Seminars: Economics of Science & Engineering

Description:  Focus on work force and career issues. Topics include: Effects of globalization on work force and innovation, growth of networks in work; impact of career incentives on productivity; university policies; mobility between academe and industry; link between ideas and outputs.
List Serve/Announcements:  Graduate Level Seminar 

SPRING 2020

SEMINAR THIS WEEK:

DATE:  Friday, Feb 28 @ 12pm
SPEAKER:  Ryan Hill (MIT)
TITLE:  "Scooped! Estimating Rewards for Priority in Science" (joint with Carolyn Stein)

FULL SCHEDULE BELOW:
2020 SPEAKER  TITLE  
Feb 7 SPEAKER: Richard B. Freeman (Harvard University) TITLE:  The Contribution of Diaspora REsearchers to China's Advance in Science & Engineering" (paper by Qingnan Xie (Nanjing Univ) and Richard B. Freeman)

ABSTRACT:     
Chinese named scientists and engineers conducting research outside China, the diaspora researchers of our title,  contributed to China's rise to the forefront of science in two ways: (1) by the exceptional quality of their research; and (2) by connecting China-based and non-China based research in the network of scientific knowledge. We develop an index of “closeness” to China for papers and authors that provides a more comprehensive
Feb 14 SPEAKER: David Yang (Harvard University)
TITLE: Data-intensive Innovation and the State: Evidence from AI Firms in China

ABSTRACT:
Data-intensive technologies such as AI may reshape the modern world. We propose that two features of data interact to shape innovation in data-intensive economies: first, states are key collectors and repositories of data; second, data is a non-rival input in innovation. We document the importance of state-collected data for innovation using comprehensive data on Chinese facial recognition AI firms and government contracts. Firms produce more commercial software and patents, particularly data-intensive ones, after receiving government public security contracts. Moreover, effects are largest when contracts provide more data. We then build a directed technical change model to study the state’s role in three applications: autocracies demanding AI for surveillance purposes, data-driven industrial policy, and data regulation due to privacy concerns. When the degree of non-rivalry is as strong as our empirical evidence suggests, the state’s collection and processing of data can shape the direction of innovation and growth of data-intensive economies.
Feb 21 SPEAKER: 
Dr. David Egilman (Never Again Consulting) 

TITLE: How Externalities Impact on Product Research

DISCLOSURE: 
Dr. Egilman consults on litigation at the request of plaintiff and defense lawyers in asbestos litigation and has consulted at the request of plaintiffs in opioid, Vioxx, Zyprexa, glyphosate, pig CAFOs, RTI safe injection needles, talc and other topics.

BACKGROUND MATERIAL:

1. Egilman, David, Molly Biklen, and Joyce Kim. 2003. "Proving Causation: The use and abuse of medical and scientific evidence inside the courtroom -- An epidemiologist's critique of the judicial interpretation of the Daubert Ruling" Food and Drug Law Journal 58(2):223-50 (February). Available at:
https://www.researchgate.net/publication/10656625_Proving_causation_The_use_and_abuse_of_medical_and_scientific_evidence_inside_the_courtroom_-_An_epidemiologist's_critique_of_the_judicial_interpretation_of_the_Daubert_ruling

2. Rankin Bohme, Susanno, AM, John Zorabedian, Daivd S. Egilman, MD, MPH. 2005. "Maximizing Profit and Endangering Health: Corporate STrategies to Avoid Litigation and Regulation," International Journal of Occupational and Environmental Health, 11:338-348. Available at:
https://www.academia.edu/11210729/Maximizing_Profit_and_Endangering_Health_Corporate_Strategies_to_Avoid_Litigation_and_Regulation

3. Egilman, David, and Samantha Howe. 2007. "Against Anti-health Epidemiology: Corporate Obstructions of Public Health via Manipulation of Epidemiology," International Journal of Occupational and Environmental Health, 13(1): 118-24 (January). Available at:
https://www.researchgate.net/publication/51391445_Against_Anti-health_Epidemiology_Corporate_Obstruction_of_Public_Health_via_Manipulation_of_Epidemiology

4. Greenland, S. 1999. "Relation of probability of causation to relative risk and doubling dose: a methodologic error that has become a social problem," American Journal Public Health, 89(8): 1166-1169 (August). Available at:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1508676/pdf/amjph00008-0022.pdf

5. Ong EK, Glantz SA. 2001. "Constructing “sound science” and “good epidemiology”: tobacco, lawyers, and public relations firms." Am J Public Health, 91:1749–1757. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1446868/
6. Perrotta, Tom. 2007. "N.Y. Federal Judge Faults Leak of Sealed Zyprexa Documents," Law.com, 14 February. Available at:
https://www.law.com/almID/1202424340602/NY-Federal-Judge-Faults-Leak-of-Sealed-Zyprexa-Documents/?back=TAL08/&slreturn=20190928151605
7. Ong, Elisa K., MD, MS, and Stanton A. Glantz, PhD. 2001. American Journal of Public Health, 91(11) (November).  Available at: https://ajph.aphapublications.org/doi/pdf/10.2105/AJPH.91.11.1749
Feb 28 SPEAKER: Ryan Hill (MIT) TITLE:  "Scooped! Estimating Rewards for Priority in Science" (joint with Carolyn Stein)

ABSTRACT:
The scientific community assigns credit or “priority” to individuals who publish an important discovery first. We examine the impact of losing a priority race (colloquially known as getting “scooped”) on subsequent publication and career outcomes. To do so, we take advantage of data from structural biology where the nature of the scientific process together with the Protein Data Bank — a repository of standardized research discoveries — enables us to identify priorityraces and their outcomes. We find that race winners receive more attention than losers, butthat these contests are not winner-take-all. Scooped teams are 2.5 percent less likely to publish,are 18 percent less likely to appear in a top-10 journal, and receive 28 percent fewer citations.As a share of total citations, we estimate that scooped papers receive a credit share of 42 percent. This is larger than the theoretical benchmark of zero percent suggested by classic models of innovation races. We conduct a survey of structural biologists which suggests that active scientists are more pessimistic about the cost of getting scooped than can be justifiedby the data. Much of the citation effect can be explained by journal placement, suggesting editors and reviewers are key arbiters of academic priority. Getting scooped has only modest effects on academic careers. Finally, we present a simple model of statistical discrimination in academic attention to explain how the priority reward system reinforces inequality in science,and document empirical evidence consistent with our model. On the whole, these estimates inform both theoretical models of innovation races and suggest opportunities to re-evaluate the policies and institutions that affect credit allocation in science.
Mar 6 SPEAKER: Wei Yang Tham (LISH, Laboratory for Innovation Science at Harvard) TITLE: "Science, Interrupted: How Scientists Respond to Funding Disruptions"
ABSTRACT:
Mar 13   NO SEMINAR
Mar 20   NO SEMINAR: SPRING BREAK
Mar 27 SPEAKER:
Xi Hu (Labor and Worklife Program at Harvard Law School)
TITLE: "Determinants of Business Software Reviews and Impacts of Reviews on Prices and Popularity of Software" (with Richard B. Freeman)

ABSTRACT:
      Software is altering work around the world. It is the cutting edge for improved robotics impacting blue-collar jobs and for the digitalization of white-collar cognitive work. Investment in software by US businesses increased from a negligible amount in the mid 1980s to 20% in fixed capital in the mid 2010s, with potentially huge effects on firms' performance and the work activity of employees. No study, to our knowledge, has used on-line reviews of business software to examine their prices, sales, and impact on purchasing firms and their workers.
      This paper seeks to fill this gap in the literature with a detailed analysis of data on hundreds of thousands of reviews of business software in a diverse set specialized categories including accounting, law, advertising, and human resources, as well as the functionalities that detail what the software products do, which we link to occupations. In addition, using data from 5 million+ individual software users, we find that the ratings as well as prices of business software depend on the number of features a software program contains, and the software users' company and industry as well as their occupation. The extent of dispersion in user attributes is associated with more neutral ratings for software products. Further, the use of higher-rated software with more features could alter the work activities of employees in a affected occupations more than other software, potentially benefiting those who gain complementary skills while reducing the work of those with substitutable skills.
Apr 3 SPEAKER:
Jennifer M. Logg (Georgetown University, McDonough School of Business)

TITLE: "Algorithm Appreciation: People Prefer Algorithmic to Human Judgement" (with Julia A. Minson and Don Moore)

PAPER:
Logg, Jennifer M., Julia A. Minson, and Don Moore. 2019.  "Algorithm Appreciation: People Prefer Algorithmic to Human Judgement" Organizational Behaivor and Human Decision Processes, 151:90-103 (March)

ABSTRACT:
Even though computational algorithms often outperform human judgment, received wisdom suggests that people may be skeptical of relying on them (Dawes, 1979). Counter to this notion, results from six experiments show that lay people adhere more to advice when they think it comes from an algorithm than from a person. People showed this effect, what we call algorithm appreciation, when making numeric estimates about a visual stimulus (Experiment 1A) and forecasts about the popularity of songs and romantic attraction (Experiments 1B and 1C). Yet, researchers predicted the opposite result (Experiment 1D). Algorithm appreciation persisted when advice appeared jointly or separately (Experiment 2). However, algorithm appreciation waned when: people chose between an algorithm’s estimate and their own (versus an external advisor’s; Experiment 3) and they had expertise in forecasting (Experiment 4). Paradoxically, experienced professionals, who make forecasts on a regular basis, relied less on algorithmic advice than lay people did, which hurt their accuracy. These results shed light on the important question of when people rely on algorithmic advice over advice from people and have implications for the use of “big data” and algorithmic advice it generates.

Apr 10

SPEAKER:

Janet Freilich (Fordham University, School of Law)  

TITLE: "Is the Patent System Sensitive to Information Quality?

ABSTRACT:
The patent system assesses information in numerous ways. Examiners evaluate whether an application contains sufficient credible information to be granted and also determine if information in prior art references is adequately reliable that the reference should be used to reject a patent application. Third parties read patents to learn about new technology. Here, we ask whether the patent system evaluates the quality of the information contained in patents in any of these scenarios. We identify patent-paper pairs where the paper has been retracted and the patent contains the retracted information (“unsupported patents”). We follow the trajectories of these unsupported patents both before and after the corresponding paper was retracted. We find that the patent system is not sensitive to information quality. Unsupported patents are prosecuted, maintained, and cited at rates similar to control patents, despite containing information publicly recognized as inaccurate. Examiners, both in evaluating unsupported patent applications for grant and in citing unsupported patents as prior art against downstream applications are largely unaware that the information has been retracted. Downstream applicants whose applications are rejected over unsupported prior art are similarly unaware. Insensitivity to information quality may therefore lead to erroneous grant of patents containing inaccurate information and erroneous rejection of downstream patents due to examiner citation of poor-quality prior art. Though retracted patent-paper pairs are relatively rare, our findings shed light on how the patent system assesses patents supported by incorrect and irreplicable information – a much bigger problem. This has implications for patent quality, patent disclosure, and how patents facilitate knowledge flows.

Apr 17 SPEAKER:
Christian Chacua (University of Bordeaux, and Visiting Fellow, Growth Lab, Harvard Kennedy School) 

TITLE: ​Homophily and Collaboration in Inventor Networks (Francesco Lissoni and Ernest Miguelez)

ABSTRACT: 
We analyze the effect of ethnic-cultural proximity on the formation of highly-skilled professional collaborations, in the context of contemporary international migration trends. Our main hypothesis is that ethnic networks are a source of complementarities among knowledge workers of foreign origin, which in turn increases their probability to collaborate. To this end, we evaluate the determinants of co-inventorship on a sample of knowledge workers of foreign origin residing in the United States, listed in patent applications between 1975 and 2012, in the technological fields of Computer technology and telecommunications and of Pharmaceuticals, biotechnology and organic fine chemistry. We find that inventors from similar foreign ethnic-cultural origin have, on average, a higher probability of inventingtogether, after controlling for spatial and social proximities, as well as for network structural effects. In addition, the effect of ethnic-cultural similarity varies among technological fields and cultural groups, being particularly strong for some Asian ethnicities and for the field of Computer technology and telecommunications. These findings suggest that informal factors, which usually drive social interactions, play a relevant role in explaining professional interactions. This stands in partial contrast with evidence according to which the increasing complexity and globalization of science and knowledge leads to ethnic-cultural diversity in teams.

Apr 24 SPEAKER:

Erling Barth (Institute for Social Research, University of Norway)

 

Erling Barth (University of Norway, ISF) "The Effect of Software on Demand for Labor and Firm Performance" (with James Davis and Richard B. Freeman)

ABSTRACT:

    END OF SPRING SEMESTER


 

 

 

Location of Baker 102 (Bloomberg Center), HBS

Speaker Papers and Presentations (since 2012)

2020, 4/3:  LOGG-Jennifer:  "Algorithm Appreciation: People Prefer Algorithmic to Human Judgement" (paper with Julia A. Minson and Don Moore)

2020, 2/28:  HILL-Ryan, "Scooped! Estimating Rewards for Priority in Science"

2019, 11/22: GLAESER-Edward: The Spatial Mismatch between Innovation and Joblessness_NBERchap-in-Lerner-Stern_Ec3118
2019, 11/22: GLAESER-Edward: The Rise of Non-Employed Men

2019, 11/15: NUNES-Ashley: ​Can driverless technology upend personal vehicle ownership? A bottom-up global analysis_Ec3118

2019, 9/27: BOUDREAU-Kevin and MARX-Matt: From Theory to Practice: Field Experimental Evidence on Early Exposure of Engineering Majors to Professional Work

2019, 9/20:  FRANK-Morgan: Paper 1 - Small cities face greater impact from automationJournal of the Royal Society Interface (2018).
2019, 9/20: FRANK-Morgan: Paper 2 - Unpacking the polarization of workplace skillsScience Advances (2018).
2019, 9/20: FRANK-Morgan: Paper 3 - Towards understanding the impact of AI on laborPNAS (2019).

2019, 9/13: GUZMAN-Jorge: The Impact of State-Level R&D Tax Credits on the Quantity and Quality of Entrepreneurship_NBERwp26099_ec3118

2019, 4/22: ROSSI-Francesca: AI-Ethics-for-Enterprise-AI_ec3118-HBS.pdf

2019, 3/8: Van REENAN-John: Who Becomes an Inventor in America_NBERwp24062.pdf

2019, 2/25: FURMAN-Jason: AI-and-Economy_NBERwp24689.pdf

2019, 2/22: LI-Li-and-MAK-Eric: Peer Optimal Assignment_with-Wang_4Jan19.pdf

2019, 2/1: FREEMAN-Richard and XIE-Qingnan: Bigger than You Thought_Chinas Contribution_JOURNAL_China and World Economy_Jan2019.pdf

2018, 4/20: LO-Andrew: paper-1_SBBI-4-20-18_NBT_Dec2017.pdf

2018, 4/20: LO-Andrew: paper-2_SBBI-4-20-18_Jamaoncology_Montazerhodjat_2017_oi_170004.pdf

2018, 4/20: LO-Andrew: paper-3_SBBI-4-20-18_DDT_Devices.pdf

2018, 4/20: LO-Andrew: paper-4_SBBI-4-20-18_predictive_15.pdf

2018, 4/20: LO-Andrew: paper-5_SBBI-4-20-18_ClinTrialSuccess.pdf

2018, 3/2: MALONEY-Bill: Engineering-Growth_8-31-17.pdf

2018, 2/23_TABAKOVIC-Haris: Revolving_Doors_Tabakovic_Wollmann_ec2888r.pdf

2018, 2/9: BESSEN-James: AI and Job - The Role of Demand_nberw24235.pdf

2017, 10/6: GLENNON-Britta: Offshoring Innovation in Taiwan_WP_6Oct 2017.pdf

2017, 10/4: BORJAS-George: 10-4-17_Ethnic-Complementarities-Students_NBERw21096.pdf

2017, 4/21: GREENSTEIN-Shane: ICTE-1.pdf

2017, 2/17: LANE-Julie_et-al: Research Funding and Regional Economics_NBERw23018.pdf

2017, 2/17: LANE-Julie_et-al: PPT_Research Funding and Regional Economies_2-17-17

2017, 2/3: KERR-William_et-al: Mechanics of Endogenous Innovation_Patents.pdf

2016, 12/9: DEMING: SocialSkills_NoAppendix_Aug2016.pdf

2016, 11/4: BOSSO-Christopher: JNR.pdf

2016, 10/28: MOTOYAMA-Yas: Connected Entrepreneurs.pdf

2016, 10/21: LESCHLY SEMINAR: SWARTZ_Speech_10-21-16.htm

2016, 10/21: LESCHLY SEMINAR: BERGER-Kenneth_5-Minutes Sermon.pdf

2016, 10/7: Doblinger: Governments as partners (w-Surana+Anadon).pdf

2016, 10/6: BORNER: full report_NSF-ModSTI-Conf-Report_for-SBBI-10-6-16.pdf

2016, 10/6: BORNER: Discussion-on-10-6-16_Modelling Sci Tech Innov.docx

2016, 9/16: WANG: Bias Against Novelty in Science_(with Veugelers-Stephan)_w22180.pdf

2016, 4/8: ARORA-Ashish_Killing the Golden Goose - Decline of Science in Corporate R&D

2016, 3:11: GANGULI-Ina: Mobility of Elite Life Scientists.pdf

2016, 2/5: WILLIAMS-Heidi: How Do Patents Affect Follow-on Innovation_Evidence from Human Genome_WP21666_Oct2015.pdf

2015, 12/4: Richard Freeman and Sen Chai

2015, 11/6: Jing Xia: Financing and the Market for Ideas - Evidence from Biopharma.pdf

2015, 10/30: Annamaria Conti: PhD Career-Preferences.pdf

2015, 10/30: CONTI-Annamaria: 10-30-15_Visentin_PhD_Career_Preferences.pdf

2015, 10/23: Weihua An: 2-Abstracts_Extracting Social Networks_and_Subject Citation Networks.pdf

2015, 9/22: Chunli Bai: Math Seminar

2015, 9/18: Melanie Sinche: ABSTRACT_Identifying Career Pathways for PhDs in Science.pdf

2015, 4/24: Adam Isen

2015, 3/6: Riccardo Crescenzi

2015, 3/27: David Ong

2015, 2/20: Matt Neidell: 2-Paper_Particulate Pollution and Productivity of Pear Packers

2015, 2/20: Matt Neidell: 1-Paper_Impact of Pollution on Worker Productivity

2014, 4/4: Dan Wang

2014, 4/25: Dan Wang

2014, 3/28: Ralf Martin

2014, 2/28: Paula Stephan: 3_Paper 3_Mobile Scientists-Intl Networks

2014, 2/28: Paula Stephan: 2_Paper 2_Migrant Scientists

2014, 2/28: Paula Stephan: 1b_Paper 1 supplement

2014, 2/28: Paula Stephan: 1a_Paper 1_text_Foreign Born Scientists

2014, 2/14: John Van Reenan

2014, 12/5: William Kerr

2014, 11/21: Gabriel Chan

CHAN-Gabriel_11-21-14_JMP - National Lab Patent Licensing.pdf

2014, 10/3: Susan Feng Lu

2014, 10/10: Sen CHAI

2014, 10/10: CHAI-Sen: Moving Beyond Bibliometrics_10-11-13.pdf

2013, 9/13: Nirupama Rao

2013, 3/8: Blume-Kohout

2013, 4/19: Nirupama Rao

2013, 3/15: Lubynsky: Abstract and Summary

2013, 3/15: Lubynsky: Powerpoint

2013, 11/8: Gabe Chan and Laura Diaz Anadon

2013, 10/18: George Borjas and Kirk Doran

2013, 10/11: Sen Chai

2012, 10/26: Freeman - Paper 2

2012, 10/26: Freeman - Paper 1