Category: Data Source
Do social media drive the rise in right-wing populism?
Abstract: Many observers are concerned that echo chamber effects in digital media are contributing to the polarization of publics and in some places to the rise of right-wing populism. This study employs survey data collected in France, the United Kingdom, and United States (1500 respondents in each country) from April to May 2017. Overall, we do not find evidence that online/social media explain support for right-wing populist candidates and parties. Instead, in the USA, use of online media decreases support for right-wing populism. Looking specifically at echo chambers measures, we find offline discussion with those who are similar in race, ethnicity, and class positively correlates with support for populist candidates and parties in the UK and France. The findings challenge claims about the role of social media and the rise of populism.
That is from a new paper by Shelley Boulianne, Karolina Koc-Michalska, and Bruce Bimber, via somebody on Twitter.
China fact of the day
China is set to add new coal-fired power plants equivalent to the EU’s entire capacity, as the world’s biggest energy consumer ignores global pressure to rein in carbon emissions in its bid to boost a slowing economy.
Across the country, 148GW of coal-fired plants are either being built or are about to begin construction, according to a report from Global Energy Monitor, a non-profit group that monitors coal stations. The current capacity of the entire EU coal fleet is 149GW.
While the rest of the world has been largely reducing coal-powered capacity over the past two years, China is building so much coal power that it more than offsets the decline elsewhere.
Here is more from Leslie Hook at the FT.
Is the rate of scientific progress slowing down?
That is the title of my new paper with Ben Southwood, here is one segment from the introduction:
Our task is simple: we will consider whether the rate of scientific progress has slowed down, and more generally what we know about the rate of scientific progress, based on these literatures and other metrics we have been investigating. This investigation will take the form of a conceptual survey of the available data. We will consider which measures are out there, what they show, and how we should best interpret them, to attempt to create the most comprehensive and wide-ranging survey of metrics for the progress of science. In particular, we integrate a number of strands in the productivity growth literature, the “science of science” literature, and various historical literatures on the nature of human progress. In our view, however, a mere reporting of different metrics does not suffice to answer the cluster of questions surrounding scientific progress. It is also necessary to ask some difficult questions about what science means, what progress means, and how the literatures on economic productivity and “science on its own terms” might connect with each other.
Mostly we think scientific progress is indeed slowing down, and this is supported by a wide variety of metrics, surveyed in the paper. The gleam of optimism comes from this:
And to the extent that progress in science has not been slowing down, which is indeed the case under some of our metrics, that may give us new insight into where the strengths of modern and contemporary science truly lie. For instance, our analysis stresses the distinction between per capita progress and progress in the aggregate. As we will see later, a wide variety of “per capita” measures do indeed suggest that various metrics for growth, progress and productivity are slowing down. On the other side of that coin, a no less strong variety of metrics show that measures of total, aggregate progress are usually doing quite well. So the final answer to the progress question likely depends on how we weight per capita rates of progress vs. measures of total progress in the aggregate.
What do the data on productivity not tell us about scientific progress? By how much is the contribution of the internet undervalued? What can we learn from data on crop yields, life expectancy, and Moore’s Law? Might the social sciences count as an example of progress in the sciences not slowing down? Is the Solow model distinction between “once and for all changes” and “ongoing increases in the rate of innovation” sound? And much more.
Your comments on this paper would be very much welcome, either on MR or through email. I will be blogging some particular ideas from the paper over the next week or two.
The future of higher education?
Two public four-year institutions, Maine Maritime Academy and the U.S. Merchant Marine Academy, rank in the top 10 colleges with the best long-term returns, while two four-year private colleges, St. Louis College of Pharmacy and Albany College of Pharmacy and Health Sciences, made the top 10 for short-term and long-term returns.
The report ranks 4,526 colleges and universities by return on investment.
Here is one article, with a graphic for the top ten, you will note that Harvard, Stanford, and MIT still do fine. Babson is underrated, as it does much better over longer stretches of time. Here is the Georgetown report.
Opioid deaths are not mainly about prescription opioids
A recent study of opioid-related deaths in Massachusetts underlines this crucial point, finding that prescription analgesics were detected without heroin or fentanyl in less than 17 percent of the cases. Furthermore, decedents had prescriptions for the opioids that showed up in toxicology tests just 1.3 percent of the time.
Alexander Walley, an associate professor of medicine at Boston University, and five other researchers looked at nearly 3,000 opioid-related deaths with complete toxicology reports from 2013 through 2015. “In Massachusetts, prescribed opioids do not appear to be the major proximal cause of opioid-related overdose deaths,” Walley et al. write in the journal Public Health Reports. “Prescription opioids were detected in postmortem toxicology reports of fewer than half of the decedents; when opioids were prescribed at the time of death, they were commonly not detected in postmortem toxicology reports….The major proximal contributors to opioid-related overdose deaths in Massachusetts during the study period were illicitly made fentanyl and heroin.”
The study confirms that the link between opioid prescriptions and opioid-related deaths is far less straightforward than it is usually portrayed. “Commonly the medication that people are prescribed is not the one that’s present when they die,” Walley told Pain News Network. “And vice versa: The people who died with a prescription opioid like oxycodone in their toxicology screen often don’t have a prescription for it.”
That is by Jacob Sullum at Reason, via Arnold Kling.
Pretty stunning data on dating
Interesting throughout, but most of all see pp.5-6, comparing how men rate women to how women rate men. Here is half of that story:
Here is the link, by Dan McMurtrie, via David Perell. The top of p.2 will indicate why friendship may be in decline:
You also can see that meeting on the job peaked in the 1990s, and do I need to tell you about meeting through church and the neighbors? Recommended.
Mortality sentences to ponder paging Ross Douthat too
This paper uses complete death certificate data from the Mortality Multiple Cause Files with American Community Survey data to examine age-specific mortality rates for married and non-married people from 2007 to 2017. The overall rise in White mortality is limited almost exclusively to those who are not married, for men and women…
That is from Philip N. Cohen, via Arnold Kling.
USA fact of the day
More than a third of Ph.D. students have sought help for anxiety or depression caused by Ph.D. study, according to results of a global survey of 6,300 students from Nature.
Thirty-six percent is a very large share, considering that many students who suffer don’t reach out for help. Still, the figure parallels those found by other studies on the topic. A 2018 study of mostly Ph.D. students, for instance, found that 39 percent of respondents scored in the moderate-to-severe depression range. That’s compared to 6 percent of the general population measured with the same scale.
And this:
Twenty-one percent of respondents said they’d been bullied in their programs. Of those, 48 percent said their supervisor was the perpetrator.
Here is the full story from Colleen Flaherty at Inside Higher Ed.
The winners and losers from Airbnb
Overall, renters in New York City suffer a loss of $178mm per annum, as the losses from the rent channel dominate the gains from the host channel. I find that the increased rent burden falls most heavily on high-income, educated, and white renters, because they prefer housing and location amenities most desirable to tourists. Moreover, there is a divergence between the median and the tail, where a few enterprising low-income households obtain substantial gains from home-sharing, especially during demand peaks.
That is from the job market paper of Sophie Calder-Wang of Harvard. You will note there still are likely net gains once you count tourist demand, but of course this helps explain why Airbnb rentals are unpopular in some cities.
Do the rich save more?
Yes, not a surprise but here are some details:
We identify a strong negative relationship between the consumption rate and the lifetime net resource. The predicted APC [average propensity to consume] of the highest net resource cohort about 0.03, which is two standard deviations smaller than the lowest resource cohort.
That is from a new paper by Ilin, Ye, and Yu (Yu is on the job market). Of course this relates to the recent wealth tax debate — almost all of that tax would fall on the investment of the wealthy, not their consumption. Note, however, that if the wealth tax induced more consumption by the wealthy, consumption inequality could quite easily go up.
The racial integration of the Korean War
The racial integration of the US Army during the Korean War (1950-1953) is one of the largest and swiftest desegregation episodes in American history. This paper argues that racial integration improved white survival rates at the expense of blacks, and resulted in less anti-black prejudice among white veterans decades after the war. Using a novel military casualty file, I construct a wartime similarity index to measure the extent of racial integration across military units and time. Using exogenous changes in racial integration, I show that integrated whites were 3% more likely to survive their injuries than segregated whites, whereas integrated blacks were 2% were less likely to survive their injuries than segregated blacks. Given that blacks were initially confined to noncombat support roles, the results reflect a convergence in hazardous combat assignments. To explore the long-term effects of racial integration, I link individual soldiers to post-war social security and cemetery data using an unsupervised learning algorithm. With these matched samples, I show that a standard deviation change in the wartime racial integration caused white veterans to live in more racially diverse neighborhoods and marry non-white spouses. In aggregate, these results are some of the first and only examples of large-scale interracial contact reducing prejudice on a long-term basis.
That is from the job market paper from Daniel Indacochea of the University of Toronto.
Don’t put the state capital too far away sorry Albany but Austin is fine
In this paper I exploit a novel and rich data-set with biographical information of US state legislators to investigate their sorting based on remoteness and attractiveness of the state capital. The main finding of the chapter is that in more remote US state capitals the legislators are on average less educated and experienced. The results are robust to using different measures of remoteness, based on the spatial distribution of the population, and controlling for other characteristics of the legislatures. To identify the causal effect of capitals’ remoteness, I use instrumental variables relying on proximity of capitals to the state centroids. Finally, I also find that legislators’ education affects public good provision and corruption.
That is the abstract of the job market paper of Giuseppe Rossitti from the London School of Economics.
Intelligence predicts educational achievement pretty well
This 5-year prospective longitudinal study of 70,000+ English children examined the association between psychometric intelligence at age 11 years and educational achievement in national examinations in 25 academic subjects at age 16. The correlation between a latent intelligence trait (Spearman’s g from CAT2E) and a latent trait of educational achievement (GCSE scores) was 0.81. General intelligence contributed to success on all 25 subjects. Variance accounted for ranged from 58.6% in Mathematics and 48% in English to 18.1% in Art and Design. Girls showed no advantage in g, but performed significantly better on all subjects except Physics. This was not due to their better verbal ability. At age 16, obtaining five or more GCSEs at grades A⁎–C is an important criterion. 61% of girls and 50% of boys achieved this. For those at the mean level of g at age 11, 58% achieved this; a standard deviation increase or decrease in g altered the values to 91% and 16%, respectively.
That is from a new paper by Deary, Strand, Smith, and Fernandez, via Noah Carl.
Did the medieval church make us WEIRD?
A growing body of research suggests that populations around the globe vary substantially along several important psychological dimensions and that populations characterized as Western, Educated, Industrialized, Rich, and Democratic (WEIRD) are particularly unusual. People from these societies tend to be more individualistic, independent, and impersonally prosocial (e.g., trusting of strangers) while revealing less conformity and in-group loyalty. Although these patterns are now well documented, few efforts have sought to explain them. Here, we propose that the Western Church (i.e., the branch of Christianity that evolved into the Roman Catholic Church) transformed European kinship structures during the Middle Ages and that this transformation was a key factor behind a shift towards a WEIRDer psychology.
That is a new piece in Science by Jonathan F. Schulz, Duman Bahrani-Rad, Jonathan P. Beauchamp, and Joe Henrich, try this link too. This one works for sure. Here is Harvard magazine coverage of the piece. Here is a relevant Twitter thread.
The two Jonathan co-authors are new colleagues of mine at GMU economics, so I am especially excited this work is seeing the light of day in such a good venue.
Harvard sentences to ponder
We show that Harvard encourages applications from many students who effectively have no chance of being admitted, and that this is particularly true for African Americans.
Here is the whole abstract, by Peter Arcidiacono, Josh Kinsler, and Tyler Ransom:
Over the past 20 years, elite colleges in the US have seen dramatic increases in applications. We provide context for part of this trend using detailed data on Harvard University that was unsealed as part of the SFFA v. Harvard lawsuit. We show that Harvard encourages applications from many students who effectively have no chance of being admitted, and that this is particularly true for African Americans. African American applications soared beginning with the Class of 2009, with the increase driven by those with lower SAT scores. Yet there was little change in the share of admits who were African American. We show that this change in applicant behavior resulted in substantial convergence in the overall admissions rates across races yet no change in the large cross-race differences in admissions rates for high-SAT applicants.
And from the paper’s conclusion:
If the goal of recruiting African Americans is not simply to increase the diversity of matriculants, but also to achieve racial balance in the admit pool and/or racial balance in admit rates, then the policy could be deemed a success. As an example, admit rates for African American applicants were twice as large as admit rates for Asian American applicants in 2000, but by 2017 were approximately the same. Why Harvard might careabout the racial distribution of admit rates and applicants is not obvious. What is clear is that each year there are a significant number of African American high school students who have a potentially false impression about their chances of being admitted to Harvard.
Here is the full paper. And here is a recent paper by Howell, Hurwitz, and Smith, with related results.