Thursday, 7 September 2017

Is economics getting better? Yes. It is.

I attended two great conferences last week. The first one was an econ conference I co-organized with my friends and colleagues Dr Dejan Kovac and Dr Boris Podobnik, which featured three world-class economists from Princeton and MIT: professors Josh Angrist, Alan Krueger, and Henry Farber. The second was the annual American Political Science Association conference in San Francisco. I am full of good impressions from both conferences, but instead of talking about my experiences I will devote this post to one thing in particular that caught my attention over the past week. A common denominator, so to speak. Listening to participants present their excellent research in a wide range of fields, from economics to network theory (in the first conference), from political economy to international relations (in the second), I noticed an exciting trend of increasing usage of scientific methods in the social sciences. Methods like randomized control trials or natural experiments are slowly becoming the standard to emulate. I feel we are at the beginning of embracing the science into social science. 

The emergence of the scientific method in the social sciences is not a new thing. I've been aware of it for quite some time now (ever since my Masters at LSE to be exact). However I am happy to see that many social scientists are now realizing the importance of making causal inference in their research. Especially among the young generation. It is becoming the standard. The new normal. Seldom can a paper get published in a top journal without conditioning on some kind of randomization in its research. This is not to say there aren't problems. Many young researchers still tend to overestimate the actual randomization in their research design, but the mere fact that they are thinking in this direction is a breakthrough. Economics has entered its own causal inference revolution

It will take time before the social sciences fully embrace this revolution. We have to accept that some areas of economic research will never be able to make any causal claims. For example, a lot of research in macroeconomics is unfortunate to suffer from this problem, even though there is progress there as well. Also, the social sciences will never be as precise as physics or as useful as engineering. But in my opinion these are the wrong targets. Our goal should simply be to replicate the methods used in psychology, or even better - medicine. I feel that the economics profession is at the same stage today where medicine was about a hundred years ago, or psychology some fifty years ago. You still have a bunch of quacks using leeches and electric shocks, but more and more people are accepting the new causal inference standard in social sciences. 

The experimental ideal in social sciences

How does one make causal inferences in economics? Imagine that we have to evaluate whether or not a given policy works. In other words we want to see whether action A will cause outcome B. I've already written about mistaking correlation for causality before. The danger is in the classical cognitive illusion: we see action A preceding outcome B and we immediately tend to conclude that action A caused outcome B (when explaining this to students I use the classic examples of internet explorer usage and US murder rates, average temperatures and pirates, vaccines and autism, money and sports performance, or wine consumption and student performance). However there could be a whole number of unobserved factors that could have caused both action A and outcome B. Translating this into the field, when we use simple OLS regressions of A on B we are basically only proving correlations, and cannot say anything about the causal effect of A on B.

In order to rid ourselves of any unobservable variable that can mess up our estimates we need to impose randomization in our sample. We need to ensure there is random assignment into treated and control units, where treated units represent the group that gets or does action A, while the control unit is a (statistically) identical group that does not get or do action A. And then we observe differences in outcomes. If outcomes significantly differ across the treatment and control groups, then we can say that action A causes outcome B. Take the example from medicine. You are giving a drug (i.e. treatment) to one group, the treatment group, and you are giving the placebo to the control group. Then you observe their health outcomes to figure out whether the drug worked. The same can be done in economics. 

It is very important to have the observed units randomly assigned. Why? Because randomization implies statistical independence. When we randomly pick who will be in the treatment and who will be in the control group, we make sure that the people in each group are statistically indistinguishable one from another. The greater the number of participants, the better. Any difference in outcomes between the two similar groups should be a result of the treatment itself, nothing more and nothing less.

Positive trends

Social sciences now possess the tools to do precisely these kinds of experimental tests. On one hand you can run actual randomized control trials (i.e. field experiments) where you actually assign a policy to one group of people and not to the next (e.g. health insurance coverage) and then observe how people react to it, and how it affects their outcomes (whatever we want to observe, their health, their income, etc.). A similar experiment is being conducted to examine the effectiveness of basic income to lower unemployment and inequality. There are plenty other examples. 

In addition to field experiments, we can do natural experiments. You do this when you're not running the experiment yourself, but when you have good data that allows you to exploit some kind of random assignment of participants into treatment and control units (in the next post I'll describe the basic idea behind regression discontinuity design and how it can be used to answer the question of whether grades cause higher salaries). Whatever method you use, you need to justify why the assignment into treatment and control units was random, or at least as-if random (more on that next time). 

As I said in the intro, this is becoming the new standard. Long gone is the time where you can do theory and when empirical work was limited to kitchen-sink regressions (throw in as many variables as you can). Have a look at the following figure from Bloomberg (data taken from a recent JEL paper by Daniel Hamermesh): 

The trend is clear. There is a rapid decline of papers doing pure theory (from 57% at its 1980s peak, to 19% today), a huge increase in empirical papers using their own data (from 2.4% to 34% today), and an even bigger increase of papers doing experiments (from 0.8% to 8.2%). This positive trend will only pick up over time. 

Here's another graph from the Economist that paints a similar picture. This one disentangles the empirical part in greater detail. There is a big jump in the usage of quasi-experimental methods (or natural experiments) such as regression discontinuity and difference-in-difference methods ever since the late 90s. The attractiveness and usefulness of DSGE models has also seen a jump in DSGE papers in the same period, but this trend has slightly declined in the recent decade. Even more encouraging, since the 2000s randomized control trials have picked up, and in the last few years there has even been a jump in big data and machine learning papers in the field of economics. That alone is fascinating enough. 

There are still problems however. The critics of such approaches begin to worry about the trickling down of the type of questions economists are starting to ask. Instead of the big macro issues such as 'what causes crises', we are focusing on a narrow policy within a subset of the population. There have been debates questioning the external validity of every single randomized control trial, which is a legitimate concern. If a given policy worked on one group of people in one area at one point in time, why would we expect it to work in an institutionally, historically or culturally completely different environment? Natural experiments are criticized in the same way, even when randomization is fully justified. Furthermore, due to an ever-increasing pressure to publish many young academics tend to overemphasize the importance of their findings or tend to overestimate their causal inference. There are still a lot of caveats that need to be held in mind while reading even the best randomized trials or natural experiments. This doesn't mean I wish to undermine anyone's efforts, far from it. Every single one of these "new methods" papers is a huge improvement over the multivariate regressions of the old, and a breath of fresh air from the mostly useless theoretical papers enslaved by their own rigid assumptions. Learning how to think like a real scientist is itself a steep learning curve. It will take time for all of us to design even better experiments and even better identification strategies to get us to the level of modern medicine or psychology. But we're getting there, that's for sure! 

P.S. For those who want more. Coincidentally the two keynotes we had at our conference, Angrist and Krueger, wrote a great book chapter back in 1999 talking about all the available empirical strategies in labour economics. They set the standards for the profession by emphasizing the importance of identification strategies for causal relationships. I encourage you to read their chapter. It's long but it's very good. Also, you can find this handout from Esther Duflo particularly helpful. She teaches methods at Harvard and MIT, and she is one of the heroes of the causal inference revolution (here is another one of her handouts on how to do field experiments; see all of her work here). Finally, if you really want to dig deep in the subject there is no better textbook on the market than Angrist and Pischke's Mostly Harmless Econometrics. Except maybe their newer and less technical book, Mastering Metrics. As a layman, I would start with Metrics, and then move to Mostly Harmless. Also to recommend (these are the last ones), Thad Dunning's Natural Experiments in the Social Sciences, and Gerber and Green's Field Experiments. A bit more technical, but great for graduate students and beyond.
P.P.S. This might come as a complete surprise given the topic of this post, but I will be teaching causal inference to PhD students next semester at Oxford. Drop by if you're around.

Thursday, 23 February 2017

Vote buying with intergovernmental grants (my paper published in Public Choice)

When I started working in the academia a few years back, my friend and co-author Josip Glaurdić asked me which journal would I like to be published in the most? Without hesitation I said: Public Choice

Well, that goal has now been accomplished. I have a publication in one of my all time favorite political economy journals! You can read the paper on this link, it's been published online first. Next big goal: Quarterly Journal of Economics (I will also accept American Economic Review, Journal of Political Economy or American Political Science Review). 

Our paper is on the political bias in the allocation of intergovernmental grants in Croatia. Here's the abstract: 
"Instead of alleviating fiscal inequalities, intergovernmental grants are often used to fulfill the grantors’ political goals. This study uses a unique panel dataset on more than 500 Croatian municipalities over a 12-year period to uncover the extent to which grant distribution is biased owing to grantors’ electoral concerns. Instead of the default fixed effects approach to modelling panel data, we apply a novel within-between specification aimed at uncovering the contextual source of variation, focusing on the effects of electoral concerns on grant allocation within and between municipalities. We find evidence of a substantial political bias in grant allocations both within and between municipalities, particularly when it comes to local-level electoral concerns. The paper offers researchers a new perspective when tackling the issue of politically biased grant allocation using panel data, particularly when they wish to uncover the simultaneous impact of time-variant and time-invariant factors, or when they cannot apply a quasi-experimental approach because of specific institutional contexts."
Basically, we have taken a new spin on a well-researched topic in the field of political economy: does central government allocate local government grants based on selective political criteria? There is a multitude of papers on this for various countries (just check out our references), with the overreaching conclusion being: yes, there is a political bias in the allocation of intergovernmental grants (intergovernmental meaning the flow of funds from the central to the local government). It happens for two main reasons: 1) central government helps its local co-partisans (mayors from the same party as the national government) retain office by giving them more money to buy votes in local election years, and 2) the central government helps itself (increases its own chances of re-election) by giving more money to important districts in national election years. An important district can be either a swing district, where voters often switch from one party to the other, or a core district, where voters always vote for the same party. The literature has found evidence of both. We find that money mostly goes to core districts. Politicians thus want to get as many votes as possible in districts where they are already strong. 

So what makes our paper special? The standard literature approach was mainly to uncover the within unit variation of grant allocation over time. This means that they wanted to see which factors' changes over time affect how much money does a local unit of government get. When uncovering the effect this way the literature usually discards any between-unit variation, i.e. it cannot make any inferences between local units. To clarify here is a sentence from the paper: "For example, finding that larger vote shares for the government within counties result in more allocated grants over time—clearly a within effect—often is misinterpreted as the between effect and generalized into a cross-sectional conclusion that counties received more grants because the government garnered a larger share of the votes in a previous election."

A few clarifications before moving on: A panel dataset means having observations on multiple units over time. This is opposed to a cross-section where you just have observations on multiple units in one fixed time period. Having panel data is great because it allows you to eliminate any changes across units that stay fixed over time (like gender, geography, demographics, or any slow-changing variable like institutions), and focus only on estimating the effect of the changing independent variables on your outcome of interest. It is a very neat way of making correct inferences in the social sciences. 

What we wanted to do is to use our panel dataset to explore the variation both within and between our units of interest. So not only the standard within effect in a municipality over time, but also the cross-sectional effect of the differences between units to see which non-changing factors also could affect our outcome. In our own words:
"We test how the effects of political considerations on grant allocation change over time within each entity and how they vary across them. The within-between approach thus allows for the inclusion of potentially influential time-invariant variables, which the fixed effects approach eliminates, as a separate between-entity effect, in addition to keeping all the benefits of the fixed effects estimation. Disentangling the within- and between-entity effects is important as it not only provides a more substantive interpretation, but also enables the researcher to correctly identify the source of variation by not confusing which of the two effects is driving the estimated relationship. By utilizing this particular approach our goal is to offer researchers a new perspective on tackling the issue of grant allocation when one wishes to test for the simultaneous impact of time-invariant and time-variant variables, and when a quasi-experimental setting is unfeasible owing to specific circumstances of the observed political system."
The within-between approach is a new method referenced to a great paper by Bell and Jones (2015)


What do we find? As I've said before, there is a clear conclusion that there is a significant political bias in the allocation of intergovernmental grants. The national government favors municipalities that support them in the national elections, and those that were won over by their co-partisan mayors. They give more money during election years (both national and local), and they support core municipalities rather than swing municipalities. 

The within-between approach was most helpful in examining the interaction effect of votes for government and turnout. This is best seen on the figures below:

In our own words: 
" Fig. 1 it is obvious that higher national turnout is conditioning only the within-municipality changes in grants in a positive way, whereas the between effect goes in completely the opposite direction (and also is insignificant). In other words, the government rewards only those municipalities wherein they gain support through higher voter turnout rates across time. 
In Fig. 2, representing local level estimates, the conditionality of turnout on a between-municipality level is shown to be crucial for concluding that mayors who win on higher voter turnouts are likely to receive larger grants. The within effect plays no role here, so the conclusion regarding the effect of mayoral alignment and turnout on grant allocation is valid only on a between-municipality level. In other words, aligned mayors who win their posts with high voter turnout rates do not get more intergovernmental grants (they do get more such funds, but not conditioned on turnout), while aligned mayors already holding power do get more money if they can increase voter turnout. Both findings make sense, since winning over a new municipality is good for the national party regardless of turnout, while for existing incumbents establishing their dominance with even more support is likely to be rewarded. None of these conclusions would have been possible without the use of the WB approach."

Tuesday, 31 January 2017

This Trumpian neomercantilism is ridiculous!

Protectionism never helped anyone. Particularly among the developed nations. I have yet to encounter a case of a rich country becoming even richer after imposing tariffs and trade restrictions. Even when looking at firm-level data over the long run, protectionism never helped. In many cases it arguably made them even less efficient (I provide a real-life example below). The notion that tariffs (taxes on imports) and quotas (limits on import quantities) are in general bad for the economy that imposes them could even be called a stylized fact of the profession. And it is one of those rare 'facts' a vast majority of economists would agree with; even those who like to emphasize that free trade has both winners and losers, and even those who cite the successes of South Korea or China in using state protectionism of infant industries to gain a competitive advantage abroad (although there are a lot more factors explaining their success - plus I have yet to see a good piece of research defending this argument). 

So why then, if the experts are practically unanimous, are calls for protectionism so attractive and can become so politically salient? One reason is because people don't trust experts anymore, but even when they did, they still had a misunderstanding of trade. Trade is just one of those topics everyone seems to have an opinion on, usually the wrong one. I've written before on the ills of the so-called mercantilist fallacy. This fallacy usually attracts anyone who suffers from a zero-sum game mentality. Your gain must imply my loss. If we trade with China and have a trade deficit (we import more than we export), we're "losing to China". This is the same variant of the classic saying that "exports are good while imports are bad". If I export then I get money, if I import I lose money. 

Let me emphasize just how ridiculous this argument is. Saying that imports are bad and exports are good is like saying that selling is good (cause we get money when we sell something) while buying is bad (cause we lose money when we buy something). Far from it! Both transactions are good, because when you buy/import you do it either to resell it at a higher price or consume it. If the transaction is voluntary it is by definition beneficial, both for the seller and the buyer, regardless if the seller/buyer is a foreigner. 

Also, governments, i.e. countries do not import nor export. Companies do. They sell (export) and buy (import) on the international market. In fact, the determinant of the demand for imports comes directly from the consumers themselves. Or companies buying intermediary products that are cheaper abroad. If we as customers have a greater benefit from consuming foreign rather than domestic goods, then there will be a company that will offer them to us. It will import foreign goods knowing someone back home will buy them. We as consumers therefore determine the demand for imported goods. Whether it's clothes or food, that almost any country can produce on its own, or cars and IT goods that most countries cannot.

How the import tariff affects US consumers

So how does all this link to the new US President? Well, it's got to do with the most recent set of ideas on trade policy coming from the experts in the Trump administration (btw, should we trust these experts over all the others? I guess we should, they do work for the President, right?).

Take for instance their idea for imposing a tariff of 20% on all imports coming from Mexico. Guess who will pay the ultimate price of that 20% tariff? Yes, you've guessed it - US consumers! How? Let me explain it in very simple, Trumpian terms.

I am a distribution company (let's call me 'the Middleman') which sells electric equipment (let's call it 'Stuff') all across the US. I don't make them myself, I just sell them. So when I buy the Stuff I want to sell, my main motivation for purchase will be a good (i.e. low) price. I buy most of the Stuff from Mexico, from a firm called Mexico Stuff Manufacturer (MSM) and then sell it to local shops across the country. MSM gives me a good quality product and at a lower cost than if I were to buy domestically.

Now the tariff is implemented at 20% on all imports from Mexico. If I want to buy the Stuff from MSM again I have to pay 20% more. That's not very good news for me given that this would eat up almost my entire profit margin. In other words if I buy the Stuff at a higher price I have to increase my selling price to the shops to stay in business. 

Or, if I don't want to do that I can always find a new supplier, perhaps someone in the US - call it US Stuff Manufacturer (USSM). The thing is, the reason I didn't go there in the first place was because USSM was charging me more than MSM for the same quality Stuff. Now that their prices are, let's assume, equal, I am basically at a standstill since whoever I buy from I still have to charge a higher price to the shops. So I decide to stick with the devil/supplier I know. In each case, whatever I choose to do, my prices will have to go up. 

So I go to the shops and sell them the Stuff at a 20% higher price. What do they do? They push that same price increase on the final consumer and charge them the extra 20% they had to pay me. They're in the same business I'm in - they buy the Stuff from me, and resell it at a higher price to the final consumer. 

But why would the shops pay the higher price? Why would they be the price-taker in this case? Because they are in the exact same position I'm in - they have no choice. In either case, if they buy from me or if they decide to switch and get the Stuff from USSM directly they still need to pay a higher price than before - a price that will always be shifted to the final consumer. The example holds even if the price of Mexican Stuff is now higher than the price of US Stuff, because the price at which we buy the US Stuff for will still be higher than the old pre-tariff price of Mexican Stuff. 

This is why a tariff on imports has the equivalence of a tax on domestic consumers buying foreign goods. This might sound like an attractive way to nudge consumers towards buying more stuff produced domestically, but we're talking about individual preferences here. If I like a foreign car, if I think it's more fuel efficient, I will buy a foreign car, regardless of what my government wants me to do. I would hate to have the government limiting my free choice and telling me what to buy! (Wasn't this the biggest issue some Americans had with Obamacare?)

What if the goods being traded are perfect substitutes? 

In other words what if I can easily switch between domestic and foreign brands, so that by imposing a higher price on Mexican Stuff, consumers will just switch to US Stuff as it will now be more price competitive? In theory yes, in reality - no. Why? Just look at the composition and current prices of the goods the US imports from Mexico

Source: CNN Money
Can the US produce all this stuff? Sure it can. In fact, it does, and it exports the same stuff to Mexico (see for yourself). Why is there then a demand for these products to come from Mexico? Price competitiveness due to lower wage costs in Mexico could be only one reason (the example above explained how that works). Another very important one are individual preferences. 

Of the top of my head I can remember a very similar protectionist policy applied by the US back in the 1980s against Japanese imported cars. There was a voluntary export restriction imposed by the US government in 1981 limiting the number of Japanese cars to be imported in the States to 1,68 million per year. It was later raised to 1.85 million and to 2.3 million by 1985. It was finally lifted in 1994 (read more here or here). What happened? The policy directly lowered the supply of Japanese cars on the market. With demand remaining high what was the effect? Prices went up. US consumers did not stop buying Japanese cars despite their higher price. They were simply better than US cars. More fuel efficient to be exact. Who profited from this policy? Only one group: Japanese car companies. That's right, the end effect of a protectionist policy aimed to protect the US car industry made Japanese car companies richer. (This is, mind you, an example from the classical textbook by Krugman and Obsfeld on International trade)

Finally, I don't see why Mexico is complaining. Or China. A 20% tax on imports from Mexico and an alleged 40% tax on imports from China is only going to benefit the companies in these countries. Sure, they might sell lower quantities of their products, but they will more than compensate this with higher prices. 

Who will pay the price for this? US consumers. Protectionism is a tax on them. So when Trump says he will force Mexico to pay for 'the Wall' by imposing a tariff on their imports, I hope these examples helped illustrate what this means - it means that US consumers will ultimately pay for the Wall through a tax they won't even realize hit them. 

Tuesday, 27 December 2016

2016: a bad year for predictions

Talk about Black Swans, 2016 was full of them! From elections to markets, from hacking to terrorist attacks, it was one unexpected event after another. Each a complete shocker in its own way. Especially in sports and politics. Portugal winning the Euro football tournament, Leicester winning the Premier League, Britain coming in second at the Olympic game medal count, or the Chicago Cubs winning the World Series were as big as Black Swans as Brexit or Trump. 

It goes without saying that a year of Black Swans was a terrible year for forecasters. Even the biggest names of the 'industry' have stumbled and failed to predict the biggest disruptive events of the year: Brexit and Trump. Not my company. We got Trump spot on. Just to remind my readers, we called 47 states including the most important swing states like PA, FL, NC, and OH for Trump. Our unique prediction method, that was further perfected since Brexit, has hit bull's-eye!
Our almost perfect prediction for Trump
Oraclum Intelligence Systems
I cannot say the same for myself however. I usually make my prediction at the beginning of each year. So far I boasted some big hits like the UK general election of 2015, the success of anti-establishment parties in the EU 2014 elections, the Scottish referendum, oil prices, interest rates, year-on-year economic growth projections, and even Germany as the winner of the 2014 World Cup

But this year it's been quite a few misses for my beginning-of-the-year predictions. The very title of my January 1st blog signifies the extent of the miss - "women in charge". I predicted that by the end of the year Hillary Clinton is expected to join Angela Merkel, Janet Yellen, and Christine Lagarde (and Irina Bokova as the UN secretary general) to have five out of ten most powerful political positions be held by women. That was a big miss. Hillary lost, Bokova lost, Yellen will most likely be replaced by Donald Trump, and Merkel is facing a tough election next year (although she will probably hold on). The woman I did not see coming was Theresa May, the new UK PM. Even if I had predicted Brexit back then I would have said that Cameron would have been replaced by Boris Johnson, not Theresa May. Again, a true Black Swan. 

Brexit was another big miss. I was categorical in saying that Britain won't leave the EU. I wasn't even sure the referendum would be held this year (this wasn't decided until February, as Cameron wanted to move quickly to capitalize on his general election victory). I had a bunch of rational explanations on why the Brits will not vote Leave. All of which apparently biased by my liberal worldview. I wrote a comment on this after the event, making a couple of other bold predictions on the way. I just can't get enough of predictions, apparently.  

In the US not only was I very bullish on Hillary, I didn't even predict Sanders to give her a run for her money. I did give Trump the biggest probability to win the Republican presidential nomination (I had Rubio second), but I still gave Hillary 55% to clinch the Presidency in November. Interestingly enough I didn't change my mind on Hillary's chances until the last few days of the campaign when I saw our model estimating a Trump victory. It was a shocker, but we did get it right. The lesson was to trust my data, not my guts. 

The second lesson from this was that with election forecasting I should wait for the last few weeks before the elections to figure out how the voters feel. After all I now possess a powerful method to do just that, so I will refrain from making any more election predictions a year in advance. Plus, I'll rather sell this info to our clients rather than boasting on my blog. 

Oh, and I also missed my sports predictions. I said that either Germany or Belgium will win the Euro, but in the end it was a final between Portugal and France, won by - surprise, surprise - Portugal. For the Olympics I was right that the US will win the most medals but I never even dreamed that the UK will come in second. In front of China! Now that was a surprise. 

The hits

It wasn't all misses. I had some good wins. Such as the economy, which unlike politics was rather predictable last year. Ireland was, as predicted, the best performer of the year in the EU, while Greece was the worst. The developed world grew more robustly, although the recovery is still slow, particularly in Europe. The US continued a steady growth trajectory and unemployment fell below 5%. The Fed raised interest rates only slightly in December, while other central banks (ECB, BoE) went for the opposite following the massive political uncertainty in Europe.

Oil prices did not go above $60, China did not go into recession as many were screaming early this year, and Putin came out of the year stronger than ever. Japan is still stagnating, and India overtook China as the fastest growing economy. All of these were good predictions, the kind that were slightly easier to make. 

Oh and here's one big hit - I predicted no terrorist attacks during the Olympics in Brazil or the Euro in France. This was a bold prediction but I was confident nothing would happen given the level of security usually associated with these events. Terrorists will not get away with it if everyone is paying extra careful attention to spot them out. 

Interestingly, with all the Black Swans that happened this year none of them went 'under the radar'. In other words Brexit or Trump had a realistic chance of happening, even if many estimated those chances to be low. Leicester or the Cubs on the other hand - those were the true under-the-radar Black Swans.

Anyway, if you think this year was hard to predict, think of how difficult the next one will be. No one has an idea of what a Trump presidency will look like. No one has an idea how Brexit will turn out (I'm sure Britain will Leave, the question is under which circumstances). Politically it could be another shocker with elections coming up in Germany, France, and the Netherlands. The last two could bring leaders that could spell and end to the EU itself. What about Syria and the EU refugee problem? Will Putin and Trump solve these issues? What about the potential US trade war with China? We're in for quite a ride! 

Tuesday, 29 November 2016

Is technological progress at the heart of stagnation?

In the previous text I presented several economic hypotheses explaining why the developed world has entered what could possibly be a prolonged period of economic stagnation. In today's text (note: long read) I will present my own opinion, arguing that what we are experiencing is a temporary slowdown which could last for several decades, but one that could also provide the greatest opportunity for the next huge boost in living standards. I hypothesize that the underlying factor behind both the current temporary stagnation (particularly in productivity and real wages) and the upcoming rise in living standards is - technology

As I've emphasized several times on the blog before, I believe we are currently, for the past 30 years, in the period of the Third Industrial Revolution. And in our times, it's only heating up, with the potential to bring to some new disruptive innovations that could change our world as much as the previous two industrial revolutions had. The technological progress we are currently undergoing will, without doubt, be disruptive. As it always is. But in its disruptive nature it will bring greater benefits for the future generations. Automated work being replaced by robots will surely lead to job losses. But in a new economy, the job losses could be offset by a series of new opportunities and entirely new careers. Which ones in particular, we can't really tell at this moment. But just like the first two industrial revolutions brought completely new jobs and changed the world as we know it (more than 95% of jobs that exist today didn't exist before the 18th century), so will the Third bring in new jobs and new possibilities we can't even imagine. Social networks have already introduced new types of occupations (social network experts being the most dubious one). Various bloggers and youtubers have managed to turn their hobby into a money making venture. Firms are just beginning to exploit the Internet, and its users are just uncovering the various ways they can make money on it. None of this was even conceivable back in the 1990s when our remote Internet usage was often wrapped around in frustration with our dial-up connection (remind yourself of that glorious sound, I know you want to!). Today so much more opportunities await. 

The good thing about this inevitable change is that it tends to be gradual. This meaning that even if robots and automated work start replacing low-skilled jobs, this will all still happen during a prolonged period where it will be possible to maintain a generational switch.

What does this mean in particular? Let's take the example of the taxi market and the driverless car (or if you want - Uber, which is the intermediate step). Naturally if driverless cars all start hitting the streets they will almost immediately take away all the jobs from the taxi drivers. Which is likely to cause them to rebel, quite legitimately so. One always has an incentive to protect their job and their immediate interest. A good way to achieve a peaceful transition would be to allow the technological breakthrough to enter gradually by having the taxi drivers operating and overseeing the driverless cars at first. This would, on one hand, correspond to a permanent barrier to entry for any new driver, however all the current drivers would keep their jobs. Until retirement or until they find another job, whichever comes first. Each current driver would therefore still be driving/riding the driverless car and providing for example local advice to tourists. This would then be a perfect way to tell whether or not the passengers really enjoy the conversation and demand for the actual person to be in the car, or do they just prefer the robot to take them where they need to go without speaking to it. It's all about having choices! And in this way to minimize the despair of potential job losses imposed by the new technology. But I digress. 

The current slowdown is essentially of temporary effect as we're currently in a transitional period from the old industry-driven economy (including the service industry) to the new digitally-driven economy. The industry-driven economy still rests upon the old industrial classification paradigm: the primary sector (agriculture, fishing, and mining), the secondary sector (manufacturing, production), and the tertiary sector (services). So far in the history of the West we have witnessed the transition from an agricultural-dominant economy to a manufacturing-driven economy (the First Industrial Revolution in the 18th century), a shift from manufacturing to rapid industrialization in the 19th and beginning of the 20th century (the Second Industrial Revolution driven by mass innovation), and a shift from industrialization to services in the final part of the 20th century following a period of rapid globalization.

Now we are facing something different - a shift beyond the standard paradigm. Disruptive technological progress will rapidly change our patterns of production and of specialization. It will be nothing like the world we knew so far. Just like the first two industrial revolutions brought us to a state of the economy not known to us before. In the 16th and 17th century having a locomotion and machines was unimaginable. In the 18th century having electricity, cars, airplanes, and modern medicine was unimaginable. After WWI having computers and traveling to space was unimaginable. 40 years ago a cell phone was unimaginable, while a mere 15 years ago a smartphone was unimaginable. True, there were always visionaries who offered their overly enthusiastic views of the future by simply extrapolating the current levels of technological progress. In the 1960s visions of the future included flying cars, intergalactic travel, jet-packs, and personal robots, all by the year of 2000 (check out some of the futurist visions from that time - some of them actually did come to existence; also read this piece to see which predictions came true).

How the Internet has changed things - for the better

Why don't many people see this obvious advantage of the technological progress so far? A famous quotation from Nobel prize winner Robert Sollow: "You can see the computer age everywhere except in the productivity statistics" is actually true. There is a productivity paradox where the advances in computing power haven't really made workers more productive. This is contrary to the idea that automation of work should increase total factor productivity. Essentially the idea is that despite all the benefits the Internet has brought to us (instant global communication, entirely new business and marketing models, different consumer behavior patterns, social networking, even spontaneous mass gatherings), it has made only marginal improvements in well-being, at least compared to the non-internet age of the 1980s. The technology skeptics cite similar examples where modern technologies only offered marginal improvements over the products we enjoy today. For example, whereas the first invention of the car was a huge advantage over a horse, its further improvements, after reaching a certain level of speed and safety, were marginal. Airplanes are a similar example. Yes, today they tend to be much safer, but flight times are still similar to what they were 50 years ago. The smartphone was an improvement over the regular cell phone, but not as much as the cell phone was an improvement over the landline, and both not as much as the regular landline was an improvement over letters and telegrams.

However all these examples are missing the point. We are still at the very early stages of the Third Industrial Revolution - the Digital Revolution. We are slowly entering the Information age. The Internet has made much bigger changes than standard economic indicators would suggest. Particularly since most of them were indirect. The Internet has, without doubt, changed the patterns of firm specialization and has increased the rate of trial and error as well as innovation. The vast availability of information online can improve business strategies and force businesses to adapt to the Internet revolution. Those that don't, lose customers. No matter what industry they are in. In the upcoming decades this will become even more obvious. Furthermore, the Internet has had a key role in promoting economic opportunity for all, particularly for the underprivileged. To start a business all you need is a laptop and an internet connection (of course, this varies from country to country depending on the scope of regulations). Most importantly the Internet and the increasing socialization it has brought with it can be used to foster democracy and the empowerment of the middle classes. That is one of its, by far, biggest advantages. Social networks and the Internet can do more in overthrowing dictators and holding politicians accountable in democracies than the media ever could. (Obviously they can also be used in an opposite capacity - by distributing fake news and encouraging bubble behavior; but to be fair, fake news and living in bubbles happened way before the Internet). 

For all these reasons, in the upcoming Information Age, the Internet should be free for all - a global public good. Free access to the Internet should be a human right (the UN has done a lot in promoting this idea, there is even an initiative to implement the Internet as a basic human right, but some disagreements still remain). Nevertheless, its creation of economic opportunity, its role in fostering democracy by empowering the middle classes, its ease of access to education (online courses can do wonders!) are more than enough to declare it a human right and offer it as a global public good. This is something that the future might hold for us - free Internet, worldwide. (Although, don't be so sure on the "free" part. We have access to electricity and clean water, but both still come at a cost).

Learning from Japan

Even with all those advantages at hand, we have currently reached a ceiling with our pre-IT revolution models of economic growth. Japan is perhaps the best example. A huge booming economy for 30 years following the recovery after WWII, it was hit by a housing bubble burst in 1990 and has experienced very low to zero economic growth ever since. In the past 26 years Japan grew, on average, by 0.7%. Its lost decade of the 90s has turned into two lost decades and is now in the middle of a third. Its public debt is the largest in the world, and by a long shot (public debt to GDP is 230%, higher than even Greece, with debt to GDP at 170%). For the entire 26-year period inflation has been close to 0, borderline deflation, as have their interest rates. Needless to say Japan has done a series of monetary and fiscal stimuli to prompt up their economy throughout this period, but nothing has worked. The consequences of both are highly visible in their over-expanded monetary base and huge debt.

But in reality, there is nothing wrong with Japan. Yes its economic indicators are terrible, yes the population is ageing which is always a problem in countries with high debt levels, but Japan remains one of the richest countries in the world. Their GDP per capita (PPP) is around $38,000 which is quite a lot for a country with 127 million people. The only comparable in population size is the US with a GDP per capita (PPP) of $55,000. But beyond GDP, Japan ranks highest in many of the measures of living standards and well-being. By life expectancy they are no.1 in the world (84 years on average!), their human development index, prosperity index, happiness index all put them among the top performers. Their health care and education system are flourishing, they have low crime rates, and decreasing inequality. One conventional economic indicator - unemployment - always managed to stay low, at around 4%. And their levels of innovation and technological adaptation are arguably among the world's highest if not the highest. The vast majority of the population is therefore enjoying really good living standards. It seems that low GDP growth, ongoing deflation, and high public debt (as long as it is held by the domestic population in a country stable and rich enough to have a huge demand for its debt, particularly domestic in this case), don't really hamper living standards. The two and a half decades of stagnation have not apparently taken their toll on well-being.

So what's the story here? Japan has simply reached a level of very high living standards combined with a strive for technological innovation that has perhaps even worsened some productivity numbers and possibly GDP growth as well (although there are a number of reasons why GDP growth was low in Japan). The IT revolution took huge proportions in Japan. Anyone who's ever been there speaks of its technological superiority and a number of "cool gadgets". Few of these gadgets have raised GDP, but they have contributed to the well-being of the population, and more importantly, they have opened vast new opportunities for its population. It takes time for these to be seized in order to produce a high magnitude well-being effect.

The Third Industrial Revolution IS at the heart of the current stagnation...

The point is that we are currently experiencing a stagnation caused by a series of factors, one of which is certainly the technological revolution. I've written on that before on multiple occasions. Essentially, technological progress began by shifting jobs and changing the patterns of specialization and production. This will go on for several more decades. But the further it unfolds, the more benefits it will bring that will be immediately noticeable to us. In other words, think of the current stagnation in wages and productivity (and hence economic growth) as an indirect consequence of the upcoming and ongoing technological progress. It takes time for the people to recognize the new patterns of specialization and to exploit the opportunities for new jobs. With the start of the IT revolution in the 80s we've already noticed a series of new jobs being created. It all started with companies like IBM, Dell, HP, and Apple to provide the hardware. Then came Microsoft and revolutionized the software (Apple made it even cooler later on in the second coming of Steve Jobs). Then the Internet giants started emerging: Google, Facebook, Amazon, YouTube, eBay and many others. (The criticism these companies get is that while they replaced many old jobs in manufacturing, it wasn't actually a one-for-one replacement. They created much less jobs than what manufacturing companies created.)

All these new companies emerged in the very beginning of the IT revolution (some even before like IBM). Expect many more of these to come in the following decades. Don't necessarily expect new search engines, software manufactures, online retailers, or social networks. No, the new high tech companies will be about something completely different. They will most likely strive on the benefits provided by all these companies before them (we all have laptops, running on either Windows or Mac, and we all use Google, Facebook, YouTube, etc.).

...but it will also set the stage for the next big boost in living standards

The current level of technological development set the stage for the Next Big Thing. They didn't replace all the lost jobs, and were on that front mostly disruptive innovations. For now. However the foundations have been laid. These foundations are supported by the current (temporary) industry giants. And most importantly they provide the nurturing environment for growth, for new companies that one day will be even greater and will perhaps not only change the jobs market, they could profoundly change our way of life (e.g. robot manufacturing companies, or nanotech companies, or AI producers, or fusion energy companies - a bit too much? Or is it?). In 50 or 100 years from now we may look down on manual labor and automated work as relics of the past. And no one will complain as everyone will find such jobs meaningless and will have the time to pursue their most desired careers. It might sound a bit idealistic from today's point of view but who knows. Cars, trains, and airplanes sounded idealistic back when the First Industrial Revolution was underway, but today we know of no better form of transportation. At this point, we can only imagine.

Now, all this is just a theory. I can devise a multitude of examples and arguments in support of it, but I cannot really test it. Yet. Time will tell basically whether or not this thinking has any merit at all. I do however carry a slightly optimistic bias in believing that we live in awesome times and are on the verge of a breakthrough that most of us simply aren't aware of. My optimistic bias makes me a bit subjective towards the impact of technological progress on future living standards, but drawing simply from historical patterns and the possibilities being uncovered to us, the IT revolution is nothing to be feared.