nep-sog New Economics Papers
on Sociology of Economics
Issue of 2021‒09‒20
six papers chosen by
Jonas Holmström
Axventure AB

  1. Gender Distribution across Topics in the Top 5 Economics Journals: A Machine Learning Approach By J.Ignacio Conde-Ruiz; Juan-José Ganuza; Manu García; Luis A. Puch
  2. What makes a journal questionable? An analysis using China’s early-warning list By Zhang, Lin; Wei, Yahui; HUANG, Ying; Sivertsen, Gunnar
  3. Is there a differentiated gender effect of collaboration with supercited authors? Evidence from early-career economists By Rodrigo Dorantes-Gilardi; Aurora A. Ramírez-Álvarez; Diana Terrazas-Santamaría
  5. Global dynamics and country-level development in academic economics: An explorative cognitive-bibliometric study By Ernest Aigner
  6. The impact of the COVID-19 pandemic on academic productivity By Andrew R. Casey; Ilya Mandel; Prasun K. Ray

  1. By: J.Ignacio Conde-Ruiz (Universidad Complutense de Madrid and ICAE (Spain).); Juan-José Ganuza (Universitat Pompeu Fabra and Barcelona GSE.); Manu García (Washington University in St. Louis and ICAE.); Luis A. Puch (Universidad Complutense de Madrid and ICAE (Spain).)
    Abstract: We analyze all the articles published in the top five (T5) Economics journals be- tween 2002 and 2019 in order to find gender differences in their research approach. We implement an unsupervised machine learning algorithm: the Structural Topic Model (STM), so as to incorporate gender document-level meta-data into a probabilistic text model. This algorithm characterizes jointly the set of latent topics that best fits our data (the set of abstracts) and how the documents/abstracts are allocated to each latent topic. Latent topics are mixtures over words where each word has a probability of belonging to a topic after controlling by journal name and publication year (the meta-data). Thus, the topics may capture research fields but also other more subtle characteristics related to the way in which the articles are written. We find that fe- males are unevenly distributed along the estimated latent topics, by using only data driven methods. This finding relies on “automatically” generated built-in data given the contents in the abstracts of the articles in the T5 journals, without any arbitrary allocation of texts to particular categories (as JEL codes, or research areas).
    Keywords: Machine Learning; Gender Gaps; Structural Topic Model; Gendered Language; Research Fields.
    JEL: I20 J16 Z13
    Date: 2021–06
  2. By: Zhang, Lin; Wei, Yahui; HUANG, Ying; Sivertsen, Gunnar
    Abstract: There is widespread agreement that questionable journals pose a threat to the integrity of scholarly publishing and the credibility of academic research. However, there is currently no agreed upon definition of what constitutes a questionable journal. The characteristics of questionable journals have not been delineated, standardized, nor broadly accepted. A series of policy initiatives by the central Chinese government has culminated in the now Early Warning List of International Journals, released by the National Science Library of the Chinese Academy of Sciences – 65 journals that Chinese scholars should be wary of publishing in. Taking this List as a litmus test, we analyze the characteristics of each journal focusing on a definitive set of factors that may see a journal included on the List. We not only include the factors applied by the publisher of the List, such as the article processing charges, the retraction rate etc., but also investigate several other factors. Most of the factors are found to influence the List, while some are not. In fact, many of the journals on the List are highly ranked by impact factors. Our study aims to provide empirical information supporting global attempts to mitigate the pervading phenomenon of questionable journals.
    Date: 2021–09–06
  3. By: Rodrigo Dorantes-Gilardi (El Colegio de México); Aurora A. Ramírez-Álvarez (El Colegio de México); Diana Terrazas-Santamaría (El Colegio de México)
    Abstract: Several inequalities between genders have been reported over the last decades in academia. Female researchers tend to have a lower pay, write fewer articles and receive fewer cites than their male counterparts, among other disparities. Co-authorship with highly cited scholars tend to give an advantage to early career researchers. Indeed, the impact of researchers that collaborate with super-cited (SC) authors at their early career stage tends to be greater than for those scientists who do not. The question of whether this advantage is favors male or female scientists has not been addressed yet. By conditioning on career length (at least ten years), we study the effect on male and female economists from collaborating with a SC author within the first five years of their career. Since collaboration is not likely random, we employ a matching model using pre-collaboration network characteristics to compare similar authors. We find a positive effect on the impact and the probability of being SC afterward; however, this effect is not statistically different between men and women. On the productivity side, we do not find an effect for any gender. To further explore these results, we study whether repeated collaboration with SC co-authors may be a possible mechanism in the years that follow.
    Keywords: super-cited authors, gender inequality, collaboration network, economics.
    Date: 2021–08
  4. By: Juan Acosta; Beatrice Cherrier (CNRS - Centre National de la Recherche Scientifique)
    Abstract: In this paper, we build on data on officials of the Federal Reserve System, oral history repositories, and hitherto underresearched archival sources to unpack the tortuous path toward crafting an institutional and intellectual space for postwar economic analysis within the Board of Governors of the Federal Reserve System. We show that growing attention to new macroeconomic research was a reaction to both mounting external criticisms against the Fed's decision-making process and the spread of new macroeconomic theories and econometric techniques. We argue that the rise of the number of PhD economists working at the Fed is a symptom rather than a cause of this transformation. Key to our story are a handful of economists from the Board of Governors' Division of Research and Statistics (DRS) who did not hold a PhD but envisioned their role as going beyond mere data accumulation and got involved in large-scale macroeconometric model building. We conclude that the divide between PhD and non-PhD economists may not be fully relevant to understand both the shift in the type of economics practiced at the Fed and the uses of this knowledge in the decision-making process. Equally important was the rift between different styles of economic analysis.
    Date: 2021
  5. By: Ernest Aigner
    Abstract: The structure of academic economics has received a fair amount of attention within and beyond the discipline. Less focus has been given the interdependencies of country and global dynamics. Building and advancing this tradition, this explorative study examines geographic variation and country specific developments in research practices in academic economics. More specifically I investigate the interdependencies of global dynamics with country-level developments in the US, Germany, UK, France, Switzerland and Austria. To that purpose the study investigates a large-scale data set using inequality measures and social network analysis. The dataset analysed in this study comprises 453,863 articles published in 477 journals citing each other a total of 3,807,289 times. This exploratory study confirms the high level of concentration and finds similar trends on the country level. Further, an international convergence in the discipline can be observed, possibly limiting the place-specific relevance of knowledge created in academic economics.
    Keywords: economic sociology, academic economics, citation analysis, heterodox economics, concentration, geography of economics
    JEL: N00 Z1 B3 B5 B00
    Date: 2021
  6. By: Andrew R. Casey; Ilya Mandel; Prasun K. Ray
    Abstract: 'Publish or perish' is an expression describing the pressure on academics to consistently publish research to ensure a successful career in academia. With a global pandemic that has changed the world, how has it changed academic productivity? Here we show that academics are posting just as many publications on the arXiv pre-print server as if there were no pandemic: 168,630 were posted in 2020, a +12.6% change from 2019 and $+1.4\sigma$ deviation above the predicted 162,577 $\pm$ 4,393. However, some immediate impacts are visible in individual research fields. Conference cancellations have led to sharp drops in pre-prints, but laboratory closures have had mixed effects. Only some experimental fields show mild declines in outputs, with most being consistent on previous years or even increasing above model expectations. The most significant change is a 50% increase ($+8\sigma$) in quantitative biology research, all related to the COVID-19 pandemic. Some of these publications are by biologists using arXiv for the first time, and some are written by researchers from other fields (e.g., physicists, mathematicians). While quantitative biology pre-prints have returned to pre-pandemic levels, 20% of the research in this field is now focussed on the COVID-19 pandemic, demonstrating a strong shift in research focus.
    Date: 2021–09

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