1997 and 2015

The East Asia crisis of 1997 had many causes, including a strong dollar, fixed exchange rates and poorly regulated financial systems.  One additional factor was the rise of China, which had begun competing with other East Asian exporters.  At the time, China was still fairly low tech, and hence the 1997 crisis hit the lower income countries of Southeast Asia much harder than places like Singapore, Taiwan and Japan.  Those higher income places had a more complementary relationship to China, supplying needed capital goods.

But the China of 2015 is very different from the China of 1997.  It is now a much more advanced economy, and it is beginning to offer fierce competition to the more sophisticated economies of East Asia, such as Singapore:

The Ministry of Trade and Industry on Wednesday forecast economic growth in 2015 at “close to 2 percent” and between 1 and 3 percent in 2016. It previously forecast 2015 growth at between 2 and 2.5 percent.

Singapore, a city-state at the tip of peninsula Malaysia, is the wealthiest economy in Southeast Asia but has shifted to lower growth rates in the past decade as other countries including China eroded its traditional strengths in electronics and other manufacturing. It has encouraged investment in higher value industries such as pharmaceuticals and also tried to boost services by opening two casinos, encouraging tourism and becoming a center for private banking.

And Taiwan:

Last year Taiwan grew by 3.8%. Many analysts had expected about the same this year. Instead, it will do well to hit 1%, says Gordon Sun of the Taiwan Institute of Economic Research. . . .

Until a few years ago, the economic relationship between China and Taiwan was symbiotic. Taiwanese firms, among the world’s biggest makers of electronic components, needed China’s cheap labour; China craved Taiwan’s technical know-how. But this complementarity has given way to competition. Chinese producers of petrochemicals, steel, computers and digital displays have moved into terrain once occupied by Taiwan. Taiwanese firms with operations in China are themselves buying more materials and machinery from Chinese suppliers. Chinese firms are now trying to break into semiconductors, Taiwan’s last big industrial redoubt.

Sometimes size gives producers additional monopoly power.  Ironically the size and homogeneity of the Han Chinese population (both inside of China and overseas) depresses the prices of the things the Chinese have a comparative advantage in producing.  Perhaps this partly explains the disappointing performance of China’s stock market in recent years.  With each China advance, a new set of ever more sophisticated industries are flooded with output.  Meanwhile non-Chinese industries that sell to the Chinese (BMW, Louis Vuitton, etc.) benefit from the China boom.

BTW, lots of people like to cite the “Li Keqiang index” as an alternative to the official Chinese statistics.  I’ve argued that this index does not apply to an economy rapidly shifting from heavy industry to services.  It seems as though Li himself now agrees with me:

In short, despite moderation in growth, the Chinese economy is moving in the desired direction of stronger domestic demand and innovation. One by-product is a fall in the relevance of indicators such as power consumption, rail-cargo volume and new bank credit in gauging economic performance. Yet this transition from “bigger is better” to “less is more” is a good thing. I would otherwise be worried whether the reforms were working as intended.

And now I anticipate commenters who believed Li’s early statements telling me that you can’t believe anything a Chinese leader says.

Wenzhou people

If you talk to enough Chinese people, you will eventually come across the phrase “Wenzhou people”, referring to people from a particularly entrepreneurial city on the coast of China.  They have a reputation for being good at business.

Wenzhou is a city in Zhejiang province.  Yasheng Huang says that Zhejiang province is rather special, as it embraced capitalism before the rest of China.  It’s leaders were more tolerant of private business during the 1980s, and as a result private enterprises did better than in other parts of China.  The province directly to the north (and most similar in some ways) is Jiangsu.  Because property right were less secure in Jiangsu, they relied more on foreign investment from multinationals.  Ironically, during the 1980s property rights in China were far more secure for multinationals than for local firms.

Even today Jiangsu has a higher GDP per capita than Zhejiang, due to all the multinational investment.  But Zhejiang has a higher level of domestic income, as much of the Jiangsu income earned by multinationals flows out of the country. Zhejiang is China’s richest province, excluding the independent cities such as Shanghai and Beijing.  Its 55 million people are a mix of urban and rural. (Similar population to England, in a 20% smaller area, with many more mountains and more rural people.) And it also seems to exhibit some other very interesting characteristics.

For instance, Zhejiang residents have a very long life expectancy.  Unfortunately I had a really hard time finding this data.  The data that is easy to find on the internet is out of date. Lancet has an article that suggests that in 2013, the life expectancy in Zhejiang had surged to over 81 years, nearly two years higher than the number two province (Jiangsu–again ignoring independent cities.)  That’s up from 74.7 in 2005, meaning their life expectancy grew by roughly 6 1/2 years in 8 years.  And more importantly, the gap with other top provinces widened significantly.  (Overall, life expectancy in China usually grows fastest in those areas where it is lowest.)

Reading Garett Jones’s fascinating new book “Hive Mind” got me wondering about Chinese IQ.  Shanghai’s PISA scores are highest in the world, but no one thinks Shanghai is representative of China as a whole.  Unfortunately, the figures for other provinces are not reported—in English.  I dug a round a bit on the internet and found a post that reports IQ equivalents for some other Chinese provinces.  Here’s what it says:

Happily (via commentator Jing) we learned that the PISA data for Zhejiang province and the China average had been released on the Chinese Internet. I collated this as well as data for Chinese-majority cities outside China in the table below, while also adding in their PISA-converted IQ scores, the scores of just natives (i.e. minus immigrants), percentage of the Han population, and nominal and PPP GDP per capita.

Screen Shot 2015-11-19 at 5.30.47 PM

* Twelve provinces including Shanghai, Zhejiang, Beijing, Tianjin, Jiangsu totaling 621 schools, 21,003 students. Results have been released for Shanghai, and later on for Zhejiang (59 schools, 1,800 students – of which 80% were township-village schools) and for the 12-province average.

(1) Academic performance, and the IQ for which it is a good proxy, is very high for a developing nation. Presumably, this gap can largely be ascribed to the legacy of initial historical backwardness coupled with Maoist economics.

(2) The average PISA-converted IQ of the 12 provinces surveyed in PISA is 103.0. (I do not know if provincial results were appropriately weighed for population when calculating the 12-province average but probably not). We know the identities of five of the 12 tested provinces (Shanghai, Zhejiang, Beijing, Tianjin, Jiangsu). They are all very high-income and developed by Chinese standards. Furthermore, these five provinces – with the exception of Tianjin – all perform well above average according to stats from a Chinese online IQ testing website.

The author of the post, Anatoly Karlin, then makes this claim:

Addendum 8/15: The commentator Jing graciously provided the list of all the 12 Chinese provinces that participated in the PISA 2009 study. They were: Tianjin, Shanghai, Beijing, Jiangsu, Zhejiang, Jilin, Hubei, Hebei,

Hainan, Sichuan, Yunnan, Ningxia.

This allowed me to make an interesting conclusion. No matter whether you weigh the provincial IQ scores above by population or not, the difference between the 12 provinces and China on average is only about 0.5 points in favor of the 12 provinces. This means that the PISA sample is actually pretty good – and that China’s PISA-derived IQ is in fact about 102.5 or so.

Even if that’s not exactly right, it’s probably in the ballpark.  Some of those 12 provinces are in the west, and Sichuan has a huge population.  So while the group of 12 would be somewhat upwardly biased by the three big cities, the sample includes a large number of very populous inland provinces.  Even if the actual number were 100, it would be an astoundingly high IQ for a country at China’s level of development (recall the so-called “Flynn effect.”)  For instance, Switzerland’s 101 is the highest average IQ in Europe.  I recall that Garett Jones mentioned that Hong Kong and Taiwan had scored surprisingly high when they were still poor, and of course they are ethnic Chinese.

I assume you know where I’m going with this.  Zhejiang seems to have an especially high average IQ, especially for a province with a mix of urban and rural residents.  In eastern China, one cannot point to ethnic differences to explain IQ variation, Zhejiang is more than 99% Han, and other eastern provinces are also overwhelmingly Han. Instead, the anomalous IQ must represent some sort of (local) cultural or educational difference. Did this arise recently, like their long life expectancy?  Is it caused by the fact that Zhejiang got a head start on capitalism?  Or does the cause go deep into Chinese history?  After all, Zhejiang contains the city of Hangzhou, which Marco Polo marveled over. Hangzhou is host to a top university, and the internet giant Alibaba. It’s also home to Pritzker prize winning architect Wang Shu who designed a college campus in Hangzhou.  And it’s one of China’s most (only?) beautiful cities.

And here’s what Wikipedia says about Wenzhou:

Wenzhou has given birth to more mathematicians more than any other city in the world.

No answers here, just some interesting regional differences to think about.

PS.  Possibly related (or not) I saw this astounding story:

Beijing will replace an aging overpass with a new one weighing 1,300 metric tons within 24 hours starting on Friday.

If the job is completed as planned, it will set a record in China for the shortest replacement time involving such a large structure in heavy-traffic downtown areas, the Beijing Municipal Commission of Transport said on Tuesday.

The replacement will take place at the Sanyuanqiao overpass on the northeast section of the Third Ring Road, which links the city with Beijing Capital International Airport. It is one of the busiest traffic hubs in the city.

This will be the first time in China that special dollies – low, wheeled platforms – that are able to carry 1,000 tons each will be used to move giant prefabricated bridge pieces and install them fully intact, said Hou Xiaoming, deputy director of the road management department of the commission.

The original overpass was completed in 1984.

.  .  .

In the past, building an overpass in downtown areas has taken months to complete. This project will be fast because of sophisticated engineering and careful preparations, Hou said.

Beijing has more than 200 overpasses inside its Fifth Ring Road, the most in the country. Sanyuanqiao is four times the size of the Xizhimen overpass in downtown Beijing, which was replaced six years ago using older engineering technology.

“If successful, it will serve as a good example for other cities to follow in downtown areas troubled by traffic jams,” Hou said.

And they say the Chinese don’t innovate.  The giant highway engineering project will cost $7.77 million.  In Boston it would cost many times more, and would take more like one year, not one day.

Will China get stuck in the middle-income trap?  Can you point to any countries that did get stuck in the middle-income trap, and have average IQs anywhere near 109.5? Or even 100?  Russia might be the best case, with an average IQ of 97, and (perhaps) stuck near the top of the middle-income range.  (Actually it’s too soon to tell.)  But China seems very different to me. Time will tell.

Sad news from down under

[Update:  Check out the comment section, it looks like the US is ultimately to blame for this too.  Everywhere I travel I hear people complaining about our government’s arrogance.  People tell me “I used to look up to the US.”  I no longer hear anything positive about the US.  Meanwhile we have a welcome sign out for corrupt officials from all over the world who want to launder money here in real estate, and we couldn’t care less what the rest of the world thinks about it.  Do as we say, not as we do.  What a disgraceful government. Just one more issue the media will ignore, as they cover the clown show called “debates”.]

When I visited Australia and New Zealand back in 1991 they seemed like much freer countries than America.  Probably they still are.  But I was disappointed to see this:

Over the past seven years, the team at Victoria Link have been running New Zealand’s only prediction market, iPredict. It is one of only three “commercial” prediction markets operating globally. We’ve really enjoyed turning it from research into a practical tool which has become part of the New Zealand political narrative.

Prediction markets function based on the assumption that people will be more accurate when they back their opinion with money. There is a wide academic field studying this, and it could one day result in more accurate forecasting of a huge variety of events and even change how governments make decisions.

As prediction markets do not comfortably fit within any existing regulatory boxes, we have been working closely and positively with the Financial Markets Authority (FMA) to enable us to operate economically within the financial market regulations.

Regrettably the Ministry of Justice has not been so positive. We applied for an exemption from the Anti-Money Laundering and Countering Financing of Terrorism Act. We believed we would secure an exemption due to the limited possible investment into iPredict trades and the small nature of the Prediction market transactions.

Our application has been declined by the Minister, Simon Bridges, on the grounds that we are “a legitimate money laundering risk”. This is essentially because we have no customer due diligence checks. He considered the level of regulatory burden is proportionate to the risk. He formed these views without any discussions with us.

We are an academic not-for-profit organisation and our agreement with the FMA dictates we place caps on transactions. For example, over the past seven years, we have handled a total of 3,782 withdrawals, with an average trader net worth of $41. Our withdrawal process is lengthy and we are a low risk of money laundering.

Because the cost of compliance is too high, we are forced to wind up operations in NZ.

It seems that it’s not just the US government that is anti-science, other governments are too.

Over at the blog Offsetting Behavior they printed an email from Glenn Boyle, who helped set up iPredict:

When we were setting iPredict up between 2005 and 2008, all the holdups were technological and financial, not regulatory.  Liam Mason and others at the Securities Commission were generally helpful and tried to eliminate roadblocks rather than put them in our way, and there certainly didn’t seem to be any impediments thrown at us by ministers.

I recall the money laundering bogeyman coming up only once, and then only in jest.  I don’t remember the exact wording, but it was something along the lines of “you’ll probably get hit with money laundering charges if the Americans invade or we ever elect a communist government.”  Ouch…

This wasn’t taken seriously at the time though.  Looking back through all the various memos etc I prepared during the 3+ years iPredict was being set up, I can’t find any reference to money laundering regulation at all.  I guess we were naive!

Ironically, it was a conservative government that put iPredict out of business.  Can’t have people laundering $41, which is what, $28 in US money?

HT:  Stephen Kirchner

Who cares?

The main problem in America is a lack of utilitarian thinking.  I was reminded of that recently when reading an excellent piece in Vox.com on the War on Drug Using Americans.

Most recently, these fears of drugs and the connection to minorities came up during what law enforcement officials characterized as a crack cocaine epidemic in the 1980s and ’90s. Lawmakers, judges, and police in particular linked crack to violence in minority communities. The connection was part of the rationale for making it 100 times easier to get a mandatory minimum sentence for crack cocaine over powder cocaine, even though the two drugs are pharmacologically identical. As a result, minority groups have received considerably harsher prison sentences for illegal drugs. (In 2010, the ratio between crack’s sentence and cocaine’s was reduced from 100-to-1 to 18-to-1.)

That’s right, America has patently racist laws that are aimed at locking up African Americans, and almost no one seems to care.  Even worse, people don’t even seem to understand what’s going on.  I don’t know whether to laugh or cry when I read something like the following (from an article discussing the fact that heroin addiction is spreading from the black to the white community, and originally in the NYT):

So officers like Eric Adams, a white former undercover narcotics detective in Laconia, are finding new ways to respond. He is deployed full time now by the Police Department to reach out to people who have overdosed and help them get treatment.

“The way I look at addiction now is completely different,” Mr. Adams said. “I can’t tell you what changed inside of me, but these are people and they have a purpose in life and we can’t as law enforcement look at them any other way. They are committing crimes to feed their addiction, plain and simple. They need help.”

He may not be able to tell “what changed inside” of him, but I can guess.  Heroin addicts went from being villains to victims, as soon as they started looking like the friends of our narcotics detectives.

So why aren’t people up in arms over this?  It’s not like people don’t care about racial injustice, we’ve seen lots of protests about police brutality.  I think it’s because victims of police brutality really do seem like victims, whereas those incarcerated for drug crimes, even when the punishment is clearly racially biased, don’t seem like sympathetic victims.  In the 1960s, campuses were in an uproar over the Vietnam war. In 1972 the protests pretty much ended, not because the war was over, but because college students were no longer being drafted.  Today, college students are more likely to protest inappropriate Halloween costumes that the unjust incarceration of hundreds of thousands of African-American drug users.

Alex Tabarrok points to another great example.  There is a government policy reform that could save 5,000 to 10,000 lives each year, and greatly reduce suffering, and save the government $12 billion/year.  There’s no downside. Who could be opposed to paying organ donors? Almost everyone.  As far as I know both parties are opposed to the sort of sensible policy reform that could save as many American lives as were lost in Iraq plus Afghanistan over a decade, and do so every single year.

Next year the media will try to trick you into thinking that the two major parties are discussing the great issues facing America.  Don’t be fooled; neither party is addressing the big issues.  There are some differences between the two parties, but not on the most important issues.  In my view there are really only two groups; those promoting utilitarianism, and those promoting pain and suffering under the guise of some sort of phony ethical values.  The rest is all a distraction.

Happy Thanksgiving.

Wage targeting to NGDP targeting

People often ask me for a mathematical model illustrating my views.  I’m not a fan of such models, but I do understand their appeal to others.  In any case, it’s not something I do, but I would encourage younger economists to take a stab at it. Here I’ll suggest one possible path.

We already have models showing that the central bank should target the stickiest prices.  For instance, here’s Mankiw and Reis (2003):

This paper assumes that a central bank commits itself to maintaining an inflation target and then asks what measure of the inflation rate the central bank should use if it wants to maximize economic stability. The paper first formalizes this problem and examines its microeconomic foundations. It then shows how the weight of a sector in the stability price index depends on the sector’s characteristics, including size, cyclical sensitivity, sluggishness of price adjustment, and magnitude of sectoral shocks. When a numerical illustration of the problem is calibrated to U.S. data, one tentative conclusion is that a central bank that wants to achieve maximum stability of economic activity should use a price index that gives substantial weight to the level of nominal wages.

I made this argument in 1995, but without a rigorous model.

So how do we get from wage targeting to NGDP targeting?  One problem with targeting nominal wages is that they respond to monetary policy with a lag, because they are fairly sticky.  I’m not sure if this would be a big problem under level targeting, but just to play it safe it might be wise to make sure the wage target addressed the problem of policy lags.

One approach would be to target expected future wages.  Earl Thompson (an under-appreciated genius, who taught David Glasner) got here first, with a sort of wage futures targeting standardproposed back in 1982.  Unfortunately Thompson passed away a few years ago, but his paper is still on the internet, and still being ignored by the profession.  Maybe in about 50 years they’ll catch up to him.

Until then, how can we overcome the policy lag problem if we don’t have a wage futures market?  One approach would be to target total nominal labor compensation.  Suppose there is a negative demand shock, which will eventually reduce nominal wages, or at least nominal wage growth.  But initially there is little or no impact, because of the stickiness of nominal wages.  In that case, employment would fall before wages, and that’s indeed what we tend to see in the US business cycle.  So by targeting total nominal labor compensation, the Fed could indirectly be targeting nominal hourly wages.  Shifts in employment would be a sort of leading indicator of changes in nominal wages.

What could go wrong?  Let’s suppose employment fell for reasons other than sticky wages, in other words the fall in total nominal labor compensation did not presage a fall in hourly wages.  And let’s suppose that the Fed tried to prevent total compensation for falling.  In that case, the Fed would end up pushing nominal hourly wages slightly higher, as hours worked fell.  But hours worked would decline by less than if nominal wages had been held steady.  The bottom line is that an attempt to maintain stable total compensation, if it were unwise from an hourly wage targeting perspective, would lead to a monetary policy that was slightly more countercyclical that desired.  Surely there are worse things in the world than a slightly too countercyclical monetary policy!

I like to think of potential monetary policy rules from a “robustness” perspective. As in “What’s the worst that could happen?”  With the gold standard, some pretty bad things are easy to envision, after all, the real value of gold has been highly unstable in recent decades.  That’s not to say those really bad things would happen, imposition of a gold standard might substantially reduce the volatility of real gold values, but at least one can envision a really bad outcome.  It’s a possibility.

I’d say the same thing regarding money supply rules.  They might work well, but you can envision a big velocity shock.  As far as price level targeting is concerned, you can envision a supply shock that called for a large reduction in real wages, and price level targeting preventing that from occurring in the short run.  I’m not sure that this would be a particularly significant drawback of price level targeting, but it might be.

But with NGDP targeting, it’s hard for me to envision a plausible outcome that is obviously undesirable.  Many people are confused on this point, mentioning the possibility of 5% inflation 0% growth, or 0% inflation and 5% growth.  Yes, the first of these seems undesirable, but the bad outcome doesn’t have any clear relationship to monetary policy failure.  If we had 5% inflation and 0% growth, it would not be obvious whether money was too easy or too tight, even in retrospect. (Many commenters think a monetary policy failure is “obvious” in this case, but can’t agree whether money is too easy or too tight!!  QED.)

A robust policy is one where it’s hard to envision a major policy failure—and by that criterion NGDP targeting seems like a really robust policy.  But total nominal labor compensation targeting seems even slightly more robust than NGDP targeting, because in commodity dependent economies one could imagine a sudden divergence between NGDP growth and total nominal labor compensation growth. And if they diverged, it seems like targeting total compensation would better stabilize the real economy than stabilizing NGDP.

Economics is all about tradeoffs.  The mathematical model I envision would start from the Mankiw-Reis model, but add in a policy lag problem, to justify targeting total labor compensation. As usual, there would be costs and benefits to this approach, and in a prefect information world we could probably do even better, a target that did not necessarily give equal weights to wage growth and hours worked growth.  I suppose you could add that to the model.  But in the imperfect world we actually live in, it seems like stabilizing growth in total nominal labor compensation is going to be pretty close to optimal.  It’s hard to imagine a scenario where that variable is stabilized at 4%/year growth (gradually adjusted over time to reflect changes in labor force growth), and people say, ex post, “policy was obviously too easy” or “obviously too tight”.  What data could they possibly point to, in order to make that claim?