Data Source

China fact of the day

by on January 20, 2016 at 4:21 am in Current Affairs, Data Source, Economics | Permalink

By almost all measures, China’s $3.3 trillion foreign reserves, the world’s largest, look formidable. Except one.

Compared with the amount of yuan sloshing around in the economy, a proxy for potential capital outflows, China’s firepower seems limited. The dollar reserves account for 15.5 percent of M2, a broad measure of money in circulation. That’s the lowest since 2004 and is less than levels in most Asian economies including Thailand, Singapore, Taiwan, Philippines and Malaysia, according to data compiled by Bloomberg.

That is from Ye Xie.  Please do note all of the caveats and qualifiers in the longer piece.  The claim is not that Chinese reserves are currently in some kind of crisis situation, only that we should not overestimate their import, relative to potential and indeed actual capital outflows.

This is probably one of the most useful things you will learn from MR all year.  It is from Maria Konnikova’s new book The Confidence Game:

In 2010, Nicholas Epley and Tal Eyal of Ben-Gurion University published the results of a series of experiments aimed at improving our person and mind perception skills.  The title of their paper: “How to Seem Telepathic.”  Many of our errors, the researchers found, stem from a basic mismatch between how we analyze ourselves and how we analyze others.  When it comes to ourselves, we employ a fine-grained, highly contextualized level of detail.  When we think about others, however, we operate at a much higher, more generalized and abstract level.  For instance, when answering the same question about ourselves or others — how attractive are you? — we use very different cues.  For our own appearance, we think about how our hair is looking that morning, whether we got enough sleep, how well that shirt matches our complexion.  For that of others, we form a surface judgment based on overall gist.  So, there are two mismatches: we aren’t quite sure how others are seeing us, and we are incorrectly judging how they see themselves.

If, however, we can adjust our level of analysis, we suddenly appear much more intuitive and accurate.  In one study, people became more accurate at discerning how others see them when they thought their photograph was going to be evaluated a few months later, as opposed to the same day, while in another, the same accuracy shift happened if they thought a recording they’d made describing themselves would be heard a few months later [TC: recall Robin Hanson’s near vs. far mode].  Suddenly, they were using the same abstract lens that others are likely to use naturally…

Upon reading this passage I realized I have been thinking in these terms for years, without quite realizing it so explicitly.

One implication: if you feel bad one morning, don’t let it get you down and lower your confidence.  Other people probably won’t notice your problems.

Another implication: you’ll understand yourself better if, in a given moment, you can pretend to distance yourself from some of your immediate impressions of your day, and treat yourself like a piece of your writing which you set aside for a week so you could look at it fresh.

A third implication is this: you can read other people’s moods better by ignoring some of your overall impressions of them, and by focusing on what they might perceive to be small changes in their situation, appearance, or stress levels.

The original research is here, worth a read (pdf).  And here are various reviews of the Konnikova book.

Cerebro2-XM

Wow!! Remember that increasing death rate among middle-aged non-Hispanic whites? It’s all about women in the south (and, to a lesser extent, women in the midwest). Amazing what can be learned just by slicing data.

I don’t have any explanations for this. As I told a reporter the other day, I believe in the division of labor: I try to figure out what’s happening, and I’ll let other people explain why.

That is from Andrew Gelman, there is more at the link.

south

Nature reports that some of the research most-cited by opponents of genetically modifying crops appears to have been manipulated. In particular, images appear to have been altered and images from one paper appear in another paper describing different experiments with different captions.

Papers that describe harmful effects to animals fed genetically modified (GM) crops are under scrutiny for alleged data manipulation. The leaked findings of an ongoing investigation at the University of Naples in Italy suggest that images in the papers may have been intentionally altered. The leader of the lab that carried out the work there says that there is no substance to this claim.

The papers’ findings run counter to those of numerous safety tests carried out by food and drug agencies around the world, which indicate that there are no dangers associated with eating GM food. But the work has been widely cited on anti-GM websites — and results of the experiments that the papers describe were referenced in an Italian Senate hearing last July on whether the country should allow cultivation of safety-approved GM crops.

There is a reason chess evolved the way it did:

…we find that queenly reigns participated more in inter-state conflicts, without experiencing more internal conflict. Moreover, the tendency of queens to participate as conflict aggressors varied based on marital status.

Among married monarchs, queens were more likely to participate as attackers than kings. Among unmarried monarchs, queens were more likely to be attacked than kings. These results are consistent with an account in which queens relied on their spouses to manage state affairs, enabling them to pursue more aggressive war policies. Kings, on the other hand, were less inclined to utilize a similar division of labor.

This asymmetry in how queens relied on male spouses and kings relied on female spouses strengthened the relative capacity of queenly reigns, facilitating their greater participation in warfare.

As Chris Blattman tells us, that is from “A new paper, Queens, by Oeindrila Dube and S.P. Harish.”

The optimists point to the rise in the share of services in nominal GDP, and the corresponding decline in industrial sectors, as shown in the above left graph. Measured in current prices, the rebalancing appears to be well underway, with the share of industrial sectors falling from 47 per cent in 2011 to 40 per cent now.

However, almost the whole of this rebalancing in nominal terms has occurred because of a large drop in the relative price of industrial products compared to services. In real, inflation adjusted terms (above right graph), there has been no rebalancing whatsoever in the past decade taken as a whole (though there has been a percent or two in 2014-15). The needed shift in real resources – labour and capital – out of the moribund sectors has therefore barely started.

That is from the excellent Gavyn Davies, file under “Ouch.”

Addendum: Scott Sumner comments.

There is now a paper on this topic by Azar, Raina, and Schmalz, the main result is this:

We document a secular increase of deposit account maintenance fees and fee thresholds with a new branch-level dataset, as well as substantial cross-sectional variation in these prices and in deposit rate spreads. We then examine whether variation in bank concentration helps explain the variation in prices. The standard measure of concentration, the HHI, is not correlated with any of the outcome variables. A generalized HHI (GHHI) that captures both common ownership (the degree to which banks are commonly owned by the same investors) and cross-ownership (the extent to which banks own shares in each other) is strongly correlated with higher maintenance fees, fee thresholds, and deposit rate spreads. We use the growth of index funds as a source of exogenous variation to establish a causal link from GHHI to higher prices for banking products.

In other words, if companies are owned by the same pension and mutual funds, why should they compete against each other?  Imagine managers given financial incentives for greater stability rather than greater risk-taking, so this does not require a publicly traceable conspiracy.

The first paper on this general question was in fact written by me and Ami Glazer about twenty-five years ago, although we never managed to get it published.  Our biggest problem was perhaps the lack of clear evidence at the time.  This is the best evidence I have seen so far, although I still file this under “speculative”…

For the pointer I thank Uri Bram.

A decade after their military service, white veterans of the draft were earning about 15 percent less than their peers who didn’t serve, according to studies from MIT economist Josh Angrist.

Now, new research suggests that the draft did more than dim the prospects of that earlier generation: The children of men with unlucky draft numbers are also worse off today. They earn less and are less likely to have jobs, according to a draft of a report from Sarena F. Goodman, an economist with the Federal Reserve Board of Governors, and Adam Isen, an economist at the Treasury Department. (A copy was released by the Fed in December, but research does not reflect the opinions of the government.)

The researchers have not nailed down how, exactly, any of this is happening, nor why the disadvantage appears to be over twice as potent for sons than for daughters. But the work is valuable for showing how the circumstances of one’s parents can have lasting repercussions. This is one way that inequality persists through the generations.

That is from Jeff Guo at Wonkblog.

One of the worst-hit markets has been Singapore’s ; lost over a THIRD of its value from April last year

singaporemkt

From David Ingles.

Three Words – Any Place

by on January 14, 2016 at 7:30 am in Data Source, Travel, Web/Tech | Permalink

Here’s an amazing new tool. what3words has identified every one of the 57 trillion 3mx3m squares on the entire planet with just three, easy to remember, words. My office, for example, not my building but my office, is token.oyster.whispering. Tyler’s office just down the hall is barons.huts.sneaky. (Especially easy to remember if you recall this is Tyrone’s office as well.)

Every location on the earth now has a fixed, easily-accessible and memorable address. Unpopulated places have addresses for the first time ever, of course, but now so do heavily populated places like favelas in Brazil where there are no roads or numbered houses. In principle, addressing could be done with latitude and longitude but that’s like trying to direct people to web sites with IP addresses–not good for humans.

Algorithms have assigned words to avoid homophones (sale & sail) and to place similar combos far from one another to aid in error detection. Simpler, more common words are used to address more populated areas and longer words are used in unpopulated areas.

Moreover the three word addresses are available not just in English but in French, Spanish, Portuguese, Swahili, Russian, German, Turkish and Swedish with more languages on the way. The addresses in other languages are not translations but unique 3 word addresses in those languages.

All of this is available in a small app so that it can be used even offline on a simple smartphone. Find your address here.

Hat tip: The Browser.

…it appears that there was roughly $750 billion of capital outflow in 2015

The Aaron Back WSJ story is here, or try through here.  And here is a Victor Shih paper (pdf) on the fragility of China’s reserves, from 2011 but still of relevance.  One key point is that high wealth inequality in China makes capital flight easier to accomplish.

By the way, here is a profound Andrew Batson post on why China is not specializing much in its exports.  That is the good news, the bad news is that Chinese leveraging does not seem to be slowing down at all.

Here’s the latest video from our MRUniversity course on the Principles of Macroeconomics; it’s an introduction to growth rates and comparing countries across time.

I don’t think climate change is the right framing for this effect, nonetheless this is an interesting result, with the subtitle “Evidence from a billion tweets.”  Here is the abstract:

What is the welfare cost of environmental stress? The change in amenity values resulting from temperature increases may be a substantial unaccounted-for cost of climate change. Because there is no explicit market for climate, prior work has relied on cross-sectional variation or survey data to identify this cost. This paper presents an alternative method of estimating preferences over nonmarket goods which accounts for unobserved cross-sectional and temporal variation and allows for precise estimates of nonlinear effects. Specifically, I create a rich dataset on hedonic state: a geographically and temporally dense collection of updates from the social media platform Twitter, scored using a set of both human- and machine-trained sentiment analysis algorithms. Using this dataset, I find limited evidence of temperature effects on hedonic state in low temperatures and strong evidence of a sharp decline in hedonic state above 70◦F. This finding is robust across all measures of hedonic state and to a variety of specifications.

That is the job market paper (pdf) by Patrick Baylis, a job candidate from UC Berkeley.

And here is a new result that Canadians are more polite on Twitter, I wonder what happens if you control for temperature…

For the pointer I thank Samir Varma.

Why are Amazon ebook reviews from US readers more important than reviews from international readers?

Have you noticed that reviews from Amazon.com are aggregated across all other international Amazon sites, but that the reverse is not true? If someone kindly posts a review of a book on Amazon.co.uk, it is stuck there, and not aggregated to Amazon.com. Why? Is a UK review less valuable than a US review? Are reviews from Canadians, Australians or India inferior to US reviews?

Source here, via Sofia Tania.

Max Mendez Beck emails me:

Given the advent of statistics in sports that occurred in the last five years, I am struck by how well soccer works as a metaphor for current epistemological debates regarding the use (and primacy) of quantitative versus qualitative data in social science research. While the three major American sports (football, basketball, and baseball) have been overtaken by a quantitative obsession (count how many tables and numbers you see on an average ESPN show), soccer is emblematic of a sport that is quite difficult to measure quantitatively.

Consider how easy it is to determine who did well in an average NBA game without needing to even watch it. You can just look at points, assists, rebounds, steals, turnovers, etc. In soccer, individual statistics are almost nonexistent. Even as major sports channels have attempted to incorporate quantitative measures into their soccer broadcasts–for example, by showing the number of kilometers a player has covered when he gets subbed out (a pretty uninformative statistic on its own)–these numbers have not caught on with the regular fan.

While in basketball everyone debates about who “the best ever” is by referring to their career averages in points, field goal percentage, PER, etc. In soccer the only statistic that is ever used is goals scores, and goals scored is only one small dimension of a player, even smaller if he is not a striker. It would be silly to judge Andrés Iniesta or Zinedine Zidane on how many goals they scored in a season.

So what is it about soccer that makes it so hard to quantify? Or what makes American sports so easy to measure? One obvious answer is the length of the units that can be easily separated and analyzed. In basketball its a maximum of 24 seconds, in baseball its essentially a pitch (or an at bat), and in football its each snap. For soccer, the only apparent unit to separate out is the 45 minute halftime mark. Changes in possession could be another measure, but even then a team’s single possession could be several minutes long.

However, the real challenge comes in measuring individual accomplishments. Just recently I was watching a Barcelona game and Iniesta clearly was having an amazing game (as was mentioned several times by the announcer), and yet the things that made him have a great game were only describable in words and not numbers. There was a beautiful and sudden “regate” or dribble around a defender before he passed it on to a teammate for a quick counter attack. There was the beautiful pass between defenders that led to an assist for the first goal. There was the sudden change in direction and over the top pass to the other side of the field that put the defenders on their heels. Many of these moves are incredibly situational; they have to do with the rhythm of the game and the need to speed it up or slow it down. Nothing in the boxscore could truly capture these attributes.

So the question is: Is soccer something that can’t be measured in numbers?

Here are various readings on the topic.