nep-sog New Economics Papers
on Sociology of Economics
Issue of 2022‒09‒12
six papers chosen by
Jonas Holmström
Axventure AB

  1. Nobel students beget Nobel professors By Richard S.J. Tol
  2. Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias? By Abel Brodeur, Nikolai M. Cook, Jonathan S. Hartley, Anthony Heyes
  3. We Need to Talk about Mechanical Turk: What 22,989 Hypothesis Tests Tell Us about Publication Bias and p-Hacking in Online Experiments By Abel Brodeur, Nikolai M. Cook, Anthony Heyes
  4. A Guide to Evaluate Academic Sources to Develop Research Paper: Source Selection in Academic Writing By Hamed Taherdoost
  5. Nobel and novice: Author prominence affects peer review By Jürgen Huber; Sabiou Inoua; Rudolf Kerschbamer; Christian König-Kersting; Stefan Palan; Vernon L. Smith
  6. Nobel laurates and the role of the industry in the emergence of new scientific breakthroughs By Quentin Plantec; Pascal Le Masson; Benoît Weil

  1. By: Richard S.J. Tol (Department of Economics, University of Sussex, BN1 9SL Falmer, United Kingdom)
    Abstract: It is unclear whether the hierarchy in the economics profession is the result of the agglomeration of excellence or of nepotism. I construct the professor-student network for laureates of and candidates for the Nobel Prize in Economics. I study the effect of proximity to previous Nobelists on winning the Nobel Prize. Conditional on being Nobel-worthy, students and grandstudents of Nobel laureates are not significantly more or less likely to win. Professors of Nobel Prize winners, however, are significantly more likely to win.
    Keywords: network formation, research training, Nobel prize
    JEL: A14 D85 Z13
  2. By: Abel Brodeur, Nikolai M. Cook, Jonathan S. Hartley, Anthony Heyes
    Abstract: Randomized controlled trials (RCTs) are increasingly prominent in economics, with pre-registration and pre-analysis plans (PAPs) promoted as important in ensuring the credibility of findings. We investigate whether these tools reduce the extent of p-hacking and publication bias by collecting and studying the universe of test statistics, 15,992 in total, from RCTs published in 15 leading economics journals from 2018 through 2021. In our primary analysis, we find no meaningful difference in the distribution of test statistics from pre-registered studies, compared to their non-pre-registered counterparts. However, pre-registered studies that have a complete PAP are significantly less p-hacked. These results point to the importance of PAPs, rather than pre-registration in itself, in ensuring credibility.
    Keywords: Pre-analysis plan, Pre-registration, p-Hacking, Publication bias, Research credibility
    JEL: B41 C13 C40 C93
    Date: 2022–08–24
  3. By: Abel Brodeur, Nikolai M. Cook, Anthony Heyes
    Abstract: Amazon Mechanical Turk is a very widely-used tool in business and economics research, but how trustworthy are results from well-published studies that use it? Analyzing the universe of hypotheses tested on the platform and published in leading journals between 2010 and 2020 we find evidence of widespread p-hacking, publication bias and over-reliance on results from plausibly under-powered studies. Even ignoring questions arising from the characteristics and behaviors of study recruits, the conduct of the research community itself erodes substantially the credibility of these studies’ conclusions. The extent of the problems vary across the business, economics, management and marketing research fields (with marketing especially afflicted). The problems are not getting better over time and are much more prevalent than in a comparison set of non-online experiments. We explore correlates of increased credibility.
    Keywords: online crowd-sourcing platforms, Amazon Mechanical Turk, p-hacking, publication bias, statistical power, research credibility
    JEL: B41 C13 C40 C90
    Date: 2022–08–24
  4. By: Hamed Taherdoost (Hamta Business Corporation)
    Abstract: It is significant to identify and evaluate sources in a research study to ensure their credibility to be used in an academic research paper. Each source should be evaluated in terms of being related to the research question and covering research objectives. However, despite the importance of source selection as one of the initial steps in conducting a research study, it may seem challenging for researchers to find relevant sources based on the topic of their study and evaluate them appropriately. The main aim of this chapter is to clarify the process of choosing sources for a research project. For this purpose, the process of source selection is divided into several steps including recognizing the types of available sources, their ranks, requirements of the study project, searching and searching tools, and finally the process of evaluating sources.
    Keywords: Source Selection,Academic Paper,Academic Writing,Scientific Source,Research Paper,Research Methodology
    Date: 2022–04–26
  5. By: Jürgen Huber (Institute of Banking and Finance, University of Innsbruck); Sabiou Inoua (Economic Science Institute, Chapman University); Rudolf Kerschbamer (Institute of Banking and Finance, University of Innsbruck); Christian König-Kersting (Institute of Banking and Finance, University of Innsbruck); Stefan Palan (Institute of Banking and Finance, University of Graz); Vernon L. Smith (Economic Science Institute, Chapman University)
    Abstract: Peer-review is a well-established cornerstone of the scientific process, yet it is not immune to status bias. Merton identified the problem as one in which prominent researchers get disproportionately great credit for their contribution while relatively unknown researchers get disproportionately little credit (Merton, 1968). We measure the extent of this effect in the peer-review process through a pre-registered field experiment. We invite more than 3,300 researchers to review a paper jointly written by a prominent author -- a Nobel laureate -- and by a relatively unknown author -- an early-career research associate --, varying whether reviewers see the prominent author's name, an anonymized version of the paper, or the less well-known author's name. We find strong evidence for the status bias: while only 23 percent recommend “reject†when the prominent researcher is the only author shown, 48 percent do so when the paper is anonymized, and 65 percent do so when the little-known author is the only author shown. Our findings complement and extend earlier results on double-anonymized vs. single-anonymized review (Peters and Ceci, 1982; Blank, 1991; Cox et al., 1993; Okike et al., 2016; Tomkins et al., 2017; Card and Della Vigna, 2020) and strongly suggest that double-anonymization is a minimum requirement for an unbiased review process.
    Date: 2022–08–16
  6. By: Quentin Plantec (TSM - Toulouse School of Management Research - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées - CNRS - Centre National de la Recherche Scientifique - TSM - Toulouse School of Management - UT1 - Université Toulouse 1 Capitole - Université Fédérale Toulouse Midi-Pyrénées); Pascal Le Masson; Benoît Weil
    Abstract: Since the 1980s, many companies recognized for their major scientific breakthroughs (e.g., IBM, AT&T, etc.), cut their investments in fundamental research activities. In parallel, academics from public research organizations (PRO) and universities engaged more extensively with the industry through research collaborations. The conditions, determinants, and effects of academic engagement have been deeply analyzed. But, the extent to which major scientific breakthroughs of the last century have emerged either from (1) academics and researchers with no interaction with the industry or (2) from scientists interacting with the industry-either as engaged academics belonging to PRO or universities or as corporate scientistsare yet to be more systematically documented. To fill this gap, we explored the extent to which scientists from the quasi-complete cohort of Nobel laureates in Physics, Medicine, and Chemistry were interacting with the industry before their breakthrough discoveries. We designed a unique dataset of their ties with the industry based on affiliations review of 84,423 academic papers and applicant review of 5,207 patent families. First, we showed that one-fifth of the studied cohort of laureates was interacting with the industry before their breakthrough discovery. More importantly, this share is still increasing, mainly through academic engagement, while the share of awarded corporate scientists has remained stable since 1970. Second, we were able to analyze the effects of those interactions with the industry on the post-discovery period by comparing interacting and noninteracting with industry laureates' follow-on research works. While some scientific discoveries were partly made possible thanks to Nobel laureates' industrial partners, those laureates' follow-on knowledge works were not bound to their initial sets of partners. They experienced similar knowledge diffusion-to-industry rates than other laureates but higher academic production rates and diffusion-to-academia rates. Finally, we claim that the extent to which scientific new knowledge still emerges in relation to industrial contexts in modern science has been underevaluated and opens rooms for further research.
    Keywords: Scientific discovery,University-Industry collaborations,Nobel Prize,New Product Development,Knowledge absorption,Academic engagement
    Date: 2022–08

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