Contraction of Valuations of Risk Financial Assets, Mediocre Cyclical United States Economic Growth with GDP Two Trillion Dollars below Trend in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide, Cyclically Stagnating Real Private Fixed Investment, United States Commercial Banks, United States Housing, United States House Prices, IMF View of World Economy and Finance, World Financial Turbulence, World Cyclical Slow Growth, Government Intervention in Globalization, and Global Recession Risk: Part III

Contraction of Valuations of Risk Financial Assets, Mediocre Cyclical United States Economic Growth with GDP Two Trillion Dollars below Trend in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide, Cyclically Stagnating Real Private Fixed Investment, United States Commercial Banks, United States Housing, United States House Prices, IMF View of World Economy and Finance, World Financial Turbulence, World Cyclical Slow Growth, Government Intervention in Globalization, and Global Recession Risk

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018

I Mediocre Cyclical United States Economic Growth with GDP Two Trillion Dollars below Trend in the Lost Economic Cycle of the Global Recession with Economic Growth Underperforming Below Trend Worldwide

IA Mediocre Cyclical United States Economic Growth

IA1 Stagnating Real Private Fixed Investment

II United States Commercial Banks Assets and Liabilities

IIA Transmission of Monetary Policy

IIB Functions of Banking

IIC United States Commercial Banks Assets and Liabilities

IIA United States Housing Collapse

IIA1 Sales of New Houses

IIA2 United States House Prices

IIC IMF View of World Economy and Finance

III World Financial Turbulence

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

II I United States Housing Collapse. Data and other information continue to provide depressed conditions in the US housing market in a longer perspective, with recent improvement at the margin. Table IIB-1 shows sales of new houses in the US at seasonally adjusted annual equivalent rate (SAAR). The US Census Bureau revised all seasonally-adjusted new house sales from 2013 to 2018 with the report for Apr 2018 on May 23, 2018 (https://www.census.gov/construction/nrs/pdf/newressales.pdf). House sales fell in 39 of 93 months from Jan 2011 to Sep 2018 with monthly declines of 5 in 2011, 4 in 2012, 5 in 2013, 6 in 2014, 3 in 2015, 6 in 2016, 4 in 2017 and 6 in 2018. In Jan-Apr 2012, house sales increased at the annual equivalent rate of 11.8 percent and at 22.3 percent in May-Sep 2012. There was significant strength in Sep-Dec 2011 with annual equivalent rate of 48.4 percent. Sales of new houses fell at 7.0 percent in Oct 2012 with increase of 9.5 percent in Nov 2012. Sales of new houses rebounded 11.8 percent in Jan 2013 with annual equivalent rate of 55.7 percent from Oct 2012 to Jan 2013 because of the increase at 11.8 percent in Jan 2013. New house sales decreased at annual equivalent 3.0 percent in Feb-Mar 2013. New house sales weakened, decreasing at 3.1 percent in annual equivalent from Apr to Dec 2013 with significant volatility illustrated by decline of 20.2 percent in Jul 2013 and increase of 10.2 percent in Oct 2013. New house sales fell 2.9 percent in Dec 2013. New house sales increased 2.8 percent in Jan 2014 and fell 3.6 percent in Feb 2014, decreasing 4.7 percent in Mar 2014. New house sales decreased 2.2 percent in Apr 2014 and increased 13.0 percent in May 2014. New house sales fell 8.0 percent in Jun 2014 and decreased 3.4 percent in Jul 2014. New house sales jumped 13.2 percent in Aug 2014 and increased 2.4 percent in Sep 2014. New House sales increased 1.3 percent in Oct 2014 and fell 5.9 percent in Nov 2014. House sales fell at the annual equivalent rate of 9.2 percent in Sep-Nov 2014. New house sales increased 9.9 percent in Dec 2014 and increased 7.0 percent in Jan 2015. Sales of new houses increased 6.5 percent in Feb

2015 and fell 12.8 percent in Mar 2015. House sales increased 3.7 percent in Apr 2015. The annual equivalent rate in Dec 2014-Apr 2015 was 34.8 percent. New house sales increased 0.2 percent in May 2015 and fell 6.0 percent in Jun 2015, increasing 5.1 percent in Jul 2015. New house sales fell at annual equivalent 4.0 percent in May-Jul 2015. New house sales increased 3.4 percent in Aug 2015 and fell 11.5 percent in Sep 2015. New house sales decreased at annual equivalent 41.3 percent in Aug-Sep 2015. New house sales increased 4.4 percent in Oct 2015 and increased 5.9 percent in Nov 2015, increasing 6.0 percent in Dec 2015. New house sales increased at the annual equivalent rate of 88.6 percent in Oct-Dec 2015. New house sales decreased 2.8 percent in Jan 2016 at the annual equivalent rate of minus 28.9 percent. New house sales increased 1.5 percent in Feb 2016 and increased 2.1 percent in Mar 2016. New house sales jumped at 5.2 percent in Apr 2016. New house sales increased at the annual equivalent rate of 41.3 percent in Feb-Apr 2016. New house sales decreased 1.4 percent in May 2016 and increased 0.4 percent in Jun 2016. New house sales jumped 12.9 percent in Jul 2016. New house sales increased at the annual equivalent rate of 56.0 percent in May-Jul 2016. New house sales fell 9.5 percent in Aug 2016 and decreased 1.0 percent in Sep 2016, increasing 1.2 percent in Oct 2016. New house sales fell at the annual equivalent rate of minus 32.4 percent in Aug-Oct 2016. New house sales decreased at 0.9 percent in Nov 2016 and fell at 3.9 percent in Dec 2016. New house sales fell at 25.4 percent annual equivalent in Nov-Dec 2016. New house sales increased at 9.2 percent in Jan 2017 and increased at 3.7 percent in Feb 2017. New house sales increased at 100.9 percent in Jan-Feb 2017. New house sales increased at 4.0 percent in Mar 2017 and fell at 7.8 percent in Apr 2017. New house sales decreased at annual equivalent 22.3 percent in Mar-Apr 2017. New house sales increased at 1.9 percent in May 2017 and increased at 2.0 percent in Jun 2017. New house sales increased at annual equivalent 26.1 percent in May-Jun 2017. New house sales decreased at 9.7 percent in Jul 2017 and increased at 0.4 percent in Aug 2017, increasing at 14.2 percent in Sep 2017. New house sales increased at annual equivalent 14.9 percent in Jul-Sep 2017. New house sales decreased at 3.0 percent in Oct 2017. New house sales increased at 15.2 percent in Nov 2017. New house sales increased at annual equivalent 94.7 percent in Oct-Nov 2017. New house sales decreased at 10.7 percent in Dec 2017 and decreased at 0.5 percent in Jan 2018. New house sales decreased at annual equivalent 50.8 percent in Dec 2017-Jan 2018. New house sales increased at 4.7 percent in Feb 2018, increasing at 1.4 percent in Mar 2018. New house sales increased at 43.2 percent in Feb-Mar 2018. New house sales decreased at 5.8 percent in Apr 2018 and increased at 3.2 percent in May 2018. New House sales decreased at annual equivalent 15.6 percent in Apr-May 2018. New house sales decreased at 6.3 percent in Jun 2018 and decreased at 1.5 percent in Jul 2018. New House sales decreased at annual equivalent 38.2 percent in Jun-Jul 2018. New house sales decreased at 3.0 percent in Aug 2018 and decreased at 5.5 percent in Sep 2018. New house sales decreased at annual equivalent 40.7 percent in Aug-Sep 2018. There are wide monthly oscillations. Robbie Whelan and Conor Dougherty, writing on “Builders fuel home sale rise,” on Feb 26, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324338604578327982067761860.html), analyze how builders have provided financial assistance to home buyers, including those short of cash and with weaker credit background, explaining the rise in new home sales and the highest gap between prices of new and existing houses. The 30-year conventional mortgage rate increased from 3.40 on Apr 25, 2013 to 4.58 percent on Aug 22, 2013 (http://www.federalreserve.gov/releases/h15/data.htm), which could also be a factor in recent weakness with improvement after the rate fell to 4.26 in Nov 2013. The conventional mortgage rate rose to 4.48 percent on Dec 26, 2013 and fell to 4.32 percent on Jan 30, 2014. The conventional mortgage rate increased to 4.37 percent on Feb 26, 2014 and 4.40 percent on Mar 27, 2014. The conventional mortgage rate fell to 4.14 percent on Apr 22, 2014, stabilizing at 4.14 on Jun 26, 2014. The conventional mortgage rate stood at 3.93 percent on Aug 20, 2015 and at 3.91 percent on Sep 17, 2015. The conventional mortgage rate was at 3.79 percent on Oct 22, 2015. The conventional mortgage rate was 3.97 percent on Nov 20, 2015. The conventional mortgage rate was 3.97 percent on Dec 18, 2015, and 3.92 percent on Jan 14, 2016. The conventional mortgage rate was 3.65 percent on Feb 19, 2016. The commercial mortgage rate was 3.73 percent on Mar 17, 2016 and 3.59 percent on Apr 21, 2016. The conventional mortgage rate was 3.58 on May 19, 2016. The conventional mortgage rate was 3.54 percent on Jun 19, 2016 and 3.45 percent on Jul 21, 2016. The conventional mortgage rate was 3.43 percent on Aug 18, 2016 and 3.48 percent on Sep 22, 2016. The conventional mortgage rate was 3.94 on Nov 17, 2016 and 4.30 percent on Dec 22. The conventional mortgage rate was 4.19 percent on Jan 26, 2017 and 4.15 percent on Feb 17, 2017. The conventional mortgage rate was 4.1 percent on Mar 16, 2017. The conventional mortgage rate was 3.97 percent on Apr 20, 2017. The conventional mortgage rate was 4.05 percent on May 18, 2017. The conventional mortgage rate was 3.90 percent on Jun 22, 2017. The conventional mortgage rate was 3.96 percent on Jul 20, 2017. The conventional mortgage rate was 3.90 percent on Aug 18, 2017. The conventional mortgage rate was 3.83 percent on Sep 21, 2017. The conventional mortgage rate was 3.88 percent on Oct 20, 2017. The conventional mortgage rate was 3.92 percent on Nov 22, 2017 and 3.94 on Dec 21, 2017. The conventional mortgage rate was 4.04 percent on Jan 18, 2018. The conventional mortgage rate was 4.40 percent on Feb 22, 2018. The conventional rate was 4.43 percent on Mar 1, 2018. The conventional mortgage rate was 4.45 percent on Mar 22, 2018. The conventional mortgage rate was 4.47 on Apr 19, 2018. The conventional mortgage rate was 4.87 percent in May 31, 2018. The conventional mortgage rate was 4.57 percent on Jun 21, 2018. The conventional mortgage rate was 4.52 percent on Jul 19, 2018. The conventional mortgage rate was 4.53 percent on Aug 16, 2018. The conventional mortgage rate was 4.65 percent on Sep 20, 2018. The conventional mortgage rate was 4.85 percent on Oct 18, 2018. The conventional mortgage rate measured in a survey by Freddie Mac (http://www.freddiemac.com/pmms/ http://www.freddiemac.com/pmms/abtpmms.htm) is the “interest rate a lender would charge to lend mortgage money to a qualified borrower.”

Table IIB-1, US, Sales of New Houses at Seasonally-Adjusted (SA) Annual Equivalent Rate, Thousands and % 

	SA Annual Rate Thousands	∆%
Sep 2018	553	-5.5
Aug	585	-3.0
AE ∆% Aug-Sep		-40.7
Jul	603	-1.5
Jun	612	-6.3
AE ∆% Jun-Jul		-38.2
May	653	3.2
Apr	633	-5.8
AE ∆% Apr-May		-15.6
Mar	672	1.4
Feb	663	4.7
AE ∆% Feb-Mar		43.2
Jan	633	-0.5
Dec 2017	636	-10.7
AE ∆% Dec-Jan		-50.8
Nov	712	15.2
Oct	618	-3.0
AE ∆% Oct-Nov		94.7
Sep	637	14.2
Aug	558	0.4
Jul	556	-9.7
AE ∆% Jul-Sep		14.9
Jun	616	2.0
May	604	1.9
AE ∆% May -Jun		26.1
Apr	593	-7.8
Mar	643	4.0
AE ∆% Mar-Apr		-22.3
Feb	618	3.7
Jan	596	9.2
AE ∆% Jan-Feb		100.9
Dec 2016	546	-3.9
Nov	568	-0.9
AE ∆% Nov-Dec		-25.4
Oct	573	1.2
Sep	566	-1.0
Aug	572	-9.5
AE ∆% Aug-Oct		-32.4
Jul	632	12.9
Jun	560	0.4
May	558	-1.4
AE ∆% May-Jul		56.0
Apr	566	5.2
Mar	538	2.1
Feb	527	1.5
AE ∆% Feb-Apr		41.3
Jan	519	-2.8
AE ∆% Jan		-28.9
Dec 2015	534	6.0
Nov	504	5.9
Oct	476	4.4
AE ∆% Oct-Dec		88.6
Sep	456	-11.5
Aug	515	3.4
AE ∆% Aug-Sep		-41.3
Jul	498	5.1
Jun	474	-6.0
May	504	0.2
AE ∆% May-Jul		-4.0
Apr	503	3.7
Mar	485	-12.8
Feb	556	6.5
Jan	522	7.0
Dec 2014	488	9.9
AE ∆% Dec-Apr		34.8
Nov	444	-5.9
Oct	472	1.3
Sep	466	2.4
AE ∆% Sep-Nov		-9.2
Aug	455	13.2
Jul	402	-3.4
Jun	416	-8.0
May	452	13.0
Apr	400	-2.2
Mar	409	-4.7
Feb	429	-3.6
Jan	445	2.8
AE ∆% Jan-Aug		7.6
Dec 2013	433	-2.9
Nov	446	0.5
Oct	444	10.2
Sep	403	5.8
Aug	381	1.6
Jul	375	-20.2
Jun	470	9.8
May	428	-2.9
Apr	441	-0.7
AE ∆% Apr-Dec		-3.1
Mar	444	-0.7
Feb	447	0.2
AE ∆% Feb-Mar		-3.0
Jan	446	11.8
Dec 2012	399	1.8
Nov	392	9.5
Oct	358	-7.0
AE ∆% Oct-Jan		55.7
Sep	385	2.7
Aug	375	1.6
Jul	369	2.5
Jun	360	-2.7
May	370	4.5
AE ∆% May-Sep		22.3
Apr	354	0.0
Mar	354	-3.3
Feb	366	9.3
Jan	335	-1.8
AE ∆% Jan-Apr		11.8
Dec 2011	341	4.0
Nov	328	3.8
Oct	316	3.9
Sep	304	1.7
AE ∆% Sep-Dec		48.4
Aug	299	1.0
Jul	296	-1.7
Jun	301	-1.3
May	305	-1.6
AE ∆% May-Aug		-10.3
Apr	310	3.3
Mar	300	11.1
Feb	270	-12.1
Jan	307	-5.8
AE ∆% Jan-Apr		-14.2
Dec 2010	326	13.6

AE: Annual Equivalent

Source: US Census Bureau

http://www.census.gov/construction/nrs/

There is additional information of the report of new house sales in Table IIB-2. The stock of unsold houses fell from rates of 6 to 8 percent of sales in 2011 to 4 to 5 percent in 2013 and 7.1 percent in Sep 2018. Robbie Whelan and Conor Dougherty, writing on “Builders fuel home sale rise,” on Feb 26, 2013, published in the Wall Street Journal (http://professional.wsj.com/article/SB10001424127887324338604578327982067761860.html), find that inventories of houses have declined as investors acquire distressed houses of higher quality. Median and average house prices oscillate. In Sep 2018, median prices of new houses sold not seasonally adjusted (NSA) increased 0.3 percent after decreasing 3.2 percent in

Sep 2018. Average prices decreased 1.9 percent in Sep 2018 and decreased 2.3 percent in Aug 2018. Between Dec 2010 and Sep 2018, median prices increased 32.7 percent, with increases of 6.0 percent in Feb 2016, 4.9 percent in Nov 2015, 2.2 percent in Sep 2015, 13.6 percent in Oct 2014, 4.0 percent in Aug 2014, 4.0 percent in May 2014 and 5.2 percent in Mar 2014. Average prices increased 29.3 percent between Dec 2010 and Sep 2018, with increases of 5.1 percent in Mar 2016, 4.0 percent in Sep 2015, 4.4 percent in Jul 2015 and 18.3 percent in Oct 2014. Between Dec 2010 and Dec 2012, median prices increased 7.1 percent and average prices increased 2.6 percent. Price increases concentrated in 2012 with increase of median prices of 18.2 percent from Dec 2011 to Dec 2012 and of average prices of 13.8 percent. Median prices increased 16.7 percent from Dec 2012 to Dec 2014, with increase of 13.6 percent in Oct 2014, while average prices increased 24.7 percent, with increase of 18.3 percent in Oct 2014. Median prices decreased 1.5 percent from Dec 2014 to Dec 2015 while average prices fell 5.5 percent. Median prices increased 10.1 percent from Dec 2015 to Dec 2016 while average prices increased 8.5 percent. Median prices increased 5.0 percent from Dec 2016 to Dec 2017 while average prices increased 5.3 percent. Median prices decreased 3.5 percent from Sep 2017 to Sep 2018 while average prices decreased 0.6 percent. Robbie Whelan, writing on “New homes hit record as builders cap supply,” on May 24, 2013, published in the Wall Street Journal (http://online.wsj.com/article/SB10001424127887323475304578500973445311276.html?mod=WSJ_economy_LeftTopHighlights), finds that homebuilders are continuing to restrict the number of new homes for sale. Restriction of available new homes for sale increases prices paid by buyers.

Table IIB-2, US, New House Stocks and Median and Average New Homes Sales Price

	Unsold* Stocks in Equiv. Months of Sales SA %	Median New House Sales Price USD NSA	Month ∆%	Average New House Sales Price USD NSA	Month ∆%
Sep 2018	7.1	320,000	0.3	377,200	-1.9
Aug	6.5	319,200	-3.2	384,500	-2.3
Jul	6.2	329,700	6.2	393,400	6.3
Jun	6.0	310,500	-2.0	370,100	-0.7
May	5.5	316,700	0.7	372,600	-3.2
Apr	5.7	314,400	-6.3	385,100	4.3
Mar	5.3	335,400	2.5	369,200	-1.2
Feb	5.4	327,200	-0.7	373,600	-1.1
Jan	5.6	329,600	-4.0	377,800	-6.2
Dec 2017	5.5	343,300	0.0	402,900	3.7
Nov	4.9	343,400	7.5	388,500	-1.4
Oct	5.6	319,500	-3.6	394,000	3.9
Sep	5.3	331,500	5.5	379,300	2.7
Aug	6.0	314,200	-2.7	369,200	-0.9
Jul	6.0	322,900	2.4	372,400	0.5
Jun	5.3	315,200	-2.6	370,600	-2.1
May	5.4	323,600	4.0	378,400	3.4
Apr	5.4	311,100	-3.3	365,800	-4.8
Mar	5.0	321,700	8.0	384,400	3.8
Feb	5.1	298,000	-5.5	370,500	3.6
Jan	5.3	315,200	-3.6	357,700	-6.5
Dec 2016	5.6	327,000	3.8	382,500	5.3
Nov	5.3	315,000	4.0	363,400	3.2
Oct	5.2	302,800	-3.8	352,200	-3.8
Sep	5.2	314,800	5.3	366,100	3.1
Aug	5.0	298,900	0.5	355,100	0.6
Jul	4.5	297,400	-4.4	353,000	-1.3
Jun	5.2	311,200	5.4	357,800	2.3
May	5.2	295,200	-7.3	349,700	-5.3
Apr	5.1	318,300	5.0	369,300	2.9
Mar	5.4	303,200	-0.9	359,000	5.1
Feb	5.4	305,800	6.0	341,700	-5.4
Jan	5.5	288,400	-2.9	361,200	2.5
Dec 2015	5.2	297,100	-5.0	352,500	-5.5
Nov	5.5	312,600	4.9	373,200	1.2
Oct	5.7	298,000	-0.5	368,900	3.3
Sep	5.9	299,500	2.2	357,200	4.0
Aug	5.1	293,000	0.2	343,300	0.6
Jul	5.2	292,300	2.5	341,200	4.4
Jun	5.5	285,100	-0.8	326,900	-2.8
May	5.0	287,500	-2.4	336,200	-1.2
Apr	4.9	294,500	2.8	340,400	-2.5
Mar	5.1	286,600	0.0	349,300	0.9
Feb	4.4	286,600	-1.8	346,300	-0.6
Jan	4.8	292,000	-3.2	348,300	-6.7
Dec 2014	5.2	301,500	1.1	373,200	7.0
Nov	5.7	298,300	0.4	348,900	-7.6
Oct	5.3	297,000	13.6	377,500	18.3
Sep	5.4	261,500	-10.4	319,100	-10.4
Aug	5.5	291,700	4.0	356,200	3.2
Jul	6.1	280,400	-2.3	345,200	2.1
Jun	5.7	287,000	0.5	338,100	4.5
May	5.2	285,600	4.0	323,500	-0.5
Apr	5.7	274,500	-2.8	325,100	-1.9
Mar	5.6	282,300	5.2	331,500	1.7
Feb	5.2	268,400	-0.5	325,900	-3.4
Jan	5.1	269,800	-2.1	337,300	5.0
Dec 2013	5.2	275,500	-0.6	321,200	-4.3
Nov	5.0	277,100	4.8	335,600	0.0
Oct	4.9	264,300	-2.0	335,700	4.4
Sep	5.4	269,800	5.7	321,400	3.4
Aug	5.5	255,300	-2.6	310,800	-5.8
Jul	5.5	262,200	0.9	329,900	7.8
Jun	4.1	259,800	-1.5	306,100	-2.5
May	4.6	263,700	-5.6	314,000	-6.8
Apr	4.4	279,300	8.5	337,000	12.3
Mar	4.2	257,500	-2.9	300,200	-3.9
Feb	4.1	265,100	5.4	312,500	1.8
Jan	4.0	251,500	-2.6	306,900	2.6
Dec 2012	4.5	258,300	5.4	299,200	2.9
Nov	4.6	245,000	-0.9	290,700	1.9
Oct	4.9	247,200	-2.9	285,400	-4.1
Sep	4.5	254,600	0.6	297,700	-2.6
Aug	4.6	253,200	6.7	305,500	8.2
Jul	4.6	237,400	2.1	282,300	3.9
Jun	4.8	232,600	-2.8	271,800	-3.2
May	4.7	239,200	1.2	280,900	-2.4
Apr	4.9	236,400	-1.4	287,900	1.5
Mar	4.9	239,800	0.0	283,600	3.5
Feb	4.8	239,900	8.2	274,000	3.1
Jan	5.3	221,700	1.4	265,700	1.1
Dec 2011	5.3	218,600	2.0	262,900	5.2
Nov	5.7	214,300	-4.7	250,000	-3.2
Oct	6.0	224,800	3.6	258,300	1.1
Sep	6.3	217,000	-1.2	255,400	-1.5
Aug	6.5	219,600	-4.5	259,300	-4.1
Jul	6.7	229,900	-4.3	270,300	-1.0
Jun	6.6	240,200	8.2	273,100	4.0
May	6.6	222,000	-1.2	262,700	-2.3
Apr	6.7	224,700	1.9	268,900	3.1
Mar	7.2	220,500	0.2	260,800	-0.8
Feb	8.1	220,100	-8.3	262,800	-4.7
Jan	7.3	240,100	-0.5	275,700	-5.5
Dec 2010	7.0	241,200	9.8	291,700	3.5

*Percent of new houses for sale relative to houses sold

Source: US Census Bureau

http://www.census.gov/construction/nrs/

The depressed level of residential construction and new house sales in the US is evident in Table IIB-3 providing new house sales not seasonally adjusted in Jan-Sep of various years. New house sales increased 3.6 percent from Jan-Sep 2017 to Jan-Sep 2018. Sales of new houses are higher in Jan-Sep 2018 relative to Jan-Sep 2016 with increase of 11.5 percent. Sales of new houses are higher in Jan-Sep 2018 relative to Jan-Sep 2015 with increase of 25.3 percent. Sales of new houses in Jan-Sep 2018 are substantially lower than in many years between 1971 and 2018 except for the years from 2008 to 2017. There are several other increases of 45.1 percent relative to 2014, 47.3 percent relative to Jan-Sep 2013, 71.1 percent relative to Jan-Sep 2012, 108.6 percent relative to Jan-Sep 2011, 89.8 percent relative to Jan-Sep 2010 and 67.0 percent relative to Jan-Sep 2009. New house sales in Jan-Sep 2018 are 21.5 percent higher than in Jan-Sep 2008. Sales of new houses in Jan-Sep 2018 are lower by 22.9 percent relative to Jan-Sep 2007, 41.9 percent relative to 2006, 51.6 percent relative to 2005 and 48.0 percent relative to 2004. The housing boom peaked in 2005 and 2006 when increases in fed funds rates to 5.25 percent in Jun 2006 from 1.0 percent in Jun 2004 affected subprime mortgages that were programmed for refinancing in two or three years on the expectation that price increases forever would raise home equity. Higher home equity would permit refinancing under feasible mortgages incorporating full payment of principal and interest (Gorton 2009EFM; see other references in http://cmpassocregulationblog.blogspot.com/2011/07/causes-of-2007-creditdollar-crisis.html). Sales of new houses in Jan-Sep 2018 relative to the same period in 2003 fell 42.8 percent and 35.4 percent relative to the same period in 2002. Similar percentage declines are also for 2018 relative to years from 2000 to 2004. Sales of new houses in Jan-Sep 2018 decreased 18.6 per cent relative to the same period in 1996. The population of the US was 179.3 million in 1960 and 281.4 million in 2000 (Hobbs and Stoops 2002, 16). Detailed historical census reports are available from the US Census Bureau at (http://www.census.gov/population/www/censusdata/hiscendata.html). The estimate of the US population is 418.8 million in 2015. The US population increased by 133.6 percent from 1960 to 2015. The final row of Table IIB-3 reveals catastrophic data: sales of new houses in Jan-Sep 2018 of 486 thousand units are lower by 7.3 percent relative to 524 thousand units of houses sold in Jan-Sep 1973, which is the ninth year when data become available in 1963. The civilian noninstitutional population increased from 122.416 million in 1963 to 255.079 million in 2017, or 108.4 percent (http://www.bls.gov/data/). The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (http://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.”

Table IIB-3, US, Sales of New Houses Not Seasonally Adjusted, Thousands and %

Jan-Sep 2018	486
Jan-Sep 2017	469
Jan-Sep 2018/Jan-Sep 2017	3.6
Jan-Sep 2016	436
∆% Jan-Sep 2018/Jan-Sep 2016	11.5
Jan-Sep 2015	388
∆% Jan-Sep 2018/Jan-Sep 2015	25.3
Jan-Sep 2014	335
∆% Jan-Sep 2018/Jan-Sep 2014	45.1
Jan-Sep 2013	330
∆% Jan-Sep 2018/Jan-Sep 2013	47.3
Jan-Sep 2012	284
∆% Jan-Sep 2018/Jan-Sep 2012	71.1
Jan-Sep 2011	233
∆% Jan-Sep 2018/  Jan-Sep 2011	108.6
Jan-Sep 2010	256
∆% Jan-Sep 2018/  Jan-Sep 2010	89.8
Jan-Sep 2009	291
∆% Jan-Sep 2018/  Jan-Sep 2009	67.0
Jan-Sep 2008	400
∆% Jan-Sep 2018/ Jan-Sep 2008	21.5
Jan-Sep 2007	630
∆% Jan-Sep 2018/Jan-Sep 2007	-22.9
Jan-Sep 2006	836
∆% Jan-Sep 2018/Jan-Sep 2006	-41.9
Jan-Sep 2005	1005
∆% Jan-Sep 2018/Jan-Sep 2005	-51.6
Jan-Sep 2004	935
∆% Jan-Sep 2018/ Jan-Sep 2004	-48.0
Jan-Sep 2003	849
∆% Jan-Sep 2018/ Jan-Sep 2003	-42.8
Jan-Sep 2002	752
∆% Jan-Sep 2018/ Jan-Sep 2002	-35.4
Jan-Sep 2001	710
∆% Jan-Sep 2018/ Jan-Sep 2001	-31.5
Jan-Sep 2000	678
∆% Jan-Sep 2018/Jan-Sep 2000	-28.3
Jan-Sep 1996	597
∆% Jan-Sep 2018/ Jan-Sep 1996	-18.6
Jan-Sep 1973	524
∆% Jan-Sep 2018/ Jan-Sep 1973	-7.3

*Computed using unrounded data

Source: US Census Bureau

http://www.census.gov/construction/nrs/

The revised level of 306 thousand new houses sold in 2011 is the lowest since 560 thousand in 1963 in the 53 years of available data while the level of 368 thousand in 2012 is only higher than 323 thousand in 2010. The level of sales of new houses of 437 thousand in 2014 is the lowest from 1963 to 2009 with exception of 412 thousand in 1982 and 436 thousand in 1981. The population of the US increased 129.4 million from 179.3 million in 1960 to 308.7 million in 2010, or 72.2 percent. The estimate of the US population is 418.8 million in 2015. The US population increased 133.6 percent from 1960 to 2015. The civilian noninstitutional population increased from 122.416 million in 1963 to 253.538 million in 2016, or 107.1 percent (http://www.bls.gov/data/). The civilian noninstitutional population increased from 122.416 million in 1963 to 255.079 million in 2017, or 108.4 percent (http://www.bls.gov/data/). The Bureau of Labor Statistics (BLS) defines the civilian noninstitutional population (http://www.bls.gov/lau/rdscnp16.htm#cnp): “The civilian noninstitutional population consists of persons 16 years of age and older residing in the 50 States and the District of Columbia who are not inmates of institutions (for example, penal and mental facilities and homes for the aged) and who are not on active duty in the Armed Forces.”

The civilian noninstitutional population is the universe of the labor force. In fact, there is no year from 1963 to 2013 in Table IIA-4 with sales of new houses below 400 thousand except for the immediately preceding years of 2009, 2010, 2011 and 2012.

Table IIB-4, US, New Houses Sold, NSA Thousands

Period	Sold During Period
1963	560
1964	565
1965	575
1966	461
1967	487
1968	490
1969	448
1970	485
1971	656
1972	718
1973	634
1974	519
1975	549
1976	646
1977	819
1978	817
1979	709
1980	545
1981	436
1982	412
1983	623
1984	639
1985	688
1986	750
1987	671
1988	676
1989	650
1990	534
1991	509
1992	610
1993	666
1994	670
1995	667
1996	757
1997	804
1998	886
1999	880
2000	877
2001	908
2002	973
2003	1,086
2004	1,203
2005	1,283
2006	1,051
2007	776
2008	485
2009	375
2010	323
2011	306
2012	368
2013	429
2014	437
2015	501
2016	561
2017	613

http://www.census.gov/construction/nrs/

Chart IIB-1 of the US Bureau of the Census shows the sharp decline of sales of new houses in the US. Sales rose temporarily until about mid 2010 but then declined to a lower plateau followed by increase, stability and new oscillating increase. There is decrease in the final segment.

Chart IIB-1, US, New One-Family Houses Sold in the US, SAAR (Seasonally Adjusted Annual Rate) 

Source: US Census Bureau

https://www.census.gov/construction/nrs/img/c25_curr.gif

Between 1991 and 2001, sales of new houses rose 78.4 percent at the average yearly rate of 6.0 percent, as shown in Table IB-5. Between 1995 and 2005 sales of new houses increased 92.4 percent at the yearly rate of 6.8 percent. There are similar rates in all years from 2000 to 2005. The boom in housing construction and sales began in the 1980s and 1990s. The collapse of real estate culminated several decades of housing subsidies and policies to lower mortgage rates and borrowing terms (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009b), 42-8). Sales of new houses sold in 2017 fell 8.1 percent relative to the same period in 1995 and 52.2 percent relative to 2005.

Table IIB-5, US, Percentage Change and Average Yearly Rate of Growth of Sales of New One-Family Houses

	∆%	Average Yearly % Rate
1963-2017	9.5	0.2
1991-2001	78.4	6.0
1995-2005	92.4	6.8
2000-2005	46.3	7.9
1995-2017	-8.1	NA
2000-2017	-30.1	NA
2005-2017	-52.2	NA

NA: Not Applicable

Source: US Census Bureau

http://www.census.gov/construction/nrs/

Chart IIB-2 of the US Bureau of the Census provides the entire monthly sample of new houses sold in the US between Jan 1963 and Aug 2018 without seasonal adjustment. The series is almost stationary until the 1990s. There is sharp upward trend from the early 1990s to 2005-2006 after which new single-family houses sold collapse to levels below those in the beginning of the series.

Chart IIB-2, US, New Single-family Houses Sold, NSA, 1963-2018

Source: US Census Bureau

http://www.census.gov/construction/nrs/

The available historical annual data of median and average prices of new houses sold in the US between 1963 and 2017 is in Table IIB-6. On a yearly basis, median and average prices reached a peak in 2007 and then fell substantially. There is recovery in 2012-2017.

Table IIB-6, US, Median and Average Prices of New Houses Sold, Annual Data

Period	Median	Average
1963	$18,000	$19,300
1964	$18,900	$20,500
1965	$20,000	$21,500
1966	$21,400	$23,300
1967	$22,700	$24,600
1968	$24,700	$26,600
1969	$25,600	$27,900
1970	$23,400	$26,600
1971	$25,200	$28,300
1972	$27,600	$30,500
1973	$32,500	$35,500
1974	$35,900	$38,900
1975	$39,300	$42,600
1976	$44,200	$48,000
1977	$48,800	$54,200
1978	$55,700	$62,500
1979	$62,900	$71,800
1980	$64,600	$76,400
1981	$68,900	$83,000
1982	$69,300	$83,900
1983	$75,300	$89,800
1984	$79,900	$97,600
1985	$84,300	$100,800
1986	$92,000	$111,900
1987	$104,500	$127,200
1988	$112,500	$138,300
1989	$120,000	$148,800
1990	$122,900	$149,800
1991	$120,000	$147,200
1992	$121,500	$144,100
1993	$126,500	$147,700
1994	$130,000	$154,500
1995	$133,900	$158,700
1996	$140,000	$166,400
1997	$146,000	$176,200
1998	$152,500	$181,900
1999	$161,000	$195,600
2000	$169,000	$207,000
2001	$175,200	$213,200
2002	$187,600	$228,700
2003	$195,000	$246,300
2004	$221,000	$274,500
2005	$240,900	$297,000
2006	$246,500	$305,900
2007	$247,900	$313,600
2008	$232,100	$292,600
2009	$216,700	$270,900
2010	$221,800	$272,900
2011	$227,200	$267,900
2012	$245,200	$292,200
2013	$268,900	$324,500
2014	$288,500	$347,700
2015	$294,200	$352,700
2016	$307,800	$360,900
2017	$323,100	$384,900

Source: US Census Bureau

http://www.census.gov/construction/nrs/

Prices rose sharply between 2000 and 2005 as shown in Table IIB-7. In fact, prices in 2017 are higher than in 2000. Between 2006 and 2017, median prices of new houses sold increased 31.1 percent and average prices increased 25.8 percent. Between 2016 and 2017, median prices increased 5.0 percent and average prices increased 6.7 percent.

Table IIB-7, US, Percentage Change of New Houses Median and Average Prices, NSA, ∆%

	Median New  Home Sales Prices ∆%	Average New Home Sales Prices ∆%
∆% 2000 to 2003	15.4	19.0
∆% 2000 to 2005	42.5	43.5
∆% 2000 to 2017	91.2	85.9
∆% 2005 to 2017	34.1	29.6
∆% 2000 to 2006	45.9	47.8
∆% 2006 to 2017	31.1	25.8
∆% 2009 to 2017	49.1	42.1
∆% 2010 to 2017	45.7	41.0
∆% 2011 to 2017	42.2	43.7
∆% 2012 to 2017	31.8	31.7
∆% 2013 to 2017	20.2	18.6
∆% 2014 to 2017	12.0	10.7
∆% 2015 to 2017	9.8	9.1
∆% 2016 to 2017	5.0	6.7

Source: US Census Bureau

http://www.census.gov/construction/nrs/

Chart IIB-3 of the US Census Bureau provides the entire series of new single-family sales median prices from Jan 1963 to Aug 2018. There is long-term sharp upward trend with few declines until the current collapse. Median prices increased sharply during the Great Inflation of the 1960s and 1970s and paused during the savings and loans crisis of the late 1980s and the recession of 1991. Housing subsidies throughout the 1990s caused sharp upward trend of median new house prices that accelerated after the fed funds rate of 1 percent from 2003 to 2004. There was sharp reduction of prices after 2006 with recovery recently above earlier prices.

Chart IIB-3, US, Median Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1963-2018

Source: US Census Bureau

http://www.census.gov/construction/nrs/

Chart IIB-4 of the US Census Bureau provides average prices of new houses sold from the mid-1970s to Aug 2018. There is similar behavior as with median prices of new houses sold in Chart IIB-3. The only stress occurred in price pauses during the savings and loans crisis of the late 1980s and the collapse after 2006 with recent recovery.

Chart IIB-4, US, Average Sales Price of New Single-family Houses Sold, US Dollars, NSA, 1975-2018

Source: US Census Bureau

http://www.census.gov/construction/nrs/

Chart IIB-5 of the Board of Governors of the Federal Reserve System provides the rate for the 30-year conventional mortgage, the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 1954 to 2016. All rates decline throughout the period from the Great Inflation of the 1970s through the following Great Moderation and until currently. In Apr 1971, the fed funds rate was 4.15 percent and the conventional mortgage rate 7.31 percent. In November 2012, the fed funds rate was 0.16 percent, the yield of the 30-year Treasury 2.80 percent and the conventional mortgage rate 3.35. The final segment shows an increase in the yield of the 30-year Treasury to 3.61 percent in July 2013 with the fed funds rate at 0.09 percent and the conventional mortgage at 4.37 percent. The final data point shows marginal decrease of the conventional mortgage rate to 3.60 percent in May 2016 with the yield of the 30-year Treasury bond at 2.63 percent and overnight rate on fed funds at 0.37 percent. The recent increase in interest rates if sustained could affect the US real estate market. 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 IIB-5, US, Thirty-year Conventional Mortgage, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1954-2016

Source: Board of Governors of the Federal Reserve System

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

Chart IIB-5A of the Board of Governors of the Federal Reserve System provides the yield of the 30-year Treasury bond and the rate of the overnight federal funds rate, monthly, from 2001 to 2018. The Board of Governors of the Federal Reserve System discontinued the conventional mortgage rate in its data bank. The final data point is 1.95 percent for the fed funds rate in Sep 2018 and 3.15 percent for the thirty-year Treasury bond. The conventional mortgage rate stood at 4.63 percent in Sep 2018.

Chart IIB-5A, US, Thirty-year Treasury Bond and Overnight Federal Funds Rate, Monthly, 1977-2018

Source: Board of Governors of the Federal Reserve System

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

Table IIB-8, US, Fed Funds Rate, Thirty Year Treasury Bond and Conventional Mortgage Rate, Monthly, Percent per Year, Dec 2012 to Sep 2018

	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	0.10	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
2017-01	0.65	3.02	4.15
2017-02	0.66	3.03	4.17
2017-03	0.79	3.08	4.20
2017-04	0.90	2.94	4.05
2017-05	0.91	2.96	4.01
2017-06	1.04	2.80	3.90
2017-07	1.15	2.88	3.97
2017-08	1.16	2.80	3.88
2017-09	1.15	2.78	3.81
2017-10	1.15	2.88	3.90
2017-11	1.16	2.80	3.92
2017-12	1.30	2.77	3.95
2018-01	1.41	2.88	4.03
2018-02	1.42	3.13	4.33
2018-03	1.51	3.09	4.44
2018-04	1.69	3.07	4.47
2018-05	1.70	3.13	4.59
2018-06	1.82	3.05	4.57
2018-07	1.91	3.01	4.53
2018-08	1.91	3.04	4.55
2018-09	1.95	3.15	4.63

Source: Board of Governors of the Federal Reserve System

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

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

IIB2 United States House Prices. The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). The Federal Housing Finance Agency (FHFA), which regulates Fannie Mae and Freddie Mac, provides the FHFA House Price Index (HPI) that “is calculated using home sales price information from Fannie Mae and Freddie Mac-acquired mortgages” (http://fhfa.gov/webfiles/24216/q22012hpi.pdf 1). Table IIA2-1 provides the FHFA HPI for purchases only, which shows behavior similar to that of the Case-Shiller index but with lower magnitudes. House prices catapulted from 2000 to 2003, 2005 and 2006. From IVQ2000 to IVQ2006, the index for the US as a whole rose 55.0 percent, with 62.1 percent for New England, 72.0 percent for Middle Atlantic, 71.2 percent for South Atlantic but only by 33.1 percent for East South Central. Prices fell relative to 2014 for the US and all regions from 2006 with exception of increase of 2.6 percent for East South Central. Prices for the US increased 4.9 percent in IVQ2014 relative to IVQ2013 and 12.9 percent from IVQ2012 to IVQ2014. From IVQ2000 to IVQ2014, prices rose for the US and the four regions in Table IIA2-1.

Table IIA2-1, US, FHFA House Price Index Purchases Only NSA ∆%

	United States	New England	Middle Atlantic	South Atlantic	East South Central
IVQ2000 to IVQ2003	24.0	40.6	35.8	25.9	11.0
IVQ2000 to IVQ2005	50.5	65.0	67.6	62.9	25.4
IVQ2000 to IVQ2006	55.0	62.1	72.0	71.2	33.1
IVQ2005 to IVQ2014	-1.5	-8.7	-2.3	-7.4	8.9
IVQ2006 to IVQ2014	-4.4	-7.1	-4.8	-11.9	2.6
IVQ2007 to IVQ2014	-1.9	-5.1	-5.0	-8.6	0.7
IVQ2011 to IVQ2014	18.9	7.3	6.9	19.9	11.8
IVQ2012 to IVQ2014	12.9	6.8	5.7	13.8	8.6
IVQ2013 to IVQ2014	4.9	2.5	2.2	5.1	4.2
IVQ2000 to IVQ2014	48.3 144.27	50.6 138.40	63.7 127.30	50.9 140.28	36.6 146.07

Source: Federal Housing Finance Agency

http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx

Data of the FHFA HPI for the remaining US regions are in Table IIA2-2. Behavior is not very different from that in Table IIA2-1 with the exception of East North Central. House prices in the Pacific region doubled between 2000 and 2006. Although prices of houses declined sharply from 2005 and 2006 to 2014 with exception of West South Central and West North Central, there was still appreciation relative to 2000.

Table IIA2-2, US, FHFA House Price Index Purchases Only NSA ∆%

	West South Central	West North Central	East North Central	Mountain	Pacific
IVQ2000 to IVQ2003	11.1	18.3	14.7	18.9	44.6
IVQ2000 to IVQ2005	23.9	31.0	23.8	58.0	107.7
IVQ2000 to IVQ2006	31.6	33.7	23.7	68.6	108.7
IVQ2005 to IVQ2014	26.6	4.7	-5.4	-2.6	-14.7
IVQ2006 to IVQ2014	19.1	2.6	-5.4	-8.7	-15.1
IVQ2007 to IVQ2014	15.2	3.2	-2.1	-5.6	-6.0
IVQ2011 to IVQ2014	18.1	13.5	14.2	32.9	37.6
IVQ2012 to IVQ2014	12.1	8.9	11.1	17.9	24.4
IVQ2013 to IVQ2014	5.9	4.0	4.6	5.5	7.3
IVQ2000 to IVQ2014	56.8 145.53	37.1 158.59	17.1 155.13	53.9 172.46	77.1 132.21

Source: Federal Housing Finance Agency

http://www.fhfa.gov/KeyTopics/Pages/House-Price-Index.aspx

Monthly and 12-month percentage changes of the FHFA House Price Index are in Table IIA2-3. Percentage monthly increases of the FHFA index were positive from Apr to Jul 2011 with exception of declines in May and Aug 2011 while 12-month percentage changes improved steadily from around minus 6.0 percent in Mar to May 2011 to minus 4.5 percent in Jun 2011. The FHFA house price index fell 0.6 percent in Oct 2011 and fell 3.2 percent in the 12 months ending in Oct 2011. There was significant recovery in Nov 2011 with increase in the house price index of 0.5 percent and reduction of the 12-month rate of decline to 2.4 percent. The house price index rose 0.3 percent in Dec 2011 and the 12-month percentage change improved to minus 1.4 percent. There was further improvement with revised change of minus 0.3 percent in Jan 2012 and decline of the 12-month percentage change to minus 1.3 percent. The index improved to positive change of 0.2 percent in Feb 2012 and decrease of 0.1 percent in the 12 months ending in Feb 2012. There was strong improvement in Mar 2012 with gain in prices of 0.8 percent and 1.9 percent in 12 months. The house price index of FHFA increased 0.6 percent in Apr 2012 and 2.3 percent in 12 months and improvement continued with increase of 0.7 percent in May 2012 and 3.2 percent in the 12 months ending in May 2012. Improvement consolidated with increase of 0.4 percent in Jun 2012 and 3.2 percent in 12 months. In Jul 2012, the house price index increased 0.3 percent and 3.3 percent in 12 months. Strong increase of 0.6 percent in Aug 2012 pulled the 12-month change to 4.1 percent. There was another increase of 0.5 percent in Oct 2012 and 5.0 percent in 12 months followed by increase of 0.5 percent in Nov 2012 and 5.0 percent in 12 months. The FHFA house price index increased 0.8 percent in Jan 2013 and 6.4 percent in 12 months. Improvement continued with increase of 0.6 percent in Apr 2013 and 7.1 percent in 12 months. In May 2013, the house price indexed increased 0.8 percent and 7.2 percent in 12 months. The FHFA house price index increased 0.6 percent in Jun 2013 and 7.5 percent in 12 months. In Jul 2013, the FHFA house price index increased 0.6 percent and 7.8 percent in 12 months. Improvement continued with increase of 0.3 percent in Aug 2013 and 7.5 percent in 12 months. In Sep 2013, the house price index increased 0.5 percent and 7.6 percent in 12 months. The house price index increased 0.3 percent in Oct 2013 and 7.3 percent in 12 months. In Nov 2013, the house price index increased 0.1 percent and increased 6.9 percent in 12 months. The house price index rose 0.5 percent in Dec 2013 and 6.9 percent in 12 months. Improvement continued with increase of 0.5 percent in Jan 2014 and 6.6 percent in 12 months. In Feb 2014, the house price index increased 0.5 percent and 6.6 percent in 12 months. The house price index increased 0.3 percent in Mar 2014 and 5.9 percent in 12 months. In Apr 2014, the house price index increased 0.3 percent and increased 5.6 percent in 12 months. The house price index increased 0.2 percent in May 2014 and 4.8 percent in 12 months. In Jun 2014, the house price index increased 0.5 percent and 4.7 percent in 12 months. The house price index increased 0.4 percent in Jul 2014 and 4.5 percent in 12 months. In Sep 2014, the house price index increased 0.1 percent and increased 4.2 percent in 12 months. The house price index increased 0.6 percent in Oct 2014 and 4.5 percent in 12 months. In Nov 2014, the house price index increased 0.5 percent and 4.8 percent in 12 months. The house price index increased 0.7 percent in Dec 2014 and increased 5.2 percent in 12 months. In Mar 2015, the house price index increased 0.3 percent and increased 5.2 percent in 12 months. In Apr 2015, the house price index increased 0.4 percent and 5.2 percent in 12 months. The house price index increased 0.6 percent in May 2015 and 5.6 percent in 12 months. House prices increased 0.5 percent in Jun 2015 and 5.5 percent in 12 months. The house price index increased 0.4 percent in Jul 2015 and increased 5.5 percent in 12 months. House prices increased 0.2 percent in Aug 2015 and increased 5.2 percent in 12 months. In Sep 2015, the house price index increased 0.7 percent and increased 5.9 percent in 12 months. The house price index increased 0.4 percent in Oct 2015 and increased 5.7 percent in 12 months. House prices increased 0.6 percent in Nov 2015 and increased 5.9 percent in 12 months. The house price index increased 0.4 percent in Dec 2015 and increased 5.6 percent in 12 months. House prices increased 0.6 percent in Jan 2016 and increased 6.1 percent in 12 months. The house price index increased 0.2 percent in Feb 2016 and increased 5.6 percent in 12 months. House prices increased 0.7 percent in Mar 2016 and increased 6.1 percent in 12 months. The house price index increased 0.4 percent in Apr 2016 and increased 6.0 percent in 12 months. House prices increased 0.4 percent in May 2016 and increased 5.8 percent in 12 months. The house price index increased 0.5 percent in Jun 2016 and increased 5.8 percent in 12 months. House prices increased 0.6 percent in Jul 2016 and increased 5.9 percent in 12 months. The house price index increased 0.5 percent in Aug 2016 and increased 6.3 percent in 12 months. House prices increased 0.7 percent in Sep 2016 and increased 6.3 percent in 12 months. The house price index increased 0.5 percent in Oct 2016 and increased 6.3 percent in 12 months. House prices increased 0.7 percent in Nov 2016 and increased 6.4 percent in 12 months. The house price index increased 0.4 percent in Dec 2016 and increased 6.4 percent in 12 months. House prices increased 0.2 percent in Jan 2017 and increased 6.0 percent in 12 months. In Feb 2017, the house price index increased 0.9 percent and increased 6.8 percent in 12 months. House prices increased 0.5 percent in Mar 2017 and increased 6.6 percent in 12 months. In Apr 2017, the house price index increased 0.7 percent and increased 6.9 percent in 12 months. House prices increased 0.4 percent in May 2017 and increased 6.9 percent in 12 months. The house price index increased 0.3 percent in Jun 2017 and increased 6.6 percent in 12 months. House prices increased 0.6 percent in Jul 2017 and increased 6.6 percent in 12 months. The house price index increased 0.8 percent in Aug 2017 and increased 6.9 percent in 12 months. House prices increased 0.5 percent in Sep 2017 and increased 6.7 percent in 12 months. The house price index increased 0.7 percent in Oct 2017 and increased 7.0 percent in 12 months. House prices increased 0.5 percent in Nov 2017 and increased 6.8 percent in 12 months. The house price index increased 0.5 percent in Dec 2017 and increased 6.9 percent in 12 months. The house price index increased 0.8 percent in Jan 2018 and increased 7.6 percent in 12 months. House prices increased 1.0 percent in Feb 2018 and increased 7.7 percent in 12 months. The house price index increased 0.1 percent in Mar 2018 and increased 7.3 percent in 12 months. House prices increased 0.4 percent in Apr 2018 and increased 6.8 percent in 12 months. The house price index increased 0.4 percent in May 2018 and increased 6.8 percent in May 2018. House prices increased 0.3 percent in Jun 2016 and increased 6.8 percent in 12 months. The house price index increased 0.4 percent in July 2018 and increased 6.6 percent in 12 months. House prices increased 0.3 percent in Aug 2018 and increased 6.1 percent in 12 months.

Table IIA2-3, US, FHFA House Price Index Purchases Only SA. Month and NSA 12-Month ∆%

		Month ∆% SA		12 Month ∆% NSA
8/1/2018		0.3		6.1
7/1/2018		0.4		6.6
6/1/2018		0.3		6.8
5/1/2018		0.4		6.8
4/1/2018		0.4		6.8
3/1/2018		0.1		7.3
2/1/2018		1.0		7.7
1/1/2018		0.8		7.6
12/1/2017		0.5		6.9
11/1/2017		0.5		6.8
10/1/2017		0.7		7.0
9/1/2017		0.5		6.7
8/1/2017		0.8		6.9
7/1/2017		0.6		6.6
6/1/2017		0.3		6.6
5/1/2017		0.4		6.9
4/1/2017		0.7		6.9
3/1/2017		0.5		6.6
2/1/2017		0.9		6.8
1/1/2017		0.2		6.0
12/1/2016		0.4		6.4
11/1/2016		0.7		6.4
10/1/2016		0.5		6.3
9/1/2016		0.7		6.3
8/1/2016		0.5		6.3
7/1/2016		0.6		5.9
6/1/2016		0.5		5.8
5/1/2016		0.4		5.8
4/1/2016		0.4		6.0
3/1/2016		0.7		6.1
2/1/2016		0.2		5.6
1/1/2016		0.6		6.1
12/1/2015		0.4		5.6
11/1/2015		0.6		5.9
10/1/2015		0.4		5.7
9/1/2015		0.7		5.9
8/1/2015		0.2		5.2
7/1/2015		0.4		5.5
6/1/2015		0.5		5.5
5/1/2015		0.6		5.6
4/1/2015		0.4		5.2
3/1/2015		0.3		5.2
2/1/2015		0.7		5.1
1/1/2015		0.1		4.8
12/1/2014		0.7		5.2
11/1/2014		0.5		4.8
10/1/2014		0.6		4.5
9/1/2014		0.1		4.2
8/1/2014		0.5		4.6
7/1/2014		0.4		4.5
6/1/2014		0.5		4.7
5/1/2014		0.2		4.8
4/1/2014		0.3		5.6
3/1/2014		0.3		5.9
2/1/2014		0.5		6.6
1/1/2014		0.5		6.6
12/1/2013		0.5		6.9
11/1/2013		0.1		6.9
10/1/2013		0.3		7.3
9/1/2013		0.5		7.6
8/1/2013		0.3		7.5
7/1/2013		0.6		7.8
6/1/2013		0.6		7.5
5/1/2013		0.8		7.2
4/1/2013		0.6		7.1
3/1/2013		1.0		7.2
2/1/2013		0.6		6.8
1/1/2013		0.8		6.4
12/1/2012		0.5		5.3
11/1/2012		0.5		5.0
10/1/2012		0.5		5.0
9/1/2012		0.4		3.9
8/1/2012		0.6		4.1
7/1/2012		0.3		3.3
6/1/2012		0.4		3.2
5/1/2012		0.7		3.2
4/1/2012		0.6		2.3
3/1/2012		0.8		1.9
2/1/2012		0.2		-0.1
1/1/2012		-0.3		-1.3
12/1/2011		0.3		-1.4
11/1/2011		0.5		-2.4
10/1/2011		-0.6		-3.2
9/1/2011		0.6		-2.5
8/1/2011		-0.3		-3.9
7/1/2011		0.2		-3.6
6/1/2011		0.4		-4.5
5/1/2011		-0.3		-5.9
4/1/2011		0.2		-5.7
3/1/2011		-1.0		-5.9
2/1/2011		-1.1		-5.1
1/1/2011		-0.4		-4.5
12/1/2010		-0.8		-3.9
12/1/2009		-1.0		-2.1
12/1/2008		-0.3		-10.4
12/1/2007		-0.5		-3.4
12/1/2006		0.1		2.4
12/1/2005		0.6		9.8
12/1/2004		0.9		10.2
12/1/2003		0.9		8.0
12/1/2002		0.7		7.8
12/1/2001		0.6		6.7
12/1/2000		0.6		7.1
12/1/1999		0.5		6.1
12/1/1998		0.5		5.9
12/1/1997		0.3		3.4
12/1/1996		0.3		2.7
12/1/1995		0.4		3.0
12/1/1994		0.0		2.5
12/1/1993		0.5		3.1
12/1/1992		-0.1		2.4

Source: Federal Housing Finance Agency

http://www.fhfa.gov/DataTools

The bottom part of Table IIA2-3 provides 12-month percentage changes of the FHFA house price index since 1992 when data become available for 1991. Table IIA2-4 provides percentage changes and average rates of percent change per year for various periods. Between 1992 and 2017, the FHFA house price index increased 144.9 percent at the yearly average rate of 3.6 percent. In the period 1992-2000, the FHFA house price index increased 39.3 percent at the average yearly rate of 4.2 percent. The average yearly rate of price increase accelerated to 7.5 percent in the period 2000-2003, 8.5 percent in 2000-2005 and 7.4 percent in 2000-2006. At the margin, the average rate jumped to 10.0 percent in 2003-2005 and 7.4 percent in 2003-2006. House prices measured by the FHFA house price index increased 14.3 percent at the average yearly rate of 1.2 percent between 2006 and 2017 and 17.0 percent between 2005 and 2017 at the average yearly rate of 1.3 percent.

Table IIA2-4, US, FHFA House Price Index, Percentage Change and Average Rate of Percentage Change per Year, Selected Dates 1992-2017

Dec	∆%	Average ∆% per Year
1992-2017	144.9	3.6
1992-2000	39.3	4.2
2000-2003	24.2	7.5
2000-2005	50.3	8.5
2003-2005	21.0	10.0
2005-2017	17.0	1.3
2000-2006	53.8	7.4
2003-2006	23.8	7.4
2006-2017	14.3	1.2

Source: Federal Housing Finance Agency

http://www.fhfa.gov/DataTools

The explanation of the sharp contraction of household wealth can probably be found in the origins of the financial crisis and global recession. Let V(T) represent the value of the firm’s equity at time T and B stand for the promised debt of the firm to bondholders and assume that corporate management, elected by equity owners, is acting on the interests of equity owners. Robert C. Merton (1974, 453) states:

“On the maturity date T, the firm must either pay the promised payment of B to the debtholders or else the current equity will be valueless. Clearly, if at time T, V(T) > B, the firm should pay the bondholders because the value of equity will be V(T) – B > 0 whereas if they do not, the value of equity would be zero. If V(T) ≤ B, then the firm will not make the payment and default the firm to the bondholders because otherwise the equity holders would have to pay in additional money and the (formal) value of equity prior to such payments would be (V(T)- B) < 0.”

Pelaez and Pelaez (The Global Recession Risk (2007), 208-9) apply this analysis to the US housing market in 2005-2006 concluding:

“The house market [in 2006] is probably operating with low historical levels of individual equity. There is an application of structural models [Duffie and Singleton 2003] to the individual decisions on whether or not to continue paying a mortgage. The costs of sale would include realtor and legal fees. There could be a point where the expected net sale value of the real estate may be just lower than the value of the mortgage. At that point, there would be an incentive to default. The default vulnerability of securitization is unknown.”

There are multiple important determinants of the interest rate: “aggregate wealth, the distribution of wealth among investors, expected rate of return on physical investment, taxes, government policy and inflation” (Ingersoll 1987, 405). Aggregate wealth is a major driver of interest rates (Ibid, 406). Unconventional monetary policy, with zero fed funds rates and flattening of long-term yields by quantitative easing, causes uncontrollable effects on risk taking that can have profound undesirable effects on financial stability. Excessively aggressive and exotic monetary policy is the main culprit and not the inadequacy of financial management and risk controls.

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 (Ibid). According to a subsequent restatement: “The basic idea is simply that individuals live for many years and that therefore the appropriate constraint for consumption decisions 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→∞.

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 but 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 close to zero interest rates, 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 to purchase 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 because of the penalty in the form of low interest rates and unsound credit decisions because 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).

There are significant elements of the theory of bank financial fragility of Diamond and Dybvig (1983) and Diamond and Rajan (2000, 2001a, 2001b) that help to explain the financial fragility of banks during the credit/dollar crisis (see also Diamond 2007). The theory of Diamond and Dybvig (1983) as exposed by Diamond (2007) is that banks funding with demand deposits have a mismatch of liquidity (see Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 58-66). A run occurs when too many depositors attempt to withdraw cash at the same time. All that is needed is an expectation of failure of the bank. Three important functions of banks are providing evaluation, monitoring and liquidity transformation. Banks invest in human capital to evaluate projects of borrowers in deciding if they merit credit. The evaluation function reduces adverse selection or financing projects with low present value. Banks also provide important monitoring services of following the implementation of projects, avoiding moral hazard that funds be used for, say, real estate speculation instead of the original project of factory construction. The transformation function of banks involves both assets and liabilities of bank balance sheets. Banks convert an illiquid asset or loan for a project with cash flows in the distant future into a liquid liability in the form of demand deposits that can be withdrawn immediately.

In the theory of banking of Diamond and Rajan (2000, 2001a, 2001b), the bank creates liquidity by tying human assets to capital. The collection skills of the relationship banker convert an illiquid project of an entrepreneur into liquid demand deposits that are immediately available for withdrawal. The deposit/capital structure is fragile because of the threat of bank runs. In these days of online banking, the run on Washington Mutual was through withdrawals online. A bank run can be triggered by the decline of the value of bank assets below the value of demand deposits.

Pelaez and Pelaez (Regulation of Banks and Finance 2009b, 60, 64-5) find immediate application of the theories of banking of Diamond, Dybvig and Rajan to the credit/dollar crisis after 2007. It is a credit crisis because the main issue was the deterioration of the credit portfolios of securitized banks as a result of default of subprime mortgages. It is a dollar crisis because of the weakening dollar resulting from relatively low interest rate policies of the US. It caused systemic effects that converted into a global recession not only because of the huge weight of the US economy in the world economy but also because the credit crisis transferred to the UK and Europe. Management skills or human capital of banks are illustrated by the financial engineering of complex products. The increasing importance of human relative to inanimate capital (Rajan and Zingales 2000) is revolutionizing the theory of the firm (Zingales 2000) and corporate governance (Rajan and Zingales 2001). Finance is one of the most important examples of this transformation. Profits were derived from the charter in the original banking institution. Pricing and structuring financial instruments was revolutionized with option pricing formulas developed by Black and Scholes (1973) and Merton (1973, 1974, 1998) that permitted the development of complex products with fair pricing. The successful financial company must attract and retain finance professionals who have invested in human capital, which is a sunk cost to them and not of the institution where they work.

The complex financial products created for securitized banking with high investments in human capital are based on houses, which are as illiquid as the projects of entrepreneurs in the theory of banking. The liquidity fragility of the securitized bank is equivalent to that of the commercial bank in the theory of banking (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65). Banks created off-balance sheet structured investment vehicles (SIV) that issued commercial paper receiving AAA rating because of letters of liquidity guarantee by the banks. The commercial paper was converted into liquidity by its use as collateral in SRPs at the lowest rates and minimal haircuts because of the AAA rating of the guarantor bank. In the theory of banking, default can be triggered when the value of assets is perceived as lower than the value of the deposits. Commercial paper issued by SIVs, securitized mortgages and derivatives all obtained SRP liquidity on the basis of illiquid home mortgage loans at the bottom of the pyramid. The run on the securitized bank had a clear origin (Pelaez and Pelaez, Regulation of Banks and Finance (2009b), 65):

“The increasing default of mortgages resulted in an increase in counterparty risk. Banks were hit by the liquidity demands of their counterparties. The liquidity shock extended to many segments of the financial markets—interbank loans, asset-backed commercial paper (ABCP), high-yield bonds and many others—when counterparties preferred lower returns of highly liquid safe havens, such as Treasury securities, than the risk of having to sell the collateral in SRPs at deep discounts or holding an illiquid asset. The price of an illiquid asset is near zero.”

Gorton and Metrick (2010H, 507) provide a revealing quote to the work in 1908 of Edwin R. A. Seligman, professor of political economy at Columbia University, founding member of the American Economic Association and one of its presidents and successful advocate of progressive income taxation. The intention of the quote is to bring forth the important argument that financial crises are explained in terms of “confidence” but as Professor Seligman states in reference to historical banking crises in the US, the important task is to explain what caused the lack of confidence. It is instructive to repeat the more extended quote of Seligman (1908, xi) on the explanations of banking crises:

“The current explanations may be divided into two categories. Of these the first includes what might be termed the superficial theories. Thus it is commonly stated that the outbreak of a crisis is due to lack of confidence,--as if the lack of confidence was not in itself the very thing which needs to be explained. Of still slighter value is the attempt to associate a crisis with some particular governmental policy, or with some action of a country’s executive. Such puerile interpretations have commonly been confined to countries like the United States, where the political passions of democracy have had the fullest way. Thus the crisis of 1893 was ascribed by the Republicans to the impending Democratic tariff of 1894; and the crisis of 1907 has by some been termed the ‘[Theodore] Roosevelt panic,” utterly oblivious of the fact that from the time of President Jackson, who was held responsible for the troubles of 1837, every successive crisis had had its presidential scapegoat, and has been followed by a political revulsion. Opposed to these popular, but wholly unfounded interpretations, is the second class of explanations, which seek to burrow beneath the surface and to discover the more occult and fundamental causes of the periodicity of crises.”

Scholars ignore superficial explanations in the effort to seek good and truth. The problem of economic analysis of the credit/dollar crisis is the lack of a structural model with which to attempt empirical determination of causes (Gorton and Metrick 2010SB). There would still be doubts even with a well-specified structural model because samples of economic events do not typically permit separating causes and effects. There is also confusion is separating the why of the crisis and how it started and propagated, all of which are extremely important.

In true heritage of the principles of Seligman (1908), Gorton (2009EFM) discovers a prime causal driver of the credit/dollar crisis. The objective of subprime and Alt-A mortgages was to facilitate loans to populations with modest means so that they could acquire a home. These borrowers would not receive credit because of (1) lack of funds for down payments; (2) low credit rating and information; (3) lack of information on income; and (4) errors or lack of other information. Subprime mortgage “engineering” was based on the belief that both lender and borrower could benefit from increases in house prices over the short run. The initial mortgage would be refinanced in two or three years depending on the increase of the price of the house. According to Gorton (2009EFM, 13, 16):

“The outstanding amounts of Subprime and Alt-A [mortgages] combined amounted to about one quarter of the $6 trillion mortgage market in 2004-2007Q1. Over the period 2000-2007, the outstanding amount of agency mortgages doubled, but subprime grew 800%! Issuance in 2005 and 2006 of Subprime and Alt-A mortgages was almost 30% of the mortgage market. Since 2000 the Subprime and Alt-A segments of the market grew at the expense of the Agency (i.e., the government sponsored entities of Fannie Mae and Freddie Mac) share, which fell from almost 80% (by outstanding or issuance) to about half by issuance and 67% by outstanding amount. The lender’s option to rollover the mortgage after an initial period is implicit in the subprime mortgage. The key design features of a subprime mortgage are: (1) it is short term, making refinancing important; (2) there is a step-up mortgage rate that applies at the end of the first period, creating a strong incentive to refinance; and (3) there is a prepayment penalty, creating an incentive not to refinance early.”

The prime objective of successive administrations in the US during the past 20 years and actually since the times of Roosevelt in the 1930s has been to provide “affordable” financing for the “American dream” of home ownership. The US housing finance system is mixed with public, public/private and purely private entities. The Federal Home Loan Bank (FHLB) system was established by Congress in 1932 that also created the Federal Housing Administration in 1934 with the objective of insuring homes against default. In 1938, the government created the Federal National Mortgage Association, or Fannie Mae, to foster a market for FHA-insured mortgages. Government-insured mortgages were transferred from Fannie Mae to the Government National Mortgage Association, or Ginnie Mae, to permit Fannie Mae to become a publicly-owned company. Securitization of mortgages began in 1970 with the government charter to the Federal Home Loan Mortgage Corporation, or Freddie Mac, with the objective of bundling mortgages created by thrift institutions that would be marketed as bonds with guarantees by Freddie Mac (see Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 42-8). In the third quarter of 2008, total mortgages in the US were $12,057 billion of which 43.5 percent, or $5423 billion, were retained or guaranteed by Fannie Mae and Freddie Mac (Pelaez and Pelaez, Financial Regulation after the Global Recession (2009a), 45). In 1990, Fannie Mae and Freddie Mac had a share of only 25.4 percent of total mortgages in the US. Mortgages in the US increased from $6922 billion in 2002 to $12,088 billion in 2007, or by 74.6 percent, while the retained or guaranteed portfolio of Fannie and Freddie rose from $3180 billion in 2002 to $4934 billion in 2007, or by 55.2 percent.

According to Pinto (2008) in testimony to Congress:

“There are approximately 25 million subprime and Alt-A loans outstanding, with an unpaid principal amount of over $4.5 trillion, about half of them held or guaranteed by Fannie and Freddie. Their high risk activities were allowed to operate at 75:1 leverage ratio. While they may deny it, there can be no doubt that Fannie and Freddie now own or guarantee $1.6 trillion in subprime, Alt-A and other default prone loans and securities. This comprises over 1/3 of their risk portfolios and amounts to 34% of all the subprime loans and 60% of all Alt-A loans outstanding. These 10.5 million unsustainable, nonprime loans are experiencing a default rate 8 times the level of the GSEs’ 20 million traditional quality loans. The GSEs will be responsible for a large percentage of an estimated 8.8 million foreclosures expected over the next 4 years, accounting for the failure of about 1 in 6 home mortgages. Fannie and Freddie have subprimed America.”

In perceptive analysis of growth and macroeconomics in the past six decades, Rajan (2012FA) argues that “the West can’t borrow and spend its way to recovery.” The Keynesian paradigm is not applicable in current conditions. Advanced economies in the West could be divided into those that reformed regulatory structures to encourage productivity and others that retained older structures. In the period from 1950 to 2000, Cobet and Wilson (2002) find that US productivity, measured as output/hour, grew at the average yearly rate of 2.9 percent while Japan grew at 6.3 percent and Germany at 4.7 percent (see Pelaez and Pelaez, The Global Recession Risk (2007), 135-44). In the period from 1995 to 2000, output/hour grew at the average yearly rate of 4.6 percent in the US but at lower rates of 3.9 percent in Japan and 2.6 percent in the US. Rajan (2012FA) argues that the differential in productivity growth was accomplished by deregulation in the US at the end of the 1970s and during the 1980s. In contrast, Europe did not engage in reform with the exception of Germany in the early 2000s that empowered the German economy with significant productivity advantage. At the same time, technology and globalization increased relative remunerations in highly-skilled, educated workers relative to those without skills for the new economy. It was then politically appealing to improve the fortunes of those left behind by the technological revolution by means of increasing cheap credit. As Rajan (2012FA) argues:

“In 1992, Congress passed the Federal Housing Enterprises Financial Safety and Soundness Act, partly to gain more control over Fannie Mae and Freddie Mac, the giant private mortgage agencies, and partly to promote affordable homeownership for low-income groups. Such policies helped money flow to lower-middle-class households and raised their spending—so much so that consumption inequality rose much less than income inequality in the years before the crisis. These policies were also politically popular. Unlike when it came to an expansion in government welfare transfers, few groups opposed expanding credit to the lower-middle class—not the politicians who wanted more growth and happy constituents, not the bankers and brokers who profited from the mortgage fees, not the borrowers who could now buy their dream houses with virtually no money down, and not the laissez-faire bank regulators who thought they could pick up the pieces if the housing market collapsed. The Federal Reserve abetted these shortsighted policies. In 2001, in response to the dot-com bust, the Fed cut short-term interest rates to the bone. Even though the overstretched corporations that were meant to be stimulated were not interested in investing, artificially low interest rates acted as a tremendous subsidy to the parts of the economy that relied on debt, such as housing and finance. This led to an expansion in housing construction (and related services, such as real estate brokerage and mortgage lending), which created jobs, especially for the unskilled. Progressive economists applauded this process, arguing that the housing boom would lift the economy out of the doldrums. But the Fed-supported bubble proved unsustainable. Many construction workers have lost their jobs and are now in deeper trouble than before, having also borrowed to buy unaffordable houses. Bankers obviously deserve a large share of the blame for the crisis. Some of the financial sector’s activities were clearly predatory, if not outright criminal. But the role that the politically induced expansion of credit played cannot be ignored; it is the main reason the usual checks and balances on financial risk taking broke down.”

In fact, Raghuram G. Rajan (2005) anticipated low liquidity in financial markets resulting from low interest rates before the financial crisis that caused distortions of risk/return decisions provoking the credit/dollar crisis and global recession from IVQ2007 to IIQ2009. Near zero interest rates of unconventional monetary policy induced excessive risks and low liquidity in financial decisions that were critical as a cause of the credit/dollar crisis after 2007. Rajan (2012FA) argues that it is not feasible to return to the employment and income levels before the credit/dollar crisis because of the bloated construction sector, financial system and government budgets.

Table IIA-1 shows the euphoria of prices during the housing boom and the subsequent decline. House prices rose 94.1 percent in the 10-city composite of the Case-Shiller home price index, 78.8 percent in the 20-city composite and 64.7 percent in the US national home price index between Jul 2000 and Jul 2005. Prices rose around 100 percent from Jul 2000 to Jul 2006, increasing 107.5 percent for the 10-city composite, 91.6 percent for the 20-city composite and 74.6 percent in the US national index. House prices rose 39.6 percent between Jul 2003 and Jul 2005 for the 10-city composite, 34.7 percent for the 20-city composite and 29.3 percent for the US national propelled by low fed funds rates of 1.0 percent between Jul 2003 and Jun 2004. Fed funds rates increased by 0.25 basis points at every meeting of the Federal Open Market Committee (FOMC) from Jun 2004 until Jun 2006, reaching 5.25 percent. Simultaneously, the suspension of auctions of the 30-year Treasury bond caused decline of yields of mortgage-backed securities with intended decrease in mortgage rates. Similarly, between Jul 2003 and Jul 2006, the 10-city index gained 49.1 percent; the 20-city index increased 44.4 percent; and the US national 37.1 percent. House prices have increased from Jul 2006 to Jul 2018 by 0.4 percent for the 10-city composite, increasing 3.5 percent for the 20-city composite and increasing 11.2 percent for the US national. Measuring house prices is quite difficult because of the lack of homogeneity that is typical of standardized commodities. In the 12 months ending in Jul 2018, house prices increased 5.5 percent in the 10-city composite, increasing 5.9 percent in the 20-city composite and 6.0 percent in the US national. Table IIA-1 also shows that house prices increased 108.3 percent between Jul 2000 and Jul 2018 for the 10-city composite, increasing 98.3 percent for the 20-city composite and 94.2 percent for the US national. House prices are close to the lowest level since peaks during the boom before the financial crisis and global recession. The 10-city composite increased 0.3 percent from the peak in Jun 2006 to Jul 2018 and the 20-city composite increased 3.5 percent from the peak in Jul 2006 to Jul 2018. The US national increased 11.3 percent in Jul 2018 from the peak of the 10-city composite in Jun 2006 and increased 11.2 percent from the peak of the 20-city composite in Jul 2006. The final part of Table II-2 provides average annual percentage rates of growth of the house price indexes of Standard & Poor’s Case-Shiller. The average annual growth rate between Dec 1987 and Dec 2017 for the 10-city composite was 3.9 percent and 3.6 percent for the US national. Data for the 20-city composite are available only beginning in Jan 2000. House prices accelerated in the 1990s with the average rate of the 10-city composite of 5.0 percent between Dec 1992 and Dec 2000 while the average rate for the period Dec 1987 to Dec 2000 was 3.8 percent. The average rate for the US national was 3.6 percent from Dec 1987 to Dec 2017 and 3.6 percent from Dec 1987 to Dec 2000. Although the global recession affecting the US between IVQ2007 (Dec) and IIQ2009 (Jun) caused decline of house prices of slightly above 30 percent, the average annual growth rate of the 10-city composite between Dec 2000 and Dec 2017 was 3.9 percent while the rate of the 20-city composite was 3.6 percent and 3.5 percent for the US national.

Table IIA-1, US, Percentage Changes of Standard & Poor’s Case-Shiller Home Price Indices, Not Seasonally Adjusted, ∆%

	10-City Composite	20-City Composite	US National
∆% Jul 2000 to Jul 2003	39.1	32.7	27.4
∆% Jul 2000 to Jul 2005	94.1	78.8	64.7
∆% Jul 2003 to Jul 2005	39.6	34.7	29.3
∆% Jul 2000 to Jul 2006	107.5	91.6	74.6
∆% Jul 2003 to Jul 2006	49.1	44.4	37.1
∆% Jul 2005 to Jul 2018	7.3	10.9	17.9
∆% Jul 2006 to Jul 2018	0.4	3.5	11.2
∆% Jul 2009 to Jul 2018	45.6	48.1	36.2
∆% Jul 2010 to Jul 2018	39.9	43.6	39.2
∆% Jul 2011 to Jul 2018	45.2	49.6	44.3
∆% Jul 2012 to Jul 2018	44.4	47.8	42.3
∆% Jul 2013 to Jul 2018	28.7	31.6	29.7
∆% Jul 2014 to Jul 2018	20.6	23.4	22.8
∆% Jul 2015 to Jul 2018	15.4	17.6	17.6
∆% Jul 2016 to Jul 2018	10.8	12.0	12.1
∆% Jul 2017 to Jul 2018	5.5	5.9	6.0
∆% Jul 2000 to Jul 2018	108.3	98.3	94.2
∆% Peak Jun 2006 Jul 2018	0.3		11.3
∆% Peak Dec 2006 to Jul 2018		3.5	11.2
Average ∆% Dec 1987-Dec 2017	3.9	NA	3.6
Average ∆% Dec 1987-Dec 2000	3.8	NA	3.6
Average ∆% Dec 1992-Dec 2000	5.0	NA	4.5
Average ∆% Dec 2000-Dec 2017	3.9	3.6	3.5

Source: https://us.spindices.com/index-family/real-estate/sp-corelogic-case-shiller

Price increases measured by the Case-Shiller house price indices show in data for Jul 2018 that “home prices continued their rise across the country over the last 12 months” (https://my.spindices.com/index-family/real-estate/sp-corelogic-case-shiller). Monthly house prices increased sharply from Feb 2013 to Jan 2014 for both the 10- and 20-city composites, as shown in Table IIA-2. In Jan 2013, the seasonally adjusted 10-city composite increased 0.8 percent and the 20-city increased 0.8 percent while the 10-city not seasonally adjusted changed 0.0 percent and the 20-city changed 0.0 percent. House prices increased at high monthly percentage rates from Feb to Nov 2013. House prices seasonally adjusted declined in most months for both the 10-city and 20-city Case-Shiller composites from Dec 2010 to Jan 2012, as shown in Table IIA-2. The most important seasonal factor in house prices is school changes for wealthier homeowners with more expensive houses. Without seasonal adjustment, house prices fell from Dec 2010 throughout Mar 2011 and then increased in every month from Apr to Aug 2011 but fell in every month from Sep 2011 to Feb 2012. The not seasonally adjusted index registers decline in Mar 2012 of 0.1 percent for the 10-city composite and is flat for the 20-city composite. Not seasonally adjusted house prices increased 1.4 percent in Apr 2012 and at high monthly percentage rates until Sep 2012. House prices not seasonally adjusted stalled from Oct 2012 to Jan 2013 and surged from Feb to Sep 2013, decelerating in Oct 2013-Feb 2014. House prices grew at fast rates in Mar 2014. The 10-city NSA index increased 0.2 percent in Jul 2018 and the 20-city increased 0.3 percent. The 10-city SA changed 0.0 percent in Jul 2018 and the 20-city composite SA increased 0.1 percent. Declining house prices cause multiple adverse effects of which two are quite evident. (1) There is a disincentive to buy houses in continuing price declines. (2) More mortgages could be losing fair market value relative to mortgage debt. Another possibility is a wealth effect that consumers restrain purchases because of the decline of their net worth in houses.

Table IIA-2, US, Monthly Percentage Change of S&P Corelogic Case-Shiller Home Price Indices, Seasonally Adjusted and Not Seasonally Adjusted, ∆%

	10-City Composite SA	10-City Composite NSA	20-City Composite SA	20-City Composite NSA
July 2018	0.0	0.2	0.1	0.3
June 2018	0.1	0.5	0.2	0.6
May 2018	0.1	0.6	0.2	0.8
April 2018	-0.3	0.7	-0.1	0.9
March 2018	0.8	0.9	0.8	1.0
February 2018	0.8	0.7	0.8	0.7
January 2018	0.7	0.3	0.7	0.3
December 2017	0.6	0.2	0.6	0.2
November 2017	0.7	0.3	0.7	0.2
October 2017	0.7	0.2	0.7	0.2
September 2017	1.2	0.4	1.1	0.4
August 2017	-0.1	0.4	0.0	0.4
July 2017	0.5	0.8	0.5	0.7
June 2017	0.2	0.6	0.3	0.7
May 2017	0.3	0.8	0.3	0.9
April 2017	-0.2	0.8	0.0	1.0
March 2017	0.7	0.8	0.8	1.0
February 2017	0.4	0.3	0.5	0.4
January 2017	0.7	0.3	0.7	0.2
December 2016	0.7	0.2	0.7	0.2
November 2016	0.6	0.2	0.7	0.2
October 2016	0.4	-0.1	0.5	0.0
September 2016	0.7	0.0	0.8	0.1
August 2016	-0.1	0.3	0.0	0.3
July 2016	0.2	0.5	0.3	0.6
June 2016	0.2	0.7	0.3	0.8
May 2016	0.1	0.8	0.2	0.9
April 2016	0.1	1.0	0.2	1.1
March 2016	0.7	0.9	0.7	1.0
February 2016	0.4	0.2	0.4	0.2
January 2016	0.4	-0.1	0.5	0.0
December 2015	0.4	-0.1	0.5	0.0
November 2015	0.5	0.0	0.6	0.0
October 2015	0.5	-0.1	0.6	0.0
September 2015	0.7	0.1	0.8	0.1
August 2015	0.0	0.2	0.0	0.3
July 2015	0.2	0.6	0.3	0.7
June 2015	0.2	0.9	0.3	1.0
May 2015	0.3	1.0	0.3	1.1
April 2015	0.2	1.1	0.2	1.1
March 2015	0.5	0.8	0.6	0.9
February 2015	0.8	0.5	0.8	0.5
January 2015	0.5	-0.1	0.5	-0.1
December 2014	0.6	0.0	0.6	0.0
November 2014	0.4	-0.3	0.4	-0.2
October 2014	0.5	-0.1	0.5	-0.1
September 2014	0.4	-0.1	0.5	-0.1
August 2014	-0.1	0.2	0.0	0.2
July 2014	0.0	0.6	0.1	0.6
June 2014	0.2	1.0	0.2	1.0
May 2014	0.1	1.1	0.2	1.1
April 2014	0.3	1.1	0.4	1.2
March 2014	0.5	0.8	0.5	0.9
February 2014	0.4	0.0	0.4	0.0
January 2014	0.6	-0.1	0.6	-0.1
December 2013	0.5	-0.1	0.5	-0.1
November 2013	0.7	0.0	0.7	-0.1
October 2013	0.9	0.2	0.9	0.2
September 2013	1.2	0.7	1.2	0.7
August 2013	1.1	1.3	1.1	1.3
July 2013	1.1	1.9	1.1	1.8
June 2013	1.2	2.2	1.1	2.2
May 2013	1.4	2.5	1.4	2.5
April 2013	1.9	2.6	1.9	2.6
March 2013	1.0	1.3	0.9	1.3
February 2013	0.9	0.3	0.9	0.2
January 2013	0.8	0.0	0.8	0.0
December 2012	0.9	0.2	0.9	0.2
November 2012	0.6	-0.3	0.7	-0.2
October 2012	0.6	-0.2	0.7	-0.1
September 2012	0.6	0.3	0.6	0.3
August 2012	0.6	0.8	0.7	0.9
July 2012	0.6	1.5	0.7	1.6
June 2012	1.0	2.1	1.1	2.3
May 2012	1.0	2.2	1.1	2.4
April 2012	0.8	1.4	0.9	1.4
March 2012	-0.2	-0.1	-0.3	0.0
February 2012	-0.2	-0.9	-0.1	-0.8
January 2012	-0.3	-1.1	-0.2	-1.0
December 2011	-0.5	-1.2	-0.4	-1.1
November 2011	-0.6	-1.4	-0.5	-1.3
October 2011	-0.5	-1.3	-0.5	-1.4
September 2011	-0.3	-0.6	-0.4	-0.7
August 2011	-0.2	0.1	-0.2	0.1
July 2011	0.0	0.9	0.0	1.0
June 2011	-0.1	1.0	-0.1	1.2
May 2011	-0.2	1.0	-0.2	1.0
April 2011	0.1	0.6	0.2	0.6
March 2011	-0.9	-1.0	-1.1	-1.0
February 2011	-0.4	-1.3	-0.4	-1.2
January 2011	-0.3	-1.1	-0.3	-1.1
December 2010	-0.2	-0.9	-0.2	-1.0

Source: https://us.spindices.com/index-family/real-estate/sp-corelogic-case-shiller

Table IIA-4 summarizes the brutal drops in assets and net worth of US households and nonprofit organizations from 2007 to 2008 and 2009. Total assets fell $9.6 trillion or 11.6 percent from 2007 to 2008 and $8.1 trillion or 9.8 percent to 2009. Net worth fell $9.5 trillion from 2007 to 2008 or 13.9 percent and $7.9 trillion to 2009 or 11.6 percent. Subsidies to housing prolonged over decades together with interest rates at 1.0 percent from Jun 2003 to Jun 2004 inflated valuations of real estate and risk financial assets such as equities. The increase of fed funds rates by 25 basis points until 5.25 percent in Jun 2006 reversed carry trades through exotic vehicles such as subprime adjustable rate mortgages (ARM) and world financial markets. 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 to purchase 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).

Table IIA-4, Difference of Balance Sheet of Households and Nonprofit Organizations, Billions of Dollars from 2007 to 2008 and 2009

	2007	2008	Change to 2008	2009	Change to 2009
A	82,929.2	73,344.8	-9,584.4	74,800.4	-8,128.8
Non FIN	27,989.1	24,354.7	-3,634.4	23,456.0	-4,533.1
RE	23,192.4	19,435.0	-3,757.4	18,519.5	-4,672.9
FIN	54,940.1	48,990.1	-5,950.0	51,344.4	-3,595.7
LIAB	14,522.0	14,436.4	-85.6	14,310.4	-211.6
NW	68,407.2	58,908.4	-9,498.8	60,490.0	-7,917.2

A: Assets; Non FIN: Nonfinancial Assets; RE: Real Estate; FIN: Financial Assets; LIAB: Liabilities; NW: Net Worth

Source: Board of Governors of the Federal Reserve System. 2018. Flow of funds, balance sheets and integrated macroeconomic accounts: second quarter 2018. Washington, DC, Federal Reserve System, Sep 20. https://www.federalreserve.gov/releases/z1/current/default.htm

I IMF View of World Economy and Finance. The International Financial Institutions (IFI) consist of the International Monetary Fund, World Bank Group, Bank for International Settlements (BIS) and the multilateral development banks, which are the European Investment Bank, Inter-American Development Bank and the Asian Development Bank (Pelaez and Pelaez, International Financial Architecture (2005), The Global Recession Risk (2007), 8-19, 218-29, Globalization and the State, Vol. II (2008b), 114-48, Government Intervention in Globalization (2008c), 145-54). There are four types of contributions of the IFIs:

1. Safety Net. The IFIs contribute to crisis prevention and crisis resolution.

i. Crisis Prevention. An important form of contributing to crisis prevention is by surveillance of the world economy and finance by regions and individual countries. The IMF and World Bank conduct periodic regional and country evaluations and recommendations in consultations with member countries and jointly with other international organizations. The IMF and the World Bank have been providing the Financial Sector Assessment Program (FSAP) by monitoring financial risks in member countries that can serve to mitigate them before they can become financial crises.

ii. Crisis Resolution. The IMF jointly with other IFIs provides assistance to countries in resolution of those crises that do occur. Currently, the IMF is cooperating with the government of Greece, European Union and European Central Bank in resolving the debt difficulties of Greece as it has done in the past in numerous other circumstances. Programs with other countries involved in the European debt crisis may also be developed.

2. Surveillance. The IMF conducts surveillance of the world economy, finance and public finance with continuous research and analysis. Important documents of this effort are the World Economic Outlook (http://www.imf.org/external/ns/cs.aspx?id=29), Global Financial Stability Report (http://www.imf.org/external/pubs/ft/gfsr/index.htm) and Fiscal Monitor (http://www.imf.org/external/ns/cs.aspx?id=262).

3. Infrastructure and Development. The IFIs also engage in infrastructure and development, in particular, the World Bank Group and the multilateral development banks.

4. Soft Law. Significant activity by IFIs has consisted of developing standards and codes under multiple forums. It is easier and faster to negotiate international agreements under soft law that are not binding but can be very effective (on soft law see Pelaez and Pelaez, Globalization and the State, Vol. II (2008c), 114-25). These norms and standards can solidify world economic and financial arrangements.

The objective of this section is to analyze current projections of the IMF database for the most important indicators.

Table I-1 is constructed with the database of the IMF (http://www.imf.org/external/ns/cs.aspx?id=29) to show GDP in dollars in 2017 and the growth rate of real GDP of the world and selected regional countries from 2017 to 2020. The data illustrate the concept often repeated of “two-speed recovery” of the world economy from the recession of 2007 to 2009. The IMF has changed its forecast of the world economy to 3.7 percent in 2017 and maintaining this rate of 3.7 percent in all years from 2017 to 2020. Slow-speed recovery occurs in the “major advanced economies” of the G7 that account for $36,867 billion of world output of $80,051 billion, or 46.1 percent, but are projected to grow at much lower rates than world output, 2.0 percent on average from 2017 to 2020, in contrast with 3.7 percent for the world as a whole. While the world would grow 15.6 percent in the four years from 2017 to 2020, the G7 as a whole would grow 8.0 percent. The difference in dollars of 2017 is high: growing by 15.6 percent would add around $12.5 trillion of output to the world economy, or roughly, over two times the output of the economy of Japan of $4,873 billion but growing by 8.0 percent would add $6.4 trillion of output to the world, or somewhat higher than the output of Japan in 2017. The “two speed” concept is in reference to the growth of the 150 countries labeled as emerging and developing economies (EMDE) with joint output in 2017 of $31,733 billion, or 39.6 percent of world output. The EMDEs would grow cumulatively 20.4 percent or at the average yearly rate of 4.8 percent, contributing $6.5 trillion from 2017 to 2019 or the equivalent of somewhat more than one half the GDP of $12,015 billion of China in 2017. The final four countries in Table I-1 often referred as BRIC (Brazil, Russia, India, China), are large, rapidly growing emerging economies. Their combined output in 2017 adds to $18,249 billion, or 22.8 percent of world output, which is equivalent to 49.5 percent of the combined output of the major advanced economies of the G7.

Table I-1, IMF World Economic Outlook Database Projections of Real GDP Growth

	GDP USD Billions 2017	Real GDP ∆% 2017	Real GDP ∆% 2018	Real GDP ∆% 2019	Real GDP ∆% 2020
World	80,051	3.7	3.7	3.7	3.7
G7	36,867	2.1	2.2	2.0	1.5
Canada	1,653	3.0	2.1	2.0	1.9
France	2,588	2.3	1.6	1.6	1.6
DE	3,701	2.5	1.9	1.9	1.6
Italy	1,939	1.5	1.2	1.0	0.9
Japan	4,873	1.7	1.1	0.9	0.3
UK	2,628	1.7	1.4	1.5	1.5
US	19,485	2.2	2.9	2.5	1.8
Euro Area	12,633	2.4	2.0	1.9	1.7
DE	3,701	2.5	1.9	1.9	1.6
France	2,588	2.3	1.6	1.6	1.6
Italy	1,939	1.5	1.2	1.0	0.9
POT	218	2.7	2.3	1.8	1.5
Ireland	331	7.2	4.7	4.0	3.5
Greece	201	1.4	2.0	2.4	2.2
Spain	1,314	3.0	2.7	2.2	1.9
EMDE	31,733	4.7	4.7	4.7	4.9
Brazil	2,055	1.0	1.4	2.4	2.3
Russia	1,577	1.5	1.7	1.8	1.8
India	2,602	6.7	7.3	7.4	7.7
China	12,015	6.9	6.6	6.2	6.2

Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries); POT: Portugal

Source: IMF World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

Continuing high rates of unemployment in advanced economies constitute another characteristic of the database of the WEO (https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx). Table I-2 is constructed with the WEO database to provide rates of unemployment from 2016 to 2020 for major countries and regions. In fact, unemployment rates for 2016 in Table I-2 are high for all countries: unusually high for countries with high rates most of the time and unusually high for countries with low rates most of the time. The rates of unemployment are particularly high in 2016 for the countries with sovereign debt difficulties in Europe: 11.1 percent for Portugal (POT), 8.4 percent for Ireland, 23.6 percent for Greece, 19.6 percent for Spain and 11.7 percent for Italy, which is lower but still high. The G7 rate of unemployment is 5.4 percent. Unemployment rates are not likely to decrease substantially if relative slow cyclical growth persists in advanced economies.

Table I-2, IMF World Economic Outlook Database Projections of Unemployment Rate as Percent of Labor Force

	% Labor Force 2016	% Labor Force 2017	% Labor Force 2018	% Labor Force 2019	% Labor Force 2020
World	NA	NA	NA	NA	NA
G7	5.4	5.0	4.6	4.4	4.4
Canada	7.0	6.3	6.1	6.2	6.2
France	10.1	9.4	8.8	8.5	8.1
DE	4.2	3.8	3.5	3.4	3.3
Italy	11.7	11.3	10.8	10.5	10.3
Japan	3.1	2.9	2.9	2.9	2.9
UK	4.9	4.4	4.1	4.2	4.5
US	4.9	4.4	3.8	3.5	3.4
Euro Area	10.0	9.1	8.3	8.0	7.7
DE	4.2	3.8	3.5	3.4	3.3
France	10.1	9.4	8.8	8.5	8.1
Italy	11.7	11.3	10.8	10.5	10.3
POT	11.1	8.9	7.0	6.7	6.5
Ireland	8.4	6.7	5.3	5.1	5.1
Greece	23.6	21.5	19.9	18.1	16.3
Spain	19.6	17.2	15.6	14.7	14.3
EMDE	NA	NA	NA	NA	NA
Brazil	11.3	12.8	11.8	10.7	10.2
Russia	5.5	5.2	5.5	5.3	5.1
India	NA	NA	NA	NA	NA
China	4.0	3.9	4.0	4.0	4.0

Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries)

Source: IMF World Economic Outlook

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

The database of the WEO (https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx) is used to construct the debt/GDP ratios of regions and countries in Table I-3. The concept used is general government debt, which consists of central government debt, such as Treasury debt in the US, and all state and municipal debt. Net debt is provided for all countries except for the only available gross debt for China, Russia and India. The net debt/GDP ratio of the G7 decreases from 87.9 in 2016 to 84.1 in 2020. G7 debt is pulled by the high debt of Japan that reaches 154.2 percent of GDP in 2020. US general government debt decreases from 81.2 percent of GDP in 2016 to 79.0 percent of GDP in 2020. Debt/GDP ratios of countries with sovereign debt difficulties in Europe are particularly worrisome. General government net debts of Italy, Greece and Portugal exceed 100 percent of GDP or are expected to exceed 100 percent of GDP by 2020. The only country in that group with relatively lower debt/GDP ratio is Spain with 86.2 in 2016, decreasing to 82.7 in 2020. Ireland’s debt/GDP ratio decreases from 64.4 in 2016 to 53.4 in 2020. Fiscal adjustment, voluntary or forced by defaults, may squeeze further economic growth and employment in many countries as analyzed by Blanchard (2012WEOApr). Defaults could feed through exposures of banks and investors to financial institutions and economies in countries with sounder fiscal affairs.

Table I-3, IMF World Economic Outlook Database Projections, General Government Net Debt as Percent of GDP

	% Debt/ GDP 2016	% Debt/ GDP 2017	% Debt/ GDP 2018	% Debt/ GDP 2019	% Debt/ GDP 2020
World	NA	NA	NA	NA	NA
G7	87.9	85.8	84.8	84.2	84.1
Canada	28.5	27.7	27.7	27.2	26.9
France	87.5	87.5	87.4	87.2	86.3
DE	48.2	44.9	41.5	38.3	35.6
Italy	119.5	119.5	118.3	117.0	116.2
Japan	152.8	154.9	155.7	154.8	154.2
UK	78.8	77.9	78.0	77.6	77.1
US	81.2	78.8	77.7	77.9	79.0
Euro Area	73.7	71.8	69.5	67.7	65.9
DE	48.2	44.9	41.5	38.3	35.6
France	87.5	87.5	87.4	87.2	86.3
Italy	119.5	119.5	118.3	117.0	116.2
POT	113.1	110.8	107.6	104.6	101.7
Ireland	64.4	59.2	56.9	55.1	53.4
Greece*	183.5	181.8	188.1	176.9	169.3
Spain	86.2	85.0	84.3	83.4	82.7
EMDE*	46.9	48.6	50.5	51.8	53.1
Brazil	46.2	51.6	56.5	59.9	63.4
Russia*	16.1	15.5	15.3	15.4	15.9
India*	69.5	71.2	69.6	68.1	66.5
China*	44.2	47.0	50.1	53.9	57.1

Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries); *General Government Gross Debt as percent of GDP

Source: IMF World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

The primary balance consists of revenues less expenditures but excluding interest revenues and interest payments. It measures the capacity of a country to generate sufficient current revenue to meet current expenditures. There are various countries with primary surpluses in 2016: Germany 1.8 percent and Italy 1.3 percent. There are also various countries with expected primary surpluses by 2020: Portugal 2.9 percent, Italy 1.6 percent and so on. Most countries in Table I-4 face significant fiscal adjustment in the future without “fiscal space.” Investors in government securities may require higher yields when the share of individual government debts hit saturation shares in portfolios. The tool of analysis of Cochrane (2011Jan, 27, equation (16)) is the government debt valuation equation:

(M_t + B_t)/P_t = E_t∫(1/R_{t, t+τ})s_t+τdτ (1)

Equation (1) expresses the monetary, M_t, and debt, B_t, liabilities of the government, divided by the price level, P_t, in terms of the expected value discounted by the ex-post rate on government debt, R_{t, t+τ}, of the future primary surpluses s_t+τ, which are equal to T_t+τ – G_t+τ or difference between taxes, T, and government expenditures, G. Cochrane (2010A) provides the link to a web appendix demonstrating that it is possible to discount by the ex post R_{t, t+τ}. Expectations by investors of future primary balances of indebted governments may be less optimistic than those in Table I-4 because of government revenues constrained by low growth and government expenditures rigid because of entitlements. Political realities may also jeopardize structural reforms and fiscal austerity.

Table I-4, IMF World Economic Outlook Database Projections of General Government Primary Net Lending/Borrowing as Percent of GDP

	% GDP 2016	% GDP 2017	% GDP 2018	% GDP 2019	% GDP 2020
World	NA	NA	NA	NA	NA
G7	-1.6	-1.5	-1.8	-1.8	-1.5
Canada	-0.4	-0.8	-0.9	-0.6	-0.5
France	-1.8	-0.9	-0.9	-1.1	-0.5
DE	1.8	1.8	2.2	2.1	1.9
Italy	1.3	1.3	1.8	1.7	1.6
Japan	-2.9	-3.8	-3.3	-2.6	-2.0
UK	-1.4	0.0	-0.3	-0.2	0.1
US	-2.3	-2.2	-2.9	-3.0	-2.7
Euro Area	0.4	0.9	1.0	1.0	1.0
DE	1.8	1.8	2.2	2.1	1.9
France	-1.8	-0.9	-0.9	-1.1	-0.5
Italy	1.3	1.3	1.8	1.7	1.6
POT	1.9	0.7	2.6	2.9	2.9
Ireland	1.6	1.6	1.4	1.5	1.7
Greece	3.9	4.2	3.5	3.5	3.5
Spain	-2.0	-0.8	-0.5	-0.2	-0.3
EMDE	-3.1	-2.5	-1.9	-1.7	-1.6
Brazil	-2.5	-1.7	-2.4	-1.8	-1.1
Russia	-3.2	-1.0	2.2	2.4	1.9
India	-2.5	-2.3	-1.7	-1.6	-1.6
China	-2.9	-3.0	-3.1	-3.3	-3.2

*General Government Net Lending/Borrowing

Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries)

Source: IMF World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

The database of the World Economic Outlook of the IMF (http://www.imf.org/external/pubs/ft/weo/2018/01/weodata/index.aspx) is used to obtain government net lending/borrowing as percent of GDP in Table I-5. Interest on government debt is added to the primary balance to obtain overall government fiscal balance in Table I-5. For highly indebted countries there is an even tougher challenge of fiscal consolidation. Adverse expectations on the success of fiscal consolidation may drive up yields on government securities that could create hurdles to adjustment, growth and employment.

Table I-5, IMF World Economic Outlook Database Projections of General Government Net Lending/Borrowing as Percent of GDP

	% GDP 2016	% GDP 2017	% GDP 2018	% GDP 2019	% GDP 2020
World	NA	NA	NA	NA	NA
G7	-3.1	-3.0	-3.2	-3.3	-3.1
Canada	-1.1	-1.1	-1.2	-1.1	-1.0
France	-3.6	-2.6	-2.6	-2.8	-2.2
DE	0.9	1.0	1.5	1.5	1.3
Italy	-2.5	-2.3	-1.7	-1.7	-1.9
Japan	-3.7	-4.3	-3.7	-2.8	-2.1
UK	-2.9	-1.8	-2.0	-1.7	-1.5
US	-3.9	-3.8	-4.7	-5.0	-4.8
Euro Area	-1.5	-0.9	-0.6	-0.6	-0.5
DE	0.9	1.0	1.5	1.5	1.3
France	-3.6	-2.6	-2.6	-2.8	-2.2
Italy	-2.5	-2.3	-1.7	-1.7	-1.9
POT	-2.0	-3.0	-0.7	-0.3	-0.2
Ireland	-0.5	-0.3	-0.2	-0.1	0.2
Greece	0.7	1.1	0.5	0.0	0.2
Spain	-4.5	-3.1	-2.7	-2.3	-2.4
EMDE	-4.8	-4.3	-3.8	-3.8	-3.7
Brazil	-9.0	-7.8	-8.6	-8.0	-7.8
Russia	-3.6	-1.5	1.6	1.8	1.3
India	-7.2	-7.2	-6.6	-6.5	-6.3
China	-3.7	-3.9	-4.1	-4.4	-4.3

Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries)

Source: IMF World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

There were some hopes that the sharp contraction of output during the global recession would eliminate current account imbalances. Table I-6 constructed with the database of the WEO (https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx) shows that external imbalances have been maintained in the form of current account deficits and surpluses. China’s current account surplus is 1.8 percent of GDP for 2016 and is projected to stabilize at 0.7 percent of GDP in 2020. At the same time, the current account deficit of the US is 2.3 percent of GDP in 2016 and is projected at 3.2 percent of GDP in 2020. The current account surplus of Germany is 8.5 percent for 2016 and remains at a high 7.7 percent of GDP in 2020. Japan’s current account surplus is 3.9 percent of GDP in 2016 and increases to 4.1 percent of GDP in 2020.

Table I-6, IMF World Economic Outlook Databank Projections, Current Account of Balance of Payments as Percent of GDP

	% CA/ GDP 2016	% CA/ GDP 2017	% CA/ GDP 2018	% CA/ GDP 2019	% CA/ GDP 2020
World	NA	NA	NA	NA	NA
G7	-0.3	-0.2	-0.4	-0.6	-0.6
Canada	-3.2	-2.9	-3.0	-2.5	-2.2
France	-0.8	-0.6	-0.9	-0.7	-0.5
DE	8.5	7.9	8.1	7.9	7.7
Italy	2.6	2.8	2.0	1.6	1.4
Japan	3.9	4.0	3.6	3.8	4.1
UK	-5.2	-3.8	-3.5	-3.2	-3.0
US	-2.3	-2.3	-2.5	-3.0	-3.2
Euro Area	3.6	3.5	3.0	2.9	2.9
DE	8.5	7.9	8.1	7.9	7.7
France	-0.8	-0.6	-0.9	-0.7	-0.5
Italy	2.6	2.8	2.0	1.6	1.4
POT	0.6	0.5	0.0	-0.3	-0.6
Ireland	-4.2	8.5	7.4	6.7	6.1
Greece	-1.1	-0.8	-0.8	-0.4	-0.3
Spain	1.9	1.9	1.2	1.2	1.3
EMDE	-0.2	0.0	0.0	0.0	-0.2
Brazil	-1.3	-0.5	-1.3	-1.6	-1.7
Russia	1.9	2.2	6.2	5.2	4.3
India	-0.6	-1.9	-3.0	-2.5	-2.4
China	1.8	1.4	0.7	0.7	0.7

Notes; DE: Germany; EMDE: Emerging and Developing Economies (150 countries)

Source: IMF World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

The G7 meeting in Washington on Apr 21, 2006 of finance ministers and heads of central bank governors of the G7 established the “doctrine of shared responsibility” (G7 2006Apr):

“We, Ministers and Governors, reviewed a strategy for addressing global imbalances. We recognized that global imbalances are the product of a wide array of macroeconomic and microeconomic forces throughout the world economy that affect public and private sector saving and investment decisions. We reaffirmed our view that the adjustment of global imbalances:

• Is shared responsibility and requires participation by all regions in this global process;
• Will importantly entail the medium-term evolution of private saving and investment across countries as well as counterpart shifts in global capital flows; and
• Is best accomplished in a way that maximizes sustained growth, which requires strengthening policies and removing distortions to the adjustment process.

In this light, we reaffirmed our commitment to take vigorous action to address imbalances. We agreed that progress has been, and is being, made. The policies listed below not only would be helpful in addressing imbalances, but are more generally important to foster economic growth.

• In the United States, further action is needed to boost national saving by continuing fiscal consolidation, addressing entitlement spending, and raising private saving.
• In Europe, further action is needed to implement structural reforms for labor market, product, and services market flexibility, and to encourage domestic demand led growth.
• In Japan, further action is needed to ensure the recovery with fiscal soundness and long-term growth through structural reforms.

Others will play a critical role as part of the multilateral adjustment process.

• In emerging Asia, particularly China, greater flexibility in exchange rates is critical to allow necessary appreciations, as is strengthening domestic demand, lessening reliance on export-led growth strategies, and actions to strengthen financial sectors.

• In oil-producing countries, accelerated investment in capacity, increased economic diversification, enhanced exchange rate flexibility in some cases.

• Other current account surplus countries should encourage domestic consumption and investment, increase micro-economic flexibility and improve investment climates.

We recognized the important contribution that the IMF can make to multilateral surveillance.”

The concern at that time was that fiscal and current account global imbalances could result in disorderly correction with sharp devaluation of the dollar after an increase in premiums on yields of US Treasury debt (see Pelaez and Pelaez, The Global Recession Risk (2007)). The IMF was entrusted with monitoring and coordinating action to resolve global imbalances. The G7 was eventually broadened to the formal G20 in the effort to coordinate policies of countries with external surpluses and deficits.

The database of the WEO (http://www.imf.org/external/pubs/ft/weo/2018/01/weodata/index.aspx) is used to construct Table I-7 with fiscal and current account imbalances projected for 2016 and 2018. The WEO finds the need to rebalance external and domestic demand (IMF 2011WEOSep xvii):

“Progress on this front has become even more important to sustain global growth. Some emerging market economies are contributing more domestic demand than is desirable (for example, several economies in Latin America); others are not contributing enough (for example, key economies in emerging Asia). The first set needs to restrain strong domestic demand by considerably reducing structural fiscal deficits and, in some cases, by further removing monetary accommodation. The second set of economies needs significant currency appreciation alongside structural reforms to reduce high surpluses of savings over investment. Such policies would help improve their resilience to shocks originating in the advanced economies as well as their medium-term growth potential.”

The IMF (2012WEOApr, XVII) explains decreasing importance of the issue of global imbalances as follows:

“The latest developments suggest that global current account imbalances are no longer expected to widen again, following their sharp reduction during the Great Recession. This is largely because the excessive consumption growth that characterized economies that ran large external deficits prior to the crisis has been wrung out and has not been offset by stronger consumption in .surplus economies. Accordingly, the global economy has experienced a loss of demand and growth in all regions relative to the boom years just before the crisis. Rebalancing activity in key surplus economies toward higher consumption, supported by more market-determined exchange rates, would help strengthen their prospects as well as those of the rest of the world.”

The IMF (http://www.imf.org/external/pubs/ft/weo/2014/02/pdf/c4.pdf) analyzes global imbalances as:

• Global current account imbalances have narrowed by more than a third from

their peak in 2006. Key imbalances—the large deficit of the United States and

the large surpluses of China and Japan—have more than halved.

• The narrowing in imbalances has largely been driven by demand contraction

(“expenditure reduction”) in deficit economies.

• Exchange rate adjustment has facilitated rebalancing in China and the United

States, but in general the contribution of exchange rate changes (“expenditure

switching”) to current account adjustment has been relatively modest.

• The narrowing of imbalances is expected to be durable, as domestic demand in

deficit economies is projected to remain well below pre-crisis trends.

• Since flow imbalances have narrowed but not reversed, net creditor and debtor

positions have widened further. Weak growth has also contributed to still high

ratios of net external liabilities to GDP in some debtor economies.

• Risks of a disruptive adjustment in global current account balances have

decreased, but global demand rebalancing remains a policy priority. Stronger

external demand will be instrumental for reviving growth in debtor countries and

reducing their net external liabilities.”

Table I-7, Fiscal Deficit, Current Account Deficit and Government Debt as % of GDP and 2016 Dollar GDP

	GDP $B 2017	FD %GDP 2017	CAD %GDP 2017	Debt %GDP 2017	FD%GDP 2019	CAD%GDP 2019	Debt %GDP 2019
US	19485	-2.2	-2.3	78.8	-3.0	-3.0	77.9
Japan	4873	-3.8	4.0	154.9	-2.6	3.8	154.8
UK	2628	0.0	-3.8	77.9	-0.2	-3.2	77.6
Euro	12633	0.9	3.5	71.8	1.0	2.9	67.7
Ger	3701	1.8	7.9	44.9	2.1	7.9	38.3
France	2588	-0.9	-0.6	87.5	-1.1	-0.7	87.2
Italy	1939	1.3	2.8	119.5	1.7	1.6	117.0
Can	1653	-0.8	-2.9	27.7	-0.6	-2.5	27.2
China	12015	-3.0	1.4	47.0	-3.3	0.7	53.9
Brazil	2055	-1.7	-0.5	51.6	-1.8	-1.6	59.9

Note: GER = Germany; Can = Canada; FD = fiscal deficit; CAD = current account deficit

FD is primary except total for China; Debt is net except gross for China

Source: IMF World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

Brazil faced in the debt crisis of 1982 a more complex policy mix. Between 1977 and 1983, Brazil’s terms of trade, export prices relative to import prices, deteriorated 47 percent and 36 percent excluding oil (Pelaez 1987, 176-79; Pelaez 1986, 37-66; see Pelaez and Pelaez, The Global Recession Risk (2007), 178-87). Brazil had accumulated unsustainable foreign debt by borrowing to finance balance of payments deficits during the 1970s. Foreign lending virtually stopped. The German mark devalued strongly relative to the dollar such that Brazil’s products lost competitiveness in Germany and in multiple markets in competition with Germany. The resolution of the crisis was devaluation of the Brazilian currency by 30 percent relative to the dollar and subsequent maintenance of parity by monthly devaluation equal to inflation and indexing that resulted in financial stability by parity in external and internal interest rates avoiding capital flight. With a combination of declining imports, domestic import substitution and export growth, Brazil followed rapid growth in the US and grew out of the crisis with surprising GDP growth of 4.5 percent in 1984.

The euro zone faces a critical survival risk because several of its members may default on their sovereign obligations if not bailed out by the other members. The valuation equation of bonds is essential to understanding the stability of the euro area. An explanation is provided in this paragraph and readers interested in technical details are referred to the Subsection IIIF Appendix on Sovereign Bond Valuation. Contrary to the Wriston doctrine, investing in sovereign obligations is a credit decision. The value of a bond today is equal to the discounted value of future obligations of interest and principal until maturity. On Dec 30, 2011, the yield of the 2-year bond of the government of Greece was quoted around 100 percent. In contrast, the 2-year US Treasury note traded at 0.239 percent and the 10-year at 2.871 percent while the comparable 2-year government bond of Germany traded at 0.14 percent and the 10-year government bond of Germany traded at 1.83 percent. There is no need for sovereign ratings: the perceptions of investors are of relatively higher probability of default by Greece, defying Wriston (1982), and nil probability of default of the US Treasury and the German government. The essence of the sovereign credit decision is whether the sovereign will be able to finance new debt and refinance existing debt without interrupting service of interest and principal. Prices of sovereign bonds incorporate multiple anticipations such as inflation and liquidity premiums of long-term relative to short-term debt but also risk premiums on whether the sovereign’s debt can be managed as it increases without bound. The austerity measures of Italy are designed to increase the primary surplus, or government revenues less expenditures excluding interest, to ensure investors that Italy will have the fiscal strength to manage its debt exceeding 100 percent of GDP, which is the third largest in the world after the US and Japan. Appendix IIIE links the expectations on the primary surplus to the real current value of government monetary and fiscal obligations. As Blanchard (2011SepWEO) analyzes, fiscal consolidation to increase the primary surplus is facilitated by growth of the economy. Italy and the other indebted sovereigns in Europe face the dual challenge of increasing primary surpluses while maintaining growth of the economy (for the experience of Brazil in the debt crisis of 1982 see Pelaez 1986, 1987).

Much of the analysis and concern over the euro zone centers on the lack of credibility of the debt of a few countries while there is credibility of the debt of the euro zone as a whole. In practice, there is convergence in valuations and concerns toward the fact that there may not be credibility of the euro zone as a whole. The fluctuations of financial risk assets of members of the euro zone move together with risk aversion toward the countries with lack of debt credibility. This movement raises the need to consider analytically sovereign debt valuation of the euro zone as a whole in the essential analysis of whether the single-currency will survive without major changes.

Welfare economics considers the desirability of alternative states, which in this case would be evaluating the “value” of Germany (1) within and (2) outside the euro zone. Is the sum of the wealth of euro zone countries outside of the euro zone higher than the wealth of these countries maintaining the euro zone? On the choice of indicator of welfare, Hicks (1975, 324) argues:

“Partly as a result of the Keynesian revolution, but more (perhaps) because of statistical labours that were initially quite independent of it, the Social Product has now come right back into its old place. Modern economics—especially modern applied economics—is centered upon the Social Product, the Wealth of Nations, as it was in the days of Smith and Ricardo, but as it was not in the time that came between. So if modern theory is to be effective, if it is to deal with the questions which we in our time want to have answered, the size and growth of the Social Product are among the chief things with which it must concern itself. It is of course the objective Social Product on which attention must be fixed. We have indexes of production; we do not have—it is clear we cannot have—an Index of Welfare.”

If the burden of the debt of the euro zone falls on Germany and France or only on Germany, is the wealth of Germany and France or only Germany higher after breakup of the euro zone or if maintaining the euro zone? In practice, political realities will determine the decision through elections.

The prospects of survival of the euro zone are dire. Table I-8 is constructed with IMF World Economic Outlook database (https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx) for GDP in USD billions, primary net lending/borrowing as percent of GDP and general government debt as percent of GDP for selected regions and countries in 2018.

Table I-8, World and Selected Regional and Country GDP and Fiscal Situation

	GDP 2018 USD Billions	Primary Net Lending Borrowing % GDP 2018	General Government Net Debt % GDP 2018
World	84,835
Euro Zone	13,738	1.0	69.5
Portugal	238	2.6	107.6
Ireland	366	1.4	56.9
Greece	218	3.5	188.1**
Spain	1,437	-0.5	84.3
Major Advanced Economies G7	39,037	-1.8	84.8
United States	20,513	-2.9	77.7
UK	2,809	-0.3	78.0
Germany	4,029	2.2	41.5
France	2,795	-0.9	87.4
Japan	5,071	-3.3	155.7
Canada	1,734	-0.9	27.7
Italy	2,087	1.8	118.3
China	13,457	-3.1	50.1***

*Net Lending/borrowing**Gross Debt

Source: IMF World Economic Outlook

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

The data in Table I-8 are used for some very simple calculations in Table I-9. The column “Net Debt USD Billions 2018” in Table I-9 is generated by applying the percentage in Table I-8 column “General Government Net Debt % GDP 2018” to the column “GDP 2018 USD Billions.” The total debt of France and Germany in 2018 is $4114.8 billion, as shown in row “B+C” in column “Net Debt USD Billions 2018.” The sum of the debt of Italy, Spain, Portugal, Greece and Ireland is $4554.8 billion, adding rows D+E+F+G+H in column “Net Debt USD billions 2018.” There is some simple “unpleasant bond arithmetic” in the two final columns of Table I-9. Suppose the entire debt burdens of the five countries with probability of default were to be guaranteed by France and Germany, which de facto would be required by continuing the euro zone. The sum of the total debt of these five countries and the debt of France and Germany is shown in column “Debt as % of Germany plus France GDP” to reach $8669.6 billion, which would be equivalent to 127.0 percent of their combined GDP in 2018. Under this arrangement, the entire debt of selected members of the euro zone including debt of France and Germany would not have nil probability of default. The final column provides “Debt as % of Germany GDP” that would exceed 215.3 percent if including debt of France and 154.6 percent of German GDP if excluding French debt. The unpleasant bond arithmetic illustrates that there is a limit as to how far Germany and France can go in bailing out the countries with unsustainable sovereign debt without incurring severe pains of their own such as downgrades of their sovereign credit ratings. A central bank is not typically engaged in direct credit because of remembrance of inflation and abuse in the past. There is also a limit to operations of the European Central Bank in doubtful credit obligations. Wriston (1982) would prove to be wrong again that countries do not bankrupt but would have a consolation prize that similar to LBOs the sum of the individual values of euro zone members outside the current agreement exceeds the value of the whole euro zone. Internal rescues of French and German banks may be less costly than bailing out other euro zone countries so that they do not default on French and German banks. Analysis of fiscal stress is quite difficult without including another global recession in an economic cycle that is already mature by historical experience.

Table I-9, Guarantees of Debt of Sovereigns in Euro Area as Percent of GDP of Germany and France, USD Billions and %

	Net Debt USD Billions 2018	Debt as % of Germany Plus France GDP	Debt as % of Germany GDP
A Euro Area	9,547.9
B Germany	1,672.0		$8669.6 as % of $4029 =215.3% $6226.8 as % of $4029 =154.6%
C France	2,442.8
B+C	4,114.8	GDP $6824 Total Debt $8,669.6 Debt/GDP: 127.0%
D Italy	2,468.9
E Spain	1,211.4
F Portugal	256.1
G Greece	410.1
H Ireland	208.3
Subtotal D+E+F+G+H	4,554.8

Source: calculation with IMF data IMF World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

World trade projections of the IMF are in Table I-10. There is increasing growth of the volume of world trade of goods and services from 5.2 percent in 2017 to 4.2 percent in 2018, stabilizing to 4.0 percent in 2019. Growth stabilizes at 4.2 percent on average from 2017 to 2023. World trade would be slower for advanced economies while emerging and developing economies (EMDE) experience faster growth. World economic slowdown would be more challenging with lower growth of world trade.

Table I-10, IMF, Projections of World Trade, USD Billions, USD/Barrel and Annual ∆%

	2017	2018	2019	Average ∆% 2017-2023
World Trade Volume (Goods and Services)	5.2	4.2	4.0	4.2
Exports Goods & Services	5.3	3.8	3.7	3.9
Imports Goods & Services	5.2	4.5	4.3	4.3
Exports Goods & Services
G7	4.4	3.0	2.5	3.0
EMDE	6.9	4.7	4.8	4.9
Imports Goods & Services
G7	4.4	3.4	4.1	3.2
EMDE	7.0	6.0	4.8	5.6
Terms of Trade Goods & Services
G7	-0.5	0.1	0.4	-0.1
EMDE	0.6	1.6	-0.2	0.3
World Crude Oil Price $/Barrel	52.81	69.38	68.76	63.05

Crude Oil: Simple Average of three spot prices: Dated Brent, West Texas Intermediate and the Dubai Fateh

Source: International Monetary Fund World Economic Outlook databank

https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/index.aspx

© Carlos M. Pelaez, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018.