Brett Romero

Data Inspired Insights

Tag: Economics

Uber Vs Taxi – A Follow-Up

Hi everyone – welcome to 2017! I hope you all had a good Christmas and New Year’s Eve and are geared up for a big 2017.

Kicking off the year, this week, I happened to stumble on a series of articles written by Hubert Horan, who has spent the last 40 years working in the transportation industry, particularly the management and regulation of airlines. In a four-part series (two pieces were later added to respond to reader comments and look at newer evidence) published at nakedcapitalism.com, he takes a critical look at the Uber business model and dispels a bunch of myths.

Some of my longer-time readers may remember a two-piece series I wrote looking at the relative advantages of Uber and traditional taxis (Part I and Part II). This series of articles (links at the bottom) actually expands on many of the points I brought up in those articles, particularly Part II where I took a more critical look at some of Uber’s practices. The TLDR is as follows:

  1. Despite huge expansion across the globe, Uber is continuing to burn through investors’ cash at an unprecedented rate (around $2 billion a year).
  2. Although there have been large increases in revenues, there are no signs to date that Uber’s profitability (currently sitting at around -140%!) is improving due to ‘economies-of-scale’, older markets maturing, or other ‘optimizations’. In fact the only thing that has had a measurable impact on profitability has been cutting driver pay.
  3. Uber’s huge losses are primarily due to one thing – the expansion across the globe is being driven by subsidies. According to Horan, current Uber passengers are only paying around 41% of the cost of their rides due to these subsidies (I do note that no source was provided for this number).
  4. Paying drivers more than regular taxi services is one of the main ways Uber is attracting drivers. However, one of the things that allows Uber to do this is the fact that they have pushed one of the most significant costs of running a taxi onto the drivers – the actual ownership and maintenance of the car. Once the expenses of running and maintaining a car are taken into account, it is not clear that drivers are actually any better off, and in many cases, are probably worse off.
  5. This is something I touched on in my articles – many (most?) Uber drivers are simply not across concepts like depreciation and capital risk. For them net profit is simply ‘my share of fare revenue’ minus ‘gas costs’, which leads to a large proportion of Uber drivers continuing to drive when it is does not make economic sense for them to do so. A big part of Uber’s success has been their ability to take advantage of this ignorance.

So why are investors continuing to pour money into Uber if it isn’t making money and the current business model does not seem to make sense? I have heard two theories raised in response to this question.

The first is that Uber is simply buying time to get self-driving cars on the road, at which point, it can replace (a.k.a. fire) all its ‘driver-partners’ and Uber’s share of fare revenue goes from 30% to 100%. I was actually a believer in this theory until recently when Noah Smith made the counter-intuitive argument that self-driving technology is likely to be terrible for Uber. Why? Because every person with a self-driving car becomes a potential competitor for Uber. By simply renting out their car when they are not using it, they are competing with Uber and can do so at very low cost because they have none of the overheads Uber has. Sure, Uber will have the app, but the app is easy and cheap to recreate (as is evidenced by the 17 Uber clones in most cities already). But even without an app at all, a large portion of the market is going to go through the minimal hassle of calling or texting (or whatever else the kids are doing these days) someone for a ride if the price is even a couple of dollars better. Finally, even if Uber lowers prices to drive (pun intended) those people out of the market, as soon as prices rise again, all those individuals will re-enter the market due to the close to zero cost of doing so.

The second (and more realistic theory in my mind) is that Uber is aiming to drive all its competitors out of business and create a monopoly. Once it has a monopoly, it can lower driver pay and raise fare prices to extract monopoly profits. Uber’s behavior to date (the subsidies are simply predatory pricing with good publicity), as well as comments from prominent investors, would seem to lend credence to this theory. But even this theory has issues, the biggest of which would seem to be that it has a very limited window to operate in due to the imminent arrival of self-driving cars. I am probably more skeptical than most people on how soon self-driving cars will be on the streets of cities (10-20 years, with long haul probably coming sooner), but even if we take the best case scenario for Uber and said it is going to be 20 years before self-driving cars are on the streets of cities, is that going to be long enough to generate the returns needed to justify the huge sums investors have poured into the company? And if this is the plan, why are Uber trying to speed up the introduction of self-driving cars? I don’t have good answers to either of these questions unfortunately.

For those with any interest in this topic, I strongly encourage you to read at least the first 4 parts over at nakedcapitalism.com – here are the links:

Part 1 – Understanding the Economics

Part 2 – Understanding Cost Structures

Part 3 – Innovation and Competitive Advantages

Part 4 – Understanding that Monopoly was Always the Goal

Part 5 – Addressing Reader Comments

Part 6 – Further Evidence

Finally, on an anecdotal note, I have recently moved to Amman where Uber operates, along with a local competitor (Careem) and a large local taxi industry. For those that may be thinking that people will probably be happy to pay a little extra for the improved Uber experience, Amman would offer an example of the opposite case. In Amman, Uber and Careem both cost around 1.5-2 times as much as a metered taxi. Either way it is still cheap (a ride from downtown to the western edge of the city would be $2.50-$3.50 in a taxi, $5.50-$6.50 in an Uber), but even with the truly horrible state of most taxis in Amman, and the inconvenience of having to flag one down, this price differential is enough to make the ride sharing portion of the transport business practically non-existent.

Hours Worked Are Going Up – Here is the Evidence

A couple of weeks back, I posted a blog that seemed to tap a nerve. The blog addressed what many white-collar workers, particularly in the private sector, have been feeling for some time: pressure to put in longer hours at the office. This week, I wanted to look into the statistics to see if there is evidence to support the anecdotal stories of increasingly common 60-hour weeks.

To address this question, we are going to look at data from a range of sources, including Australia, the US, and the OECD.

The Picture in the US

Starting in the US, the Bureau of Labor Statistics (BLS) produces data on average weekly hours. This data has a lot of fine level detail on average weekly hours by sector and subsector, but unfortunately, only goes back to March 2006. Still, if there is a trend towards longer hours in recent times, it should be apparent.

Chart 1 – Average Weekly Hours by Industry

Chart 1 above shows the average weekly hours for the three main sectors for white-collar workers, Financial Services, Information, and Professional and Business Services. The first thing that stands out is there does appear to be an upwards trend in the average weekly hours for Financial Services workers and for Profession and Business Services workers. Both sectors look like they have added an extra hour on average over the past 9 years. Given the short time frame and the number of people involved in those sectors, that should be considered substantial. Multiplying extra hour by the number of employees in those sectors, (approximately 8 million and 19 million respectively), works out to an additional 3,375,000 working days (assuming 8 hours a day) every week – between those two sectors alone.

Drilling down into the detail, Chart 2 shows the Professional and Business Services sector broken down into its various subsectors.

Chart 2 – Average Weekly Hours – Professional and Business Services

At this level of detail, the data shows us that the increase in the sector as a whole is far from uniform:

  • Accounting, Tax Preparation, Bookkeeping and Payroll Services, Advertising and Related Services, and Other Professional Scientific and Technical Services have added around 2 hours per week
  • Legal Services and Management, Scientific and Technical Consulting Services have added approximately 1 hour a week
  • The remaining subsectors have remained flat, or even declined slightly.

Interestingly, data for the most infamous subsectors for long hours, Legal (Legal Services) and consulting (Management, Scientific and Technical Consulting Services) show employees averaging between 36 and 37 hours a week, which would seem to be very normal. This is probably indicative of two things:

  1. People in legal and consulting generally aren’t working as many hours as we assume (or they tell us).
  2. The people working long hours in these subsectors are limited to a few top tier firms. Their long hours are being drowned out by large numbers of people working normal hours.

There is also another thing to keep in mind when looking at this data. These statistics are based on surveys that are voluntary for people to respond to. As a result, there is likely to be some bias in the data towards lower hours due to people who do work long hours opting out of the survey altogether. This bias would impact all sectors and subsectors, but could be masking more dramatic increases in the averages.

What about Technology?

In Chart 1, the average weekly hours for the information sector (of which technology based industries are subsectors) barely moved over the last 9 years. However, as seen previously, looking at the information sector in aggregate can be deceiving. Chart 3 shows the information sector broken down into its various subsectors.

Chart 3 – Average Weekly Hours – Information Sector

Looking at this breakdown, the expected increase in average hours worked becomes more apparent. The Data Processing, Hosting and Related Services subsector has added close to 3 hours a week since 2006, while the Other Information Services subsector has added around 2 hours a week.

An interesting point to note is that for the Other Information Services subsector, the average weekly hours have been decreasing for the past 12-18 months. Looking at the period 2006-2013, it looked like this sector was on course to add 4 hours a week. However, after peaking at 36.4 hours a week in December 2013, the subsector has steadily lost hours to the point that for the first 6 months of 2015, the average was just 34.6 hours per a week. Whether this is the result of more work friendly policies, more competition for staff or some other factor remains to be seen.

Hard Working Aussies?

Moving on to Australian data from the Australian Bureau of Statistics (ABS), the dataset available is longer than what was available from the BLS, but it is lacking fine level detail. The ABS data goes back to 1978 and is split by different brackets of hours worked. For example, 1-15 hours, 16-29 hours, 60+ hours and so on. Chart 4 below shows the percentage of employed people in each bracket[1] (based on a 12-month moving average).

Chart 4 – Australian Employees by Average Weekly Hours

The most striking aspect of the chart is the decline in the number of people working between 30 and 40 hours a week – or what most people would consider a regular full time job. As late as January 1986, more than half of Australian workers were working between 30 and 40 hours a week. By the turn of the century, that percentage was closing in on 40%. From the data, most of the people who moved out of the 30-40 hours a week category appear to have moved into the ‘less than 30 hours a week’ category. This substitution of full time jobs for part time and/or casual employees is sometimes referred to as ‘casualization’.

In Australia, the ‘casualization’ of the workforce has been a much-discussed topic. Some argue that it is the natural result of more modern, flexible working arrangements. Others see negatives in reduced job security and reduced benefits (casual employees do not get access to paid leave for example). One thing that is for certain is the number of people affected continues to increase.

Moving on to the other end of the spectrum, those working 50+ hours a week, there are two distinct phases. The first phase, from 1979 through to the year 2000 shows a strong increase in the number of people working 50+ or more hours. The second phase, from 2000 onwards shows a decrease in the number of people in this category that almost completely unwinds the previous increase. Another interesting observation is that the decrease in people working 50+ hours from 2000 onwards is almost exactly mirrored by the gain in people working 30-40 hours a week over that period.

It is difficult to say what exactly is driving this change. Are employees leaving jobs that require longer hours for jobs with better work life balance? Are companies becoming more serious about looking after their employees? Has the recent mining boom, which has led to huge economic changes, caused a shift away from industries that have longer hours? All these questions are a topic for another blog post.

What can be said is that, at a high level, there is little to indicate that longer hours are becoming the norm for Australian workers. But, like the US example, without looking at the data at a sector and subsector level, this data tells us very little about what is happening in legal offices and tech startups in inner city Sydney and Melbourne.

The International Perspective

The OECD also provides statistics on average yearly hours across a range of countries. Looking at yearly hours worked is slightly different to weekly hours because of differing leave allowances and expectations between countries, but it does allow us to look at how things have changed over time within each country. Chart 5 shows the average yearly hours for a selection of countries.

Chart 5 – Average Annual Hours Worked – Selected Countries

Again, this data is at the highest level (all sectors, all employees), making it difficult to detect a small increase in average hours worked that is limited to some subsectors. However, this chart does provide some perspective on how much average hours worked a year has declined in pretty much all developed nations over the past 60 years. The decline in hours worked in France in particular is striking – falling from over 2,300 hours a year (almost 48 hours a week if 4 weeks of leave is assumed) to under 1,500 hours a week (just over 31 hours a week).

The other interesting point to note is the increase in hours in Sweden since the early 80s. Not having any knowledge of Swedish history outside of the recent Thor movies (which I assume are completely factually accurate), any explanation anyone could offer about what is happening here would be very welcome.

The Long Term Perspective

The final data source for comparison is a paper[2] released in 2007 by Michael Huberman and Chris Minns. The paper takes a look at the question of how hours worked have changed over time from a very long-term perspective. Chart 6 shows a summary of the main results from the paper.

Chart 6 – Huberman and Minns; Hours of work per week; 1870–2000

Similar to the OECD data, this data provides perspective on how far the average hours worked has fallen over time. The biggest gains were made in the interwar period as Henry Ford and other business owners realized lowering the hours of their employees actually ended up boosting output, and many countries adopted statutory hours.

We also see how cultural and policy differences in France has led to continued declines in hours worked post World War II, while the Anglo-Saxon nations have essentially had no real change.

Table 1 – Huberman and Minns; Hours of work per week; 1870–2000

  1900 1913 1929 1938 1950 1960 1970 1980 1990 2000
U.K. 56.0 56.0 47.0 48.6 45.7 44.7 42.0 40.0 42.4 40.5
France 65.9 62.0 48.0 39.0 44.8 45.9 44.8 40.7 39.9 35.8
Australia 48.1 44.7 45.5 45.0 39.6 39.6 39.6 39.2 40.1 40.6
U.S. 59.1 58.3 48.0 37.3 42.4 40.2 38.8 39.1 39.7 40.3

Another thing that is not so obvious from the chart, but is clearer in the underlying data (see Table 1), is that in Australia and the US, there has been an increase in hours worked from 1980 onwards. Although not significant when compared to hours worked by previous generations, this could be representative of more recent trends. One caveat on that is that this data series only runs to the year 2000, and, at least in the case of Australia, there were declines in the number of people working 50+ hours from 2000 onwards.

Wrapping Up

Overall, the evidence that people are working longer hours is mixed. When drilling down to specific subsectors in the BLS data from the US, the data indicates there has been an increase in average hours worked in most of the expected places. However, the gains appear small (1-3 hours a week) and no sector or subsector analyzed averaged over 40 hours a week.

The ABS data from Australia did show a significant increase in people working 50+ hours from the late 70s through to the turn of the century, but that trend then stopped and reversed. Meanwhile, the longer-term perspective provided by the OECD data and Huberman and Minns showed significant declines over the last 150 years, with little indication average hours worked were going back up in recent years.

Taking all this data into account, there are two main conclusions to be taken away:

  1. When looking at data aggregated across sectors, there is little indication that average hours worked are increasing. That doesn’t mean average hours worked are not increasing anywhere, but that it is not happening on a big enough scale to move the high level aggregate numbers.
  2. When drilling down into specific subsectors where anecdotal evidence suggests there should be increases, the data indicates that average hours worked have been increasing. Although the averages still seem low (i.e. less than 40 hours a week), when you take into account the spread of hours making up those averages, even a 1-2 hour average increase represents an increasingly large proportion of people in those subsector working very long hours.

 

[1] Note – I have aggregated some of the brackets to simply the picture.

[2] M. Huberman, C. Minns; The times they are not changin’: Days and hours of work in Old and New Worlds, 1870–2000; Explorations in Economic History 44 (2007) 538–567

Does Wealth Inequality Impact Growth?

I recently read a paper entitled Does wealth inequality matter for growth? The effect of billionaire wealth, income distribution, and poverty[1] that has been getting some coverage in economic circles. One of the reasons for the coverage is that income and wealth inequality has become a major discussion point in economics, since the release of Thomas Piketty’s Capital in the Twenty-First Century.

The other reason for the attention is that the paper, although implicitly agreeing with Thomas Piketty’s conclusion that inequality is detrimental to economic growth, puts a twist on the conclusion. This paper, through a series of statistical models, provides evidence to suggest wealth inequality in itself does not impact economic growth, but that wealth inequality that arises due to government corruption impacts on economic growth.

Reading between the lines, this conclusion essentially reverses the prescription Piketty has been arguing for (greater intervention from government to redistribute wealth) and instead implies the opposite, that government should be reduced and basically get out of the way.

At a high level, there are two reasons I wanted to review this paper. These reasons are:

  1. To highlight the importance of skepticism when reading headlines based on scientific literature, and
  2. To provide an example of how a lack of domain knowledge[2] can cause problems in the world of statistics.

The Setup

The basic experiment setup is as follows. The authors (Sutirtha Bagchi and Jan Svejnar) took the Forbes List of World Billionaires for four years, 1987, 1992, 1996[3] and 2002. They then split the billionaires in these lists into two groups: those that have seemingly gained their wealth through political connections, and those that apparently gained their wealth independent of political connections.

Once grouped, the researchers aggregated the wealth of the billionaires by country to calculate politically connected billionaire wealth, politically unconnected wealth, and (adding these two pools together) total billionaire wealth – for each country in the dataset.

To normalize this measure of billionaire wealth across countries, they then divided the billionaire wealth for each country in each year by the total GDP for that country in that year. This provided a measure of billionaire wealth (politically connected, politically unconnected and total) as a percentage of GDP[4], which was taken to be a measure of inequality.

In addition to the three variables for each country – politically connected wealth inequality, politically unconnected wealth inequality, and total wealth inequality the authors also added a number of other variables, including measures of poverty, income inequality, income level (as measured by real GDP per capita), levels of schooling and the price level of investment[5].

Using linear regression, these variables were then used to predict GDP growth per capita for the following five years (after the year the variables corresponds to). For example, the variables for the year 1987 were used to predict the GDP growth per capita for the years 1988 to 1992.

Without getting too deep into how linear regression works, this approach was informative because it allowed for an assessment of the impact of each variable on growth, assuming all the other variables were held constant. With a variety of models constructed, the authors were able to assess what impact politically connected inequality had on growth, assuming politically unconnected inequality, income, poverty levels, schooling levels and the price level of investment were held constant.

The other big benefit of using linear regression is that it provides information about which of the variables used in a model are actually useful (“found to be significant”) in making a prediction. Essentially, variables that are found to not be significant can be excluded from the model with little or no decrease in the accuracy of the model.

Before moving on to the results, please be aware, for the sake of brevity, I am greatly simplifying the experimental setup, and completely ignoring a range of robustness and other testing the authors did. For those details, you will need to read the full paper.

The Results

At a high level, the results of the models constructed suggested the following in relation to the impact of inequality on growth:

  1. Politically connected wealth inequality (regardless of how it is normalized) was found to be a statistically significant predictor of growth. In all cases the coefficient was negative, indicating the higher the level of wealth, the lower the predicted growth.
  2. Politically unconnected wealth inequality (regardless of how it is normalized) was not found to be a significant predictor of growth.
  3. Wealth inequality (when political connectedness is ignored) can be a significant predictor of growth depending on how it is normalized[6]. When found to be significant, higher levels of billionaire wealth led to lower levels of predicted growth.
  4. Income inequality was found to be a significant predictor of growth in only one of the 12 models constructed. In the case where it was found to be significant, greater income inequality led to predictions of higher growth.

In addition, the model also provided some other interesting conclusions:

  1. The level of income in a country was found to be a significant predictor of growth in all cases. The models suggested that the higher the level of income in a country, the lower the predicted growth[7].
  2. The level of poverty was not found to be a significant predictor of growth in any of the models constructed.
  3. The level of schooling (for males or females) was not found to be a significant predictor of growth in any of the models constructed.

Caveats and Problems

Already from some of the findings above, you probably have some questions about the results. Poverty and schooling and income inequality have no impact on economic growth? The conclusions can change based on how billionaire wealth is normalized? You are right to be skeptical, but lets break down why.

Determining Wealth is Difficult

The first problem, and it is one explicitly acknowledged by the authors, is that measuring wealth (and therefore wealth inequality) is very difficult. Most of the difficulty arises from determining the wealth of the rich. In some cases, it is relatively straightforward to determine wealth – for example if the billionaire’s wealth is tied up in one company (e.g. Bill Gates and Mark Zuckerberg). But in other cases, particularly with inherited wealth, the assets are diversified, held in a large number of holdings, trusts and companies across the world. In some further cases, it is extremely difficult to value the assets of a billionaire due to the unique nature of the assets (this is why Donald Trump’s worth is always the subject of debate).

To get around this, problem, the authors have relied on the Forbes list of billionaires. In terms of billionaire wealth, this is probably the best researched list of billionaires available, but by Forbes own admission “It’s less about the [net worth] number, per se… this is a scorecard of who the most important people are.”

Is Billionaire Wealth a Good Predictor of Wealth Inequality?

The authors built their measure of wealth inequality using the wealth of billionaires. But does this make sense even if we assume the Forbes list is accurate? There are two main problems I see with this approach.

The first is that ‘billionaire’ is an arbitrary cutoff point. Extremely wealthy people with wealth over the billion-dollar cut off one year regularly fall out of the three comma club the following year. For smaller countries with very few billionaires, this can have an outsized impact on their measure of wealth inequality from year to year.

The second issue is that looking at billionaire wealth tells you nothing about the distribution of wealth below the $1 billion mark. An example is provided in Chart 1 below.

Chart 1 – Wealth Distribution Across Two Hypothetical Countries

What Chart 1 shows is two hypothetical countries with 10 people each and the same amount of total wealth. Country 1 has two people who are extremely wealthy (but not billionaires), while the rest are far less wealthy. In Country 2, we have one billionaire but a much more even distribution of wealth amongst the rest of the population. Looking at the chart we would conclude that Country 1 has a higher level of inequality, but if we calculate inequality based on methodology used in the paper, Country 2 will be determined to be more unequal than Country 1. In fact, Country 1 would be assigned an inequality value of 0.

Obviously this is an exaggerated example, but it illustrates the point that there is a lot that could be happening below the $1 billion mark that is completely ignored by the measure used. I would also argue that the distribution of wealth amongst the population who are not billionaires is going to be much more important for growth than the ratio of billionaires to everyone else.

What is Politically Connected?

This is the part of the experiment setup that will probably end up being the most contentious, and relates back to the lack of domain knowledge. The problem is the authors could not possibly know of every billionaire on the list, the circumstances of how they accrued their wealth, and make a judgment call on whether political connections were a necessary precondition. As a result, they had to rely on various news sources to draw their conclusions and this led to some interesting outcomes.

For those that read the Wonkblog piece I linked to earlier, you may have noticed a chart in which Australia was adjudged to have 65% of billionaire wealth over the four years looked at being politically connected, putting it the same range as India and Indonesia. To most Australians this would be a hugely surprising result given Australia’s strong democratic tradition, strong separation of powers and prominence of tall poppy syndrome[8].

Generously, the authors of the paper provided me with the classifications that led to this number and it boils down to the fact that they have classified Kerry Packer as a politically connected billionaire. For those that know of Packer (pretty much every Australian) it would seem ridiculous to class him in the same bracket as Russian oligarchs or Indonesian billionaires who benefitted from the corrupt Suharto regime. But for someone who is not from Australia, they had to make this judgment based on newspaper clippings talking about Packer’s lobbying efforts.

In the case of Australia, having a high percentage of politically connected billionaire wealth has little impact. Once politically connected billionaire wealth (i.e. Kerry Packer’s wealth) is taken as a ratio of GDP, the number becomes very small because Packer’s wealth is dwarfed by the relatively large Australian economy. But what about other countries? How have various judgment calls impacted their inequality measures and therefore the model?

As I mentioned at the start of this section, it is unreasonable to expect the authors to be able to know how every billionaire worldwide accrued their wealth and the role of the government in that process. Additionally, the fact that there may be issues with some classifications does not mean we should throw away the results. However, it does mean any conclusions we draw from the results should be caveated with this problem in mind.

Unknown Unknowns

The final problem comes down to the high level question of what drives economic growth.

When you consider all the different things that can impact on the economic growth of a country over the course of five years, you quickly realize there are an almost unlimited number of factors. Commodity prices, what is happening in the economies of major trading partners, weather patterns, population growth, war, immigration, fiscal policy, monetary policy, the level of corruption and the regulatory environment are just some of the factors that can have a major impact on growth.

When economists build models to predict growth, they make choices about what factors they believe are the major drivers of growth. In this case, the authors have used factors like income levels, schooling and poverty levels. But what about some of the other factors mentioned above? Could these factors have better explained growth than politically connected wealth inequality?

This choice of variables is further complicated by the interrelatedness of the factors impacting growth. Is population growth driving economic growth, or is it because population growth indicates higher levels of immigration? Is government corruption holding back growth, or is it that corruption is siphoning off money from schooling and other public services?

When it comes to the models in the paper, the key question is if politically connected billionaire wealth is really impacting growth, or if it is simply acting as a proxy for some other measure (or measures). For example, are high levels of politically connected billionaire wealth dragging on growth, or is this measure acting as a proxy for the level of corruption in an economy and/or the prevalence of inefficient government created monopolies – which are the real drags on growth? Unfortunately, there is no definitive way to know the answer to these questions.

Conclusions

As mentioned at the outset, inequality and its impact on growth and the economy in general has been a popular topic of discussion in economic circles for the last 1-2 years. In many ways, it is the defining economic discussion of our time and has the potential to shape economic policy for a generation.

In an effort to provide more information in that debate, the authors of this particular paper deserve plenty of credit for taking an innovative approach to a difficult problem. However, at least in my mind, the results raise more questions then they answer.

That, it should be noted, is not a criticism, but is often the outcome of research and experiments. Results can often be confusing or misleading, and can only later be explained properly through further research. This is all part of the scientific method. Hypotheses are created, challenged, and either proved incorrect or strengthened. They are always subject to be proven wrong.

Unfortunately this nuanced process is not one that lends itself to catchy headlines and this is where we find one of the key problems with reporting of scientific results. Most authors, including the authors of this paper, are fully aware of the limitations of their findings. That is why you will find the conclusions section filled with words like ‘suggests’, ‘possibly’ and ‘could’. But those words do not make for good stories and so the qualifiers tend to get left out.

It is for this reason, if you are interested in the results of a particular paper or study, it always worth looking at the detail. With that, I’ll leave the final word to Sutirtha and Jan (emphasis mine):

“These and other examples, together with our econometric results, suggest that the policy debate about sources of economic growth ought to focus on the distribution of wealth rather than on the distribution of income. Moreover, particular attention ought to be paid to politically connected concentration of wealth as a possible cause of slower economic growth. Further research in this area is obviously needed, especially with respect to the effects of wealth inequality at different parts of the wealth distribution, the possibly declining effect of unequal distribution of income on growth, and the role of poverty.”

 

[1] S. Bagchi, J. Svejnar, Does wealth inequality matter for growth? The effect of billionaire wealth, income distribution, and poverty, Journal of Comparative Economics(2015), http://dx.doi.org/10.1016/j.jce.2015.04.002

[2] Domain knowledge is knowledge of the field that the data relates to.

[3] A change in the methodology used by Forbes to compile the list between 1997 and 2000 led them to instead choose 1996.

[4] The authors also try normalizing by other factors, such as population and physical capital stock, but this doesn’t substantially change the results of the model.

[5] A measure of how expensive it is to invest in capital within a country.

[6] When normalized by population, billionaire wealth is found to be a better predictor of growth than politically connected wealth.

[7] This may seem strange, but actually nicely captures a phenomenon in economics where lower income countries experience higher growth as they ‘catch-up’ to higher income countries.

[8] A perceived tendency to discredit or disparage those who have achieved notable wealth or prominence in public life.

4 Reasons Fiat Money is a Great Idea and One Catch

In the world of economics and finance there are many complex topics that are poorly understood in the wider community. Differential calculus, options trading and multiple regression to take three examples. However, money and the monetary system is another topic I would quickly add to this list. The difference when it comes to money, however, is the number of people who believe they do understand the system. This leads to a range of misunderstandings including:

  1. Money in modern economies is still exchangeable for gold
  2. Printing of money will always lead to high levels of inflation
  3. Balancing a household budget is a suitable analogy for balancing the budget of a Government
  4. Paper money is worthless and doomed to fail

There is a lot of myths to dispel in that list. In this article we are going to tackle the question of why money works, even when not tied to a physical commodity (known as “fiat money”[1]). To do this, let’s start by imagining a world where there is no money. Instead of paying for things for money, everyone now has to barter for goods. What issues would people have in this system?

1. Coincidence of Wants

In a world where everyone has to barter to exchange goods, the first problem you are likely to encounter is described as the coincidence of wants. Imagine you are a pig farmer and, sick of eating pork for every meal (hard to imagine I know), you decide you would like to trade a pig for some wheat. The first hurdle is finding a wheat farmer who actually wants a pig. This is the coincidence – that you have pigs and want wheat, and that someone else has wheat and wants pigs and that both these wants occur at the same time.

Even in a simple agrarian village with only a limited number of food related products, you can already see the difficulties that will arise. Wheat is only available at certain times of the year and that will not coincide with the production of many other products. Some people may simply not like certain products, making it difficult for people producing those products to do any trading with them.

Introducing money into this scenario cleanly solves this problem by providing something to trade for what everybody wants at any given time.

2. Divisibility of Money

The second major problem in a barter system is the indivisibility of goods. Let’s go back to the pig farmer example and imagine again you want to trade pig for wheat. Assuming we find someone who wants to trade with us, how much of each do we actually trade? A pig is probably worth quite a large quantity of wheat, so what do I do if I only want a little bit of wheat? I’d have to kill my pig, give some of it to the wheat farmer, then hope I could find someone to buy the other parts of my pig. What about people producing even larger goods that can’t be sold in parts at all, such as a horse trainer or a house builder? They would constantly be forced to trade their goods for huge quantities of other goods.

Money solves this problem because it has the property of divisibility. I can sell a pig for $100, then split that money up to buy as many different types of goods as I want.

3. A Store of Value

The third problem in our moneyless world is that many of the goods we trade have limited lifespans. As a wheat farmer, if I have a good year and have extra wheat, what can I do with my extra wheat? I need to trade it for something or it will go off and be wasted.

In the past, this was such a problem, nearly every culture developed ways to preserve seasonal produce. Think about how many cultures have cured meats (prosciutto, jerky, spec), preserved fish (bacalao, pickled herring), pickled vegetables (cucumbers, onions, beans, peppers, achar) and fruit preserves and jams. Many of the most popular foods today were developed largely as a way to store produce over extended time periods in the days before freezers and refrigeration.

Although money doesn’t stop food spoiling, it does allow a farmer to sell off their seasonal produce for something that does not need to be preserved. That money can then be spent as needed in the future to purchase other goods. In the simple world of our example, that may simply mean buying preserved goods during the winter to survive until the next season. In a more complicated world it helps us do many things including to save for more expensive purchases such as a TV, a car, a house or our retirement.

4. Practicality

Continuing to build on the farming example, let’s imagine that the people of this particular farming village are trying to decide on a given (non-money) product that will become the unit of trade for everyone. Keeping in mind the points above, what would be the best options?

It would need to be something that could be easily divisible, which rules out livestock and any large objects such as furniture, tractors, houses and so on. It has to be something that doesn’t go off or require preservation, which rules out vegetables, fruits, grains and so on. What if they decided to use something that met these basic conditions like salt or honey?

Here we run into another issue that is neatly solved by money – practicality. Even trading in a commodity such as salt or honey would face a number of hurdles:

  1. Every transaction would need to be weighed or measured out to ensure the quantity exchanged is correct
  2. People would have to carry around honey or salt to complete transactions, and for large transactions, that could be a significant burden
  3. There would need to be some measure of the quality of the commodity being traded. How pure is the honey or salt? Do salts from certain places carry more value? Has the honey been diluted?
  4. People would have to find ways to store large amounts of these commodities in a such a way that they are safe and don’t get stolen, eaten or washed away

Problems 3 and 4 could be alleviated by some third party fulfilling the role of a salt or honey bank. This bank could verify the quality of the commodity and store large quantities of these commodities on behalf of their customers (for a small fee of course). It could even provide facilities allowing customers to access their deposits. This could be done by allowing access to the commodity itself, or by issuing some sort of official document or paper that the holder could bring to the bank to exchange for the commodity (it’s starting to sound pretty close to money at this point right?). But even in this case, the commodity would still need to be stored somewhere physically.

All these concerns are things we don’t have to worry about in a world with money. Notes and coins are extremely portable, meaning people can carry even very large amounts in small leather foldy things (let’s call them “wallets”). They come in predefined amounts which mean they don’t have to be measured out and quantities can be quickly verified. Finally, in a fiat money system, the vast majority of money doesn’t need to be physically stored, it is stored as numbers in a bank account.

The Catch

The catch in a fiat monetary system is that it is essentially a system built on mutual trust. For me to accept money as payment for goods I am selling or services I am providing, I must believe that I will be able to trade that money for goods and services, of approximately the same value, in the future. The person I purchase those goods or services from, in turn, must also believe the same thing, and so on down the line. If, at any given point, people in general stopped believing money would be able to be traded for goods or services in the future, the fiat money system would collapse very quickly.

We can see some examples of this in the real world in countries where hyperinflation and/or currency controls have occurred. In most cases, the local currency often becomes close to worthless as people substitute to either a more stable currency (typically the US dollar) or hard commodities. Luckily, these occurrences have usually been limited to small and poorly run economies and have not seriously impacted the legitimacy of the fiat money system overall.

What would happen if the population of a major developed economy stopped believing their currency would be accepted in the future? At that stage all bets are off. There is a substantial community of people that does have this concern, and are preparing for this scenario by buying hard commodities such as precious metals. But realistically, they should also be buying guns, canned food and digging a shelter in the backyard, because a failure of the monetary system would be a complete catastrophe.

Overall

Looking at the above points, we can see there are a large number of advantages to fiat money. Many of the transactions we undertake every day would become extremely burdensome in the absence of money. A lot of larger business transactions would be impossible in a barter system. Although money introduces some its own complications, it is hard to argue that the world would be anywhere near as complex or advanced if we had persisted with a barter system or a commodity based trade system.

Money in Three Charts

To finish off, let’s take a look at some stats on the values and volume of currency on issue for the worlds reserve currency, the US dollar. All the underlying data and more is available at the Federal Reserve website for those interested. I’m trying out some new interactive charts so please click, play and let me know what you think!

Chart 1 – Value of US Currency in Circulation by Denomination

Chart 2 – Volume of US Currency in Circulation by Denomination

Chart 3 – Comparison of Different Measures of Money Supply

For those that are unfamiliar, there are different ways of measuring the total money supply. The following chart compares 3 different measures – M1 money supply, M2 money supply and cash. This data is from the Federal Reserve of St. Louis website.

[1] Fiat money is money that derives it value only from Government rule or regulation. This is as opposed to commodity or representative money which is tied to the value of an underlying commodity.

Greek Debt Crisis Enters Final Stage

The never-ending saga of the Greek debt crisis appears to be finally entering its final phase this week. After 5 months of negotiations, Greece’s creditors, led by the IMF, have made a final offer to the Greek government, and it is an offer of more of the same – i.e. austerity. For its part, the Greek government needs to make a decision before Tuesday next week when it is expected to run out of cash.

Background

For those that have seen the headlines, but have not had the time to dig into what is actually happening in Greece, first a little background.

As early as 2010, it became clear that the Greek government was in trouble financially. It was running large deficits and was quickly accumulating a debt that bond markets increasingly believed were unlikely to be repaid. As a result, the yields on Greek Government bonds (the rate of interest that the Greek government has to pay to borrow money) began to spike, further increasing the risk that Greece would be unable to repay its debt.

To avert a crisis, the IMF, the European Commission and the European Central Bank (“the Troika”) provided loans to the Greek government to help pay off their existing debt. By doing this, these organizations essentially took the majority of Greek government debt off the books of a range of mostly German and French banks, and put it on their own books.

However, in exchange for the provision of these loans, the Troika insisted that the Greek government implement a series of measures to improve the budgetary situation. These measures mainly consisted of cuts to the public service and pensions, but also tax increases, and other measures. Generally these measures are referred to as “austerity measures”. Despite the warnings of many prominent economists that cutting government spending in a recession would cause further damage to the Greek economy, the measures were pushed through – as they were in a range of other countries.

Sadly, the warnings provided proved accurate. By January 2015, the Greek economy was suffering from 25%+ unemployment and GDP had fallen 25%, far more than had been forecasted by the Troika at the outset of austerity. As the economy shrunk, so did government revenues and so further cuts were required to try meet the surplus target.

In January this year, the Greek people tired of years of crushing austerity, elected what has been called a ‘far-left’ government[1]. Syriza, a party that for most of the recent past had been attracting less than 10% of the vote, was all of a sudden front and center, and with a clear mandate to renegotiate and bring an end to austerity – but also to keep Greece in the Eurozone.

What is Happening Now?

After 5 months of increasingly bitter negotiations between the Syriza government and the Troika, and with the deadline approaching (Tuesday next week), there were two final offers made.

For their part, the Greek government proposed a range of austerity measures that more or less met the Troika’s demands in terms of net budgetary impact. The difference was that they proposed smaller cuts to pensions with the gap being made up with a range of tax increases. Hilariously, the proposed measures were rebuffed over concerns it would hurt the growth of the Greek economy.

The Troika then made their final offer to Greece. Even after all the evidence of how destructive and counterproductive austerity, the offer was basically the same as the original demand. Many took this as a sign that the Troika are aiming to force Syriza out of government, or Greece out of the Eurozone.

What happened next appears to have caught most observers by surprise. On Friday night, the Greek Prime Minister Alex Tsipras announced he would take the final offer to a referendum to be held on July 5th. Although this is sure to further aggravate the Troika (if that is even possible), this would actually appear to be a very clever move on the part of Syriza.

The biggest issue for Syriza since their election has been how they would manage to maintain their two key promises – to stay in the Eurozone and bring an end to austerity. After 5 months of failed negotiations, they have almost certainly proved beyond doubt that the Troika are not going to give any ground on austerity. By calling a referendum, they force the Greek people to choose what they want more – Eurozone membership or the freedom to run their own economy. Either the Greek people willingly accept further austerity in exchange for staying the Eurozone, or they accept exiting and take their chances on their own.

For their part, Syriza have made it clear they believe going on their own is the better option. As part of his announcement to the Greek people, Tsipras took the chance to lambast the institutions making up the Troika (translated from Greek):

“These proposals -– which directly violate the European social acquis and the fundamental rights to work, equality and dignity — prove that certain partners and members of the institutions are not interested in reaching a viable and beneficial agreement for all parties, but rather the humiliation of the Greek people.”

“Greek citizens, I call on you to decide –- with sovereignty and dignity as Greek history demands — whether we should accept the extortionate ultimatum that calls for strict and humiliating austerity without end, and without the prospect of ever standing on our own two feet, socially and financially.”

What Happens if the Greeks Choose to Exit?

No one knows for sure – but it won’t be pretty. Essentially, a chain of events will mean Greece will need to revert back to their own currency (essentially a new Drachma), which in itself leads to further impacts. The first and most serious of which is that the Greek government would need to impose capital controls – basically stopping people from moving their money out of Greece.

In anticipation of this measure, Greeks have been pulling Euros out of Greek banks at a record pace the last few weeks and either moving it offshore, or effectively stuffing their mattresses. After the announcement of the referendum, the pace further quickened with pictures flooding into Twitter of lines at ATMs on Saturday morning and reports that many ATMs had already run out of cash.

Looking further forward, after the change to a new currency, there is an expectation that it would depreciate very quickly against the Euro. As a result, vital imports like oil and medical supplies would suddenly become hugely more expensive causing problems in the health sector as well as for business in general. On the flip side, this depreciation should provide a boost to Greek exports (primarily tourism and agriculture). However, it is questionable how much benefit this can provide given the large internal devaluation that has already occurred.

The only possibly good news is that the Greek government is already running a primary budget surplus (surplus before the costs of borrowing are included). By defaulting on its existing debt, it would not need to issue new debt to meet payment obligations in the short run (although a depreciating currency could impact that). Longer term, by most measures, the Greek budget is actually in a strong structural surplus (i.e. if the economy wasn’t hugely depressed, the budget would be in a much better position than it currently is). If the Greeks could manage even a small amount of growth after leaving the Euro, they could find they are quickly running large surpluses.

For the Eurozone, a Greek exit is no longer the risk to financial stability that it once was, but it could be a risk to political stability. If Greece does exit the Eurozone, there will be several countries monitoring the situation very closely. Spain, Portugal and Ireland (not to mention Italy) have all undergone differing levels of austerity over the past 4-5 years, and all have seen very high levels of unemployment and significant falls in GDP as a result. If (and it is a big if) Greece exits the Eurozone AND manages to keep the country from falling apart completely, these other countries may be tempted to do something similar.

From there, the Eurozone project could completely unravel. And make no mistake; this would also be disastrous for the northern European economies, including Germany. Without the relatively unproductive southern European countries in the shared currency zone, the Euro would be expected to appreciate strongly, doing serious damage to Germany’s export driven economy and even more so to less efficient countries like France and Italy.

This scenario has led to some speculation that the Europeans will try to make any Greek exit as difficult as possible – to deter other countries from exiting. But this strategy has its own political ramifications. Essentially the European Union would start to look like a union held together by the threat of economic ruin rather than goodwill and mutual benefit. At that point, the question becomes what kind of union does Europe really have?

What Happens Next

Even though the Greeks have declared their intention to hold a referendum to decide on whether they will accept the bailout conditions, they don’t actually have enough cash to survive until the referendum date. As such, they are asking the creditors to provide an extension for a few days to get to the referendum.

Early indications are that they will be refused even this small extension (the creditors are really pissed off…). To do this would appear to be a dumb move politically and with very little gained financially, but it took a lot of dumb moves to get to this point, so nothing can be ruled out. If they do hold the line and deny Greece the extension, essentially everything gets moved forward. On Tuesday, assuming the European Central Bank stops providing liquidity (cash) to Greece’s banks, the Greek government would be forced to step in with a new currency and we will officially have the first example of a country leaving the Eurozone.

The Greeks have put the gun to their collective heads and shown they are ready to pull the trigger. The only question left is will Europe stop them, or hand them a bigger gun?

Further Reading

For further details of why a Greek exit from the Eurozone will not be a panacea to the countries woes, Greek finance minister Yanis Varoufakis actually provides one of the best explanations I have seen here. In fact, Varoufakis, who has a master’s degree in Mathematical Science and a PhD in Economics, has been very active on Twitter and his blog throughout the negotiation process, often taking to the public to deny claims of insults and walkouts. To my mind he has remained the perfect professional throughout this process.

For Australians, there is also a personal connection to Varoufakis, who was senior lecturer in the Economics department at Sydney University for 11 years from 1989 to 2000. He also regularly provided commentary on the crisis (before being elected) on Late Night Live – a radio program hosted by Phillip Adams (is there anyone with a better voice for radio?). I highly recommending listening to an interview conducted just after Varoufakis was elected to get a sense of the man – and that most Australian of traits, self-deprecation.

 

[1] If anyone can point me to a policy that could reasonably be called far-left, I’d love to see it.

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

RBA_chart_1_1

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

RBA_chart_1_2

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

RBA_chart_1_3

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

RBA_chart_1_5

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

RBA_chart_1_6

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

4 Economics Concepts to Improve Decision Making

Rightly or wrongly, the reputation of the economics profession has taken a battering over the last 6 years. Largely this is because of the perceived inability of economists to foresee the Global Financial Crisis, and the anemic recovery that occurred afterwards in most countries. Leaving the debates over austerity vs. stimulus, liquidity traps and the zero lower bound for another day, I thought I would go back to some basic economic concepts and how, with a little bit of imagination, they can be useful in situations most people encounter in everyday life.

1. Opportunity Costs

Opportunity cost is a concept used in economics to help determine the cost of a particular action or choice. At the most basic level, the opportunity cost of doing something is the cost of NOT doing the most (economically) beneficial alternative.

For example, it is Saturday morning and you are going to drive to your friend’s house for a morning session of Call of Duty. The cost of doing this might appear to simply be the cost of gas to get there and any marginal wear and tear on the car. However, economically the cost is more than that – it also includes the benefit of the most productive activity forgone. Depending on your circumstances, that might have been picking up the breakfast shift at the local café, or putting time into that startup idea you have been working on. Whatever the case may be, you have unconsciously placed a higher value on time spent playing games then the alternative and, in many cases, that is a tangible value (the café example).

This may change nothing in your mind, you may really get a lot out of hanging out and playing games – video games are a huge industry precisely because people place a high value on playing games. But once you start consciously thinking about the cost of your actions in this manner, it tends to have an impact on how and where you spend your time.

2. Incentives

One of the parts of general economic theory that tends to rub people up the wrong way is the idea that everything can be quantified and compared. This is not a particularly romantic way to look at the world, but this line of thinking can be used to help understand and change behaviors.

For example, many people have trouble finding the motivation to go to the gym. Why is it so hard to go consistently? When you hit that sleep button on the alarm, you are making a choice based on the incentives in front of you – and you are valuing an additional hours’ sleep higher than the benefit of a gym session. There can be a range of very rational reasons for that – the benefits of any given gym session are tiny and hard to identify even if the long term benefits of consistent gym work (e.g. improved fitness, a more appealing physique) are highly valued. When you look at it that way, every morning you are faced with a choice between an immeasurably small improvement in health/fitness, or an additional hour of sleep and relaxation.

However, once we realize what the problem is, we can use incentives to reweight the choice to get the desired result. In the gym example, this can be done in a range of ways that will probably sound familiar. You can set a goal like running a half marathon – that provides additional incentive in the form of not letting yourself and/or others down or avoiding embarrassment. It could be a more immediate incentive such as treating yourself to a better lunch if you go to the gym. Organizing a gym or exercise partner will provide incentive in the form of not wanting to let your partner down by cancelling (this only really works if they don’t happen to sleep in the same bed as you). Sometimes, all that is needed is to clearly identify what the benefits will be and remind yourself of them.

Expanding this way of thinking, it can be used to look at a lot of different aspects of your life. If you have a goal to cook at home more regularly, what steps can you take to make that more appealing after a day at work (or make eating out less appealing)? Trying to commit to further study? Perhaps clearly identifying and reminding yourself of how the it will get you closer to that dream job will help provide the needed motivation.

The next logical step for this way of thinking is using it to understand the behaviors of those around you, and then utilizing that understanding to make changes. If you are a manager, how can you rearrange the incentives for the people you are managing to improve productivity or eliminate some unwanted behavior? If you have children, what incentives can you use to motivate them to help with the housework or clean up their room?

One thing to be aware of is that the incentives people are responding to can be complicated and counterintuitive. Understanding your own motivations, never mind the motivations of those around you, is something that takes time. However, the simple act of stopping to consider what may be causing you or someone else to make a choice can often lead to meaningful insights.

3. Sunk Costs

The standard definition of a sunk cost is a cost that has already been paid and is unrecoverable. In economics the concept is usually used in relation to firms (businesses), but we all deal with sunken costs on a pretty regular basis.

The classic example is where you have bought a ticket to a concert but when the day arrives you have come down with a cold (or worse). You spent all that money on the ticket so you should definitely go, right? Actually, to make the economically optimal decision in this situation, you should ignore how much you spent on the ticket (the sunk cost) and simply base your decision to go or not on whether you would still enjoy the concert more than the next best alternative (lying in bed and binge watching season 4 of the Walking Dead). Depending on how bad you are feeling, that decision could go either way.

That sounds simple right? Let’s try a thought experiment to see how difficult this can be in practice. Using the same example from above, let’s first imagine the concert ticket only cost you $10. You probably don’t have to be feeling very sick before The Walking Dead is sounding pretty appealing. Now imagine the ticket cost you $1,000. In your mind, what would it take to stop you going to that concert? Broken leg? Getting stung by an Irukandji jellyfish? If you were being rational (at least in an economic sense), you would be just as willing to forgo the concert regardless of what the ticket cost.

Applying this thinking can be tough in practice (I would have to be close to death to pass up a $1,000 concert ticket), but being conscious of it can help to avoid some poor decision-making. Should you keep pouring time and money into an unsuccessful business because you spent a bunch of money and time getting it started? You probably shouldn’t. Is it acceptable not to drink yourself into a coma after you spent money on an all-you-can-drink pass? Yes, it certainly is. It can even apply to relationships – should you continue dating your current jerk boyfriend because you have already spent 5 years with him? This isn’t a relationship advice blog, so I’ll leave that one to you, but you can see where this is going.

4. Expected Cost/Benefit

Calculating the expected cost or benefit of a set of choices can be a great way to analyze a situation where there are multiple possible outcomes, even if you don’t have specific numbers to attach to certain outcomes. Used in the right situations, it helps to identify, clarify and compare the expected outcomes of different courses of action.

Let’s look at an example where you need to decide whether to apply for a promotion or not. Let’s imagine you are in a work place and a position opens up at the next level that will be filled internally. Here is the scenario:

  • The next level pays $10,000 p.a. more than your current job
  • You are one of two people that can go for the position, but the other candidate is much better qualified and you believe they will get the job if they apply
  • When you speak to the other candidate, they are on the fence as to whether they want the promotion (let’s say there is a 50% chance they will apply)
  • Your current boss is a bit of a possessive jerk and if you apply for the job and don’t get it, you estimate he will cut your bonus by $5,000
  • Your current boss can also be a generous possessive jerk, and if you don’t apply, you estimate he will bump up your bonus by $2,000 for showing loyalty

Should you go for the position? We can calculate the expected benefit/cost of each course of action to help us make the decision. Here are all the possible outcomes:

Candidate 2 Applies

Candidate 2 Doesn’t Apply

You Apply

-$5,000

+$10,000

You Don’t Apply

+$2,000

+$2,000

Given these outcomes, we can now weight the outcomes by the probability of them occurring to determine what the best course of action is:

Expected Benefit of Applying: 50% × -$5,000 + 50% × $10,000 = $2,500

Expected Benefit of Not Applying: 50% × $2,000 + 50% × $2,000 = $2,000

Based on this calculation, you should apply for the job, as the expected benefit is $500 higher than not applying.

Of course this is a stylized example, in reality it is unlikely that you have all the information given above. However, even missing some pieces of information, you can still use this approach to provide a baseline for your thinking. You may not know what the chances of the other person applying are, but by doing this calculation, you can determine that if there is anything more than a 50% chance of them applying, you will be better off not applying. You may not be able to estimate the impact on your bonus of an unsuccessful application, but you can work out how big the cut would have to be to stop you applying (a $6,000 cut in the above case) and then decide whether that is likely to occur. In short, you can use the information you do have to help you make your decision.

Initially it may be difficult to picture many scenarios where this type of thinking may be useful. However, with a little imagination, you may be surprised how often these situations present themselves. Some example scenarios may include:

  • Trying to buy a car knowing someone else is also interested – should I increase my offer, stick to my original low-ball offer, or pull out altogether?
  • Salary negotiations at work – should I accept the first offer or hold out for more money?
  • Deciding who to have lunch with when you are double booked – who would be the most offended and/or who can I most easily make it up to?

BONUS POINT: Getting Over Decisions That Don’t Pan Out

One of the key benefits of approaching your decision-making in a more rational, fact-based manner (aside from hopefully better decision-making) is that there will be less regret when you make a decision that does turn out badly.

Sometimes, even when you make the correct decision based on the information you have on hand, things will turn out badly – and the reverse can also be true. What changes when you start approaching your decision-making in a more calculated way is you provide yourself with an audit trail of assumptions and reasoning used. Now, instead of wondering why you made a particular decision, you can analyze the assumptions and reasoning used and work out what, if anything, went wrong. Did I underestimate how annoyed my current boss would be with me for applying for other jobs? Did I let sunk costs influence my decision? From that point, the only thing left to do is to learn and readjust for next time.

 

Used any other economic concepts in your day to day life? Had any interesting experiences using the ones mentioned above? Please leave a comment and share the story!

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