Brett Romero

Data Inspired Insights

Category: 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.

The argument for taxing capital gains at the full rate

Politicians, both in Australia and the US, when asked how they will find the money to fund various policy proposals, often resort to the magic pudding of funding sources that is “closing the loop holes in the tax code”. After all, who can argue with stopping tax dodgers rorting the system? But as Megan McArdle recently pointed out, raising any significant revenue from closing loop holes requires denying deductions for things that a lot of middle and lower class people also benefit from. This includes, among other things, deductions for mortgage interest, employee sponsored health insurance, lower (or no) tax on money set aside for pensions and no tax on capital gains when the family house is sold.[1]

Broadly, I agree with McArdle’s point. The public, in general, are far too easily convinced by simplistic arguments about changes to taxation – as if after decades of tax policy changes there are still simple ways to increase revenues without anyone suffering. Any changes made at this point are going to cause winners and losers, and often, the people intended to be the losers (usually the rich) are less affected than some other group that also happened to be taking advantage of a particular deduction.

That said, there is one point, addressed breifly in McArdle’s article, that I thought deserved greater attention – the concessional taxation of capital gains. In the list provided in the article, it was the second most expensive tax deduction in the US at $85 billion a year[2]. You see, for a while now I have been somewhat of a closet skeptic of the need for lower tax rates on capital income (i.e. capital gains and dividends). The reason for my skepticism is two fold:

  1. Everyone seems to be in agreement that concessional rates for capital income are absolutely necessary, but no one seems to really understand why.
  2. Capital income makes up a much larger percentage of income for the wealthy than for the lower or middle class. When you hear that story about billionaire Warren Buffet paying a lower rate of tax than his secretary, it is because of the low rate of tax on capital income.

So, now that I am finally voicing my skepticism, this article is going to look at what arguments are made for lower tax rates on capital income (focusing on capital gains for individuals) and whether those arguments hold water.

Why are capital gains taxed at a lower rate?

Once you start digging, you quickly find there is a range of arguments (of variable quality) being made for why capital gains should be taxed at a lower rate. These arguments can largely be grouped into the following broad categories:

  1. Inflation
  2. Lock-In
  3. Double Taxation
  4. Capital is Mobile
  5. The Consumption – Savings tradeoff

Inflation

Taxing capital gains implies taxing the asset holder for any increases in the price of that asset. In an economy where inflation exists (i.e. every economy) this means you are taxing increases in the price of the asset due to inflation, as well as any increase in the value of the asset itself. Essentially, even if you had an asset which had only increased in value at the exact same rate as inflation (i.e. the asset was tradable for the same amount of goods as when you bought it), you would still have to pay capital gains tax.

The inflation argument although legitimate, is relatively easy to legislate around by allowing asset holders to adjust up the cost base of their assets by the inflation rate each year.

Lock In

‘Lock-in’ is the idea that investors, to avoid paying capital gains tax, will stop selling their assets. An investor holding onto assets to avoid tax implies they are being incentivized, through the tax system, to invest suboptimally – something economists really dislike. However, as far as ‘lock-in’ would occur, it cannot be considered anything other than an irrational reaction. Holding onto assets does not avoid tax, it only delays it, and given inflation is factored into the asset price (as discussed above), there is not even the benefit of time reducing the tax burden. The bottom line is this – to pay more capital gains tax, there must be larger capital gains. That is, even if the capital gains tax rate was 99%, an investor would still be better off making larger capital gains than smaller ones.

The other point to remember when it comes to ‘lock-in’ is that in both the US and Australia, the lower rate of capital gains tax only applies to assets held for more than a year. That means if ‘lock-in’ exists, it is already a major problem. Because asset holders can access a lower rate of tax by holding an asset for a year, they are already strongly incentivized to hold onto their underperforming assets longer than is optimal to access the concessional tax rate. In fact, increasing the long-term capital gains tax rate to the same level as the short-term rate should actually reduce lock-in by removing this incentive.

Double Taxation

The double taxation argument is a genuine concern for economists. The double tax situation arises because companies already pay tax on their profits. Taxing those profits in the hands of investors again, either as capital gains (on that company’s stock) or dividends, implies some high marginal tax rates on investment. This is one of the main reasons capital income is taxed at low rates in most countries.

Ideally, to avoid this situation, the tax code would be simplified by removing company tax altogether, as McArdle herself has argued in the past. However, we should probably both accept that, at best, the removal of corporate tax is a long way away. Nevertheless, this idea can form the basis for policies that achieve similar goals without the political issue of trying to sell the removal of corporate tax.

For dividends, for example, double taxation can be avoided by providing companies with a deduction for the value of dividends paid out to investors. Investors would then pay their full marginal tax rate on the dividends, more than replacing the lost company tax revenues.

Preventing double taxation of capital gains is a little more complicated, but the answer may lie in setting up a quarantined investment pool that companies can move profits into. Profits moved into this pool would not be subject to tax and, once in the pool, the money could only be used for certain legitimate investment activities. This would effectively remove taxation on profits going toward genuine reinvestment, as opposed to fattening bonus checks.

The overall point here is not that I have the perfect policy to avoid double taxation of company profits, but that there are other worthwhile avenues worth exploring that are not simply giving huge tax breaks to wealthy investors.

Capital is Mobile

This is one of the two arguments McArdle briefly mentions in her article. The ‘capital is mobile argument’ is the argument that if we tax wealthy investors too much, they will do a John Galt, take their money and go to another country that won’t be so “mean” to them.

When it comes to moving money offshore, obviously, not everyone is in a position to make the move. Pension funds and some investment vehicles cannot simply move country. Companies and some other investment vehicles do not receive a capital gains tax discount currently, meaning raising tax rates for capital gains for individuals would not impact them at all. Finally, even for investors that would be affected and do have the means, a hike in the capital gains rate does not automatically move all their investments below the required rate of return.

This argument also overlooks the vast array of complications in moving money offshore and the risks involved with that action. Moving assets offshore exposes investors to new risks such as exchange rate risk[3] and sovereign risk[4]. It also significantly complicates the administrative, compliance and legal burden the investor has to manage.

However, even if we concede that yes, some money would move offshore as a result of higher taxes on capital gains, let’s look at the long term picture. What is the logical end point for a world where each country employs a policy of attracting wealthy investors by lowering taxes on capital? A world where no country taxes capital!

Of course, there are alternatives. Countries (and developed countries in particular should take the lead on this) can stop chasing the money through tax policy and focus on other ways of competing for investment capital. Education, productivity, infrastructure, network effects, low administrative and compliance costs are all important factors in the assessment of how attractive a location is for investors. California, for example, is not the home of Silicon Valley because it has low taxes on capital. Pulling the ‘lower taxes to attract investment’ lever is essentially the lazy option.

Consumption vs. Savings

The second point raised by McArdle is the argument that if you reduce the returns from investing (by raising tax rates), people will substitute away from saving and investing (future consumption) and instead spend the money now (immediate consumption).

The way to think of this is not of someone cashing in all their assets and going on a spending spree because the capital gains tax rate increased. That is extremely unlikely to happen and would actually make no sense. The change will come on the margin – because the returns on investment have decreased slightly (for certain asset types), there will be slightly less incentive to save and invest. As a result, over time, less money ends up being invested and is instead consumed.

But let’s consider who would be affected. If we think about the vast majority of people, their only exposure to capital gains is through their pension fund and the property they live in, neither of which would be affected by increasing the individual capital gains tax rate. Day traders, high frequency traders and anyone holding stocks for less than a year on average would also be unaffected. Most investors in start-ups do so through investment vehicles that are, again, not subject to individual capital gains tax[5]. That leaves two main groups of investors impacted by an increase in the capital gains tax rate for individuals:

  1. Property investors
  2. High net worth individual investors

Given property investing is not what most people are thinking about when concerns about capital gains tax rates reducing investment are raised, let’s focus on high wealth investors.

The key issue when considering how these investors would be affected by an increase in the capital gains tax rate is identifying what drives them to invest in the first place. Many of them literally have more money than they could ever spend, which means their investment decisions cannot be driven by a desire for future consumption. Many of their kids will never want for anything either, so even ensuring the financial security of their kids is not an issue. The only real motivation that can be left is simply status, power and prestige. Or as the tech industry has helpfully rebadged it – ‘making the world a better place.’

If that is the motivation though, does a rise in the capital gains tax rate change that motivation?

To my mind, the answer to that question is ‘No’. These people are already consuming everything they want, or in economic parlance, their desire for goods and services has been satiated. They will gain no additional pleasure (‘utility’) from diverting savings to consumption, so there is no incentive to do so even when the gains from investing are reduced.

Of course, there are exceptions, and it is quite possible (even likely) that there are high net worth individuals who live somewhat frugally and as a result of this policy change would really start splashing out. The question is how significant is this amount of lost investment, and does the loss of that investment capital outweigh the cost to society more widely of a deduction that flows almost entirely to the wealthy.

The Research

Putting this piece together, I have studiously attempted to avoid confirmation bias.[6] Despite the fact that I would benefit personally from lower tax rates on capital gains (well, at least I would if my portfolio would increase in value for a change), I definitely want to believe that aligning capital gains tax rates with the tax rates on normal income would raise significant amounts of tax, mostly from wealthy individuals, with few negative consequences.

In my attempts to avoid confirmation bias, I have deliberately searched for articles and research papers that provide empirical evidence that lower capital gains tax rates were found to lead to higher rates of savings, investment and/or economic growth. I have not been able to find any. There were some papers that claimed to show that decreasing capital gains tax rates actually increased tax revenue, but reading the Australian section of this paper (about which I have some knowledge), it quickly became clear this conclusion had been reached using a combination of cherry picking dates[7] and leaving out important details.[8]

I did also find some papers that, through theoretical models, concluded higher taxes on capital income would cause a range of negative impacts. But the problem with papers that rely on theoretical models is that for every paper based on a theoretical model that concludes “… a capital income tax… reduces the number of entrepreneurs…” there is another paper based on a theoretical model that concludes “… higher capital income taxes lead to faster growth…

Leaving research aside, there were a number of articles supporting the lowering or removing of capital income taxes. The problem is they all recite the same old arguments (“it will cause lock-in!”) and tend to come from a very specific type of institution. Without going too much into what type of institution, let me just list where almost all the material I located was coming from (directly or indirectly):

Even when I found an article from a less partisan source (Forbes), it turned out to be written by a senior fellow at the Cato Institute, and was rebutted by another article in the same publication.

Of course we should not ignore what people say because they work for a certain type of institution – just because they have an agenda does not mean they are wrong. In fact, it stands to reason that organizations interested in reducing taxation and limiting government would research this particular topic. The problem is that if there are genuine arguments being made, they are being lost amongst the misleading and the nonsensical.

Take this argument for lower taxes on capital as an example. First there is a chart taken from this textbook:

Capital per Worker vs. Income per Worker

The article then uses this as evidence to suggest more capital equals more income for workers. As straightforward as this seems, what this conclusion misleadingly skips over is:

  • income per worker is not equivalent to income for workers, and
  • almost all the countries towards the top right hand corner of this chart (i.e. the rich ones) got to their highly capital intensive states despite having high taxes on capital.

A Change in Attitude?

The timing of this article seems to have conveniently coincided with the announcement by Hilary Clinton of a new policy proposal – a ‘Fair Share Surcharge’. In short, the surcharge would be a 4% tax on all income above $5 million, regardless of the source. Matt Yglesias has done a good job of outlining the details in this article if you are interested.

The interesting aspect of this policy is, given the lower rate of tax typically applied to dividends and capital gains, it is a larger percentage increase in taxes on capital income than wage income. Of course, unless something major changes, this policy is very unlikely to make it past Congress and so may simply be academic, but at least it shows one side of politics may be starting to question the idea that taxes on capital should always be lower.

The Data

Finally, I want to finish up with a few charts. The charts below show how various economic indicators changed as various changes were made to the rate of capital gains tax, historically and across countries. Please note, these charts should not be taken as conclusive evidence one way or the other. The curse of economics is the inability to know (except in rare circumstances) what would have happened if a tax rate had not been raised, or if an interest rate rise had been postponed. The same applies with changes to the capital gains tax rate. Without knowing what would have happened if the capital gains tax rate had not been changed, we cannot draw firm conclusions as to what the result of that change was.

However, what we can see is that the indicators shown below do not seem to be significantly affected by changes in the capital gains tax rate, one way or the other – the effects appear to be drowned out by larger changes in the economy. That could be considered a conclusion in itself.

Chart1 – Maximum Long Term CGT Rate vs. Personal Savings rate, US 1959 to 2014

Chart 2 – Maximum Long Term CGT Rate vs. Annual GDP Growth, US 1961 to 2014

Chart 3 – Maximum Long Term CGT Rate vs. Gross Savings, Multiple Countries, 2011-2015 Average

Gross savings are calculated as gross national income less total consumption, plus net transfers. This amount is then divided by GDP (the overall size of the economy to normalize the value across countries.

Chart 4 – Maximum Long Term CGT Rate vs. Gross Fixed Capital Formation, Multiple Countries, 2011-2015 Average

Gross fixed capital formation is money invested in assets such as land, machinery, buildings or infrastructure. For the full definition, please see here. This amount is then divided by GDP (the overall size of the economy to normalize the value across countries.

Chart 5 – Maximum Long Term CGT Rate vs. Gini Index, 2011-2015 Average

The Gini index is a measure of income inequality within a country. A Gini index of 100 represents a country in which one person receives all of the income (i.e. total inequality). An index of 0 represents total equality.

 

[1] Interestingly, two of these four deductions (mortgage interest and employee sponsored health insurance) will be completely foreign to Australians.

[2] A similar policy (50% tax discount for capital gains) in Australia costs around AUD$6-7 billion per year.

[3] The risk that the exchange rate changes and has an adverse impact on the value of your investments.

[4] The risk that the government of the country you are investing in will change the rules in such a way to hurt your investments.

[5] Capital Gains Tax Policy Toward Entrepreneurship, James M. Poterba, National Tax Journal, Vol. 42, No. 3, Revenue Enhancement and Other Word Games: When is it a Tax? (September, 1989), pp. 375-389

[6] Confirmation basis is the tendency of people, consciously or subconsciously, to disregard or discount evidence that disagrees with their preconceived notions while perceiving evidence that confirms those notions as more authoritative.

[7] “After Australian CGT rates for individuals were cut by 50% in 1999 revenue from individuals grew strongly and the CGT share of tax revenue nearly doubled over the subsequent nine years.” Note the carefully selected time period includes the huge run up in asset prices from 2000 to 2007 and avoids the 2008 financial crisis, which caused huge declines in CGT revenues.

[8] “Individuals enjoyed a larger discount under the 1999 reforms than superannuation funds (50% versus 33%), yet yielded a larger increase in CGT payable.” This neglects to mention that even after the discounts were applied, the rate for of capital gains tax for almost all individuals was still higher than for superannuation funds.

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.

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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