Month: August 2015

4 Reasons Working Long Hours is Crazy

August 28, 2015 / Brett Romero / 1 Comment

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 Harvard 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:

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.
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].
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.
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:

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.
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.
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.
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.
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

Uber vs Taxis – The Taxi Perspective

August 15, 2015 / Brett Romero / 0 Comments

Previously, in Part I, we looked at some of the major ways that Uber had improved the market for people who use taxis and/or ride sharing[1]. This week we are going to look at the less rosy side of ride sharing services.

When you consider the main player in ride sharing, Uber, there seems to be a general agreement that they are improving the situation for everyone. The customer is happier because they are getting better service than they were from a taxi. The drivers (who in the Uber model are also the owners) are happier because they are making all this extra cash. How can this be? If the reason taxi services are so intolerable is because taxi owners are squeezing all the profit they can out of the business, how can Uber drivers be making more money and offering a better service?

As we saw last week, part of the answer lies in some genuine innovations that Uber brought to the market. These innovations help their drivers to be more efficient and maximize the time spent with a customer in the car. But, as we also saw, most of these advantages have already been replicated by taxi services[2]. If taxis are replicating most of Uber’s technology based advantages, what advantages are left to explain the seemingly unstoppable spread of Uber?

Unfair Advantages

The taxi industry in most countries and cities is a highly regulated industry. Although it varies by location, the rules and regulations that are in place typically include requiring drivers to carry a commercial drivers license, more expensive insurance policies, more regular vehicle checks, regular health checks for drivers, as well as various fees and taxes. As this article from the Boston Globe highlights, all this results in a significant overhead for the taxi owners and their drivers.

Many Uber and Lyft drivers on the other hand avoid most or all of these extra costs. This allows drivers to operate at a lower cost than traditional taxis and still be profitable – which is the main complaint of most existing taxi owners and drivers. There is a strong argument to be made that the rules and regulations in many cities are overly onerous and should be reformed, but that doesn’t change the fact that people who are following those rules are at a significant disadvantage. It is also hard to think of another example where a company operating outside existing laws has such popular support. Imagine for a second if this was a finance company flouting the rules[3] to build market share – what would people’s reaction be?

Lack of Insurance

Of biggest concern in these avoided expenses is the lack of appropriate insurance. In a survey conducted on the therideshareguy.com, almost 90% of the 500+ respondents didn’t currently have insurance that would cover them as a commercial driver. This is hardly a representative sample[4], but it does suggest a significant number of drivers are driving uninsured.

It should be noted that in many cases this isn’t just a case of people not purchasing insurance. In many states (and countries), there is no legitimate insurance available for ride share drivers. Additionally, even in places where it is available, there is often a lack of clarity as to when the driver is and isn’t covered. However, none of this negates the fact that a major expense, one that taxi owners are forced to wear, is being avoided.

Subsidies

Aside from avoiding costs, there is another slightly underhanded tactic that Uber and Lyft are using to undermine taxis and attract new drivers to their service – providing subsidies to drivers.

How this typically works is that Uber/Lyft will provide a guarantee to their drivers that they will make $X per hour driving for the service[5]. When the drivers don’t (or can’t) make that much through driving alone, Uber/Lyft provides a top up. This account of driving for Lyft reveals just how big those subsidies can be worth – in this case the driver received $1,500 in pay for a period in which he only made $600 in actual fares.

In many cases, these subsidies are required to help offset the reductions in fares that Uber and Lyft use to attract more customers to the service. Chart 1 and Chart 2 show recent reductions in average fares in Chicago and New York City. It should be noted that in both cases, the drivers’ earnings per hour increased on average, but at the expense of significantly more trips taken.

Chart 1 – Reductions in Uber Fares Chicago 2013 to 2014

Source: http://newsroom.uber.com/2015/01/beating-the-winter-slump-price-cuts-for-riders-with-guaranteed-earnings-for-drivers/

Chart 2 – Reductions in Uber Fares in New York City 2012 to 2014

Source: http://newsroom.uber.com/nyc/2014/10/three-septembers-of-uberx-in-new-york-city/

Chart 3 – Increase in Uber Driver Earnings in New York City 2012 to 2014

Source: http://newsroom.uber.com/nyc/2014/10/three-septembers-of-uberx-in-new-york-city/

As great as this sounds for drivers and customers, the problem is that this clearly isn’t a sustainable business model for ridesharing services. It is being used to attract new drivers and grow the business. But the impact of this is that it very difficult for any incumbents in the marketplace to remain competitive.

Think about an analogous scenario that most people in the US or Australia will be familiar with – supermarkets. A big chain (Walmart in the US, Coles or Woolies in Australia) moves into a small town, opens up a big new supermarket, and starts selling products at a loss. Great for consumers right? In this case, most people recognize this for what it is – predatory pricing designed to drive the incumbents out of business.

So what is the difference with Uber and Lyft doing this and driving the taxi services out of business? If you are about to argue that Uber is a start up competing against a rich, well-funded and protected taxi industry, let’s just remember who the competitors are. On one side we have Uber, a company recently valued at $50 billion and that has now raised almost $10 billion in funding. On the other side, there is a group of mostly small business owners, many of whom have probably borrowed against their house to buy their taxi license.

Bad Accounting

The final unfair advantage is one that is more incidental than a deliberate strategy. This advantage specifically benefits Uber and Lyft, rather than their drivers and arises from rideshare drivers’ lack of experience in the commercial driving business. Basically, many Uber and Lyft drivers simply aren’t accounting for all their costs correctly and, as a result, there is an oversupply of drivers.

The mistake many drivers are making is that they aren’t properly taking into account the long-term costs of the service they are providing. In addition to the cost of the gas/petrol, significant costs are incurred through the increased maintenance requirements for the car. These include the extra: sets of tires; oil changes; brake pads; timing belt replacements; and so on.

On top of increased maintenance costs, arguably the biggest expense being ignored by many drivers is the increased depreciation of their vehicles worth. By some estimates, a taxi doing 60 hours of driving a week in NYC (which probably has shorter average rides than most cities) does just over 46,000 miles (75,000 kms) a year. At that rate, just one or two years of driving for Uber or Lyft is going to put a serious dent in the resale value of your new Camry. And let’s not even start on how that shiny 5 year/100,000 mile warranty ran out after year 2 and your gearbox just threw a cog.

Finally, going back to the lack of appropriate insurance held by many rideshare drivers, there is a risk of large expenses incurred through an accident. If we assume the probability of an accident is greater than zero for any given drive, uninsured drivers should be factoring in an expected cost of an accident[6] into the costs of their business.

Once these costs are properly accounted for, many drivers are reporting incomes below or at minimum wage level. To make things worse, as this piece from uberdriverdiaries.com points out, this is a minimum wage job where you have to supply a $20,000+ piece of equipment.

Unfortunately, many drivers will learn the hard way that driving wasn’t as profitable as they thought it was. In some cases it is possible that driving will actually cost the driver more than they made. At some point though, probably after the subsidies end, there is going to be a major consolidation in the number of drivers working for these services as they arrive at this realization.

What Happens Next?

At the moment, rideshare companies Uber and Lyft are in extreme expansion mode and there is a huge amount of excitement around them. Unfortunately for taxi owners and drivers, this is not likely to end soon, and relying on regulators to enforce caps seems misguided. Even in cites where rideshare services have been banned, Uber has already shown it is willing to undermine those attempts to take its drivers off the road by paying drivers fines. You could argue that paying all their drivers fines may not be sustainable business model in the long term, but a company last valued at $50 billion probably figures they can keep paying fines longer than taxi owners can go without getting rides.

At some point, there will probably be some changes to align regulations for taxi drivers and rideshare services. This will probably make life more difficult (read: expensive) for rideshare drivers, but also life easier for taxi owners. But at least it should put everyone on an equal playing field.

What is likely to happen after that is a big consolidation. As mentioned above, once ride share drivers are brought back into the regulatory regime, and are forced to face the reality of the full costs of the service they provide, one of two outcomes are likely:

They stop driving altogether, or
They cut costs to stay profitable (cheaper and older cars, maintenance short cuts, no more chocolates and water bottles).

In short, the rideshare drivers that do stay in the market start to look and feel a lot more like taxis. There are already complaints in some more established markets that this is starting to happen.

Ultimately, the market will reach an equilibrium. For consumers, that is likely to be a world with a better, technology assisted, experience than was available 5 years ago. It probably also means slightly cheaper rides than the old taxi monopoly was providing, simply because the number of cars on the road is no longer capped. Thanks to the concept of surge pricing, you are also likely to spend far less time waiting in lines at taxi ranks, even at peak times – but you will pay extra for the convenience.

For the drivers and owners, driving is likely to become less profitable. In the future, driving probably becomes the equivalent of a minimum wage job[7]. Maybe the idea of working for something like minimum wage, but with the flexibility to choose your hours and work from your own car is not such a bad deal, but it is very different from the experience many rideshare drivers are having today.

Taxi Visualizations

Finally, I wanted to leave you with a couple of very cool visualizations of data obtained from taxi drivers.

The first is an app that tracks a taxi driver in New York City as they pick up and drop of passengers over a 24 hour period:

http://nyctaxi.herokuapp.com

The second shows all taxi pick ups and drop offs in New York City over the course of 24 hours in a hypnotic visualization. The fascinating thing is seeing the peak times at different times of the day, with midtown particularly busy during the day, while the Lower East Side and the Meatpacking district peak in the early hours of the morning. This visualization (and many more) can be accessed here:

http://www.nyctaxiviz.com

[1] “Ride sharing” is a bit of a misnomer – you have to pay after all – but this is the commonly used name for these services.

[2] Whether they are being used is a separate question.

[3] Let’s face it, rules and regulations in the financial industry are an order of magnitude more onerous than for taxi services.

[4] The people reading that article are probably doing so because they are looking for insurance. But there are also plenty of people who wouldn’t even be conscientious enough to look in the first place.

[5] Typically, there are various conditions applied that lock the driver in, such as a minimum number of hours worked in a week, 90%+ of rides accepted and so on.

[6] The probability of an accident occurring on that drive multiplied by the expected cost to the driver of that accident.

[7] As individual contractors, Uber and Lyft drivers aren’t subject to minimum wage requirements, but drivers will only continue to drive if they can make more than they could bussing tables or flipping burgers.

Women in the Workplace – Where is Everyone?

August 13, 2015 / Brett Romero / 0 Comments

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!

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.

Uber vs Taxi – The Uber Perspective

August 9, 2015 / Brett Romero / 0 Comments

Inspired by a recent piece by Oliver Blanchard I was put onto by a friend[1] (warning: it is a very long piece and gets very ranty), I thought I would put together some thoughts on the “Sharing Economy”, and in particular Uber. As there is a bit of ground to cover, I’ll split this into two parts. This first part will look at how Uber has improved taxi services and why taxi services may never be able to close that gap. The second part will look at some of the unfair advantages Uber has and why those advantages probably won’t last.

Before we dive into it though, I first want to say the economist in me loves the idea behind Uber and similar services such as Airbnb. They take some of the most valuable assets that most people will own (e.g. houses and cars) and helps their owners to derive economic value from them when they would otherwise be sitting idle. From the perspective of the wider economy, this is undoubtedly a good thing. Cars in particular are something that we spend a lot of money purchasing and maintaining, yet, end up sitting in a garage or parking lot for close to 90% of their existence.

How Uber Changed the Market

Since its founding in March 2009, there has always been a lot of hype around Uber. From their official launch in San Francisco in early 2011, they rapidly expanded to numerous other cities around the US and made their first move internationally to Paris in December 2011. As of today, Uber is available in 58 countries worldwide, and at a recent capital raising the company was valued in the ballpark of $50 billion. If publicly listed at that value, Uber would be among the largest 100 companies in the S&P500. Charts 1 and 2 show some of the explosive growth in driver numbers from a recent Uber paper.

Chart 1 – Total Active Drivers

Chart 2 – Active Drivers by City

Aside from the rapid growth, one of the more impressive things about Uber is the amount of good will there seems to be towards Uber. Despite ‘disrupting’ an industry that has been around for decades and taking an aggressive approach to protecting its drivers and business model, the only people who seem to have anything bad to say about Uber are taxi drivers. Outside that obviously vested interest, there seems to be the general consensus that Uber is improving the situation for everyone. The customer is happier because they are getting much better service than they were from a taxi, and the drivers are happier because they are making all this extra cash. To work out why that is, let’s take a look at some of the key ways Uber has improved the taxi experience.

1. Getting a ride is now easy

Having an app that allows people to request a car at the tap of a button and know exactly when it will turn up is a big improvement for customers. No more automated phones services forcing you to scream “OPERATOR!” into the phone. No waiting on the side of the road trying to flag down a cab. No waiting for 2 hours in line at the taxi rank at 2am on a Saturday night. And finally, no sitting in silence in your home waiting for the honk of the horn to make sure you don’t miss the taxi you ordered.

2. So is getting to your destination

The app also allows you to enter a destination, which is then used to determine the best route and guides the driver. This again is a big improvement over the taxi experience in most countries. No waiting for the driver to type the address into his circa-1996 dashboard GPS – if he has one at all. No missing the freeway exit because you weren’t paying attention. No more risk of been taken on ‘the scenic route’ because you are from out of town.

3. Bad drivers and passengers get penalized

As a customer, think about the things you dislike about taxis. Now consider how many of those things are as a result of taxi drivers having to deal with bad passengers. Clunky plastic screens separating drivers from passengers. Inability to sit in the front seat of the cab at all in some cases. Cars that haven’t been cleaned in the past 6 months. The overall surliness of drivers.

Having a system where drivers rate their passengers and have the ability to refuse rides to people with low ratings, creates a lot of positive incentives for both driver and passenger. Passengers can no longer act like douche bags towards the driver or trash the cab without affecting their ability to get a taxi in the future. Drivers can maintain nicer cabs knowing their passengers are likely to be well behaved.

On the flip side, passengers rating their drivers also creates positives incentives for drivers to be much more helpful to their customers. As a result, Uber drivers are generally much more pleasant, cheerful, helpful and generous towards their customers. In my own personal Uber experience we have had drivers provide free water bottles, chocolates and other goodies.

4. Surge pricing means you rarely have to wait long

This is a controversial one, but I firmly believe this is positive, and anyone who has spent hours waiting for a taxi should as well.

The reason you had to wait so long for a taxi is because there are spikes in demand for taxi services and little to no increase in supply to meet that demand. There are two main reasons for that:

In almost all cities, the number of taxi licenses available is capped
If there are any taxis currently off duty, there is no incentive for the driver/owner to clock back on

Uber avoids both these problems. By not capping the number of drivers in a given city, Uber ensures there are plenty of spare drivers around when needed. By significantly increasing the rates drivers can charge in periods of peak demand, Uber also provides a strong incentive for drivers to get in their cars and start picking up passengers at 2am on a cold morning.

Surge pricing has drawn criticism and negative press in some parts, but reading the details of some of these stories, it really is difficult to have too much sympathy. Some will argue surge pricing is taking advantage of desperate people, but they are misunderstanding the options. The two options available in that moment are not an expensive ride at surge prices and a normal priced ride. The two options are an expensive ride at surge prices or no ride at all.

Now, that said, there is an argument to be made for stopping surge prices in disaster situations. But the best way to do that is not to stop providing drivers with higher prices to pick up people in those situations, but to change who is paying for it. Whether this is the government, Uber or some third party is a separate discussion.

Playing Catchup

If we look at the four advantages that Uber has (as listed above), and add in the fact that in many cities Uber is significantly cheaper than the taxi services, it makes a pretty compelling case that taxi services are in big trouble. Following the news and seeing taxi driver strikes[2], taxi lobbyists pushing for cities to outlaw Uber and police spending significant resources pulling over and fining Uber drivers, it can look like the last desperate throws of the dice for a dying industry.

However, in the face of this threat to their business, there has been some positive outcomes for taxi owners. Apps (Hailo and myTaxi) are now available that put taxis on par with Uber for 3 of the 4 advantages listed above. You can now order a taxi easily from an app, provide a destination and have access to a ratings system.

It is also not difficult to picture a world where taxi services start using some form of surge pricing to encourage drivers to be on the road at peak hours. To some degree this is already in place with many services charging higher rates at different times and days. But the problem is surge pricing only really works if you have a bunch of drivers off duty at any given time that can be, through monetary incentive, convinced to clock on and start picking up passengers.

This gets us to the underlying problem facing taxi services – the capping of the number of available taxi licenses. Capping taxi licenses has led to a situation where each taxi license is extremely valuable because of the amount of cash it can generate. In New York City for example, the cost of a single license peaked at over $1 million in recent years. Because of the cost of a license, and its consistent appreciation in value over the past few decades, for many taxi owners, their taxi license represents their retirement savings. Now, due to competition from Uber, many cities (Sydney, Toronto and many others) are seeing the cost of taxi licenses falling.

You could argue taxi owners should have been smarter and diversified their investment. However, the fact is they made an investment decision on the basis of the rules as they stood at the time, and have since been severely undermined. Besides, they would hardly be the first people to invest all their savings in one overpriced asset class.

Leaving aside judgements on investment decisions though, it is difficult to see a scenario where taxi owners end up the winners in this battle. Now that people have experienced the higher level of service that can be provided by services like Uber, they will be very reluctant to go back to the old way of doing business. Taxi services can (and have) improved as a response to Uber, but unfortunately, as long as taxi services want to cling to the idea of a capped number of taxi licenses, customers will continue to be frustrated by a lack of availability at key times.

All that isn’t to say Uber has everything worked out or that shouldn’t be criticized for their own failings and dodgy practices. In fact Uber faces several large problems of its own. To find out more about those, tune in next week.

[1] Thank you Bek Chew

[2] Seriously, it’s like they want everyone to hate them

Women in the Workplace – Understanding the Data

August 5, 2015 / Brett Romero / 0 Comments

Cross Posted from OpenDataKosovo.org:

Continuing our series on Gender Inequality and Corruption in Kosovo (see Part I and Part II), in Part III and the next few parts, we are going to take a detailed look at the problems women face in the labour market in Kosovo.

To do this, we will be using information from several sources, including data on participation rates, by gender, from the Gender Statistics database at the World Bank, and a range of labour market statistics from various Kosovo Labour Force Surveys, released by the Kosovo Agency of Statistics.

High Level Concepts

Before diving into the statistics, let’s first visualize and explain some of the high level concepts in labour market statistics.

Chart 1 – Population Breakdown 2014

At the highest level, the section of the population that is relevant when looking at labour market statistics is people who are of working age and are able to work. In Kosovo, this population includes all people aged 15 to 64 and is known as the ‘working age population’.

Labour Force and Inactive Populations

At the next level, the working age population can be broken down into two main subgroups – those that are considered in the labour force (i.e. ‘participating’) and those that are ‘inactive’. It is important to note that someone who is ‘inactive’ is not the same as someone who is ‘unemployed’. In Kosovo, to be considered ‘actively looking for work’ (and therefore be classified in the labour force) the following criteria must be met. The person must be:

currently available for work, that is, available for paid employment or self- employment within two weeks; and
seeking work, that is, have taken specific steps in the previous four weeks to seek paid employment or self-employment.

If either of the above criteria is not met, the person is classified as inactive.

Calculating the Participation Rate

Once the population is classified as either in the labour force or inactive, it is possible to calculate the participation rate, one of the key labour market statistics. The participation rate measures the labour force population (people employed and/or actively looking for work) as a percentage of the working age population.

In Kosovo, the participation rates in 2014 were as follows:

Male Participation Rate (2014): 61.8%
Female Participation Rate (2014): 21.4%
Overall Participation Rate (2014): 41.6%

Unlike the unemployment rate, described below, the participation rate tends to provide more stable and reliable data than the unemployment rate, as it is not affected by short-term fluctuations and the business cycle.

Employed vs. Unemployed

Analyzing the population further, the ‘labour force’ can be subdivided into two populations – those that are employed and those that are unemployed. In most cases it is obvious whether someone is employed or not, but in some situations it may not be so clear (e.g. when a person is working for the family business in an unpaid capacity). To handle these scenarios, the agency tasked with compiling the labour market statistics in each country typically has a specific definition (or definitions) of what qualifies as employment. In Kosovo, to be classified as ‘employed’ a person must meet the following high-level criteria:

“People who during the reference week performed some work for wage or salary, or profit or family gain, in cash or in kind or were temporarily absent from their jobs.”

In addition, the Kosovo Agency of Statistics includes some more detailed criteria in their methodology that clarifies when work done on family owned farms classifies as employment. This will become important later.

Calculating the Unemployment Rate

Having separated the employed from the unemployed, it is now possible to calculate the unemployment rate. To do this, we divide the number of unemployed people by the total number of people in the labour force.

In Kosovo, the unemployment rates in 2014 were as follows:

Male Unemployment Rate (2014): 33.1%
Female Unemployment Rate (2014): 41.6%
Overall Unemployment Rate (2014): 35.3%

The unemployment rate is useful as a more immediate indicator of conditions in the economy. The obvious information is provides is an indicator of how many people without a job are currently looking for employment. But, in addition, it also provides information about how much spare capacity an economy has, the risk that inflation may pick up, whether structural issues are keeping people out of work and so on.

Chart 1 – Participation and Unemployment Rates by Gender 2014

What is Next?

In the next article, we will take a look at how the participation rate (for both males and females) in Kosovo compares across the region and internationally. In the meantime, please feel free to play around with the interactive visualization below, which shows the entire working age population of Kosovo broken down into its various subgroups.

Click on the chart below to interact with the data!

Sunburst chart created by Festina Ismali

4 Reasons Fiat Money is a Great Idea and One Catch

August 3, 2015 / Brett Romero / 0 Comments

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

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

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

1. Coincidence of Wants

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

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

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

2. Divisibility of Money

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

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

3. A Store of Value

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

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

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

4. Practicality

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

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

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

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

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

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

The Catch

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

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

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

Overall

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

Money in Three Charts

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

Chart 1 – Value of US Currency in Circulation by Denomination

Chart 2 – Volume of US Currency in Circulation by Denomination

Chart 3 – Comparison of Different Measures of Money Supply

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

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