Edward Conard

Top Ten New York Times Bestselling Author

  • “…a must-read for serious students of economic policy.” - Glenn Hubbard, Dean, Columbia Business School, and former Chairman of the Council of Economic Advisers
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “…a comprehensive explanation of the modern economy.” - Julian Robertson, Founder, Tiger Management
  • “Unintended Consequences offers deep and well-argued analyses on almost every issue.” - The New York Times
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “Unintended Consequences is far smarter and more thought-provoking than most economics written for the general public” - Greg Mankiw, Harvard University, Former Chairman of the Council of Economic Advisors
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…challenges misconceptions that distort our economic debates.” - Arthur Brooks, President of the American Enterprise Institute
  • “…a fresh argument for the productive value of inequality.” - David Autor, Professor of Economics, Massachusetts Institute of Technology
  • “A full-throated defense of economic dynamism.” - The Wall Street Journal
  • “…serious thinking for serious thinkers. …a thought-provoking blueprint for growing middle- and working-class incomes.” - Mitt Romney, former Governor of Massachusetts
  • “…a very valuable contribution.” - Larry Summers, former Secretary of the Treasury and director of the National Economic Council, president emeritus, Harvard University
Upside of Inequality Oxford Unintended Consequences

Macro Roundup


Friday, July 24, 2015

AI Summary. Household total hours worked rise with income, with top-quintile households consistently working more hours than lower-quintile households.

Shaylee Wheeler N/A
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@ShayleeWheeler The data on household total hours worked by income quintile reveals significant disparities in labor distribution across economic strata, with the top quintile consistently working more hours compared to lower quintiles.

Do higher-income households work more hours than lower-income households?

Core argument: Household total hours worked rise with income in the PISD data, with the top quintile consistently working more hours than lower quintiles.

The data on household total hours worked by income quintile reveals significant disparities in labor distribution across economic strata. The top quintile, representing the highest earners, consistently works more hours compared to lower quintiles, highlighting a potential correlation between income and labor input. This trend underscores the economic divide, as higher-income households not only earn more but also contribute more labor hours, potentially influencing productivity and GDP growth. Conversely, lower quintiles exhibit fewer hours worked, which may reflect barriers to employment or access to opportunities. These insights are crucial for policymakers aiming to address income inequality and improve labor market participation across all income levels. Understanding these dynamics can inform strategies to enhance economic inclusivity and optimize workforce utilization.

Takeaways by Macro Roundup® AI

  1. Household total hours worked rise with income in the PISD data, with the top quintile consistently working more hours than lower quintiles.
  2. Lower income quintiles record fewer hours worked in the PISD data, which may reflect barriers to employment or access to opportunities.
  3. The PISD breakdown by quintile shows higher-income households both earn more and contribute more labor hours, a pattern relevant to policymakers addressing income inequality and labor market participation.

I copied that specific sheet into an entirely new notebook - hopefully this works. (I assumed you wanted the data sheet on Household Total Hours Worked by Income Quintile, but I can grab any of them).

I made a copy - try this one?

Spreadsheet attached.

https://www.dropbox.com/s/a7711k65nahsqmn/Basic%20results.xlsx

Monday, January 25, 2016

AI Summary. Only 10% of the 1982 Forbes 400 remained on the list in 2012, evidence cited to argue that wealth at the top is dynamic and increasingly self-made rather than inherited. Economists analyzing the data attribute rising inequality to changes in the marginal productivity of labor, alongside technology and globalization, rather than a rigged system.

James Pethokoukis American Enterprise Institute
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Larry Summers challenges Thomas Piketty’s view on wealth inequality, emphasizing that it is driven by labor dynamics rather than inherent flaws in capitalism.

Is rising wealth inequality driven by labor productivity rather than inheritance?

Core argument: Only 10% of the 1982 Forbes 400 remained on the list in 2012, which Larry Summers cites as evidence of dynamic wealth creation rather than entrenched fortunes.

Larry Summers challenges Thomas Piketty's view on wealth inequality, emphasizing that it is driven by labor dynamics rather than inherent flaws in capitalism. Summers highlights that only 10% of the 1982 Forbes 400 remained in 2012, illustrating the dynamic nature of wealth creation. This shift is attributed to economic processes that reward creators and directors who eliminate middlemen, allowing them to capture more benefits. Economists Steven Kaplan and Joshua Rauh support this by showing that rising inequality is linked to changes in the marginal productivity of labor. Their analysis indicates a trend towards self-made fortunes, with many wealthy individuals coming from modest backgrounds and starting their own businesses. This suggests that technology and globalization, rather than a rigged system, are key drivers of inequality.

Takeaways by Macro Roundup® AI

  1. Only 10% of the 1982 Forbes 400 remained on the list in 2012, which Larry Summers cites as evidence of dynamic wealth creation rather than entrenched fortunes.
  2. Larry Summers argues, against Thomas Piketty, that wealth inequality stems from labor dynamics rewarding creators and directors who eliminate middlemen and capture more of the benefits, not from inherent flaws in capitalism.
  3. Steven Kaplan and Joshua Rauh link rising inequality to changes in the marginal productivity of labor and find a trend toward self-made fortunes, with many wealthy individuals from modest backgrounds starting their own businesses.

James Pethokoukis, "No, the US economy is not a rigged game," American Enterprise Institute, January 11, 2016, https://www.aei.org/technology-and-innovation/no-the-us-economy-is-not-a-rigged-game/

No, the US economy is not a rigged game

No, the US economy is not a rigged game: Extended Excerpt Image 1


In a 2014 a16z podcast from the Andreessen Horowitz venture capital firm, economist Larry Summers clearly analyzed the inequality theories of Thomas Piketty, author of the best-selling “Capital in the Twenty-First Century.” While Summers thinks Piketty’s description of “what” is happening is important, he is skeptical of the “why” of perpetually rising wealth inequality — particularly Piketty’s assertions about rates of return on capital and savings. These are criticisms which many other economists have since offered and expanded upon.

So rather than highlight that bit of the chat, here is Summers on Piketty’s fundamental misunderstanding about how American capitalism works:

His is a theory of accumulation by the fortunate, but what is striking — and you certainly see it out here in particularly dramatic form is how dynamic the process of wealth accumulation is. Forbes looked in 2012 at its 1982 Forbes 400 and found that less than 10 percent of those who were on the 1982 Forbes 400 were still part of the 2012 Forbes 400.

Now in part 30 years is a long time and people die, and their fortunes get split up several ways. But Mark Zuckerberg had not been born in 1982. So there are all kinds of fortunes being created, and I think a better way to think about the sources of large fortunes is in terms of the economic processes that take all the middlemen out and allow the creator, director, designer to capture a larger part of the benefits.

And that’s what’s behind entrepreneurial fortunes. In a different sense, with much better information systems, much greater capacity to execute, the judgments that a CEO makes as to broad strategy and direction become a larger part of business success than they once were. So entrepreneurial CEOs are rewarded heavily, not just in new companies but in traditional companies. And in a world of this kind of ferment there’s enormous gains to be had by being able to judge the ways in which things are moving and judge who’s doing the best job of taking advantage of the opportunities — and that contributes to the fortunes that are made in the financial sector. So I think it is much better to analyze inequality in terms of the fundamental forces of technology and globalization than in terms of any kind of inherent cultural contradiction of capitalism.

Summers’s intuition and observation is supported by the work of economists Steven Kaplan and Joshua Rauh. In their 2013 paper, “It’s the Market: The Broad-Based Rise in the Return to Top Talent,” they conclude the increase in high-end “inequality has been driven by economic factors that have altered the marginal productivity of certain kinds of labor.” Shorter: The economy is not a rigged game that only benefits those born wealthy or CEOs who manipulate pliant corporate boards.

Likewise, their analysis of the Forbes data shows a trend “away from people who grew up wealthy and inherited businesses towards those who grew up with more who grew up wealthy and inherited businesses towards those who grew up with more modest wealth in the family and started their own businesses.” While it is important to note the rich are getting richer, it is equally important to understand why.

AI Summary. A study in the American Economic Journal analyzing 50,000 papers from top American economics journals estimates that 10–20% of results reported as statistically significant may be questionable. The authors find a double-humped distribution of test statistics, with results "missing" just short of the standard significance cutoff, suggesting reporting bias.

Economist Staff The Economist
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A recent study found 10-20% of results in top American journals may be questionable, raising concerns about the reliability of economic research.

Are statistically significant results in top economics journals reliable?

Core argument: A study in the American Economic Journal analyzing 50,000 papers published in top American journals from 2005–2011 finds that 10–20% of results deemed statistically significant may be questionable.

A recent study published in the American Economic Journal analyzed 50,000 papers from 2005-2011 in top American journals, revealing that 10-20% of results deemed statistically significant may be questionable. The study identified a peculiar double-humped distribution of z-scores, indicating "missing" results just outside the standard significance cut-off. This suggests potential bias in reporting, as some results may have been manipulated to achieve significance. Such findings raise concerns about the reliability of economic research, emphasizing the need for rigorous scrutiny and transparency in data reporting to ensure the integrity of published results. This has significant implications for economists, policymakers, and business professionals who rely on these studies for data-driven decision-making.

Takeaways by Macro Roundup® AI

  1. A study in the American Economic Journal analyzing 50,000 papers published in top American journals from 2005–2011 finds that 10–20% of results deemed statistically significant may be questionable.
  2. The study identified a double-humped distribution of z-scores with "missing" results just outside the standard significance cut-off, pointing to potential bias in reporting.
  3. The findings point to a need for rigorous scrutiny and transparency in data reporting, with implications for economists, policymakers, and business professionals who rely on published studies for decision-making.

Economist Staff, "Are results in top journals to be trusted?"The Economist, January 21m 2016. Available at:http://www.economist.com/blogs/freeexchange/2016/01/fudging-hell

"....But apaperjust published in the American Economic Journal finds evidence of a different sort of bias, closer to the source. Called "Star Wars, the empirics strike back", it analyses 50,000 papers published between 2005 and 2011 in three top American journals. It finds that the distribution of results (as measured by z-score, a measure of how far away a result is from the expected mean) has a funny double-humped shape (see chart). The dip between the humps represents "missing" results, which just happen to be in a range just outside the standard cut-off point for statistical significance (where significance is normally denoted with stars, though the name may also be something to do with a film recently released—file under 'economists trying to be funny').Their results suggest that among the results that are only just significant, 10-20% have been fudged...."

Ed Comment:A study of 3 of the most prestigious economic journals—American Economic Review, the Quarterly Journal of Economics, and the Journal of Political Economy—suggests 10 to 20% of the results may be fudged. The results suggest researches find ways to push near-significant results over the statistical threshold necessary for publication. Rather than finding smoothly distributed study results across the range of statistical significant the find an odd 2-hump curve where the frequency of results just beyond the threshold.…Is this too hard to understand? A study estimates 20% of economic studies are fudged. The selection of studies by academic journals should logically increase as the statistical significance of the studies’ results increase. This should lead to a logically shaped distribution of studies in journals as a function of their statistical significance. But rather than find such a curve, the study finds, “the distribution of test statistics published in three of the most prestigious economic journals over the period 2005-2011 exhibits a sizable under-representation of marginally insignificant statistics relatively to significant statistics but also to (very) insignificant ones. In a nutshell, once tests are normalized…the distribution has a two-humped camel shape … cannot be explained by selection alone. …10% to 20% percent of tests with are misallocated: there are missing test statistics just before the threshold that…can retrieve after the” The study notes, “The two-humped shape is an empirical regularity that can be observed consistently across journals, years and fields. … Similarly, the two-humped camel shape is less visible in articles with theoretical models, articles using data from randomized control trials or laboratory experiments and papers published by tenured and older researchers. More generally, we find a larger residual in cases in which we would expect higher incentives for researchers to respond to selection,” i.e., “ …evidence that academic economists respond to publication incentives.” The researchers concludes, “Our analysis suggests that the pattern of this misallocation is consistent with what we dubbed an inflation bias: researchers might be tempted to inflate the value of those almost-rejected tests by choosing a slightly more ‘significant’ specification. … Among the tests that are marginally significant, 10% to 20% are misreported. These figures are likely to be lower bounds of the true misallocation as we use conservative collecting and estimating processes.”

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Various solutions have been proposed. One is to publish 'pre-analysis plans', where researchers say how they will do their analysis before they actually do it. Another is to encourage more replication. Anew NBER working paperby Marcel Fafchamps and Julien Labonne suggests another, related, method. The idea is that researchers send their data to a third party, who randomly splits the data sample in half. The researchers do their analysis based on the first dataset, finalise their method, and submit for publication. If and when the paper is accepted, the same analysis is carried out on the second sample, and the unadulterated results published. If the initial result only showed up because of manipulation, then the chances of the same result in the second sample are relatively low. To avoid the embarassment of a non-result, researchers should be stricter with themselves when it comes to tweaking their results. When sample sizes are small, this fix is difficult, as halving the sample saps power from tests. But in a world of big data, it could work. The bigger barrier might be getting career-conscious researchers to sign up.

The paper does look at the results split into subgroups, and there seem to be some factors that are associated with a less humpy distribution (which could suggest less fudging). Although the overall pattern holds across all three prestigious journals the paper considers (theAmerican Economic Review, theQuarterly Journal of Economicsand theJournal of Political Economy), papers by older researchers and ones describing randomised control trials have less marked humps—though they are still there. This is worrying for those trying to interpret and communicate the latest research, as it is impossible to tell if there has been foul play in any individual study. But more fundamentally it is worrying for the profession and policymakers making decisions based on economic evidence; fiddling and running multiple, slightly different tests on the same data rapidly sucks meaning from the reported size and accuracy of the final results.

One explanation is that if a result shows up as significant at the 5% significance level (the industry standard) then researchers crack open the champagne and move on to making economicsjokes. But if the result is tantalisingly close to a positive result then perhaps the researchers will fiddle a bit with their method...and celebrate their nice publisher-friendly result. Yanos Zylberbe, one of the paper's authors, explains that in economics it is difficult to conduct controlled experiments, which ultimately gives a lot of freedom to researchers to tweak their methods. Sometimes researchers are tweaking because they want to find the best way of estimating an effect, but sometimes it's in the search for a significant effect. The distinction might be hazy, even in their own minds.

This should skew the distribution of published results, towards more 'significant' findings. But apaperjust published in theAmerican Economic Journalfinds evidence of a different sort of bias, closer to the source. Called "Star Wars, the empirics strike back", it analyses 50,000 papers published between 2005 and 2011 in three top American journals. It finds that the distribution of results (as measured by z-score, a measure of how far away a result is from the expected mean) has a funny double-humped shape (see chart). The dip between the humps represents "missing" results, which just happen to be in a range just outside the standard cut-off point for statistical significance (where significance is normally denoted with stars, though the name may also be something to do with a film recently released—file under 'economists trying to be funny'). Their results suggest that among the results that are only just significant, 10-20% have been fudged.

PUBLICATION bias in academic journals is nothing new. Afindingof no correlation between sporting events and either violent crime or property crime may be analytically top class, but you couldn’t be blamed, frankly, for not giving a damn. But if journal editors are more interested in surprising or dramatic results, there is a danger that the final selection of published papers offer a distorted vision of reality.

Are results in top journals to be trusted?