nep-knm New Economics Papers
on Knowledge Management and Knowledge Economy
Issue of 2022‒09‒19
four papers chosen by
Laura Nicola-Gavrila
Centrul European de Studii Manageriale în Administrarea Afacerilor

  1. Testing the knowledge-capital model of foreign direct investment: New evidence By Kox, Henk L.M.
  2. Language Barriers and the Speed of Knowledge Diffusion By Kyle HIGHAM; NAGAOKA Sadao
  3. Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction By Lei Li; Zhiyuan Zhang; Ruihan Bao; Keiko Harimoto; Xu Sun
  4. Intellectual Property Rights Protection and Trade: An Empirical Analysis By Auriol, Emmanuelle; Biancini, Sara; Paillacar, Rodrigo

  1. By: Kox, Henk L.M.
    Abstract: The knowledge-capital (KC) model of FDI explains the international distribution of FDI, assuming that firms own proprietary knowledge assets that can at low costs also be exploited in foreign subsidiaries. The model's implication is that countries with much outward FDI should have a relative abundance of proprietary knowledge assets, which has up to now not been adequately tested, partly due to a lack of data. This paper extends the KC model by a module that formalises the encapsulation of public national knowledge assets into proprietary firm-level assets. It provides a way to test the basic tenet of the KC model. We exploit a new dataset (80 indicators, 209 countries, period 2000-2020) to identify the impact and statistical significance of national knowledge assets for explaining outward FDI. Our test confirms the validity of the KC model for explaining patterns of outward FDI. Several robustness tests confirm the stability of our findings.
    Keywords: foreign direct investment; knowledge-capital model; empirical testing; 2000-2020; 209 countries
    JEL: D21 F21 F23
    Date: 2022–08
  2. By: Kyle HIGHAM; NAGAOKA Sadao
    Abstract: While language barriers are well-known obstacles to knowledge diffusion, quantitative research on this topic is sparse. In this work, we attempt to fill this gap by providing causal evidence on their effects on the speed of knowledge diffusion by exploiting the introduction of pre-grant publications by the American Inventors Protection Act (AIPA) in 2000. We find that this policy significantly accelerated, relative to Japanese inventors, US inventors’ use of Japan-originating technical knowledge in their patents. Our analysis controls for biases of patent citations as proxies of knowledge flow, including preference for citing local prior art. Consistent with incentives for translation, this acceleration is much larger for small firms and the firms with little investment in the Japanese market. Consistent with high uncertainty of foreign patents before translation, we see much larger effects of the AIPA on the patent applications with higher quality. Our findings suggest that pre-grant publication provides a significant public good for cumulative innovation through earlier translations of foreign patents.
    Date: 2022–08
  3. By: Lei Li; Zhiyuan Zhang; Ruihan Bao; Keiko Harimoto; Xu Sun
    Abstract: Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available in regression problems like stock trading volume prediction. To remedy this, we present a novel distillation framework for training a light-weight student model to perform trading volume prediction given historical transaction data. Specifically, we turn the regression model into a probabilistic forecasting model, by training models to predict a Gaussian distribution to which the trading volume belongs. The student model can thus learn from the teacher at a more informative distributional level, by matching its predicted distributions to that of the teacher. Two correlational distillation objectives are further introduced to encourage the student to produce consistent pair-wise relationships with the teacher model. We evaluate the framework on a real-world stock volume dataset with two different time window settings. Experiments demonstrate that our framework is superior to strong baseline models, compressing the model size by $5\times$ while maintaining $99.6\%$ prediction accuracy. The extensive analysis further reveals that our framework is more effective than vanilla distillation methods under low-resource scenarios.
    Date: 2022–08
  4. By: Auriol, Emmanuelle; Biancini, Sara; Paillacar, Rodrigo
    Abstract: The paper proposes an empirical analysis of the determinants of the adoption of Intellectual Property Rights (IPR) and their impact on innovation in manufac- turing. The analysis is conducted with panel data covering 112 countries. First we show that IPR protection is U-shaped with respect to a country’s market size and inverse-U-shaped with respect to the aggregated market size of its trade partners. Second, reinforcing IPR protection reduces on-the-frontier and inside-the-frontier innovation in developing countries, without necessarily increasing innovation at the global level.
    Keywords: Intellectual Property Rights; Innovation; Developing Countries; Market Potential; Trade
    JEL: F12 F13 F15 L13 O31 O34
    Date: 2022–09–02

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