Brett Romero

Data Inspired Insights

Tag: employment

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

4 Reasons Working Long Hours is Crazy

I recently read an article in the Harvard Business Review about why working long hours is bad for business. This article resonated with me for several reasons, but mainly because over the past 2-3 years, I have been concerned with, and viewed first hand, the growing cult of working long hours.

Unfortunately, there are an increasing number of people who equate productivity and working hard with spending long hours at work. Consulting and Legal are arguably the worst culprits, but finance, tech, and various other sectors can be just as bad. Many in the tech startup world in particular seem to consider it a badge of honor to work excessive hours and sleep as little as possible.

In a lot of cases, companies have been making genuine attempts to improve work-life balance with various initiatives. These range from sending corporate communications to shutting down the office for a period each year to force people to take leave. Yet despite this, unused vacation leave reached a 40-year high in 2014.

Anyway, in an effort to fight this rising tide, I thought I would put together 4 good reasons why working long hours is detrimental and a waste of time.

1. Longer Hours Means Reduced Output

When I say reduced output, I don’t mean that each extra hour worked is less productive then the previous one (although that is also true). I mean your actual total output falls – you work longer and produce less. And the longer you work long hours, the less productive you become.

There is an exception here of course. Working longer hours for a short period (e.g. a couple of weeks to meet a deadline) can boost productivity – but this boost quickly erodes and then reverses. A good illustration of this was provided by a report from the Business Roundtable Report from 1980. The report detailed how the initial gains from extra hours were quickly eaten up by increasingly poor productivity. From the Executive Summary:

“Where a work schedule of 60 or more hours per week is continued longer than about two months, the cumulative effect of decreased productivity will cause a delay in the completion date beyond that which could have been realized with the same crew size on a 40-hour week.”

For physical workers this is one thing (the report was based on construction projects), but what about office workers? Unfortunately the story only gets worse. Shifting concrete mix or laying bricks when you are tired is one thing, but problem solving, complex reasoning and the intricacies of office politics require a higher level of focus.

Think about managing a software development project and having a team that is mentally exhausted after working long hours for months. It is not hard to imagine a scenario where the productivity of the team actually becomes negative as important files are mistakenly deleted or code is committed with catastrophic errors that then require significant time and effort to fix.

There are many reasons for this drop in productivity including mental exhaustion, depression and declining health. However the biggest driver of lost productivity is sleep deprivation.

2. Sleep Deprivation Is the Silent Killer

In addition to being a serious productivity killer, the biggest issue with sleep deprivation is that, as Dr. Charles A. Czeisler [1] explains in this interview with Havard Business Review, people consistently underestimate its impact. He goes on to explain that a person averaging four hours of sleep a night for four or five days has the same level of cognitive impairment as someone who has been awake for 24 hours – equivalent to legal drunkenness. Within 10 days, the level of impairment is the same as someone who has gone 48 hours without sleep.

The problem is, in many cases, very few people are taking this productivity loss as seriously as they should be. Consider how your boss would react if you decided to start dropping Jaeger bombs in the morning before coming to work. They are likely to be pretty unhappy, and not just because of your juvenile choice of drink. In fact, you would probably be lucky to keep your job. Yet, work long hours for an extended period, which has a similar impact on your productivity (and is a lot less fun), and you are more likely to be promoted than get a reprimand.

Again, there is a caveat here. An estimated 1-3% of people can function at a normal level on 5-6 hours sleep a night. But before you start reassuring yourself you are that person – research also shows that of 100 people who think they can function with 5-6 hours sleep, only 5 actually can. The rest have no idea they are even impaired. Which takes us to reason number 3.

3. People Do Not Realize When They Are Not Productive

A simple mistake that many people make is confusing being busy with being productive. Anyone who has spent any decent amount of time working in an office will know at least one person who seems to be perpetually busy, but never seems to get anything done[2].

The fact is that ‘busy’ and ‘productive’ are often very different things. Frantically sending emails, multitasking, scheduling pointless meetings or just doing a bunch of work that is completely unnecessary are not productive activities, but they are often the hallmarks of busy people.

But it is not just the frantically busy people who are not being productive. There is a limited amount of time that everyone is productive during a day. Consider the following scenario, which I am sure many people will recognize.

You are working late at night on a problem. You are spending hours trying to fix a seemingly intractable problem (for example, searching for the source of a bug, or trying to identify why the numbers do not add up). Eventually you give up, resolving to get in early the next day and fix it. Then something amazing happens. You get in the next morning, and within 10 minutes you have fixed the problem. In fact, you are amazed you spent so long worrying about something that was so simple to fix.

When you were trying to solve the problem the night before, did you feel impaired or less productive? Tired, frustrated, sure, but did you believe you were any less capable of solving the problem?

Here is where the downward cycle can start. People who consistently work 60-80 hours a week are (with some exceptions) mentally exhausted, but are not aware this is the case. All they see is that they have a significant amount of work that needs to get done and not enough hours to finish it. What is the first solution that comes to a weary mind in that scenario? Put in a couple of late ones and get over the hump. Maybe spend Saturday working and try get ahead a little bit.

Unfortunately, this is unlikely to work, and as they continue to increase their sleep deficit, they are increasingly likely to make mistakes and/or fall further behind.

4. We Have Already Learnt This Lesson

“We learn from history that we do not learn from history.” ― Georg Wilhelm Friedrich Hegel

The tragedy of this move towards longer hours is that we have been down this path before. The conclusion that shorter hours actually boost absolute productivity is not new, or even controversial. Ernst Abbe as early as 1900 moved his workers from a 9 hour to an 8 hour work day and noted that overall output increased. Henry Ford is another famous example. In 1926, he moved his workers from a 6-day to a 5-day workweek and again saw output increase.

These are not one-off cases. Although this push initially came from the union movement, business after business found that the overall output per worker actually increased with shorter hours.

What Can You Do?

It is easy to blame a highly competitive labor market and/or evil corporations for this trend towards ever-increasing hours. The fact is we all have some power to change the culture of our workplaces through our own actions.

As an employee, you are somewhat limited by your surroundings, particularly if you work somewhere that judges your performance on hours rather than productivity. However, assuming you do not work in a place that thinks work life balance is a list of priorities in descending order[3], there is still a lot you can do to improve your situation:

  1. Get your rest. If you want to get back to a 40-hour week, you need to be well rested and switched on when you arrive at the office.
  2. Be prepared to actually work. Working does not include reading blogs, regularly checking Facebook/Twitter, getting into pointless arguments, or wondering the hallways. If you turn up to work and are focused on work, you will be amazed how much you can get done in 8 hours[4].
  3. Be organized. Making the most of your 8 hours means being organized. Make lists, prioritize, plan well ahead and finish tasks early to get them off your plate. Whatever method(s) works for you, ensure when you turn up to work, you already know exactly what you need to do.
  4. Know when to leave. It is hard to understate the importance of this. Spending hours and hours late at night trying to solve a problem is possibly the single biggest time suck in the modern workplace. If it is not due that night, leave it for the morning. Go home, relax and have dinner. You will be doing everyone a favor.

Employers and managers obviously also have a key role to play. If you really want to encourage better habits in your employees (and you really, really should want to), you need to lead by example. This means:

  1. Cut down your own hours. At the very least, work from home outside business hours. If you say one thing and do another, your staff will choose to follow actions over instructions every time.
  2. Schedule emails for business hours. If you find yourself writing emails after hours or on weekends and you do not need a response immediately, schedule the emails to go out during business hours.
  3. Push for realistic deadlines. If you repeatedly provide unrealistic deadlines for tasks and projects, staff will be forced to put in extra hours to meet them, and will often fail anyway. Set generous deadlines and aim to finish early.
  4. Tell people to go home. If you see staff repeatedly staying late and you know there is no real reason they should be staying late, send them home. Every hour they stay working late is decreasing what you will get out of them the next day.
  5. Address poor time management. Consulting, for example, is littered with examples of people being praised for pulling all-nighters to finish off a piece of work. Sure, they met the deadline. Congratulations. Now let us talk about the weeks of poor time and resource management that led to that situation in the first place.

 

[1] Dr. Czeisler is the incumbent of an endowed professorship donated to Harvard by Cephalon and consults for a number of companies, including Actelion, Cephalon, Coca-Cola, Hypnion, Pfizer, Respironics, Sanofi-Aventis, Takeda, and Vanda.

[2] If you work in an office and do not know anyone like this, it is probably you.

[3] If this is your case, consider an alternative job. Or alternatively, start planning for your Eat Pray Love moment to strike in a few years time.

[4] A side effect of this is you are likely to become a lot less tolerant of long pointless meetings. Be wary of anyone who doesn’t mind long pointless meetings

 

 

Women in the Workplace – Where is Everyone?

Cross posted from OpenDataKosovo.org:

Continuing our series on Gender Inequality and Corruption in Kosovo, in Part IV we are going to build on Part III and use our understanding of the participation rate to compare the participation rate in Kosovo across a range of countries, as well as look at the reasons for non-participation (“inactivity”). If you don’t understand what a participation rate is (SPOILER: it is not the same as the unemployment rate), or just want to make sure you get the full picture, please go back and read Part III.

Click on the chart below to interact with the data!

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Sunburst chart created by Festina Ismali

Comparing Participation Rates

Comparing participation rates across countries provides insight into broad demographic trends and the specific employment situation in a country relative to other countries. For most high income nations, the participation rate tends to be around 60%. That is, 6 out of every 10 people of working age are actively engaged in the employment market (whether they currently have a job or not). While that may sound low, this accounts for parents who stay home to raise children, students, retirees and discouraged workers[1].

Once we leave high income countries, there is a much larger range of participation rates. Many very poor low income nations in Asia and Africa have extremely high participation rates of well over 80%. This is driven by pure necessity as, in many cases, there is simply no option for one partner to stay home, retire, or even for young people to continue studying.

Conversely, we also see many countries with very low participation rates of just over 40%. In some cases, these countries are involved in ongoing conflicts or are post-conflict countries (Syria, Iraq and Afghanistan all had participation rates below 50% in 2013). But in other cases, the cause is harder to identify.

Unfortunately, Kosovo is one of these harder to understand cases. In 2013, Kosovo had the second lowest participation rate of any country in the World Bank database, at 40.5%. In 2014 that number picked up slightly to 41.6%, but that was still low enough to keep Kosovo in the bottom 10, based on 2013 figures. Notably, Kosovo’s low participation rate has actually decreased substantially over the past decade (see Chart 1). In 2002, the participation rate stood at 52.8%. If that participation rate applied today, there would be an extra 134,600 people in the labour force – an increase of 26.9%.

Chart 1 – Participation Rate in Kosovo 2002 to 2014

Looking at Chart 1, another data point that immediately stands out is the low participation rate for women. In fact, with a participation rate for women of 21.1% in 2013, Kosovo has one of the lowest participation rates for women in the world. In terms of the rankings, Kosovo places between Saudi Arabia (20.2%) and Lebanon (23.3%). Looking around the region, Kosovo is also a significantly outlier (see Chart 2).

Chart 2 – Female Participation Rate for Selected Countries 2002 to 2013

Methodology Matters

Previously, in Part III, we mentioned that there were some more detailed criteria for determining whether a person is considered ‘employed’ in Kosovo. Specifically, there is one particular criteria that may partially explain Kosovo’s notably lower participation compared to its neighbors (and everyone else).

In the 2014 Kosovo Labour Force Survey, a specific methodological difference with Albania is highlighted. In Kosovo, people who work on a family run farm are not considered employed if the produce of the farm is not considered an “important source of consumption” (let’s call these people ‘family farm workers’). In contrast, these same people in Albania are classified as employed. From the 2014 Kosovo Labour Force Survey Results paper (emphasis mine):

“It is important to note that when respondents answer code 5B[2], that they do some agricultural activity but it is not an important contribution, this is not counted as employed. In 2014 69% of this group were categorized as inactive and 31% as unemployed. An important contribution is a subjective term and could depend on overall household income.”

The key takeaway here is that there is a significant population of family farm workers that are currently being classified as inactive, when in fact they are working. This at least partially explains the low participation rate in Kosovo.

Unfortunately, the paper does not provide enough information to be able to determine how many people are  family farm workers. As such, we are unable to quantify exactly how much impact adding family farm workers back into the labour force would have on the headline participation rate.

Even if we could though, this would not be fully correct either (welcome to the surprisingly complex world of labour market statistics). Many family farm workers probably do not consider themselves employed – working 1 hour a week[3] on a family farm is a pretty low bar after all. The fact that 31% of them qualified as unemployed, meaning they actively sought other work, reveals that this is not homogenous group of full time farm workers being incorrectly classified.

Worrying Trends

Methodological anomalies aside, there is also a concerning trend in the data – the participation rate for women in Kosovo has been declining for much of the past decade[4]. Despite the improving economy and significant international development assistance, the participation rate for women fell from over 34.5% in 2002 to 21.4% in 2014. There is some good news – the fall appears to have bottomed out, with 2013 and 2014 both recording higher participation rates for women than the low point in 2012 (17.8%!).

This slight uptick in recent years could be the impact of numerous initiatives to get women into the workforce in Kosovo. These range from the prioritization of grants for projects that provide jobs for women, to supporting women in registering property in their own names to help provide collateral for loans. There has also been a push by Kosovo’s first and current female President to boost participation among women. Several more years of data will be required to determine whether this is the beginning of a more substantial trend or simply noise in the data.

In the meantime, let’s get a better understanding of the current labour market by looking at a break down (see Table 1), provided in the 2014 Kosovo Labour Force Survey, of the inactive population sorted by reason for not participating.

Table 1 – Inactive Persons by Category

(A) Men (B) Women (C) = (B) minus (A)
1,000s 1,000s  (C1) 1,000s (C2) % of total
Looking after children or incapacitated adults 0.1 14.3 14.2 5.8%
Own illness or disability 13.3 8.6 -4.7 -1.9%
Other personal or family responsibilities 13.5 233.4 219.9 90.2%
In education or training 104.7 97.3 -7.4 -3.0%
Retired 6.9 5 -1.9 -0.8%
Believes that no work is available 49.5 78.9 29.4 12.1%
Waiting to go back to work (laid-off people) 0.8 0.5 -0.3 -0.1%
Other reasons 20.7 16.2 -4.5 -1.8%
No reason given 1.9 3.4 1.5 0.6%
Total  229.2 473.0 243.8 100.0%

Looking at the breakdown, there is one category in particular in which there was a large discrepancy between the sexes – ‘Other personal or family responsibilities’. In this category, 233,400 were women, amounting to 38.8% of the total population of working age women. By contrast, only 13,500 were men, amounting to 2.2% of the total population of working age men. The table also shows the calculated difference between the number of inactive women and men (see column C1). Looking at these calculated differences, we see that for the total calculated difference across all categories (243,800 – see ‘Total’ row in column C1), 219,900, or over 90%, arose from this category. This breakdown is also shown in Chart 3 below.

Chart 3 – Inactive People by Category of Inactivity – 2014

Going back to the family farm workers discussed earlier, we expect that those classified as inactive would be included in the ‘Other personal or family responsibilities’ category. However, if a significant number of women in this category were family farm workers and this was a full time role, we would also expect to see large numbers of men in the same category. The fact that we do not suggests that many men who are family farm workers also have other more formal jobs and lends support to the decision to exclude family farm workers from the employed population.

The other category where we see a meaningful gap between the sexes is the ‘Believes that no work is available’ category. As mentioned earlier, these are the people that are considered discouraged workers (i.e. those that would take a job, but are no longer actively looking). Why would significantly more women be discouraged than men? Typically, discouraged workers are the end product of long and unsuccessful searches for employment. At times of high unemployment, it will often be the case that the number of discouraged workers will also increase. Seeing that women are more likely to be discouraged than men suggests they are having a more difficult time finding employment.

To confirm this hypothesis, we need to look at unemployment rates. This will be the focus of the next piece in this series – Part V.

 

[1] People who would like a job but who haven’t actively sought work in the past 4 weeks

[2] Code 5b text: “Worked (at least one hour) on a farm owned or rented by you or a member of your household (even unpaid) whether in cultivating crops or in other farm maintenance tasks, or you have cared for livestock belonging to you or a member of your household (if the whole production is only for own consumption and this production does not constitute an important contribution to the total consumption of the household.

[3] Employed are considered all the persons who have worked even for one hour with a respective salary or profit during the reference week.

[4] There is no mention of when the current methodology was implemented, but it is possible that the large drop in participation rate between 2009 and 2012 was due to a change.

US Labor Market Update – The Grind Continues

On June 5, the Federal Reserve released its latest Employment Situation Summary. The results were slightly better than expected – 280,000 jobs added in the month of May compared to an expected 226,000. There were also small upward revisions to the previously released numbers for March and April.

In terms of the long-term trends in the participation rate identified previously (see here), this update didn’t really change much. The participation rate has more or less stopped falling over the past 12 months, currently sitting at just under 63% (see Chart 1). The percentage of the civilian non-institutional population[1] that is employed continues to climb slowly back towards to 60%, but is still well below the peak of over 63% reached in 2007.

Chart 1 – Participation Rate vs. Employed as Percentage of Civilian Population

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The benchmark unemployment rate for May was 5.5%, a slight increase from 5.4% in April and was matched by a slight increase in the number of people unemployed, up to 8.7 million. Even though this goes against the general downwards trend in unemployment since 2010, Chart 2 shows how this slight uptick doesn’t really impact on the broader trend.

Chart 2 – Unemployment Rate

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Unemployed Breakdown

Looking at the breakdown of the unemployed (see Chart 3), the average period of unemployment continuing to normalize, with the number of people unemployed for 5-14 weeks now below the number unemployed for less than 5 weeks. The group of people unemployed for 15 weeks or more, although still large by historical standards, also continues to fall in both percentage and absolute terms. To provide some indication of just how far the size of this group has fallen, in mid-2010 there were over 9 million people who had been unemployed for 15 weeks or more. That number is now less than 4 million, a decrease of over 55%.

Chart 3 – Unemployed Persons by Length of Unemployment

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The improving situation for the unemployed is also evident in the average weeks people spend unemployed (see Chart 4).

Chart 4 – Average Period of Unemployment

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Industry Breakdown

In Part 4 of this series, we looked at what was happening to the number of people employed in various industries in the US economy. Chart 5 provides an update for some of the more interesting stories from that piece.

Chart 5 – Employment by Various Industries

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By and large we see long standing trends continuing. Manufacturing continues to undergo a renaissance, bucking a long downwards trend. Nearly 1 million jobs have been added since the low point in early 2010. Education and health services, and professional and business services continue to grow strongly, while the government sector is basically still going nowhere.

Previously, we also looked in some detail at the Information sector, in particular the technology related subsectors. Chart 6 shows the breakdown of the information sector and its various subsectors.

Chart 6 – Employment in the Information Sector

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What Chart 6 reveals is that the ‘Other information services’ subsector is clearly adding jobs at a fast pace, with data processing, hosting and related services also increasing employment. Chart 7 shows the employment growth rate in these two subsectors combined since 2006.

Chart 7 – Tech Subsectors Employment Growth

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Since 2011, these sectors have been adding jobs at an annualized rate of between 6% and 8%. In total this has led to a 35% increase in jobs in these sectors since the start of 2011 – which is fantastic growth. But these subsectors are starting from a very low base –a 35% increase only translates into an additional 139,000 jobs. By way of comparison, over that same period, professional and business services added over 2.6 million jobs, education and health services added 1.9 million and even manufacturing added 700,000 jobs.

One thing to keep in mind though is that the tech boom is causing jobs to be created in other fields that service the technology sector. Lawyers, accountants, talent recruiters and HR personnel, among others, all provide support to the technology sector. Most of these roles are likely to sit in the professional and business services, which we just saw has added a lot of jobs. A big part of that story could be the tech boom.

 

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

Labor Statistics Part IV – The Employed

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

Declining Industries

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

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Decline in Manufacturing

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

Chart 2 – US Manufacturing Jobs 1939 to 2015

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

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

Where is the Tech Boom?

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

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

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

The Telecommunication Boom

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

Expanding Industries

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

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

Professional and Business Services

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

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

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

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

Inexorable Rise of Education and Health

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

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

chart_4_5

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

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

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

Childcare Catch 22

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

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

The Financial Sector Reflects the Market

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

Chart 6 – Financial Sector, All Employees 1990 to 2015

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

Stable Industries

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

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The Government Sector

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

Chart 8 – Government Sector, All Employees 1955 to 2015

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

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

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

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

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

A Strange Observation

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

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

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

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

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

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

 

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

 

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

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