Working Full-Time, Part-Time, and Either in 2012

Below, Corrections depicts the proportion of the population working full-time, part-time, or either in 2012 according to the Current Population Survey (click to enlarge).

Chaotic Systems

Corrections has yet to meet anyone who is good at forecasting much of anything.  Why might this be?  One reason is bad statistical models.  Another reason (that we are not overly sympathetic towards!) might be chaotic systems.  The present may perfectly and completely determine the future, but the near present may not have any power at predicting the future.

One simple example of this is the sequence x(t+1)=4x(t)*(1-x(t)).  The sequence will bounce around for a while between 0 and 1 (given we avoid a few bad starting states like {0, 0.25, 0.5, 0.75, 1}) and be completely deterministic.  Surely it wouldn't be hard to forecast, right?  

Wrong.  If your starting point (initial information used for forecasting) deviates the slightest amount, your sequence soon becomes completely different than if you had used the true starting point.  Below, Corrections depicts two such starting values:  X(0)=0.1 and X(0)=0.24, and plot the series for 100 periods (click to enlarge).

What if we were really, really, really close?  If we start out with a percent error of merely 0.0001%, then shouldn't our forecasts match up?  They do, for a while, but diverge rather quickly for having a one-part-in-ten-million difference (click to enlarge).

Does one series provide any forecast of the other, or have a recognizable pattern?  Below, Corrections depicts the two series against one another after the 20th period:  they no longer have a discernible relationship (click to enlarge).

This is one possible reason why the vast majority of sophisticated forecasts Corrections has heard (that don't suffer from selection) have been wrong.   We don't put much stock in it, however.

The Trends of Federal Receipts and Outlays

Below, Corrections depicts log Federal outlays and log Federal receipts under Reagan, Bush-I, Clinton, Bush-II, and Obama up until August 2013.  We also display the Reagan-Bush I-Clinton trend extrapolated out through Bush and Obama's terms.  We attribute the split January to the outgoing President, as he exits around the end of the third week of that month.

Log outlays tell a clear story:  outlays under Reagan, Bush I, Clinton, and Bush II continued on trend.  They saw a dramatic jump, and then a fairly stark arrest under most of Obama's term (click to enlarge).

Log receipts tell a different story:  while outlays have gone according to trend, receipts were halted under Bush, and again under Obama (click to enlarge).  For both, this was a result in part of tax cuts (or tax cut extensions) and bad economies.

Finally, we depict the two together (click to enlarge):  the short time the blue line was above the red line represents the Clinton surpluses, and the near-zero deficit of the Bush term before the financial crisis ended hopes of a balanced budget.

U.S. Federal Debt: Who Holds it, Who is Buying it?

Since 2007:Q4, over the last 21 quarters, U.S. Federal debt has gone up by about 7.5 trillion dollars.  Three of the most common misconceptions Corrections has heard have been:

1. The Federal Reserve is buying all the debt!
2. Foreigners are buying all the debt!
3. Banks and the public are buying all the debt!

All three are incorrect.  U.S. debt purchases  have been fairly balanced.  Below, Corrections depicts the three owners of U.S. debt (click to enlarge):

It can be helpful to see the proportion of U.S. debt held by each of the entities (click to enlarge):

Neither of these is particularly helpful.  Instead, we depict how the three entities have changed their holdings since 2007:Q4 (click to enlarge).  Of the new debt, the public has purchased 41%, the federal reserve has purchased 14%, and foreign entities have purchased 45%.  

Any dramatic stories you hear about U.S. debt eschew the facts in favor of hyperbole: disbelieve them. Debt has risen sharply, but none of these three entities has purchased more than 50% of new U.S. bonds.

Note: Millions should read billions in the relevant graphs!  (E.g. U.S. debt has been in the 16 trillions range recently.

SNAP Benefits by Income and Household Size

Below, Corrections depicts SNAP (food stamp) benefits by income and household size (click to enlarge).  We assume no elderly individuals in the household, but do assume 3% of income is spent on childcare.  ($300/year for (So an income of $30,000 would spend $900/year on child care).

Note the sudden drop-offs. For a family of two, going from earning $19500 to earning $19,750 (earning $250 more) sees a net decrease of total income (including food stamps) from $19,935 to $19,750:  a decrease of  $185 in total income in return for earning $250 more prebenefit.

Whatever one's beliefs about government programs, everyone can agree it makes little sense to implicitly tax the poor at rates above 100% (end up with less for making more), as programs like SNAP do.  

How Big Were the Payroll Revisions this Month?

The news today was filled with hyperbolic reports of how bad the revisions to payroll growth have been.  Below, Corrections depicts nonfarm payrolls from April 2012-August 2013 with and without August revisions (click to enlarge).

 Below, we offer a more blown up view (click to enlarge).

The revisions represented about half a month's loss in payroll employment growth.  Unfortunate, but not the steep revisions many news reports suggested.

Long-Run Geometric Annual Return by Industry: 1970-2012

Below, Corrections depicts the long-run geometric annual return by industry, from January 1970-December 2012, from Kenneth French's industry data.

Probability a Person Lives with Parents by Age

Below, Corrections takes the cross-sectional data on 2012 from the Current Population Survey and calculates the probability that one is the child of the head of household, by age from 16 to 85 (click to enlarge).  The probability a child is head of household with a parent present is small (single-digit percentages), even as they grow older (not shown).

Additionally, we display the time series evidence for four ages:  23, 25, 27 and 29 years of age (click to enlarge).