Raviv Murciano-Goroff
Job Market Candidate

Stanford University
Department of Economics
579 Serra Mall
Stanford, CA 94305
617-953-9608
ravivmg@stanford.edu

Curriculum Vitae

Fields:
Economics of Innovation, Technology,
and Entrepreneurship

Expected Graduation Date:
June, 2018

References:
Jonathan Levin (primary):
jdlevin@stanford.edu

Timothy Bresnahan:
tbres@stanford.edu

Muriel Niederle:
niederle@stanford.edu

Richard B. Freeman:
freeman@nber.org

Job Market Paper

Missing Women in Tech: The Labor Market for Highly Skilled Software Engineers - slides
This paper examines the behavior of job seekers and recruiters in the labor market for software engineers. I obtained data from a recruiting platform where individuals can self-report their computer programming skills and recruiters can message individuals they wish to contact about job opportunities. I augment this dataset with measures of each individual's previous programming experience based on analysis of actual computer source code they wrote and shared within the open-source software community. This novel dataset reveals that candidates' self-reported technical skills are quantitatively one of the most important predictors of recruiter interest. Consistent with social psychology and behavioral economics studies, I also find female programmers with previous experience in a programming language are 9.36% less likely than their male counterparts to self-report knowledge of that programming language on their resume. Despite public pronouncements, however, recruiters do not appear more inclined toward recruiting female candidates who self-report knowing programming languages. Indeed, recruiters are 12.37% less likely to message a woman than a man with comparable observable qualifications, even if those qualifications are very strong. Ultimately, neither the labor supply nor the labor demand side is adjusting their behavior with regard to the self-reported technical skills in ways that could increase the representation of women among software engineering recruits.


Publications

Why and Wherefore of Increased Scientific Collaboration
Richard B. Freeman, Ina Ganguli, Raviv Murciano-Goroff
Chapter in NBER book The Changing Frontier: Rethinking Science and Innovation Policy (2015), Adam Jaffe and Benjamin Jones, editors (p. 17 - 48)
We examine international and domestic collaborations using an original survey of corresponding authors and Web of Science data of articles that had at least one US coauthor in Particle and Field Physics, Nanoscience and Nanotechnology, and Biotechnology and Applied Microbiology. The data identify the connections among coauthors and the views of corresponding authors about the collaboration. We find that collaborations have increased across US cities and between US researchers and researchers abroad. However, they show sufficient similarity to indicate that collaborations are best viewed in many regards as occurring across space broadly rather than in terms of international vs. domestic collaborative activity. We also document that the main reason scientists give for collaborations is to combine the specialized knowledge and skills of coauthors. The vast majority report that face-to-face meetings are important; most collaborators first met working in the same institution and communicate often through meetings with coauthors from distant locations. Finally, we find that for biotech, citations to international papers are higher compared to papers with domestic collaborators only, but not for the other two fields. Moreover, in all three fields, papers with the same number of coauthors had lower citations if they were international collaborations.


Works in Progress

Worker Mobility and Spillovers in Technology Adoption Decisions
When highly skilled workers switch employers, they bring their knowledge of and experience in the technologies used at their previous workplace. Thus, the movement of workers can influence the diffusion new technologies across firms. By collecting and analyzing the actual source code of websites over time, I created a panel dataset of the web technologies used on hundreds of popular public-facing websites over the past seven years. I augment this panel with data on the rate of worker transitions between employers. Using this novel dataset, I investigate how the movement of workers influence web technology adoption decisions. My results indicate that the technology adoption decisions of firms have spillover effects on the decisions of other firms through the channel of worker mobility.

The Dynamics of Consumer Smartphone Update and Upgrade Decisions
I analyze the dynamics of smartphone update and upgrade decisions by professional and amateur photographers. The data for this research comes from a random sample of 100 million photos posted to Flickr, a photo-sharing website. For each photograph taken by a smartphone, embedded meta-data indicates both the model and the software installed on the phone that took the photograph. I extract this data and construct a panel dataset of each photographer’s devices and their usage. Thus far, I find two results. First, when new smartphones are released, individuals who do not update their phone and do not update the software on the phone also increase the number of photos they take with their phone. Second, while the installation of new software on a smartphone increases the usage of that device, it also prolongs the time until a user purchases a new device. These results provide insights into the trade-offs that device manufacturers face when developing strategies for the release of new devices and software updates.


Research Awards

B.F. Haley and E.S. Shaw Fellowship for Economics (2017)
Received a dissertation fellowship from the Stanford Institute for Economic Policy Research (SIEPR).

Kauffman Foundation Dissertation Fellowship (2016)
Received a $20,000 grant for research related to hiring and entrepreneurship.

Oxford University Wellcome Unit for the History of Science, Medicine, and Technology Scholarship (2009)

Harvard University Carol K. Pforzheimer History Award (2007)



Teaching Awards

Stanford University's Outstanding Teaching Assistant Award (2017)
Economics 101: Economic Policy Analysis, Corporate and Business Strategy.

Stanford University's Outstanding Teaching Assistant Award (2016)
Economics 101: Economic Policy Analysis, Corporate and Business Strategy.

Harvard University's Derek Bok Center Certificate of Distinction in Teaching (2009)
Quantitative Reasoning 20: Algorithms and Data Structures.



Teaching Experience

Econ 101: Economic Policy Analysis, Corporate and Business Strategy (Undergraduate course), Stanford University (2016)
Teaching Assistant for Prof. Hamilton Helmer
Stanford University's Outstanding Teaching Assistant Award (2016)

Advertising and Monetization (MBA Course), Stanford University (2016)
Course Grader for Prof. Susan Athey

Platform Competition in Digital Markets (MBA Course), Stanford University (2016)
Teaching Assistant for Prof. Susan Athey

Econ 101: Economic Policy Analysis, Corporate and Business Strategy (Undergraduate course), Stanford University (2015)
Teaching Assistant for Prof. Hamilton Helmer
Stanford University's Outstanding Teaching Assistant Award (2015)

Platform Competition in Digital Markets (MBA Course), Stanford University (2015)
Course Grader for Prof. Susan Athey

Math 23: Linear Algebra and Real Analysis (Undergraduate course), Harvard University (2011)
Proof Assistant for Prof. Paul Bamberg

Algorithms and Data Structures (Undergraduate course), Harvard University (2009)
Teaching Assistant for Prof. William Bossert
Harvard University's Derek Bok Center Certificate of Distinction in Teaching (2009)