Relevant and even prescient commentary on news, politics and the economy.

Stock market performance by presidential administrations

It is leap year and Wall St. is having its usual discussion of which candidate will be better for the stock market. I do not know, but it is interesting to look at the historic record. Starting with Truman makes a good comparison as Republican and Democrats have served almost the same number of years and it covers all of the post WW II era. Moreover, each party has  an admiration with two presidents –JFK & LBJ and Nixon & Ford.  In addition, each has one president that only served for four years and lost their bid for a second term.

So this comparisons shows that the average annual stock market gain under Republican has been 7.3% while under Democrats it has been more than double that at 14.9%.


stocks by president
The primary reason for this were the two republican presidents where the S&P 500 actually was lower when they left office than when they took office. In modern times only three presidents suffered a falling market over their entire term–Hoover, Nixon and the second Bush. On the democratic side, they have the president with the largest average returns – -Clinton. Maybe the question should be, can Hillary repeat her husband’s record?


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Industrial Production as a share of real GDP

I regularly see people comment about the decline in the importance of industrial production but I never see any data on how much it has declined.  Finally, I saw a libertarian blogger talk about as a point in his argument but he also revealed that he   had not idea what had happened to industrial production relative to the economy.

The Federal Reserve actually publishes the value of industrial production as part of its monthly industrial production report, so it is quite easy to get the  raw data to construct a series of industrial production as a share of GDP.  It has fallen from 33% of real GDP in 1972 — as far back as the data goes — to 23% currently.  The trend is for the share to fall 2.4% annually.

Manufacturing employment has had a similar fall, from 31% to 13.7% last quarter. The employment has seen a 2.4% annual decline.


For a while I agreed that there was an error in using this data to calculate industrial production as a share of GDP.   But after more consideration and checking with the Fed I have concluded that the original chart is correct.  There is no conceptional difference between the Fed data and the real GDP accounts.



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Federal Deficit by President

If you look at the federal deficit as a share of GDP by presidential administration an interesting pattern emerges.

Every Republican administration left office with a larger deficit than they inherited.

Every Democratic administration left office with a smaller deficit than they inherited.

fed deficits

Why should we pay any attention to anything any Republican says about the deficit.

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Wages and the Fed.

Both bulls and bears are examining wage growth for signs of incipient inflationary pressures. The current debate seems to assume that wages are completely determined by how much slack there is in the labor market and overall economy. Both conservatives and liberals seem to believe that if employment fall below current levels that wage growth must accelerate. Standard analysis seems to completely ignore the point that inflation expectations plays a significant role in the wage setting mechanisms.

I have been using a wage equation that I first developed some 20 years ago and it has worked extremely well to explain average hourly earnings growth as far back as the wage data goes, 1964. The equation has three variables, unemployment, manufacturing capacity utilization and the trailing three year change in the CPI. This is used as a proxy for inflation expectations because other measures of inflation expectations do not have a long enough consistent history. For those of you that like to duplicate work they see online, the equation does have a fourth variable that I call Nixon. It is a dummy variable for wage price controls in the early 1970s.3 yr cpi
As you can see the equation explains wages very well through the acceleration of wage growth in the 1960s and 1970 and wage moderation in the 1980s and 1990s. The only time it fails is when it called for wages to fall after the great recession. I believe this is just another example of how wages are sticky and that business had good reasons to not implement widespread wage cuts after the Great Recession.
cpi 3

The second chart shows the three year trailing CPI. At 1.3% is at the lowest level experienced since the 1950s. Moreover, it is in line with other widely quoted measures of inflation expectations. This means that low inflation expectations are offsetting some of the upward pressure on wages from the low unemployment rate and high capacity utilization. Consequently, I believe that the Fed – as well as those who have been warnings that runaway inflation is just around the corner — are overly concerned with the risk of employment gains leading to higher wages and inflation. This fed can easily leave rates at low levels with little fear that wages growth and inflation will accelerate.

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The recent stock market fall appears to be in reaction

to weakness in foreign economies, not domestic developments

in the US.




One measure to watch is world trade.

from December to May world trade volume fell -3.4%.

Interestingly, in the first five months of the 1971

decline trade fell -3.5%, essentially the same as this drop.

The year over year growth rate is 0.4%.  The year over year

change in world trade has only turned negative twice sine 1990,

as far back as this data series goes.  For what it is worth

those two declines also coincided with US recessions.

For now, the critical question is how much of the weak growth

abroad impact US growth. Almost certainly the impact is likely

to be significant.




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Why is productivity so weak?

One of the major reasons I expects sluggish growth and weak earnings is the secular decline in the growth of real net fixed nonresidential investment. The dominant factor driving productivity growth is workers being provided additional capital equipment to assist them. It is the old simple story of getting a ditch dug. Would you rather have a dozen men with shovels or one guy with a back-hoe to do the job? It is important to look at net, not gross investment, because more and more business capital spending is on high technology equipment. But high tech has a much shorter life span than traditional capital goods. Consequently, more and more of gross investment is just to replace obsolete equipment. We are having to run faster and faster just to stay even. The growth of net nonresidential capital equipment averaged 3.3% from 1950 to 1980 and 2.5% from 1980 to 2008. Over the past five years its’ average growth was 1.1%, or 0.0% growth on a per employee basis. No wonder productivity growth is so weak and is most likely to remain so..cap stock

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Economic Cycles

Economic Cycles

Even with the rebound in second quarter growth, first half growth was still under 2%. Moreover, newly revised real GDP data reports even slower growth over the past few years. For some five years the consensus has forecast stronger growth right around the corner, despite the fact that it has consistently been too optimistic. This has clearly been the weakest economic expansion on record. On the other hand, the expansion is now 73 months old, which ties it with the early 2000s expansion as the fourth longest on record. The longer ones were the 1960s expansion of 106 months; the 1980s one of 92 months and the 1990s one of 119 months.

economic expansions













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Real GDP Seasonal Factors

In recent weeks there has been much discussion about the seasonal adjustment factors for GDP understating growth. The evidence seems pretty strong and BEA says they are working on an adjustment.

But remember, the seasonal adjustment factor have to sum to one (1.00). If you change the seasonal adjustment factors to add two percentage points to first quarter growth you must also subtract two percentage points from the other quarters. Changing the seasonal adjustment factors will not change the annual value of GDP. If annual growth was reported to be 2.5% before the correction it will still be 2.5% after the seasonals are changed. This also means that the annual values for other economic data, like productivity will not change either.

Maybe the good thing that could come out of the issue is that people will give less weight to the volatile monthly and quarterly economic reports and more weight to the longer run growth rates.

Remember, the game of estimating monthly and quarterly data releases was originally started by the brokerage houses to increase volume — their profits are extremely sensitive to market volume.

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Jeff Jacoby had an interesting article in the Boston Globe on Sunday where he argue that if the demand for something doubled the price must increase—it is basic introductory economics. What price is too high for a miracle drug?

His argument is so typical of how so many columnist and/or bloggers demonstrate that a little knowledge is a dangerous thing.
In basic theory many economist draw supply and demand curve that show if demand doubles the price will increase. But most are aware that this is actually a special case where all the assumptions of the perfect competition model  hold.  But, practical economists realize that this is a special case and that there are many exceptions to this theory.

Specifically, Jacoby writes that a doubling of a demand for a medical drug must lead to a price increase.

But the drug industry is a clear case where introductory economic analysis does not work because the drug industry is one of those industries where much of the cost of bring a new product to market is sunk or fixed cost. The specific sunk or fixed cost in the drug industry is the fortunes spent on research, development and testing before a new drug can be brought to market. These expenses are capitalized and incorporated into the price of a drug. The rough and ready rule of thumb is that these sunk or fixed cost account for about half of the price of a new drug. Exactly how much of this sunk cost is incorporated depends heavily on the estimate of how large the market for the drug will be. For example, if a drug firm has $1 billion in these sunk cost and they expect to sell two billion units of the drug they can assign $0.50 to the price of each pill for the sunk cost and another $0.50 for other costs of manufacturing, distribution, advertising, profits, etc., etc., so the final price is $1.00 per pill. But if the demand suddenly doubles, as Jacoby writes, the price does not have to increase. Rather the $1billion in fixed or sunk cost can now be spread over four billion units rather than original two billion units so the cost per unit falls from $.50 to $0.25. Consequently, the drug firm can cut the price of the drug from $1.00 per pill to $0.75 per unit and still make more profits than they did when the price was $1.00.

Jacoby’s analysis is a classic example that if so many bloggers or public pundits knew half as much as they though they knew, they would be geniuses. But the real problem is that the Globe editorial staff allows such shoddy analysis to gain the credibility it gets from being published in a highly regarded paper like the Boston Globe.

Jacoby claims that the doubling of demand for the drug Maloxone completely justifies the more than doubling of the price from $19.56 to $41.43 and that state officials should not investigate that increase. But maybe,  just maybe, the state officials know something that Jacoby does not know.

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