nep-knm New Economics Papers
on Knowledge Management and Knowledge Economy
Issue of 2017‒11‒26
four papers chosen by
Laura Ştefănescu
Centrul European de Studii Manageriale în Administrarea Afacerilor

  1. Exploring the Roles of Market-Driven Capability and Functional Diversification in Market Knowledge Transfer within MNCs By Sirisuhk Rakthin
  2. Knowledge Exchange and Productivity Spill-overs in Bangladeshi Garment Factories By Andreas Menzel
  3. Diffusing New Technology without Dissipating Rents: Some Historical Case Studies of Knwoledge Sharing By James Bessen; Alessandro Nuvolari
  4. The STEM requirements of "non-STEM" jobs: evidence from UK online vacancy postings and implications for skills & knowledge shortages By Grinis, Inna

  1. By: Sirisuhk Rakthin (College of Management, Mahidol University)
    Abstract: Drawing upon theory from transnational management and market-driven organization literatures, this study uses both survey and case study data of 162 respondents who works in three multinational corporations (MNCs) to examine the influence of functional diversification, market-driven capability, and cultural distance on a transfer of market knowledge from foreign affiliates to headquarter. The empirical results indicate a strong positive influence of both functional diversification and market-driven capability on a transfer of market knowledge, i.e., customer and competitor intelligence. The qualitative data collected through in-depth interviews with top executives were then leveraged to provide further explanation and support empirical results. Implications for academics and practitioners are also addressed.
    Keywords: knowledge transfer in MNCs; actor network theory; market-driven; functional diversification
    JEL: F23 C30 M10
    Date: 2017–10
  2. By: Andreas Menzel
    Abstract: Productivity spill-overs within firms have commonly been used as a proxy measure for organizational learning. Using novel data from more than 200 production lines in three garment factories in Bangladesh, this paper extends the evidence on such productivity spill-over in two directions. First, I find that spatial distance within firms matters greatly for the strengths of productivity spill-overs, while product complexity matters little. This has important implications for firms in rapidly developing countries such as Bangladesh, as spill-over strength seems less affected when firms upgrade to more complex products, but seems more affected if firms grow larger. Second, I provide evidence from a randomized communication intervention in the three factories to determine the extent to which productivity spill-overs are indeed a measure of knowledge exchange within firms, and not of other types of peer effects, such as competition. In the intervention, randomly selected line supervisors were instructed by their superiors to share production knowledge when their lines were allocated the same garment for production. The intervention increased the strength of the productivity spill-overs between the targeted production lines. It thus supports the view that productivity spill-overs can be used as a measure of knowledge exchange within firms.
    Keywords: learning; productivity; firms
    JEL: D2 L2 M5 O3
    Date: 2017–11
  3. By: James Bessen; Alessandro Nuvolari
    Abstract: The diffusion of innovations is supposed to dissipate inventors' rents. Yet in many documented cases, inventors freely shared knowledge with their competitors. Using a model and case studies, this paper explores why sharing did not eliminate inventors' incentives. Each new technology coexisted with an alternative for one or more decades. This allowed inventors to earn rents while sharing knowledge, attaining major productivity gains. The technology diffusion literature suggests that such circumstances are common during the early stages of a new technology.
    Keywords: technological change, technology diffusion, knowledge sharing, collective invention, patents
    Date: 2017–11–15
  4. By: Grinis, Inna
    Abstract: Do employers in "non-STEM" occupations (e.g. Graphic Designers, Economists) seek to hire STEM (Science, Technology, Engineering, and Mathematics) graduates with a higher probability than non-STEM ones for knowledge and skills that they have acquired through their STEM education (e.g. "Microsoft C#", "Systems Engineering") and not simply for their problem solving and analytical abilities? This is an important question in the UK where less than half of STEM graduates work in STEM occupations and where this apparent leakage from the "STEM pipeline" is often considered as a wastage of resources. To address it, this paper goes beyond the discrete divide of occupations into STEM vs. non-STEM and measures STEM requirements at the level of jobs by examining the universe of UK online vacancy postings between 2012 and 2016. We design and evaluate machine learning algorithms that classify thousands of keywords collected from job adverts and millions of vacancies into STEM and nonSTEM. 35% of all STEM jobs belong to non-STEM occupations and 15% of all postings in non-STEM occupations are STEM. Moreover, STEM jobs are associated with higher wages within both STEM and non-STEM occupations, even after controlling for detailed occupations, education, experience requirements, employers, etc. Although our results indicate that the STEM pipeline breakdown may be less problematic than typically thought, we also find that many of the STEM requirements of "non-STEM" jobs could be acquired with STEM training that is less advanced than a full time STEM education. Hence, a more efficient way of satisfying the STEM demand in non-STEM occupations could be to teach more STEM in non-STEM disciplines. We develop a simple abstract framework to show how this education policy could help reduce STEM shortages in both STEM and non-STEM occupations.
    Keywords: STEM Education; Skills Shortages; Machine Learning
    JEL: N0 R14 J01 J50
    Date: 2017–05–01

This nep-knm issue is ©2017 by Laura Ştefănescu. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.