Why Standard Economic Models Don’t Work–Our Economy is a Network

Starts with an interesting premise but I’m not too sure about the conclusions drawn

Our Finite World

The story of energy and the economy seems to be an obvious common sense one: some sources of energy are becoming scarce or overly polluting, so we need to develop new ones. The new ones may be more expensive, but the world will adapt. Prices will rise and people will learn to do more with less. Everything will work out in the end. It is only a matter of time and a little faith. In fact, the Financial Times published an article recently called “Looking Past the Death of Peak Oil” that pretty much followed this line of reasoning.

Energy Common Sense Doesn’t Work Because the World is Finite 

The main reason such common sense doesn’t work is because in a finite world, every action we take has many direct and indirect effects. This chain of effects produces connectedness that makes the economy operate as a network. This network behaves…

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The New Econometrics?

First, let me get this out of the way. This is going to be an emotional post. Knowing myself, it will end up reading like a rant riddled with spelling errors (so not much of difference there). Why, you ask? Because I care about Economics and I’m mad we were robbed a good topic that any Econometrics student should be offered. We are still robbed. So read this like a letter to both students and professors.

Econometrics is about data. Econometrics is about analysis and distilling information to obtain the best picture within the data to mimic the population at large. This isn’t statistics used to unearth correlations but, like any self respecting economists, to unearth causations; always trying to answer the why within a phenomenon. So, on one hand I can forgive the unfortunate avoidance of economists shying away from heavy data crunching. However, it is an unforgivable sin to not mention, at least in passing, the wealth of options surrounding students.

So what is this thing I keep raving about? Well, it is known as data mining and/or machine learning(ML). I will avoid explaining the differences between the two (mostly because the answer is a bit vague, especially for the scope of this article) 1. To explain the field itself, it is using algorithms seep through data and obtain meaningful relationships. Alright, that sounds kinda like Econometrics. And that is exactly my point. Having knowledge of the field makes you an even more complete econometrician. Remember all the linear regressions you made in Econometrics? Well, that is the first algorithm found in intro to ML. Basically, the entire semester I spent learning Mathematical Economics (which is like advanced Econometrics) was over in a week. Then came logistic regression (an even more useful algorithm). Then came neural networks. Then came feed-forward neural networks and (wait for it!) backpropagating networks. OK, I will stop with the forced revision. My point still stands on exciting and useful algorithms that can be used to detect relationships and avoid errors.

Ignoring the hype with big data 2, think of how much data is generated every single second. Think of events happening that were once hard to measure/track: mobile phones, geo-location, PaaS, SaaS and multiple ways fixed costs have become variable costs. Hal Varian puts it best,

“There is now a computer in the middle of most economic transactions. These computer­mediated transactions enable data collection and analysis, personalization and customization, continuous experimentation, and contractual innovation.Taking full advantage of the potential of these new capabilities will require increasing sophistication in knowing what to do with the data that are now available” 3

I should note that Hal is the main reason I am writing this post. He works as the chief economist at Google. He has also written one of the most intriguing papers (Big Data: New Tricks for Econometrics) 4 concerning the future of Econometrics – a must read if you have read this far.

I pointed out exciting algorithms that might change the way we approach analysis. Some algorithms correct and anticipate their own errors! Not even joking! Remember when we had to account for bias in sampling? Well, ML has a better solution for correcting for this automatically 5. All me to quote Varian again –

“Our goal with prediction is typically to get good out-of-sample predictions. Most of us know from experience that it is all too easy to construct a predictor that works well in-sample, but fails miserably out-of-sample. To take a trivial example, ‘n’ linearly independent regressors will fit ‘n’ observations perfectly but will usually have poor out-of-sample performance. Machine learning specialists refer to this phenomenon as the ‘overfitting problem.’ ”

To be on point, you end up having algorithms that penalize themselves 6

It will be unfair to blame undergrad Economics’ syllabus for not including ML concepts. I should note that most of these concepts are relatively new. Case in point, Varian’s paper is still a working paper (last revision a week ago as of publishing this post). ML is also mostly computer science driven. The algorithms are not written with Economic theories in mind. This should not be an excuse however because inter-disciplinary studies are not uncommon. There is also the lack of basic coding knowledge associated with most economics students. I, personally, believe any student taking econometrics and wants to go into the field should, at least, have basic coding skills but that is an argument for another day.

In hindsight, this stopped being angsty rather quickly. However, I am still disappointed I missed out on exciting new topics during my earlier economic analysis lessons. Let this be a lesson to any econometrics student. There are mind-blowing projects and ventures popping up. You should not, however, think you will stop predicting wages versus education and age. That thing haunts you everywhere. Seriously, it’s everywhere!

1. [Stack Exchange has a good discussion on the differences.]

2. [I don’t think it’s even hype anymore. You know it’s mainstream when government scandals are invited to the party!]

3. [Varian, Hal. 2014. Beyond Big Data.]

4. [Varian, Hal. 2013. Big Data: New Tricks for Econometrics.]

5. [I understand that some of these methods are already applied in certain Econometrics works. Feel free to point out other interesting projects using these methods.]

6. [One of the funniest tweets from ML Hipster. You should follow him.]

The XY Problem

The beauty of technology problems is that they are applicable in so many different fields, including daily tasks. Back in high-school, I used to deliberately leave some parts of assignments half-assed just so I would be asked to re-do them. I would always make sure these were sections that I was more more confident in my ability. It would always lead to instructors ignoring the parts I was less confident in their quality. Later on, I had my moment of clarity when I learnt about Parkinson’s law of triviality. It is a phenomenon that spreads as far as management, one among many. I cannot count the number of times I have abused this technique.

Of those problems that seem to persist in a cross-field basis, I have recently been guilty of one that tends to lurk under the radar. Let’s say an arcade owner has a problem with counting coins. He decides to employ ten people for $8/hour. He then struggles with the logistics of organizing the 10 people to finish the task in a timely manner. He goes out and asks his friend on the best procedures to organize 10 people in a factory line. He ends up with even more complicated situation after his friends mentions the fact that two five-person groups seem to work better than one ten-person group. He now has another problem on deciding on how to divide the group of ten into the best five-man packs.

Ignoring the terrible thought-out hypothetical scenario, the arcade owner is at fault for not realizing what problem he was solving in the first place. He needed to find the optimal way to count coins at the end of the work day. In the way he went to seek for help, he avoided to mention his primary problem. Alternatively, he needed to mention his coin-counting problem that led to his decision to hire ten people in the first place. His friend might even mention about the possibility of leasing a coin-counting machine, a much cheaper alternative used by all arcade owners.

Like any developer, I tend to scour online help forums. I cannot count the number of times when the first response to most questions is “What exactly are you trying to do?”. An old post from Usenet describes it as an XY-problem. In short, one wants to accomplish task X. He is not sure on the solution to X. So he comes up with a solution Y. He is not sure on the best way to implement Y. He asks for the solution to Y, assuming that by solving Y he will end up solving X. Those trying to help fail to understand why one would want to solve Y, usually because Y is a strange problem to solve. In the end, no one is usually happy.

I think it is a safe guess a good number of these questions were trying to obtain the file extension. Instead of directly referring to their main problem, they came up with a solution which assumes that all file extensions are three characters long (HINT: not true). The issue is so pervasive to deserve its own wiki with numerous examples.

In all this seemingly noob-bashing (and by extension self-bashing), I feel some of it is accidental. In the arcade owner example, he probably does not know that other arcade owners are faced with the same problem. Maybe he is the only arcade owner in the area. Maybe he is a new arcade owner without a clue on the best practices. While it is easier to blame the asker for their lack of knowledge, ignoring their position is equally unfair. It is too easy to forget the number of times we all assume our problems are unique. The main lesson should not be how to ask questions but rather most problems are not unique. Sometimes that lightbulb might just be a firefly.

The Interview

A mathematician, an accountant and an economist apply for the same job.
The interviewer calls in the mathematician and asks “What do two plus two equal?” The mathematician replies “Four.” The interviewer asks “Four, exactly?” The mathematician looks at the interviewer incredulously and says “Yes, four, exactly.”
Then the interviewer calls in the accountant and asks the same question “What do two plus two equal?” The accountant says “On average, four – give or take ten percent, but on average, four.”
Then the interviewer calls in the economist and poses the same question “What do two plus two equal?” The economist gets up, locks the door, closes the shades, sits down next to the interviewer and says, “What do you want it to equal”?

Some Sub-Saharan African Data

I think I’m due a data post. I’ve been searching for some relevant good looking data but I think this one’ll do for now. I fully do not agree going through with serious regression analysis because it will take me some precious hours (and will involve me working with STATA which I’m trying to avoid at the moment). Plus, people online like them some simple graphs.

I’ll start with inflation. The current plight of sub-Saharan Africa is everything to do with price hikes. Below is a graph comparing inflation from around 1965 to 2012.

(PS: You can click on all graphs to enlarge them)

Inflation from 1965 to 2012

Angola had some major inflation in mid-90s compared to the rest of the countries. They all seem to behave after 2000. At least until you expand the graph to explore between 2005 and 2012.

Inflation between 2005-2012

Ethiopia seems to be leading the race with around 30% inflation (this is bad!) while the East African crew (Kenya, Uganda & Tanzania) are following up close-by with inflation of between 10-20%. Notice however the slope of Ethiopia’s rise.

Let’s take a look at the gender imbalance (hopefully the lack thereof) in the respective parliaments. Coming from a parent who studies Gender issues hopefully this will get me off the hot seat.

Percentage of Women in the Parliament from 1995

In general, gender equality seems to be on the rise (at least in terms of parliamentary seats). I found it surprising that Rwanda has around 55% of it’s parliament consisting of women. That’s pretty impressive. To those who know Rwanda, this isn’t surprising considering the amount of progress they’ve achieved in the last 15 or so years.

It’s noteworthy to consider Nigeria’s position. I’m surprised they have such a high gender imbalance. It seems to have been under 10% from the beginning with no significant signs of improvement.

I also had to consider the aid flow from around 2000. The massive spike you see below is Nigeria sudden massive increase in aid around mid 2000s with its consequent and similar decline.

Aid in US$ from 2000

This next bit includes my favourite graph. It shows the relationship between external debt to gross national income. Total external debt is the sum of public, publicly guaranteed, and private nonguaranteed long-term debt, use of IMF credit, and short-term debt. Short-term debt includes all debt having an original maturity of one year or less and interest in arrears on long-term debt. GNI (formerly GNP) is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.
The graph is from 1970 to show the full picture of these countries’ journey through the debt burden.

Total Debt divided by GNI from 1970

It’s not surprising that all countries had a sudden decrease in the ratio around mid 2000s. There was a debt forgiveness agreement during G8 summit in 2005.

Finally, let’s see my least favourite graph. Internet subscribers for every 100 people would be a good way to see how far the technology spread has been so far. I chose to start from 2005 because all the countries before that had less than 0.1 subscribers per 100 people.

Internet subscribers per 100 people

While the countries involved have less than 1% of it’s population subscribed to the internet, Namibia and Botswana seem to separate themselves from the pack by showing an impressive spike. The rest seem to have somewhat of an increase but nothing deviating enough from the norm to attract attention. Except the dead last in the pack, never showing any improvement for nearly 10 years is Tanzania. No surprises there.

Fix The Machine, Not The Person


The General Motors plant in Fremont was a disaster. “Everything was a fight,” the head of the union admits. “They spent more time on grievances and on things like that than they did on producing cars. They had strikes all the time. It was just chaos constantly. … It was considered the worst workforce in the automobile industry in the United States.”

“One of the expressions was, you can buy anything you want in the GM plant in Fremont,” adds Jeffrey Liker, a professor who studied the plant. “If you want sex, if you want drugs, if you want alcohol, it’s there. During breaks, during lunch time, if you want to gamble illegally—any illegal activity was available for the asking within that plant.” Absenteeism was so bad that some mornings they didn’t have enough employees to start the assembly line; they had to go across the street and drag people out of the bar.

When management tried to punish workers, workers tried to punish them right back: scratching cars, loosening parts in hard-to-reach places, filing union grievances, sometimes even building cars unsafely. It was war.

In 1982, GM finally closed the plant. But the very next year, when Toyota was planning to start its first plant in the US, it decided to partner with GM to reopen it, hiring back the same old disastrous workers into the very same jobs. And so began the most fascinating experiment in management history.

Toyota flew this rowdy crew to Japan, to see an entirely different way of working: The Toyota Way. At Toyota, labor and management considered themselves on the same team; when workers got stuck, managers didn’t yell at them, but asked how they could help and solicited suggestions. It was a revelation. “You had union workers—grizzled old folks that had worked on the plant floor for 30 years, and they were hugging their Japanese counterparts, just absolutely in tears,” recalls their Toyota trainer. “And it might sound flowery to say 25 years later, but they had had such a powerful emotional experience of learning a new way of working, a way that people could actually work together collaboratively—as a team.”

Three months after they got back to the US and reopened the plant, everything had changed. Grievances and absenteeism fell away and workers started saying they actually enjoyed coming to work. The Fremont factory, once one of the worst in the US, had skyrocketed to become the best. The cars they made got near-perfect quality ratings. And the cost to make them had plummeted. It wasn’t the workers who were the problem; it was the system.1

An organization is not just a pile of people, it’s also a set of structures. It’s almost like a machine made of men and women. Think of an assembly line. If you just took a bunch of people and threw them in a warehouse with a bunch of car parts and a manual, it’d probably be a disaster. Instead, a careful structure has been built: car parts roll down on a conveyor belt, each worker does one step of the process, everything is carefully designed and routinized. Order out of chaos.

And when the system isn’t working, it doesn’t make sense to just yell at the people in it — any more than you’d try to fix a machine by yelling at the gears. True, sometimes you have the wrong gears and need to replace them, but more often you’re just using them in the wrong way. When there’s a problem, you shouldn’t get angry with the gears — you should fix the machine.

If you have goals in life, you’re probably going to need some sort of organization. Even if it’s an organization of just you, it’s still helpful to think of it as a kind of machine. You don’t need to do every part of the process yourself — you just need to set up the machine so that the right outcomes happen.

For example, let’s say you want to build a treehouse in the backyard. You’re great at sawing and hammering, but architecture is not your forte. You build and build, but the treehouses keep falling down. Sure, you can try to get better at architecture, develop a better design, but you can also step back, look at the machine as a whole, and decide to fire yourself as the architect. Instead, you find a friend who loves that sort of thing to design the treehouse for you and you stick to actually building it. After all, your goal was to build a treehouse whose design you like — does it really matter whether you’re the one who actually designed it?2

Or let’s say you really want to get in shape, but never remember to exercise. You can keep beating yourself up for your forgetfulness, or you can put a system in place. Maybe you have your roommate check to see that you exercise before you leave your house in the morning or you set a regular time to consistently go to the gym together. Life isn’t a high school exam; you don’t have to solve your problems on your own.

In 1967, Edward Jones and Victor Harris gathered a group of college students and asked them to judge another student’s exam (the student was a fictional character, but let’s call him Jim). The exam always had one question, asking Jim to write an essay on Fidel Castro “as if [he] were giving the opening statement in a debate.” But what sort of essay Jim was supposed to write varied: some of them required Jim to write a defense of Castro, others required Jim to write a critique of Castro, the rest left the choice up to Jim. The kids in the experiment were asked to read Jim’s essay and then were asked whether they thought Jim himself was pro- or anti-Castro.

Jones and Harris weren’t expecting any shocking results here; their goal was just to show the obvious: that people would conclude Jim was pro-Castro when he voluntarily chose write to a pro-Castro essay, but not when he was forced to by the teacher. But what they found surprised them: even when the students could easily see the question required Jim to write a pro-Castro essay, they still rated Jim as significantly more pro-Castro. It seemed hard to believe. “Perhaps some of the subjects were inattentive and did not clearly understand the context,” they suspected.

So they tried again. This time they explained the essay was written for a debate tournament, where the student had been randomly assigned to either the for or against side of the debate. They wrote it in big letters on the blackboard, just to make this perfectly clear. But again they got the same results — even more clearly this time. They still couldn’t believe it. Maybe, they figured, students thought Jim’s arguments were so compelling he must really believe them to be able to come up with them.

So they tried a third time — this time recording Jim on tape along with the experimenter giving him the arguments to use. Surely no one would think Jim came up with them on his own now. Again, the same striking results: students were persuaded Jim believed the arguments he said, even when they knew he had no choice in making them.3

This was an extreme case, but we make the same mistake all the time. We see a sloppily-parked car and we think “what a terrible driver,” not “he must have been in a real hurry.” Someone keeps bumping into you at a concert and you think “what a jerk,” not “poor guy, people must keep bumping into him.” A policeman beats up a protestor and we think “what an awful person,” not “what terrible training.” The mistake is so common that in 1977 Lee Ross decided to name it the “fundamental attribution error”: we attribute people’s behavior to their personality, not their situation.4

Our natural reaction when someone screws up is to get mad at them. This is what happened at the old GM plant: workers would make a mistake and management would yell and scream. If asked to explain the yelling, they’d probably say that since people don’t like getting yelled at, it’d teach them be more careful next time.

But this explanation doesn’t really add up. Do you think the workers liked screwing up? Do you think they enjoyed making crappy cars? Well, we don’t have to speculate: we know the very same workers, when given the chance to do good work, took pride in it and started actually enjoying their jobs.

They’re just like you, when you’re trying to exercise but failing. Would it have helped to have your friend just yell and scream at you for being such a lazy loser? Probably not — it probably would have just made you feel worse. What worked wasn’t yelling, but changing the system around you so that it was easier to do what you already wanted to do.

The same is true for other people. Chances are, they don’t want to annoy you, they don’t like screwing up. So what’s going to work isn’t yelling at them, but figuring out how to change the situation. Sometimes that means changing how you behave. Sometimes that means bringing another person into the mix. And sometimes it just means simple stuff, like changing the way things are laid out or putting up reminders.

At the old GM plant, in Fremont, workers were constantly screwing things up: “cars with engines put in backwards, cars without steering wheels or brakes. Some were so messed up they wouldn’t start, and had to be towed off the line.” Management would yell at the workers, but what could you do? Things were moving so fast. “A car a minute don’t seem like it’s moving that fast,” noted one worker, “but when you don’t get it, you’re in the hole. There’s nobody to pull you out at General Motors, so you’re going to let something go.”

At the Toyota plant, they didn’t just let things go. There was a red cord running above the assembly line, known as an andon cord, and if you ever found yourself in the hole, all you had to do was pull it, and the whole line would stop. Management would come over and ask you how they could help, if there was a way they could fix the problem. And they’d actually listen — and do it!

You saw the results all over the factory: mats and cushions for the workers to kneel on; hanging shelves traveling along with the cars, carrying parts; special tools invented specifically to solve problems the workers had identified. Those little things added up to make a big difference.

When you’re upset with someone, all you want to do is change the way they’re acting. But you can’t control what’s inside a person’s head. Yelling at them isn’t going to make them come around, it’s just going to make them more defiant, like the GM workers who keyed the cars they made.

No, you can’t force other people to change. You can, however, change just about everything else. And usually, that’s enough.

This article was part of Raw Nerve series.