CHANGES IN U.S. MANUFACTURING

Aside from the persistent and important question of manufacturing job losses in the U.S., and whether these losses can be regained or at least stopped, it may be instructive to see the changes in the manufacturing industry's structure over the last 20 years or so. Changes in the structure, regardless of the job losses, have an impact on the average wages paid in the sector and, thus, impact the incomes of U.S. consumers.

As I pointed out in an earlier post in this blog, manufacturing shipments have been relatively robust. Overall they have followed a positive trend, of course allowing for declines associated with economic recessions. Since 1992 shipments have doubled, translating into a 4.4% annual growth rate.
However, during this same period prices measured by the GDP Implicit Price Deflator rose by slightly over 50%; that is a 2.4% annual rate. After deflating the shipments data we find that the 4.4% annual growth is only 1.3% in real terms. Naturally, applying such a broad price measure does not give an accurate measure because price inflation varies among industries. Case in point is the sharp drop in petroleum and natural gas over the last couple of years, a decline that did not result in similar price reductions in other industries that depend in oil products.

Changes in Industry Structure
Since 1992 there have been several important changes in the structure of manufacturing, as one would expect. An economy is not a static entity but, rather, is one in which change is prevalent. At the micro level, old factories close and new factories spring up, production in individual factories goes up and down depending on the whims of consumers who may want more or less of the products made by those factories. At the macro level, we see new industries being born making products that nobody may have thought of up to that point, event though many make claims to it, or we see whole industries lose significance or disappear altogether.

The chart to the right compares 21 broad manufacturing sectors in 1992 and 2015, where we use shipments data through September for each of the years. The charts allow us to see two types of changes. One is in the percentage of manufacturing accounted by each sector, such as the 1.4 percentage points gained by Transportation Equipment (that includes autos, trucks, aircrafts, etc.) or by the Food sector.
The second type of change is the relative ranking of the various sectors within manufacturing- these changes are indicated by the green and red arrows and they highlight significant changes. Such is the case of Petroleum and Coal that, unsurprisingly, jumped to number four with 5.1 percentage point increase in share. At the same time we see sectors such as Computer and Electronics that fell three places to number seven with a 3.4 percentage point decline. Although I am not investigating causes for these declines, one can surmise that this reflects production shifted to overseas locations for cost considerations.
The good thing is that four out of the top five manufacturing sectors, that account for nearly half of manufacturing shipments, are also among the ones with the highest hourly earnings; the exception is Food Products Manufacturing with workers in this sector earning 24% below the average hourly manufacturing wage.

Manufacturing Losing Ground
Compared to other industries within the U.S. economy we find that manufacturing is not keeping up in general with overall growth in the economy. In terms of total output, manufacturing accounted for over one quarter of the output of all private industries in 1997 (the earliest year for which we have data). By last year, the share of manufacturing had fallen to just over a fifth of gross output.
Some of the decline in manufacturing is due to the long-term shift towards increased reliance on services. For instance, health services is an area where usage has increased, as shown by the two percentage points increase in the charts to the right. An given Obamacare's mandates, we should expect health's share of gross output to increase further in the next few years.

HOUSING STARTS AND EMPLOYMENT

Is there a relationship or link between new jobs and housing starts? Economic reasoning would logically lead you to answer positively- the greater number of people who get a job, the more likely some of them would purchase a new home. Of course this is all contingent on other factors such as the availability of vacant housing, ability to get a mortgage, and a growing population, to name a few. That is, any direct link between new jobs and new housing is constrained by demographic and other factors.

However, the search for a way of predicting the course of new housing starts led some economists, a few years ago, to posit a fixed or constant relationship between new jobs and housing starts. They sought and came up with a constant value that could be used as a rule of thumb to calculate how many new houses we should expect given the growth in employment. Today I examine whether such a constant does in fact exist or can be calculated meaningfully. I do this analysis at two levels; first at the national level looking at aggregate data that we normally see on a day to day, and then using the less commonly seen metropolitan area. Does such a relationship hold?

U.S. New Jobs and Housing Starts
At the national level we find that the ratio of new jobs to starts hovers somewhere between zero and three, ignoring those periods when employment falls in negative territory. A simple mathematical average using data from 1960 on, a calculation that one can always do with numbers even if the result is totally meaningless, shows the ratio for the U.S. equal to 1.1. Taking this number as a rule would mean that a new job translates into a slightly more than a new housing start. Applying this ratio to the number of new jobs between 2010 and 2014 results in over eight million housing starts driven by the new jobs created. But in fact, over this five year period, there were 3.9 million new houses started, and not eight million as the ratio suggests.

In reality, as a visual inspection of the graph to the right clearly shows, there is no stable ratio value. The blue line is the annual ratio of new jobs to housing starts going back to 1960. The red line reflects the 1.1 average over all those years (I excluded the years since 2008.) The way the blue line fluctuates around the average red line shows that the average does not carry much predictive power. In many years it underestimates the ratio and, conversely for others the ratio is overestimated; the size of the discrepancy is of an order of magnitude of more than two. One can only conclude that if indeed employment growth leads to new housing construction, we can't say with any degree of confidence how large or small the impact will be; that is, how many housing starts we should expect from employment growth alone.

Is there a ratio for MSAs?
We find that the relationship new jobs to starts is even more tenuous at the local level. We examined data for the ten largest metropolitan areas in the U.S. based upon the number of housing permits; we are using housing permits instead of starts, since the latter are not readily available for metropolitan areas.

A review of these data, graphed on the right, shows that there is not a consistent pattern for these metropolitan areas. In fact, the opposite seems to be true, there is great variation both within and between metro areas. Following any single line, for instance looking at the top line that is Los Angeles, we can see that the ratio ranges from 5 to 11- a huge difference. Also looking at the values for a given year, say 2011, we can see large differences between metro areas. The ratio can range from a low of around 3 for Atlanta to nearly 10 for New York. Thus there is not an accurate rule of thumb that we can use given there is not a stable value that can be used for any of these metro areas.

All of this simply shows that suggesting a specific number of housing starts given the growth in new jobs is close to economic nonsense. It is correct to say that more jobs will likely lead to more housing starts, the same that it will lead to more automobile sales or any other consumer product. People work because they want to buy stuff. However to say that X number of new jobs will produce exactlyY housing starts is a totally false statement.

WATCH OUT FOR MISLEADING DATA!

The Social Security Administration, the agency managing the largest Ponzi scheme in the world that some would call the "mother of all Ponzi schemes", has just released data on the wage and income compensation paid to workers in 2014. The data is somewhat interesting, mostly from a curiosity angle since its analytical value is minimal. Nonetheless, I think it's worthwhile reviewing it, and pointing to its limitations since the data may easily lead the unwary to incorrect conclusions. In fact, I've seen a couple of references to this data drawing incorrect comparison to poverty levels.

The data shows the total wage income of individuals that is subject to social security payments. You can look at the raw data for last year here: https://www.ssa.gov/cgi-bin/netcomp.cgi?year=2014. Data for previous years is also available in the same web page.

The chart to the right shows on the horizontal axis the wages and income paid to all workers in the U.S. in 2014, in ranges of five thousand dollars up to those who earned $200,000. From that point the data are grouped into $50,000 ranges up to the million dollar mark, etc. On the vertical axis is the number of workers falling within each range. We can see the first point on the left represents 22.5 million workers, earning each under $5,000 a year; this is just over 14% of the workers. Wow, is the data telling me that one out of seven workers in the U.S. made less than $5,000 last year? Things are pretty bad then.

Well, not really. This is where the data is misleading if it's interpreted wrongly. First of all, the data includes all workers; teenagers working part time in the Summer, retired workers who also work part time in temporary jobs, housewives and others who take temporary jobs around the Christmas season, etc. Secondly, many of these workers are not the primary income earners in a household, they may have a spouse who is the main income earner in the family. That is, this is not the normal household or family income data that is more meaningful for analysis. For instance, the median household income is currently about $53,000, but using the Social Security data we find that the median compensation per person is about $44,500- the difference is made by other working persons living in the same household who presumably contribute to the household's economic wellbeing.

Comparisons to other data, such as poverty levels for instance, should not be made using these data. Poverty levels are usually meaningful only in terms of the number of persons who live within a family, while the social security data above does not provide a hint whether the person is living alone or with other members. If one were to use these data to estimate the number of people who are below the poverty level, we get that about 40.5 million individuals fall below the poverty line of $11,670 for one individual- this is 25% of the wage earners. But this is the wrong conclusion, as I said, because that individual may or may not be part of a larger family.


However an interesting comparison is to see how the data changes over time. The chart to the right displays the changes in the number of wage earners over the ten years ended in 2014. The horizontal axis displays data for individuals making less than $100 thousand, split again in groups of $5,000 each. The bars highlight changes over two 5-year periods; one is the change from 2004 to 2009 shown in the blue bars, and from 2009 to 2014 in the red bars.
It is immediately apparent that the number of individuals earning less than $35,000 fell over the ten-year period; it fell by nearly 8.4 million workers (although it's hard to discern that from the graph alone.) The graph suggests that most of that change occurred between 2004 and 2009, when in fact the number of individuals making less than $35,000 fell by 6.9 million.

Another caution with the data can be inferred from the last paragraph and chart. Yes, the number of workers earning less than $35,000 fell sharply; this is not necessarily because their incomes improved but because many of them lost their jobs as a consequence of the 2008-2009 recession.

GDP - THE MEASURE THAT ISN'T

In a recent post I discussed the issue of accuracy of economic data, focusing particularly on Gross Domestic Product or GDP (http://econlives.blogspot.com/2015/08/us-growth-worse-than-we-thought.html). Here I want to address misconceptions commonly held about the definition and content of GDP itself, that lead to misunderstandings and incorrect policy prescriptions. It is commonly held by many people, including journalists and economists, that GDP is a measure of an economy's size, and sometimes even a measure of an economy's health and the wellbeing of the population. These misconceptions lead people to think that an increasing GDP, commonly referred as GDP Growth, is the end-all purpose of an economic system. Positive GDP growth is automatically taken to be a good thing, that unquestionably it is a sign of progress. Moreover, a high GDP growth rate is automatically seen as better and more desirable than a low one. This high growth is interpreted as a signal that the economy and the well being of the population are improving. But in reality such is not always the case, these interpretations are often incorrect.

GDP is not the same as Production
The most commonly made mistake is to confuse GDP with production within a region, the U.S. in our case. Typical is the view of Sho Chandra, for instance, a reporter at Bloomberg who recently referred to Gross Domestic Product as "the value of all goods and services produced." Also, the common error of equating GDP with "total" production can be found not only in Wikipedia, as one would perhaps expect, but also in statements by such venerable institutions as the OECD, that defines GDP as

"an aggregate measure of production equal to the sum of the gross values added of all resident, institutional units engaged in production"

Which is not an improvement over the commonly held understanding of Bloomberg and others.

Also, the U.S. agency that comes up with the GDP data, the Bureau of Economic Analysis, defines GDP slightly different by adding the phrase

"less the value of the goods and services used up in production..."

This statement brings to the forth a key point to understand what GDP measures. It does not include any goods or services that were used in the production of other goods. For instance, a chair that is purchased by a consumer in a given quarter will show up under Consumption in the GDP accounting. But the materials used to make that chair, such as the wood, glue, nails, paint, etc. are not counted on GDP because they want to avoid the so-called "double-counting." All those things (wood, nails, etc.) were produced but are not counted, thus GDP is not a measure of production as such. A true measure of production would include all those "intermediate" goods that are used to make the final goods that consumers purchase.

A more subtle misconception is the implicit assumption that GDP represents the value of things or services produced in the period in question, whether it's a quarter or year. The first term in the equation making up GDP, "consumption," attempts to capture the purchases of goods and services made by consumers in the quarter in question. But those purchases may be of goods that could have been produced in a previous quarter or year. Naturally perishable goods such as food items, were very likely produced within the same quarter; but this may not be the case with more durable items that last longer than a quarter, such as cell phones, appliances or canned food, that were very likely produced in previous quarters once we take account of the time lapsed between their production, inventorying and shipment to the final destination where a consumer may purchase it. That is, consumption of goods in a specific quarter is not equal to production of goods in that quarter.

Thus, the largest component of GDP, consumption, does not truly reflect production but rather consumer purchases of goods that may have been produced at different times.

GDP is not Demand
Another common mistake is to equate GDP with demand, such as the statement coming from none other than the chief economist at JPMorgan Chase, who said that this is a "pretty broad-based pickup in domestic demand." This also is not entirely true when we see that one of the components of the arithmetic definition of GDP as

GDP = Consumption + Investment + Government Spending + Exports - Imports

Includes the element "Government Spending" which reflects the amount of money that the various governmental entities spend on providing "services." Now, only a confused mind would equate the services of government, many if not all of which are foisted unto the public whether or not such public wanted them. So, in a strict sense, government services can not be equated with demand and therefore defining GDP as national or domestic demand is incorrect.

GDP does not represent the health of an economy
But the most troublesome, and perhaps misleading, interpretation is to take Gross Domestic Product as an indicator of the health of an economy. This is a common misunderstanding. Investopedia, a website that is presumably designed to give advice to investors, defines that GDP is one of the primary indicators used to gauge the health of a nation's economy. But, why is it wrong to equate GDP with an economy's health?

After hurricane Sandy hit the Northeast back in 2012, Forbes published an article pointing out that the devastation caused by the hurricane could reach $50 billion but "at the end of the day, Sandy may end up being beneficial to the U.S. economy." (Forbes, Nov 6, 2012). In addition to other comments touting how despite Sandy leaving "many casualties in its path...it could have a positive economic impact in the near-term." The conclusion one draws is that disasters are good for the economy since we should expect the post-disaster reconstruction and rebuilding to boost GDP. Somehow they just see the cost of reconstruction as a good thing but ignore the huge losses of wealth caused by the disaster. The economy is not healthier because of the post-disaster spending- it is poorer because what is lost can never be recovered.

The economists at Forbes, Goldman and similar outfits who focus on the growth resulting from a disaster, fall prey to the broken window fallacy. They only see the spending that occurs after a disaster but ignore two important things. One is the loss of wealth that occurs in a disaster that is much greater than the spending after the disaster. The other is the fact that the funds used in the post-disaster reconstruction have been diverted from other uses. The broken-window fallacy was cleverly discussed by the French economist Frederick Bastiat more than a century and a half ago (here is an article explaining the fallacy in a more recent context https://mises.org/library/broken-window-fallacy)

GDP does not reflect how an economic system works
Another misinterpretation is to take this definition of GDP as a true representation of how an economic system works. Thus, the equation becomes a tool that can be used to finesse the economy's performance. If GDP is falling, or even GDP growth slowing down, the usual prescription is to try to change one of the components, such as increasing government expenditures, and by definition the problem is solved. Such was the case after the 2008 recession when the Federal government engaged in extraordinary spending to boost GDP. But all this spending did not improve economic performance- GDP growth has remained anemic since then.

Unfortunately they do not realize that the economic system is not like a machine whose performance can be improved by moving some levers.

What is the alternative?
In reality there is not a single alternative to GDP. Not even the silly concept of Gross National Happiness that was introduced about 50 years ago in Bhutan, a country that ironically at that time was an absolute monarchy. The solution lies in taking a broader view and inspecting a number of economic statistics, such as employment indicators, production statistics, price information, etc. Only such a holistic view can give a true assessment of the health status of an economic system and whether the economy is prospering or not.

In a future post I will discuss Gross Domestic Output, an alternative measure that the Bureau of Economic Analysis has been releasing periodically and that can serve as a better measure of the nation's production output. 

JOB GROWTH - WHAT SHOULD WE EXPECT

Last week's release of employment figures for the month of August came in below "expectations," which brought further losses in the stock markets since the weak jobs figure does not provide any indication on the direction of the Fed's actions in the near future. While expectations hovered around 220-225 thousand new jobs for August, employment grew by 173 thousand jobs, a disappointing number. Moreover the private sector added only 140 thousands workers to its employment rolls, the lowest since March of this year when private employers added only 117 thousand new jobs. Year to date, total employment has increased by just under 1.7 million, this is over 10% below the comparable figure for last year, when employment through August already was 1.89 million.

Given the size of the U.S. economy we should ask not only the question of why are the employment numbers so low, but also whether those expectations are warrranted. Should employment in the U.S. grow by over 200 thousand workers month after month? Or should this figure be higher yet? This is an issue that I examine in this post and come to the conclusion that indeed we should expect employment to increase by more than 220 thousand workers, in fact historical data suggest we should be adding between 300 thousand and 350 thousand new jobs a month.

Strong Job Growth

When we look at number of new jobs created month after month, we get the impression that job growth since the end of the recession is comparable if not better than what we've seen in the past.The chart to the right displays the number of new jobs created annually since 1946 (the annual data are not calendar years but, rather, are calculated as the 12 months ended in August of each year, in order to include the latest data available.) We can see that in the last five years, 2011 to 2015, jobs have grown by a solid two million or more per year, this is better than any other five year period with exceptions- the 1990s for instance.

But we must realize that employment growth of two million when the total base of workers is around 140 million is very different than when employment is smaller than this level, as in the late 60s when U.S. employment averaged less than half of today's with fewer than 62 million workers total.

Weaker relative growth
Consequently, when we look at the number of new jobs compared to total employment, we get a different pattern; this is shown on the chart to the right. The top graph is the same as the one above, "U.S. Jobs Growth- 000s", except that we are excluding all years in which employment fell. The red line is a moving average that reflects a five-year trend.
The bottom graph displays the percentage change in employment, with the red line also indicating the five-year moving trend of the percentages. We can easily discern that employment growth since the beginning of the century is under 2% annually, which contrasts with that maintained in the second half of last century that averages nearly 3%.

What should be the job expectations number?
Summarizing the data into decades allows for easier visual interpretation. This is done on the chart nearby where we display percentage job growth in ten year periods since 1946- again we exclude all years in which employment fell.
We can see that job growth over the last ten years, at 1.7%, lags any other 10-year period since the end of the Second World War. Naturally the 4.3% growth seen by 1955 captures the robust private employment that absorbed all the soldiers discharged at the end of WWII. In the mid-1980s, the second highest period, reflects the impact of tax changes and other policies implemented in the early 1980s.
All in all, employment growth between 1946 and 1999 averaged 2.9%.

Thus we can calculate what job growth would have been in the last five years if we maintained this 2.9% average. This is shown on the table to the right.
On average, we should expect nearly 350 thousand new jobs added every month to stay in line with our historical growth. Last year, only three times did employment increase by more than 300 thousand jobs. The figure, which may seem high to some, should obviously be expected. The question that should be asked is why aren't we achieving these results more frequently? A proper answer requires an in-depth investigation of economic policies and their impact on employment and the nation's economy overall. But this is the subject of a future post.

U.S. GROWTH - WORSE THAN WE THOUGHT

Once a year the Bureau of Economic Analysis revises the Gross Domestic Product data series, from which the often-quoted GDP growth is derived. The revisions aim to improve the quality and accuracy of the data, and they are the result of a very extenuating evaluation of the source data used to calculate GDP.  The revisions typically show several major findings, however the media tends to focus on overall growth figure, such as the headlines following Friday's release of the second quarter, 2015 estimates:

"Economy bounces back: GDP grows 2.3% in second quarter (USAToday)"

"First reading on Q2 US GDP at 2.3% vs 2.6% expected (CNBC)"

this is all true. Reading further in those articles sometimes we may read commentary on the impact of those revisions, such as the latest one which resulted in a lowering of GDP for both 2012 and 2013. 

Although, as the bottom graph on the chart to the right shows, some quarters were revised upwards and others downwards, the net impact has been to reduce growth over the last few years. While previously average annual growth was pegged at 2.3% between 2011 and 2014, currently the estimate is for only 2.0%. These dismal figures reinforce the weak growth that the U.S. economy has had since the end of the recession, a full six years ago. 

Annual growth since the second quarter of 2009, when the recession was officially declared over, has averaged only 2.1%. This is close to European standards, the example that Krugman and other economists wish the U.S. should follow; that is, adopt policies like the European countries have favored for the last 30 years or so, and that have brought them continuous stagnation, high unemployment, and unbearable debt loads.

Failed Government Policies

But the sad truth is that all the "GDP boosting" policies of both the Federal government and the Federal Reserve Bank have failed. The Federal government debt has nearly doubled since the third quarter 2007, at the onset of the recession. At that time, debt amounted to $10.2 trillion, and it has jumped to $18.2 trillion today for a total increase of $8.1 trillion- this is an 81% increase in just seven years. To put it in a dramatic perspective, in seven years we've acquired debt equal to that incurred in the 226 years between the time the U.S. became a country, in 1789, and 2005- a remarkable feat. 

At the same time, the Federal Reserve Bank has increased what is misleadingly called its balance sheet, by well over $3.8 trillion. Just out of thin air, the Fed has purchased $3.75 trillion worth of U.S. securities since August 2008. That is, in the earlier period it held securities worth $479.6 billion, and today it is the proud owner of $4,231 billion of U.S. securities. It is interesting to note that nearly half of the U.S. government debt issued since the recession has been acquired by the Fed; that is, it has been monetized. 

But all of this spending and money pumping has not produced anything other than creating a stock market bubble. The impact on the economy has been minimal, at best. Actual GDP has risen by only 9% in total since the recession onset. Real GDP in the fourth quarter of 2007 was running at an annual rate of just under $15.0 trillion; it has risen to only $16.3 trillion since then. This translates into a 1.1% annual rate over the 30 quarters in this time. This is very poor performance indeed; both by itself and compared to our experience in prior recoveries. The bottom graph on the chart to the right shows the U.S. performance in all recoveries since the end of the Second World War, beginning with the 1949 recession. Since it's been 24 months since the end of the Great Recession (that should be called the "Lingering Recession" perhaps), we calculate average GDP growth, at annual rates, for the same period after each recession ends. The lowest average growth for all post-recessions is the latest one, at only 2.1% - clearly this is the worst by far of all other periods.

How Accurate are the Data?

One issue that is usually ignored in all the discussions on GDP growth is the accuracy of the statistics. Aside from the fact that they try to measure perhaps the wrong things, the revisions suggest that the data can not be reliably used as a guide for business. Take the first quarter of this year. The initial data release suggested that the economy had actually slowed significantly, in fact it had gone backwards since GDP shrank two-tenths of a percent (-0.2%) (what many economists state nonsensically as "negative growth," an oxymoronic word if I ever heard one.) But contrary to the initial release that the economy had shrunk in the first quarter, the revised figure tells us that in fact the economy grew modestly by slightly over half a percent. And these revisions are a common occurrence. I am not suggesting that I would want them not to revise the data, but that we should look at these figures with some degree of skepticism.
A few years ago, well actually many years ago, the economist Oskar Morgestern (see the brief biographical note below) wrote a book aptly titled On the Accuracy of Economic Observations. There he deplored the practice of government agencies issuing data that typically give the impression of being very accurate and precise, while they have a high degree of uncertainty. In all fairness, I should admit that the agencies usually footnote the sampling errors in the data, but they are not strongly highlighted. Analysis of the revisions to historical GDP data and their revisions, reveals that the growth rate for a specific period may be revised in the future by an average of 1.6 percentage points. For example, this means that a 2.5% GDP growth rate could end up being either as high as 4.1% or as low as 0.9%- which is a wide variation in fact.

EconLives
My plan for this blog was to provide brief biographies of economists from time to time. Let me start with Oskar Morgerstern.

He was an economist born in Germany  in 1902, grew up in Austria and studied at the University of Vienna, where he obtained a PhD in Political Science in 1925. After this he attained a scholarship from Rockefeller Foundation to study in the U.S., where he stayed until 1929. Subsequently he moved back to Vienna and taught at the University of Vienna for a few years until an invitation to visit Princeton University came in 1938. He was in the U.S. just about the time that Hitler took over Austria in the infamous Anschcluss, so Oskar decided it was wiser to remain here. He was a faculty member in Economics at Princeton until his retirement in 1970, when he took a position in New York University until his death in 1977 (like many people today he continued to work past retirement age!)
Besides the book on economic measurement mentioned above, he wrote jointly with the mathematician John Von Neumann the first book on game theory, titled Theory of Games and Economic Behavior, the book for which he is best known. Morgenstern was one of the economists in the "Austrian Schoool" of economics- a group known for voicing an economic theory radically different and opposed to the Keynesian paradigm. He worked with Ludwig von Mises, Friedrich von Hayek and others while in Vienna, although his work on economic theory tended to be more technocratic, and emphasized a mathematical approach that is anathema to the Austrian school. But at heart he was a strong believer in free markets and free association.

Game theory has been famously brought to prominence recently by Yanis Varoufakis, who was touted and feared as an expert in game theory when he was appointed as Finance Minister in the Greek government. Underlying all the media statements and opinions when Varoufakis was appointed, was the implication that the Greeks had now the upper hand in all discussions with the European Union, and the Euro group primarily, regarding the huge Greek debt load. Yanos had in game theory the silver bullet that would solve the problem. We all know now how it all ended up. The Euro group, dominated and led by the Germans, particularly by the influence of Wolfgang Schauble the German Finance Minister, crushed the Greek government's claims and forced them to surrender their position. Game theory met its match, German might.

EXPANDING WORLD...SHRINKING COUNTRIES

The recent news that Japan's population count continues to fall brought to mind the similar seemingly irreversible trend that other countries are facing, several of them in the developed first world. We see an scenario like Japan's playing for those countries over the next years. But also, more revealingly, many of these countries with falling population are in what was formerly the Soviet Union or its satellites, such as Cuba. Russia itself, still the 9th most populous country in the world, has seen its population drop dramatically since the fall of the Iron Curtain. However, as the chart nearby shows, Russia's population appears to have stabilized this decade- perhaps Putin's expansionary policies of slowly swallowing the Ukraine, after taking Crimea last year, may be working after all. But aside from this lull in the long term trend, Russia's population is projected to fall a further 2% by the year 2015.

Shrinking Countries
In total, we find that 23 countries, out of the 158 with population above one million inhabitants, will see their total population fall over the next 10 years. The majority of them, shown in light blue bars, were part of the former Soviet Union. In fact, only 4 out of these 23 are outside the former Soviet Union area. Most notably is Germany, the so-called economic engine for Europe, whose population 10 years from now will be about 2% lower. And poor, sad Greece, on top of its intractable economic debt problems, will see its population fall by another 1% over the next decade. This trend only compounds the likelihood that it will never be able to pay its debts.

Now a declining population is not necessarily a bad thing; in fact, if you are an ardent follower of Paul Ehrlich you'd think it's a good thing. But ignoring Ehrlich and his always-wrong predictions about the end of the world caused by high population growth and limited resources, the fact is that the declining population in these countries is accompanied with fewer working-age people who are expected to sustain its elderly folks. And this is where a crisis could originate.

Thus we find that many of these countries also face an "elderly population" problem, that is an increasing elderly population with all the potential burdens that this implies. Living longer is a blessing if one is a healthy individual; it could be curse if infirmities take over the body. Although there is no hard and fast rule at which point an elderly population becomes an onerous burden, we can assume that a critical point may be reached when this group exceeds one fifth, that is 20%, of the population. Given the relatively high unemployment rates in these countries, except Germany of course, combined with declining labor participation rates, we find fewer working people forced to sustain the elderly population (through taxes or debt to maintain the levels of support services required.

The chart to the right displays the percentage of the population that is 65 years and older for the 23 countries whose population is falling. Nearly half of them are above 20%, and several others are hovering just under this percentage.
The chart shows that leading all is Japan that by the year 2025 will have over 30% of its population aged 65 years and over. Germany is not far behind with fully one quarter of its population in this age group; Greece posts a percentage slightly over 23%.




Rest of the World
There are several other countries, twelve in fact, that also have a relatively high percentage of their population among the elderly. These countries are shown in the chart to the right, above the 20% horizontal line. But these countries have the advantage that their population is growing- this means that the number of working people to help sustain the elderly is not necessarily falling.
many countries, xx in fact, that also have a relatively high percentage of their population among the elderly, but at least they will see their population grow over the next few years.
We see that the United States falls in this group, with population growth of 8.1% over the next decade or just under one percent per year. Canada will have about 23% of its population in the 65+ years group, but has modest growth of 7% over the next ten years. But Finland and Italy, have nearly a quarter of their population in the elderly group and show very low rates of population growth- 2.8% and 1.2% each, respectively.

Is all downhill from here?
But there is some sunshine behind these gray clouds. Many of the elderly are not necessarily quitting the workforce altogether, since they are continuing to work. This is particularly evident in the United States where we find that the labor participation rate among the "65 year and older" group has doubled over the last 10-15 years (see my post for evidence: http://econlives.blogspot.com/2015/03/fewer-workers-must-we-worry-when.html.) Moreover, as Anthony de Jasay has pointed out recently, more elderly people will be able to work in the future as the nature of work will be much less demanding physically, he says "fewer and fewer jobs in the modern world call for much physical exertion for which the old would be unfit."