Author: Brett Romero (Page 4 of 11)

Web Analytics – Looking Under the Hood

January 4, 2016 / Brett Romero / 1 Comment

On occasion I get the sense from bloggers that talking about your traffic statistics is a bit like talking about salary – not something to be done amongst polite company. However, unlike discussing pay, which can generate bad feelings, jealousy, poor morale and a range of other negative side effects, discussing website stats should provide a great learning opportunity for everyone taking part. With that said, in the name of transparency, let me offer a peak under the hood here at BrettRomero.com.

Overall Traffic

For those that have not looked at web traffic statistics, first a quick introduction. When it comes to web traffic, there are two primary measures of volume – sessions and page views. A session is a continuous period of time that one user spends on a website. One session can result in multiple page views – or just the one if the user leaves after reading one article as is often the case. Chart 1 below shows the traffic to BrettRomero.com, as measured in sessions per day.

Chart 1 – All Traffic – Daily

There are a couple of large peaks worth explaining in this chart. The first peak, on 3 November 2015, was the day I discovered just how much traffic Reddit.com can generate. Posting to the TrueReddit subreddit, I posted what, to that point, had been by far my most popular article – 4 Reasons Working Long Hours is Crazy. The article quickly gained over 100 upvotes and, over the course of the day, generated well over 500 sessions. To put that in perspective, the traffic generated from that one post on Reddit in one day is greater than all traffic from LinkedIn and Twitter combined… for the entire time the blog has been online.

The second big peak on 29 December 2015 was also a Reddit generated spike (in fact, all four spikes post 3 November were from Reddit). In this instance it was the posting of the Traffic Accidents Involving Cyclists visualization to two subreddits – the DataIsBeautiful subreddit and the Canberra subreddit.

Aside from these large peaks though, the data as represented in Chart 1 is a bit difficult to decipher – there is too much noise on a day-to-day basis to really see what is going on. Chart 2 shows the same data at a weekly level.

Chart 2 – All Traffic – Weekly

Looking at the weekly data the broader trend seems to show two different periods for the website. The first period, from March to around August has more consistent traffic, around 200 sessions a week, but with smaller spikes. The second period, from August onwards shows less consistent traffic, around 50 sessions a week, but with much larger spikes. But how accurate is this data? Let’s break some of the statistics down.

Breakdown by Channel

When looking at web traffic using Google Analytics, there are a couple of breakdowns worth looking at. The first is the breakdown by ‘channel’ – or how users got to your website for a given session. The four channels are:

Direct – the user typed your website URL directly into the address bar
Referral – the user navigated to your site from another (non-social media) website by clicking on a link
Social – the user accessed your website from a social media website (Facebook, Twitter, Reddit, LinkedIn and so on)
Organic Search – a user searched for something in a search engine (primarily Google) and clicked on a search result to access your site.

The breakdown of sessions by channel for BrettRomero.com is shown in Table 1 below:

Table 1 – Breakdown by Channel

Channel Grouping	Sessions
Direct	2,923
Referral	2,776
Social	2,190
Organic Search	567
Total	8,456

Referral Traffic

Looking at referral traffic specifically, Google Analytics allows you to view which specific sites you are getting referral traffic from. This is shown in Table 2.

Table 2 – Top Referrers

Rank	Source	Sessions
1	floating-share-buttons.com	706
2	traffic2cash.xyz	177
3	adf.ly	160
4	free-share-buttons.com	152
5	snip.to	74
6	get-free-social-traffic.com	66
7	www.event-tracking.com	66
8	claim60963697.copyrightclaims.org	63
9	free-social-buttons.com	57
10	sexyali.com	50
Total	All Referral Traffic	2,776

Looking at the top 10 referrers to BrettRomero.com, the first thing you may notice is that these site addresses look a bit… fake. You would be right. What you are seeing above is a prime example of what is known as ‘referrer spam’. In order to generate traffic to their sites, some unscrupulous people use a hack that tricks Google Analytics into recording visitors to your site coming from a URL they want you to visit. In short, they are counting on you looking at this data, getting curious and trying to work out where all this traffic is coming from. Over time these fake hits can build up to significant levels.

There are ways to customize your analytics to exclude traffic from certain domains, and initially I was doing this. However, I quickly realized that this spam comes from an almost unlimited number of domains and trying to block them all is basically a waste of time.

Looking at the full list of sites that have ‘referred’ traffic to my site, I can actually only find a handful of genuine referrals. These are shown in Table 3.

Table 3 – Genuine Referrers

Rank	Source	Sessions
17	uberdriverdiaries.com	35
18	vladimiriii.github.io	33
72	australiancraftbeer.org.au	3
76	alexa.com	2
95	opendatakosovo.org	1
Total	Genuine Referral Traffic	74
Total	Referrer Spam	2,702

What does the total traffic look like if I exclude all the referrer spam? Chart 3 below shows the updated results.

Chart 3 – All Traffic Excluding Referrals

As can be seen, a lot of the traffic in the period March through August was actually coming from referrer spam. Although May still looks to have been a strong month, April, June and July now appear to be hovering around that baseline 50 sessions a month.

Search Traffic

Search traffic is generally the key channel for website owners in the long term. Unlike traffic from social media or from referrals, it is traffic that is generated on an ongoing basis without additional effort (posting, promotion and so on) on the part of the website. As you would expect though, to get to the first page of search results for any combination of key words that is searched regularly is very difficult. In fact it is so difficult, an entire industry has developed around trying to achieve this – Search Engine Optimization or SEO.

For BrettRomero.com, search traffic has been difficult to come by for the most part. Below is a chart showing all search traffic since the website started:

Chart 4 – Search Traffic – All

Keeping in mind the y-axis in this chart is on a smaller scale than the previous charts, there doesn’t seem to be much pattern to this data. August again seemed to be a strong month, as well as the weeks in late May and early June. Recent months have been flatter, but more consistent.

Going one step further, Table 4 shows the keywords that were searched by users to access BrettRomero.com.

Table 4 – Top Search Terms

Rank	Keyword	Sessions
1	(not provided)	272
2	beat with a shovel the weak google spots addons.mozilla.org/en-us/firefox/addon/ilovevitaly/	47
3	erot.co	45
4	непереводимая.рф	40
5	“why you probably don’t need a financial advisor”	33
6	howtostopreferralspam.eu	32
7	sexyali.com	16
8	vitaly rules google ☆:.｡.゚゚･ヽ(^ᴗ^)丿･゚゚.｡.:☆ ¯\_(ツ)_/¯(•ิ_•ิ)(ಠ益ಠ)(ಥ‿ಥ)(ʘ‿ʘ)ლ(ಠ_ಠლ)( ͡° ͜ʖ ͡°)ヽ(゚д゚)ノʕ•̫͡•ʔᶘ ᵒᴥᵒᶅ(=^. .^=)oo	14
9	http://w3javascript.com	13
10	ghost spam is free from the politics, we dancing like a paralytics	11

Again, we see something unexpected – most of the keywords are actually URLs or nonsensical phrases (or both). As you might suspect, this is another form of spam. Other website promoters are utilizing another hack – this one tricks Google Analytics into recording a search session, with the keyword being a message or URL the promoter wants to display. Looking at the full list, the only genuine search traffic appears be the records for which keywords are not provided[1]. Chart 5 shows search traffic with the spam excluded.

Chart 5 – Search Traffic – Spam Removed

With the spam removed, we see something a little bit more positive. After essentially nothing from March through July, we see a spike in activity in August and September, before falling back to a new baseline of around 5-10 sessions per week. Although this is obviously still miniscule, it does suggest that the website is starting to show up regularly in people’s searches.

Referring back to the total sessions over time, Chart 6 shows how removing the spam search impacts our overall number of sessions chart.

Chart 6 – All Traffic Excluding Referrals and Spam Search

Social Traffic and the Reddit Effect

As was shown in Table 1, one of the two main sources of (real) traffic for the website is social media.

Social media provides a real bonus for people who are starting from zero. Most people now have large social networks they can utilize, allowing them to get their content in front of a lot of people from a very early stage. That said, there is a line and spamming your friends with content continuously is more likely to get you muted than generate additional traffic.

Publicizing content on social media can also be a frustrating experience. Competing against a never-ending flood of viral memes and mindless, auto-generated content designed specifically to generate clicks, can often feel like a lost cause. However, even though it seems like posts simply get lost amongst the tsunami of rubbish, social media is still generally a good indicator of how ‘catchy’ a given article is. Better content will almost always generate more likes/retweets/shares.

In terms of the effectiveness of each social media platform, Reddit and Facebook have proven to be the most effective for generating traffic by some margin. Table 5 shows sessions by social media source.

Table 5 – Sessions by Social Media Source

Rank	Social Network	Sessions
1	Reddit	999
2	Facebook	868
3	Twitter	224
4	LinkedIn	69
5	Blogger	26
6	Google+	3
7	Pocket	1

When looking at the above data, also keep in mind, I only started posting to Reddit at the start of November, effectively giving Facebook a 7 month head start. This means Reddit is by far the most effective tool I have found to date to get traffic to the website. However, there is a catch to posting on Reddit – the audience can be brutal.

Generally on Facebook, Twitter and LinkedIn, people who do not agree with your article will just ignore it. On Reddit, if people do not agree with you – or worse still, if they do not like your writing – they will comment and tell you. They will not be delicate. They will down vote your post (meaning they are actively trying to discourage other people from viewing it). Finally, just to be vindictive, they will down vote any comments you make as well. If you are planning to post on Reddit, make sure you read the rules of the subreddit (many explicitly ban people from promoting their own content) and try to contribute in ways that are not just self‑promotional.

Pages Visited

Finally, let’s look at one final breakdown for BrettRomero.com. Table 5 shows the top 10 pages viewed on BrettRomero.com.

Table 6 – 10 Most Viewed Pages

Rank	Page	Pageviews
1	/	4,345
2	/wordpress/	1,450
3	/wordpress/4-reasons-working-long-hours-is-crazy/	1,038
4	/cyclist-accidents-act/	773
5	/wordpress/climbing-mount-delusion-the-path-from-beginner-to-expert/	306
6	/wordpress/the-dark-side-of-meritocracy/	205
7	/wordpress/why-australians-love-fosters-and-other-beer-related-stories/	194
8	/blog.html	192
9	/?from=http://www.traffic2cash.xyz/	177
10	/wordpress/visualizations/	165

As mentioned earlier, 4 Reasons Working Long Hours is Crazy has been by some margin my popular article. Although Reddit gave this article a boost traffic wise, it was also by some margin the best performing article I have posted to Reddit with over 100 upvotes. The next best performing, the Traffic Accidents Involving Cyclists visualization, only managed 20 upvotes.

Overall

As I mentioned at the outset, web traffic statistics tend to be a subject that is not openly discussed all that often. As a result, I have little idea how good or bad these statistics are. Given I have made minimal effort to promote my blog, generate back links (incoming links from other websites) or get my name out there by guest blogging, I suspect that these numbers are pretty unimpressive in the wider scheme of things. Certainly I am not thinking about putting up a pay wall any time soon anyway.

As unimpressive as the numbers may be though, I hope they have provided an interesting glimpse into the world of web analytics and, for those other bloggers out there, some sort of useful comparison.

Spotted something interesting that I missed? Please leave a comment!

[1] For further information on why the keywords are often not provided, this article has a good explanation.

5 Things I Learned in 2015

December 31, 2015 / Brett Romero / 2 Comments

2015 has been an interesting year in many respects. A new country[1], a new language, a new job, and plenty of new experiences – both at work and in life in general. To get into the year-end spirit, I thought I would list out 5 key things I learned this year.

1. I Love Pandas

Yes, those pandas as well, who doesn’t? But I knew that well before 2015. The pandas I learned to love this year is a data analysis library for the programming language Python. “Whoa, slow down egg head” I hear you say. For those that are not regular coders, what that means is that pandas provides a large range of ways for people writing Python code to interact with data that makes life very easy.

Reading from and writing to Excel, CSV files and JSON (see lesson number 2) is super easy and fast. Manipulating large datasets in table like structures (dataframes) – check. Slicing, dicing, aggregating – check, check and check. In fact, as a result of pandas, I have almost entirely stopped using R[2]. All the (mostly basic) data manipulation for which I used to use R, I now use Python. Of course R still has an important role to play, particularly when it comes to complex statistical analysis, but that does not tend to come up all that regularly.

2. JSON is Everywhere

JSON, JavaScript Object Notation for the uninitiated, is a data interchange format that has become the default way of transferring data online. Anytime you are seeing data displayed on a webpage, including all the visualizations on this website, JSON is the format the underlying data is in.

JSON has two big advantages that have led to its current state of dominance. The first is that, as the name suggests, it is native to JavaScript – the key programming language, alongside HTML, that is interpreted by the browser you are reading this on. The second is that JSON is an extremely flexible way of representing data.

However, as someone who comes from a statistics and data background, as opposed to a technology background, JSON can take a while to get used to. The way data is represented in JSON is very different to the traditional tables of data that most people are used to seeing. Gone are the columns and rows, replaced with key-value pairs and lots of curly brackets – “{“ and “}”. If you are interested in seeing what it looks like, there are numerous CSV to JSON convertors online. This one even has a sample dataset to play with.

If you do bother to take a look at some JSON, you will note that it is also much more verbose than your standard tabular format. A table containing 10 columns by 30 rows – something that could easily fit into one screen on a spreadsheet – runs to 300+ lines of JSON, depending on how it is structured. That does not make it easy to get an overview of the data for a human reader, but that overlooks what JSON is designed for – to be read by computers. The fact that a human can read it at all is seen as one of JSON’s strengths.

For those interested in working with data (or any web based technology), knowing how to read and manipulate JSON is becoming as important as knowing how to use a spreadsheet.

3. Free Tools are Great

There are some people working for software vendors who will read this and be happy I have a very small audience. Having worked in the public sector, for a large corporate and now for a small NGO, one thing I have been pleasantly surprised by in 2015 is the number and quality of free tools available online.

For general office administration there are office communicator applications (Slack), task management tools (Trello) and Google’s free replacements for Excel, Word and PowerPoint. For version control and code management there is GitHub. For data analysis, the aforementioned Python and R are both free and open source. For data storage, there is a huge range of free database technologies available, in both SQL (PostgreSQL, MySQL, SQLite3) and NoSQL (MongoDB, Redis, Cassandra) variations.

To be fair to my previous larger employers and my software-selling friends, most of these tools/applications do have significant catches. Many operate on a ‘freemium’ model. This means that for individuals and small organizations with relatively few users, the service is free (or next to free), but costs quickly rise when you need larger numbers of users and/or want access to additional features, typically the types of features larger organizations need. Many of the above also provide no tech support or guarantees, meaning that executives have no one to blame if the software blows up. If you are responsible for maintaining the personal data of millions of clients, that may not be a risk you are willing to take.

For small business owners and entrepreneurs however, these tools are great news. They bring down barriers to entry for small businesses and make their survival more dependent on the quality of the product rather than how much money they have. That is surely only a good thing.

4. Blogging is a Full Time Job

Speaking of starting a business, a common dream these days is semi-retiring somewhere warm and writing a blog. My realization this year from running a blog (if only part time) is just how difficult it is to get any traction. Aside from being able to write reasonably well, there are two main hurdles that anyone planning to become a full time blogger needs to overcome – note that I have not come close to accomplishing either of these:

You have to generate large amounts of good quality content – at least 2-3 longer form pieces a week if you want to maintain a consistent audience. That may seem easy, but after you have quickly bashed out the 5-10 article ideas you have been mulling over, the grind begins. You will often be writing things that are not super interesting to you. You will often not be happy with what you have written. You will quickly realize that your favorite time is the time immediately after you have finished an article and your least favorite is when you need to start a new piece.
You will spend more time marketing your blog than writing. Yep, if you want a big audience (big enough to generate cash to live on) you will need to spend an inordinate amount of time:
- cold emailing other blogs and websites, asking them to link to your blog (‘generating back links’ in blogspeak)
- ensuring everything on your blog is geared towards your blog showing up in peoples’ Google search results (Search Engine Optimization or SEO)
- promoting yourself on Facebook
- building a following on Twitter
- contributing to discussions on Reddit and LinkedIn to show people you are someone worth listening to, and
- writing guest blogs for other sites.

None of this is easy. Begging strangers for links, incorporating ‘focus words’ into your page titles and headings, posting links on Facebook to something you spend days writing, only to find you get one like (thanks Mum!). Meanwhile, some auto-generated, barely readable click-bait trash from ‘viralnova’ or ‘quandly’ (yes, I am deliberately not linking to those sites) is clocking up likes in the 5 figures. It can be downright depressing.

Of course, there are an almost infinite number of people out there offering their services to help with these things (I should know, they regularly comment on my articles telling me how one weird trick can improve my ‘on page SEO’). The problem is, the only real help they can give you is adding more things to the list above. On the other hand, if you are thinking about paid promotion (buying like’s or a similar strategy) I’d recommend watching this video:

Still want to be a blogger? You’re welcome.

5. Do not be Afraid to Try New Things

One of the things that struck me in 2015 is how attached people get to doing things a certain way. To a large degree this makes sense, the more often you use/do something, the better you get at it. I am very good at writing SQL and using Excel – I have spent most of the last 10 years using those two things. As a result, I will often try to use those tools to solve problems because I feel most comfortable using them.

Where this becomes a problem is when you start trying to shoehorn problems into tools not just because you are comfortable with the tool, but to avoid using something you are less comfortable with. As you have seen above, two of the best things I learned this year were two concepts that were completely foreign to a SQL/Excel guy like me. But that is part of what made learning them so rewarding. I gained a completely new perspective on how data can be structured and manipulated and, even though I am far from an expert in those new skills, I now know they are available and which sorts of problems they are useful for.

So, do not be afraid to try new things, even if the usefulness of that experience is not immediately apparent. You never know when that skill might come in handy.

Happy New Year to everyone, I hope you have a great 2016!

[1] Or ‘Autonomous Province’ depending on your political views

[2] R is another programming language designed specifically for statistical analysis, data manipulation and data mining.

Traffic Accidents Involving Cyclists in the ACT

December 29, 2015 / Brett Romero / 0 Comments

I’ve had a few days off lately and I decided to try something a bit different. Instead of writing an(other) lengthy article, I thought I would go back to my roots and actually look at some data. To that end I recently discovered a website for open data in Australia, data.gov.au. This website has literally thousands of interesting datasets released from all levels of government, covering everything from the tax bills of Australia’s largest companies to the locations of trees in Ballarat.

One of the first datasets that caught my eye was one published by the Australian Capital Territory (ACT) Government on traffic accidents involving cyclists. For those that don’t know, Canberra (the main city in the ACT) is a very bike friendly city and is home to a large number of recreational and more serious cyclists, so seeing where the accidents were/are occurring was something I thought would be interesting.

Using a few new things I have not used before (primarily Mapbox and leaflet.js), I put (slapped?) together an interactive map that uses the data provided and also gives you a few different ways of viewing it. The full version of the map can be accessed by clicking the picture below:

See a bug? Found it particularly useful? Hate it? Leave a comment below!

Should the Wealthy be able to pay for Better Healthcare?

November 20, 2015 / Brett Romero / 0 Comments

Commenting on an article on reddit.com, I recently got into an argument[1] with someone about healthcare and more specifically the role of private healthcare. The article was this NY times piece that talks about how US hospitals provide a range of benefits for wealthier ‘clients’ (at significant additional cost of course). These benefits can be anything from nicer rooms to gourmet food and access to business centers.

My first reaction to the piece was, what I expect, the desired response – indignation. In a country like the US where there are countless healthcare horror stories (the story of a carpenter having to choose which fingers to reattach as covered in Sicko is particularly famous), this seems outrageous. How can some people not afford access to healthcare at all, and yet others are paying huge sums to stay in private rooms and eat soft cheeses?

I believe in my case, this sense of indignation was particularly strong because I come from one of the many non-US developed countries in the world with a basic but functioning universal healthcare system. No one avoids going to hospital for fear of being bankrupted by the cost. No one has to make horrible decisions about which appendages to reattach. The only major drawback in most universal healthcare systems is procedures that are non-life threatening can have significant wait times.

A good example of this is getting surgery to repair an ACL. You can get it done through the public health system (Medicare in Australia), free of charge – or close to free. However, because you are not going to die from a ruptured ACL, you are likely to have to wait for 1-2 years to get that surgery done through Medicare. If, on the other hand, you have something like $5,000-$10,000 you can have it done next week[2].

As you may have observed from this example though, this sounds very close to what I was getting all indignant about in the first place – wealthy people buying access to better healthcare. In fact, in most universal healthcare systems, including Australia’s, the wealthy do have the option to pay more to receive access to better care and/or skip to the front of the queue. In reality, the NY Times article could easily have been talking about Australian hospitals. What is more, the ability of richer patients to pay for better service is often viewed as necessary for the system in Australia – the extra money paid by wealthy patients helps to fund the system for others. So why does it feel different?

After several days of mentally dissecting this issue I think I have come to a conclusion as to why the NY Times story got such a reaction out of me and yet I had a generally positive impression of the private health system in Australia. The key difference (at least in my mind) is the extent of the privatization of the healthcare system. In the US, healthcare has been privatized to such an extent that some people have been priced out of the market completely. When this is contrasted with the opposite end of the spectrum – private rooms, nicer robes, lobster stuffed with tacos – it highlights that the problem with the system is not an overall lack of resources, but that those resources are being allocated in such a way that some people do not get access.

Contrast this to the existence of private health systems in countries with universal healthcare. Even though some patients are able to access better facilities (and potentially doctors), everyone has access to a (generally) good level of healthcare, regardless of wealth or insurance policy. Because of this, the fact that some people can pay extra for nicer rooms seems much less important. The system has enough resources for everyone – so it is not perceived as resources being taken from poorer patients.

However, it is worth asking the question of whether this is right or simply a convenient piece of logic.

To assess the morality of the wealthy having the ability to purchase better healthcare services, we have to recognize the two main constraints on a healthcare system. The first constraint is the supply of personnel, equipment and medical supplies. The second constraint is the supply of money. These constraints are not unrelated. An endless supply of money will not help if there is a shortage in equipment/personnel at a given point in time. But money can help to increase the supply of these things in the future.

If we accept the premise that wealthy patients benefit healthcare systems by adding additional money into the system, going back to the constraints above, we can see that essentially this is a short term sacrifice for a longer term gain. Assuming that the demand for healthcare will always exceed supply, wealthy patients skipping to the front of the queue will take resources away from poorer patients in the present. They occupy beds, take time away from doctors and require access to equipment just like any other patient. However, they also pay money into the system that allows future patients to access treatment they might not otherwise have had access to.

Here is where it gets a bit murkier. If we are saying that payments from wealthy patients are needed for the system to function in the future, are we not then implying that the system is underfunded? Why can that money not come from other sources such as higher tax rates or lower spending in other areas of the budget? The problem with that line of thinking is that in any realistic government budget, there will always be room for additional healthcare funding. No government is ever likely to fund a healthcare system to the point that everyone gets the best possible treatment instantaneously[3]. So even in a much better funded public health system than currently exists in most countries, additional funds provided by wealthy patients will still allow for better treatment of other patients in the future.

All this does not mean we have to like the US model of healthcare where money plays far too big a role for the comfort of many. Denying patient access to healthcare (or bankrupting them for emergency care) in a modern developed country is a deplorable situation. But my overall conclusion is that it is best to focus your indignation on the real issues with the system – the excessive insurance premiums, the tying of affordable insurance to employment, the huge markups charged by many hospitals and the unnecessary expensive treatments added to patients bills.

As outrageous as it seems to picture wealthy patients receiving lavish treatment in private rooms while others are avoiding necessary treatment for fear of the cost, it is not the real issue. In fact it is probably providing a net benefit in a deeply flawed system.

[1] Those that know me will find this very unsurprising

[2] This cost, it should be noted, is still a fraction of the $55,000+ my health insurance company paid for that procedure in the US.

[3] If that was the case, you would also have idle resources for much of the year

The Darker Side of Meritocracy

October 28, 2015 / Brett Romero / 1 Comment

Meritocracy. An ideal world where everyone is rewarded based on his or her individual qualities. The intelligent and the hardworking become the rock stars, the lazy and ignorant are doomed to a life of mediocrity. But would a true meritocracy be as idyllic as it sounds?

As was covered in Part I, there are a number of problems with defining and identifying merit. In Part II, we are going to overlook these issues and imagine what a real meritocracy might look like, and why, despite what they might say, the vast majority of people actively undermine meritocracy on a regular basis.

What Would a True Meritocracy Look Like?

Assuming we have some agreed upon way to define and identify merit, what would a true meritocracy look like? For a pure meritocracy (i.e. one in which the success of a given person is solely determined by their own actions and intelligence) to exist, each individual’s merit needs to be determined solely by his or her own individual quality.

The problem with this is, in the real world, parents have a huge influence on a child’s chances of success. This influence comes in an infinite number of forms, but includes intangible things like advice, help with homework, introductions to influential people, and being a positive role model, as well as tangible resources such as money and access to the best schools.

If parents have such a large influence on the success (or failure) of their children, how can a true meritocracy exist? Realistically, to achieve a true meritocracy, the government (or some independent body) needs to equalize parents’ influence on their child.

This equalization can take two forms. The first form is providing resources and assistance to less well off parents to try bring them up to level of parents in the upper classes. This typically includes things like welfare payments, subsidized/free health care and housing assistance, but also includes scholarships and other programs offered to help disadvantaged kids.

The second form of equalization is typically more controversial and involves reducing the ability of upper class parents to provide advantages to their children. These types of measures are far more rare, but they do exist – policies such as inheritances taxes and the removal or restriction of private schools[1] are two examples.

The reason this second type of measure is so rare is because it starts to reveal the underlying tradeoff. The tradeoff being that ensuring everyone gets the same quality upbringing means that, for some children, the quality of their upbringing has to decrease.

But even if we were willing to accept more extreme policies, they can only realistically go so far. No government can legislate to ensure every child is read to at night, and nor can they implement a ban on reading to children to make sure no child gets an advantage. No government can legislate away deadbeat Dads or Moms that get drunk in front of the kids. Which means that if you are going to create a true meritocracy, there is only really one option – take the parents out of the picture completely. This is where things start to get a little scary.

To guarantee every child receives the exact same upbringing and education, the government (or some independent body) would need to remove parents from their children’s lives. This could take various forms. A Logan’s Run style scenario where everyone is ‘terminated’ at age 30 – essentially creating a nation of orphans is one potential option. Another would be taking children at birth and raising them in industrial scale nurseries and boarding schools out of reach of parents, somewhat akin to Aldous Huxley’s Brave New World (without the presence of castes or the extreme social conditioning).

There a numerous ways that one can envisage removing a parents’ influence from their children, but the difficulty is imagining one that does not sound like a good premise for a movie about a dystopian future. In fact, the options are so unappealing that even the most repressive and extreme regimes in history have shied away from this kind of intervention.

If this is what a society would need to do to implement a true meritocracy, are there at least some upsides?

A Fairer System?

One of the key arguments made for meritocracy is that it is a ‘fairer’ system. But is it fairer (whatever that means), or are we simply replacing one lottery with another?

The current system is one in which your future success is dictated by some combination of who your parents and/or role models are (‘nurture’) and your own individual abilities (‘nature’). A true meritocracy, as we have been describing it, is simply a system in which the ‘nurture’ component has been standardized.

Is that actually fairer though? There will still be winners and losers, but now the people born with a dud genetic hand are probably worse off then in our current less meritocratic world. Unlike the current world we live in, there is no chance that a superior work ethic instilled by charmingly humble parents will get someone ahead. There are no inspiring stories of underdogs beating their better-credentialed rivals through pure determination. Rocky Balboa never even gets to fight against Apollo Creed. In a true meritocracy, the favorite always wins – that is the point of system.

A Better System?

By ensuring that the best and brightest are the ones that rise to the most influential positions, are we at least guaranteeing the fastest possible rate of progress for humanity? The answer to that question depends on how you believe progress is made.

Someone who believes that progress is only really made by rare transformative geniuses, like Einstein and Hawking, should be in favor of a more meritorious society. The risk is that a genius will be born to bad parents or in the wrong country, and as a result, that genius is wasted and substantial progress is forgone. To minimize the risk of this happening, a rational person should be willing to sacrifice certain freedoms (through more government intervention) to make sure that fewer geniuses are ‘wasted’.

On the other hand, if a person believes that progress is made by the cumulative effort of many, many intelligent (but not unique people), they should not be so worried about a true meritocracy. In this case, the loss of some geniuses to bad upbringings and poverty is much less consequential as they will be replaced by other equally or slightly less intelligent people. Maximizing the overall level of child welfare should be the priority, which, to most people, would mean allowing parents to raise their own children as far as possible.

Saying versus Doing

Stepping away from the theoretical, there is a lot that can be learned about people’s preferences in regard to meritocracy by simply looking at their actions in our world today. There is an Italian proverb that I enjoy reciting from time to time to make myself sound intelligent:

“Between saying and doing, many a pair of shoe is worn out”

Aside from the aforementioned reason, I bring this up now because people’s actions often reveal their true preferences much more accurately than their words. This is particularly true when it comes to meritocracy. In my experience, there are few people that do not actively attempt to give themselves (or those they care about) some advantage over others, and even fewer that would not take advantage of an opportunity that was presented to them.

A common example is private schools. These schools, by definition, are unmeritorious. Their business model is that parents will pay money (often large amounts of it) to send their children to a certain school exactly because they believe it will provide their child with an advantage over other children that don’t go to that school. If they did not believe it provided their children with an advantage, no rational parent would pay to send their child there.

Inheritances, giving someone a job because you know them, private tutors, moving to a better (i.e. more expensive) school district, helping out the kids with homework or even reading to them at night are just some of the endless ways that everyone, myself included, undermines a true meritocracy.

Summary

Despite the platitudes and mainstream acceptance, a true meritocracy is not what we really want as a society. Any serious thought on the subject quickly reveals a true meritocracy it is all but impossible to implement, and if implemented, the reality would be a dystopian world worthy of a George Orwell novel.

However, once the realization is made that a true meritocracy is impossible and undesirable, the remaining conclusion is that no one is truly arguing for or against meritocracy, everyone is simply arguing for a different shade of grey. The introduction and removal of various policies simply makes that shade slightly darker or lighter.

This is an important conclusion because it changes the perspective of the argument. There is no right vs. left, haves vs. have nots, good vs. evil. There is just people arguing for incremental changes. Each country, with every election, is simply working out what shade of grey they prefer.

[1] Many countries in Europe do not have private school systems, including education pinup nation Finland.

The Dark Side of Meritocracy

October 8, 2015 / Brett Romero / 0 Comments

In recent years, discussion about economic concepts like inequality and income mobility has been everywhere. Thrust into the spotlight by the global financial crisis, they have rarely left the front pages thanks to Thomas Piketty’s Capital in the Twenty-First Century and a series of rolling financial crises in Europe. These days you can’t even enjoy your artisanal quail egg omelet and fair trade coffee without some bearded, tweed wearing, artisanal whiskey distilling, overgrown trust fund baby complaining about how unfair it all is, in between cashing rent checks from his parents.

When it comes to discussions of inequality though, one of the underlying assumptions that few are willing to challenge is that the drivers of inequality largely boil down to nepotism and inherited wealth, while the answer to most inequality based problems comes down to one idea: meritocracy.

What is a Meritocracy?

Meritocracy is a system in which the people who hold power (through democratically elected means or otherwise) are those that are most deserving based on individual merit. In common use, it is usually taken to be slightly broader than that – a world in which money and success are allocated, perfectly, to those who are deemed to deserve it the most.

In an increasingly polarized political system in the US, meritocracy – or ‘the right to rise’ – is often the only thing that politicians on both sides of the aisle seem to agree on. The ideal of meritocracy is so ingrained in the US that Americans are famous for their belief that hard work will be rewarded with untold wealth and success. But this belief is far from unique to the US. In Australia meritocracy has long been considered part of the national identity with politicians of all stripes often talking in jingoistic terms about ‘the fair go’.

For all the talk of meritocracy though, how feasible is it in the real world? What would be some of the major hurdles to implementing a more meritocratic system?

Defining Merit

The first question that should arise whenever meritocracy is discussed is how is merit defined? There are 4 basic criteria that most commonly are thought of as contributing to merit:

Qualifications
Work ethic
Intelligence
Experience

For almost all competitions where there are winners and losers – jobs, university positions or other – some combination of these traits will generally be used to decide a winner. To keep things simpler, let’s focus on the job market for now.

The first thing to consider when defining merit for a given job is that to have an accurate measure of merit, the criteria need to be modified for every position. Jobs requiring manual labor place a higher value on work ethic but little value on qualifications. Jobs in tech often place higher value on intelligence, but less on formal qualifications and, depending on the role, large amounts of experience can be seen as detrimental. Most jobs will require applicants to possess experience in one or more specific areas.

For the most part, this customization of criteria for each job is already being done – a job advertisement is essentially a statement of the criteria that merit will be assessed by. But the question is, are those criteria actually the correct ones to identify the best possible person for a given job? I believe the answer to that question is a resounding “no”. Let me explain why.

Let’s look at a common example that anyone who has tried changing sector, industry or country in his or her career will be able to relate to.

Imagine you have been working for around 10-15 years and have spent all of that time in one industry[1]. During that time you have picked up a lot of useful workplace skills, spreadsheets, experience with various applications, writing skills, general how-not-to-piss-everyone-off skills and so on. Now you want a new challenge that will require a lot of the skills you have, but in a different industry. You approach a recruiter, bright eyed and excited by the possibilities, but despite your best efforts to sell your skills as relevant, the recruiter basically discards your experience as worthless and tries to push you towards a low level role or something in your old industry.

This experience is a simple example that reveals an underlying truth – if we were being truly meritorious, there could be no fixed criteria for merit for any job because there is no way to preemptively identify what combination of skills and experience will ultimately prove to be the most valuable.

The possibilities for what combination of skills and experience lead to the best performance in a role are endless. Many successful business owners do not have MBAs. Many of the best investors on Wall Street do not come from finance backgrounds. Some of the best NFL punters are ex-Australian Rules Football players. What people who excel tend to have in common is a combination of skills and experiences that allows them to bring a different perspective to a problem.

Yet, despite history proving time[2] and time again[3] that different perspectives are often vital to important insights, it is a rare employer or recruiter that will take a bet on a candidate with ‘unusual experience’ rather than a candidate who ticks all the boxes. The reason for that is simple – it is safer. Choosing the candidate that ticks the boxes provides cover (“I gave you what you asked for”) and it gives a better guarantee of an acceptable level of performance. The unusual candidate could be fantastic – but they could also be a complete flop who turns out to be way out of their depth.

Unfortunately, this is only the first hurdle for a true meritocracy – if merit is a difficult thing to define, it is an even more difficult thing to measure.

Measuring Merit

Once we get past the step of deciding what criteria will determine the most deserving applicant, the next step is deciding who best meets those criteria. A quick look at the application process for college admission or a technical job will give you an instant appreciation for the lengths that people will go to try and get an accurate assessment of an applicant’s true merit.

Tests, interviews and essays are probably the most common tools used to assess merit but all can be (and are) gamed by people who understand the system. Material for tests can be rote learned with little to no understanding necessary. Interviews are notorious for being poor predictors of talent, which makes sense when you consider that the most confident people are often delusional. Essays, aside from providing evidence of basic writing skills, are assessed subjectively.

Even if these tools for assessing merit were designed in such a way as to prevent gaming the system, these are still three very narrow tests of ability. As Megan McArdle explains, the experience in China shows that selecting for people who do well on exams gives you… a selection of people who do well on exams.

Assumptions and Prejudice

One interesting side effect of the difficulty in determining merit is it leads to people basing their assessment on completely superficial qualities (at least partially). A good dress sense, physical attractiveness, and being an eloquent speaker are just some examples of relatively superficial qualities people use to assess intelligence and merit. As frustrating as this can be for the unshapely, poorly dressed, mumblers out there, these are all things that can be improved and worked on (at least to some degree). Others are subject to prejudices that cannot be addressed – the impact of race on the ability to get interviews, for example, is well established.

Another concerning trend is the increasing use of someone’s current level of success/wealth as an indicator of merit. That is, if someone is wealthy and/or successful, they must be someone who is highly intelligent and works harder than everyone else. This line of thinking is dangerous for two reasons:

Too much value is placed on the opinions of wealthy and successful people – particular on topics outside their domain. Anyone who has listened to Clive Palmer or Donald Trump speak should know that is a mistake.
The implicit assumption made when you believe wealthy and successful people are fully deserving of their place in the world is that anyone who is poor and unsuccessful is also fully deserving of their situation.

Evidence of this thinking is present everywhere to some degree, but seems particularly prevalent in the US[4], where TV shows like Shark Tank are extremely popular and prominent CEOs are regularly asked for their opinions on public policy issues.

This belief system can largely be explained as the flip side of the optimistic view American’s have of their economic prospects. As this paper from the Brookings Institution highlights, American’s are far more likely to believe hard work and intelligence will be rewarded and yet are second only to the UK in terms of how closely correlated a son’s earnings are to his fathers (i.e. hard work has the least chance of improving your situation). If you truly believe that hard work and intelligence is all you need to be successful, you must also believe that people who are currently experiencing success have those attributes.

Best Person for the Job

Going back to our problems with creating a meritocracy, everything discussed so far has overlooked a key factor in this endless quest to find the most deserving – people are not cogs that can be simply transferred in and out of a machine seamlessly. The person who ‘deserves’ the job on merit (provided we can define it and measure it accurately) is often NOT the best person for the job. The best person for the job is often determined by qualities such as:

how that person fits in with the team culture,
their personality type, and
how they respond to authority (or the lack thereof).

These traits are all key factors in how well someone will perform in a given role and yet none would typically be thought of as meritorious qualities.

This realization is not new. Employers and hiring firms have been pushing the idea of the ‘beer test’ (asking yourself which of the candidates would you most like to go for an after-work beer with) for some time now. But it does beg the question – what would happen if a company simply hired the ‘best’ candidates for each position without considering whether these people will work well together? Would that team be more productive than a team that hired less ‘deserving’ candidates but aimed to build a harmonious work place? The entire body of management knowledge (and every buddy cop movie ever made) would tell us otherwise.

Summary

One thing that becomes obvious when you start thinking about how a true meritocracy would actually work is how difficult it would be to implement:

Many of the criteria we associate with someone being deserving of a role or position are subjective or exclude applicants who would in fact be far superior.
Our methods of assessment are often deeply flawed, subject to gaming and our own prejudices.
Selecting the most objectively deserving candidates is not guaranteed to provide the best results anyway.

Yet, despite the reasons above hopefully being enough to give pause the next time someone begins expressing frustration with the current lack of meritocracy, all of these issues are only really logistical problems.

There is a good argument to be made that we can and should try to improve on all of these things – that we should aim to get better at identifying the right people and refine our methods of assessing skills. We should pay more attention to team fit and personalities when selecting the best candidate. It is hard to imagine the world being a worse place if employers were more open minded about what skills might be valuable to their company and sociopaths were less likely to impress during an interview.

However, the next question is how far should we take this. What does a truly meritocratic society look like and is it something we really want? That is the subject of discussion in Part II in this series on meritocracy.

[1] You can easily replace ‘industry’ in this story with ‘company’, ‘field’ or ‘country’.

[2] Einstein famously worked at a patent office where his work often exposed him to the transmission of electric signals and electrical-mechanical synchronization of time. Exposure to these topics helped him to arrive at his conclusions about the nature of light and the connection between space and time.

[3] Steve Jobs often talked about the importance of a calligraphy class he took in shaping what fonts were best used in operating systems.

[4] Australia’s proclivity to tall poppy syndrome does have some positive side effects

Climbing Mount Delusion – The Path from Beginner to Expert

September 22, 2015 / Brett Romero / 0 Comments

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

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

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

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

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

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

September 12, 2015 / Brett Romero / 0 Comments

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:

People in legal and consulting generally aren’t working as many hours as we assume (or they tell us).
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:

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.
When drilling down into specific subsectors where anecdotal evidence suggests there should be increases, the data indicates that average hours worked have been increasing. Although the averages still seem low (i.e. less than 40 hours a week), when you take into account the spread of hours making up those averages, even a 1-2 hour average increase represents an increasingly large proportion of people in those subsector working very long hours.

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

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

Does Wealth Inequality Impact Growth?

September 3, 2015 / Brett Romero / 0 Comments

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

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

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

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

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

The Setup

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

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

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

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

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

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

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

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

The Results

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

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

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

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

Caveats and Problems

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

Determining Wealth is Difficult

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

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

Is Billionaire Wealth a Good Predictor of Wealth Inequality?

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

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

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

Chart 1 – Wealth Distribution Across Two Hypothetical Countries

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

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

What is Politically Connected?

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

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

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

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

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

Unknown Unknowns

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

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

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

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

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

Conclusions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4 Reasons 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

Overall Traffic

Chart 1 – All Traffic – Daily

Chart 2 – All Traffic – Weekly

Breakdown by Channel

Table 1 – Breakdown by Channel

Referral Traffic

Table 2 – Top Referrers

Table 3 – Genuine Referrers

Chart 3 – All Traffic Excluding Referrals

Search Traffic

Chart 4 – Search Traffic – All

Table 4 – Top Search Terms

Chart 5 – Search Traffic – Spam Removed

Chart 6 – All Traffic Excluding Referrals and Spam Search

Social Traffic and the Reddit Effect

Table 5 – Sessions by Social Media Source

Pages Visited

Table 6 – 10 Most Viewed Pages

Overall

1. I Love Pandas

2. JSON is Everywhere

3. Free Tools are Great

4. Blogging is a Full Time Job

5. Do not be Afraid to Try New Things

What Would a True Meritocracy Look Like?

A Fairer System?

A Better System?

Saying versus Doing

Summary

What is a Meritocracy?

Defining Merit

Measuring Merit

Assumptions and Prejudice

Best Person for the Job

Summary

1. Initial Optimism

2. The Summit of Mount Delusion

3. The Clearing of the Fog of Ignorance

4. The Valley of Self-Pity

5. Exiting the Valley

6. The Never-ending Slope of True Mastery

How I Can Relate to Fred

The Picture in the US

Chart 1 – Average Weekly Hours by Industry

Chart 2 – Average Weekly Hours – Professional and Business Services

What about Technology?

Chart 3 – Average Weekly Hours – Information Sector

Hard Working Aussies?

Chart 4 – Australian Employees by Average Weekly Hours

The International Perspective

Chart 5 – Average Annual Hours Worked – Selected Countries

The Long Term Perspective

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

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

Wrapping Up

The Setup

The Results

Caveats and Problems

Determining Wealth is Difficult

Is Billionaire Wealth a Good Predictor of Wealth Inequality?

Chart 1 – Wealth Distribution Across Two Hypothetical Countries

What is Politically Connected?

Unknown Unknowns

Conclusions

1. Longer Hours Means Reduced Output

2. Sleep Deprivation Is the Silent Killer

3. People Do Not Realize When They Are Not Productive

4. We Have Already Learnt This Lesson

What Can You Do?

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