the week as the yen continues oscillating but relatively weaker and the stock market gains in expectations of success of fiscal stimulus by a new administration and monetary stimulus by a new board of the Bank of Japan. Dow Asia Pacific TSM decreased 0.1 percent on Jan 13 and increased 1.3 percent in the week. Shanghai Composite that decreased 1.0 percent on Mar 8 and decreased 1.7 percent in the week of Mar 8, falling below 2000 at 1974.38 on Mar 12, 2014 but closing at 3112.76 on Jan 13, 2017, for decrease of 0.2 percent and decreasing 1.3 percent in the week. The Shanghai Composite increased 57.7 percent from March 12, 2014 to Jan 13, 2017. There is deceleration with oscillations of the world economy that could affect corporate revenue and equity valuations, causing fluctuations in equity markets with increases during favorable risk appetite. The global hunt for yield induced by central bank policy rates of near zero percent motivates wide portfolio reshufflings among classes of risk financial assets.

Commodities were mixed in the week of Jan 13, 2017. Table III-1 shows that WTI decreased 3.0 percent in the week of Jan 13 while Brent decreased 2.9 percent in the week with turmoil in oil producing regions but oscillating action by OPEC. Gold decreased 0.3 percent on Jan 13 and increased 2.0 percent in the week.

Table III-I, Weekly Financial Risk Assets Jan 9 to Jan 13, 2017

Fri 6	Mon 9	Tue 10	Wed 11	Thu 12	Fri 13
USD/ EUR 1.0533 -0.1% -0.7%	1.0573 -0.4% -0.4%	1.0555 -0.2% 0.2%	1.0582 -0.5% -0.3%	1.0612 -0.8% -0.3%	1.0645 -1.1% -0.3%
JPY/ USD 116.99 0.0% -1.4%	116.05 0.8% 0.8%	115.77 1.0% 0.2%	115.41 1.4% 0.3%	114.72 1.9% 0.6%	114.50 2.1% 0.2%
CHF/ USD 1.0181 0.1% -0.8%	1.0152 0.3% 0.3%	1.0169 0.1% -0.2%	1.0141 0.4% 0.3%	1.0109 0.7% 0.3%	1.0086 0.9% 0.2%
CHF/ EUR 1.0723 0.0% -0.1%	1.0733 -0.1% -0.1%	1.0733 -0.1% 0.0%	1.0731 -0.1% 0.0%	1.0727 0.0% 0.0%	1.0736 -0.1% -0.1%
USD/ AUD 0.7299 1.3701 1.3% -0.5%	0.7354 1.3598 0.8% 0.8%	0.7369 1.3570 1.0% 0.2%	0.7441 1.3439 1.9% 1.0%	0.7484 1.3362 2.5% 0.6%	0.7501 1.3332 2.7% 0.2%
10Y Note 2.416	2.374	2.375	2.378	2.361	2.381
2Y Note 1.226	1.194	1.186	1.190	1.173	1.189
German Bond 2Y -0.73 10Y 0.30	2Y -0.74 10Y 0.28	2Y -0.73 10Y 0.29	2Y -0.71 10Y 0.26	2Y -0.72 10Y 0.24	2Y -0.72 10Y 0.34
DJIA 19963.80 1.0% 0.3%	19887.38 -0.4% -0.4%	19855.53 -0.5% -0.2%	19954.28 0.0% 0.5%	19891.00 -0.4% -0.3%	19885.73 -0.4% 0.0%
Dow Global 2582.66 2.0% 0.1%	2575.11 -0.3% -0.3%	2580.71 -0.1% 0.2%	2580.08 -0.1% 0.0%	2590.59 0.3% 0.4%	2598.07 0.6% 0.3%
DJ Asia Pacific 1456.53 2.4% -0.5%	1457.03 0.0% 0.0%	1463.78 0.5% 0.5%	1464.03 0.5% 0.0%	1477.63 1.4% 0.9%	1475.49 1.3% -0.1%
Nikkei 19454.33 1.8% -0.3%	19454.33 0.0% 0.0%	19301.44 -0.8% -0.8%	19364.67 -0.5% 0.3%	19134.70 -1.6% -1.2%	19287.28 -0.9% 0.8%
Shanghai 3154.32 1.6% -0.4%	3171.24 0.5% 0.5%	3161.67 0.2% -0.3%	3136.75 -0.6% -0.8%	3119.29 -1.1% -0.6%	3112.76 -1.3% -0.2%
DAX 11599.01 1.0% 0.1%	11563.99 -0.3% -0.3%	11583.30 -0.1% 0.2%	11646.17 0.4% 0.5%	11521.04 -0.7% -1.1%	11629.18 0.3% 0.9%
DJ UBS Comm. NA	NA	NA	NA	NA	NA
WTI $/B 53.99 0.5% 0.4%	51.96 -3.8% -3.8%	50.82 -5.9% -2.2%	52.25 -3.2% 2.8%	53.01 -1.8% 1.5%	52.37 -3.0% -1.2%
Brent $/B 57.10 0.5% 0.4%	54.94 -3.8% -3.8%	53.64 -6.1% -2.4%	55.10 -3.5% 2.7%	56.01 -1.9% 1.7%	55.45 -2.9% -1.0%
44Gold 1171.9 1.9% -0.7%	1183.5 1.0% 1.0%	1184.2 1.0% 0.1%	1195.6 2.0% 1.0%	1198.9 2.3% 0.3%	1195.3 2.0% -0.3%

Note: USD: US dollar; JPY: Japanese Yen; CHF: Swiss

Franc; AUD: Australian dollar; Comm.: commodities; OZ: ounce

Sources: http://www.bloomberg.com/markets/

http://professional.wsj.com/mdc/page/marketsdata.html?mod=WSJ_hps_marketdata

The euro has devalued 49.4 percent relative to the dollar from the high on Jul 15, 2008 to Jan 13, 2017. There are complex economic, financial and political effects of the withdrawal of the UK from the European Union or BREXIT after the referendum on Jun 23, 2016 (https://next.ft.com/eu-referendum for extensive coverage by the Financial Times). The British pound (GBP) devalued 13.9 percent from the trough of ₤1.388 on Jan 2, 2009 to ₤1.2183 on Jan 13, 2017 and devalued 64.7 percent from the high of ₤2.006 on Sep 15, 2008. Such similar event actually occurred in the week of Sep 23, 2011 reversing the devaluation of the dollar in the form of sharp appreciation of the dollar relative to other currencies from all over the world including the offshore Chinese yuan market. The Bank of England reduced the Bank Rate to 0.25 percent on Aug 4, 2016, and announced new measures of quantitative easing (http://www.bankofengland.co.uk/publications/Pages/news/2016/008.aspx).

Table VI-6, Exchange Rates

	Peak	Trough	∆% P/T	Jan 13, 2017	∆% T Jan 13, 2017	∆% P Jan 13, 2017
EUR USD	7/15 2008	6/7 2010		01/13/17 2017
1Rate	1.59	1.192		1.0645
∆%			-33.4		-12.0	-49.4
JPY USD	8/18 2008	9/15 2010		01/13/17 2017
Rate	110.19	83.07		114.50
∆%			24.6		-37.8	-3.9
CHF USD	11/21 2008	12/8 2009		01/13/17 2017
Rate	1.225	1.025		1.0086
∆%			16.3		1.6	17.7
USD GBP	7/15 2008	1/2/ 2009		01/13/17 2017
Rate	2.006	1.388		1.2183
∆%			-44.5		-13.9	-64.7
USD AUD	7/15 2008	10/27 2008		01/13/17 2017
Rate	1.0215	1.6639		0.7501
∆%			-62.9		19.9	-30.5
ZAR USD	10/22 2008	8/15 2010		01/13/17 2017
Rate	11.578	7.238		13.5138
∆%			37.5		-86.7	-16.7
SGD USD	3/3 2009	8/9 2010		01/13/17 2017
Rate	1.553	1.348		1.4281
∆%			13.2		-5.9	8.0
HKD USD	8/15 2008	12/14 2009		01/13/17 2017
Rate	7.813	7.752		7.7549
∆%			0.8		0.0	0.7
BRL USD	12/5 2008	4/30 2010		01/13/17 2017
Rate	2.43	1.737		3.2214
∆%			28.5		-85.5	-32.6
CZK USD	2/13 2009	8/6 2010		01/13/17 2017
Rate	22.19	18.693		25.392
∆%			15.7		-35.8	-14.4
SEK USD	3/4 2009	8/9 2010		01/13/17 2017
Rate	9.313	7.108		8.9099
∆%			23.7		-25.4	4.3
CNY USD	7/20 2005	7/15 2008		01/13/17 2017
Rate	8.2765	6.8211		6.8998	-1.2	16.6
∆%			17.6

Symbols: USD: US dollar; EUR: euro; JPY: Japanese yen; CHF: Swiss franc; GBP: UK pound; AUD: Australian dollar; ZAR: South African rand; SGD: Singapore dollar; HKD: Hong Kong dollar; BRL: Brazil real; CZK: Czech koruna; SEK: Swedish krona; CNY: Chinese yuan; P: peak; T: trough

Note: percentages calculated with currencies expressed in units of domestic currency per dollar; negative sign means devaluation and no sign appreciation

Source:

http://professional.wsj.com/mdc/public/page/mdc_currencies.html?mod=mdc_topnav_2_3000

ESIII Unconventional Monetary Policy and Valuations of Risk Financial Assets. Unconventional monetary policy since 2003 has consisted of near zero nominal interest rates, negative real rates of interest, massive purchases of securities for the balance sheet of the Fed and intervention in allocation of credit. Professor John B. Taylor (2016Dec 7, 2016Dec20), in Testimony to the Subcommittee on Monetary Policy and Trade Committee on Financial Services, on Dec 7, 2016, analyzes the adverse effects of unconventional monetary policy:

“My research and that of others over the years shows that these policies were not effective, and may have been counterproductive. Economic growth was consistently below the Fed’s forecasts with the policies, and was much weaker than in earlier U.S. recoveries from deep recessions. Job growth has been insufficient to raise the percentage of the population that is working above pre-recession levels. There is a growing consensus that the extra low interest rates and unconventional monetary policy have reached diminishing or negative returns. Many have argued that these policies widen the income distribution, adversely affect savers, and increase the volatility of the dollar exchange rate. Experienced market participants have expressed concerns about bubbles, imbalances, and distortions caused by the policies. The unconventional policies have also raised public policy concerns about the Fed being transformed into a multipurpose institution, intervening in particular sectors and allocating credit, areas where Congress may have a role, but not a limited-purpose independent agency of government.”

A counterfactual consists of theory and measurements of what would have occurred otherwise if economic policies or institutional arrangements had been different. This task is quite difficult because economic data are observed with all effects as they actually occurred while the counterfactual attempts to evaluate how data would differ had policies and institutional arrangements been different (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008b), 125, 136; Pelaez 1979, 26-8). Counterfactual data are unobserved and must be calculated using theory and measurement methods. The measurement of costs and benefits of projects or applied welfare economics (Harberger 1971, 1997) specifies and attempts to measure projects such as what would be economic welfare with or without a bridge or whether markets would be more or less competitive in the absence of antitrust and regulation laws (Winston 2006). The “new economic history” of the United States used counterfactuals to measure the economy with or without railroads (Fishlow 1965, Fogel 1964) and in analyzing slavery (Fogel and Engerman 1974). A critical counterfactual in economic history is how Britain surged ahead of France (North and Weingast 1989). There is similarly path-breaking research on railroads in Latin America by Coastworth (1981) and Summerhill (1997, 1998, 2003). Coastworth (2006, 176) argues that: “We already have so many history books that tells us so much about what really occurred in the past, that what we need now are books about what did not happen—but might have, or perhaps even should have happened: Counterfactual History, that is, history that is contrary to fact.”

The counterfactual of avoidance of deeper and more prolonged contraction by fiscal and monetary policies is not the critical issue. As Professor John B. Taylor (2012Oct25) argues, the critically important counterfactual is that the financial crisis and global recession would have not occurred in the first place if different economic policies had been followed. The counterfactual intends to verify that a combination of housing policies and discretionary monetary policies instead of rules (Taylor 1993) caused, deepened and prolonged the financial crisis (Taylor 2007, 2008Nov, 2009, 2012FP, 2012Mar27, 2012Mar28, 2012JMCB, 2015, 2012 Oct 25; 2013Oct28, 2014 Jan01, 2014Jan3, 2014Jun26, 2014Jul15, 2015, 2016Dec7, 2016Dec20 http://www.johnbtaylor.com/; see http://cmpassocregulationblog.blogspot.com/2017/01/rules-versus-discretionary-authorities.html and earlier http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html). The experience resembles that of the Great Inflation of the 1960s and 1970s with stop-and-go growth/inflation that coined the term stagflation (http://cmpassocregulationblog.blogspot.com/2012/06/rules-versus-discretionary-authorities.html http://cmpassocregulationblog.blogspot.com/2011/05/slowing-growth-global-inflation-great.html http://cmpassocregulationblog.blogspot.com/2011/04/new-economics-of-rose-garden-turned.html http://cmpassocregulationblog.blogspot.com/2011/03/is-there-second-act-of-us-great.html and Appendix I).

Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 24.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.4 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 percent below trend under this alternative calculation.

Valuations of risk financial assets have reached extremely high levels in markets with fluctuating volumes. For example, the DJIA has increased 105.3 percent since the trough of the sovereign debt crisis in Europe on Jul 2, 2010 to Jan 13, 2016; S&P 500 has gained 122.4 percent and DAX 105.1 percent. The overwhelming risk factor is the unsustainable Treasury deficit/debt of the United States (http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html and earlier http://cmpassocregulationblog.blogspot.com/2016/07/unresolved-us-balance-of-payments.html and earlier http://cmpassocregulationblog.blogspot.com/2016/04/proceeding-cautiously-in-reducing.html and earlier http://cmpassocregulationblog.blogspot.com/2016/01/weakening-equities-and-dollar.html). A competing event is the high level of valuations of risk financial assets (Section I and earlier http://cmpassocregulationblog.blogspot.com/2016/01/unconventional-monetary-policy-and.html and earlier http://cmpassocregulationblog.blogspot.com/2015/01/peaking-valuations-of-risk-financial.html and earlier http://cmpassocregulationblog.blogspot.com/2014/01/theory-and-reality-of-secular.html). Matt Jarzemsky, writing on “Dow industrials set record,” on Mar 5, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324156204578275560657416332.html), analyzes that the DJIA broke the closing high of 14,164.53 set on Oct 9, 2007, and subsequently also broke the intraday high of 14,198.10 reached on Oct 11, 2007. The DJIA closed at 19,885.73 on Jan 13, 2017, which is higher by 40.4 percent than the value of 14,164.53 reached on Oct 9, 2007 and higher by 40.1 percent than the value of 14,198.10 reached on Oct 11, 2007. Values of risk financial assets have been approaching or exceeding historical highs.

The financial crisis and global recession were caused by interest rate and housing subsidies and affordability policies that encouraged high leverage and risks, low liquidity and unsound credit (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 157-66, Regulation of Banks and Finance (2009b), 217-27, International Financial Architecture (2005), 15-18, The Global Recession Risk (2007), 221-5, Globalization and the State Vol. II (2008b), 197-213, Government Intervention in Globalization (2008c), 182-4). Several past comments of this blog elaborate on these arguments, among which: http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html http://cmpassocregulationblog.blogspot.com/2011/01/professor-mckinnons-bubble-economy.html http://cmpassocregulationblog.blogspot.com/2011/01/world-inflation-quantitative-easing.html http://cmpassocregulationblog.blogspot.com/2011/01/treasury-yields-valuation-of-risk.html http://cmpassocregulationblog.blogspot.com/2010/11/quantitative-easing-theory-evidence-and.html http://cmpassocregulationblog.blogspot.com/2010/12/is-fed-printing-money-what-are.html

Table VI-1 shows the phenomenal impulse to valuations of risk financial assets originating in the initial shock of near zero interest rates in 2003-2004 with the fed funds rate at 1 percent, in fear of deflation that never materialized, and quantitative easing in the form of suspension of the auction of 30-year Treasury bonds to lower mortgage rates. World financial markets were dominated by monetary and housing policies in the US. Between 2002 and 2008, the DJ UBS Commodity Index rose 165.5 percent largely because of unconventional monetary policy encouraging carry trades from low US interest rates to long leveraged positions in commodities, exchange rates and other risk financial assets. The charts of risk financial assets show sharp increase in valuations leading to the financial crisis and then profound drops that are captured in Table VI-1 by percentage changes of peaks and troughs. The first round of quantitative easing and near zero interest rates depreciated the dollar relative to the euro by 39.3 percent between 2003 and 2008, with revaluation of the dollar by 25.1 percent from 2008 to 2010 in the flight to dollar-denominated assets in fear of world financial risks. The dollar revalued 10.7 percent by Fri Jan 13, 2017. Dollar devaluation is a major vehicle of monetary policy in reducing the output gap that is implemented in the probably erroneous belief that devaluation will not accelerate inflation, misallocating resources toward less productive economic activities and disrupting financial markets. The last row of Table VI-1 shows CPI inflation in the US rising from 1.9 percent in 2003 to 4.1 percent in 2007 even as monetary policy increased the fed funds rate from 1 percent in Jun 2004 to 5.25 percent in Jun 2006.

Table VI-1, Volatility of Assets

DJIA	10/08/02-10/01/07	10/01/07-3/4/09	3/4/09- 4/6/10
∆%	87.8	-51.2	60.3
NYSE Financial	1/15/04- 6/13/07	6/13/07- 3/4/09	3/4/09- 4/16/07
∆%	42.3	-75.9	121.1
Shanghai Composite	6/10/05- 10/15/07	10/15/07- 10/30/08	10/30/08- 7/30/09
∆%	444.2	-70.8	85.3
STOXX EUROPE 50	3/10/03- 7/25/07	7/25/07- 3/9/09	3/9/09- 4/21/10
∆%	93.5	-57.9	64.3
UBS Com.	1/23/02- 7/1/08	7/1/08- 2/23/09	2/23/09- 1/6/10
∆%	165.5	-56.4	41.4
10-Year Treasury	6/10/03	6/12/07	12/31/08	4/5/10
%	3.112	5.297	2.247	3.986
USD/EUR	6/26/03	7/14/08	6/07/10	01/13/2017
Rate	1.1423	1.5914	1.192	1.0645
CNY/USD	01/03 2000	07/21 2005	7/15 2008	01/13/ 2017
Rate	8.2798	8.2765	6.8211	6.8998
New House	1963	1977	2005	2009
Sales 1000s	560	819	1283	375
New House	2000	2007	2009	2010
Median Price $1000	169	247	217	222
	2003	2005	2007	2010
CPI	2.3	3.4	2.8	1.6

Sources: http://professional.wsj.com/mdc/page/marketsdata.html?mod=WSJ_hps_marketdata

http://www.census.gov/const/www/newressalesindex_excel.html

http://federalreserve.gov/releases/h10/Hist/dat00_eu.htm

Percentage changes of risk financial assets from the last day of the year relative to the last day of the earlier year are in Table I-1 from 2007 to 2016. Risk financial assets mostly increased in 2016. DJIA increased 13.4 percent and S&P increased 9.5 percent while the NYSE gained 10.4 percent. Dow Global increased 8.4 percent with Dow Asia Pacific increasing 2.4 percent. NIKKEI Average increased 0.4 percent while Shanghai Composite fell 12.3 percent. The US dollar appreciated 3.1 percent relative to the euro and the ten-year Treasury yield increased to 2.447 percent. There is mixed performance in 2015 with declines of 2.2 for DJIA, 0.7 percent for S&P 500, 6.0 percent for NYSE Financial, 6.6 percent for Dow Global and 2.5 percent for Dow Asia Pacific. There were increases of 9.1 percent for the Nikkei Average, 9.4 percent for Shanghai Composite and 9.6 percent for DAX of Germany. The US dollar appreciated 10.2 percent relative to the euro. Calendar year 2014 was satisfactory for most equity indexes but not as excellent as 2013. Shanghai Composite outperformed all equity indexes in Table I-1 in 2014 with increase of 52.9 percent after falling 6.7 percent in 2013. The second highest increase is 11.4 percent for the Standard and Poor’s 500 (S&P 500). DAX of Germany gained 2.7 percent. NYSE Financial increased 5.6 percent and Dow Global gained 0.6 percent. Dow Asia Pacific decreased 1.6 percent while the Dow Jones Industrial Average (DJIA) increased 7.5 percent. The USD appreciated 12.0 percent relative to the EUR. Equities also outperformed in calendar year 2012. DAX gained 29.1 percent and NYSE Financial 25.9 percent. Equities soared in 2013. The Nikkei Average increased 56.7 percent. DJIA gained 26.5 percent and S&P 500 29.6 percent. DAX of Germany increased 25.5 percent. The dollar depreciated 4.2 percent relative to the euro. DJ UBS Commodities index fell 9.6 percent. Equities enjoyed a good year in 2012. Nikkei Average gained 22.9 percent in 2012. S&P increased 13.4 percent and DJIA 7.3 percent. Shanghai Composite increased 3.2 percent. Dow Global increased 10.7 percent and Dow Asia Pacific 13.1 percent. DJ UBS Commodities fell 1.8 percent. The only gain for a major equity index in Table I-1 for 2011 is 5.5 percent for the DJIA. S&P 500 is better than other equity markets by remaining flat for 2011. With the exception of a drop of 8.4 percent of the European equity index STOXX 50, all declines of equity markets in 2011 are in excess of 10 percent. China’s Shanghai Composite lost 21.7 percent. The equity index of Germany DAX fell 14.7 percent. The DJ UBS Commodities Index dropped 13.4 percent. Robin Wigglesworth, writing on Dec 30, 2011, on “$6.3tn wiped off markets in 2011,” published in the Financial Times (http://www.ft.com/intl/cms/s/0/483069d8-32f3-11e1-8e0d-00144feabdc0.html#axzz1i2BE7OPa), provides an estimate of $6.3 trillion erased from equity markets globally in 2011. The Bureau of Economic Analysis (BEA) estimates US nominal GDP in 2011 at $15,517.9 billion (http://www.bea.gov/iTable/index_nipa.cfm). The loss in equity markets worldwide in 2011 of $6.3 trillion is equivalent to about 40.6 percent of US GDP or economic activity in 2011. Table I-1 also provides the exchange rate of number of US dollars (USD) required in buying a unit of euro (EUR), USD/EUR. The dollar appreciated 3.1 percent on the last day of trading in 2011 relative to the last day of trading in 2010, suggesting risk aversion. Depreciation of the dollar by 1.8 percent in 2012 and 4.2 percent in 2013 suggests more favorable environment of risk appetite for carry trades from zero interest rates into risk financial assets. The final row of Table I-1 provides the yield of the ten-year Treasury, decreasing to 2.172 percent in 2014 and 2.269 percent in 2015. The yield of the ten-year Treasury increased to 3.030 percent in 2013, which is the highest since 3.292 percent in 2010 and 3.844 percent in 2008. The yield at year-end 2007 was 4.077 percent.

Table I-1, Percentage Change of Year-end Values of Financial Assets Relative to Earlier Year-end Values 2007-2016 and Year-end Yield of 10-Year Treasury Note

∆%	2015	2014	2013	2012	2011	2010	2009	2008	2007
DJIA	-2.2	7.5	26.5	7.3	5.5	11.0	18.8	-33.8	6.4
S&P 500	-0.7	11.4	29.6	13.4	0.0	12.8	23.5	-38.5	3.5
NYSE Fin	-6.0	5.6	24.2	25.9	-18.1	5.0	22.7	-53.6	-13.1
Dow Global	-6.6	0.6	24.5	10.7	-13.6	5.2	30.0	-45.4	30.5
Dow Asia-Pacific	-2.5	-1.6	10.2	13.1	-17.6	16.0	36.4	-44.2	14.0
Nikkei Av	9.1	7.1	56.7	22.9	-17.3	-3.0	19.0	-42.1	-11.1
Shanghai	9.4	52.9	-6.7	3.2	-21.7	-14.3	80.0	-65.4	96.7
DAX	9.6	2.7	25.5	29.1	-14.7	16.1	23.8	-40.4	22.3
USD/ EUR*	10.2	12.0	-4.2	-1.8	3.1	6.6	-2.5	4.3	-10.6
DJ UBS** Com		NA	-9.6	-1.1	-13.4	16.7	18.7	-36.6	11.2
Year-end Yield 10-Year Treasury %	2.269	2.172	3.030	1.758	2.027	3.292	3.844	2.157	4.077

∆%	2016
DJIA	13.4
S&P 500	9.5
NYSE Financial	10.4
Dow Global	8.4
Dow Asia Pacific	2.4
Nikkei Average	0.4
Shanghai Composite	-12.3
DAX	6.9
USD/EUR*	3.1
DJ UBS Commodities**	NA
Year-end Yield 10 Year Treasury	2.447

*Negative sign is dollar devaluation; positive sign is dollar appreciation

**DJ UBS available only for 2013 and earlier years

Sources: http://professional.wsj.com/mdc/public/page/marketsdata.html?mod=WSJ_PRO_hps_marketdata

The other yearly percentage changes in Table I-2 are also revealing wide fluctuations in valuations of risk financial assets. To be sure, economic conditions and perceptions of the future do influence valuations of risk financial assets. It is also valid to contend that unconventional monetary policy magnifies fluctuations in these valuations by inducing carry trades from zero interest rates to exposures with high leverage in risk financial assets such as equities, emerging equities, currencies, high-yield structured products and commodities futures and options. In fact, one of the alleged channels of transmission of unconventional monetary policy is through higher consumption induced by increases in wealth resulting from higher valuations of stock markets. Bernanke (2010WP) and Yellen (2011AS) reveal the emphasis of monetary policy on the impact of the rise of stock market valuations in stimulating consumption by wealth effects on household confidence. Unconventional monetary policy could also result in magnification of values of risk financial assets beyond actual discounted future cash flows, creating financial instability. Separating all these effects in practice may be quite difficult because they are observed simultaneously. Conclusive evidence would require contrasting what actually happened with the counterfactual of what would have happened in the absence of unconventional monetary policy and other effects (on counterfactuals see Pelaez and Pelaez, Globalization and the State Vol I (2008a), 125, 136, Harberger (1971, 1997), Fishlow 1965, Fogel 1964, Fogel and Engerman 1974, North and Weingast 1989, Coastworth 1981, 2006, Summerhill 1997, 1998, 2003,Pelaez 1979, 26-7). There is no certainty or evidence that unconventional policies attain their intended effects without risks of costly side effects. Yearly fluctuations of financial assets in Table I-1 are quite wide. In 2007, for example, the equity index Dow Global increased 30.5 percent while DAX gained 22.3 percent and the Shanghai Composite jumped 96.7 percent. The DJIA gained only 6.4 percent as recession began in IVQ2007. The flight to government obligations in 2008 (Cochrane and Zingales 2009, Cochrane 2011Jan) was equivalent to the astronomical declines of world equity markets and commodities. The flight from risk is also in evidence in the appreciation of the dollar by 4.3 percent in 2008 with unwinding carry trades and with renewed carry trades in the depreciation of the dollar by 2.5 percent in 2009. Recovery still continued in 2010 with shocks of the European debt crisis in the spring and in Nov 2010. The flight from risk exposures dominated declines of valuations of risk financial assets in 2011.

Table I-2 is designed to provide a comparison of valuations of risk financial assets at the end of 2015 relative to valuations at the end of every year from 2007 to 2016. There were increases in major indexes in 2016: 13.4 percent for DJIA, 9.5 percent for S&P 500, 10.4 percent for NYSE Financial, 8.4 percent for Dow Global and 2.4 percent for Dow Asia Pacific. There are increases in major indexes: 0.4 percent for Nikkei and 6.9 percent for DAX of Germany. Shanghai Composite fell 12.3 percent. The DJIA index is 10.9 percent higher at the end of 2016 relative to the valuation at the end of 2014, 49.0 percent above the valuation at the end of 2007 and 58.6 percent higher relative to the valuation at the end of 2006. DJIA is higher by 125.2 percent at the end of 2016 relative to the depressed valuation at the end of 2008. Several indexes are still lower at the end of 2016 relative to the values at the end of 2007 with exception of gains of 49.0 for DJIA, 52.5 percent for S&P 500, 24.9 percent for Nikkei Average and 42.3 percent for DAX. Some equity indexes are higher at the end of 2016 relative to the end of 2006: DJIA by 58.6 percent, S&P by 57.9 percent, Dow Global by 18.3 percent, Nikkei Average by 11.0 percent, Shanghai Composite by 16.0 percent and DAX by 74.0 percent. The USD is 27.9 stronger at the end of 2016 relative to 2007 and 20.3 percent stronger relative to 2006. Zero interest rates do not devalue the dollar during prolonged bouts of relative risk aversion and portfolio reallocations. Low valuations of risk financial assets are intimately related to risk aversion in international financial markets because of the European debt crisis, weakness and unemployment in advanced economies, fiscal imbalances and slowing growth worldwide. Valuations of stock indexes for the US and Germany are peaking at the turn of 2014 into 2015 relative to 2007 and 2006 with recent sharp declines into 2016.

Table I-2, Percentage Change of Year-end 2015 Values of Financial Assets Relative to Year-end Values 2006-2014

	∆% 16/ 14	∆% 16/ 13	∆% 16/ 12	∆% 16/ 11	∆% 16/ 10	∆% 16/ 09	∆% 16/ 08	∆% 16/ 07
DJIA	10.9	19.2	50.8	61.8	70.7	89.5	125.2	49.0
S&P 500	8.7	21.1	57.0	78.0	78.0	100.8	147.9	52.5
NYSE Fin	3.8	9.6	36.1	71.3	40.4	47.5	80.9	-16.1
Dow Global	1.2	1.8	26.8	40.4	21.3	27.6	65.9	-9.4
Dow Asia-Pacific	-0.2	-1.8	8.3	22.4	0.9	17.0	59.6	-11.0
Nikkei Av	9.5	17.3	83.9	126.1	86.9	81.2	115.7	24.9
Shanghai	-4.1	46.7	36.8	41.1	10.5	-5.3	70.5	-41.0
DAX	17.1	20.2	50.8	94.6	66.1	92.7	138.7	42.3
USD/EUR*	13.1	23.5	20.3	18.8	21.3	26.5	24.7	27.9
DJ UBS** Com	NA	-9.6	-10.6	-22.6	-9.7	7.3	-32.0	-24.4

	∆% 16/15
DJIA	13.4
S&P 500	9.5
NYSE Fin	10.4
Dow Global	8.4
Dow Asia Pacific	2.4
Nikkei Average	0.4
Shanghai Composite	-12.3
DAX	6.9
USD/EUR*	3.1
DJ UBS Commodities**	NA

*Negative sign is dollar devaluation; positive sign is dollar appreciation

**DJ UBS available only for 2013 and earlier years; percentage change is to 2013.

Sources: http://professional.wsj.com/mdc/public/page/marketsdata.html?mod=WSJ_PRO_hps_marketdata

ESIV Recovery without Hiring. Professor Edward P. Lazear (2012Jan19) at Stanford University finds that recovery of hiring in the US to peaks attained in 2007 requires an increase of hiring by 30 percent while hiring levels increased by only 4 percent from Jan 2009 to Jan 2012. The high level of unemployment with low level of hiring reduces the statistical probability that the unemployed will find a job. According to Lazear (2012Jan19), the probability of finding a new job in early 2012 is about one third of the probability of finding a job in 2007. Improvements in labor markets have not increased the probability of finding a new job. Lazear (2012Jan19) quotes an essay coauthored with James R. Spletzer in the American Economic Review (Lazear and Spletzer 2012Mar, 2012May) on the concept of churn. A dynamic labor market occurs when a similar amount of workers is hired as those who are separated. This replacement of separated workers is called churn, which explains about two-thirds of total hiring. Typically, wage increases received in a new job are higher by 8 percent. Lazear (2012Jan19) argues that churn has declined 35 percent from the level before the recession in IVQ2007. Because of the collapse of churn, there are no opportunities in escaping falling real wages by moving to another job. As this blog argues, there are meager chances of escaping unemployment because of the collapse of hiring and those employed cannot escape falling real wages by moving to another job (Section I and earlier (http://cmpassocregulationblog.blogspot.com/2016/12/rising-values-of-risk-financial-assets.html). Lazear and Spletzer (2012Mar, 1) argue that reductions of churn reduce the operational effectiveness of labor markets. Churn is part of the allocation of resources or in this case labor to occupations of higher marginal returns. The decline in churn can harm static and dynamic economic efficiency. Losses from decline of churn during recessions can affect an economy over the long-term by preventing optimal growth trajectories because resources are not used in the occupations where they provide highest marginal returns. Lazear and Spletzer (2012Mar 7-8) conclude that: “under a number of assumptions, we estimate that the loss in output during the recession [of 2007 to 2009] and its aftermath resulting from reduced churn equaled $208 billion. On an annual basis, this amounts to about .4% of GDP for a period of 3½ years.”

There are two additional facts discussed below: (1) there are about ten million fewer full-time jobs currently than before the recession of 2008 and 2009; and (2) the extremely high and rigid rate of youth unemployment is denying an early start to young people ages 16 to 24 years while unemployment of ages 45 years or over has swelled.

Hiring in the nonfarm sector (HNF) has declined from 63.327 million in 2006 to 61.680 million in 2015 or by 1.647 million while hiring in the private sector (HP) has declined from 59.128 million in 2006 to 57.557 million in 2015 or by 1.571 million, as shown in Table I-1. The ratio of nonfarm hiring to employment (RNF) has fallen from 47.0 in 2005 to 43.5 in 2015 and in the private sector (RHP) from 52.7 in 2005 to 48.0 in 2015. Hiring has not recovered as in previous cyclical expansions because of the low rate of economic growth in the current cyclical expansion. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 250.801 million in 2015 or by 21.986 million. Hiring has not recovered prerecession levels while needs of hiring multiplied because of growth of population by more than 21 million. Private hiring of 59.128 million in 2006 was equivalent to 25.8 percent of the civilian noninstitutional population of 228.815, or those in condition of working, falling to 57.557 million in 2015 or 22.9 percent of the civilian noninstitutional population of 250.801 million in 2015. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 64.957 million of hiring in 2015, which would be 7.400 million higher than actual 57.557 million in 2015. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 24.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.4 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 percent below trend under this alternative calculation.

Table I-1, US, Annual Total Nonfarm Hiring (HNF) and Total Private Hiring (HP) in the US in Thousands and Percentage of Total Employment

	HNF	Rate RNF	HP	Rate HP
2001	62,633	47.4	58,501	52.7
2002	58,479	44.8	54,665	50.1
2003	56,949	43.7	53,584	49.3
2004	60,263	45.7	56,573	51.4
2005	62,951	47.0	59,179	52.7
2006	63,327	46.4	59,128	51.7
2007	62,104	45.0	57,797	49.9
2008	54,745	39.9	51,316	44.8
2009	45,931	35.0	42,703	39.3
2010	48,740	37.4	44,903	41.7
2011	50,283	38.1	47,179	43.0
2012	52,367	39.0	48,916	43.6
2013	54,241	39.8	50,787	44.3
2014	58,657	42.2	55,048	47.0
2015	61,680	43.5	57,557	48.0

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-1 shows the annual level of total nonfarm hiring (HNF) that collapsed during the global recession after 2007 in contrast with milder decline in the shallow recession of 2001. Nonfarm hiring has not recovered, remaining at a depressed level. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 250.801 million in 2015 or by 21.986 million. Hiring has not recovered precession levels while needs of hiring multiplied because of growth of population by more than 21 million.

Chart I-1, US, Level Total Nonfarm Hiring (HNF), Annual, 2001-2015

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-2 shows the ratio or rate of nonfarm hiring to employment (RNF) that also fell much more in the recession of 2007 to 2009 than in the shallow recession of 2001. Recovery is weak in the current environment of cyclical slow growth.

Chart I-2, US, Rate Total Nonfarm Hiring (HNF), Annual, 2001-2015

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Yearly percentage changes of total nonfarm hiring (HNF) are provided in Table I-2. There were much milder declines in 2002 of 6.6 percent and 2.6 percent in 2003 followed by strong rebounds of 5.8 percent in 2004 and 4.5 percent in 2005. In contrast, the contractions of nonfarm hiring in the recession after 2007 were much sharper in percentage points: 1.9 in 2007, 11.8 in 2008 and 16.1 percent in 2009. On a yearly basis, nonfarm hiring grew 6.1 percent in 2010 relative to 2009, 3.2 percent in 2011, 4.1 percent in 2012 and 3.6 percent in 2013. Nonfarm hiring grew 8.1 percent in 2014 and increased 5.2 percent in 2015. The relatively large length of 27 quarters of the current expansion reduces the likelihood of significant recovery of hiring levels in the United States because lower rates of growth and hiring in the final phase of expansions.

Table I-2, US, Annual Total Nonfarm Hiring (HNF), Annual Percentage Change, 2002-2015

Year	Annual ∆%
2002	-6.6
2003	-2.6
2004	5.8
2005	4.5
2006	0.6
2007	-1.9
2008	-11.8
2009	-16.1
2010	6.1
2011	3.2
2012	4.1
2013	3.6
2014	8.1
2015	5.2

Source: US Bureau of Labor Statistics

http://www.bls.gov/jlt/

Total private hiring (HP) 12-month percentage changes of annual data are in Chart I-3. There has been sharp contraction of total private hiring in the US and only milder recovery from 2010 to 2015.

Chart I-3, US, Total Nonfarm Hiring Level, Annual, ∆%, 2001-2015

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Total private hiring (HP) annual data are in Chart I-5. There has been sharp contraction of total private hiring in the US and only milder recovery from 2010 to 2015.

Chart I-5, US, Total Private Hiring, Annual, 2001-2015

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-5A plots the rate of total private hiring relative to employment (RHP). The rate collapsed during the global recession after 2007 with insufficient recovery.

Chart I-5A, US, Rate Total Private Hiring, Annual, 2001-2015

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Total nonfarm hiring (HNF), total private hiring (HP) and their respective rates are in Table I-3 for the month of Nov in the years from 2001 to 2016. Hiring numbers are in thousands. There is recovery in HNF from 3541 thousand (or 4.0 million) in Nov 2009 to 3657 thousand in Nov 2010, 3814 thousand in Nov 2011, 4001 thousand in Nov 2012, 4257 thousand in Nov 2013, 4674 thousand in Nov 2014, 4873 thousand in Nov 2015 and 4850 thousand in Nov 2016 for cumulative gain of 37.0 percent at average rate of 4.6 percent per year. HP rose from 3338 thousand in Nov 2009 to 3436 thousand in Nov 2010, 31613 thousand in Nov 2011, 3791 thousand in Nov 2012, 4029 thousand in Nov 2013, 4432 thousand in Nov 2014, 4599 in Nov 2015 and 4563 thousand in Nov 2016 for cumulative gain of 37.0 percent at the average yearly rate of 4.6 percent. HNF has decreased from 4915 thousand in Nov 2006 to 4850 thousand in Nov 2016 or by 1.3 percent. HP has decreased from 4654 thousand in Nov 2006 to 4563 thousand in Nov 2016 or by 2.0 percent. The civilian noninstitutional population of the US, or those in condition of working, rose from 229.905 million in Nov 2006 to 254.540 million in Nov 2016, by 24.635 million or 10.7 percent. There is often ignored ugly fact that hiring decreased by around 2.0 percent while population available for working increased around 10.7 percent. Private hiring of 59.128 million in 2006 was equivalent to 25.8 percent of the civilian noninstitutional population of 228.815, or those in condition of working, falling to 57.557 million in 2015 or 22.9 percent of the civilian noninstitutional population of 250.801 million in 2015. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 64.957 million of hiring in 2015, which would be 7.400 million higher than actual 57.557 million in 2015. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. Cyclical slow growth over the entire business cycle from IVQ2007 to the present in comparison with earlier cycles and long-term trend (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html) explains the fact that there are many million fewer hires in the US than before the global recession. The labor market continues to be fractured, failing to provide an opportunity to exit from unemployment/underemployment or to find an opportunity for advancement away from declining inflation-adjusted earnings.

Table I-3, US, Total Nonfarm Hiring (HNF) and Total Private Hiring (HP) in the US in

Thousands and in Percentage of Total Employment Not Seasonally Adjusted

	HNF	Rate RNF	HP	Rate HP
2001 Nov	4253	3.2	3998	3.6
2002 Nov	4274	3.2	3998	3.6
2003 Nov	4125	3.1	3914	3.6
2004 Nov	4591	3.4	4326	3.9
2005 Nov	4682	3.4	4423	3.9
2006 Nov	4915	3.6	4654	4.0
2007 Nov	4616	3.3	4368	3.7
2008 Nov	3499	2.6	3301	2.9
2009 Nov	3541	2.7	3338	3.1
2010 Nov	3657	2.8	3436	3.1
2011 Nov	3814	2.8	3613	3.2
2012 Nov	4001	2.9	3791	3.3
2013 Nov	4257	3.1	4029	3.5
2014 Nov	4674	3.3	4432	3.7
2015 Nov	4873	3.4	4599	3.8
2016 Nov	4850	3.3	4563	3.7

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-6 provides total nonfarm hiring on a monthly basis from 2001 to 2016. Nonfarm hiring rebounded in early 2010 but then fell and stabilized at a lower level than the early peak not-seasonally adjusted (NSA) of 4815 in May 2010 until it surpassed it with 5006 in Jun 2011 but declined to 3092 in Dec 2012. Nonfarm hiring fell to 2997 in Dec 2011 from 3814 in Nov 2011 and to revised 3629 in Feb 2012, increasing to 4197 in Mar 2012, 3092 in Dec 2012 and 4238 in Jan 2013 and declining to 3690 in Feb 2013. Nonfarm hires not seasonally adjusted increased to 4257 in Nov 2013 and 3223 in Dec 2013. Nonfarm hires reached 3730 in Dec 2014, 3919 in Dec 2015 and 4850 in Dec 2016. Chart I-6 provides seasonally adjusted (SA) monthly data. The number of seasonally-adjusted hires in Oct 2011 was 4239 thousand, increasing to revised 4470 thousand in Feb 2012, or 5.4 percent, moving to 4345 in Dec 2012 for cumulative increase of 2.6 percent from 4234 in Dec 2011 and 4488 in Dec 2013 for increase of 3.3 percent relative to 4345 in Dec 2012. The number of hires not seasonally adjusted was 5006 in Jun 2011, falling to 2997 in Dec 2011 but increasing to 4110 in Jan 2012 and declining to 3092 in Dec 2012. The number of nonfarm hiring not seasonally adjusted fell by 40.1 percent from 5006 in Jun 2011 to 2997 in Dec 2011 and fell 40.1 percent from 5162 in Jun 2012 to 3092 in Dec 2012 in a yearly-repeated seasonal pattern. The number of nonfarm hires not seasonally adjusted fell from 5114 in Jun 2013 to 3223 in Dec 2013, or decline of 37.0 percent, showing strong seasonality. The number of nonfarm hires not seasonally adjusted fell from 5570 in Jun 2014 to 3730 in Dec 2014 or 33.0 percent. The level of nonfarm hires fell from 5918 in Jun 2015 to 3919 in Dec 2015 or 33.8 percent.

Chart I-6, US, Total Nonfarm Hiring (HNF), 2001-2016 Month SA

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Similar behavior occurs in the rate of nonfarm hiring in Chart I-7. Recovery in early 2010 was followed by decline and stabilization at a lower level but with stability in monthly SA estimates of 3.2 in Aug 2011 to 3.2 in Jan 2012, increasing to 3.3 in May 2012 and stabilizing to 3.3 in Jun 2012. The rate stabilized at 3.2 in Jul 2012, increasing to 3.3 in Aug 2012 but falling to 3.2 in Dec 2012 and 3.3 in Dec 2013. The rate not seasonally adjusted fell from 3.8 in Jun 2011 to 2.2 in Dec 2011, climbing to 3.8 in Jun 2012 but falling to 2.3 in Dec 2012. The rate of nonfarm hires not seasonally adjusted fell from 3.7 in Jun 2013 to 2.3 in Dec 2013. The NSA rate of nonfarm hiring fell from 4.0 in Jun 2014 to 2.6 in Dec 2014. The NSA rate fell from 4.1 in Jun 2015 to 2.7 in Dec 2015. Rates of nonfarm hiring NSA were in the range of 2.7 (Dec) to 4.4 (Jun) in 2006. The rate of nonfarm hiring SA stood at 3.6 in Nov 2016 and at 3.3 NSA.

Chart I-7, US, Rate Total Nonfarm Hiring, Month SA 2001-2016

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

There is only milder improvement in total private hiring shown in Chart I-8. Hiring private (HP) rose in 2010 with stability and renewed increase in 2011 followed by almost stationary series in 2012. The number of private hiring seasonally adjusted fell from 4043 thousand in Sep 2011 to 3933 in Dec 2011 or by 2.7 percent, decreasing to 4015 in Jan 2012 or decline by 0.7 percent relative to the level in Sep 2011. Private hiring fell to 3961 in Sep 2012 or lower by 2.0 percent relative to Sep 2011, moving to 4049 in Dec 2012 for increase of 0.8 percent relative to 4015 in Jan 2012. The number of private hiring not seasonally adjusted fell from 4626 in Jun 2011 to 2817 in Dec 2011 or by 39.1 percent, reaching 3855 in Jan 2012 or decline of 16.7 percent relative to Jun 2011 and moving to 2911 in Dec 2012 or 38.8 percent lower relative to 4757 in Jun 2012. Hires not seasonally adjusted fell from 4761 in Jun 2013 to 3059 in Dec 2013. The level of private hiring NSA fell from 5151 in Jun 2014 to 3532 in Dec 2014 or 31.4 percent. The level of private hiring fell from 5475 in Jun 2015 to 3697 in Dec 2015 or 32.5 percent. Companies reduce hiring in the latter part of the year that explains the high seasonality in year-end employment data. For example, NSA private hiring fell from 5614 in Jun 2006 to 3579 in Dec 2006 or by 36.2 percent. Private hiring NSA data are useful in showing the huge declines from the period before the global recession. Hiring in the nonfarm sector (HNF) has declined from 63.327 million in 2006 to 61.680 million in 2015 or by 1.647 million while hiring in the private sector (HP) has declined from 59.128 million in 2006 to 57.557 million in 2015 or by 1.571 million, as shown in Table I-1. The ratio of nonfarm hiring to employment (RNF) has fallen from 47.0 in 2005 to 43.5 in 2015 and in the private sector (RHP) from 52.7 in 2005 to 48.0 in 2015. Hiring has not recovered as in previous cyclical expansions because of the low rate of economic growth in the current cyclical expansion. The civilian noninstitutional population or those in condition to work increased from 228.815 million in 2006 to 250.801 million in 2015 or by 21.986 million. Hiring has not recovered prerecession levels while needs of hiring multiplied because of growth of population by more than 21 million. Private hiring of 59.128 million in 2006 was equivalent to 25.8 percent of the civilian noninstitutional population of 228.815, or those in condition of working, falling to 57.557 million in 2015 or 22.9 percent of the civilian noninstitutional population of 250.801 million in 2015. The percentage of hiring in civilian noninstitutional population of 25.9 percent in 2006 would correspond to 64.957 million of hiring in 2015, which would be 7.400 million higher than actual 57.557 million in 2015.

Chart I-8, US, Total Private Hiring Month SA 2001-2016

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

Chart I-9 shows similar behavior in the rate of private hiring. The rate in 2011 in monthly SA data did not rise significantly above the peak in 2010. The rate seasonally adjusted fell from 3.7 in Sep 2011 to 3.5 in Dec 2011 and reached 3.6 in Dec 2012 and 3.6 in Dec 2013. The rate not seasonally adjusted (NSA) fell from 3.7 in Sep 2011 to 2.5 in Dec 2011, increasing to 3.8 in Oct 2012 but falling to 2.6 in Dec 2012 and 3.4 in Mar 2013. The NSA rate of private hiring fell from 4.8 in Jul 2006 to 3.4 in Aug 2009 but recovery was insufficient to only 3.9 in Aug 2012, 2.6 in Dec 2012 and 2.6 in Dec 2013. The NSA rate increased to 3.0 in Dec 2015 and 3.7 in Nov

2016.

Chart I-9, US, Rate Total Private Hiring Month SA 2001-2016

Source: Bureau of Labor Statistics

http://www.bls.gov/jlt/

ESV Ten Million Fewer Full-time Jobs. There is strong seasonality in US labor markets around the end of the year.

• Seasonally adjusted part-time for economic reasons. The number employed part-time for economic reasons because they could not find full-time employment fell from 9.166 million in Sep 2011 to 7.775 million in Mar 2012, seasonally adjusted, or decline of 1.391 million in six months, as shown in Table I-9. The number employed part-time for economic reasons rebounded to 8.671 million in Sep 2012 for increase of 697,000 in one month from Aug to Sep 2012. The number employed part-time for economic reasons declined to 8.203 million in Oct 2012 or by 468,000 again in one month, further declining to 8.166 million in Nov 2012 for another major one-month decline of 37,000 and 7.943 million in Dec 2012 or fewer 223,000 in just one month. The number employed part-time for economic reasons increased to 8.074 million in Jan 2013 or 131,000 more than in Dec 2012 and to 8.119 million in Feb 2013, declining to 7.864 million in May 2013 but increasing to 8.096 million in Jun 2013. The number employed part-time for economic reasons fell to 7.804 million in Aug 2013 for decline of 279,000 in one month from 8.083 million in Jul 2013. The number employed part-time for economic reasons increased 207,000 from 7.804 million in Aug 2013 to 8.011 million in Sep 2013. The number part-time for economic reasons rose to 7.995 million in Oct 2013, falling by 265,000 to 7.730 million in Nov 2013. The number part-time for economic reasons increased to 7.792 million in Dec 2013, decreasing to 7.298 million in Jan 2014. The number employed part-time for economic reasons fell from 7.298 million in Jan 2014 to 7.262 million in Feb 2014. The number employed part-time for economic reasons increased to 7.403 million in Mar 2014 and 7.466 million in Apr 2014. The number employed part-time for economic reasons fell to 7.170 million in May 2014, increasing to 7.469 million in Jun 2014. The level employed part-time for economic reasons fell to 7.430 million in Jul 2014 and 7.173 million in Aug 2014. The level employed part-time for economic reasons fell to 7.123 million in Sep 2014, 7.033 million in Oct 2014 and 6.870 million in Nov 2014. The level employed part-time for economic reasons fell to 6.819 million in Dec 2014, increasing to 6.836 million in Jan 2015. The level employed part-time for economic reasons fell to 6.664 million in Feb 2015, increasing to 6.646 million in Mar 2015. The level of employed part-time for economic reasons fell to 6.563 million in Apr 2015, increasing to 6.544 million in May 2015. The level employed part-time for economic reasons fell to 6.463 million in Jun 2015 and 6.292 million in Jul 2015. The level employed part-time for economic reasons increased to 6.438 million in Aug 2015, declining to 6.031 million in Sep 2015. The level employed part-time for economic reasons fell to 5.734 million in Oct 2015, increasing to 6.113 million in Nov 2015. The level of part-time for economic reasons fell to 6.057 million in Dec 2015, decreasing to 6.035 million in Jan 2016. The level employed part-time for economic reasons decreased to 6.019 million in Feb 2016 and increased to 6.120 million in Mar 2016. The level employed part-time for economic reasons fell to 5.970 million in Apr 2016 and increased to 6.409 million in May 2016. The level of part-time for economic reasons fell to 5.820 million in Jun 2016, increasing to 5.936 million in Jul 2016. The level of part-time for economic reasons increased to 6.027 million in Aug 2016, decreasing to 5.874 million in Sep 2016. The level of part-time for economic reasons reached 5.850 million in Oct 2016, decreasing to 5.659 million in Nov 2016 and 5.598 million in Dec 2016.
• Seasonally adjusted full-time. The number employed full-time increased from 112.923 million in Oct 2011 to 115.024 million in Mar 2012 or 2.101 million but then fell to 114.233 million in May 2012 or 0.791 million fewer full-time employed than in Mar 2012. The number employed full-time increased from 114.736 million in Aug 2012 to 115.570 million in Oct 2012 or increase of 0.834 million full-time jobs in two months and further to 115.724 million in Jan 2013 or increase of 0.988 million more full-time jobs in five months from Aug 2012 to Jan 2013. The number of full time jobs decreased slightly to 115.674 million in Feb 2013, increasing to 116.247 million in May 2013 and 116.126 million in Jun 2013. Then number of full-time jobs increased to 116.155 million in Jul 2013, 116.435 million in Aug 2013 and 116.895 million in Sep 2013. The number of full-time jobs fell to 116.362 million in Oct 2013 and increased to 117.046 in Nov 2013. The level of full-time jobs increased to 117.351 million in Dec 2013, increasing to 117.504 million in Jan 2014 and 117.747 million in Feb 2014. The level of employment full-time increased to 117.941 million in Mar 2014 and 118.516 million in Apr 2014. The level of full-time employment reached 118.816 million in May 2014, decreasing to 118.238 million in Jun 2014. The level of full-time jobs increased to 118.450 million in Jul 2014 and 118.707 million in Aug 2014. The level of full-time jobs increased to 119.338 million in Sep 2014, 119.763 million in Oct 2014 and 119.645 million in Nov 2014. The level of full-time jobs increased to 120.075 million in Dec 2014 and 120.575 million in Jan 2015. The level of full-time jobs increased to 120.776 million in Feb 2015 and 120.963 million in Mar 2015. The level of full-time jobs decreased to 120.870 million in Apr 2015, increasing to 121.523 million in May 2015 and decreasing to 121.066 million in Jun 2015. The level of full-time jobs increased to 121.629 million in Jul 2015 and increased to 121.934 million in Aug 2015, decreasing to 121.829 million in Sep 2015. The level of full-time jobs increased to 122.071 million in Oct 2015 and increased to 122.110 million in Nov 2015. The level of full-time jobs increased to 122.700 million in Dec 2015 and 123.116 million in Jan 2016. The level of full-time jobs increased to 123.210 million in Feb 2016 and increased to 123.513 million in Mar 2016. The level of full-time jobs decreased to 123.259 million in Apr 2016 and 123.232 million in May 2016. The level of full-time jobs increased to 123.618 million in Jun 2016, increasing to 123.888 million in Jul 2016. The level of full-time jobs increased to 124.256 million in Aug 2016, decreasing to 124.253 million in Sep 2016 and 124.190 million in Oct 2016. The level of full-time jobs increased to 124.213 million in Nov 2016 and 124.248 million in Dec 2016. Adjustments of benchmark and seasonality-factors at the turn of every year could affect comparability of labor market indicators (http://cmpassocregulationblog.blogspot.com/2016/02/fluctuating-risk-financial-assets-in.html http://cmpassocregulationblog.blogspot.com/2015/02/job-creation-and-monetary-policy-twenty.html http://cmpassocregulationblog.blogspot.com/2014/02/financial-instability-rules.html http://cmpassocregulationblog.blogspot.com/2013/02/thirty-one-million-unemployed-or.html).
• Not seasonally adjusted part-time for economic reasons. The number of employed part-time for economic reasons actually increased without seasonal adjustment from 8.271 million in Nov 2011 to 8.428 million in Dec 2011 or by 157,000 and then to 8.918 million in Jan 2012 or by an additional 490,000 for cumulative increase from Nov 2011 to Jan 2012 of 647,000. The level of employed part-time for economic reasons then fell from 8.918 million in Jan 2012 to 7.867 million in Mar 2012 or by 1.051 million and to 7.694 million in Apr 2012 or 1.224 million fewer relative to Jan 2012. In Aug 2012, the number employed part-time for economic reasons reached 7.842 million NSA or 148,000 more than in Apr 2012. The number employed part-time for economic reasons increased from 7.842 million in Aug 2012 to 8.110 million in Sep 2012 or by 3.4 percent. The number part-time for economic reasons fell from 8.110 million in Sep 2012 to 7.870 million in Oct 2012 or by 240.000 in one month. The number employed part-time for economic reasons NSA increased to 8.628 million in Jan 2013 or 758,000 more than in Oct 2012. The number employed part-time for economic reasons fell to 8.298 million in Feb 2013, which is lower by 330,000 relative to 8.628 million in Jan 2013 but higher by 428,000 relative to 7.870 million in Oct 2012. The number employed part time for economic reasons fell to 7.734 million in Mar 2013 or 564,000 fewer than in Feb 2013 and fell to 7.709 million in Apr 2013. The number employed part-time for economic reasons reached 7.618 million in May 2013. The number employed part-time for economic reasons jumped from 7.618 million in May 2013 to 8.440 million in Jun 2013 or 822,000 in one month. The number employed part-time for economic reasons fell to 8.324 million in Jul 2013 and 7.690 million in Aug 2013. The number employed part-time for economic reasons NSA fell to 7.522 million in Sep 2013, increasing to 7.700 million in Oct 2013. The number employed part-time for economic reasons fell to 7.563 million in Nov 2013 and increased to 7.990 million in Dec 2013. The number employed part-time for economic reasons fell to 7.771 million in Jan 2014 and 7.397 million in Feb 2014. The level of part-time for economic reasons increased to 7.455 million in Mar 2014 and fell to 7.243 million in Apr 2014. The number of part-time for economic reasons fell to 6.960 million in May 2014, increasing to 7.805 million in Jun 2014. The level of part-time for economic reasons fell to 7.665 million in Jul 2014 and 7.083 million in Aug 2014. The level of part-time for economic reasons fell to 6.711 million in Sep 2014 and increased to 6.787 million in Oct 2014. The level of part-time for economic reasons reached 6.713 million in Nov 2014 and 6.970 million in Dec 2014, increasing to 7.269 million in Jan 2015. The level of part-time for economic reasons fell to 6.772 million in Feb 2015 and 6.672 million in Mar 2015, falling to 6.356 million in Apr 2015. The level of part-time for economic reasons increased to 6.363 million in May 2015 and to 6.776 million in Jun 2015, decreasing to 6.511 million in Jul 2015. The level of part-time for economic reasons fell to 6.361 million in Aug 2015 and 5.693 million in Sep 2015. The level of part-time for economic reasons fell to 5.536 million in Oct 2015, increasing to 5.967 million in Nov 2015. The level of part-time for economic reasons increased to 6.179 million in Dec 2015, increasing to 6.406 million in Jan 2016. The level of part-time for economic reasons decreased to 6.106 million in Feb 2016 and increased to 6.138 million in Mar 2016. The level of part-time for economic reasons decreased to 5.771 million in Apr 2016 and increased to 6.238 million in May 2016. The level of part-time for economic reasons decreased to 6.119 million in Jun 2016, increasing to 6.157 million in Jul 2016. The level of part-time for economic reasons fell to 5.963 million in Aug 2016, decreasing to 5.550 million in Sep 2016. The level of part-time for economic reasons increased to 5.648 million in Oct 2016, decreasing to 5.518 million in Nov 2016 and increasing to 5.707 million in Dec 2016.
• Not seasonally adjusted full-time. The number employed full time without seasonal adjustment fell from 113.138 million in Nov 2011 to 113.050 million in Dec 2011 or by 88,000 and fell further to 111.879 in Jan 2012 for cumulative decrease of 1.259 million. The number employed full-time not seasonally adjusted fell from 113.138 million in Nov 2011 to 112.587 million in Feb 2012 or by 551.000 but increased to 116.214 million in Aug 2012 or 3.076 million more full-time jobs than in Nov 2011. The number employed full-time not seasonally adjusted decreased from 116.214 million in Aug 2012 to 115.678 million in Sep 2012 for loss of 536,000 full-time jobs and rose to 116.045 million in Oct 2012 or by 367,000 full-time jobs in one month relative to Sep 2012. The number employed full-time NSA fell from 116.045 million in Oct 2012 to 115.515 million in Nov 2012 or decline of 530.000 in one month. The number employed full-time fell from 115.515 in Nov 2012 to 115.079 million in Dec 2012 or decline by 436,000 in one month. The number employed full time fell from 115.079 million in Dec 2012 to 113.868 million in Jan 2013 or decline of 1.211 million in one month. The number of full time jobs increased to 114.191 in Feb 2012 or by 323,000 in one month and increased to 114.796 million in Mar 2013 for cumulative increase from Jan by 928,000 full-time jobs but decrease of 283,000 from Dec 2012. The number employed full time reached 117.400 million in Jun 2013 and increased to 117.688 in Jul 2013 or by 288,000. The number employed full-time reached 117.868 million in Aug 2013 for increase of 180,000 in one month relative to Jul 2013. The number employed full-time fell to 117.308 million in Sep 2013 or by 560,000. The number employed full-time fell to 116.798 million in Oct 2013 or decline of 510.000 in one month. The number employed full-time rose to 116.875 million in Nov 2013, falling to 116.661 million in Dec 2013. The number employed full-time fell to 115.744 million in Jan 2014 but increased to 116.323 million in Feb 2014. The level of full-time jobs increased to 116.985 in Mar 2014 and 118.073 million in Apr 2014. The number of full-time jobs increased to 119.179 million in May 2014, increasing to 119.472 million in Jun 2014. The level of full-time jobs increased to 119.900 million in Jul 2014. Comparisons over long periods require use of NSA data. The number with full-time jobs fell from a high of 123.219 million in Jul 2007 to 108.777 million in Jan 2010 or by 14.442 million. The number with full-time jobs in Dec 2016 is 123.570 million, which is higher by 0.351 million relative to the peak of 123.219 million in Jul 2007.
• Loss of full-time jobs. The magnitude of the stress in US labor markets is magnified by the increase in the civilian noninstitutional population of the United States from 231.958 million in Jul 2007 to 254.742 million in Dec 2016 or by 22.784 million (http://www.bls.gov/data/). The number with full-time jobs in Dec 2016 is 123.570 million, which is higher by 0.351 million relative to the peak of 123.219 million in Jul 2007. The ratio of full-time jobs of 123.219 million in Jul 2007 to civilian noninstitutional population of 231.958 million was 53.1 percent. If that ratio had remained the same, there would be 135.268 million full-time jobs with population of 254.742 million in Dec 2016 (0.531 x 254.742) or 11.698 million fewer full-time jobs relative to actual 123.570 million. There appear to be around 10 million fewer full-time jobs in the US than before the global recession while population increased around 20 million. Mediocre GDP growth is the main culprit of the fractured US labor market. Long-term economic performance in the United States consisted of trend growth of GDP at 3 percent per year and of per capita GDP at 2 percent per year as measured for 1870 to 2010 by Robert E Lucas (2011May). The economy returned to trend growth after adverse events such as wars and recessions. The key characteristic of adversities such as recessions was much higher rates of growth in expansion periods that permitted the economy to recover output, income and employment losses that occurred during the contractions. Over the business cycle, the economy compensated the losses of contractions with higher growth in expansions to maintain trend growth of GDP of 3 percent and of GDP per capita of 2 percent. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 24.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.4 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 percent below trend under this alternative calculation.

Table I-9, US, Employed Part-time for Economic Reasons, Thousands, and Full-time, Millions

	Part-time Thousands	Full-time Millions
Seasonally Adjusted
Dec 2016	5,598	124.248
Nov 2016	5,659	124.213
Oct 2016	5,850	124.190
Sep 2016	5,874	124.253
Aug 2016	6,027	124.256
Jul 2016	5,936	123.888
Jun 2016	5,820	123.618
May 2016	6,409	123.232
Apr 2016	5,970	123.259
Mar 2016	6,120	123.513
Feb 2016	6,019	123.210
Jan 2016	6,035	123.116
Dec 2015	6,057	122.700
Nov 2015	6,113	122.110
Oct 2015	5,734	122.071
Sep 2015	6,031	121.829
Aug 2015	6,438	121.934
Jul 2015	6,292	121.629
Jun 2015	6,463	121.066
May 2015	6,544	121.523
Apr 2015	6,563	120.870
Mar 2015	6,646	120.963
Feb 2015	6,664	120.776
Jan 2015	6,836	120.575
Dec 2014	6,819	120.075
Nov 2014	6,870	119.645
Oct 2014	7,033	119.763
Sep 2014	7,123	119.338
Aug 2014	7,173	118.707
Jul 2014	7,430	118.450
Jun 2014	7,469	118.238
May 2014	7,170	118.816
Apr 2014	7,466	118.516
Mar 2014	7,403	117.941
Feb 2014	7,262	117.747
Jan 2014	7,298	117.504
Dec 2013	7,792	117.351
Nov 2013	7,730	117.046
Oct 2013	7,995	116.362
Sep 2013	8,011	116.895
Aug 2013	7,804	116.435
Jul 2013	8,083	116.155
Jun 2013	8,096	116.126
May 2013	7,864	116.247
Apr 2013	7,936	116.044
Mar 2013	7,658	115.785
Feb 2013	8,119	115.674
Jan 2013	8,074	115.724
Dec 2012	7,943	115.791
Nov 2012	8,166	115.655
Oct 2012	8,203	115.570
Sep 2012	8,671	115.252
Aug 2012	7,974	114.736
Jul 2012	8,082	114.575
Jun 2012	8,072	114.749
May 2012	8,101	114.233
Apr 2012	7,913	114.371
Mar 2012	7,775	115.024
Feb 2012	8,238	114.141
Jan 2012	8,305	113.755
Dec 2011	8,171	113.774
Nov 2011	8,447	113.213
Oct 2011	8,657	112.923
Sep 2011	9,166	112.544
Aug 2011	8,788	112.723
Jul 2011	8,281	112.193
Not Seasonally Adjusted
Dec 2016	5,707	123.570
Nov 2016	5,518	123.960
Oct 2016	5,648	124.588
Sep 2016	5,550	124.278
Aug 2016	5,963	125.892
Jul 2016	6,157	125.507
Jun 2016	6,119	124.903
May 2016	6,238	123.548
Apr 2016	5,771	122.742
Mar 2016	6,138	122.522
Feb 2016	6,106	121.757
Jan 2016	6,406	121.411
Dec 2015	6,179	122.013
Nov 2015	5,967	121.897
Oct 2015	5,536	122.466
Sep 2015	5,693	122.303
Aug 2015	6,361	123.420
Jul 2015	6,511	123.142
Jun 2015	6,776	122.268
May 2015	6,363	121.863
Apr 2015	6,356	120.402
Mar 2015	6,672	119.981
Feb 2015	6,772	119.313
Jan 2015	7,269	118.840
Dec 2014	6,970	119.394
Nov 2014	6,713	119.441
Oct 2014	6,787	120.176
Sep 2014	6,711	119.791
Aug 2014	7,083	120.110
Jul 2014	7,665	119.900
Jun 2014	7,805	119.472
May 2014	6,960	119.179
Apr 2014	7,243	118.073
Mar 2014	7,455	116.985
Feb 2014	7,397	116.323
Jan 2014	7,771	115.774
Dec 2013	7,990	116.661
Nov 2013	7,563	116.875
Oct 2013	7,700	116.798
Sep 2013	7,522	117.308
Aug 2013	7,690	117.868
Jul 2013	8,324	117.688
Jun 2013	8,440	117.400
May 2013	7,618	116.643
Apr 2013	7,709	115.674
Mar 2013	7,734	114.796
Feb 2013	8,298	114.191
Jan 2013	8,628	113.868
Dec 2012	8,166	115.079
Nov 2012	7,994	115.515
Oct 2012	7,870	116.045
Sep 2012	8,110	115.678
Aug 2012	7,842	116.214
Jul 2012	8,316	116.131
Jun 2012	8,394	116.024
May 2012	7,837	114.634
Apr 2012	7,694	113.999
Mar 2012	7,867	113.916
Feb 2012	8,455	112.587
Jan 2012	8,918	111.879
Dec 2011	8,428	113.050
Nov 2011	8,271	113.138
Oct 2011	8,258	113.456
Sep 2011	8,541	112.980
Aug 2011	8,604	114.286
Jul 2011	8,514	113.759
Jun 2011	8,738	113.255
May 2011	8,270	112.618
Apr 2011	8,425	111.844
Mar 2011	8,737	111.186
Feb 2011	8,749	110.731
Jan 2011	9,187	110.373
Dec 2010	9,205	111.207
Nov 2010	8,670	111.348
Oct 2010	8,408	112.342
Sep 2010	8,628	112.385
Aug 2010	8,628	113.508
Jul 2010	8,737	113.974
Jun 2010	8,867	113.856
May 2010	8,513	112.809
Apr 2010	8,921	111.391
Mar 2010	9,343	109.877
Feb 2010	9,282	109.100
Jan 2010	9,290	108.777 (low)
Dec 2009	9,354 (high)	109.875
Nov 2009	8,894	111.274
Oct 2009	8,474	111.599
Sep 2009	8,255	111.991
Aug 2009	8,835	113.863
Jul 2009	9,103	114.184
Jun 2009	9,301	114.014
May 2009	8,785	113.083
Apr 2009	8,648	112.746
Mar 2009	9,305	112.215
Feb 2009	9,170	112.947
Jan 2009	8,829	113.815
Dec 2008	8,250	116.422
Nov 2008	7,135	118.432
Oct 2008	6,267	120.020
Sep 2008	5,701	120.213
Aug 2008	5,736	121.556
Jul 2008	6,054	122.378
Jun 2008	5,697	121.845
May 2008	5,096	120.809
Apr 2008	5,071	120.027
Mar 2008	5,038	119.875
Feb 2008	5,114	119.452
Jan 2008	5,340	119.332
Dec 2007	4,750	121.042
Nov 2007	4,374	121.846
Oct 2007	4,028	122.006
Sep 2007	4,137	121.728
Aug 2007	4,494	122.870
Jul 2007	4,516	123.219 (high)
Jun 2007	4,469	122.150
May 2007	4,315	120.846
Apr 2007	4,205	119.609
Mar 2007	4,384	119.640
Feb 2007	4,417	119.041
Jan 2007	4,726	119.094
Dec 2006	4,281	120.371
Nov 2006	4,054	120.507
Oct 2006	4,010	121.199
Sep 2006	3,735 (low)	120.780
Aug 2006	4,104	121.979
Jul 2006	4,450	121.951
Jun 2006	4,456	121.070
May 2006	3,968	118.925
Apr 2006	3,787	118.559
Mar 2006	4,097	117.693
Feb 2006	4,403	116.823
Jan 2006	4,597	116.395

Source: US Bureau of Labor Statistics

http://www.bls.gov/

People lose their marketable job skills after prolonged unemployment and face increasing difficulty in finding another job. Chart I-18 shows the sharp rise in unemployed over 27 weeks and stabilization at an extremely high level.

Chart I-18, US, Number Unemployed for 27 Weeks or Over, Thousands SA Month 2001-2016

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Another segment of U6 consists of people marginally attached to the labor force who continue to seek employment but less frequently on the frustration there may not be a job for them. Chart I-19 shows the sharp rise in people marginally attached to the labor force after 2007 and subsequent stabilization.

Chart I-19, US, Marginally Attached to the Labor Force, NSA Month, Thousands, 2001-2016

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20 provides the level of full-time jobs from 2001 to 2016. The magnitude of the stress in US labor markets is magnified by the increase in the civilian noninstitutional population of the United States from 231.958 million in Jul 2007 to 254.742 million in Dec 2016 or by 22.784 million (http://www.bls.gov/data/). The number with full-time jobs in Dec 2016 is 123.570 million, which is higher by 0.351 million relative to the peak of 123.219 million in Jul 2007. The ratio of full-time jobs of 123.219 million in Jul 2007 to civilian noninstitutional population of 231.958 million was 53.1 percent. If that ratio had remained the same, there would be 135.268 million full-time jobs with population of 254.742 million in Dec 2016 (0.531 x 254.742) or 11.698 million fewer full-time jobs relative to actual 123.570 million. There appear to be around 10 million fewer full-time jobs in the US than before the global recession while population increased around 20 million. Mediocre GDP growth is the main culprit of the fractured US labor market.

There is current interest in past theories of “secular stagnation.” Alvin H. Hansen (1939, 4, 7; see Hansen 1938, 1941; for an early critique see Simons 1942) argues:

“Not until the problem of full employment of our productive resources from the long-run, secular standpoint was upon us, were we compelled to give serious consideration to those factors and forces in our economy which tend to make business recoveries weak and anaemic (sic) and which tend to prolong and deepen the course of depressions. This is the essence of secular stagnation-sick recoveries which die in their infancy and depressions which feed on them-selves and leave a hard and seemingly immovable core of unemployment. Now the rate of population growth must necessarily play an important role in determining the character of the output; in other words, the com-position of the flow of final goods. Thus a rapidly growing population will demand a much larger per capita volume of new residential building construction than will a stationary population. A stationary population with its larger proportion of old people may perhaps demand more personal services; and the composition of consumer demand will have an important influence on the quantity of capital required. The demand for housing calls for large capital outlays, while the demand for personal services can be met without making large investment expenditures. It is therefore not unlikely that a shift from a rapidly growing population to a stationary or declining one may so alter the composition of the final flow of consumption goods that the ratio of capital to output as a whole will tend to decline.”

The argument that anemic population growth causes “secular stagnation” in the US (Hansen 1938, 1939, 1941) is as misplaced currently as in the late 1930s (for early dissent see Simons 1942). This is merely another case of theory without reality with dubious policy proposals.

Inferior performance of the US economy and labor markets, during cyclical slow growth not secular stagnation, is the critical current issue of analysis and policy design.

Chart I-20, US, Full-time Employed, Thousands, NSA, 2001-2016

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20A provides the noninstitutional civilian population of the United States from 2001 to 2016. There is clear trend of increase of the population while the number of full-time jobs collapsed after 2008 without sufficient recovery as shown in the preceding Chart I-20.

Chart I-20A, US, Noninstitutional Civilian Population, Thousands, 2001-2016

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20B provides number of full-time jobs in the US from 1968 to 2016. There were multiple recessions followed by expansions without contraction of full-time jobs and without recovery as during the period after 2008. The problem is specific of the current cycle and not secular.

Chart I-20B, US, Full-time Employed, Thousands, NSA, 1968-2016

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

Chart I-20C provides the noninstitutional civilian population of the United States from 1968 to 2016. Population expanded at a relatively constant rate of increase with the assurance of creation of full-time jobs that has been broken since 2008.

Chart I-20C, US, Noninstitutional Civilian Population, Thousands, 1968-2016

Sources: US Bureau of Labor Statistics

http://www.bls.gov/data/

ESVI Youth and Middle Age Unemployment. Table EMP provides the comparison between the labor market in the current whole cycle from 2007 to 2016 and the whole cycle from 1979 to 1989. In the entire cycle from 2007 to 2016, the number employed increased 5.389 million, full-time employed increased 2.670 million, part-time for economic reasons increased 1.542 million and population increased 21.491 million. The number employed increased 3.7 percent, full-time employed increased 2.2 percent, part-time for economic reasons increased 35.0 percent and population increased 9.3 percent. There is sharp contrast with the contractions of the 1980s and with most economic history of the United States. In the whole cycle from 1979 to 1989, the number employed increased 18.518 million, full-time employed increased 14.715 million, part-time for economic reasons increased 1.317 million and population increased 21.530 million. In the entire cycle from 1979 to 1989, the number employed increased 18.7 percent, full-time employed increased 17.8 percent, part-time for economic reasons increased 36.8 percent and population increased 13.1 percent. The difference between the 1980s and the current cycle after 2007 is in the high rate of growth after the contraction that maintained trend growth around 3.0 percent for the entire cycle and per capital growth at 2.0 percent. The evident fact is that current weakness in labor markets originates in cyclical slow growth and not in imaginary secular stagnation.

Table EMP, US, Annual Level of Employed, Full-Time Employed, Employed Part-Time for Economic Reasons and Noninstitutional Civilian Population, Millions

	Employed	Full-Time Employed	Part Time Economic Reasons	Noninstitutional Civilian Population
2000s
2000	136.891	113.846	3.227	212.577
2001	136.933	113.573	3.715	215.092
2002	136.485	112.700	4.213	217.570
2003	137.736	113.324	4.701	221.168
2004	139.252	114.518	4.567	223.357
2005	141.730	117.016	4.350	226.082
2006	144.427	119.688	4.162	228.815
2007	146.047	121.091	4.401	231.867
2008	145.362	120.030	5.875	233.788
2009	139.877	112.634	8.913	235.801
2010	139.064	111.714	8.874	237.830
2011	139.869	112.556	8.560	239.618
2012	142.469	114.809	8.122	243.284
2013	143.929	116.314	7.935	245.679
2014	146.305	118.718	7.213	247.947
2015	148.834	121.492	6.371	250.801
2016	151.436	123.761	5.943	253.358
∆2007-2016	5.389	2.670	1.542	21.491
∆% 2007-2016	3.7	2.2	35.0	9.3
1980s
1979	98.824	82.654	3.577	164.863
1980	99.303	82.562	4.321	167.745
1981	100.397	83.243	4.768	170.130
1982	99.526	81.421	6.170	172.271
1983	100.834	82.322	6.266	174.215
1984	105.005	86.544	5.744	176.383
1985	107.150	88.534	5.590	178.206
1986	109.597	90.529	5.588	180.587
1987	112.440	92.957	5.401	182.753
1988	114.968	95.214	5.206	184.613
1989	117.342	97.369	4.894	186.393
∆1979-1989	18.518	14.715	1.317	21.530
∆% 1979-1989	18.7	17.8	36.8	13.1

Source: Bureau of Labor Statistics

http://www.bls.gov/

The theory of secular stagnation cannot explain sudden collapse of the US economy and labor markets. There are accentuated cyclic factors for both the entire population and the young population of ages 16 to 24 years. Table Summary Total provides the total noninstitutional population (ICP) of the US, full-time employment level (FTE), employment level (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP), employment/population ratio (EPOP) and unemployment level (UNE). Secular stagnation would spread over long periods instead of immediately. All indicators of the labor market weakened sharply during the contraction and did not recover. Population continued to grow but all other variables collapsed and did not recover. The theory of secular stagnation departs from an aggregate production function in which output grows with the use of labor, capital and technology (see Pelaez and Pelaez, Globalization and the State, Vol. I (2008a), 11-16). Hansen (1938, 1939) finds secular stagnation in lower growth of an aging population. In the current US economy, Table Summary shows that population is dynamic while the labor market is fractured. There is key explanation in the behavior of the civilian labor force participation rate (CLFP) and the employment population ratio (EPOP) that collapsed during the global recession with inadequate recovery. Abandoning job searches are difficult to capture in labor statistics but likely explain the decline in the participation of the population in the labor force. Allowing for abandoning job searches, the total number of people unemployed or underemployed is 24.2 million or 14.4 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html).

Table Summary Total, US, Total Noninstitutional Civilian Population, Full-time Employment, Employment, Civilian Labor Force, Civilian Labor Force Participation Rate, Employment Population Ratio, Unemployment, NSA, Millions and Percent

	ICP	FTE	EMP	CLF	CLFP	EPOP	UNE
2006	228.8	119.7	144.4	151.4	66.2	63.1	7.0
2009	235.8	112.6	139.9	154.1	65.4	59.3	14.3
2012	243.3	114.8	142.5	155.0	63.7	58.6	12.5
2013	245.7	116.3	143.9	155.4	63.2	58.6	11.5
2014	247.9	118.7	146.3	155.9	62.9	59.0	9.6
2015	250.8	121.5	148.8	157.1	62.7	59.3	8.3
2016	253.5	123.8	151.4	159.2	62.8	59.7	7.8
12/07	233.2	121.0	146.3	153.7	65.9	62.8	7.4
9/09	236.3	112.0	139.1	153.6	65.0	58.9	14.5
12/16	254.7	123.6	151.8	159.0	62.4	59.6	7.2

ICP: Total Noninstitutional Civilian Population; FT: Full-time Employment Level, EMP: Total Employment Level; CLF: Civilian Labor Force; CLFP: Civilian Labor Force Participation Rate; EPOP: Employment Population Ratio; UNE: Unemployment

Source: Bureau of Labor Statistics

http://www.bls.gov/

The same situation is present in the labor market for young people in ages 16 to 24 years with data in Table Summary Youth. The youth noninstitutional civilian population (ICP) continued to increase during and after the global recession. There is the same disastrous labor market with decline for young people in employment (EMP), civilian labor force (CLF), civilian labor force participation rate (CLFP) and employment population ratio (EPOP). There are only increases for unemployment of young people (UNE) and youth unemployment rate (UNER). If aging were a factor of secular stagnation, growth of population of young people would attract a premium in remuneration in labor markets. The sad fact is that young people are also facing tough labor markets. The application of the theory of secular stagnation to the US economy and labor markets is void of reality in the form of key facts, which are best explained by accentuated cyclic factors analyzed by Lazear and Spletzer (2012JHJul22).

Table Summary Youth, US, Youth, Ages 16 to 24 Years, Noninstitutional Civilian Population, Full-time Employment, Employment, Civilian Labor Force, Civilian Labor Force Participation Rate, Employment Population Ratio, Unemployment, NSA, Millions and Percent

	ICP	EMP	CLF	CLFP	EPOP	UNE	UNER
2006	36.9	20.0	22.4	60.6	54.2	2.4	10.5
2009	37.6	17.6	21.4	56.9	46.9	3.8	17.6
2012	38.8	17.8	21.3	54.9	46.0	3.5	16.2
2013	38.8	18.1	21.4	55.0	46.5	3.3	15.5
2014	38.7	18.4	21.3	55.0	47.6	2.9	13.4
2015	38.6	18.8	21.2	55.0	48.6	2.5	11.6
2016	38.4	19.0	21.2	55.2	49.4	2.2	10.4
12/07	37.5	19.4	21.7	57.8	51.6	2.3	10.7
9/09	37.6	17.0	20.7	55.2	45.1	3.8	18.2
12/16	38.3	18.3	20.7	54.0	49.1	1.9	9.0

ICP: Youth Noninstitutional Civilian Population; EMP: Youth Employment Level; CLF: Youth Civilian Labor Force; CLFP: Youth Civilian Labor Force Participation Rate; EPOP: Youth Employment Population Ratio; UNE: Unemployment; UNER: Youth Unemployment Rate

Source: Bureau of Labor Statistics

http://www.bls.gov/

Chart I-24 provides longer perspective with the rate of youth unemployment in ages 16 to 24 years from 1948 to 2016. The rate of youth unemployment rose to 20 percent during the contractions of the early 1980s and also during the contraction of the global recession in 2008 and 2009. The data illustrate again the argument in this blog that the contractions of the early 1980s are the valid framework for comparison with the global recession of 2008 and 2009 instead of misleading comparisons with the 1930s. During the initial phase of recovery, the rate of youth unemployment 16 to 24 years NSA fell from 18.9 percent in Jun 1983 to 14.5 percent in Jun 1984. In contrast, the rate of youth unemployment 16 to 24 years was nearly the same during the expansion after IIIQ2009: 17.5 percent in Dec 2009, 16.7 percent in Dec 2010, 15.5 percent in Dec 2011, 15.2 percent in Dec 2012, 17.6 percent in Jan 2013, 16.7 percent in Feb 2013, 15.9 percent in Mar 2013, 15.1 percent in Apr 2013. The rate of youth unemployment was 16.4 percent in May 2013, 18.0 percent in Jun 2013, 16.3 percent in Jul 2013 and 15.6 percent in Aug 2013. In Sep 2006, the rate of youth unemployment was 10.5 percent, increasing to 14.8 percent in Sep 2013. The rate of youth unemployment was 10.3 in Oct 2007, increasing to 14.4 percent in Oct 2013. The rate of youth unemployment was 10.3 percent in Nov 2007, increasing to 13.1 percent in Nov 2013. The rate of youth unemployment was 10.7 percent in Dec 2013, increasing to 12.3 percent in Dec 2013. The rate of youth unemployment was 10.9 percent in Jan 2007, increasing to 14.9 percent in Jan 2014. The rate of youth unemployment was 10.3 percent in Feb 2007, increasing to 14.9 percent in Feb 2014. The rate of youth unemployment was 9.7 percent in Mar 2007, increasing to 14.3 percent in Mar 2014. The rate of youth unemployment was 9.7 percent in Apr 2007, increasing to 11.9 percent in Apr 2014. The rate of youth unemployment was 10.2 percent in May 2007, increasing to 13.4 percent in May 2014. The rate of youth unemployment was 12.0 percent in Jun 2007, increasing to 15.0 percent in Jun 2014. The rate of youth unemployment was 10.8 percent in Jul 2007, increasing to 14.3 percent in Jul 2014. The rate of youth unemployment was 10.5 percent in Aug 2007, increasing to 13.0 percent in Aug 2014. The rate of youth unemployment was 11.0 percent in Sep 2007, increasing to 13.6 percent in Sep 2014. The rate of youth unemployment increased from 10.3 in Oct 2007 to 12.2 in Oct 2014. The rate of youth unemployment increased from 10.3 percent in Nov 2007 to 11.7 percent in Nov 2014. The rate of youth unemployment increased from 10.7 in Dec 2007 to 11.2 in Dec 2014. The rate of youth unemployment increased from 9.7 in Mar 2007 to 12.3 in Mar 2015. The rate of youth unemployment increased from 9.7 in Apr 2007 to 10.7 in Apr 2015. The rate of youth unemployment increased from 10.2 in May 2007 to 12.3 in May 2015. The rate of youth unemployment increased from 12.0 in Jun 2007 to 13.7 in Jun 2015. The rate of youth unemployment increased from 10.8 in Jul 2007 to 12.2 in Jul 2015. The rate of youth unemployment increased from 10.5 in Aug 2007 to 10.9 in Aug 2015. The rate of youth unemployment decreased from 11.0 in Sep 2007 to 10.9 in Sep 2015. The rate of youth unemployment increased from 10.3 in Oct 2007 to 10.6 in Oct 2015, decreasing to 10.4 in Nov 2015. The rate of youth unemployment decreased to 10.1 in Dec 2015. The rate of youth unemployment stood at 10.8 in Jan 2016, 10.8 in Feb 2016, 10.4 in Mar 2016 and 9.9 in Apr 2016. The rate of youth unemployment increased to 10.6 in May 2016 and 12.3 in Jun 2016. The rate of youth unemployment fell to 11.5 in Jul 2016, decreasing to 10.1 in Aug 2016. The rate of youth unemployment increased to 10.2 in Sep 2016, decreasing to 10.1 in Oct 2016 and 9.3 in Nov 2016. The rate of youth unemployment decreased to 90.0 in Dec 2016. The actual rate is higher because of the difficulty in counting those dropping from the labor force because they believe there are no jobs available for them. The difference originates in the vigorous seasonally adjusted annual equivalent average rate of GDP growth of 5.9 percent during the recovery from IQ1983 to IVQ1985 and 4.5 percent from IQ1983 to IVQ1989 compared with 2.1 percent on average during the first 28 quarters of expansion from IIIQ2009 to IIIQ2016. US economic growth has been at only 2.1 percent on average in the cyclical expansion in the 29 quarters from IIIQ2009 to IIIQ2016. Boskin (2010Sep) measures that the US economy grew at 6.2 percent in the first four quarters and 4.5 percent in the first 12 quarters after the trough in the second quarter of 1975; and at 7.7 percent in the first four quarters and 5.8 percent in the first 12 quarters after the trough in the first quarter of 1983 (Professor Michael J. Boskin, Summer of Discontent, Wall Street Journal, Sep 2, 2010 http://professional.wsj.com/article/SB10001424052748703882304575465462926649950.html). There are new calculations using the revision of US GDP and personal income data since 1929 by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) and the third estimate of GDP for IIIQ2016 (https://www.bea.gov/newsreleases/national/gdp/2016/pdf/gdp3q16_3rd.pdf). The average of 7.7 percent in the first four quarters of major cyclical expansions is in contrast with the rate of growth in the first four quarters of the expansion from IIIQ2009 to IIQ2010 of only 2.7 percent obtained by diving GDP of $14,745.9 billion in IIQ2010 by GDP of $14,355.6 billion in IIQ2009 {[$14,745.9/$14,355.6 -1]100 = 2.7%], or accumulating the quarter on quarter growth rates (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The expansion from IQ1983 to IVQ1985 was at the average annual growth rate of 5.9 percent, 5.4 percent from IQ1983 to IIIQ1986, 5.2 percent from IQ1983 to IVQ1986, 5.0 percent from IQ1983 to IQ1987, 5.0 percent from IQ1983 to IIQ1987, 4.9 percent from IQ1983 to IIIQ1987, 5.0 percent from IQ1983 to IVQ1987, 4.9 percent from IQ1983 to IIQ1988, 4.8 percent from IQ1983 to IIIQ1988, 4.8 percent from IQ1983 to IVQ1988, 4.8 percent from IQ1983 to IQ1989, 4.7 percent from IQ1983 to IIQ1989, 4.7 percent from IQ1983 to IIIQ1989, 4.5 percent from IQ1983 to IVQ1989. 4.5 percent from IQ1983 to IQ1990 and at 7.8 percent from IQ1983 to IVQ1983 (http://cmpassocregulationblog.blogspot.com/2016/12/mediocre-cyclical-united-states.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). The US maintained growth at 3.0 percent on average over entire cycles with expansions at higher rates compensating for contractions. Growth at trend in the entire cycle from IVQ2007 to IIIQ2016 would have accumulated to 29.5 percent. GDP in IIIQ2016 would be $19,414.4 billion (in constant dollars of 2009) if the US had grown at trend, which is higher by $2687.4 billion than actual $16,727.0 billion. There are about two trillion dollars of GDP less than at trend, explaining the 24.2 million unemployed or underemployed equivalent to actual unemployment/underemployment of 14.4 percent of the effective labor force (http://cmpassocregulationblog.blogspot.com/2017/01/twenty-four-million-unemployed-or.html and earlier http://cmpassocregulationblog.blogspot.com/2016/12/rising-yields-and-dollar-revaluation.html). US GDP in IIIQ2016 is 13.8 percent lower than at trend. US GDP grew from $14,991.8 billion in IVQ2007 in constant dollars to $16,727.0 billion in IIIQ2016 or 11.6 percent at the average annual equivalent rate of 1.3 percent. Professor John H. Cochrane (2014Jul2) estimates US GDP at more than 10 percent below trend. Cochrane (2016May02) measures GDP growth in the US at average 3.5 percent per year from 1950 to 2000 and only at 1.76 percent per year from 2000 to 2015 with only at 2.0 percent annual equivalent in the current expansion. Cochrane (2016May02) proposes drastic changes in regulation and legal obstacles to private economic activity. The US missed the opportunity to grow at higher rates during the expansion and it is difficult to catch up because growth rates in the final periods of expansions tend to decline. The US missed the opportunity for recovery of output and employment always afforded in the first four quarters of expansion from recessions. Zero interest rates and quantitative easing were not required or present in successful cyclical expansions and in secular economic growth at 3.0 percent per year and 2.0 percent per capita as measured by Lucas (2011May). There is cyclical uncommonly slow growth in the US instead of allegations of secular stagnation. There is similar behavior in manufacturing. There is classic research on analyzing deviations of output from trend (see for example Schumpeter 1939, Hicks 1950, Lucas 1975, Sargent and Sims 1977). The long-term trend is growth of manufacturing at average 3.1 percent per year from Nov 1919 to Nov 2016. Growth at 3.1 percent per year would raise the NSA index of manufacturing output from 108.2316 in Dec 2007 to 142.1081 in Nov 2016. The actual index NSA in Nov 2016 is 103.0759, which is 27.5 percent below trend. Manufacturing output grew at average 2.1 percent between Dec 1986 and Dec 2015. Using trend growth of 2.1 percent per year, the index would increase to 130.3109 in Nov 2016. The output of manufacturing at 103.0759 in Nov 2016 is 20.9 percent below trend under this alternative calculation.

Chart I-24, US, Unemployment Rate 16-24 Years, Percent NSA, 1948-2016

Source: US Bureau of Labor Statistics http://www.bls.gov/data/

Chart I-25 provides the level unemployed ages 45 years and over. There was an increase in the recessions of the 1980s, 1991 and 2001 followed by declines to earlier levels. The current expansion of the economy after IIIQ2009 has not been sufficiently vigorous to reduce significantly middle-age unemployment. Recent improvements could be illusory because many abandoned job searches in frustration that there may not be jobs for them and are not counted as unemployed.

Chart I-25, US, Unemployment Level Ages 45 Years and Over, Thousands, NSA, 1976-2016

Source: US Bureau of Labor Statistics http://www.bls.gov/data/

ESVII United States International Trade. Table IIA-1 provides the trade balance of the US and monthly growth of exports and imports seasonally adjusted with the latest release and revisions (http://www.census.gov/foreign-trade/). Because of heavy dependence on imported oil, fluctuations in the US trade account originate largely in fluctuations of commodity futures prices caused by carry trades from zero interest rates into commodity futures exposures in a process similar to world inflation waves (http://cmpassocregulationblog.blogspot.com/2016/12/of-course-economic-outlook-is-highly.html). The Census Bureau revised data for 2016, 2015, 2014 and 2013. Exports decreased 0.2 percent in Nov 2016 while imports increased 1.1 percent. The trade deficit increased from $42,360 million in Oct 2016 to $45,240 million in Nov 2016. The trade deficit deteriorated to $48,189 million in Mar 2015. The trade deficit improved to $40,885 million in Apr 2015 and $40,170 million in May 2015. The trade deficit deteriorated to $42,973 million in Jun 2015 and improved to $39,900 million in Jul 2015, deteriorating to $44,639 million in Aug 2015. The trade deficit improved to $41,072 million in Sep 2015, deteriorating to $41,600 million in Oct 2015 and improving to $41,122 million in Nov 2015. The trade deficit deteriorated to $45,259 million in Feb 2016, improving to $36,930 million in Mar 2016. The trade deficit deteriorated to $38,191 million in Apr 2016, deteriorating to $41,835 million in May 2016 and $44,719 million in Jun 2016. The trade deficit improved to $39,626 million in Jul 2016, deteriorating to $40,641 million in Aug 2016. The trade deficit improved to $36,166 million in Sep 2016, deteriorating to $42,360 million in Oct 2016. The trade deficit deteriorated to $45,240 million in Nov 2016.

Table IIA-1, US, Trade Balance of Goods and Services Seasonally Adjusted Millions of Dollars and ∆%  

	Trade Balance	Exports	Month ∆%	Imports	Month ∆%
Nov 2016	-45.240	185,833	-0.2	231,072	1.1
Oct	-42,360	186,281	-1.8	228,641	1.2
Sep	-36,166	189,774	0.9	225,940	-1.2
Aug	-40,641	187,991	0.9	228,632	1.2
Jul	39,626	186,351	1.8	225,978	-0.8
Jun	-44,719	183,003	0.8	227,723	1.9
May	-41,835	181,577	-0.2	223,412	1.5
Apr	-38,191	181,850	1.7	220,041	2.0
Mar	-36,930	178,798	-1.2	215,729	-4.7
Feb	-45,259	181,003	1.1	226,262	1.9
Jan	-43,027	179,069	-2.2	222,096	-1.1
Dec 2015	-41,487	183,074	-0.3	224,561	-0.1
Nov	-41,122	183,576	-1.1	224,698	-1.1
Oct	-41,600	185,587	-1.0	227,186	-0.6
Sep	-41,072	187,550	0.5	228,622	-1.1
Aug	-44,639	186,620	-1.8	231,259	0.5
Jul	-39,900	190,106	-0.1	230,006	-1.4
Jun	-42,973	190,347	0.0	233,320	1.2
May	-40,170	190,361	-0.7	230,531	-0.9
Apr	-40,885	191,675	0.6	232,560	-2.5
Mar	-48,189	190,448	0.3	238,637	5.5
Feb	-36,268	189,852	-1.1	226,121	-3.4
Jan	-42,057	191,968	-2.8	234,024	-3.0
Jan-Dec 2015	-500,361	2,261,163	-4.9	2,761,525	-3.7

Note: Trade Balance of Goods = Exports of Goods less Imports of Goods. Trade balance may not add exactly because of errors of rounding and seasonality. Source: US Census Bureau, Foreign Trade Division

http://www.census.gov/foreign-trade/

Table IIA-1B provides US exports, imports and the trade balance of goods. The US has not shown a trade surplus in trade of goods since 1976. The deficit of trade in goods deteriorated sharply during the boom years from 2000 to 2007. The deficit improved during the contraction in 2009 but deteriorated in the expansion after 2009. The deficit could deteriorate sharply with growth at full employment.

Table IIA-1B, US, International Trade Balance of Goods, Exports and Imports of Goods, Millions of Dollars, Census Basis

	Balance	∆%	Exports	∆%	Imports	∆%
1960	4,608	(X)	19,626	(X)	15,018	(X)
1961	5,476	18.8	20,190	2.9	14,714	-2.0
1962	4,583	-16.3	20,973	3.9	16,390	11.4
1963	5,289	15.4	22,427	6.9	17,138	4.6
1964	7,006	32.5	25,690	14.5	18,684	9.0
1965	5,333	-23.9	26,699	3.9	21,366	14.4
1966	3,837	-28.1	29,379	10.0	25,542	19.5
1967	4,122	7.4	30,934	5.3	26,812	5.0
1968	837	-79.7	34,063	10.1	33,226	23.9
1969	1,289	54.0	37,332	9.6	36,043	8.5
1970	3,224	150.1	43,176	15.7	39,952	10.8
1971	-1,476	-145.8	44,087	2.1	45,563	14.0
1972	-5,729	288.1	49,854	13.1	55,583	22.0
1973	2,389	-141.7	71,865	44.2	69,476	25.0
1974	-3,884	-262.6	99,437	38.4	103,321	48.7
1975	9,551	-345.9	108,856	9.5	99,305	-3.9
1976	-7,820	-181.9	116,794	7.3	124,614	25.5
1977	-28,352	262.6	123,182	5.5	151,534	21.6
1978	-30,205	6.5	145,847	18.4	176,052	16.2
1979	-23,922	-20.8	186,363	27.8	210,285	19.4
1980	-19,696	-17.7	225,566	21.0	245,262	16.6
1981	-22,267	13.1	238,715	5.8	260,982	6.4
1982	-27,510	23.5	216,442	-9.3	243,952	-6.5
1983	-52,409	90.5	205,639	-5.0	258,048	5.8
1984	-106,702	103.6	223,976	8.9	330,678	28.1
1985	-117,711	10.3	218,815	-2.3	336,526	1.8
1986	-138,279	17.5	227,159	3.8	365,438	8.6
1987	-152,119	10.0	254,122	11.9	406,241	11.2
1988	-118,526	-22.1	322,426	26.9	440,952	8.5
1989	-109,399	-7.7	363,812	12.8	473,211	7.3
1990	-101,719	-7.0	393,592	8.2	495,311	4.7
1991	-66,723	-34.4	421,730	7.1	488,453	-1.4
1992	-84,501	26.6	448,164	6.3	532,665	9.1
1993	-115,568	36.8	465,091	3.8	580,659	9.0
1994	-150,630	30.3	512,626	10.2	663,256	14.2
1995	-158,801	5.4	584,742	14.1	743,543	12.1
1996	-170,214	7.2	625,075	6.9	795,289	7.0
1997	-180,522	6.1	689,182	10.3	869,704	9.4
1998	-229,758	27.3	682,138	-1.0	911,896	4.9
1999	-328,821	43.1	695,797	2.0	1,024,618	12.4
2000	-436,104	32.6	781,918	12.4	1,218,022	18.9
2001	-411,899	-5.6	729,100	-6.8	1,140,999	-6.3
2002	-468,263	13.7	693,103	-4.9	1,161,366	1.8
2003	-532,350	13.7	724,771	4.6	1,257,121	8.2
2004	-654,830	23.0	814,875	12.4	1,469,704	16.9
2005	-772,373	18.0	901,082	10.6	1,673,455	13.9
2006	-827,971	7.2	1,025,967	13.9	1,853,938	10.8
2007	-808,763	-2.3	1,148,199	11.9	1,956,962	5.6
2008	-816,199	0.9	1,287,442	12.1	2,103,641	7.5
2009	-503,582	-38.3	1,056,043	-18.0	1,559,625	-25.9
2010	-635,362	26.2	1,278,495	21.1	1,913,857	22.7
2011	-725,447	14.2	1,482,508	16.0	2,207,954	15.4
2012	-730,446	0.7	1,545,821	4.3	2,276,267	3.1
2013	-689,470	-5.6	1,578,517	2.1	2,267,987	-0.4
2014	-735,194	6.6	1,621,172	2.7	2,356,366	3.9
2015	-745,660	1.4	1,502,572	-7.3	2,248,232	-4.6

Source: US Census Bureau, Foreign Trade Division

http://www.census.gov/foreign-trade/

Chart IIA-1 of the US Census Bureau of the Department of Commerce shows that the trade deficit (gap between exports and imports) fell during the economic contraction after 2007 but has grown again during the expansion. The low average rate of growth of GDP of 2.1 percent during the expansion beginning since IIIQ2009 does not deteriorate further the trade balance. Higher rates of growth may cause sharper deterioration.

Chart IIA-1, US, International Trade Balance, Exports and Imports of Goods and Services USD Billions

Source: US Census Bureau

http://www.census.gov/briefrm/esbr/www/esbr042.html

Table IIA-2B provides the US international trade balance, exports and imports of goods and services on an annual basis from 1992 to 2015. The trade balance deteriorated sharply over the long term. The US has a large deficit in goods or exports less imports of goods but it has a surplus in services that helps to reduce the trade account deficit or exports less imports of goods and services. The current account deficit of the US not seasonally adjusted decreased from $137.2 billion in IIIQ2015 to $133.2 billion in IIIQ2016 (http://www.bea.gov/international/index.htm). The current account deficit seasonally adjusted at annual rate increased from 2.7 percent of GDP in IIIQ2015 to 2.6 percent of GDP in IIQ2016, decreasing to 2.4 percent of GDP in IIIQ2016 (http://www.bea.gov/international/index.htm http://www.bea.gov/iTable/index_nipa.cfm). The ratio of the current account deficit to GDP has stabilized around 3 percent of GDP compared with much higher percentages before the recession (see Pelaez and Pelaez, The Global Recession Risk (2007), Globalization and the State, Vol. II (2008b), 183-94, Government Intervention in Globalization (2008c), 167-71). The last row of Table IIA-2B shows marginal improvement of the trade deficit from $548,625 million in 2011 to lower $536,773 million in 2012 with exports growing 4.3 percent and imports 3.0 percent. The trade balance improved further to deficit of $461,876 million in 2013 with growth of exports of 3.4 percent while imports virtually stagnated. The trade deficit deteriorated in 2014 to $490,176 million with growth of exports of 3.6 percent and of imports of 4.0 percent. The trade deficit deteriorated in 2015 to $500,361 million with decrease of exports of 4.9 percent and decrease of imports of 3.7 percent. Growth and commodity shocks under alternating inflation waves (http://cmpassocregulationblog.blogspot.com/2016/12/of-course-economic-outlook-is-highly.html) have deteriorated the trade deficit from the low of $383,774 million in 2009.

Table IIA-2B, US, International Trade Balance of Goods and Services, Exports and Imports of Goods and Services, SA, Millions of Dollars, Balance of Payments Basis

	Balance	Exports	∆%	Imports	∆%
1960	3,508	25,940	NA	22,432	NA
1961	4,195	26,403	1.8	22,208	-1.0
1962	3,370	27,722	5.0	24,352	9.7
1963	4,210	29,620	6.8	25,410	4.3
1964	6,022	33,341	12.6	27,319	7.5
1965	4,664	35,285	5.8	30,621	12.1
1966	2,939	38,926	10.3	35,987	17.5
1967	2,604	41,333	6.2	38,729	7.6
1968	250	45,543	10.2	45,293	16.9
1969	91	49,220	8.1	49,129	8.5
1970	2,254	56,640	15.1	54,386	10.7
1971	-1,302	59,677	5.4	60,979	12.1
1972	-5,443	67,222	12.6	72,665	19.2
1973	1,900	91,242	35.7	89,342	23.0
1974	-4,293	120,897	32.5	125,190	40.1
1975	12,404	132,585	9.7	120,181	-4.0
1976	-6,082	142,716	7.6	148,798	23.8
1977	-27,246	152,301	6.7	179,547	20.7
1978	-29,763	178,428	17.2	208,191	16.0
1979	-24,565	224,131	25.6	248,696	19.5
1980	-19,407	271,834	21.3	291,241	17.1
1981	-16,172	294,398	8.3	310,570	6.6
1982	-24,156	275,236	-6.5	299,391	-3.6
1983	-57,767	266,106	-3.3	323,874	8.2
1984	-109,072	291,094	9.4	400,166	23.6
1985	-121,880	289,070	-0.7	410,950	2.7
1986	-138,538	310,033	7.3	448,572	9.2
1987	-151,684	348,869	12.5	500,552	11.6
1988	-114,566	431,149	23.6	545,715	9.0
1989	-93,141	487,003	13.0	580,144	6.3
1990	-80,864	535,233	9.9	616,097	6.2
1991	-31,135	578,344	8.1	609,479	-1.1
1992	-39,212	616,882	6.7	656,094	7.6
1993	-70,311	642,863	4.2	713,174	8.7
1994	-98,493	703,254	9.4	801,747	12.4
1995	-96,384	794,387	13.0	890,771	11.1
1996	-104,065	851,602	7.2	955,667	7.3
1997	-108,273	934,453	9.7	1,042,726	9.1
1998	-166,140	933,174	-0.1	1,099,314	5.4
1999	-258,617	969,867	3.9	1,228,485	11.8
2000	-372,517	1,075,321	10.9	1,447,837	17.9
2001	-361,511	1,005,654	-6.5	1,367,165	-5.6
2002	-418,955	978,706	-2.7	1,397,660	2.2
2003	-493,890	1,020,418	4.3	1,514,308	8.3
2004	-609,883	1,161,549	13.8	1,771,433	17.0
2005	-714,245	1,286,022	10.7	2,000,267	12.9
2006	-761,716	1,457,642	13.3	2,219,358	11.0
2007	-705,375	1,653,548	13.4	2,358,922	6.3
2008	-708,726	1,841,612	11.4	2,550,339	8.1
2009	-383,774	1,583,053	-14.0	1,966,827	-22.9
2010	-494,658	1,853,606	17.1	2,348,263	19.4
2011	-548,625	2,127,021	14.8	2,675,646	13.9
2012	-536,773	2,218,989	4.3	2,755,762	3.0
2013	-461,876	2,293,457	3.4	2,755,334	0.0
2014	-490,176	2,376,577	3.6	2,866,754	4.0
2015	-500,361	2,261,163	-4.9	2,761,525	-3.7

Source: US Census Bureau

http://www.census.gov/foreign-trade/

Chart IIA-2 of the US Census Bureau provides the US trade account in goods and services SA from Jan 1992 to Nov 2016. There is long-term trend of deterioration of the US trade deficit shown vividly by Chart IIA-2. The global recession from IVQ2007 to IIQ2009 reversed the trend of deterioration. Deterioration resumed together with incomplete recovery and was influenced significantly by the carry trade from zero interest rates to commodity futures exposures (these arguments are elaborated in Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 157-66, Regulation of Banks and Finance (2009b), 217-27, International Financial Architecture (2005), 15-18, The Global Recession Risk (2007), 221-5, Globalization and the State Vol. II (2008b), 197-213, Government Intervention in Globalization (2008c), 182-4 http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html http://cmpassocregulationblog.blogspot.com/2011/01/professor-mckinnons-bubble-economy.html http://cmpassocregulationblog.blogspot.com/2011/01/world-inflation-quantitative-easing.html http://cmpassocregulationblog.blogspot.com/2011/01/treasury-yields-valuation-of-risk.html http://cmpassocregulationblog.blogspot.com/2010/11/quantitative-easing-theory-evidence-and.html http://cmpassocregulationblog.blogspot.com/2010/12/is-fed-printing-money-what-are.html). Earlier research focused on the long-term external imbalance of the US in the form of trade and current account deficits (Pelaez and Pelaez, The Global Recession Risk (2007), Globalization and the State Vol. II (2008b) 183-94, Government Intervention in Globalization (2008c), 167-71). US external imbalances have not been fully resolved and tend to widen together with improving world economic activity and commodity price shocks. There are additional effects for revaluation of the dollar with the Fed orienting interest rate increases while the European Central Bank and the Bank of Japan determine negative nominal interest rates.

Chart IIA-2, US, Balance of Trade SA, Monthly, Millions of Dollars, Jan 1992-Nov 2016

Source: US Census Bureau

http://www.census.gov/foreign-trade/

Chart IIA-3 of the US Census Bureau provides US exports SA from Jan 1992 to Nov 2016. There was sharp acceleration from 2003 to 2007 during worldwide economic boom and increasing inflation. Exports fell sharply during the financial crisis and global recession from IVQ2007 to IIQ2009. Growth picked up again together with world trade and inflation but stalled in the final segment with less rapid global growth and inflation.

Chart IIA-3, US, Exports SA, Monthly, Millions of Dollars Jan 1992-Nov 2016

Source: US Census Bureau

http://www.census.gov/foreign-trade/

Chart IIA-4 of the US Census Bureau provides US imports SA from Jan 1992 to Nov 2016. Growth was stronger between 2003 and 2007 with worldwide economic boom and inflation. There was sharp drop during the financial crisis and global recession. There is stalling import levels in the final segment resulting from weaker world economic growth and diminishing inflation because of risk aversion and portfolio reallocations from commodity exposures to equities.

Chart IIA-4, US, Imports SA, Monthly, Millions of Dollars Jan 1992-Nov 2016

Source: US Census Bureau

http://www.census.gov/foreign-trade/

There is deterioration of the US trade balance in goods in Table IIA-3 from deficit of $62,486 million in Nov 2015 to deficit of $66,627 million in Nov 2016. The nonpetroleum deficit increased by $3418 million while the petroleum deficit increased $811 million. Total exports of goods increased 0.9 percent in Nov 2016 relative to a year earlier while total imports increased 2.8 percent. Nonpetroleum exports changed 0.0 percent from Nov 2015 to Nov 2016 while nonpetroleum imports increased 2.0 percent. Petroleum imports increased 15.0 percent.

Table IIA-3, US, International Trade in Goods Balance, Exports and Imports $ Millions and ∆% SA

	Nov 2016	Nov 2015	∆%
Total Balance	-66,627	-62,486
Petroleum	-6,021	-5,210
Non Petroleum	-59,284	-55,866
Total Exports	122,351	121,286	0.9
Petroleum	8,243	7,193	14.6
Non Petroleum	113,603	113,598	0.0
Total Imports	188,978	183,772	2.8
Petroleum	14,264	12,402	15.0
Non Petroleum	172,886	169,463	2.0

Details may not add because of rounding and seasonal adjustment

Source: US Census Bureau

http://www.census.gov/foreign-trade/

US exports and imports of goods not seasonally adjusted in Jan-Nov 2016 and Jan-Nov 2015 are in Table IIA-4. The rate of growth of exports was minus 4.0 percent and minus 3.1 percent for imports. The US has partial hedge of commodity price increases in exports of agricultural commodities that increased 0.1 percent and of mineral fuels that decreased 11.9 percent both because prices of raw materials and commodities increase and fall recurrently as a result of shocks of risk aversion and portfolio reallocations. The US exports an insignificant but growing amount of crude oil, decreasing 13.3 percent in cumulative Jan-Nov 2016 relative to a year earlier. US exports and imports consist mostly of manufactured products, with less rapidly increasing prices. US manufactured exports decreased 6.1 percent while manufactured imports decreased 3.1 percent. Significant part of the US trade imbalance originates in imports of mineral fuels decreasing 21.5 percent and petroleum decreasing 22.6 percent with wide oscillations in oil prices. The limited hedge in exports of agricultural commodities and mineral fuels compared with substantial imports of mineral fuels and crude oil results in waves of deterioration of the terms of trade of the US, or export prices relative to import prices, originating in commodity price increases caused by carry trades from zero interest rates. These waves are similar to those in worldwide inflation.

Table IIA-4, US, Exports and Imports of Goods, Not Seasonally Adjusted Millions of Dollars and %, Census Basis

	Jan-Nov 2016 $ Millions	Jan-Nov 2015 $ Millions	∆%
Exports	1,327,862	1,382,633	-4.0
Manufactured	961,909	1,024,743	-6.1
Agricultural Commodities	121,960	121,788	0.1
Mineral Fuels	84,005	95,366	-11.9
Petroleum	68,452	78,963	-13.3
Imports	2,004,953	2,068,507	-3.1
Manufactured	1,754,211	1,790,206	-2.0
Agricultural Commodities	104,871	104,379	0.5
Mineral Fuels	139,317	177,427	-21.5
Petroleum	129,557	165,331	-22.6

Source: US Census Bureau

http://www.census.gov/foreign-trade/

ESVIII Collapse of United States Dynamism of Income Growth and Employment Creation. The departing theoretical framework of Bordo and Haubrich (2012DR) is the plucking model of Friedman (1964, 1988). Friedman (1988, 1) recalls, “I was led to the model in the course of investigating the direction of influence between money and income. Did the common cyclical fluctuation in money and income reflect primarily the influence of money on income or of income on money?” Friedman (1964, 1988) finds useful for this purpose to analyze the relation between expansions and contractions. Analyzing the business cycle in the United States between 1870 and 1961, Friedman (1964, 15) found that “a large contraction in output tends to be followed on the average by a large business expansion; a mild contraction, by a mild expansion.” The depth of the contraction opens up more room in the movement toward full employment (Friedman 1964, 17):

“Output is viewed as bumping along the ceiling of maximum feasible output except that every now and then it is plucked down by a cyclical contraction. Given institutional rigidities and prices, the contraction takes in considerable measure the form of a decline in output. Since there is no physical limit to the decline short of zero output, the size of the decline in output can vary widely. When subsequent recovery sets in, it tends to return output to the ceiling; it cannot go beyond, so there is an upper limit to output and the amplitude of the expansion tends to be correlated with the amplitude of the contraction.”

Kim and Nelson (1999) test the asymmetric plucking model of Friedman (1964, 1988) relative to a symmetric model using reference cycles of the NBER and find evidence supporting the Friedman model. Bordo and Haubrich (2012DR) analyze 27 cycles beginning in 1872, using various measures of financial crises while considering different regulatory and monetary regimes. The revealing conclusion of Bordo and Haubrich (2012DR, 2) is that:

“Our analysis of the data shows that steep expansions tend to follow deep contractions, though this depends heavily on when the recovery is measured. In contrast to much conventional wisdom, the stylized fact that deep contractions breed strong recoveries is particularly true when there is a financial crisis. In fact, on average, it is cycles without a financial crisis that show the weakest relation between contraction depth and recovery strength. For many configurations, the evidence for a robust bounce-back is stronger for cycles with financial crises than those without.”

The average rate of growth of real GDP in expansions after recessions with financial crises was 8 percent but only 6.9 percent on average for recessions without financial crises (Bordo 2012Sep27). Real GDP declined 12 percent in the Panic of 1907 and increased 13 percent in the recovery, consistent with the plucking model of Friedman (Bordo 2012Sep27). Bordo (2012Sep27) finds two probable explanations for the weak recovery during the current economic cycle: (1) collapse of United States housing; and (2) uncertainty originating in fiscal policy, regulation and structural changes. There are serious doubts if monetary policy is adequate to recover the economy under these conditions.

Lucas (2011May) estimates US economic growth in the long-term at 3 percent per year and about 2 percent per year in per capita terms. There are displacements from this trend caused by events such as wars and recessions but the economy grows much faster during the expansion, compensating for the contraction and maintaining trend growth over the entire cycle. Historical US GDP data exhibit remarkable growth: Lucas (2011May) estimates an increase of US real income per person by a factor of 12 in the period from 1870 to 2010. The explanation by Lucas (2011May) of this remarkable growth experience is that government provided stability and education while elements of “free-market capitalism” were an important driver of long-term growth and prosperity. Lucas sharpens this analysis by comparison with the long-term growth experience of G7 countries (US, UK, France, Germany, Canada, Italy and Japan) and Spain from 1870 to 2010. Countries benefitted from “common civilization” and “technology” to “catch up” with the early growth leaders of the US and UK, eventually growing at a faster rate. Significant part of this catch up occurred after World War II. Lucas (2011May) finds that the catch up stalled in the 1970s. The analysis of Lucas (2011May) is that the 20-40 percent gap that developed originated in differences in relative taxation and regulation that discouraged savings and work incentives in comparison with the US. A larger welfare and regulatory state, according to Lucas (2011May), could be the cause of the 20-40 percent gap. Cobet and Wilson (2002) provide estimates of output per hour and unit labor costs in national currency and US dollars for the US, Japan and Germany from 1950 to 2000 (see Pelaez and Pelaez, The Global Recession Risk (2007), 137-44). The average yearly rate of productivity change from 1950 to 2000 was 2.9 percent in the US, 6.3 percent for Japan and 4.7 percent for Germany while unit labor costs in USD increased at 2.6 percent in the US, 4.7 percent in Japan and 4.3 percent in Germany. From 1995 to 2000, output per hour increased at the average yearly rate of 4.6 percent in the US, 3.9 percent in Japan and 2.6 percent in Germany while unit labor costs in USD fell at minus 0.7 percent in the US, 4.3 percent in Japan and 7.5 percent in Germany. There was increase in productivity growth in Japan and France within the G7 in the second half of the 1990s but significantly lower than the acceleration of 1.3 percentage points per year in the US. The key indicator of growth of real income per capita, which is what a person earns after inflation, measures long-term economic growth and prosperity. A refined concept would include real disposable income per capita, which is what a person earns after inflation and taxes.

Table IB-1 provides the data required for broader comparison of long-term and cyclical performance of the United States economy. Revisions and enhancements of United States GDP and personal income accounts by the Bureau of Economic Analysis (BEA) (http://bea.gov/iTable/index_nipa.cfm) provide important information on long-term growth and cyclical behavior. First, Long-term performance. Using annual data, US GDP grew at the average rate of 3.2 percent per year from 1929 to 2015 and at 3.2 percent per year from 1947 to 2015. Real disposable income grew at the average yearly rate of 3.2 percent from 1929 to 2015 and at 3.7 percent from 1947 to 1999. Real disposable income per capita grew at the average yearly rate of 2.0 percent from 1929 to 2015 and at 2.3 percent from 1947 to 1999. US economic growth was much faster during expansions, compensating contractions in maintaining trend growth for whole cycles. Using annual data, US real disposable income grew at the average yearly rate of 3.5 percent from 1980 to 1989 and real disposable income per capita at 2.6 percent. The US economy has lost its dynamism in the current cycle: real disposable income grew at the yearly average rate of 1.7 percent from 2006 to 2015 and real disposable income per capita at 0.9 percent. Real disposable income grew at the average rate of 1.7 percent from 2007 to 2015 and real disposable income per capita at 0.8 percent. Table IB-1 illustrates the contradiction of long-term growth with the proposition of secular stagnation (Hansen 1938, 1938, 1941 with early critique by Simons (1942). Secular stagnation would occur over long periods. Table IB-1 also provides the corresponding rates of population growth that is only marginally lower at 0.8 to 0.9 percent recently from 1.1 percent over the long-term. GDP growth fell abruptly from 2.6 percent on average from 2000 to 2006 to 1.3 percent from 2006 to 2015 and 1.2 percent from 2007 to 2015 and real disposable income growth fell from 2.9 percent on average from 2000 to 2006 to 1.7 percent from 2006 to 2015. The decline of growth of real per capita disposable income is even sharper from average 2.0 percent from 2000 to 2006 to 0.9 percent from 2006 to 2015 and 0.8 percent from 2007 to 2015 while population growth was 0.8 percent on average. Lazear and Spletzer (2012JHJul122) provide theory and measurements showing that cyclic factors explain currently depressed labor markets. This is also the case of the overall economy. Second, first four quarters of expansion. Growth in the first four quarters of expansion is critical in recovering loss of output and employment occurring during the contraction. In the first four quarters of expansion from IQ1983 to IVQ1983: GDP increased 7.8 percent, real disposable personal income 5.3 percent and real disposable income per capita 4.4 percent. In the first four quarters of expansion from IIIQ2009 to IIQ2010: GDP increased 2.7 percent, real disposable personal income 0.2 percent and real disposable income per capita decreased 0.7 percent. Third, first 29 quarters of expansion. In the expansion from IQ1983 to IQ1990: GDP grew 37.8 percent at the annual equivalent rate of 4.5 percent; real disposable income grew 31.8 percent at the annual equivalent rate of 3.9 percent; and real disposable income per capita grew 23.4 percent at the annual equivalent rate of 2.9 percent. In the expansion from IIIQ2009 to IIIQ2016: GDP grew 16.5 percent at the annual equivalent rate of 2.1 percent; real disposable income grew 15.4 percent at the annual equivalent rate of 2.0 percent; and real disposable personal income per capita grew 9.2 percent at the annual equivalent rate of 1.2 percent. Fourth, entire quarterly cycle. In the entire cycle combining contraction and expansion from IQ1980 to IQ1990: GDP grew 37.1 percent at the annual equivalent rate of 3.1 percent; real disposable personal income grew 39.4 percent at the annual equivalent rate of 3.2 percent; and real disposable personal income per capita 26.6 percent at the annual equivalent rate of 2.3 percent. In the entire cycle combining contraction and expansion from IVQ2007 to IIIQ2016: GDP grew 11.6 percent at the annual equivalent rate of 1.3 percent; real disposable personal income increased 17.4 percent at the annual equivalent rate of 1.8 percent; and real disposable personal income per capita grew 9.5 percent at the annual equivalent rate of 1.0 percent. The United States grew during its history at high rates of per capita income that made its economy the largest in the world. That dynamism is disappearing. Bordo (2012 Sep27) and Bordo and Haubrich (2012DR) provide strong evidence that recoveries have been faster after deeper recessions and recessions with financial crises, casting serious doubts on the conventional explanation of weak growth during the current expansion allegedly because of the depth of the contraction of 4.2 percent from IVQ2007 to IIQ2009 and the financial crisis. The proposition of secular stagnation should explain a long-term process of decay and not the actual abrupt collapse of the economy and labor markets currently.

Table IB-1, US, GDP, Real Disposable Personal Income, Real Disposable Income per Capita and Population Long-term and in 1983-89 and 2007-2016, %

Long-term Average ∆% per Year	GDP	Population
1929-2015	3.2	1.1
1947-2015	3.2	1.2
1947-1999	3.6	1.3
1980-1989	3.5	0.9
2000-2015	1.8	0.9
2000-2006	2.6	0.9
2006-2015	1.3	0.8
2007-2015	1.2	0.8
Long-term Average ∆% per Year	Real Disposable Income	Real Disposable Income per Capita	Population
1929-2015	3.2	2.0	1.1
1947-1999	3.7	2.3	1.3
2000-2015	2.2	1.3	0.9
2000-2006	2.9	2.0	0.9
2006-2015	1.7	0.9	0.8
2007-2015	1.7	0.8	0.8
Whole Cycles Average ∆% per Year
1980-1989	3.5	2.6	0.9
2006-2015	1.7	0.9	0.8
2007-2015	1.7	0.8	0.8
Comparison of Cycles	# Quarters	∆%	∆% Annual Equivalent
GDP
I83 to IV83 I83 to IQ87 I83 to II87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 I83 to IV88 I83 to I89 I83 to II89 I83 to III89 I83 to IV89 I83 to I90	4 17 18 19 20 21 22 23 24 25 26 27 28 29	7.8 23.1 24.5 25.6 27.7 28.4 30.1 30.9 32.6 34.0 35.0 36.0 36.3 37.8	7.8 5.0 5.0 4.9 5.0 4.9 4.9 4.8 4.8 4.8 4.7 4.7 4.5 4.5
RDPI
I83 to IV83 I83 to I87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 I83 to IV88 I83 to I89 I83 to II89 I83 to III89 I83 to IV89 I83 to I90	4 17 19 20 21 22 23 24 25 26 27 28 29	5.3 19.5 20.5 22.1 23.8 25.1 26.3 27.5 29.1 28.7 29.6 30.7 31.8	5.3 4.3 4.0 4.1 4.2 4.2 4.1 4.1 4.2 4.0 3.9 3.9 3.9
RDPI Per Capita
I83 to IV83 I83 to I87 I83 to III87 I83 to IV87 I83 to I88 I83 to II88 I83 to III88 I83 to IV88 I83 to I89 I83 to II89 I83 to III89 I83 to IV89 I83 to I 90	4 17 19 20 21 22 23 24 25 26 27 28 29	4.4 15.1 15.5 16.7 18.2 19.2 20.0 20.9 22.1 21.5 22.0 22.6 23.4	4.4 3.4 3.1 3.1 3.2 3.2 3.2 3.2 3.2 3.0 3.0 3.0 2.9
Whole Cycle IQ1980 to IQ1990
GDP	42	37.1	3.1
RDPI	42	39.4	3.2
RDPI per Capita	42	26.6	2.3
Population	42	10.1	0.9
GDP
III09 to II10 III09 to III16	4 29	2.7 16.5	2.7 2.1
RDPI
III09 to II10 III09 to III16	4 29	0.2 15.4	0.2 2.0
RDPI per Capita
III09 to II10 III09 to III16	4 29	-0.7 9.2	-0.7 1.2
Population
III09 to II10 III09 to II16	4 29	0.8 5.7	0.8 0.8
IVQ2007 to IIIQ2016	35
GDP	35	11.6	1.3
RDPI	35	17.4	1.8
RDPI per Capita	35	9.5	1.0
Population	35	7.2	0.8

RDPI: Real Disposable Personal Income

Source: US Bureau of Economic Analysis http://www.bea.gov/iTable/index_nipa.cfm

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017.