Thursday, September 29, 2016

Larry Summers on the decline of the middle class

Summers at the WaPo - decline of the middle class is causing economic damage. Short quote:

I have just come across an International Monetary Fund working paper on income polarization in the United States that makes an important contribution to the secular stagnation debate. The authors — Ali Alichi, Kory Kantenga and Juan Solé — use standard econometric techniques to estimate the impact of declines in middle class incomes on total consumer spending. They find that polarization has reduced consumer spending by more than 3 percent or about $400 billion annually. If these findings stand up to scrutiny, they deserve to have a policy impact.

This level of reduction in spending is huge. For example, it exceeds by a significant margin the impact in any year of the Obama stimulus program. Alone it would be enough to account for a significant reduction in neutral real interest rates. If consumers were spending 3 percent more, there would be scope to maintain full employment at interest rates much closer to normal. And there would be much less of a problem of monetary policy’s inability to respond to the next recession.

Well, the gig is up. Here I thought a childishly easy Ph.D. thesis would be the quantification of exactly how much right-wing government policy is responsible for secstag: now everyone's writing about it.

Oh well. Maybe I can use this for my undergrad Political Economy essay this winter.

Friday, September 23, 2016

Lars Syll on the political economy of inequality

Lars Syll - being especially grumpy today. Here's a good quote:

No one ought to doubt that the idea that capitalism is an expression of impartial market forces of supply and demand, bears but little resemblance to actual reality. Wealth and income distribution, both individual and functional, in a market society is to an overwhelmingly high degree influenced by institutionalized political and economic norms and power relations, things that have relatively little to do with marginal productivity in complete and profit-maximizing competitive market models [....]

In other words... economic rent?

Thursday, September 22, 2016

Dietrich Vollrath says "this isn't the service sector productivity you were looking for...."

Dietrich Vollrath - service sector productivity, Roy, Baumol, etc.

This is just so cool:

Alwyn Young published a paper in 2014 on the “cost disease of services” and productivity growth. In short, the cost disease of services is an idea from William Baumol, and it is intended to explain why productivity growth tends to slow down during a structural transformation from manufacturing to services. The idea is that services have low inherent productivity growth compared to manufacturing, mainly because services are often provided in fixed units of time (you can’t get a one-hour massage in less than one hour). Combine that with the fact that services are highly income elastic, meaning we demand more and more of them as we get richer, and labor moves into services over time. Hence the average growth rate of productivity falls because everyone is now working in this low-growth sector. It is certainly one of my leading candidates for an explanation of why productivity growth is relatively low in the last 15-20 years compared to prior periods of time.

Young’s paper questions whether in fact services really do have slower productivity growth than manufacturing. His argument is going to be that we may be falsely under-stating productivity growth in services because we are not accounting for the fact that the labor flowing into services is, compared to existing service workers, relatively bad at doing service work. What looks like low productivity growth is in fact low growth (or negative growth) in average human capital.

At the same time, productivity growth in manufacturing is over-stated because the workers that remains behind in manufacturing as people leave are only the very best workers, and hence the average ability of manufacturing workers (to do manufacturing work) is going up over time. What looks like high productivity growth is in fact high growth in average human capital.

The idea comes from the Roy model (1951), a classic economics paper about the distribution in earnings. At it’s heart, the Roy model is about self-selection. Imagine that each person has some built-in ability to work in manufacturing, and some built-in ability to work in services. There may be some elements of these abilities that are correlated (maybe you are really smart and could figure out how to do either efficiently), but regardless you’ll have a comparative advantage in one of them. Yes, the same idea of comparative advantage as in trade. You may be good at both activities, but relatively speaking you’ll be better at one or the other when I compare you to someone else.

Young works out an exact case of the Roy model where your comparative advantage is positively correlated to your absolute advantage. That is, people who are relatively good at manufacturing are also absolutely good at manufacturing. Let me be specific, because I know I always found these concepts hard to keep straight in my head. My daughter once took a few hockey lessons, but now has been swimming her whole life. She has a comparative advantage in swimming over hockey. I’ve played hockey for a long time, but I never really took swim lessons as a kid, and I’m always worried about losing my contacts in the water, so I know just enough to keep myself from drowning. My comparative advantage is in hockey.

But not only that, our absolute advantages are correlated with our comparative advantages. If we had a swim race, my daughter would win - absolute advantage matches comparative advantage. If we played hockey, I would win - absolute advantage matches comparative advantage. (This wasn’t true a few years ago. Despite my lack of swimming skill, when she was 7 I could still outswim her just because I was bigger. Not any more. But I can still own her on the ice.)

In the Roy model, if there was demand for one swimmer and one hockey player, my daughter would self-select into the swim sector and I would self-select into the hockey sector. Now, if the economy changes and the demand for hockey falls and swimming rises (global warming?), then I will have to switch. This will unambiguously lower the average skill level of swimmers, as I suck.

When comparative advantage and absolute advantage are correlated in a Roy model, any movement of a worker from one sector to another will lower the average skill of the receiving sector, and raise the average skill of the sending sector. If I get moved from hockey to swimming, I lower the average skill of swimmers. The dudes that remain in my hockey league will be the ones who are still better than me (i.e essentially all of them), and so I just raised the average skill level of my league by leaving.

This is the essence of Young’s story, and he shows that if this is how things work, then it has an effect on measured productivity growth in each sector. Just another example of how measured productivity growth is a garbage pile of all the things we don’t know how to keep track of.

Really starting to like Branko Milanovic

Nature - income inequality is cyclical.

As an aside, which of these two methods of doing economics is better?

On the one hand, you have Branko Milanovic's nuanced explanation of some explicit features which have affected income inequality over the long term:

In modern times, economic factors seem to have been the most important drivers of change. In the United Kingdom and the United States, an upswing of inequality, which lasted most of the nineteenth century, followed the introduction of inventions such as the steam engine and the cotton gin. With demand high and competition low, people who invested in the new products and services could enjoy large 'rents' (payments over and above what is needed to cover production costs). Inequality was also probably pushed up by the movement of people from the countryside into cities — to better paid, more diverse (and hence more unequally paid) jobs.

In the United Kingdom, the data suggest that inequality peaked around 1870. Around this time, demand rose for labour (driven in part by people leaving the country) and legislation limited child labour, hours of work and so on. Thus workers' conditions began to improve. In the United States, inequality seems to have peaked in the 1920s to 1930s, its decline probably held back longer than in the United Kingdom by immigration from Europe4.

Next came the great decline in inequality that many have associated with progressive modernization. For the many countries involved, the First World War destroyed assets (particularly in Germany, France and Russia), and brought large taxes on the rich to finance the conflict. These changes, along with the emergence of socialist movements and trade unions, the massive scale up of public education (fuelled in part by an increased need for skilled and educated labourers) and the greater participation of women in the workforce, ushered a period of more than half a century of growing equality in all developed countries. For the West, the period from the end of the First World War to the early 1980s saw a 'great levelling'.

Ultimately, this levelling occurred worldwide. Policies such as the distribution of land to landless people, the introduction of widespread education and the creation of state-owned enterprises — such as those running the railways, or producing coal or sugar — boosted equality in developing nations (particularly in Turkey, Iran, South Korea and Egypt). The nationalization of factories, narrowing of wage distribution, and the elimination of almost all capital income (which tends to be more unequally distributed than are wages) accomplished the same in Communist economies such as the Soviet Union and Czechoslovakia.

And so on.

On the other hand, you have Daron Acemoglu shoehorning a μ term into

an infinite-horizon non-overlapping generations model with bequests. A continuum of agents of mass λ live for only one period and each begets a single offspring. A proportion of these agents are “poor,” while the remaining 1-λ form a rich “elite.”

Where μ means "likelihood of revolution" - except if you actually read it, all it is is a policy decision term where the economic policy, if not decided by the government, is decided by the mass of revolutionary proletariat.

In other words, μ has only whatever meaning anyone bothers to give it. And in any case it doesn't matter what it is, really, since everybody dies each period anyway. It certainly doesn't yield any policy suggestions.

I think I've figured out why I'd prefer doing my Ph.D. in political economy instead of economics.

Wednesday, September 21, 2016

Chris Dillow convinced me technology shocks are a thing! But then I quickly got over it.

Dillow - technology shocks are a thing!

I haven't studied RBC yet, but from my position of ignorance, to the extent people have explained it to me, it always seemed nonsensical.

I mean, how can anyone assert that "productivity variations" are the cause of "business cycles" in the face of the 70s Arab oil embargo, the 1980 Iranian revolution, Volcker, the S&L crisis, LTCM, the Asian crisis, the NASDAQ collapse and the real estate collapse?

That's all either political economy (Arabs, Iran, Volcker), asset price shocks (S&L, LTCM, NASDAQ, real estate), or international finance (Asia).

Where is Solow's z in all that? Not one of those has anything to do with "productivity", right?

Then Chris Dillow posts this, and for a while I was convinced that RBC might be a thing:

However, if we ditch representative agent thinking and think instead of firms as being inherently heterogenous, the notion of a negative technology shock seems more reasonable.

Xavier Gabaix points out that even in a large economy aggregate fluctuations can arise from the failure of one or two big firms. This is especially possible if those firms are important hubs, whose troubles plunge supplier or customer firms into trouble: as Daron Acemoglu shows, networks are crucial in transmitting (or dampening) firm-level shocks throughout the economy.

It seems to me that this is a plausible description of the financial crisis. Banks became less able to supply credit than we thought; this was a firm- or industry-level negative technology shock. And because banks were key hubs, this shock was transmitted to the wider economy.

You can squeeze this into DSGE-style models, as (for example) Michael Wickens (pdf) and Hashem Pesaran (pdf) have done.

OK, fine. Institutions are part of z; they are necessary if you want to multiply f(K,L) to get Y. So then institutions could indeed cause shocks! I'm convinced.

But then I thought about this at a more basic level and became unconvinced.

"The residual" is simply everything in an economy that's not capital, labour, or the production function of the two, right? So by this line of reasoning, all that's being said here is that some vague, unknown thing that's not capital, labour, or the production function is going to be the cause of business cycles.

Or, using logic,

P1) B is a set that contains K, L and f, as well as an indefinite* large number of other things (including phlogiston).

P2) Anything which is not K, L or f can cause a recession.

C1) P1 true + P2 true means that there is an indefinite large number of things (including phlogiston) that cause a recession.

I'm sorry, but that seems to be the sort of emptiness that Romer was mocking this week. Want banks to originate technology shocks? Make them explicit in your model.

I'll repeat what I've said before: show me an RBC model where an exogenous political economy variable, or even better where an endogenous asset price variable, causes regular cycles, and I'll be impressed.

* - I use "indefinite" to mean "some huge, undefineable number that certainly won't be anywhere near infinity, but is still big enough for us to treat it that way". E.g., the mathematical term "gazillion".

Romer gets better and better at calling out your mom

Hello to new subscribers. This is just a blog on economics, I'm just an undergrad so don't expect anything original from me, I'm really just running this blog so that I have sufficient ideas stored up for when I need to write theses.

But feel free to keep reading.

Paul Romer - more on my love letter to economics. Quote:

One suggestion is that it would have been better if I had written one of those passive-voice “mistakes were made” documents that firms issue after a PR disaster.

I name names because this is how science works. The standard practice calls for an individual to put his or her reputation behind a claim; to listen to the claims that others make; and to admit that the claim is false when this is what the evidence shows. I did try to restrict my criticism to people who have received Nobel Prizes in Economics or to Smets and Wouters, who have received wide recognition for their work, so it is not like I’m picking off the stragglers at the back of the head.

Seriously? An important part of Romer's withering attack on Lucas, Prescott and Sargeant was the illustration that economists, far from dispassionately pursuing truth, were instead publishing nonsense papers full of statistical fraud to back up each other's fancy new theory.

The social anthropology of economics needs to be discussed, and Romer did it. It really needs to be discussed, because economics is the only "social science" discipline that still refuses to analyze its own power structures and truth statements, even from a simple historical perspective.

I mean, I'm sure the Marxist heterodox crowd have been writing critically about economics for decades, but their analysis doesn't count. The analysis has to take place within the mainstream. The orthodoxy in every other scientific field analyzes itself: even engineers are postmodern nowadays. Why not economists? Are they scared to look in the mirror?


Why Use Humor and Sarcasm?

Wait... seriously, dude? Is someone honestly suggesting you need a reason? Whoever said that, he sounds like a crybaby to me.

The whine I hear regularly from the post-real crowd is that “it is really, really hard to do research on macro so you shouldn’t criticize any of our models unless you can produce one that is better.”

This is just post-real Calvinball used as a shield from criticism. Imagine someone saying to a mathematician who finds an error in a theorem that is false, “you can’t criticize the proof until you come up with valid proof.” Or try this one on and see how it feels: “You can’t criticize the claim that vaccines cause autism unless you can come up with a better explanation for autism.”

On the topic of Calvinball, do you remember the old methodological rule, “in economics, we don’t make assumptions about preferences.” At some point, this became “in macroeconomics, risk aversion and leisure shocks to preferences cause fluctuations in investment and labor supply.” One suspects that Orwell would have been amused.

I'm going to take note of this, then use it in my 4th year micro class.

Monday, September 19, 2016

Chris Blattman learned something from transvestites today

Chris Blattman - the most interesting email I received today. The concluding paragraph is the real kernel of wisdom, but I'll just repost the whole thing cos it's rather funny:

From my email inbox this morning, one of my research assistants reports issues on a large field experiment and crime victimization survey I am running in Colombia:

In Santa Fe the prostitutes and transvestites haven’t let enumerators do the surveys. The survey firm is contacting two leaders of this transvestites community they have worked with before, but the leaders are currently outside Bogota, so the survey firm is waiting for their return on the next few days.

One of the things I enjoy about my work is collecting data on populations that few people have tried to survey. Often the problems are more harrowing, for the subjects and my staff. The emails from Colombia are considerably better than the ones I got accustomed to trying to track ex-combatants in Liberia (“The Land Cruiser fell through a bridge again, but fortunately no one was seriously hurt”), working with street youth (“some of the research subjects got so enthusiastic they decided to stop drugs, and so we’ve had to turn one of the vehicles into an ambulance”) or an African country not to be named (“Yesterday one of the field managers was arrested by the secret police”).

One of the underappreciated aspects of running large surveys, and especially field experiments, is having to deal with entirely unexpected logistical and political and economic problems. I now miss the pre-teaching, pre-toddler days when I got to do most of this myself, rather than pay assistants to do the really hard work.

In retrospect, these everyday problems of running a project were the most educational part of my research. Everything I know about weak states, crime, and violence came not from surveying bureaucrats or criminals or victims, but navigating problems with them in an attempt to get the study done.

I’ve said this before: field experiments will have a huge effect on international development not because they will give us clean treatment effects, but because the act of doing difficult things in the real world will change the way academics think the world works, and what the real problems are.

Paul Romer just called your mom fat

WaPo - the state of macro is not good. Paul Romer's latest missive, unfortunately not titled "Hey Economists: Your Mom Is Fat", has caused a bit of a stir in the academy:
One could argue that the most high-profile contribution by macroeconomists to the post-2008 global economy has been an emphasis on fiscal austerity as a solution to stagnation. That prescription has been, well, pretty much disastrous.

I bring all of this up because Paul Romer has a lulu of a paper entitled “The Trouble with Macroeconomics” that rocketed around the social media of the social sciences. If you think the title implies criticism, read the abstract:
For more than three decades, macroeconomics has gone backward. The treatment of identification now is no more credible than in the early 1970s but escapes challenge because it is so much more opaque. Macroeconomic theorists dismiss mere facts by feigning an obtuse ignorance about such simple assertions as “tight monetary policy can cause a recession.” Their models attribute fluctuations in aggregate variables to imaginary causal forces that are not influenced by the action that any person takes. A parallel with string theory from physics hints at a general failure mode of science that is triggered when respect for highly regarded leaders evolves into a deference to authority that displaces objective fact from its position as the ultimate determinant of scientific truth.
It gets more brutal from there, as Romer mocks the logic of real business cycles (a core component behind much of modern macro) and relabels its key explanatory variable as “phlogiston.” In history of science circles, that is a sick burn.
And it provides an opportunity for other people in economics to comment:

Noah Smith - the new heavyweight macro critics.

Chris Dillow - an academic problem.

Mean Squared Errors - the microfoundations hoax.

And Romer himself had to put his paper up for download, since it was already being distributed without him. Read it!

Paul Romer - economists' moms are fat (pdf).

Friday, September 16, 2016

Barkley Rosser on agent-based modelling

Barkley Rosser - whither agent-based modeling? This part was a bit weird:

Anyway, there are these pretty interesting ABMs out there that seem to be able to do interesting stuff, but somehow they are not being picked up by the central banks. Furthermore, I heard rumors that funding from the EU and INET and some other places may be cut for this kind of research. If this turns out to be the case, I think it will be too bad.

Don't tell me INET is dumping ABM in favour of... what, then?More anti-West Putin-funded propaganda?

Anyway, this article got me looking at rents in Genoa, and apparently they're cheap. I wonder if it's at all possible to do a Ph.D. there?

Paul Romer and Simon Wren-Lewis on the trouble with macro

Paul Romer - a draft of "the trouble with macroeconomics". I'll probably read it on the bus to school today.

But for an unexpectedly differing opinion, see:

Simon Wren-Lewis - economics, DSGE and reality, a personal story.

I'm not in this to rubbish mainstream macro, btw: I just want to learn what's true and what's not, without the misleading political propaganda of either the left or the right.

Thursday, September 15, 2016

Jared Bernstein on income gains versus income deciles

Jared Bernstein - my comments on the CBPPS census data press call. Here's the important bit:

So we find in today’s data that while households throughout the pay scale saw real gains, the largest gains accrued to those at the households at the bottom of the income scale. I’m sure you’ve heard the topline income number: real median HH income up 5.2 percent, the largest one-year gain on record in this series, which starts in the mid-1960s, and the first real gain since 2007.

But real HH income went up 8 percent at the 10th percentile and 6 percent at the 20th percentile. Poverty rates for whites fell about one percentage point; the rate for blacks and Hispanics fell twice that much (though from much higher levels).

Meanwhile, income gains at the 90th and 95th percentiles were between 2 and 4 percent, below those of lower income households. This too is a familiar pattern of the benefits of full employment. It’s not that tight labor markets don’t help the wealthy. It’s that they tend to do well in good times in bad, while less well-off households depend on tight job markets to give them the bargaining clout they otherwise lack.

So a big part of the story today is that strong labor markets make a big difference in helping to connect low- and middle-income working families to the broader economy. That also points the way forward. I’m glad to see a great year in these data, but middle and low-income HH’s need a lot more than one good year.

Which seems to suggest that monetary policy has a differential effect on low- and high-income earners, doesn't it?

Wednesday, September 14, 2016

Steven Levitt said there's no such thing as a real world demand curve, gets pwned

Jayson Lusk - real world demand curves. Oh you didn't!:

On a recent flight, I listened to the latest Freakonomics podcast in which Stephen Dubner interviewed the University of Chicago economist Steven Levitt about some of his latest research.  The podcast is mainly about how Levitt creatively estimated demand for Uber and then used the demand estimates to calculate the benefits we consumers derive from the new ride sharing service.

Levitt made some pretty strong statements at the beginning of the podcast that I just couldn't let slide.  He said the following:
“And I looked around, and I realized that nobody ever had really actually estimated a demand curve. Obviously, we know what they are. We know how to put them on a board, but I literally could not find a good example where we could put it in a box in our textbook to say, “This is what a demand curve really looks like in the real world,” because someone went out and found it.”

As someone whose spent the better part of his professional career estimating consumer demand curves, I was a bit surprised to hear Levitt claim "nobody ever had really estimated a demand curve."  He also said, "we completely and totally understand what a demand curve is, but we’ve never seen one."  The implication seems to be that Levitt is the first economist to produce a real world estimate of a demand curve.  That's sheer baloney.

Angus Deaton is perhaps most well known for his work on estimating consumer demand curves.

In fact, agricultural economists were among the first people to estimate real world demand curves (see this historical account I coauthored a few years ago).  Here is a screenshot of a figure out of a paper by Schultz in the Journal of Farm Economics in 1924 who estimated demand for beef.  Yes - in 1924!  I'm pretty sure that figure was hand drawn!

Or, here's Working in a paper in the Quarterly Journal of Economics in 1925 estimating demand for potatoes:

Two years later in 1927, Working's brother was perhaps the first to discuss "endogeneity" in demand (how do we know we're observing a demand curve and not a supply curve?), an insight that had a big influence on future empirical work.

Fast forward to today and there are literally thousands of studies that have estimated consumer demand curves.  The USDA ERS even has a database which, in their words, "contains a collection of demand elasticities-expenditure, income, own price, and cross price-for a range of commodities and food products for over 100 countries." 

Well, I've never seen a real-world demand curve in my own education yet, so I guess Levitt's not really to blame.

Branko Milanovic gets very long-winded about robots

Branko Milanovic - robots or fascination with anthropomorphism. He could have been a lot more concise:
Recent discussions about the “advent of robots” have some rather unusual features. The threat of robots replacing humans is seen as something truly novel possibly changing our civilization and way of life. But in reality this is nothing new. Introduction of machinery to replace repetitive (or even more creative) labor has been applied on a significant scale since the beginning of the Industrial Revolution. Robots are not different from any other machine.

The obsession with, or fear of, robots has to do, I believe, with our fascination with their anthropomorphism. Some people speak of great profits reaped by “owners of robots”, as if these owners of robots were slaveholders. But there are no owners of robots: there are only companies that invest and implement these technological innovations and indeed they will reap the benefits. It could happen that the distribution of net product will shift even more toward capital, but again this is not different from the introduction of new machines that substitute labor—a thing which has been with us for at least two centuries.

Robotics leads us to face squarely three fallacies.
And so on.

Which could have been summarized more concisely as "we've seen automation replace labour for 200 years already and it just made us richer".

Though I watched an old documentary a while ago which suggested that a contribution to the Great Depression was that Ford-style assembly lines increased productivity far faster than consumption increased, leading to a supply glut.

I don't know if this is just a standard left-wing argument, or if there's merit to it. I'd expect it depends on the rate of productivity increase and the consequent rate of change of the labour-capital income split that would determine the rate of change of demand shortfall increase.

But that's just another way of saying that if you screw the poor you'll screw the economy, and that more general thesis seems like the important topic, not just that robots will take our jerbs.

Monday, September 12, 2016

David Ricardo

Today in school I learned that David Ricardo made his fortune by manipulating the stock market.

Sunday, September 11, 2016

Interesting secstag idea

This is very interesting:

Vox EU - secular stagnation and the fat tail. Quote:

Existing theories about why the crisis took place assume that the shocks that triggered it were persistent. Yet such shocks in previous business cycle episodes were not so persistent. This differential in persistence is just as puzzling as the origin of the crisis. What most explanations of the Great Recession miss is a mechanism that takes some large, transitory shocks and then transforms them into long-lived economic responses.

Perhaps the fact that this recession has been more persistent than others is because, before it took place, it was perceived as an extremely unlikely event. Today, the question of whether the financial crisis might repeat itself arises frequently. Financial panic is a new reality that was never perceived as a possibility before.

Our explanation for persistently low output hinges on people’s assessment of tail risk.

Basically, their thesis is that the financial crisis resulted in a permanent re-evaluation of tail risk, and this should be expected to last for years in any part of the economy where finance is important.

One important reason why tail risk has such large aggregate effects is the fact that firms finance investment, at least in part, by issuing debt. Debt inflicts bankruptcy costs in the event of default. The cost of issuing debt, the credit spread, depends on the probability of default. Default risk depends on the likelihood of large aggregate shocks, or tail events. Thus, when the probability of a left tail event rises, financing investment with debt becomes less attractive. As a result, when tail risk rises, an economy with more highly-leveraged (indebted) firms experiences a larger drop in long-run investment and output. We show that the extent to which debt amplifies bad shocks is greater for large tail shocks than it is for events closer to the mean. Thus, debt financing interacts with tail risk to accentuate the difference between extreme recessions and their milder counterparts.

And that's where they include their "gotcha!" chart, the Skew:

And that does seem to back up their thesis, no?

Thursday, September 8, 2016

China's insufficient investment in education, or more likely not

Tim Taylor - China's insufficient investment in education.

Someone at PIIE just wrote a paper that makes me think PIIE have no idea what they're doing. Here's Tim Taylor's summary:

Jacob Funk Kirkegaard suggests that one substantial hindrance may be China's education system is not keeping up. He lays out the case in "China’s Surprisingly Poor Educational Track Record," which appears as Chapter 3 in
China’s New Economic Frontier: Overcoming Obstacles to Continued Growthpublished by the Peterson Institute for International Economics (PIEE Briefing 16-5, September 2016, edited by Sean Miner).

As a starting point, compare countries by per capita GDP and what share of the adult population has at least an upper secondary education. As shown in the figure, the education level of China's adult population ranks well below other countries with a roughly similar level of per capita GDP.

China has made dramatic gains in its education level in the last few decades. One standard measure of gains over time is to compare the education level of a younger age group to an older age group, like the average education level of adults age 25-34 with adults age 55-64. The red bars--with China shown in yellow--shows how much the education level of the younger group exceeds that of the older age group. Clearly, China has made substantial gains. But just as clearly, the gains in China's education attainment are below those for France, Spain, Brazil, Korea, and others. Moreover, China was starting at a much lower level of educational attainment (the hollow box showing educational attainment for the 55-64 age group is lower for China than for the comparison countries shown here) and so middling gains for China in educational attainment aren't helping it to catch up.

Kirkegaard sums up the situation this way:
"In some ways, China may have been a victim of its own success. The pull effects of its sustained economic boom and rapidly rising wage levels appear to have led too many young people to leave education too early to acquire the skills needed to sustain them (and Chinese economic growth rates) throughout their lifetimes. As Chinese economic expansion shifts toward more skill-intensive growth, those without a secondary education will be less able to find jobs. ...  The Chinese government and society appear to have failed to keep enough of the country’s young people in school during the recent decades of economic growth. This is likely to have long-term scarring effects, as public underinvestment in human capital and individual acquisition of needed skills are difficult
to undo. People’s “lower than otherwise would have been the case” skill levels cannot easily be restructured. Skill shortages at the upper secondary level will make it harder for China to move into the production of higher value added goods and services, lead to increased income inequality and geographic wealth diversity, and complicate the transition to a widespread consumption-based economy."

I think the problem here is that JF Kirkegaard fails to note that educational attainment in China has been "lagging" GDP growth precisely because their GDP growth has been so fast for so long. That explains all the other countries you find below the curve in that first chart (except Saudi Arabia, which has other understandable reasons for low educational attainment): they've experienced a human generation of fast growth.

The countries above the curve have seen GDP deflation over the past generation.

And education is capital that takes a generation to produce.

So sure, China's spent a decade getting way ahead of themselves in transportation infrastructure and physical capital, which is wise because (a) they built it when construction costs were lower and (b) as long as you can pay the upkeep, you can let this capital sit there and do nothing til you've grown into it. But that capital is far faster to form.

And still China was mocked for the past decade for its excess physical capital.

How much more would China have been mocked if Xi Jinping hopped into a time machine and travelled back in time 30 years to convince Deng Xiaoping to spend a pile of money on increasing highschool completion rates?

Excess human capital left to sit idle does not provide a future benefit. In China's case, I'd think their politicians of a generation ago would have even thought idle human capital would produce social unrest.

And I eagerly await someone to tell me the names of those trade theorists 30 years ago who would have encouraged China to spend a fortune on education.

This paper (or, at least, Tim Taylor's condensation of it) makes no sense to me.

Tuesday, September 6, 2016

WSJ - could reducing tax cheating close the deficit? Me - has anyone figured out 1+1 yet?

WSJ - could reducing tax cheating reduce the deficit? Short answer is duh:

The most recent estimates for the size of the “tax gap” (basically, how much tax revenue should be collected but isn’t) are for tax years 2008-2010. Thanks to the financial crisis, both the economy and the deficit looked especially bad then — incomes were depressed and a very expensive stimulus package had just passed — so it’s not a perfect analogy to today’s world. Rather than looking at the raw number of tax dollars that went unpaid those years, then, I’m going to rely on the percent of tax dollars that went unpaid, and apply that figure to today’s fiscal situation.

In each of those years, an average of 81.7 percent of taxes were paid “voluntarily and timely.” Another 2 percent were ultimately collected late and after enforcement actions, which brings the “net compliance rate” to 83.7 percent. These estimates of compliance rates have been relatively stable over the last three decades.

The Congressional Budget Office’s detailed revenue estimates show that about $3.083 trillion in tax revenues will be collected in fiscal year 2016 (counting only individual income, corporate income, payroll, excise, estate and gift taxes). If we assume that figure represents only 83.7 percent of what’s legally owed, that means the real total tax liability is closer to $3.684 trillion.

It also suggests we’re leaving about $600 billion on the table in uncollected tax revenue ($3.684T -$3.083T = $600B). The budget deficit for 2016, according to the C.B.O., is $590 billion.

That’s right: The estimated amount of dollars lost to tax cheating is almost exactly equal to the size of the annual deficit.

I'll go one further:

I'd bet that, from a savings/consumption perspective, something approaching 80-90% of tax-evaded money is going into savings. Which means there's $600 billion too much in savings being generated in the US each year.

(It's "too much" because real rates are negative, so there must be too much savings for the level of investment demand.)

If the government didn't use this $600B to reduce the deficit, which would be stupid to do when real borrowing rates are negative, but instead spent it on infrastructure and capital generation with a positive 30-year rate of return, that would mean $600B/y less in savings, $600B/y more in government "consumption", and thus $600B/y more in annual capital generation.

That would fix the whole secular stagnation problem, no?

Let me know when I'm taking an economics class that makes this point.

Sunday, September 4, 2016

WSJ on natural rate of interest, economists fail at stats edition

WSJ - think you know the natural rate of interest? This part here stuns me:

Economists — and investors — tend to ignore the level of confidence in a calculation. (If you doubt that, look at the attention paid to small beats or misses of expectations for the nonfarm payrolls report, which has a margin of error of plus or minus 100,000 jobs, with 95% confidence.) Holston, Laubach and Williams helpfully published the margin of error around their estimates, and it is big enough to drive a truckload of economists through.

The error margins they produced allow a 95% confidence interval to be calculated, and for some regions it is just silly: They are 95% sure that the natural rate of interest in the eurozone is currently somewhere between plus 12% and minus 12%. Frankly, I’m 100% sure the natural rate sits in a much narrower band than that, without even picking up a calculator.

I think we can take that confidence interval as an indication that their "natural rate of interest" is the deranged fantasy of someone who fell off a bike at high speed while not wearing a helmet.

Seriously, this right here is why I'm taking extra stats classes beyond what is required for an economics BA. I could probably do one paper a year just on bad statistics.

Chris Dillow on incompetence

Chris Dillow points out that incompetence is something that should be part of economic models, but isn't:

Chris Dillow - on incompetence. Quote:

Should ineliminable incompetence play a bigger role in economic and political thinking?

My trigger for asking is a piece in the Times by Oliver Kay on the appalling mismanagement of football clubs such as Blackburn Rovers, Charlton, Leeds and Blackpool. But of course, incompetence is much wider than that. Trains are late and overcrowded; building projects run over time and budget; utilities, banks and broadband providers often have poor quality service that can’t wholly be explained by profit-maximizing; and you all have examples of bad management in your own workplace.

Which brings me to a paradox. Whereas our own eyes tell us that incompetence is ubiquitous, standard economic theory regards it as merely a temporary deviation. It thinks that agents are incentivized to optimize; that badly managed assets will be bought cheaply by people better equipped to run them; and that competition will drive incompetent firms out of business.

But here's where he sums up the problem in one sentence:

I have an incentive to become a Premier League footballer or Nobel laureate, but you’d be ill-advised to bet on me becoming either.

It's not really a case of "incompetence", but rather one of unequal endowment of human capital. Unfortunately, recognizing that would mean using human capital as an input to every model, and it would also mean learning how to quantify human capital in some way if you want numbers to come out the other end.