New Approaches to Ranking Economics Journals
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Publishing and Promotion in Economics: the Curse of the Top Five
Publishing and Promotion in Economics: The Curse of the Top Five James J. Heckman 2017 AEA Annual Meeting Chicago, IL January 7th, 2017 Heckman Curse of the Top Five Top 5 Influential, But Far From Sole Source of Influence or Outlet for Creativity Heckman Curse of the Top Five Table 1: Ranking of 2, 5 and 10 Year Impact Factors as of 2015 Rank 2 Years 5 Years 10 Years 1. JEL JEL JEL 2. QJE QJE QJE 3. JOF JOF JOF 4. JEP JEP JPE 5. ReStud JPE JEP 6. ECMA AEJae ECMA 7. AEJae ECMA AER 8. AER AER ReStud 9. JPE ReStud JOLE 10. JOLE AEJma EJ 11. AEJep AEJep JHR 12. AEJma EJ JOE 13. JME JOLE JME 14. EJ JHR HE 15. HE JME RED 16. JHR HE EER 17. JOE JOE - 18. AEJmi AEJmi - 19. RED RED - 20. EER EER - Note: Definition of abbreviated names: JEL - Journal of Economic Literature, JOF - Journal of Finance, JEP - Journal of Economic Perspectives, AEJae-American Economic Journal Applied Economics, AER - American Economic Review, JOLE-Journal of Labor Economics, AEJep-American Economic Journal Economic Policy, AEJma-American Economic Journal Macroeconomics, JME-Journal of Monetary Economics, EJ-Economic Journal, HE-Health Economics, JHR-Journal of Human Resources, JOE-Journal of Econometrics, AEJmi-American Economic Journal Microeconomics, RED-Review of Economic Dynamics, EER-European Economic Review; Source: Journal Citation Reports (Thomson Reuters, 2016). Heckman Curse of the Top Five Figure 1: Articles Published in Last 10 years by RePEc's T10 Authors (Last 10 Years Ranking) (a) T10 Authors (Unadjusted) (b) T10 Authors (Adjusted) Prop. -
Alberto Abadie
ALBERTO ABADIE Office Address Massachusetts Institute of Technology Department of Economics 50 Memorial Drive Building E52, Room 546 Cambridge, MA 02142 E-mail: [email protected] Academic Positions Massachusetts Institute of Technology Cambridge, MA Professor of Economics, 2016-present IDSS Associate Director, 2016-present Harvard University Cambridge, MA Professor of Public Policy, 2005-2016 Visiting Professor of Economics, 2013-2014 Associate Professor of Public Policy, 2004-2005 Assistant Professor of Public Policy, 1999-2004 University of Chicago Chicago, IL Visiting Assistant Professor of Economics, 2002-2003 National Bureau of Economic Research (NBER) Cambridge, MA Research Associate (Labor Studies), 2009-present Faculty Research Fellow (Labor Studies), 2002-2009 Non-Academic Positions Amazon.com, Inc. Seattle, WA Academic Research Consultant, 2020-present Education Massachusetts Institute of Technology Cambridge, MA Ph.D. in Economics, 1995-1999 Thesis title: \Semiparametric Instrumental Variable Methods for Causal Response Mod- els." Centro de Estudios Monetarios y Financieros (CEMFI) Madrid, Spain M.A. in Economics, 1993-1995 Masters Thesis title: \Changes in Spanish Labor Income Structure during the 1980's: A Quantile Regression Approach." 1 Universidad del Pa´ıs Vasco Bilbao, Spain B.A. in Economics, 1987-1992 Specialization Areas: Mathematical Economics and Econometrics. Honors and Awards Elected Fellow of the Econometric Society, 2016. NSF grant SES-1756692, \A General Synthetic Control Framework of Estimation and Inference," 2018-2021. NSF grant SES-0961707, \A General Theory of Matching Estimation," with G. Imbens, 2010-2012. NSF grant SES-0617810, \The Economic Impact of Terrorism: Lessons from the Real Estate Office Markets of New York and Chicago," with S. Dermisi, 2006-2008. -
Rankings of Economics Journals and Departments in India
WP-2010-021 Rankings of Economics Journals and Departments in India Tilak Mukhopadhyay and Subrata Sarkar Indira Gandhi Institute of Development Research, Mumbai October 2010 http://www.igidr.ac.in/pdf/publication/WP-2010-021.pdf Rankings of Economics Journals and Departments in India Tilak Mukhopadhyay and Subrata Sarkar Indira Gandhi Institute of Development Research (IGIDR) General Arun Kumar Vaidya Marg Goregaon (E), Mumbai- 400065, INDIA Email (corresponding author): [email protected] Abstract This paper is the first attempt to rank economics departments of Indian Institutions based on their research output. Two rankings, one based on publications in international journals, and the other based on publications in domestic journals are derived. The rankings based on publications in international journals are obtained using the impact values of 159 journals found in Kalaitzidakis et al. (2003). Rankings based on publications in domestic journals are based on impact values of 20 journals. Since there are no published studies on ranking of domestic journals, we derived the rankings of domestic journals by using the iterative method suggested in Kalaitzidakis et al. (2003). The department rankings are constructed using two approaches namely, the ‘flow approach’ and the ‘stock approach’. Under the ‘flow approach’ the rankings are based on the total output produced by a particular department over a period of time while under the ‘stock approach’ the rankings are based on the publication history of existing faculty members in an institution. From these rankings the trend of research work and the growth of the department of a university are studied. Keywords: Departments,Economics, Journals, Rankings JEL Code: A10, A14 Acknowledgements: The auhtors would like to thank the seminar participants at Indira Gandhi Institute of Development Research. -
Journal Rankings Paper
NBER WORKING PAPER SERIES NEOPHILIA RANKING OF SCIENTIFIC JOURNALS Mikko Packalen Jay Bhattacharya Working Paper 21579 http://www.nber.org/papers/w21579 NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2015 We thank Bruce Weinberg, Vetla Torvik, Neil Smalheiser, Partha Bhattacharyya, Walter Schaeffer, Katy Borner, Robert Kaestner, Donna Ginther, Joel Blit and Joseph De Juan for comments. We also thank seminar participants at the University of Illinois at Chicago Institute of Government and Public Affairs, at the Research in Progress Seminar at Stanford Medical School, and at the National Bureau of Economic Research working group on Invention in an Aging Society for helpful feedback. Finally, we thank the National Institute of Aging for funding for this research through grant P01-AG039347. We are solely responsible for the content and errors in the paper. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer- reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications. © 2015 by Mikko Packalen and Jay Bhattacharya. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. Neophilia Ranking of Scientific Journals Mikko Packalen and Jay Bhattacharya NBER Working Paper No. 21579 September 2015 JEL No. I1,O3,O31 ABSTRACT The ranking of scientific journals is important because of the signal it sends to scientists about what is considered most vital for scientific progress. -
Intra-Industry Trade and North-North Migration: How TTIP Would Change the Patterns
Model Data Results References Intra-Industry Trade and North-North Migration: How TTIP Would Change the Patterns Mario Larch1 Steffen Sirries1 1University of Bayreuth ETSG 2014 1 / 38 Model Data Results References Funding and Notes on the Project This project is funded by DFG within the Project \Gravity with Unemployment" part of a bigger research agenda: Heid and Larch (2012) for unemployment, Heid (2014) for informality up to now without labor market imperfections work in progress ! input is very welcome! 2 / 38 Model Data Results References Why trade and migration? I Traditional view: H-O-type trade models with migration: Trade substitutes for migration (Mundell (1957)). Signing free trade agreements lowers the pressure of migration. Expected (bilateral) correlation between trade and migration ! negative! Gaston and Nelson (2013) BUT... 3 / 38 Model Data Results References Why trade and migration? II 15 10 5 0 TRADEFLOWS in logs (normalized) −5 −5 0 5 10 15 20 MIGRATION STOCKS in logs (normalized) 4 / 38 Model Data Results References Why trade and migration? III 2 0 −2 Residuals of trade gravity −4 −5 0 5 Residuals of migration gravity corr(^etrade ; e^migr ) = 0:2304 5 / 38 Model Data Results References Why trade and migration? IV There might be more than just a spurious correlation between trade and migration. New trade models with intra-industry trade and north-north migration ! complementarity of trade and migration (e.g OECD). This is not new (Head and Ries (1998)): Migrants also bring along preferential access to the markets of the country of origin. We think simpler: Migrants are not just a factor but consumers and though they bring their demand! Migrants have idiosyncratic shocks which drive the migration decision! 6 / 38 Model Data Results References Why \A Quantitative Framework"?I One of the core issues in empirical international trade is the quantification of the welfare gains from trade liberalization. -
Econometrics Conference May 21 and 22, 2015 Laudation By
Grant H. Hillier – Econometrics Conference May 21 and 22, 2015 Laudation by Kees Jan van Garderen ©GHHillier University of Amsterdam Welcome to this conference in honour of Grant Highmore Hillier. It is great to see you all here and it is great that we can jointly celebrate Grant’s continuing contributions to Econometrics and his non-retirement. A few years ago, before Grant was going to turn 65 and retire, there was some talk about having a conference like the one we are having now. He didn’t retire, however, and nothing eventuated. This year I had time and money, with the University of Southampton also contributing, to organize a “party” and allows me to settle part of my debt. I am deeply indebted, as many of you are also. Many of you are indebted to his teaching, research training and academic approach, insight, solving ability, and friendship. Of course this differs per person but we are all indebted to him for his contributions to science. The scientific community owes him. Grant contributed significantly to exact small sample distribution theory. In particular multivariate structural models, Hypothesis testing , likelihood and regression techniques. No integral over a manifold was avoided, whether it was the Stiefel manifold or very general, as in his Econometrica 1996 paper. The formula used there is one of his favourites and he put this to canvas. This is the painting which is on the Book of Abstracts of this conference in his honour. No zonal- or other polynomials he avoided. No shortcuts to find out the deep meaning of the structure of the problem. -
The Journal Ranking System Undermining the Impact of 2 Brazilian Science 3 4 Rodolfo Jaffé1 5 6 1 Instituto Tecnológico Vale, Belém-PA, Brazil
bioRxiv preprint doi: https://doi.org/10.1101/2020.07.05.188425; this version posted July 6, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 QUALIS: The journal ranking system undermining the impact of 2 Brazilian science 3 4 Rodolfo Jaffé1 5 6 1 Instituto Tecnológico Vale, Belém-PA, Brazil. Email: [email protected] 7 8 Abstract 9 10 A journal ranking system called QUALIS was implemented in Brazil in 2009, intended to rank 11 graduate programs from different subject areas and promote selected national journals. Since this 12 system uses a complicated suit of criteria (differing among subject areas) to group journals into 13 discrete categories, it could potentially create incentives to publish in low-impact journals ranked 14 highly by QUALIS. Here I assess the influence of the QUALIS journal ranking system on the 15 global impact of Brazilian science. Results reveal a steeper decrease in the number of citations 16 per document since the implementation of this QUALIS system, compared to the top Latin 17 American countries publishing more scientific articles. All the subject areas making up the 18 QUALIS system showed some degree of bias, with social sciences being usually more biased 19 than natural sciences. Lastly, the decrease in the number of citations over time proved steeper in a 20 more biased area, suggesting a faster shift towards low-impact journals ranked highly by 21 QUALIS. -
Ranking Forestry Journals Using the H-Index
Ranking forestry journals using the h-index Journal of Informetrics, in press Jerome K Vanclay Southern Cross University PO Box 157, Lismore NSW 2480, Australia [email protected] Abstract An expert ranking of forestry journals was compared with journal impact factors and h -indices computed from the ISI Web of Science and internet-based data. Citations reported by Google Scholar offer an efficient way to rank all journals objectively, in a manner consistent with other indicators. This h-index exhibited a high correlation with the journal impact factor (r=0.92), but is not confined to journals selected by any particular commercial provider. A ranking of 180 forestry journals is presented, on the basis of this index. Keywords : Hirsch index, Research quality framework, Journal impact factor, journal ranking, forestry Introduction The Thomson Scientific (TS) Journal Impact Factor (JIF; Garfield, 1955) has been the dominant measure of journal impact, and is often used to rank journals and gauge relative importance, despite several recognised limitations (Hecht et al., 1998; Moed et al., 1999; van Leeuwen et al., 1999; Saha et al., 2003; Dong et al., 2005; Moed, 2005; Dellavalle et al., 2007). Other providers offer alternative journal rankings (e.g., Lim et al., 2007), but most deal with a small subset of the literature in any discipline. Hirsch’s h-index (Hirsch, 2005; van Raan, 2006; Bornmann & Daniel, 2007a) has been suggested as an alternative that is reliable, robust and easily computed (Braun et al., 2006; Chapron and Husté, 2006; Olden, 2007; Rousseau, 2007; Schubert and Glänzel, 2007; Vanclay, 2007; 2008). -
Fractional Cointegration and Aggregate Money Demand Functions
CORE Metadata, citation and similar papers at core.ac.uk Provided by Brunel University Research Archive FRACTIONAL COINTEGRATION AND AGGREGATE MONEY DEMAND FUNCTIONS Guglielmo Maria Caporale Brunel University, London Luis A. Gil-Alana University of Navarre Abstract This paper examines aggregate money demand relationships in five industrial countries by employing a two-step strategy for testing the null hypothesis of no cointegration against alternatives which are fractionally cointegrated. Fractional cointegration would imply that, although there exists a long-run relationship, the equilibrium errors exhibit slow reversion to zero, i.e. that the error correction term possesses long memory, and hence deviations from equilibrium are highly persistent. It is found that the null hypothesis of no cointegration cannot be rejected for Japan. By contrast, there is some evidence of fractional cointegration for the remaining countries, i.e., Germany, Canada, the US, and the UK (where, however, the negative income elasticity which is found is not theory-consistent). Consequently, it appears that money targeting might be the appropriate policy framework for monetary authorities in the first three countries, but not in Japan or in the UK. Keywords: Money Demand, Velocity, Fractional Integration, Fractional Cointegration JEL Classification: C32, C22, E41 We are grateful to an anonymous referee for useful comments, and to Mario Cerrato for research assistance. The second named author also gratefully acknowledges financial support from the Gobierno de Navarra, (“Ayudas de Formación y Desarrollo”), Spain. Corresponding author: Professor Guglielmo Maria Caporale, Brunel Business School, Brunel University, Uxbridge, Middlesex UB8 3PH, UK. Tel. +44 (0)1895 203 327. Fax: +44 (0)1895 269 770. -
Australian Business Deans Council 2019 Journal Quality List Review Final Report 6 December 2019
Australian Business Deans Council 2019 Journal Quality List Review Final Report 6 December 2019 1 About the Australian Business Deans Council The Australian Business Deans Council (ABDC) is the peak body of Australian university business schools. Our 38 members graduate one-third of all Australian university students and more than half of the nation’s international tertiary students. ABDC’s mission is to make Australian business schools better, and to foster the national and global impact of Australian business education and research. ABDC does this by: • Being the collective and collegial voice of university business schools • Providing opportunities for members to share knowledge and best practice • Creating and maintaining strong, collaborative relationships with affiliated national and international peak industry, higher education, professional and government bodies • Engaging in strategic initiatives and activities that further ABDC’s mission. Australian Business Deans Council Inc. UNSW Business School, Deans Unit, Level 6, West Lobby, College Road, Kensington, NSW, Australia 2052 T: +61 (0)2 6162 2970 E: [email protected] 2 Table of Contents Acknowledgements 4 Background and Context 4 Method and Approach 7 Outcomes 10 Beyond the 2019 Review 13 Appendix 1 – Individual Panel Reports 14 Information Systems 15 Economics 20 Accounting 37 Finance including Actuarial Studies 57 Management, Commercial Services and Transport and Logistics 63 (and Other, covering 1599) Marketing and Tourism 78 Business and Taxation Law 85 Appendix 2 – Terms -
Putting Behavioral Economics to Work: Testing for Gift Exchange in Labor Markets Using Field Experiments
Econometrica, Vol. 74, No. 5 (September, 2006), 1365–1384 PUTTING BEHAVIORAL ECONOMICS TO WORK: TESTING FOR GIFT EXCHANGE IN LABOR MARKETS USING FIELD EXPERIMENTS BY URI GNEEZY AND JOHN A. LIST1 Recent discoveries in behavioral economics have led scholars to question the under- pinnings of neoclassical economics. We use insights gained from one of the most influ- ential lines of behavioral research—gift exchange—in an attempt to maximize worker effort in two quite distinct tasks: data entry for a university library and door-to-door fundraising for a research center. In support of the received literature, our field evi- dence suggests that worker effort in the first few hours on the job is considerably higher in the “gift” treatment than in the “nongift” treatment. After the initial few hours, however, no difference in outcomes is observed, and overall the gift treatment yielded inferior aggregate outcomes for the employer: with the same budget we would have logged more data for our library and raised more money for our research center by using the market-clearing wage rather than by trying to induce greater effort with a gift of higher wages. KEYWORDS: Gift exchange, field experiment. 1. INTRODUCTION NEOCLASSICAL ECONOMICS treats labor as a hired input in much the same manner as capital. Accordingly, in equilibrium, the firm pays market-clearing wages and workers provide minimum effort. The validity of this assumption is not always supported by real-life observations: some employers pay more than the market-clearing wage and workers seemingly invest more effort than nec- essary (Akerlof (1982)). The “fair wage–effort” hypothesis of Akerlof (1982) and Akerlof and Yellen (1988, 1990) extends the neoclassical model to explain higher than market-clearing wages by using a gift exchange model, where “On the worker’s side, the ‘gift’ given is work in excess of the minimum work stan- dard; and on the firm’s side the ‘gift’ given is wages in excess of what these women could receive if they left their current jobs” (Akerlof (1982, p. -
Understanding Research Metrics INTRODUCTION Discover How to Monitor Your Journal’S Performance Through a Range of Research Metrics
Understanding research metrics INTRODUCTION Discover how to monitor your journal’s performance through a range of research metrics. This page will take you through the “basket of metrics”, from Impact Factor to usage, and help you identify the right research metrics for your journal. Contents UNDERSTANDING RESEACH METRICS 2 What are research metrics? What are research metrics? Research metrics are the fundamental tools used across the publishing industry to measure performance, both at journal- and author-level. For a long time, the only tool for assessing journal performance was the Impact Factor – more on that in a moment. Now there are a range of different research metrics available. This “basket of metrics” is growing every day, from the traditional Impact Factor to Altmetrics, h-index, and beyond. But what do they all mean? How is each metric calculated? Which research metrics are the most relevant to your journal? And how can you use these tools to monitor your journal’s performance? For a quick overview, download our simple guide to research metrics. Or keep reading for a more in-depth look at what’s in the “basket of metrics” and how to interpret it. UNDERSTANDING RESEACH METRICS 3 Citation-based metrics Citation-based metrics IMPACT FACTOR What is the Impact Factor? The Impact Factor is probably the most well-known metric for assessing journal performance. Designed to help librarians with collection management in the 1960s, it has since become a common proxy for journal quality. The Impact Factor is a simple research metric: it’s the average number of citations received by articles in a journal within a two-year window.