Category: Data Source
That new paper by Daniel Barth, Nicholas W. Papageorge and Kevin Thom is attracting a great deal of attention and also some controversy. Here is the first sentence of the abstract:
We show that genetic endowments linked to educational attainment strongly and robustly predict wealth at retirement.
But it’s not mainly about IQ. I found this to be the most interesting part of the paper, noting that EA is a polygenic score:
Our use of the EA score as a measure of biological traits linked to human capital is related to previous attempts in the literature to measure ability through the use of tests scores such as IQ or the AFQT…We note two important differences between the EA score and a measure like IQ that make it valuable to study polygenic scores. First, a polygenic score like the EA score can overcome some interpretational challenges related to IQ and other cognitive test scores. Environmental factors have been found to influence intelligence test results and to moderate genetic influences on IQ (Tucker-Drob and Bates, 2015). It is true that differences in the EA score may reflect differences in environments or investments because parents with high EA scores may also be more likely to invest in their children. However, the EA score is fixed at conception, which means that post-birth investments cannot causally change the value of the score. A measure like IQ suffers from both of these interpretational challenges. High IQ parents might have high IQ children because of the genes that they pass on, but also because of the positive investments that they make…Compared to a cognitive test score like IQ, the EA score may also measure a wider variety of relevant endowments. This is especially important given research, including relatively recent papers in economics, emphasizing the importance of both cognitive and non-cognitive skills in shaping life-cycle outcomes (Heckman and Rubinstein, 2001). Existing evidence suggests a correlation of approximately 0.20 between a cognitive test score available for HRS respondents and the EA score (Papageorge and Thom, 2016). This relatively modest correlation could arise if both variables measure the same underlying cognitive traits with error, or if they measure different traits. However, Papageorge and Thom (2016) find that the relationship between the EA score and income differs substantially from the relationship between later-life cognition scores and income, suggesting that the EA score contains unique information…
…we interpret the EA score as measuring a basket of genetic factors that influence traits relevant for human capital accumulation.
If I understand the paper correctly, the polygenic score is what predicts well from the genetic data set, it is not a “thing with a known nature.” And I believe the results are drawn from the same 1.1 million person data set as is used in this Nature paper.
The bottom of the educational distribution is doing very very poorly:
Changing mortality rates among less educated Americans are difficult to interpret because the least educated groups (e.g. dropouts) become smaller and more negatively selected over time. New partial identification methods let us calculate mortality changes at constant education percentiles from 1992–2015. We find that middle-age mortality increases among non-Hispanic whites are driven almost entirely by changes in the bottom 10% of the education distribution. Drivers of mortality change differ substantially across groups. Deaths of despair explain a large share of mortality change among young non-Hispanic whites, but a small share among older whites and almost none among non-Hispanic blacks.
That is from Paul Novosad and Charlie Rafkin.
There has been a 12 percentage point decline in the share of Democrats who view the impact of churches positively (from 50% to 38%); Democrats are now evenly divided in these attitudes (38% positive, 40% negative). In five previous Pew Research Center surveys over the past decade, significantly more Democrats viewed the impact of religious organizations positively than negatively. Republicans continue to view religious organizations much more positively than do Democrats (68% positive in the new survey).
Republicans and Democrats also have moved further apart in opinions about how banks and large corporations affect the country. Half of Republicans say banks and other financial institutions have a positive effect on the U.S., compared with just 27% of Democrats. Two years ago, the partisan gap was less pronounced (46% of Republicans, 33% of Democrats).
Today, Republicans are more than twice as likely than Democrats to say large corporations have a positive effect on the way things are going in the U.S. (45% vs. 17%). In 2016, 34% of Republicans and 26% of Democrats said they had a positive effect.
There are further topics considered at the link. At least labor unions are more popular.
Read the whole post, but are is an excerpt:
With oil hovering around $100 a barrel we did see airfares rise 2011-2014 but then return to long run trend, and indeed real airfares inclusive of fees were lower 2016-2018 than in 2010…
Indeed the drivers of increased airline profits are:
- lower fuel prices
- richer co-brand credit card deals.
As I’ve pointed out in many recent quarters the entirety of American Airlines profit has been accounted for by its co-brand credit card deals and not flying. The richness of these deals for airlines has grown markedly. This may be partly attributable to industry consolidation (fewer airlines for banks to negotiate with) and partly due to American Express losing its deal with Costco which set off a chain of renegotiations at higher price points.
Consolidation has improved airlines’ bargaining position vis-a-vis banks more so than consumers. And indeed with fuel prices up from three and four years ago profits are down…
Moreover it’s the ultra low cost carriers – Spirit, Frontier, and to a lesser extent Allegiant – that have been the driving forces in the U.S. airline industry.
Do read the whole thing.
Here is an email from Daniel Stone at Bowdoin, I am not imposing a double-formatting on it for ease of reading and formatting:
“Dear Tyler (if I may),
I’m a big fan of your work in general, and MR in particular, and think that you do as good a job as anyone at exploring a variety of political perspectives, and sharing related (diverse) research.
Still, you’re human after all J. I’ve always been curious if there are systematic patterns in your writing or links you post.
It occurred to me a couple weeks ago that you sometimes describe research as speculative or imply this by adding a question mark to the end of the link (the example that made me notice this was: “Minimum wage effects and monopsony?”https://marginalrevolution.com/marginalrevolution/2019/07/thursday-assorted-links-215.html). At other times your link simply states the main research finding or directly quotes from the paper or its title.
So, while it might be hard to identify a general bias in your links – even if the majority were, say, “pro-liberal”, this wouldn’t necessarily mean *you* were biased, since the majority of good research out there could be pro-liberal, using the added “?”s provides an identification strategy: if you were more likely to add a ? for research that leans one political direction or the other, that would suggest a bias on your part.
As a fun side project, that I thought might also have some value given the importance of MR and understanding bias more generally, I had my RA (Maggie Hanson, cc’d) grab all your links from Assorted Links posts to social science research this year (as of a few days ago). Together we coded the ‘slant’ of each as L, R or N (neutral) – depending on whether the research supports regulation, indicates market failure, etc (admittedly our process here was not extremely scientific). She also recorded whether your link text is phrased as a question (or notes that the finding is speculative, which you did a couple times and seems similar). In addition, for link text phrased as a question, we also noted whether this text is a direct reference to the research paper’s title, as this means you didn’t actually add the “?”.
We did a bit of very basic analysis, here are results:
The distribution of slant across links is quite balanced, but leans left:
. tab sla
(L/N/R) | Freq. Percent Cum.
L | 35 29.17 29.17
N | 58 48.33 77.50
R | 27 22.50 100.00
Total | 120 100.00
But you were slightly more likely to phrase your link as a question for “L” links vs for Rs (9/35 for Ls vs 5/27 for Rs):
. tab slant endswith
Slant | Ends with ?
(L/N/R) | n y | Total
L | 26 9 | 35
N | 48 10 | 58
R | 22 5 | 27
Total | 96 24 | 120
And you were a bit more likely to do this for links that were not direct quotes of article titles that were questions (7/33 = 0.21 for Ls vs 2/24=0.083 for Rs):
tab slant endswith if linktex==”n”
Slant | Ends with ?
(L/N/R) | n y | Total
L | 26 7 | 33
N | 48 8 | 56
R | 22 2 | 24
Total | 96 17 | 113
But the magnitude of this difference is not large (and I bet not statistically significant), and the large majority of both L and R links were presented by you without questions marks.
Bottom line: you do present a quite balanced set of research findings, the general distribution leans left but it is hard to interpret this (without knowing the slants of research in general or the slant of research you post elsewhere, aside from Assorted Links). And there is suggestive evidence of a small tendency for you to be more questioning of research supportive of liberal/leftist policies.
Here’s a link to the data:
This includes a sheet with all the links that end in ?, that aren’t quotes of article titles, and their slants.
I wanted to share this with you before sharing with others. Please feel free to let me know any questions or comments!
Thanks, and thanks again for all your work. All the best – Dan”
We find the chained direct-out-of-pocket CPI for generic prescription drugs declines by about 50% between 2007 and 2016, while the total CPI [what the dispensing pharmacy receives, the difference being generated by co-pay rates] falls by nearly 80% over the same time period. The smaller decline in the direct out-of-pocket CPI than in the total CPI is due in part to consumers’ increasingly moving away from fixed copayment benefit plans to pure coinsurance or a mixed package of coinsurance and copayments. While consumers are experiencing more cost sharing that in fact shifts more of the drug cost burden on to them, on balance in the US consumers have experienced substantial price declines for generic drugs.
That is from a new NBER working paper by Richard G. Frank, Andrew Hicks, and Ernst R. Berndt.
The social conservatives are turning out to be right about many things:
In this paper we evaluate the degree to which the adverse parental divorce effect on university education operates through deprivation of economic resources. Using one million siblings from Taiwan, we first find that parental divorce occurring at ages 13-18 led to a 10.6 percent decrease in the likelihood of university admission at age 18. We then use the same sample to estimate the effect of parental job loss occurring at the same ages, and use the job-loss effect as a benchmark to indicate the potential parental divorce effect due to family income loss. We find the job-loss effect very little. Combined, these results imply a minor role played by reduced income in driving the parental divorce effect on the child’s higher education outcome. Non-economic mechanisms, such as psychological and mental shocks, are more likely to dominate. Our further examinations show that boys and girls are equally susceptible, and younger teenagers are more vulnerable than the more mature ones, to parental divorce.
That is from a recent NBER Working Paper by Yen-Chien Chen, Elliott Fan, and Jin-Tan Liu.
Nonetheless, I suspect there is more to it than this. I can’t speak to the circumstances of Taiwan, but on average I think of women as suffering the most from non-divorce, not men. It is not sufficiently discussed how much the higher growth rates of earlier times might have been achieved at the expense of women, at least in the short run. It might in some ways boost economic growth to, through discrimination, allocate more very smart women to the teaching of grade school, and to keep them in unhappy marriages, “for the sake of the children.” And yet those outcomes are entirely unjust, and the contemporary world has decided it will not accept them.
Here is the VoxEu piece, excerpt:
Gross or net product includes gross or net investment when it occurs, and includes the corresponding present value a second time when additional rental income results from the enhanced stock of capital. Thus, from the standpoint of the intertemporal budget constraint for consumption, aggregates such as GDP and national income overstate the resources available for consumption.
I quantify the double-counting problem within a standard model used by economists, the steady state of the neoclassical growth model (Barro 2019). With reasonable parameters, GDP overstates the potential for consumption by 28%, while national income exaggerates this potential by 9%. Thus, for example, in international comparisons, countries that invest and save larger fractions of their incomes artificially appear to be too rich when gauged by per capita GDP.
Using typical parameter values, the capital income share based on GDP is around 40%. With the conventional adjustment to allow for depreciation, the computed capital income share for national income is reduced to 24%. With the additional adjustment to calculate permanent income, the share falls further, to 16%. Hence, the proper accounting treatment of investment makes a major difference in calculating the division of aggregate income between capital and labour.
I wish I knew this area of national accounting better than I do — opinions?
Here is the full NBER working paper. All via Ilya Novak.
That is the title of a new and important paper by Andrea L. Eisfeldt, Antonio Falato, and Mindy Z. Xiaolan. It seems that perhaps the share of labor in gdp has not fallen much after all:
The widespread and growing practice of equity-based compensation has transformed high-skilled labor from a pure labor input into a class of “human capitalists”. We show that high-skilled labor income in the form of equity claims to firms’ future dividends and capital gains has dramatically increased since the 1980s. Indeed, in recent years, equity-based compensation represents almost 45% of total compensation to high-skilled labor. Ignoring such income results in incorrect measurement of the returns to high-skilled labor, with important implications for macroeconomics. Including equity-based compensation to high-skilled labor cuts the total decline in the labor share since the 1980’s by over 60%, and completely reverses the decline in the high skilled labor share to an increase of almost 1%. Correctly measuring the return to high-skilled labor can thus resolve the puzzling lack of a skill premium in recent data, as well as the corresponding lack of evidence of complementarity between high-skilled labor and new-economy physical capital. Moreover, tackling the capital structure question of who owns firms’ profits is necessary to provide a link between changing factor shares and changing income and wealth shares. We use an estimated model to understand the rise of human capitalists in an economy with declining capital goods prices. Finally, we present corroborating cross section and time series evidence for complementarity between high-skilled labor and physical capital using our corrected measure of the total return to human capitalists.
Since smart people are bearing more and more risk, this may be another reason why income inequality is rising.
Via the excellent Kevin Lewis.
Most accounts of international negotiations suggest that global agreements are individually crafted and distinct, while some emerging scholarship suggests a heavy reliance on models and templates. In this research, we present a comprehensive test of whether new international treaties are heavily copied and pasted from past ones. We specify several reasons to expect widespread copying and pasting, and argue that both the most and least powerful countries should be most likely to do so. Using text analysis to examine several hundred preferential trade agreements (PTAs), we reveal that most PTAs copy a sizable majority of their content word for word from an earlier agreement. At least one hundred PTAs take 80 percent or more of their contents directly from a single, existing treaty—with many copying and pasting 95 percent or more. These numbers climb even higher when we compare important substantive chapters of trade agreements, many of which are copied and pasted verbatim. Such copying and pasting is most prevalent among low-capacity governments that lean heavily on existing templates, and powerful states that desire to spread their preferred rules globally. This widespread replication of existing treaty language reshapes how we think about international cooperation, and it has important implications for literatures on institutional design, policy diffusion, state power, and legal fragmentation.
One in five U.S. high-technology firms are led by CEOs with hands-on innovation experience as inventors. Firms led by “Inventor CEOs” are associated with higher quality innovation, especially when the CEO is a high-impact inventor. During an Inventor CEO’s tenure, firms file a greater number of patents and more valuable patents in technology classes where the CEO’s hands-on experience lies. Utilizing plausibly exogenous CEO turnovers to address the matching of CEOs to firms suggests these effects are causal. The results can be explained by an Inventor CEO’s superior ability to evaluate, select, and execute innovative investment projects related to their own hands-on experience.
The AEA has long had a data repository but no one was responsible for examining the data or replicating a paper’s results and confidential data was treated as an exception. All that is about to change. The AEA has hired a Data Editor, Lars Vilhuber. Vilhuber will be responsible for verifying that the author’s code produces the claimed results from the given data. In some cases Vilhuber will even verify results from raw data all the way to table output.
The new data policy is a significant increase in the requirements to publish in an AEA journal. It takes an immense amount of work to document in a replicable way every step of the empirical process. It’s all to the good, of course, but it is remarkable how little economists train our students in these techniques and make no mistake writing code to be replicable from day one is an art and a science and it needs to be part of the econometrics sequence. All hail Gentzkow and Shapiro!
Here’s more information:
On July 10, 2019, the Association adopted an updated Data and Code Availability Policy, which can be found at https://www.aeaweb.org/journals/policies/data-code. The goal of the new policy is to improve the reproducibility and transparency of materials supporting research published in the AEA journals by providing improved guidance on the types of materials required, increased quality control, and more review earlier in the publication process.
What’s new in the policy? Several items of note:
A central role for the AEA Data Editor. The inaugural Data Editor was appointed in January 2018 and will oversee the implementation of the new policy.
The policy now clearly applies to code as well as data and explains how to proceed when data cannot be shared by an author. The Data Editor will regularly ask for the raw data associated with a paper, not just the analysis files, and for all programs that transform raw data into those from which the paper’s results are computed. Replication archives will now be requested prior to acceptance, rather than during the publication process after acceptance, providing more time for the Data Editor to review materials.
Will the Data Editor’s team run authors’ code prior to acceptance? Yes, to the extent that it is feasible. The code will need to produce the reported results, given the data provided. Authors can consult a generic checklist, as well as the template used by the replicating teams.
Will code be run even when the data cannot be posted? This was once an exemption, but the Data Editor will now attempt to conduct a reproducibility check of these materials through a third party who has access to the (confidential or restricted) data. Such checks have already been successfully conducted using the protocol outlined here.
Scholars of foreign policy preference formation have accepted what Rathbun et al. (2016) call the “vertical hierarchy model,” which says that policy attitudes are determined by more abstract moral ideas about right and wrong. This paper turns this idea on its head by introducing the prejudice first model, arguing that foreign policy preferences and orientations are in part driven by attitudes towards the groups being affected by specific policies. Three experiments are used to test the utility of this framework. First, when conservatives heard about Muslims killing Christians, as opposed to the opposite scenario, they were more likely to support a humanitarian intervention and agree that the United States has a moral obligation to help those persecuted by their governments. Liberals showed no religious preference. When the relevant identity group was race, however, liberals were more likely to want to help blacks persecuted by whites, while conservatives showed no racial bias. In contrast, the degree of persecution mattered relatively little to respondents in either experiment. In another experiment, conservatives adopted more isolationist policies after reading a text about the country becoming more liberal, as opposed to a paragraph that said the United States was a relatively conservative country. The treatment showed the opposite effect on liberals, although the results fell just short of statistical significance. While not necessarily contradicting the vertical hierarchy model, the results indicate that prejudices and biases not only help influence foreign policy attitudes, but moral perceptions of right and wrong in international politics.
That is from Richard Hanania and Robert Trager. File under “Mood Affiliation…”
This paper presents on three new styled facts: first, schools of public affairs hire many economists; second, those economists are disproportionately female; and third, salaries in schools of public affairs are, on average, lower than salaries in mainline departments of economics. We seek to understand the linkage, if any, among these facts. We assembled a unique database of over 2,150 faculty salary profiles from the top 50 Schools of Public Affairs in the United States as well as the corresponding Economics and Political Science departments. For each faculty member we obtained salary data to analyze the relationship between scholarly discipline, department placement, gender, and annual salary compensation. We found substantial pay differences based on departmental affiliation, significant differences in citation records between male and female faculty in schools of public affairs, and no evidence that the public affairs discount could be explained by compositional differences with respect to gender, experience or scholarly citations.
That is the abstract of a new NBER working paper by Lori L. Taylor, Kalena E. Cortes, and Travis C. Hearn. I have a vague sense that the same might be true of public policy schools as well. Why?