Flow of Funds: Net Borrowing as a Percent of U.S. GDP at Annual Rates by Sector

Corrections displays the Fed Flow of Funds data.  The Flow of Funds data breaks the economy, for example, into seven sectors:  the household sector, nonfinancial corporate businesses, nonfinancial noncorporate businesses, state and local governments, federal government, rest of world, and financial sector.  Net lending in the world must add up to zero:  there are two sides to every loan.  The flow of funds breaks up the U.S. and the rest of the world, and then breaks up the U.S.  Nevertheless, the sum must still be zero.

Below, Corrections displays the flow of funds for each sector over time (click to enlarge).

The same graph zoomed into the recent period is depicted graphically below (click to enlarge).  Note that while the Federal government is borrowing much more than it used to, as a country we're receiving less than we used to from the rest of the world:  the Federal deficit is being made up by the financial sector. 

What is the financial sector?  The lending portion is made up of the Monetary authority, chartered banks, foreign banking offices in the U.S., credit unions, insurance companies, private and public pension funds, money market mutual funds, mutual funds, closed-end funds, exchange-traded funds, government sponsored enterprises, agency and GSE-backed mortgage pools, ABS issuers, finance companies, real estate investment trusts, brokers and dealers, holding companies, and funding corporations.  The borrowing portion is similar.  We organize these sources into four main sources:  1) private/stock market, such as mutual funds, exchange traded funds, and private pensions 2) government sans monetary authority, such as GSE-backed mortgage pools, government retirement funds 3)  foreign banking offices in the U.S. 4)  the monetary authority and funding companies (AIG and Bear Stearns, for instance).  We graph these four graphically below (click to enlarge):  they add up to the light blue line in the above graph.

From the second graph, we note that the Federal government is borrowing more and that this is financed by the financial sector.  We further note that within the financial sector, it is being financed primarily by domestic funds and the stock market, rather than the monetary authority.  

Indexed Employment by State

Below, Corrections displays indexed total employment by state over time.  The high outlier is North Dakota, the low outlier is Nevada.

The Impact of War on Economic Growth

Corrections took the dataset present in Growth Dynamics:  The Myth of Economic Recovery: Comment by Hannes Mueller and collapsed the dataset down to a single interesting table, giving the present period growth rates given whether a country was at war last year, this year, and next year.

Interestingly, lapsing back into war:  war last year, no war this year, but war next year, has the lowest growth rate, while failing to lapse back into war: war last year, no war this year, and no war next year, has the highest.

Bond Yields

Below, Corrections plots out bond yields in the recent period: corporate AAA, BBB, and CCC yields according to Merrill Lynch, and Treasury Constant-Maturity yields.  Note that during the housing crisis, corporate bond yields went up, while treasury bond yields went down:  

Women's Labor Force Decisions by Marital Status over Time

Below, Corrections depicts the proportion of women by marital status and labor force status over time from the Current Population Survey (click to enlarge).

What could cause this?  Why are married women working so much more?  It can't be anything special about being married--nonmarried and married women show the same pattern.  Women in general are working more.  

Even though we know it isn't anything to do with martial status, we then depict the proportion of married women by labor force status over time (click to enlarge).  Women used to work about 35% of time (other sources give 35% extending back further) to around 60% of the time, an increase of 35% (doubling the proportion of women working).  

Corrections then offers the proportion of women by martial status, educational status, and labor force status over time.  One trend dominates the pattern: women are becoming more educated, so both red lines decline while both blue increase (click to enlarge).

This is depicted more clearly below (click to enlarge):

Instead, we can normalize by the population of all women within an educational category (click to enlarge):

And we can normalize that to the proportion that was working in 1965, to see how much these proportions change over time (click to enlarge):

Households by Number of Earners

Below, Corrections depicts Households by number of earners from 1980-2011 (click to enlarge):

Making the same graph with proportions (click to enlarge):

State Unemployment

Below, Corrections displays the difference between state unemployment levels in a given month and its minimum from 2002 to that date (click to enlarge) and the difference between state unemployment levels in a given month and its maximum from 2002 to that date (click to enlarge).

U.S. Treasury Holdings by Foreign Entity

Who owns U.S. Treasuries?  Below, Corrections depicts holdings of U.S. Treasuries by Foreign Entity (non-US), from the Treasury International Capital System (click to enlarge).

While Chinese holdings of U.S. Treasuries have leveled off, the holdings of other countries have more than picked up the slack.  This can also be seen by normalizing each period to 100% and seeing the proportion of foreign-owned U.S. Treasuries owned by each actor (click to enlarge).

Measurement without Theory on the Empire State Manufacturing Survey

Corrections examines today's Empire State Manufacturing Survey:  the release contains 88 variables:, current and future estimates of New York manufacturers for: business conditions, orders, future shipments, delivery time, inventories, unfulfilled orders, prices paid, prices received  number of employees, and average employee workweek, along with future capital expenditures and future technology spending.

Corrections takes each series, normalizes it, HP filters it, multiplies it by -1 if it correlates negatively with the average of all series together (so variables that are high when conditions are bad flip sign), and finally sorts the series vertically based on the value in July 2009.  We then graph the series in a heat map:  red when the normalized, filtered, and signed variable is "high" and blue when it is "cold."

The result is blind economics (click to enlarge), without theory, seeing if these variables are telling us anything related to one another, suggesting they're measuring something.

Consumer Price Index Components and Transportation Breakout

Below, Corrections displays the components of the urban CPI (click to enlarge).  Weighted by expenditure, they make up the headline CPI.

Transportation looks interesting enough to break out further.  Below, we display the components of the transportation component (click to enlarge):

Ricardian Equivalence

From the Flow of Funds accounts, Corrections presents net private and net government savings, along with net total U.S. savings (click to enlarge).  Notice the mirror trends of net private and government savings as a percent of GDP.  

The graph certainly doesn't discount Ricardian Equivalence as a first-order effect:  households know that deficit spending means future taxes:  to smooth consumption, they save more  If true, this equivalence would suggest that financing government consumption through deficits or through taxation is equivalent, to a first order approximation:  households know net present value of taxation will rise and save for the event.