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Recovering the Sunk Costs of R&D: the Moulds Industry Case

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  • Carlos Daniel Santos
Abstract
Sunk costs for R&D are an important determinant of the level of innovation in the economy. In this paper I recover them using a Markov equilibrium framework. The contribution is twofold. First, a model of industry dynamics which accounts for selection into R&D, capital accumulation and entry/exit is proposed. The industry state is summarized by an aggregate state with the advantage that it avoids the 'curse of dimensionality'. Second, the estimated sunk costs of R&D for the Portuguese moulds industry are shown to be important (3.4 million Euros). They become particularly relevant since the industry is mostly populated by small firms. Institutional changes in the early 1990s generated an increase in demand from European car makers and created the incentives for firms to pay the costs of investment. Trade-induced innovation reinforced the selection effect by which international trade leads to productivity growth. Finally, using the estimated parameters, simulations evaluate the effects of changes in market size, sunk costs and entry costs.

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  • Carlos Daniel Santos, 2009. "Recovering the Sunk Costs of R&D: the Moulds Industry Case," CEP Discussion Papers dp0958, Centre for Economic Performance, LSE.
    • Handle: RePEc:cep:cepdps:dp0958
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    Cited by:

    1. Carlos Daniel Santos, 2009. "Recovering the Sunk Costs of R&D: the Moulds Industry Case," CEP Discussion Papers dp0958, Centre for Economic Performance, LSE.
    2. Bonnín Roca, Jaime & Vaishnav, Parth & Morgan, Granger M. & Fuchs, Erica & Mendonça, Joana, 2021. "Technology Forgiveness: Why emerging technologies differ in their resilience to institutional instability," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    3. Pere Arqué-Castells, 2013. "Persistence in R&D Performance and its Implications for the Granting of Subsidies," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 43(3), pages 193-220, November.
    4. Srisuma, Sorawoot, 2010. "Estimation of structural optimization models: a note on identification," LSE Research Online Documents on Economics 58071, London School of Economics and Political Science, LSE Library.
    5. Amoroso, S., 2013. "Heterogeneity of innovative, collaborative, and productive firm-level processes," Other publications TiSEM f5784a49-7053-401d-855d-1, Tilburg University, School of Economics and Management.
    6. Prajogo, Daniel I., 2016. "The strategic fit between innovation strategies and business environment in delivering business performance," International Journal of Production Economics, Elsevier, vol. 171(P2), pages 241-249.
    7. Sara Amoroso, 2014. "The hidden costs of R&D collaboration," JRC Working Papers on Corporate R&D and Innovation 2014-02, Joint Research Centre.

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    More about this item

    Keywords

    Aggregate state; industry dynamics; Markov equilibrium; moulds industry; R&D; structural estimation; sunk costs;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L22 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Organization and Market Structure
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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