Brett Romero

Data Inspired Insights

Category: Economy (page 2 of 2)

Australian Housing Bubble – Further Reading

Over the past 2-3 months, the mainstream media coverage of housing prices in Australia has exploded. Every commentator appears to have had a piece on this topic and was waiting for the right time to publish it. That right time is apparently now. For those interested in additional reading on this topic, here are some of the better pieces I’ve come across:

The banks and real estate: a Ponzi scheme that could ruin us? – Ian Verrender | ABC News

The housing crash we had to have: A Gen Y perspective on the bubble – Matt Ellis | Rational Radical

Another interest rate cut will fuel a housing bubble in danger of bursting – Greg Jericho | The Guardian

It’s not Hockey’s job comment that should worry us most – Michael Janda | ABC News

Blowing bubbles: the tricky task of tackling Sydney’s property market – Amy Auster | The Conversation

4 charts of the ‘largest housing bubble on record’ – Wolf Richter | Wolf Street

The Sydney housing bubble to pop – but how? – Michael Pascoe | The SMH

The mother of all housing bubbles – Chris Joye | The Australian Financial Review

Australian Housing Bubble Redux

In the recent piece about the Australian economy we touched on the issue of the bubble in Australian house prices. Over the weekend, Saul Eslake, Chief Economist at Bank of America Merrill Lynch and one of Australia’s most respected economists, added his thoughts to the debate. A lot of his concern is around the longer term affects on people who are locked out of the housing market:

“I would say [rising house prices] are causing social harm because they are widening the gap between those who have houses and those who don’t, and freezing younger generations out of home ownership,”

In a country like Australia where, much like the US, owning your house is seen as a noble goal that everyone should be able to achieve, this could signal a cultural change. Home ownership in Australia is at its lowest level since 1950 as investors increasingly snap up properties, not for the rent/income they will generate, but for the assumed capital gains. In recently released data from the Australian Taxation Office (ATO) for the 2012-13 financial year, 1,967,260 (or just over 15% of all taxpayers) claimed rental income. Of those, 64% declared a net loss (i.e. they claimed deductions for negative gearing). Think about that for a second – almost 2 out of every 3 people with an investment property in Australia are actively losing money on that investment. What do these investors do if their expectation of further capital gains changes?

“2 out of every 3 people with an investment property in Australia are actively losing money on that investment.”

With all these statistics, why is there still an argument about whether a housing bubble exists? A big part of the problem is that there is no qualitative measure of a bubble. In hindsight they tend to be blindingly obvious, but one of the reasons bubbles occur at all is that most people don’t notice them as they are inflating. Adding to the problem is the reluctancy of politicians and commentators to call out bubbles or even use the word ‘bubble’ because of the negative connotations – bubbles tend to burst. The following was the response of Australian Assistant Treasurer Josh Frydenberg when asked about the possibility of a housing bubble on the ABC Insiders program on Sunday morning:

“I don’t think there is a housing bubble… In the early 2000s housing prices increased by 20 per cent for three years in a row and then were steady for a decade. And there wasn’t a bubble that led to a major correction.”

However, as the situation becomes more extreme, more and more respected commentators are starting to sound the alarm on this issue, even if they avoid calling it a bubble. Saul Eslake again:

“What I do say, without any hesitation at all, is that Australian prices of housing in most Australian cities, and particularly in Sydney, are, as [Reserve Bank governor] Glenn Stevens called them in September last year, ‘elevated’,”

So, leaving aside talk of bubbles, what are the facts?

  1. Australians have record levels of housing debt as a percentage of income
  2. Almost 2 out of 3 property investors are losing money on their properties
  3. The median house price in Sydney is now over AU$900,000
  4. Rates of home ownership are at their lowest levels in over 60 years

Whether or not you want to call it a bubble, that seems unsustainable to me.

Why the RBA doesn’t want to cut rates

The first Tuesday of the month is interest rate day in Australia, the day the Reserve Bank of Australia – the Australian equivalent of the Federal Reserve – announces any changes to the official cash rate. The decision for June was to leave interest rates on hold at 2.0%.

In a situation that will feel relatively alien to readers in the US, Australian interest rates have never really been close to 0, but have been falling since late 2011 (see Chart 1).

Chart 1 – Australian Cash Rate vs. US Federal Funds Rate

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What has been leading to falling rates in Australia over a period where the US has been slowly recovering and the Fed Reserve is slowly edging back to normalizing interest rate policy? As is usually the case, a mix of factors are involved.

Iron Ore and Coal Prices Return to Earth

A story that most people outside Australia have at least heard about is the large mining boom Australia has been enjoying over the past decade or so, and that it was largely driven by demand from China. What they may not know is that this mining boom has been largely driven by just two commodities (well technically three) – iron ore and coal (two types of coal – thermal and metallurgical). Chart 2 shows the prices of iron ore and thermal coal[1] in AUD/tonne since the 1995.

Chart 2 – Iron Ore and Thermal Coal Prices 1995 to Present

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From this chart, we can clearly see the huge increase in prices that boosted the Australian economy. This was particularly pronounced for iron ore which went from between AU$16-AU$17 a tonne for most of the 90s to over AU$180 a tonne in 2010 and 2011.

Aside from generating huge profits for anyone who happened to own a coal or iron ore mine, what this price rise also led to was a large amount of employment in areas that weren’t just digging up the commodities themselves. This included:

  • Exploration of possible new mining sites – at AU$180 a tonne everyone wanted an iron ore mine
  • Building infrastructure that facilitated the large-scale digging up and exportation of these commodities – ports needed to be built and/or expanded, mining pits dug, roads paved and so on
  • Providing services to mining companies – lawyers, accounts, caterers and so on

After peaking in 2010/11 though, things started to go into reverse. By late 2013, much of the investment in infrastructure had run its course and the people who were employed to build that infrastructure were no longer needed. Prices were falling, bringing into question the viability of a lot of higher cost mines (and the mining companies running these mines) set up during the boom period. In short, a lot of people formerly employed on mine sites or in mining services roles were finding themselves looking for a new job and the rest of the economy was (and still is) struggling to pick up the slack. This in part is because of the …

High Exchange Rate

For those that haven’t decided to brave the 20+ hours of flight time to visit Australia in the recent past, Australia has become an extraordinarily expensive place. Sydney and Melbourne have been consistent fixtures in the world’s most expensive cities to live lists over the past 10 years.

Most of this was driven by a very strong Australian dollar, which was in turn driven mostly by the mining boom. In addition to buyers of commodities needing Australian dollars to buy the products they wanted, Australia became the target of a large volume of carry trade with currency traders looking for a relatively stable economy to park money at a relatively high interest rate. As a result of this, at the height of the mining boom, the AUD was buying almost $1.10USD.

Since that peak, the Australian dollar has depreciated around 30% (see Chart 3), easing a lot of the price pressure. However, as of 2015, Sydney and Melbourne still rank 5th and 6th on the world’s most expensive city list, as provided by the Economist Intelligence Unit’s (EIU) bi-annual Worldwide Cost of Living report.

Chart 3 – AUD/USD Exchange Rate 1995 to Present

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The RBA has publically been stating that they believe the value of the Australian dollar is too high in an attempt to talk down the value of the Australian dollar (often called ‘jawboning’) and provide a boost to the non-mining sectors of the Australian economy. They have also progressively lowered the cash rate from 4.75% in 2011 to 2.0% today, in an attempt to stem the carry trade. As we have seen, to some degree they have been successful, but the exchange rate is still higher than they (and many other commentators) believe is optimal.

Unfortunately, some bumbling on the part of the RBA (or the execution of a plan that no one else understands) has blunted some of their efforts. At the previous monetary policy meeting at the start of May, the RBA lowered the official cash rate from 2.25% to 2.0%, but removed any talk of further cuts from the publically released meeting minutes (removing the “easing bias”). Doing this then had the opposite of the desired result and caused a spike in the Australian dollar.

Chart 4 – Consumer price index; year-ended change 2000 to 2015

RBA_chart_1_4

So why are they removing the easing bias? Why don’t they just slash rates further – after all inflation is running below the target band (see Chart 4)? The problem is they are worried about the…

Bubble in House Prices

The RBAs hesitancy to cut interest rates further is mostly due to a concern about further encouraging investment in housing and contributing to rising house prices, which look to be well into bubble territory.

For those that aren’t too familiar with Australia, particularly the modern, post ‘put-another-shrimp-on-the-Barbie’, Australia, being a property tycoon has become something of a national obsession. Home renovation shows are everywhere and are getting huge ratings. Morning news regularly holds interviews with the latest property ‘success story’.

This obsession has led to Australia becoming a world-beater when it comes to levels of household debt. The Australian Bureau of Statistics (ABS) produced a great series of charts in May 2014 showing some alarming statistics. See below for some of the highlights:

Chart 5 – Household Debt vs. Annual Income[2] in Australia 1987 to 2013

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Charts 6 and 7 – Household Debt vs. Annual Income – Various Countries 2001 to 2013

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RBA_chart_1_7

After 20 years of Australians continually buying properties off each other for ever-increasing prices, funded mostly by increasing level of mortgage debt, something changed. Perhaps it was the median house price in Sydney soaring past AU$900,000 (approximately US$700,000 at today’s exchange rate). What ever triggered it, in recent months, the talk in Australia has become all about a bubble in house prices, particularly in Sydney and parts of Melbourne. The Secretary of the Department of the Treasury, John Fraser, recently became the latest high profile public figure to weigh in:

“When you look at the housing price bubble evidence, it’s unequivocally the case in Sydney, unequivocal,”

More over, he drew a direct link between high house prices and low interest rates:

“It does worry me that the historically-low level of interest rates are encouraging people to perhaps over-invest in housing,”

And there is plenty of evidence to support the notion that the rise in housing prices is increasingly due to investors as opposed to owner-occupiers (see Chart 8).

Chart 8 – Investor Housing Credit as a Percentage of Total Housing Credit 1990 to 2014

RBA_chart_1_8

Meanwhile, belying the sparkling reputation the Australian Government has earned internationally in recent times[3], the Government has all but ruled out taking any meaningful action to reverse key policies that are currently encouraging investment in property – negative gearing and the capital gains tax concession being two of the main culprits. When asked in a recent session of question time by the leader of the Opposition Bill Shorten to respond to the comments from John Fraser, Prime Minister Tony Abbott responded as follows:

“As someone who, along with the bank, owns a house in Sydney I do hope our housing prices are increasing,”

Summing Up

All this leaves the RBA in quite a pickle. Relatively high interest rates (by the standards of developed nations internationally) continue to keep the exchange rate at higher than desired levels, which makes Australia an expensive place to do business. This in turn harms Australia’s two big non-commodity exports – higher education and tourism – just when they need to pick up the slack from a cooling mining sector. But lowering interest rates risks further fueling a bubble in house prices which the Government seems quite happy to ignore.

I don’t imagine there are too many people who would like to be in the shoes of RBA Governor Glenn Stevens right now.

Keep an eye on this space for further updates as this all unwinds.

 

[1] This is an example of that classic Australian trait – sarcasm

[2] Gross disposable household income received during the previous year.

[3] If anyone can find a good historical price series for metallurgical coal, I’d love to hear from you

Labor Statistics Part IV – The Employed

Previously in Parts II and III, we focused on two subsets of the population that are not employed – non-participants and the unemployed. In Part IV, we finally move on to looking at the population of employed people. However, in a slight change of tack, instead of focusing on the characteristics of these people, we are going to look at the changes in the employment market in general and more specifically at the changes at the industry level.

Declining Industries

Chart 1 – Industries with Declining Shares of the Employment Market 1939 to 2015

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Decline in Manufacturing

Looking at the data, the big story since the end of World War II (1945 for those who skipped History class) is the decline of the manufacturing industry. Manufacturing was far and away the biggest sector in the US in terms of employment at the end of the war, but has seen its share of the employment market decline to less than 9% as of 2015. The reasons for this have been the subject of a lot of discussion (see here for example), but if we look at the number of manufacturing jobs (see Chart 2), as opposed to the percentage of the non-farm employment market, we see there are two phases to this decline.

Chart 2 – US Manufacturing Jobs 1939 to 2015

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As Chart 2 makes clearer, manufacturing in the US was actually still adding jobs from 1945 through to the late 70s, it was just that the other sectors were adding more jobs, causing the manufacturing sector’s share of the employment market to decline.

From the mid 80s onwards though, the manufacturing sector started declining in both percentage and absolute terms. Increasing automation and the shift of jobs to low cost manufacturing countries such as China, India and other developing nations started what would be a long decline for the industry. There is one ray of light though, and that is that the US has actually been adding manufacturing jobs for the past 6 years. Although this looks like a positive change, it is hard to say whether this is the start of a new trend or just an aberration representing the recovery of jobs lost in the last downturn. The 90s boom saw similar gains before they were reversed very quickly in the new century.

Where is the Tech Boom?

The sector that you may be surprised to see in the declining chart is the Information sector. Information Technology (“tech”) seems to be the only sector that anyone is talking about right now – glitzy product launches, podcasts (the excellent Startup) and TV shows (Silicon Valley is fantastic if you haven’t seen it). So why don’t we see it in the employment data? To explain that, it helps to break the sector down into its component industries (see Chart 3).

Chart 3 – Information Sub Industries, All Employees 1990 to 2015

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The finer level data only goes back to 1990, but this is the key period we are interested in anyway. What we see is that despite the hype, the two tech related sub industries (Data processing, hosting and related services, and Other information services) are still very small, even within the Information sector. In terms of the number of people employed, these two sectors are drowned out by the traditional publishing industry and the telecommunications sector. So even though the two tech sub industries have been adding jobs, it has simply not been enough to outweigh the job losses in the larger sub industries.

The Telecommunication Boom

The other interesting point on Chart 3 is how much the tech boom in the late 90s impacted on the telecommunications sector. Despite the popular perception that this boom was a tech boom (it is called the dot com bubble after all), the boom led to far greater increases in job numbers (and job losses after the bust) in the telecommunications sector than in the tech sectors. The boom in telecommunications was primarily driven by telecom companies rushing to upgrade networks and infrastructure in response to exploding demand for the two hot new products of the time: the internet and mobile phones. After the bubble popped, some large companies went bust, others consolidated, but the net result was a lot of job losses.

Expanding Industries

Chart 4 – Industries with Expanding Shares of the Employment Market 1939 to 2015

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Moving on from declining industries, let’s look at the industries that have grown their share of the employment market over the past half a century. The clear winners here are the Education and Health sector, and the Professional and Business Services sector.

Professional and Business Services

Professional and Business Services cover a range of services that have gone from non-existent, or the domain of niche firms, to being the domain of some of the world’s largest firms. Additionally, being employed to provide services within this sector has become very prestigious (legal services and management consulting are good examples), allowing these firms to attract some of the top talent in the market place.

Overall, the growth in the number of people employed to provide these services is largely explained by the increasing complexity of doing business. Increasing complexity creates demand in several ways, including the need for:

  • People who are experts in one or a small subset of specific business functions
  • People who are experienced in navigating an increasingly complex regulatory environment, and/or
  • Agility to quickly respond to certain business needs that preclude hiring and training staff internally

In recent times there has been talk about larger businesses attempting to ‘in-house’ some of the services that professional services firms typically provide, particularly legal services and various compliance functions. As of yet, this does not appear to be impacting the employment growth of professional services firms.

Inexorable Rise of Education and Health

The Education and Health sector has shown the strongest and most consistent growth of any industry over the last 50 years. But what explains this strong growth? Chart 5 provides a breakdown of the subsectors within this industry.

Chart 5 – Education and Health Sub Industries, All Employees 1990 to 2015

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The first thing to note is that all the sub sectors have been adding a large number of jobs over the past 25 years, but there are two standouts:

  • Social Assistance (child care workers, personal and home care aides, social and human service assistants) has gone from easily the smallest sub-sector in 1990 to employing as many people as the Education sub-sector, tripling the number of people employed.
  • Ambulatory Health Care Services (outpatient medical services like dentists, GPs, diagnostic centers and so on) has become easily the largest sub-sector over the past 25 years, adding over 4 million jobs.

Generally this provides further confirmation of what we saw in Part II of this series – that there are larger numbers of Americans retiring and as they do, the demand for certain services, particularly health care is also growing.

Childcare Catch 22

One additional point to make on this subject is regarding the growth in childcare services, a key component of the overall growth of the sector. As the model of the family has changed to one with two parents in full-time employment, there has been a corresponding growth in demand for childcare services. For a lot of families this has presented a question – is it worth paying for childcare (does the parent earning the least still earn more than the cost of childcare?).

This causes a catch 22 for the childcare industry in most countries – childcare typically struggles to attract enough suitable employees due to a combination of parents’ (understandably) high expectations and generally low pay. However, if businesses in the childcare industry were to offer higher pay to childcare workers to attract more candidates, they would need to raise the cost of the childcare to parents, leading to more parents simply dropping out of the workforce to stay home and raise their children instead. Because of a parent’s ability to provide their own childcare services, without Government intervention, it will be difficult for the wages of childcare workers to ever significantly exceed the average income for parents in the area they service.

The Financial Sector Reflects the Market

The last sector I want to spend some time on in this section is the financial sector. One of the noticeable things from Chart 4 is, as a percentage of the total non-farm employment market, the financial sector hasn’t grown since the late 80s. This would seem to contrast with the general notion of an ever-expanding financial sector that is taking over the US economy. Again, the fact that we are looking at the data in terms of the percentage of total non-farm employees can be deceptive. Chart 6 shows the total financial sector employees from 1990 to 2015.

Chart 6 – Financial Sector, All Employees 1990 to 2015

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Looking at this chart, we see the Financial sector did add a significant number of jobs between 1990 and 2015, but the number of jobs in the financial sector is still relatively small compared to the economy as a whole. Additionally, the number of jobs in the Finance sector appears to change in line with with the economy as a whole. Does that mean the Financial sector doesn’t need to be reigned in or that it isn’t sucking talent out of the US economy into relatively unproductive industry? That is a topic for a separate article, but the one thing that can be said is that in terms of the number of people being employed by the Financial sector, everything looks very much like business as usual.

Stable Industries

Chart 7 – Industries with Stable Shares of the Employment Market 1939 to 2015

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The Government Sector

Despite there being observations we could make about both the other two industries on this chart, I am going to focus on the most interesting story on this chart – the Government sector. The basic story in the chart is the build up in the percentage of non-farm employees in the Government sector from 1945 to the mid 70s, and then a slow decline through to 2015. Again looking at the percentages can be deceiving, so let’s look at the number of employees in the Government sector (Chart 8):

Chart 8 – Government Sector, All Employees 1955 to 2015

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The period from 1955 to 2009 saw a pretty consistent build up in the Government sector – close to 15 million jobs were added in this time. But since 2009, ignoring census hiring in 2010 (you can also see corresponding spikes in all years ending with ‘0’ for the same reason), the number of people employed by the Government had its biggest decline since the early 80s. To help determine what is happening, let’s look at the Government sector broken down into its three sub-sectors, Local, State and Federal (see Chart 9):

Chart 9 – Government Sub Sectors, All Employees 1955 to 2015

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At first this would seem to show a slightly confusing picture. This decline from early 2009 through to late 2014 represents almost 6 years right in the middle of Barrack Obama’s presidency, but for all the noise about political stalemate in Washington, the sequester and the Government shutdown, there appears to have been minimal impact on the number of Federal Government employees. At the same time, Local Governments have been slashing payrolls and State Governments have essentially been in a hiring freeze. The explanation for this is largely due to:

  1. The nature of Local and State Government revenue sources – the three main types of taxes that Local and State Governments collect are income tax, forms of sales tax, and property tax. All three sources took sharp downturns in the recession, with property tax continuing to decline even as income and sales tax collections were recovering.
  2. Balanced budget requirements – many State and Local Governments have balanced budget requirements, which meant in the face of sharply falling revenues, they were forced to slash expenditures. In many cases this meant cutting payrolls, which unfortunately only exacerbated the effects of the recession locally.

The combination of sharply falling revenues and the inability to use debt financing led to large job losses at the State and Local Government level. On the other hand, it is well known that Federal Government does not have a balanced budget requirement (much to the chagrin to some on Capitol Hill) and, in contrast to the State and Local Governments, significantly increased spending going into the recession (the American Recovery and Reinvestment Act of 2009). The merits and impact of Government financed stimulus may be debated, but the impact on employment within the Government sector is pretty obvious.

A Strange Observation

The other surprising observation from Chart 9 is that the Federal Government has employed more or less the same number of people since the late 1960s – all the growth in the Government sector has come from the Local and State Government sectors. The growth in Local Government makes sense, the population of the US has increased significantly in that period and providing Governance for that population requires more employees. We also see growth in State Government for the same reasons – but nothing at the Federal level.

Technology and other efficiency gains should allow fewer people to do the same amount of work over time, and the productivity gains between now the 1960s have been huge. Additionally, the impact of these efficiencies would be greatest at the Federal level where the scale of the work is typically bigger and there is less need to maintain a physical presence all over the country/state in the same way that Local or State Government has to. But the efficiencies wouldn’t apply everywhere:

  • To audit the same percentage of businesses over time, the IRS would need to continually hire additional auditors to keep up with the growing number of people and businesses
  • For Social Security to continue to service a growing population, the number of locations (and the staff to keep them running) would also need to expand significantly

Even allowing for a more efficient work force, it seems unlikely that the Federal Government has been able to maintain the same levels of service, regulatory effectiveness and Government advisory when the country has grown so much in population and complexity.

From here it would be easy to launch into a diatribe about an understaffed Federal Government leading to issues like the financial crisis, the failure to detect various huge frauds (Enron, Bernie Madoff), and the generally poor quality of Government services (the torturous immigration process comes to mind[1]). I could then also go on to talk about how using the points above to argue for further reductions in the Federal Government seems crazily wrong-headed. However, linking all these events to a shortage of Federal Government employees is far too simplistic. These events were caused by a range of factors and simply adding more Federal public servants would not have solved the problem on its own.

All that said, not increasing staffing levels for 50+ years does have an impact. The next time you are forced to suffer through some unnecessarily archaic (Federal) Government process, read about another fraud that the SEC and/or FinCEN failed to pick up, or lament that lobbyists are writing a significant amount of legislation that gets put before congress, keep in mind that collectively the Government agencies providing these functions are today operating with the same number of people as they were when Neil Armstrong took his first steps on the moon.

 

[1] Please don’t tell me that this is done intentionally to discourage applicants – there are plenty of ways to discourage applicants without wasting huge amounts of time and money.

 

Have any thoughts on what impact constant levels of Federal Government staffing since the 1960s might have had? Please leave them in the comments!

Labor Statistics Part III – The Unemployed

Following on from Part II where I looked at the population of people who had left the labor force completely, this week I turn my attention to the unemployed. The unemployed are defined as those who are currently not employed but have made “specific efforts to find employment some time during the previous 4 week-period ending with the reference week”. Chart 1 maps the unemployment rate since 1948.

Chart 1 – US Unemployment Rate 1948 to 2015chart_3_1

Courtesy of the Bureau of Labor Statistics, there are several ways we can divide up the population of unemployed people to better understand what is driving the changes over time.

Cause of Unemployment

The first breakdown (shown in Chart 2) is the unemployed population (as a percentage of the total civilian labor force) broken down according to the cause of unemployment:

  1. Lost a job
  2. Left a job
  3. Rejoining the labor force after some hiatus
  4. Joining the labor force for the first time

Chart 2 – Unemployed Persons by Cause 1967 to 2015

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From this breakdown, the first conclusion we can draw is that people losing their jobs drives almost all the variation in the total unemployment rate over time. This stands in stark contrast to the population of job leavers and new entrants to the labor force, both of which have remained remarkably consistent over a long period of time.

The second thing to note is that the changes for those reentering the labor force appear to track the changes for job losers, but with smaller peaks and troughs. This suggests that when there is a spike in people losing their jobs (due to a recession for example), a population of people who had left the labor force is returning to look for jobs. Although counterintuitive (why would you rejoin the labor force in the middle of a downturn?), this likely reflects cases such as a family where the primary breadwinner loses their job, and both parents begin the hunt for jobs to make ends meet.

This is interesting primarily because it shows a feedback loop that potentially increases the spike in unemployment in a downturn. That is, just as large numbers of people are getting laid off from their jobs, an additional population of people who weren’t in the labor force also begins looking for jobs, further boosting the population of unemployed. Conversely, this also means that unemployment can fall much quicker than anticipated (for example when one parent becomes employed and the other drops out of the labor force again).

Education Level of the Unemployed

Chart 3 shows the unemployed population broken down by education level and the obvious conclusion to draw is that your teachers were right; finishing school will help you get (and keep) a job. The rates of unemployment for those people that didn’t finish high school are significantly higher than for everyone else, keeping in mind this is for people actively looking for work (as opposed to cruising on their parents couch or living off a wealthy spouse). Conversely, the unemployment rate for those that completed a bachelor’s degree or higher is by far the lowest of the four groups.

Chart 3 – Unemployed Persons by Education Level 1992 to 2015

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The other observation to be made is that there is not a huge difference in the unemployment rates for those that finished high school but didn’t go on to further studies, and those that went on to get an associates degree or attend, but not finish, college (university for those not in the US). Contrast this with the large gap between the ‘Some College/Associates Degree’ group and the ‘Bachelor’s or Higher’ group, and the advantage of graduating from college (at least in regards to getting employed) becomes plain to see.

Length of Unemployment

One of the more interesting and discussed breakdowns of the BLS unemployment data is the breakdown by length of time unemployed. Chart 3 shows how these percentages have changed over time for three groups:

Chart 4 – Unemployed Persons by Length of Unemployment 1948 to 2015

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The main feature that immediately stands out on this chart is the huge spike in the percentage of people unemployed for more than 15 weeks in 2009. This peak is well well above anything since the end of World War II and remains high today. This indicates that in addition to unemployment spiking in the global financial crisis (as we saw in Chart 1), people tended to stay out of work for significantly longer than in any other downturn since the end of World War II.

What this chart also shows us is how far the US economy is from what would be considered ‘historically normal’. For most of the past 60 years, the majority of unemployed people were unemployed for less than 5 weeks, followed by those unemployed for 5-14 weeks, and then finally the smallest group was those unemployed for 15 weeks or more. However, with the financial crisis we saw this split reverse and, unlike previous downturns, over 6 years after the financial crisis the population of people unemployed for 15+ weeks is still significantly higher than the population of people unemployed for less than 5 weeks.

Further confirming this shift, an additional series that the BLS produces is the average weeks unemployed (see Chart 5). From this chart we see that the latest downturn caused a huge spike in the average weeks unemployed, but also that the average period of unemployment remains at a level higher than at any other point pre-crisis.

Chart 5 – Average Period of Unemployment 1948 to 2015

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The other interesting point from Chart 5 is that even before the spike in 2009, if we look past the ups and downs of the recessions and recoveries, there appears to a trend of slowly increasing average time unemployed in the preceding 60 years. What would cause this average to creep up over time? It is likely to be a combination of a number of factors. Below are some factors that have occurred over time that could help explain this trend:

  1. Professionalization of recruiting – recruiting is increasingly a function that is handled by a professional team within an organization, or is outsourced to a professional firm, even for smaller companies. This practice generally ensures a certain minimum standard of hire, but also means it is increasingly rare that a firm will take a chance on someone with a long period of unemployment or a spotty employment history.
  2. Increasingly technical nature of jobs – with many professional jobs, even outside of the tech world, there is increasing pressure to continually develop new skills and adapt to new software and best practices just to keep up with the requirements of the job. As difficult as this can be for someone in the job, it is essentially impossible for someone who is unemployed leaving that person heavily disadvantaged in the job market.
  3. Improved ability to validate work history – previously, if a person had been unemployed for an extended period, they could fudge the dates (or flat out lie) with little chance of being found out. In 2015, with online networks such as LinkedIn and generally more thorough background check processes in place, it is much more difficult to get away with this type of deception (although it definitely still happens).

Many of these changes would appear to be positive changes, such as increasing professionalism in the recruitment process and less room to mislead potential employers, so surely we are just reducing the number of dishonest people and under qualified children of bosses/friends getting jobs? That is probably true to some extent. But what is also true is that those underdog stories that we love to hear about and watch, like a mother becoming hugely successful after years of staying home to raise the kids, or a super smart kid scamming his way into a prestigious law firm, are becoming close to impossible in reality. For better or worse, the job market is becoming a place for the Louis Litts of the world, not the Mike Rosses.

People in Part Time Work

Finally, although officially classified as employed, the BLS also tracks the number of people who want full time work but that are currently only working part time (also referred to as ‘under employed’). The change in this population is shown in Chart 6.

Chart 6 – Persons at Work Part Time for Economic Reasons 1956 to 2015

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One of the criticisms of the recovery post-2009 has been that it is a “part-time recovery” (see here and here for example). In other words, the belief is that the jobs being created are mostly part-time jobs and so the unemployment rate is not accurately reflecting the poor state of the economy. However, we can see that although the peak in 2009 was high (but not the highest, the peak in this series was actually 6.2% in October 1982), it has since fallen back to around average for the period and continues to fall in both absolute and percentage terms.

Watch this space for the final part of this series, Part IV, where we will explore the employed population.

Labor Statistics Part II – The Non-Participants

Previously in Labor Statistics Part I, we looked at data from the Bureau of Labor Statistics that showed, among other things, a falling participation rate since the turn of the century (see Chart 1). We also saw that even though unemployment has fallen over the last 5 years or so, that fall was at least partially a result of people leaving the labor force entirely rather than finding employment. Here we will take a more detailed look at those people classified as not in the labor force (those deemed to be not participating) to see if we can explain why the participation rate is falling.

Chart 1 – Participation Rate vs. Employed Population as a Percentage of Total Population 1947 to 2014

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Courtesy of Bureau of Labor Statistics (BLS) data we can actually break the population of people not in the labor force into several different subgroups, starting with two main groups: people who want a job (but don’t fall under the unemployed category); and people who don’t want a job. These populations can then be further broken down into subgroups based on: gender; race; and age. For our purposes we are going to focus mainly on the two main groups, people who want work and people who do not, with the latter further split into three age brackets (16-24, 25-54 and 55+). Chart 2 shows the break down of these groups (12 month moving averages have been used to smooth the series).

Chart 2 – Breakdown of Population Not in the Labor Force 1995 to 2015

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Looking at Chart 2, aside from an increase of over 20 million people over the last 20 years, no obvious trends emerge – none of the subgroups really appears to be shrinking or expanding significantly and all seem to be more or less moving in line with population growth. And, in fact, if we look at the percentages, this is more or less what we see:

Chart 3 – Breakdown of Population Not in the Labor Force – Percent of Total – 1995 to 2015

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Keeping in mind that the left hand axis of these charts has been zoomed in to exaggerate any changes, we can see that over 20 years, the proportion of the total population of people not in the labor force in each age bracket has not changed significantly. Summarizing:

  • Don’t Want a Job – 55+ years: fell a couple of percent from 2001 through to 2011, but in the past four years has basically recovered to where it was pre-2001.
  • Don’t Want a Job – 25-54 years: slowly increased around 1.5% from 1995 to 2005, but has now fallen back below 1995 levels.
  • Don’t Want a Job – 16-24 years: Increased around 5% from 1995 to 2011 but has fallen back a couple of percent since.
  • Do want a job – All Ages: Fell 4% from 1995 to 2001, was flat from 2001 to 2009 but has increased around a percentage point since.

Looking at the data this way, there aren’t many conclusions to draw. There is not enough real movement in the numbers to suggest anything is fundamentally changing in the pool of people considered not in the labor force.

However, one of the issues with analyzing data like this can be that the large pool of existing people can tend to obscure more rapid changes happening when you look at new entrants to, and exits from, that pool. So what does the picture look like if we analyze only the changes in the population from year to year?

Chart 4 – Annual Change to the Population Not in the Labor Force 1995 to 2015

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* 2015 figures are only for January, February and March

Looking at Chart 4, we can see several interesting trends and changes.

The first trend that becomes clear is how much of the growth in the population of people not in the labor force since the global recession in 2008-2009 is due to over 55 years olds. In fact, from 2010 to March 2015, the 55+ years age group contributed just over 75% of the growth in the population of people not in the labor force. And this trend has been accelerating. In 2014, the last year for which we have complete data, the 55+ years age group contributed close to 95% of total growth.

The second trend that stands out concerns 16-24 year olds. In this case there are large increases in the number of 16-24 year olds entering the population not in the labor force immediately after the two major downturns, the bursting of the tech bubble in 2000 and the financial crisis in 2008-2009. This suggests that following a downturn, young people are electing to either delay entering the workforce to stay in school longer, or are leaving the workforce and going back to school.

The third and final observation relates to those who indicated that they want a job, but do not meet the formal criteria to be classified as unemployed. In 2008 and 2009 we see large increases in the population of people who wanted a job but are not in the labor force. This is expected and corresponds with the financial crisis when millions of Americans lost their jobs. However, from 2010 onwards, the rate of this increase slowed, and, in some years, even reversed.

Drawing all the threads together, what are the main conclusions we can make from the above observations? My main takeaways are as follows:

  1. The growth in the population of people not in the labor force (and the decline in the participation rate), particularly over the past 5 years, has been mostly driven by over 55 year olds. This actually agrees with one of the more prevalent theories for why the participation rate is falling – the baby boomer generation is reaching retirement age and retiring en masse. Additionally, this trend of decreasing participation seems set to continue, meaning a decreasing percentage of the population will be required to fund the government for an ageing population.
  2. 16-24 year olds tend to drop out of the labor force in the immediate aftermath of downturns. Given the age range, a logical explanation for this is that, when the job market is poor, young people elect to either stay in school longer or return to school to pursue further studies. The positive news is that the spikes in young people leaving the labor force appear to be short term. Additionally, taking a long-term perspective, more young people pursuing higher education will arguably benefit the wider economy in later years.
  3. Since 2013, the number of people not in the labor force who do want a job appears to be on the decline (or has at least leveled off). This in turn suggests the current recovery is a genuine one, even in the face of a falling participation rate.

Finally, I want to leave you with one last chart. Chart 5 shows the changes in the proportion of men and women in the population of people not in the labor force. The positive news is that, although there is still a gender divide, it appears that the gap is continuing to close. In fact, 2014 was the year in which women represented the smallest proportion of the population not in the labor force, and the first time (probably since the US existed) that the percentage has dropped under 60%.

Chart 5 – Gender Split of Not in the Labor Force Population 1947 to 2014

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Labor Statistics Part I – Setting the Scene

The unemployment rate: in western countries this tends to be one of the most discussed and politicized of the official statistics produced by Governments. In the US, the unemployment rate has been under a higher than usual level of scrutiny since the 2008 financial meltdown led to historically high levels of unemployment.

However, as of February 2015 the unemployment rate has fallen to 5.5%, meaning it is back within the normal historical range and is expected to keep falling. So everything is all good right? Maybe.

Chart 1 – US Unemployment Rate Jun 1976 to Feb 2015

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Before we get into that, first a bit more detail on how the unemployment rate is calculated. For such a well-known statistic, it appears to be relatively poorly understood outside of the world of policy wonks. Ask the average person to guess how the unemployment rate is calculated and they are likely to guess something along the lines of the following:

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Someone with a bit more time to think on it may consider the fact that not all of the population are of a working age and factor that into their guess:

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But even this more refined calculation would result in an unemployment rate well into the 30%-40% range. The reason for that is, as the Bureau of Labor Statistics (BLS) outlines, to qualify as unemployed, a person has to be part of the Civilian non-institutional population [1] and meet one of the following two criteria (emphasis mine):

  • had no employment during the reference week, were available for work, except for temporary illness, and had made specific efforts to find employment some time during the 4 week-period ending with the reference week, or
  • were waiting to be recalled to a job from which they had been laid off.

As I am sure you can imagine, this definition leaves a lot of people that many would consider “unemployed” in some third pool, neither employed nor unemployed as the BLS defines it. These people are actually in a pool labeled “not in the labor force”.

From Bureau of Labor Statistics data, in 2014, people in the labor force made up 62.9% of the Civilian non-institutional population, leaving just over 92 million people outside the labor force. But this percentage of people in the labor force (also called the “Participation Rate”) has changed significantly over time. Chart 2 shows the Participation Rate since 1947:

Chart 2 – US Participation Rate 1947 to 2014

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There are several interesting things to notice here.

Firstly, from the early 60s until the late 90’s, the percentage of people considered in the labor force surged – from 58.7% in 1963 to 67.1% in the year 2000. To give you an idea of how that translates into numbers of people, that increase meant there were an additional 17.8 million people in the labor force in the year 2000 than there would have been if the participation rate from 1963 had remained unchanged.

Who were all these extra people? Most of this increase represents the movement of women into the labor force over time and the rise of the two income household, both of which can be seen in the increasing participation rate for woman (see Chart 3). Although this indirectly led to a lower participation rate for men, the overall result was an increase in the participation rate in general.

Chart 3 – Participation Rate by Gender 1948 to 2014

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The other major trend we can see in the Participation Rate over time is the downwards trend since 2000. By the end of 2014 the US had returned to a Participation Rate not seen since 1977. The result is that even though the headline unemployment rate has dropped back down to 5.5% as of February 2015, once the lower participation rate is factored, the picture isn’t nearly as rosy, as shown in Chart 4:

Chart 4 – Participation Rate vs. Employed Population as a Percentage of Total Population 1947 to 2014

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This has not gone unnoticed (see ForbesCNN and Bloomberg for example), but what is the cause and what impact does this have for public policy going forward? This will be something we will explore over the next few weeks in a series of articles – watch this space.

In the meantime, keep an eye on the Datasets section of this website for downloads of the various datasets being used in these articles.

 


[1] Persons 16 years of age and older residing in the 50 states and the District of Columbia, who are not inmates of institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces.

Eurozone Perceptions

It has long been a perception held by many in the western world that the people of southern Europe (Spain, Portugal, Italy and Greece for the purposes of this article) have a particularly easy-going approach to work, life and financial responsibility. Whether this is a good or bad thing depends on who you ask and even what time of year you ask them as Ed Vulliamy describes.

However, with the onset of the European debt crisis, these perceptions have taken on a new prominence as they are now used to justify the harsh austerity being forced on Southern European nations, with special scorn and head shaking reserved for Greece in particular. At the deepest level, the enforcement of austerity is being spun as a moral tale – the people of Southern Europe are suffering for their laziness and financial irresponsibility. The financial irresponsibility aspect of this is a topic for another article, but here we will look at the evidence supporting the proposition that people in Southern European nations are ‘lazier’ than their northern European neighbors.

The first step in analyzing this proposition is defining how we measure ‘laziness’. In general, laziness refers to a lack of willingness to work or expend energy. Given we have no quantitative way of comparing how much energy people are expending, or their willingness to perform work (what a different world it would be if we could!), a good proxy to determine relative energy expenditure, and therefore laziness, is the number of hours worked. Conveniently, the OECD produces statistics on average hours worked per person per a year for most OECD nations, which includes most of the European nations we are interested in.

An argument can be made about the productivity of the respective workers but productivity has its own larger distortions due to the impact of differing levels of capital investment. A German working in a car manufacturing plant controlling a high tech automated assembly line will be much more productive (in terms of the value of his output) than an Italian waiting tables in a coffee shop – but this tells us nothing about the level of effort (or lack thereof) being expended, and also nothing about the time being spent at work.

So looking at the OECD statistics on hours worked, what do we see for the countries we are talking about?

Table 1

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What we see is actually the opposite of the commonly assumed situation. The famously hardworking Germans are averaging less than 1,400 hours a year of work or under 27 hours a week averaged over 52 weeks. This is actually the 2nd lowest of all countries in the OECD in 2012. Meanwhile, the Greeks, often held up as the epitome of laziness (at least in Europe) actually work some of the longest hours in the OECD – the third longest in fact, behind only the Koreans and the Mexicans. In 2012 the average Greek clocked up 2,034 hours of work, or the hours of almost 1.5 Germans. So how do we explain this? How can the perception be so different to what we are seeing here?

When we look at the data, some trends begin to emerge that explain some of the differences in hours worked. The first and most obvious trend that emerges when we expand our dataset to the full OECD and for all years covered is a negative correlation between hours worked and GDP (PPP) per capita (a rough proxy for wealth – see Chart 1). The trend is clear, both across countries and across time, and intuitively this makes sense – as people get wealthier, they feel less need to work long hours.

Chart 1 – GDP per capita (PPP) Vs. Average hours worked per person per year – OECD Countries, 2000-2012

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Asides from the negative correlation between GDP (PPP) per capita and average hours worked, there are a few other observations we can make looking at this chart. The first observation is that the minimum hours that people work seems to bottom out at around 1400 hours a year – more or less where Germany and the Netherlands sit currently. Again this conclusion checks out logically. Subject to the social expectations and the demands of a given job, people aim to reach a comfortable balance between work and leisure time. Once this is achieved, they generally let any further increases in income accrue to their wealth rather than further reduce their working hours.

The second observation is that at any given level of GDP (PPP) per capita, there is a still a high level of variability between countries as to how many hours the average person will work. More than anything, this shows there is a range of factors that create variances in hours worked between countries. Labor force restrictions, minimum wage, unemployment benefits, education levels, inequality and the general structure of the economy will all affect the hours worked at a given level of GDP (PPP) per capita.

What else can we determine looking at this information? If we believe wealth to be a major factor in how many hours a person will work, what would it look like if we could remove the impact of wealth? In fact we can remove the wealth effect from this data by building a simple linear model that estimates the average amount of hours a person would work given a certain level of GDP per capita. From there we can then see where countries lie relative to the model prediction, effectively telling us which countries are working more hours than we would expect for their relative level of wealth, and which countries are working less. The 2012 data, with a linear model applied is shown in Chart 2.

Chart 2 – Average hours worked per person per year Vs. GDP per Capita (PPP) – OECD Countries, 2012

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What this model tells us is that for every extra $1,000 of GDP (PPP) per capita, the average person will work 16.4 hours less per year. When we use this model to predict the number of hours the average person will work per year based on the GDP (PPP) per capita of their country, we come up with an estimated hours worked per person per year for each country, which we can then compare to the actual value for each country. The results of this comparison are shown in Chart 3.

Chart 3 – Average hours per person per year – Actual vs. Forecast, 2012

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What we see is that even if we remove the differences in wealth from average hours worked per person per year, the average citizen in many northern European countries (particularly Germany and Denmark) are still working less hours than we would expect. The verdict for Southern European nations is more mixed. People in Portugal and Spain are also working fewer hours than we would expect, the Italians are more or less in line with expectations, while the Greeks are again well ahead of what would be expected.

So what is the bottom line here? What conclusions can we take away from this? The answer is surprisingly little. There are a huge range of incentives and disincentives that are unique to each country that we are completely ignoring in this analysis. We also have no way of identifying how effective or productive different people are while they are at work, which as I’m sure anyone who has worked with another human being can testify, can vary pretty dramatically. So, despite the above evidence, no one should be prepared to believe the people of Germany or Denmark are ‘lazier’ than people in Mexico, the US or Korea. What we can say though is what evidence is missing from the above analysis – and what is clearly missing is any evidence that the people of southern European nations are ‘lazier’ than their northern European counterparts.

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