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Impact of R&D Activities on Pricing Behaviors with Product Turnover

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  • HARA Yasushi
  • TONOGI Akiyuki
  • TONOGI Konomi

Abstract

This study empirically investigates the impact of research and development (R&D) activity on product turnover based on Point-of-Sales (POS) data. When measuring the inflation rate in an economy, the effects of quantitative, qualitative and volume changes must be isolated from changes in nominal sales figures. Changes in quality can be attributed to corporate R&D activities. In order to examine the effect of R&D activities on price changes in sales data, we construct a unique dataset by combining three datasets: weekly POS data, patent database (IIP Patent DB) data, and the Survey of Research and Development data. We use regression analysis with pooling and panel regression. We observe that while R&D activity may have a causal effect on price increases, a negative effect on the price of incumbent products is also observed. In addition, the relative prices of new and incumbent products tended to be higher for companies with active R&D expenditures. We suggest that continuous R&D is necessary to keep introducing high value products that place upward pressure on prices.

Suggested Citation

  • HARA Yasushi & TONOGI Akiyuki & TONOGI Konomi, 2020. "Impact of R&D Activities on Pricing Behaviors with Product Turnover," Discussion papers 20006, Research Institute of Economy, Trade and Industry (RIETI).
    • Handle: RePEc:eti:dpaper:20006
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    References listed on IDEAS

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    1. Abe, Naohito & Inakura, Noriko & Tonogi, Akiyuki, 2017. "Effects of the Entry and Exit of Products on Price Indexes," RCESR Discussion Paper Series DP17-2, Research Center for Economic and Social Risks, Institute of Economic Research, Hitotsubashi University.
    2. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    3. Feenstra, Robert C, 1994. "New Product Varieties and the Measurement of International Prices," American Economic Review, American Economic Association, vol. 84(1), pages 157-177, March.
    4. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Introduction to "Scanner Data and Price Indexes"," NBER Chapters, in: Scanner Data and Price Indexes, pages 1-13, National Bureau of Economic Research, Inc.
    5. Robert C. Feenstra & Matthew D. Shapiro, 2003. "Scanner Data and Price Indexes," NBER Books, National Bureau of Economic Research, Inc, number feen03-1, January.
    6. IKEUCHI Kenta & MOTOHASHI Kazuyuki & TAMURA Ryuichi & TSUKADA Naotoshi, 2017. "Measuring Science Intensity of Industry using Linked Dataset of Science, Technology and Industry," Discussion papers 17056, Research Institute of Economy, Trade and Industry (RIETI).
    7. YAMAGUCHI, Shotaro & 山口, 翔太郎 & NITTA, Ryuji & HARA, Yasushi & SHIMIZU, Hiroshi & 清水, 洋, 2018. "Staying Young at Heart or Wisdom of Age: Longitudinal Analysis of Age and Performance in US and Japanese Firms," IIR Working Paper 18-41, Institute of Innovation Research, Hitotsubashi University.
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