The Index of Economic Freedom suggests Economic Freedom is Unimportant for Growth

The Index of Economic Freedom is correlated with GDP/capita.  Whatever our qualms about the Index's creation (it is fatally flawed), the manner in which it is almost always cited is wrong.  Not only is the instrument useless, but the traditional conclusions made with it are logically fallacious.

Corrections notes that in order for the Index of Economic Freedom to be useful in discussing growth, changes in the index should correlate with changes in GDP per capita growth.  Below, we take the difference in the rankings for the IEF and plot them against the difference in GDP per capita in Geary-Khamis dollars (PPP) (click to enlarge).  96 countries have data in both the Penn World Tables and IEF in 1995 and 2010.

The fit is so poor that a traditional OLS regression finds IEF on real per capita GDP change is worse than chance (it explains less of the variance than the average totally random sample would).  

Disability Insurance over Time

Below, Corrections depicts the number of people accruing disability benefits as well as the number of disabled over time (wives and children can also collect benefits) (click to enlarge) along with benefits (click to enlarge):

 We can normalize the count by looking at disability benefit counts divided by population (click to enlarge) and real benefit amounts (click to enlarge).

 Finally, we plot them all, normalized to January 2000 (click to enlarge).

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.

Decomposition of the U.S. Federal Deficit: Receipt Shortfall & Expenditure Excess

 Below, Corrections decomposes the reasons behind the U.S. Federal deficit as a percent of GDP.  We attribute a deficit to two reasons:  a shortfall in revenue, or an excess of expenditure.  Because the U.S. Federal Government has run a historical deficit (receipts average 17% and expenditures have averaged 19.9%) we close the historical gap by blaming both receipts and expenditures equally:  the "baseline" for both is therefore 18.7%.  

Our method of decomposition is to take the deviation of each from its historical norm and attribute that portion of the deficit to its deviation, as the two deviations will always sum to the deficit that year.  For instance, if revenues ran at 18.6% while expenditures ran at 19%, then we would have a deficit of 0.4% per year:  0.1% of it would be attributed to revenues, and 0.3% would be attributed to expenditures.

Finally, we graph both the levels and the combined contribution of both (click to enlarge).  The blue and red lines represent the simple contributions of each to the deficit, and add up to the black line, which denotes the deficit.  The blue and red areas depict the stacked expenditure and receipts, and also sum up to the black line.

Our takeaway is that from 2008:Q4 to present, expenditures have been 4.62% above historical norms as a fraction of GDP, while receipts have been 2.32% below, giving the "reason" for deficits to be 33.4% receipt shortfall, and 66.5% expenditure excess.

There are, of course, other decompositions one can offer:  perhaps a more promising one would be to attribute a constant growth rate to the level of GDP, expenditures, and revenues, and decompose the shortfall into three parts:  a fall in the trend growth of GDP, a rise above trend in growth of expenditures, and a fall in the trend growth of receipts.  

Employment Falls and Recoveries: By Industry

Below, Corrections depicts the loss and gain of jobs over the 2007-present business cycle.  We measured the industry peak (defined as the maximum employment between May 2007 and April 2009) and the industry trough (defined as minimum employment between May 2007 and December 2010).  This difference is the "millions of jobs gained between Jan-2007 to Industry 2010 Trough," and is negative for all industries, denoting a loss of employment.

We then calculated the gain from that trough by taking the present employment and subtracting the trough employment, and graphed the two against one another (click to enlarge). Finally, we included a -45 degree line.  Being above that line means expansion from trough past industry peak:  mining, leisure and hospitality, education and health, and professional and business services all succeeded in expanding past their old peaks.  Being below that line means failure to expand past your old peak.

Remarkably, only government (Federal, state and local) jobs fell both during the recession and the recovery, though both losses were fairly mild.  A second graph includes the same procedure for the entire economy (click to enlarge).  While we lost 8 million jobs, we have regained 6.4 million jobs, and with average net job growth in the last 12 months at about 182,000, we should reach that peak in 9 months following June 2013, around March 2014.

GDP Per Capita as a Fraction of US GDP Per Capita over Time

Below, Corrections depicts the GDP per capita as a fraction of US GDP per capita for a series of OECD countries (click to enlarge).  GDP per capita is in constant prices, PPP with 2005 as the basket reference year.  We exclude Luxembourg and Norway, the two countries that have higher values than the U.S. in these terms.  A few things stand out:

1. Korea's growth in the last decade has been astounding.  Far faster in per capita terms than China's.
2. China is exceedingly poor.
3. The Russian Federation is exceedingly poor.  
4. Ireland's "Celtic Tiger" growth is astounding, even taken with its catastrophic fall.

Labor Force Participation Rates by Age

Below, Corrections depicts labor force participation rates by age (click to enlarge).

FDA Approval Times

From the article "An exploratory study of FDA new drug review times, prescription drug user fee act, and R&D spending," Corrections depicts the time until FDA approval from submission (click to enlarge).

A hastening of the review process by about 3.6 years to 1 year represents an reduction by 71% of wait time:  a dramatic technological improvement (or reduction of waste) accessible to the pharmaceutical industry, holding quality of drug constant.

Detrended Productivity, Hours/Capita and GDP/Capita

Below, following and extending the data of Cociuba, Prescott and Ueberfeldt (Simona Cociuba's website), Corrections displays Productivity (Production/Hour Worked), GDP/Capita (Production/Person) and Hours/Capita (Total Hours/Persons) (click to enlarge).  Population is restricted to ages 16-64.

All series are detrended:  any straight horizontal line therefore represents "typical" constant exponential growth in growth variables (production/cap and productivity).

Particularly interestingly, productivity growth has been lower than trend, rather than higher.  Firms are not "squeezing out more per hour."  Or they are, but the rate of increase at which they are able to squeeze out more per hour is lower.

Disability Rolls-II

Below, Corrections displays the growth of Disability Rolls, to supplement the comparison below (click to enlarge):  as explained, we conservatively imputed (likely overstated) the growth of rolls in 2013 and their decline as a proportion of population.

Changes in Government Purchases and Investment as a Percent of GDP: By Presidency

Below, Corrections depicts the change in government expenditure as a fraction of GDP by President (click to enlarge).  (To be clear, if your predecessor ended with a share of 18%, and your first quarter was 18.1%, then this graph would show 0.1 for your first quarter).  

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.

Yield Curve

Below, Corrections depicts the daily yield curve from Jan1990-Aug2012 (click to enlarge).

Below, Corrections takes each day's yield curve and breaks it up into a constant, slope (by duration), and quadratic (by duration squared) term (click to enlarge):

Finally, we normalize each components to have mean zero and standard deviation one, and graph them together (click to enlarge).  Insofar as interest rates predict bad times, the three components point to lower growth (the low level, shallow slope, and less curvature).   

Business Employment Dynamics: Where Jobs Losses and Gains Come From

Below, Corrections graphically depicts transformed Business Employment Dynamics data.  The two data series are the proportion of gross job losses generated by closing establishments, rather than contracting establishments (click to enlarge).  Similarly for gross job gains generated by opening establishments, rather than expanding establishments.

Three things seem to jump out of the figure:

• Generally, around 20% of gross job gains and losses come from opening and closing establishments.
• Compared to the proportion of gross job losses that come from closings, generally a higher proportion of gross job gains come from openings.
• There has been a secular downward trend in the impact of closings and openings on employment.

The last point is probably bad news for the U.S. economy.

US GDP, Log GDP, and Percent Deviations from Trend

GDP from 1947-2011, log GDP for the same period, and deviations from that log trend (which can be interpreted as percent deviations).

AAA vs. BAA Bond Spreads

Below, Corrections takes the difference between daily AAA and BAA rated bonds (Moody's, from the FRED database) and displays recessions (click to enlarge).

When the data from Operation Twist and the subsequent days become available, we'll put those graphs up too.

U.S. Log GDP with Error Bands of +/-3%

Below, Corrections depicts our own version of Figure 2.4 in Ed Leamer's Macroeconomic Patterns and Stories.  It depicts, from the perspective of 1970, log GDP.  Then, from 1970+3% GDP and 1970-3% GDP, it simulates a permanent 3% growth trend.  One can see that growth, remarkably, stays within this rather narrow corridor.  

Forecasting June's Payroll Change

Below, I depict on the Y-axis the change in payroll jobs.  On the x-axis the average growth in payroll jobs in the three months preceding that month.  The red line is the average growth in the three months preceding July 2011.  This joint distribution may give an idea of what to expect from this month's payroll figures.