Brett Romero

Data Inspired Insights

Month: September 2015

Climbing Mount Delusion – The Path from Beginner to Expert

In our careers there are various skillsets that we will be required to develop over time. Whether that is carrying multiple plates at a time, while working in a restaurant, or something more technically challenging, such as learning a programming language or learning to write good. Regardless of the skillset, there is always a learning curve that must be conquered.

It is tempting to think of this learning curve as a steady slope where knowledge is accumulated over time, or perhaps a steep initial slope that flattens out. In my experience though, this is rarely the case. I believe there is a reoccurring pattern in the way most people move from beginner to expert in a given subject, with distinct phases. What’s more, I believe many others will identify with these phases.

If you do identify with these phases, you will also realize there are risks that emerge at different times, and that being aware of those risks can help you avoid them. These risks typically occur where a persons’ belief in their mastery of a given subject diverges from their actual abilities. Sometimes it will be a lack of confidence that causes more experienced people to not speak up when they should. Other times, the person will exhibit there far too much confidence relative to their knowledge. The latter case is so common it has become cliché: A little bit of knowledge is a dangerous thing.

To help illustrate the various phases of the journey from beginner to expert, I am going to tell the story through a fictional character, Fred, who is learning Economics.

1. Initial Optimism

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When Fred first begins to learn economics, he has a large burst of excitement. Everything is new and interesting, he is learning new ways to think about problems, and he can’t seem to get enough. He knows very little about the subject but is enthralled with how quickly he is absorbing all this new information, and how quickly the pieces seem to be fitting together.

For Fred though, the best part is that he can clearly see the point at which he believes he will be an expert.

2. The Summit of Mount Delusion

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Finally, after months/years of working hard, Fred reaches the peak of Mount Delusion. He finishes his degree and he can feel the knowledge coursing through him. Fred loves spending hours enlightening his friends and family about the intricacies of interest rate policy and why minimum wage increases are wrong headed. He feels great. He set out to master something and did it. Already his mind is turning to what is next on the list of topics to master.

The problem with standing on the summit of Mount Delusion is the fog often blocks the view.

Fred, like many who have stood on the summit of Mount Delusion, espouses advice without realizing the risks of that advice. He provides clear, unambiguous recommendations because he lacks the experience and/or knowledge to realize what caveats are needed. Ironically, this often makes Fred all the more convincing to his colleagues. While the true experts are hedging their responses, Fred is completely convinced option 1 is the best. People like decisiveness and, as a result, they like and trust Fred.

3. The Clearing of the Fog of Ignorance

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For Fred (and most people), a moment comes when the fog clears. Someone who is much further along this journey than Fred clears the fog completely unintentionally. With an innocuous comment and a simple question, this person – who does not even regard themselves as an expert – completely shakes Fred’s confidence to the core. For a horrible moment, Fred is left looking out over the vast expanse of knowledge and concepts he had not even known existed until 30 seconds ago. All the knowledge and experience accumulated to that point only seems to highlight how little he really knows. From here, it is a long way down …

It should be noted at this point that, for some people, the fog never clears. They simply lack the level of self-reflection required to ever critically review their performance and continue their development. They will go through life claiming they are an advanced user of X or an expert on Y without ever realizing just how misguided they are. To be frank, these people are often some of the most dangerous in the workplace.

4. The Valley of Self-Pity

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After that horrifying moment when the fog cleared, our former expert Fred was left in a depressed state. His mind continually racing through all the times he fearlessly dispensed his advice, advice he now realizes was off base or often just completely wrong. What’s worse, he now realizes that anyone with any real knowledge could have identified him as a fraud based purely on that misguided advice. In short, he feels amazingly stupid.

He revises his resume, removes all words like “advanced” and “expert” and prays his ill formed advice doesn’t come back to haunt him. People who used to rely on Fred for unambiguous advice are completely mystified as to what happened. Where did his confidence go? They will speculate about what happened but most will never really realize the truth.

At this point in the learning process, there are two main risks. The first is that Fred gives up on economics altogether. In his depressed state, he feels like he is back at square one. He views his own skills as trivial and meaningless, while over valuing the skills of others. Many people will never exit the valley of self-pity for this reason.

The second risk is that, in this state, Fred (and people like him) begins to significantly undersell his expertise. He defers decision making to those around him, even though in many instances he will be much better placed to make decisions.

5. Exiting the Valley

After what feels like the world’s longest meal of humble pie, some strange things start happening to Fred.

Firstly, he will start bumping into people who are still standing on the peak of Mount Delusion. He will identify them, because, despite their claims of being experts, he knows significantly more than them. He will realize that they do not even realize what they do not know yet, exactly like he did, not so long ago. This provides comfort because he realizes he is unlikely to be the first or last person to fall from the top of Mount Delusion. In fact, compared to some of the people he is now meeting, he was amazingly restrained.

Secondly, Fred will meet people who didn’t study economics and realize that skills and knowledge that, in the Valley of Self-Pity, he assumed everyone had are, in fact, exceedingly rare. Fred will realize that many of the basic skills he has are actually not so basic and are quite valuable.

With each of these encounters, Fred’s confidence begins to recover. He will remain painfully conscious of how much he still has to learn, but for the first time since this journey began, his actual knowledge level and his assumed knowledge level will come into alignment.

6. The Never-ending Slope of True Mastery

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Fred is finally on a sustainable path. He has acquired a large amount of knowledge and experience, but is fully aware of the limits of his knowledge. He has revised his resume again to include words like “advanced” and “expert”, but now seeks to play these down.

He continues to run into many people standing on the summit of Mount Delusion, but mostly just feels sorry for them – most have a large and embarrassing fall coming, and many will not recover from it. He attempts to coach these people where possible, to help lessen the pain from their fall. Some take his advice, some do not.

How I Can Relate to Fred

In my own life, I have taken the journey to the summit of Mount Delusion several times. With each subsequent visit I have learned to be more cautious, to pay more attention to people who have more experience than I do, but the scars of previous falls remain.

From SQL to Excel, writing blogs to learning Spanish, there has always been a specific depressing moment when the illusion of expertise disappeared and only a sense of inadequacy remained. I would always recover and continue to build knowledge (I have a reputation for being a little stubborn), but to this day, the words “expert” and “advanced user” continue to stick in the throat, the fear of being exposed as a fraud (again) always present.

So far I have been fortunate. Even my most reckless declarations and advice have only served to cause personal embarrassment rather than any significant damage to my career. It could have been so much worse.

To those that are beginning the journey, my only advice is to remain humble. To those that have already endured a fall or two, don’t give up. The world will be a better place for your continued contributions.

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.

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