IT'S NOT WHAT THEY SAY, IT'S WHAT THEY DO

Do presidential candidates deliver on what they promise during a campaign once they are elected? This is a question that many people this year, when we are in the middle of another campaign, are probably skeptically answering with a resounding no. There is indeed a high likelihood that most people dismiss the promises made by candidates, although there could be a general expectation that at some fundamental level the candidate may deliver on the underlying spirit of his or her promises.

Although I do not attempt to explore this question here, I will deal with a related issue. That is examine the presidential priorities as revealed in the patterns of spending the Federal budget. I will focus my analysis on the volume of Federal government expenditures under each U.S. president going back to the beginning of the Second World War in 1940.

Federal Spending Always Increases...No Surprise
Let's start with total spending by the Federal government, illustrated by the blue line in the chart to the right. The vertical lines reflect each successive president whose name is identified at the top. In a couple of cases I have combined two presidents and treat them as one, Kennedy/Johnson and Nixon/Ford, since the second president of the pair came to office by accident and more or less followed the policies of the rightfully elected preceding president (although Johnson won a rightful term on his own and may have gone much farther than Kennedy ever intended or even thought of going). Also, the breaks denoted by the vertical lines are placed one year after each individual takes office- I did this because for all practical purposes Federal spending during the first few months of a new president's term, perhaps up to a year, has been determined to a great extent by the budget adopted by the previous president. Finally, the numbers in the chart underneath each president's name reflect the difference in spending between the first and the last year of each president's term in office.
A quick inspection of the chart clearly reveals that spending by the Federal government increases steadily year after year, regardless of who is in office. The only periods where we spot a decline are those following a period of excessive spending resulting from a war (WWII in the 1940s, Korean war in the 1950s, and the Afghanistan/Irak wars in the early 2000s.) Thus, one thing we can be sure of is that Federal spending will continue to increase. Moreover, the increase in spending incurred by each successive president is higher than that of the previous office holder. In only two instances we can see a smaller increase; one is during Bush I (Bush Papa) when the $238 billion increase in spending at the end of his term was smaller than the previous president's (Reagan)- but Bush I was in office only four years, compared to Reagan's eight years. The second one is under Obama, whose increase in the rate of spending is less than his predecessor's because Bush II courageously or foolishly, take your pick, engaged in two wars, in Afghanistan and Irak. Although, as we will see below, defense spending under Obama is still double what it was at the beginning of the Bush II term.


The Change in Spending Always Increases Too
An alternative way of viewing this trend in Federal spending is to look at the change in spending relative to total Federal spending, that is, as a percentage of total Federal spending for the final year. For instance, Reagan's change in spending is almost 75% higher than his predecessor's, that is Carter; but in relative terms we can see that both are very close as percentages of total spending\- 69% for Reagan versus 66% for Carter. Similarly, we can see that Bush II's increase of nearly $1.7 trillion dwarfs the spending of all other presidents. However, this huge increase becomes in relative terms second to the spending committed by Nixon/Ford- 89% for Bush II against 125% for the Nixon/Ford duo. Note that by insisting on a relative comparison I am not suggesting that we should accept those large increases laying down.


Does Spending Reveal the Priorities of Presidents?
Aside from the overall increase in spending, that every president achieves regardless of party or political tendencies, we can get a clearer view of the presidents tendencies and preferences by analyzing the way they allocate funds among the various departments or functions.

The chart to the right ranks the changes in spending on the major government functions; again we are comparing changes in spending from beginning to end of each president's term. For ease of visual interpretation we have color-coded the principal functions. Note that some of the cells at the bottom have a red arrow- the arrows denote areas where spending actually drops under the corresponding president- Nixon/Ford take the prize for downplaying science.
We can see how increases in Defense spending loom large for many presidents; Truman and the Korean war, JFK/LBJ for the Vietnam war, Carter flip-flopping on defense (he started cutting defense spending but towards the end of his term he actually increased it.) Also Reagan and the successful military build-up that drove the Soviet Union out of business, although in some quarters there is nostalgia for that system (Bernie Sanders?),  and Bush II for the misguided wars. We can also can see how Social Security spending increases are top, or near the top, for most periods. And, as we will see below, they will continue to demand more and more increases in Federal spending as the baby boomers retire in droves.
Medicare is another function that is requiring greater spending every year, unless congress opts for modifying the law, something that is not politically palatable.

Changes in Priorities Over Time

The priorities and plans of an incoming president are normally altered by practical circumstances or unforeseen geopolitical world events. Although in the end Presidents may be able to attain and meet their plans. The bars on each of the nine graphs in the chart at the right, representing the spending shares for every president since FDR, provide a visual image of these shifting priorities. The bar with a percentage number simply identifies the period when that particular function peaked as a share of total federal spending. But we must be careful not to be fooled by these charts and conclude, for instance, that defense spending has declined. Its share of total spending has fallen but, as shown below spending for national defense (some think of it as spending for intrusion into other countries affairs) has increased sharply. One thing is clear though, spending for Social Security, Medicare and Health (that includes Medicaid) has been increasing, and will continue to do so, because of legal committments. The first two primarily to aging population that will increase rapidly over the next few years as the baby boomers retire.

The Relentless Increase in Defense Spending
The chart to the right displays defense spending back to 1940. We can see that in only three presidential terms spending for defense fell, but only temporarily: under Truman, Clinton and Obama. But even in these cases, we can see that spending began to increase towards the end of their presidential term. In the case of Truman the rise was driven by the military reaction to North Korea's invasion of the South Korea. Clinton, who began requesting smaller defense budgets, but reversed course in the middle of his term responding to a number of trouble spots around the world (Somalia, Bosnia, etc.), with his defense spending at the end of his term virtually identical to where he started. Finally, Obama reduced defense spending by removing all forces from Irak and lessening our involvement in Afghanistan, but has increased the budget in the last two years in response to Russia's and Isis' threats (an argument can be said that the first is the result of the famous "reset" with Russia at the beginning of his term, and the second caused by the abandoning Irak without support.)

Mandatory and Discretionary Spending

But something has been happening over the years. As Congress approves more and more laws and regulations that guarantee benefits to segments of the U.S. population, a greater portion of the budget falls into the so-called "mandatory" spending. That is, spending that the Federal government is committed unless Congress specifically obviates it by law- and this hardly every happens. So we see that a really big chunk of the budget, slightly over 70% today, is out of the discretion of the President, he has to spend the funds by the force of law. The chart clearly shows the rise in mandatory spending over the last 50 years or so. The only way that Congress can increase discretionary spending is by changing existing laws, raising taxes further or simply incurring additional debt. The latter is the normally preferred method since it shifts the burden to future taxpayer generations who can't complain about the burden.

What Do We Make of This?
First of all, this analysis confirms and provides factual evidence to what we already knew, that no matter what the political leanings or beliefs of the candidate, spending during his term will increase. Spending rose even under Reagan, the 'government is the problem' president. Yes, you will say, but it was for a good purpose, to bring the Soviet Union to its knees. And indeed that was a good motive; the problem is that there is always a good motive to justify more spending.
Secondly, the larger mandatory spending as a share of total spending increases the likelihood that Congress and the President will simply resort to more debt to fund additional programs. Raising taxes, the more correct alternative because current beneficiaries should carry the burden, is harder to implement because of the re-election consequences for congressmen.

93 MILLION, OR SHOULD IT BE 32 MILLION?

Much has been written lately about the 93 million working-age people in the U.S. who are today neither working nor looking for a job. These millions of people translate into the lowest rate of labor participation since the mid-70s, and even though it had risen to a peak of 67.6% in 1997 the rate has been declining since that year, as can be appreciated in the chart.
The chart shows that the labor participation rate was fairly stable between 1948 and the late 60s, hovering around 60%. Then it began a gradual and steady rise for the following 30 years until it peaked at the above-mentioned rate of 67.6% in 1997, only to fall continuously over the last 18 years. The increase in participation between the 1960s and the 90s is attributed primarily to increases in the female participation rate, as significant numbers of women joined the labor force and obtained jobs. In fact, female participation rose steadily from around 33% in 1948 to a peak of 60.4% in 1997.
 
But back to the 93 million. Often we read articles and hear opinions that, attempting perhaps to make a political point, imply or state directly that these 93 million are unemployed. They say that these people are not in the labor force because they have been shut-out of the market from a lack of employment opportunities. Granted, for some people that may be the case. Frustrated because they can't find employment they stop searching for a job altogether and, thus, are not counted anymore as part of the labor force. However a deeper analysis into the data shows that there are other reasons explaining why there are so many people not in the labor force and why this number will continue to increase in the foreseeable future.

Age Drives Participation in the Labor Force
The primary reason is the change in age structure of the population; that is, the fact that the U.S. population is getting older provides a partial explanation. The chart to the right displays today's population mix by age group (the red bars) compared to what it was 25 years ago in 1990 (the blue bars.) Each bar stands for the percentage of the year's population accounted by each age group. We can see the aging of the population in that the red bars are higher than the blue ones for the older three groups, that is people 45 years and older. This group represents today 52% of the working age population, up from 40% in 1990. Conversely, the under 45 years population fell from 60% in 1990 to today's 48%. Also note that the percentage drops for every bracket under 45 years.

The pie chart conveys a more clear view of the change in mix by age group since 1990. Over the last 25 years, the working-age population rose by 61.7 million persons, to this year's 251 million. Each slice in the pie represents the percent of the total change between 1990 and 2015 for each of the seven age groups. Thus, we can see that the largest change occurred among the 55 to 64 years group with 32% of the total change, or just under 20 million persons. Second in line are the two groups that bracket the 55-64 years one, each with 28% of the total.
These three age groups, 45 and older, thus represent 89% of the total change in working population, while they account for just over half (52%) of the total working age population.

Alternatively, we can examine this phenomenon by simply looking at the participation rates, shown in the chart. Statically, there is a significant drop in participation when a person moves from the 45-54 yrs to the next age bracket- the rate differential is 15.7 percentage points (from 79.3% to 63.6%). This means that the number of people in the labor force will drop by 15.7% over the next decade simply by the number of people in the 45-54 group who fall now in the 55-64 group. And the drop is more dramatic for the 65 yrs and older, the change in rte is nearly 45 percentage points. Again, this means that simply due to aging, in a year nearly half of the persons who move to the next higher age bracket will fall out of the labor force. There is virtually nothing that can be done about this trend- it's a demographic factor.

But Rates Are Dropping Among Younger Population
The curious thing is that the declines in participation rates are occurring mostly among the younger population. That is, persons under 45 years of age are the ones leaving (or not joining) the labor force. We find that, over the last 30 years, the younger a person is the more likely that he or she is leaving the labor force. Thus we see that for those aged 20 to 24 years the participation rate has fallen by 8.4 percentage points since 1985.

In contrast, we find that older people are bucking this trend, they are in general becoming more active in the labor force. One reason for this phenomenon is the fact that people are healthier today, live longer and have more productive years, and are capable to work when they are older- very likely they enjoy work. A second reason is economic necessity; there is a large number of people at retirement or near retirement age who do not have sufficient funds and thus are forced to work. Many of them may have lost their homes or savings in the financial crisis.
It is interesting to note that more people 70 years and older are joining the labor force. The participation rate for both men and women in that age group has increased by around five percentage points over the last 30 years. One would like to say that they enjoy working so much they've returned to the labor force, but it's more likely they are doing so out of sheer economic necessity. (The chart displays separately the data for men and women 70 years and older, this is because we don't have readily available the combined figure, although we know that it is around five.)

If participation rates had remained at their 1985 levels we would have today nearly 2 million fewer in the labor force- 155.7 million at the '85 rates compared to 157.5 million actual. But the mix is radically different; we would have nearly 7 million more in the labor force who are younger than 45 years and, conversely, about 8.9 million in the 45 and over age group. The more younger people are in the labor force, the greater promise of larger economic output in the future (younger people have more working years in their future naturally) and paying more to many government pension plans, such as Social Security at the Federal level, that depend on the ongoing contribution from working people to remain viable.

So the more relevant figure to discuss is 32 million, rather than the touted 93 million. Thirty two million is the number of people under 45 years of age who are not in the labor force. That is, who for one reason or another are not interested in joining the labor force and becoming productive members of the U.S. economy.

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.