Canada's Economic Position Relative to the United Kingdom as Illustrated by the Monthly Average Noon Spot Rate

Source: Statistics Canada, CANSIM using CHASS, v37430 Canada; United Kingdom pound sterling, noon spot rate, average

This chart demonstrates the reaction of global investors to the 'graceful' decline of the UK after the second world war, as its colonies were gaining independence. This effect was magnified by Canada's booming post war economy, not having suffered the massive decrease in the capital stock brought on from Nazi Luftwaffe bombs.

The current fiscal position of the UK is no significantly better off than their 'European Union' sisters affectionally known as the "PIIGS" (Portugal, Ireland, Italy, Greece, and Spain). The massive welfare economy of Britain, and its appearance from abroad of a disintegrating society, illustrated by muslim street protests professing its ascendance into an Islamic country are certainly not an image that will excite international investors looking to dump their money.

It appears that Canada's economic and fiscal mess is not as bad as our cousins across the Atlantic.

Canada's Economic Position Relative to the US as Illustrated by the Monthly Average Noon Spot Rate

Source: Statistics Canada, CANSIM using CHASS, v37426 Canada; United States dollar, noon spot rate, average

This is an interesting chart to illustrate how international investors view Canada's economy relative to the United States since 1950. The relative stability between the two currencies from 1950 to approximately the early 1970's can be explained by the gold standard where Canada (and many others) pegged their currency to the United States greenback, which was in turn pegged to the value of gold. Under such a system Canada could not pursue an independent monetary policy (see trillema).

Since the end of the currency peg, the Canadian dollar has depreciated relative to the US$ for many reasons that will have to be further examined. Possible reasons include Canada's lower productivity growth compared to the US, or perhaps Canada's poor taxation policies and larger government.

The relative weakness of the Canadian economy (or strength of the US economy) peaked around 2001-2002. This could be attributed to the massive asset bubble in the US for technology stocks, where global investors were selling local currencies to buy the greenback to purchase technology stocks, forcing the US dollar upwards.

The relative strength of the Canadian economy was visible after 2003 as the government debt problem was seen as being under control. This was compounded by the massive US government debt being piled up to fight wars in Afghanistan and the invasion of Iraq.

At this point in time the two currencies are pretty well at par, similar to when the Bretton Woods agreement was enacted.

Federal Government Surplus / Deficit (1989-2009)

This chart shows how Canada's Federal Government under PM Chretien pursued the necessary strategy of cutting expenditures to return federal finances to a slight surplus. The generation of revenues and the cutting of expenses will be looked into further, program by program, to determine how this has affected provinces and federal services especially the military. Despite the problems the cuts generated it was necessary to pursue to avoid near bankruptcy (see Greece 2010). We can also see that the surpluses that were considered 'massive' in the media were hardly enough to offset the previous decade's prolific spending and deficits.

The period under PM Harper when the world was hit by a massive recession, and Keynesian economics was brought back into favor among Western democracies will also need to be examined. In a period of a few years the decrease in tax revenues and a massive increase in spending sent the federal government back into huge deficits, that will likely erase all the gains from the surplus years.

The analysis of federal finances must take into account the cyclical economy (business cycles). Politicians are all to eager to take responsibility for strong finances through the crest of the business cycle, and to eager to assign blame for poor finances on the weak global/national economy. Proper analysis must look into how the government grew spending on poor programs, or how previous governments helped to grow the economy in the long-run (signing free trade agreements, eliminated inter-provincial trade barriers etc.).

Gross Canadian Federal Debt, 1867-2008

Source: Statistics Canada, CANSIM using CHASS, v151537 Canada; Gross federal government debt

This chart shows a lot about how Canada has evolved as a nation. I will use this chart as a starting point to analyze Canada's economic, political, military and economic history. An required next step would be to analyze other economic data such as GDP, population, labour force, trade, Consumer Price Index, and Government Revenues and Spending. A proper analysis will also require comparisons with other nations, comparisons between provinces, and a deeper look into the different eras and the corresponding role of the federal government played, and how that has affected individual freedoms. This will also require an understanding of history and politics.

A preliminary look at the federal gross debt time series, we can separate Canada's economic history into a few eras:

1. Early Nationhood (1867-1914) Characterized by little federal debt and a federal government that was not intrusive in people's lives.

2. World Wars (1914-1945) Increased government spending (and debt) to finance two world wars, growing role of the federal government including increased taxation and new forms of taxation (supposedly temporary).

3. Peace 'Dividend' (1945-1993) Emergence of the welfare state, growing federal spending (and debt), more taxation.

4. Belt Tightening (1993-2008) Cutting services and federal transfer payments to provinces, repaying some debt.

5. New Keynesian (2008-present) Massive growth in federal spending, some tax cuts.

6. Fiscal Conservatism ?

These eras will be examined in closer detail, especially the period from 1945-1993.

The Role of Leverage in the Asset Bubble in Housing in the USA

The Role of Leverage in the Asset Bubble in Housing in the USA

The hypothesis is that in the major metropolitain areas studied in this paper, Atlanta, Boston, Los Angeles, and Miami, the size of each bubble as measured by the Case-Schiller (CS) index would be larger for cities that experienced the largest increase in leverage, as measured by the US census’ American Housing Survey (AHS) in Mortgage Characteristics of Owner Occupied Units and their median outstanding principle. The results were different than expected; in the cities with the smallest housing bubble, Atlanta and Boston, the growth in the CS index was similar to the growth in total outstanding principle and the current total loan as a percent of value was essentially unchanged, whereas in the cities with the largest housing bubble, Los Angeles and Miami, the growth in total principle outsanding was significantly less than the growth in the index and the current total loan as a percent of value fell.

The expectation that the rise in asset prices was related to an increase in leverage is due to the behavioural aspect of finance where people will buy an asset with the expectation that they can sell it in the future at a higher price, known as ‘the castle-in-the-air theory’. (Malkiel, pg. 30)[1] If this belief is paired with greater access to liquidity (mortgage financing) people who would otherwise not be able to enter the market will be able to purchase the asset by putting little or no of their money as a down payment. Prices will rise due to the increase in demand for the asset likely out pacing the increase in supply spurred on from higher prices.

Leverage also increases investors risk because with a certain percentage rise (or fall) of the asset there is a greater total rise (or fall) in the investors’ return even after deducting interest costs. In the housing market this means that the higher the loan to value ratio (current total loan as percent of value) the more risk there is of homeowners defaulting if the value of housing prices drops. (MacGee, pg. 1)[2]

The asset bubble in housing was noticeably different in the 20 major metropolitan areas of the US, as shown on page 2.[3] The differences in the four cities compared to the 20 city composite index are illustrated on page 6. This is interesting considering that all US cities had the same monetary policy of low interest rates, and federal regulations allowed for easier access to mortgage financing across the US. (MacGee)

In Atlanta, the median total principal amount grew at a rate of 6.10% from 1996 to 2004, whereas the CS index in the same period grew at 5% (page 7), the current total loan as percent of value was flat at -0.02%.[4]

In Boston, the median total outstanding principal amount grew at a rate of 9.66% from 1998 to 2007, while the CS index grew in that period a similar 8.10% (page 7), and the current total loan as percent of value decreased at a rate of 1.36%.[5]

In Los Angeles, median total outstanding principal grew 5.60% from 1999 to 2003, the CS index grew 13.18% (page 8), and current total loan as percent of value decreased at a rate of 13.18%.[6]

In Miami, the median total outstanding principal grew 10.26% from 2002 to 2007, the CS index grew an amazing 14.44% (page 8), and current total loan as a percent of value decreased by 4.66%.[7]

The values from the CS index and the AHS are summarized in Table 1.

Table 1

Medians	Atlanta		Boston		Los Angeles		Miami
Total Outstanding Principal Amount	1996	70,341	1998	79,667	1999	117,295	2002	75,342
	2004	113,004	2007	182,740	2003	145,884	2007	122,764
	Growth rate	6.10%	Growth rate	9.66%	Growth rate	5.60%	Growth rate	10.26%
Case-Schiller Index Growth	1996	82.17	1998	83.97	1999	96.12	2002	132.58
	2004	121.45	2007	169.28	2003	157.18	2007	260.25
	Growth rate	5.00%	Growth rate	8.10%	Growth rate	13.18%	Growth rate	14.44%
Current Total Loan as Percent of Value	1996	67.5	1998	45.8	1999	61.1	2002	56.5
	2004	67.4	2007	40.5	2003	47.2	2007	44.5
	Growth rate	-0.02%	Growth rate	-1.36%	Growth rate	-6.25	Growth rate	-4.66%

These bizarre results may be caused by a few different factors. The US Census Bureau conducts the American Housing Survey (AHS) and they measures mortgage characteristics. For the four cities the median term of the primary mortgage at origination was 30 years[8]. There could be problems in the AHS data used to measure leverage, because some people who responded simply may not have known how much outstanding principle they had. Especially because the AHS includes the total number of households in each surveyed area that have 1, 2, and 3 or more mortgages, and there was a considerable number of households with 2 mortgages or 3 or more mortgages. They might have only reported information for the first mortgage. The AHS survey is conducted in different years (with no regularity) for different metropolitan areas. This makes comparisons difficult both between cities and to observe changes between years. For the 4 cities compared the survey was conducted since 1974 at intervals of between 3 to 9 years for Boston, 3 to 8 years for Atlanta, 3 to 6 years for LA, and 3 to 7 years for Miami.[9] Due to this irregularity growth in mortgages outstanding could not be compared between the cities because the time periods were different, but trends were still noticeable in the time period leading to the peak in the housing bubble (peak in July 2006 in CS index). High quality yearly mortgage data separated by metropolitan areas would be preferable to improve this research. The CS index is based on census and Fiserv data[10] and is likely very accurate.

The magnitude of the housing bubble in the four cities does not seem to be linked to the increase in leverage according to the limited data provided by the AHS. An alternative that would improve this research is to collect mortgage information from all the lending institutions in a metropolitan area, monthly or quarterly, which would provide better more accurate information than the AHS which relies on people knowing their principle outstanding on all their mortgages published at varying intervals.

References

“American Housing Survey for the Atlanta Metropolitan Area in 1996”. U.S. Department of Commerce. Bureau of the Census. Nov 1997. Pg 59-60.

“American Housing Survey for the Atlanta Metropolitan Area: 2004”. U.S. Department of Commerce. Bureau of the Census. Oct 2005. Pg. 76-77.

“American Housing Survey for the Boston Metropolitan Area 1998”. U.S. Department of Commerce. Bureau of the Census. Nov 2000. Pg. 70.

“American Housing Survey for the Boston Metropolitan Area: 2007”. U.S. Department of Commerce. Bureau of the Census. Feb 2009. Pg. 80-1.

“American Housing Survey for the Los Angeles-Long Beach Metropolitan Area 1999”. U.S. Department of Commerce. Bureau of the Census. March 2001. Pg. 71.

“American Housing Survey for the Los Angeles-Long Beach Metropolitan Area: 2003”. U.S. Department of Commerce. Bureau of the Census. Dec 2004. Pg. 76-7.

“American Housing Survey for the Miami-Ft. Lauderdale Metropolitan Area: 2002”. U.S. Department of Commerce. Bureau of the Census. July 2003. Pg. 74-5.

“American Housing Survey for the Miami-Ft. Lauderdale Metropolitan Area: 2007”. U.S. Department of Commerce. Bureau of the Census. Feb 2009. Pg. 80-1.

Gjerstad, Steven and Smith, Vernon L. “Monetary Policy, Credit Extension, and Housing Bubbles: 2008 and 1929”. Critical Review. 21:2, Pg. 269-300.

“Index Methodology”. S&P/Case-Shiller Home Price Indices. Nov 2009. real estate > S&P/Case-Schiller Home Price Indices > Methodology>.

MacGee, James. “Why Didn’t Canada’s Housing Market Go Bust?”. Federal Reserve Bank of Cleveland. Sept. 2009 .

Malkiel, Burton. A Random Walk Down Wall Street. New York: W. W. Norton & Company, 2007.

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[1] Malkiel, Burton. A Random Walk Down Wall Street. New York: W. W. Norton & Company, 2007. Pg. 30

[2] http://www.clevelandfed.org/research/commentary/2009/0909.pdf

[3] Source for all Case-Schiller data used in this report: http://www2.standardandpoors.com/spf/pdf/index/CSHomePrice_History_022445.xls

[4] American Housing Survey for the Atlanta Metropolitan Area in 1996, pg. 60; American Housing Survey for the Atlanta Metropolitan Area: 2004, pg. 75-6

[5] American Housing Survey for the Boston Metropolitan Area 1998, pg. 70; American Housing Survey for the Boston Metropolitan Area: 2007, pg 80-1

[6] American Housing Survey for the Los Angeles-Long Beach Metropolitan Area 1999, pg 71; American Housing Survey for the Los Angeles-Long Beach Metropolitan Area: 2003, pg 76-7

[7] American Housing Survey for the Miami-Ft. Lauderdale Metropolitan Area: 2002, pg 74-5; American Housing Survey for the Miami-Ft. Lauderdale Metropolitan Area: 2007, pg 80-1

[8] AHS Atlanta 1996, 2004; Boston 1998, 2007; Los Angeles 1999, 2003; Miami 2002, 2007

[9] http://www.census.gov/hhes/www/housing/ahs/metrodates.html

[10] Fiserv uses census data for the number of single family housing units in each metropolitan region. Fiserv calculates the average and aggregate value of single family homes. The value of each unit is based on the sale date and price of a single family home, which is compared to historical records of the same home (if this data is available) which gives two points to compare for a yield rate. The yield rates are aggregated using their proprietary algorithm. Fiserv uses a number of techniques to maintain data integrity. To avoid upward bias data is excluded if there have been improvements or additions to the home since the previous sale. If there it appears that the transaction was not arms length (same family name, or the name of a property developer) it is excluded. The index is normalized to January 2000.

Source: “Index Methodology” S&P/Case-Shiller Home Price Indeces. Pg 6-18