Dennis Alexis Valin Dittrich

@davdittrich.economicscience.net

838 Followers 746 Following

Interested in bounded rationality, trust, discrimination, fair compensation, labor economics, quantitative methods, dataviz, rstats, open source 1stgen Professor of Economics (2005-2022) Senior Economist at the Stepstone Group https://economicscience.net

A three-panel chart detailing replication rates across six academic fields (Political science, Economics, Sociology, Psychology, Business, and Education) and an "All fields" summary. Panel a ("Attempts (%)"): Shows the percentage of studies where a reproduction was "Attempted" (dark blue) versus "Not attempted" (light grey). Political science has the highest attempt rate (around 20%), while all other fields remain under 10%. Panel b ("Attempts (%)"): A stacked bar chart of attempt outcomes: "Precisely reproduced" (dark blue), "Approximately reproduced" (light blue), and "Not reproduced" (magenta). Economics and Political Science lead with precise reproduction rates over 50%, while Education shows 0% precise reproductions. Panel c ("Counts"): A dot plot showing total study counts (0 to 150) color-coded by outcome, revealing that Psychology and Business have the largest total sample sizes in the dataset. The chart highlights a dual-layered reality in social science replication. While fields like Economics and Political Science are much more proactive at attempting to reproduce past research (Panel a) and boast the highest rates of precise reproducibility when they do try (Panel b), the vast majority of published papers across all fields are still never attempted. When attempts are made, roughly half across all fields can be precisely reproduced, leaving a significant portion that can only be partially replicated or fail entirely.
A line graph tracking changes in "Percentage points" on the y-axis (ranging from -2 to 4) over time on the x-axis (from 2015 to 2024). The graph compares two categories, each plotted with a line and a shaded confidence interval: "Remotable occupations" (black line with circular markers and grey shading) and "Non-remotable occupations" (blue line with triangular markers and light blue shading). Both lines converge at 0 in 2019. A vertical grey shaded block highlights the pandemic period around 2020 and 2021. In 2020, both lines spike sharply, but the blue line peaks much higher (around 2.5) than the black line (around 1.5). By 2021, the blue line drops sharply and remains lower, while the black line for remotable occupations steadily climbs upward from 2022 through 2024, ending near 1.5 percentage points. The chart illustrates a distinct post-pandemic divergence in employment trends based on how adaptable a job is to remote work. While non-remotable occupations saw a massive, immediate spike in 2020 followed by a swift stabilization, **remotable occupations have experienced a sustained, upward trajectory in percentage points from 2022 through 2024**. This suggests a structural, long-term shift or growing challenge within the remote job market compared to traditional, in-person roles.
A line graph titled "Happiness 1972-2018 and 2021-2024." The y-axis ranges from 0 to 30, and the x-axis shows years from 1970 to 2025. A black line tracks the percentage difference between people reporting they are "very happy" versus "not so happy." For decades (1972–2018), the data fluctuates mostly between 15 and 30, with a historical average of 22.01 indicated by a dashed horizontal line. There is a gap in the data for 2019–2020. In 2021, the line plummets to approximately -2, marking the first time the value is negative. While it recovers slightly by 2024, it remains historically low at around 6. Source: GSS variable happy. The chart highlights a "happiness crash" that broke a nearly 50-year trend: Historical Stability: From 1972 to 2018, Americans were consistently more likely to say they were "very happy" than "not so happy," maintaining a steady average of about 22 points. The Post-2020 Pivot: The data for 2021 shows a shocking reversal where, for the first time in the recorded history of this survey, the number of "not so happy" people actually outweighed the "very happy" group (resulting in a negative score). The "New Normal": Although there is a slight upward tick between 2022 and 2024, the current happiness level (approx. 6) is still significantly lower than even the worst historical dips seen in the mid-80s or 2010. This suggests a lasting shift in the national mood following the events of 2020.
New research from the University of Zurich (Dell et al., 2026) shows that "Choice Architecture" isn't just for small purchases—it shapes high-stakes career moves. By studying thousands of job seekers, researchers found: 1️⃣ The Power of Ranking: Users engage significantly more with top-ranked results, even when others are a perfect match. 2️⃣ Visuals > Text: Moving from text-heavy lists to interactive, visual interfaces reduces "Cognitive Load." 3️⃣ The Watch List Effect: Better design leads seekers to use "watch lists," keeping more diverse career options in their memory and leading to more applications. The Takeaway: Small design tweaks can help people overcome "tunnel vision" and discover a wider range of professional opportunities.
A line chart titled "Establishment survey response rates" shows data from 2015 to early 2025. The y-axis measures the percentage response rate from 0% to 80%. Five colored lines represent different Bureau of Labor Statistics (BLS) surveys: CPI-C&S (dark blue), CES (light orange), ECI (light blue), IPP-Exports (yellow), and IPP-Imports (dark orange). All five lines show a steady downward trend. In 2015, most rates were between 55% and 72%; by 2025, they have dropped significantly, with some surveys (IPP) crashing to around 30%. A sharp, synchronized drop is visible across most categories in early 2020. Source: Bureau of Labor Statistics / Brookings. The chart reveals a growing "crisis of participation" in economic data collection: The "COVID Cliff": Every survey experienced a sharp, permanent drop in early 2020. While we might have expected a recovery post-pandemic, the rates continued to stagnate or slide further, suggesting a fundamental change in how businesses prioritize government requests. A Growing Blind Spot: The IPP (Import/Export Price Indexes) surveys are in the most trouble, plummeting from roughly 55% to 30% in just a decade. When only 1 in 3 businesses respond, the data becomes much "noisier" and less reliable for tracking inflation. The "Gold Standard" is Fading: Even the CPI (Consumer Price Index) and ECI (Employment Cost Index)—critical tools for setting interest rates and wages—have fallen from the 70% range to the 45%–50% range.
A scatter plot titled "Intolerance of uncertainty in U.S. and Canadian college students, 1992–2022." The x-axis shows the year, and the y-axis shows IU scores in standard deviation units ranging from 2.5 to 4.0. Green and orange dots (representing individual study data points) populate the graph, with a thick purple trend line showing a steady, accelerating climb from approximately 2.8 in the early 90s to 3.7 by 2022. A shaded purple area indicates the confidence interval. Note: The data is attributed to Van Baar et al. (2026). The visualization suggests a profound shift in the psychological profile of the "average" college student over the last three decades: Steady Escalation: Between 1992 and roughly 2010, the increase was gradual. However, the curve steepens significantly after 2010, suggesting that more recent cohorts find uncertain situations increasingly stressful or "unacceptable." The "Certainty Gap": The trend line moves from the bottom third of the scale to near the top. In clinical terms, higher IU is often correlated with increased levels of anxiety and perfectionism, as individuals struggle to cope with the absence of a guaranteed outcome. Recent Volatility: The cluster of data points near 2020–2022 shows some of the highest recorded scores, likely reflecting the impact of global instability and the pandemic on student mental health.
Three-panel speedup plot. Panel A: robScale, robLoc, adm vs revss (4–5× at small n, 2–4× at large n). Panel B: Qn, Sn vs robustbase (up to 9× at large n). Panel C: GMD vs GiniDistance, IQR vs stats (37× at small n), MAD vs stats (26× at small n). Sample sizes n=3 to n=10 million.
A two-panel benchmark charting performance multipliers of the optimized C++ robscale package against legacy pure-R implementations across sample sizes from $n = 3$ up to $10^7$, with the vertical axis starting honestly at 0x. The left panel reveals a massive speedup for M-estimators (robLoc, robScale, adm vs. revss), pushing up to ~28x for robScale. The right panel tracks scale estimators ($Q_n$, $S_n$ vs. robustbase), showing the speedup curve upward from 1.6x, approaching 10x at large sample sizes. Shaded ribbons show 95% bootstrap confidence intervals, visually confirming dramatic computational efficiency.
WHY DO PEOPLE KEEP BELIEVING IN MALTHUSGIAN THEORIES WHEN THEY KEEP NOT PANNING QUT? THE NUMBER OF PROBLEMS THAT SEEM MALTHUSIAN GROWS GEOMETRICALLY, WHILE THE NUMBER OF MALTHLS DEBOUNKINGS ONLY GROWS LINEARLY.
A Calvin & Hobbs cartoon: RESOLUNIONS ? ME?? JUST WHAT ARE You IMPLYING? THAT I NEED TO CHANGE ?? WELL, BUDDY, AS FAR AS I'M CONCERNED, I'M PERFECT THE WAY I AM!
This figure displays the average perceived earnings impact of AI chatbots among adopters, categorized by workers’ occupations and whether their employers encourage AI. They are all close to zero.
Comprehension in economic games Many disciplines rely on economic games to measure prosocial behavior. However, there is a concern that participants may misunderstand these games, complicating interpretation of results. This study combines online and laboratory data (total n = 1568) to assess subject comprehension of five standard economic games: the Dictator Game, Ultimatum Game, Trust Game, Public Goods Game, and Prisoner's Dilemma. The online and lab data collections are carried out separately and for the online data collection we collect data for two separate platforms (Prolific and CloudResearch’s MTurk Toolkit). Within each data collection participants carry out all five games, and are randomized to comprehension questions with or without incentives for correct answers. Results indicate that misunderstanding is common: the proportion of participants who misunderstood ranged from 22 % (Dictator Game) to 70 % (Trust Game) in the online samples and from 22 % (Dictator Game) to 53 % (Public Goods Game) in the lab sample. Incentivizing the comprehension questions had no significant impact on misunderstanding, but numeracy was associated with lower misunderstanding. Misunderstanding also predicted increased prosocial behavior in several of the games. Our findings suggest that misunderstanding may be important in explaining prosocial behavior, making it more complicated to draw clear inferences about social preferences from experimental data.
Statistical Significance, p-Values, and the Reporting of Uncertainty Theodor Geisel, better known by the nom de plume Dr. Seuss, published The Sneetches in 1961. In this children’s story, the Star-Belly Sneetches viewed themselves as superior to the Plain-Belly Sneetches. When the character of Sylvester McMonkey McBean arrives with a machine that can add or remove belly stars (for a modest fee), social upheaval results. In empirical work in economics, stars have long been attached to numbers in tables and figures to indicate the level of statistical significance: one star typically refers to an estimate that is statistically significant at a 10 percent level; two stars, the 5 percent level; and the coveted three stars, the 1 percent level. In the word of Dr. Seuss: “Those stars weren’t so big. They were really so small./You might think such a thing wouldn’t matter at all./But, because they had stars, all the Star-Belly Sneetches/Would brag, ‘We’re the best kind of Sneetch on the beaches.” In empirical studies, estimates with one, two, or three stars are often viewed as superior to those without such adornments.
THIS WAS PROVEN EMPIRICALLY BY DRAWING SQUARES OF EQUAL AREA, SAMPLING THEM VIA THROWING n DARTS, AND DETERMINING THAT AS N GROWS LARGE, THE RATIO OF DARTS IN EACH SQUARE APPROACHES 1. You could always tell the future statisticians.
Summer Institute on BOUNDED RATIONALITY Decision-Making in a Digital World
Screenshot from the Microsoft 365 subscription page, showing the "Turn off recurring billing" item as highlighted.
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Two scientists share an office. The first, whose half of the room is neat and tidy, says “My new year's resolutions are to jog to work every day, choose the healthy option at lunch, and run a pleasant, well-ordered workspace.” The second, working in a total mess, replies “In the interests of scientific rigour, I'll be the control, and change absolutely nothing.”
Return to Office Mandates and Brain Drain By tracking over 3 million tech and finance workers’ employment histories reported on LinkedIn, we analyze the effect of S&P 500 firms’ return-to-office (RTO) mandates on employee turnover and hiring. We find that these firms experience abnormally high employee turnover following RTO mandates. The increase in turnover rates is more pronounced for female employees, more senior employees, and more skilled employees. Further, it takes significantly longer time for these firms to fill their job vacancies after the mandates. Their hire rates also significantly decrease. These results are consistent with firms losing their best talent and female employees and facing greater difficulties with talent attraction after RTO mandates. Our study highlights brain drain as a significant cost of RTO mandates even for the largest firms in the world. Keywords: Return-to-office Mandate, Employee Turnover, Employee Hiring, Human Capital
Some scientists stand at a workbench looking closely at a tiny speck. One says “this is version 2.0 - it still doesn’t work, but now it’s not working at a quantum level!”
CALL FOR SUBMISSIONS: Research in Labor Economics (RLE) Submission Deadline November 29, 2024 Notification Deadline December 15, 2024 Research in Labor Economics (RLE) is planning to publish a volume highlighting research on New Developments in Labor Economics. The editors Solomon Polachek (SUNY Binghamton and IZA) and Benjamin Elsner (University College Dublin and IZA) are soliciting up to ten new papers showcasing new developments in labor economics. They welcome submissions tackling new questions as well as submissions that look at traditional labor economics questions with new methods and/or interesting new data. The editors seek to publish papers from a broad range of topics, for example: The impact of new technologies on the labor market New findings on the influence of labor market institutions New developments in research on migration Measuring labor market dynamics with new data Taxation, labor supply, and inequality Education, skill formation, and the labor market Gender and the labor market New developments in personnel economics In order to meet the publication deadline, please submit a detailed abstract (full papers are even more desirable) by November 29, 2024, to the RLE website:
Hands type at a computer with bar graphs on screen, other hands hold questionnaires.
A ~Trojan~ wooden horse and two men: "I NO IDEA! BUT IT'S FREE" IT ALWAYS WORKED
International Conference Cyborg workers 2.0 The past, present and future of automated labour - the social and environmental costs of big tech 13-14 February 2025
This is figure 1, which shows key indicators for several models in GPT (OpenAI), LLaMA (Meta) and BLOOM (BigScience) families.
Panel one: Two small figures stand before a huge machine. One says “Our startup has innovated the human writer out of the publishing process. This machine can write sixty-five books an hour.” A conveyor belt at the front of the machine dispatches a continuous stream of books. Panel two: They walk across the workshop. The second figure, who is reading one of the books, says “but will people actually want to read this stuff?” Panel three: They have reached a second huge machine. “The real beauty of our integrated system” says the first figure “is that it also frees companies from the tyranny of human readers”. A conveyor belt at the front of the machine dispatches a continuous stream of robot readers, happily consuming the content from the other machine.
Poster for 2024 Ihaka Lectures Black background, white lettering, hexmap of New Zealand. Series title "Engaging through data" Picture of Yihui Xie. "Reimagining literate programming and automated report generation"
Cartoon: A space craft is falling towards the sun: 'Tragedy has struck the mission; the spacecraft's engines have failed... Gravity draws it inexorably towards the sun' the spacecraft catches fire: 'the team of brave scientists will perish in the pitiless fires' th sun grows ever larger: '... with the exception of Alex, who is attending remotely' A screen in the boring spacecraft" 'sorry guys. The kids are off school with colds & Sam is away at a conference'!
After see a 100 Dollar bill on the street, the economist conscience joins little angel and devil and argues: That 100 Dollars can't exist. If it were real, someone would've picked it up by now. Move on.
Economic analyses and policy decisions require timely, accurate, nationally representative data on household employment conditions — for which the CPS is the sole source. Allowing the reliability of the CPS to erode would impair economic policymaking by the Federal Reserve and Congress, and undercut effective decision making by businesses, financial-market participants, state and local governments, community organizations, and the general public. Even in an environment of scarce budgetary resources, AEAStat views maintaining the reliability of the CPS as a priority.
Alt-text 1/2: All data in USD, as of 2023 (per adult) Top 25 in average wealth Switzerland Luxembourg Hong Kong SAR United States Australia Denmark New Zealand Singapore Norway Canada Belgium Netherlands United Kingdom France Sweden Taiwan Germany Israel Austria Ireland Korea Spain Japan Italy Qatar Ranging from 709,612 to 199,430 USD