The above chart shows a historical view of who holds (owns) U.S. consumer credit (does not include mortgage debt). The main observation from this chart is the dramatic role securitization has played in the growth of consumer debt.
Data Source:
U.S. Federal Reserve
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The above chart shows a historical view of total U.S. consumer credit per household. It also breaks out the data for revolving and non-revolving credit. In March 2009, U.S. per household consumer credit was $22,857. This was off of the record of $23,140 hit in July 2008.
The purpose of this chart is not to imply that every household has this much consumer debt. The purpose is to help provide some perspective as to how much consumer debt is outstanding. When we see numbes like $2.5 trillion dollars of consumer credit it is difficult to understand exactly what it means. I find that seeing this type of data on a per capita or per household basis helps me understand the magnitude. Below is a chart that shows per capita U.S. consumer credit - just another perspecitive.
Data source:
U.S. Federal Reserve
U.S. Census Bureau
Note: Does not include mortgage debt.
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The above chart is a historical view of the growth in U.S. credit card debt per household. In March 2009, U.S. credit card debt per household was $8,475. This is off of the record high reached in September 2008, of $8,729. Americans haven’t always been addicted to plastic. In March 1969, the U.S. credit card debt per household was only $37.
Data Source:
U.S. Federal Reserve
U.S. Census Bureau
Data is seasonally adjusted
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The above chart shows the growth in consumer credit in the U.S. over the past 40 years. The chart breaks out the data for revolving and non-revolving credit. Note that revolving credit is basically credit card debt, and non-revolving credit is debt from things like autos, boats, student loans. Consumer credit does not include mortgage debt.
Check back tomorrow to see a historical view of U.S. credit card debt on a per household basis.
Data Source:
U.S. Federal Reserve. G.19
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This is the first in a series of posts on consumer debt. The above charts show two views of consumer credit in the U.S. The first chart is a view of consumer credit in March 1969, and the second chart is a view of consumer credit in March 2009. Two observations: 1) consumer credit has greatly expanded in the past 40 years (big surprise), and 2) Revolving credit (credit card debt) has grown from almost nothing to over a third of consumer credit now.
Notes:
I tried to make the two charts to scale. PieR2 calculation (it was a little difficult to measure on my screen, but I think it is pretty close).
Revolving credit is basically credit card debt, and non-revolving credit is debt from things like autos, boats, student loans. All data is seasonally adjusted.
Please note that consumer credit does not include mortgage debt.
Data Source:
U.S. Federal Reserve. G.19.
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The above chart shows the projected sources of funding (receipts) for the U.S. Government for 2009. Borrowing (the federal budget deficit) is projected to account for nearly half of all funding. If you read yesterday’s post, you also know that the amount of borrowing in 2009 is likely to increase (because the Obama administration’s budget assumptions are too optimistic).
Data source:
Updated Summary Tables, May 2009, Budget of the U.S. Government Fiscal Year 2010. Table S-4.
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Last week the Obama administration released its new budget projections, and the overall deficit was revised upward. The above chart shows the budget deficit as a percentage of GDP based on the Obama administration’s February 2009 budget projections and on their new projections.
In March, I said that the Obama administration’s budget was optimistic (here’s a link to several articles I wrote back then on the budget: http://chartingtheeconomy.com/?cat=7). So, I hope no readers were surprised last week when the administration came out with a revised budget projecting the 2009 deficit would increase by $89 billion and the 2010 deficit would increase by $87 billion. I think there will be another negative revision later this year because the assumptions in the administration’s budget are still too optimistic.
First, I think the budget is too optimistic in its growth projections. It has real GDP declining only by 1.2% in 2009 and growing by 3.2% in 2010 (4% in 2011, and 4.6% in 2012). The next chart shows three differing views of budget year 2009 year-over-year real GDP growth. As you can see the Obama administration’s projection is far more optimistic than the Blue Chip concensus and the Congressional Budget Office (CBO) projection.
Second, the administration’s unemployment figures are too optimistic. It has the 2009 average unemployment rate at 8.1% and the average for 2010 at 7.9%. The unemployment assumption is an important input in the budget process. A higher unemployment rate has several adverse impacts on the deficit including: 1) higher government expenditures on unemployment benefits, 2) lower income and payroll tax receipts, and 3) reduced consumer spending which leads to lower GDP growth. The chart below shows how the unemployment rate would need to trend in coming months in order for the Obama administration’s assumption to be accurate. Note: The U.S. budget year starts October 1st of the prior year (so we are more than halfway through the 2009 budget year).
The projections in the above chart hit the administration’s assumption of a 8.1% unemployment rate in 2009 and 7.9% in 2010. As you can see in this example the unemployment rate would need to start improving now and continue to improve steadily over the next 17 months. Of course the administration’s assumption can also be hit if the unemployment rate continues to increase in the next few months. If this happens, the recovery would need to be much more extreme. I believe the administration’s unemployment assumption in their budget projections will be very difficult to hit.
Given the Obama Administration’s optimistic assumptions I think we are in for another negative revision in the federal deficit later this year. This administration definitely does not have a budget strategy to set low public expectations, and then exceed them. Just the opposite. It appears that their budget strategy is to set optimistic public expectations, and then to gradually revise the expectations downward. Get use to it. The administration’s budget numbers are still a best case scenario.
Data Source:
Budget of the U.S. Government Fiscal Year 2010, Updated Tables May 2009. Table S-9, S-13, and S.14. Table S-13 has the April 2009 Blue Chip Economic Indicators (Aspen Publishers, Inc.), and the CBO’s March projections.
The Blue Chip Economic Indicators is published monthly by Aspen Publishers, Inc. It is a poll of America’s top business economists.
U.S. Bureau of Labor Statistics
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On Friday, the Federal Reserve reported that capacity utilization for U.S. industry hit a record low of 69.1% in April. The Fed’s calcuation is based on the percentage of total industrial capacity being utilized. This report means that there is more excess industrial capacity in the U.S. than at anytime on record.
Capacity Utilization is considered a leading indicator of inflation and future capital spending. This record low reading is an indicator that there is little inflationary pressure in the U.S. economy (at least now - I’m concerned about the effects of U.S. deficit spending on long-term inflation). This record amount of spare capacity also indicates that industry has little need to spend capital to increase production (even if demand picks up significantly).
Data Source:
U.S. Federal Reserve
The first chart shows the core capital ratios in the U.S. banking industry by asset class. Basically, the smaller the bank class the stronger the capital ratio. The second chart shows the net charge-off to loan ratio by bank asset class. Again, the smaller the bank class the better their loan quality.
It’s not the entire banking system that is in crisis. It’s the biggest banks as a class that are not as strong. The largest banks have the lowest capital ratios and the lowest loan quality (when compared to smaller banks as a class. This would not be a big concern expect that the largest banks have dramatically increased their market share in recent years. For example, the top 10 banks in the U.S. hold over 40% of U.S. banking deposits. They held only about 12% in 1994. Allowing the largest banks to consolidate market share was not in itself bad. However, at the same time to allow them to maintain the lowest capital ratios and the lowest loan quality seems irresponsible. It increased the chances of systemic risk if (when) these largest banks got into trouble.
Data source:
FDIC
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The above charts show that FDIC resources have not kept pace with the growth in the U.S. banking industry in recent years. I’m not a fan of burdensome government oversight. However, I am also not a fan of the banking crisis we have today which has lead to even more government involvement (bailouts, public investments, talk of nationalization, a “too big to fail” policy).
Data source:
FDIC
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The above charts show the change from 1994 to 2008 in the distribution of U.S. banking deposits by asset class. As you can see banks in the ”Greater than $10 Billion” asset class now control nearly three-quarters of U.S. banking deposits.
Data source:
FDIC
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The above charts show the extent to which U.S. banking deposits have become increasingly concentrated within a few large banks in recent years. Four words: “too big to fail.” In my book the ability to fail should be a basic human (and corporate) right.
Data source:
FDIC
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The above charts show the extremely high concentration of assets within the U.S. banking industry. The first chart shows the number of banking institutions by asset class as of December 31, 2008. When you combine the last two categories you see that less than 10% of banks have assets of more than $1 billion. Just over 1% of banks fall into the last asset class (banks that have assets over $10 billion).
The second chart shows the percentage of total U.S. banking assets hold by banks within each asset class. For example, banks with more than $10 billion in assets as a class hold nearly 80% of total U.S. banking assets. By looking at both charts you can see that just over 1% of the banks in the U.S. hold almost 80% of total U.S. banking assets. This wasn’t always the case. In fact the concentration of assets within a few large banks has dramatically increased in recent years. The same is true with banking deposits. Tomorrow I will have a chart showing how the concentration of banking deposits has increased since the early 1990’s.
Data Source:
FDIC
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The above chart is the first in a six part series on the U.S. banking industry. In this series I will show how the government allowed a large-scale consolidation within the U.S. banking industry in recent years. This consolidation, and a concentration of banking deposits and assets helped result in a handful of major banks becoming ”too big to fail.” I will also show that at the same time this was occuring the government greatly reduced FDIC resources that regulate the banking industry.
The above chart begins the series by showing clearly how the U.S. banking industry has consolidated since 1993. The chart shows how the total number of U.S. banking institutions has steadily declined as the number of mergers within the banking industry has steadily grown.
Tomorrow I will show just how concentrated assets are within the U.S. banking industry.
Data Source:
FDIC
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The April 2009, unemployment numbers for the U.S. were released this morning, and the offical unemployment rate increased to 8.9%. However, the offical unemployment rate does not tell the complete story. The above chart compares the offical unemployment rate, the BLS’ U-6 rate, and what I call the suffering rate. Each one of these measures unemployment differently. Let me explain.
First, the offical unemployment rate should be thought of as a baseline. Then there is the U-6 rate which is the BLS’ broadest offical measure of unemployment. It takes the offical unemployment rate and adds to it. The U-6 rate includes:
1) Those that fall into the offical unemployment rate.
2) Plus all marginally attached workers (BLS defines marginally attached as: persons who currently are neither working nor looking for work but indicate that they want and are available for a job and have looked for work sometime in the recent past).
3) Plus those employed part time for economic reasons.
The U-6 is then measured as a percent of the civilian labor force plus all marginally attached workers. The U-6 is a broad measure of unemployment, but misses some of the people. For example, individuals that want a job but have not looked for work in the recent past (last 12 months) are not included. However, the suffering rate does include these individuals.
The suffering rate includes:
1) Those that fall into the offical unemployment rate.
2) Plus all individuals that the BLS states ”want a job” (even if they have not looked for work in the recent past).
3) Plus those employed part time for economic reasons.
The suffering rate is then measured as a percent of the civilian labor force plus all workers that want a job.
To get a clear picture of the unemployment situation in the U.S. you need to look past the offical unemployment rate of 8.9%. You even need to look past the BLS’ U-6 measure of unemployment that stands at 15.8%. I think the suffering rate of 17.8% is the broadest (and best) measure of U.S. unemployment (under-empolyment).
A few other points about the April 2009, employment numbers:
1) Nonfarm payroll employment declined by 539,000 jobs which was better than expected. However, much of the improvement was due to government hiring. Employment in the private sector fell by 611,000 jobs.
2) Job losses in February and March were worse than previously reported. February job losses were revised to 681,000 from 651,000. March job losses were revised to 699,000 from 663,000.
3) The number of individuals unemployed for 27 weeks or more (long-term unemployed) increased by 498,000 to 3.7 million last month.
Data Source:
Bureau of Labor Statistics
Tables A-1, A-7, A-12, and The Employment Situation, April 2009.
All numbers are seasonally adjusted.
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