Data Source

China fact of the day

by on September 26, 2016 at 1:34 am in Data Source, Web/Tech | Permalink

The Chinese government estimates females found 55 percent of new Internet companies and more than a quarter of all entrepreneurs are women. In the U.S., only 22 percent of startups have one or more women on their founding teams, according to research by Vivek Wadhwa and Farai Chideya for their book ‘Innovating Women: The Changing Face of Technology.’

That is from Shai Oster and Selina Wang.  Some of that may stem from the one-child policy, but note ex-communist countries have relatively good records of producing female CEOs.

The Decline of Car Culture

by on September 23, 2016 at 2:52 pm in Data Source, Travels | Permalink

UMTRI: About 87 percent of 19-year-olds in 1983 had their licenses, but more than 30 years later, that percentage had dropped to 69 percent. Other teen driving groups have also declined: 18-year-olds fell from 80 percent in 1983 to 60 percent in 2014, 17-year-olds decreased from 69 percent to 45 percent, and 16-year-olds plummeted from 46 percent to 24 percent.

Cars used to represent freedom. Today WiFi does. The decline of young drivers is likely another reason the roads are getting safer.

Hat tip: @counternotions.

Addendum: Steven Kopits argues (youtube) that this has more to do with lack of employment of young people than with a change in culture.

Or just a way to get rid of political opponents?  The news on this front is by no means entirely bad.  Xi Lu and Peter L. Lorentzen report:

In order to maintain popular support or at least acquiescence, autocrats must control the rapacious tendencies of other members of the governing elite. At the same time, the support of this elite is at least as important as the support of the broader population. This creates difficult tradeoffs and limits the autocrat’s ability to enforce discipline. We explore this issue in the context of Chinese leader Xi Jinping’s ongoing anti-corruption campaign. There have been two schools of thought about this campaign. One holds that it is nothing but a cover for intra-elite struggle and a purge of Xi’s opponents, while the other finds more credibility in the CCP’s claim that the movement is sincere. In this article, we demonstrate three facts, using a new dataset we have created. First, we use the political connections revealed by legal documents and media reports to visualize the corruption network. We demonstrate that although many of the corrupt officials are connected, Xi’s most prominent political opponent, Bo Xilai, is less central by any network measure than other officials who were not viewed as challenging Xi’s leadership. Second, we use a recursive selection model to analyze who the campaign has targeted, providing evidence that even personal ties to top leaders provided little protection. Finally, using another comprehensive dataset on the prefectural-city level, we show that the provinces later targeted by the corruption campaign differed from the rest in important ways. In particular, it appears that promotion patterns departed from the growth-oriented meritocratic selection procedures evidence in other provinces. Overall, our findings contradict the factional purge view and are more consistent with the view that the campaign is indeed primarily an attempt to root out systemic corruption problems.

The pointer is from the excellent Kevin Lewis.

Equally, in a world where academics are obliged to offer up each piece of work to be evaluated as internationally significant, world leading etc., they will seek to signal such a rating discursively. A study by Vinkers et al. in the British Medical Journal uncovered a new tendency towards hyperbole in scientific reports. They found the absolute frequency of positive words increased from 2.0% (1974-80) to 17.5% (2014), which amounts to a relative increase of 880% over four decades. 25 individual positive words contributed to the increase, particularly the words “robust,” “novel,” “innovative,” and “unprecedented,” which increased in relative frequency up to 15 000%”). The authors comment upon an apparent evolution in scientific writing to ‘look on the bright side of life’.

That is by Liz Morrish, via Mark Carrigan.

Bowling alone and for peanuts too:

In 1964, “bowling legend” Don Carter was the first athlete in any sport to receive a $1 million endorsement deal ($7.6 million today). In return, bowling manufacturing company Ebonite got the rights to release the bowler’s signature model ball. At the time, the offer was 200x what professional golfer Arnold Palmer got for his endorsement with Wilson, and 100x what football star Joe Namath got from his deal with Schick razor. Additionally, Carter was already making $100,000 ($750,000) per year through tournaments, exhibitions, television appearances, and other endorsements, including Miller, Viceroys, and Wonder Bread.

…Of the 300 bowlers who competed in PBA events during the 2012-2013 season, a select few did surprisingly well. The average yearly salary of the top ten competitors was just below $155,000, with Sean Rash topping the list at $248,317. Even so, in the 1960s, top bowlers made twice as much as top football stars — today, as the highest grossing professional bowler in the world, Sean Rash makes significantly less than a rookie NFL player’s minimum base salary of $375,000.

In 1982, the bowler ranked 20th on the PBA’s money list made $51,690; today, the bowler ranked 20th earns $26,645.

The article, by Zachary Crockett, suggests numerous hypotheses for the economic decline of bowling, but ultimately the answer is not clear to me.  I would suggest the null of “non-bowling is better and now it is better yet.”  A more subtle point is that perhaps bowling had Baumol’s “cost disease,” but under some assumptions about elasticities a cost disease sector can shrink rather than ballooning as a share of gdp.

For the pointer I thank Mike Donohoo.

Total outstanding mortgage loans rose more than 30 percent and new mortgage growth clocked in at 111 percent in the past year. Since June 2012, outstanding mortgage loans have grown at an annualized rate of 30 percent. Predictably, that’s pushed prices higher and higher.

In urban China, the average price per square foot of a home has risen to $171, compared to $132 in the U.S. In first-tier cities such as Beijing and Shenzhen, prices have increased by about 25 percent in the past year. A 100-city index compiled by SouFun Holdings Ltd. surged by a worrisome 14 percent in the last year. Developers are buying up land in some prime areas that would need to sell for $15,000 per square meter just to break even.

That is from Christopher Balding, there is more at the link.  Might as much as 70% of Chinese household wealth be in housing?  Here is some follow-up analysis.

It seems so, at least subject to the usual caveats about happiness studies:

In spite of the great U-turn that saw income inequality rise in Western countries in the 1980s, happiness inequality has fallen in countries that have experienced income growth (but not in those that did not). Modern growth has reduced the share of both the “very unhappy” and the “perfectly happy”. Lower happiness inequality is found both between and within countries, and between and within individuals. Our cross-country regression results argue that the extension of various public goods helps to explain this greater happiness homogeneity. This new stylised fact arguably comes as a bonus to the Easterlin paradox, offering a somewhat brighter perspective for developing countries.

That is from a new paper by Clark AE1, Flèche S2, Senik C3. via Neuroskeptic.  In other words, for the variable that really matters for welfarism, inequality is down not up.  Shout it from the rooftops…

The elephant chart is the tool, developed by Branko Milanovic, often used to show that globalization has hurt the interests of much of the middle class, presumably due to competition with lower wage countries, most notably China.  Now from the Resolution Foundation there is a new study of the matter, based in part on updated data from Milanovic, here are excerpts from Chris Giles and Shawn Donnan at the FT:

The Resolution Foundation found that faster population growth in emerging markets made it difficult to compare the incomes of the lower middle classes over time because their position in global income rankings changed. The larger number of Chinese families made it appear that the US poor were further up the global income scale in 2008 than they were in 1988.

If incomes were unchanged in every country, this population effect alone would lead to apparent drops of 25 per cent in parts of the global income scale associated with poorer people in rich countries. That generated the characteristic “elephant” shape, according to the Resolution Foundation.

These results were exacerbated by outlying factors, such as the former Soviet states of eastern Europe, which had incomes in the same zone and saw them collapse after the fall of communism.

Adjusting the chart for constant populations and removing China, ex-Soviet states and Japan shows a relatively even spread of income growth across the world. China is a clear outlier in performing very strongly.

“Globalisation is not to blame for all the ills of the world,” Torsten Bell, director of the Resolution Foundation, said.

Is it “elephant chart,” “elephant curve,” or “elephant graph”?  I would stress two points.  First, I am not sure the highly aggregated elephant “thing” was so useful to begin with, and indeed you will not see much of it in the MR archives.  Second, there still may be significant cases where globalization has depressed or held down middle class wages.  This is an important revision to how we organize the data, but maybe not a big revision to how we should think about the world.

Here is the actual report, go to Figure 10 on p.23 (this pdf), or try this link, the Resolution Foundation is on a roll these days.

…just as the bulk of the growth in employment can be attributed to a few sectors where productivity is either low or unmeasurable, a whopping 88 per cent of the total rise in the price level boils down to four sectors of the US economy…

How did you guess it was health care, higher education, real estate, and prescription drugs?

…In January 1990, those four product categories only accounted for 30 per cent of the money spent on consumption by the average American. (Housing was about half that.) Even after more than a quarter-century in which prices of these goods and services rose significantly faster than everything else, these four sectors still account for less than 40 per cent of total consumer spending.

Within health care, dentistry has seen the highest rate of price inflation.  Televisions, however, have been falling in price at the rate of about 12 percent a year since 1990.  Luggage, “dishes and flatware,” and household linens are all down in price dramatically, as are telephone and communication services.  Durable goods are down in price by about a third.

That is from Matthew C. Klein at FT Alphaville.

Workplace sentences to ponder

by on September 9, 2016 at 3:04 am in Data Source, Economics | Permalink

It really is time to hurry up and give Bill Baumol that Nobel Prize:

…In the past sixteen years, 94 per cent of the net jobs created were in education, healthcare, social assistance, bars, restaurants, and retail, even though those sectors only employed 36 per cent of America’s workforce at the start of the millennium…

Average hourly pay in these sectors, weighted by their relative sizes, has consistently been about 30 per cent lower than in the rest of the economy…

And since typical jobs in bars, restaurants, and retail involve far fewer hours than normal, weekly pay packets for workers in these growing industries were more than 40 per cent lower than workers in the rest of the economy. Average weekly earnings are now 3 per cent lower than they would have been if the distribution of employment had stayed the same as in January, 2000…

That is from Matthew C. Klein, who is riffing on Ryan Avent, don’t forget Ryan’s new book.

That is my latest column for Bloomberg, here is the method:

Uber calculates figures for surge pricing at times of high demand, but it rounds off. So a computation of market conditions that might lead to a surge price that is 1.249 times higher than normal fares is rounded down to 1.2, but 1.251 would be rounded up to 1.3. Yet the initial, unrounded 1.249 and 1.251 estimates represent almost the same underlying market tightness.

Using data from Uber, the authors therefore could see how the demand for Uber varied with surge prices that vary (say from 20 percent to 30 percent above normal fares) even when market conditions are roughly constant.

Here is the source:

A new paper by Peter Cohen, Robert Hahn, Jonathan Hall, Steven Levitt…and Robert Metcalfe…

They conclude UberX produces about $6.8 billion in consumer surplus a year.  My caveat:

If anything, this method underestimates the worth of Uber, as it doesn’t capture what economists call “option value.” Let’s say you walk home with a guy or gal late at night, hoping something nice will happen. But you’re not quite sure, as he or she might make the wrong noises about a particular political candidate, and then you would wish to bail out quickly. Uber would be the safety net. Most of the time you don’t end up using the service or recording a transaction that would count for this study, but you can start making plans because you know you have Uber as a fallback.

Or consider those urban residents who have ditched their cars altogether. They know they can take Uber to the local market if they need to, even if most of the time they have not run out of milk and dog food. Similarly, the existence of Uber is helping some localities economize on mass transit expenditures.

The study also doesn’t measure how Uber might help get the U.S. to the next level of market innovation, which in this case might mean a network of on-demand, self-driving vehicles.

Do read the whole thing.

Barack Obama’s campaign adopted data but Hillary Clinton’s campaign has been molded by data from birth. Politico has the remarkable story:

Staff in Clinton’s analytics department sit under a sign that hangs from the ceiling with the words “statistically significant” printed on it. And overnight, in some of the few hours that headquarters isn’t whirring with activity, the team’s computers run 400,000 simulations of the fall campaign in what amounts to a massive stress-test of the possibilities on Nov. 8.

…“I have never seen a campaign that’s more driven by the analytics,” [one] strategist said. It’s not as if Kriegel’s data has ever turned around Clinton’s campaign plane; it’s that her plane almost never takes off without Kriegel’s data charting its path in the first place.

…Among the pioneering areas Kriegel’s analytics team has studied, according to people familiar with the operation, is gauging not just whom to talk to, how to talk to them and what to say — but when to say it. Is the best time to contact a voter, say, 90 days before the election? 60 days? One week? The night before? It is a question Obama’s team realized was crucial to mobilizing voters in 2012 but had never been truly analyzed. With a full calendar of competitive primaries, Kreigel and his team had plenty of chances to run rigorous, control-group experiments to ferret out answers to such questions earlier this year.

Here is one fascinating bit on the algorithms that were used to estimate delegate flippability in the primary:

First, the campaign ranked every congressional district by the probability that campaigning there could “flip” a delegate into Clinton’s column. Because every district has a different number of delegates allocated proportionally (in Ohio, for instance, 12 districts had 4 delegates each while one had 17), this involved polling and modeling Clinton’s expected support level, gauging the persuadability of voters in a particular area and then seeing how close Clinton was to a threshold that would tip another delegate in her direction. (At the most basic level, for instance, districts with an even number of delegates, say 4, are far less favorable terrain, as she and Bernie Sanders were likely split them 2-2 unless one of them achieved 75 percent of the vote.)

That so-called “flippability score” was then layered atop which media markets covered which seats. If a media market touched multiple districts with high “flippability” scores, it shot up the rankings. Then the algorithm took in pricing information, and what television programs it predicted the most “flippable” voters would be watching, to determine what to buy.

The irony? More questions are being asked, more data is being collected and more randomized experiments are being run in the effort to win the presidency than will ever be used to choose policy by the presidency. Sad.

There is a new NBER paper on this topic by Fali Huang, Ginger Zhe Jin, and Lixin Colin Xu, the results are striking:

While parental matchmaking has been widespread throughout history and across countries, we know little about the relationship between parental matchmaking and marriage outcomes. Does parental involvement in matchmaking help ensure their needs are better taken care of by married children? This paper finds supportive evidence using a survey of Chinese couples. In particular, parental involvement in matchmaking is associated with having a more submissive wife, a greater number of children, a higher likelihood of having any male children, and a stronger belief of the husband in providing old age support to his parents. These benefits, however, are achieved at the cost of less marital harmony within the couple and lower market income of the wife. The results render support to and extend the findings of Becker, Murphy and Spenkuch (2015) where parents meddle with children’s preferences to ensure their commitment to providing parental goods such as old age support.

Here is an earlier SSRN version.

And self-published “indie” authors — in part because they get a much bigger cut of the revenue than authors working with conventional publishers do — are now making much more money from e-book sales, in aggregate, than authors at Big Five publishers.

And this:

The AAP also reported, though, that e-book revenue was down 11.3 percent in 2015 and unit sales down 9.7 percent. That’s where things get misleading. Yes, the established publishing companies that belong to the AAP are selling fewer e-books. But that does not mean fewer e-books are being sold. Of the top 10 books on Amazon’s Kindle bestseller list when I checked last week, only two (“The Light Between Oceans” and “The Girl on the Train,” both mass-market reissues of novels that have just been made into movies) were the products of major publishers. All the rest were genre novels (six romances, two thrillers) published either by the author or by an in-house Amazon imprint. Their prices ranged from 99 cents to $4.99.

That is from Justin Fox at Bloomberg.