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Measuring Productivity Dynamics with Endogenous Choice of Technology and Capacity Utilization: An Application to Automobile Assembly

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  • Johannes Van Biesebroeck

Abstract

During the 1980s, all Japanese automobile producers opened assembly plants in North America. Industry analysts and previous research claim that these transplants are more productive than incumbent plants and that they produce with a substantially different production process. We compare the two production processes by estimating a model that allows for heterogeneity in technology and productivity. We treat both types of heterogeneity as intrinsically unobservable. In the model, plants choose technology before production starts. They condition subsequent input decisions on this choice. Maximum likelihood estimation is used to estimate the unconditional distribution of the technology choice, output, and inputs. The model is applied to a sample of automobile assembly plants. We control for capacity utilization, unobserved productivity differences, and price effects. The results indicate that there exist two distinct technologies. In particular, the more recent technology uses labor less intensively and it has a higher elasticity of substitution between labor and capital. Hicks-neutral productivity growth is estimated to be lower, while capital-biased (labor-saving) productivity growth is estimated significantly higher, for the new technology. Using the estimation results, we decompose industry-wide productivity growth in plant-level changes and composition effects, for both technologies separately. Plant-level productivity growth is further decomposed to reveal the importance of capital-biased productivity growth, increase in capital-labor ratio, and returns to scale.

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Bibliographic Info

Paper provided by Center for Economic Studies, U.S. Census Bureau in its series Working Papers with number 00-16.

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Date of creation: Dec 2000
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Handle: RePEc:cen:wpaper:00-16

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Keywords: CES; economic; research; micro; data; microdata; chief; economist;

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Cited by:
  1. Chad Syverson, 2011. "What Determines Productivity?," Journal of Economic Literature, American Economic Association, vol. 49(2), pages 326-65, June.
  2. Cynthia Bansak & Norman Morin & Martha Starr, 2004. "Technology, capital spending, and capacity utilization," Finance and Economics Discussion Series 2004-30, Board of Governors of the Federal Reserve System (U.S.).
  3. Lukach, R. & Kort, P.M. & Plasmans, J.E.J., 2007. "Strategic R&D with Knowledge Spillovers and Endogenous Time to Complete," Discussion Paper 2007-38, Tilburg University, Center for Economic Research.
  4. Johannes Van Biesebroeck, 2006. "Complementarities in Automobile Production," NBER Working Papers 12131, National Bureau of Economic Research, Inc.
  5. Aamir Hashmi, 2012. "The Relationship between Market Structure and Innovation in Industry Equilibrium: A Case Study of the Global Automobile Industry," 2012 Meeting Papers 356, Society for Economic Dynamics.
  6. Allan Collard-Wexler & Jan De Loecker, 2013. "Reallocation and Technology: Evidence from the U.S. Steel Industry," NBER Working Papers 18739, National Bureau of Economic Research, Inc.
  7. TAKEDA Yosuke & UCHIDA Ichihiro, 2009. "Technological Externalities and Economic Distance: A case of the Japanese automobile suppliers," Discussion papers 09051, Research Institute of Economy, Trade and Industry (RIETI).
  8. Van Reenen, John, 2011. "Does competition raise productivity through improving management quality?," International Journal of Industrial Organization, Elsevier, vol. 29(3), pages 306-316, May.
  9. Hyytinen, Ari & Maliranta, Mika, 2013. "Firm lifecycles and evolution of industry productivity," Research Policy, Elsevier, vol. 42(5), pages 1080-1098.
  10. Ruslan Lukach & Peter M. Kort & Joseph Plasmans, 2005. "Optimal R&D Investment Strategies with Quantity Competition under the Threat of Superior Entry," CESifo Working Paper Series 1385, CESifo Group Munich.
  11. T. Buchmann & D. Hain & Muhamed Kudic & M. Müller, 2014. "Exploring the Evolution of Innovation Networks in Science-driven and Scale-intensive Industries: New Evidence from a Stochastic Actor-based Approach," IWH Discussion Papers 1, Halle Institute for Economic Research.
  12. Ackerberg, Daniel & Caves, Kevin & Frazer, Garth, 2006. "Structural identification of production functions," MPRA Paper 38349, University Library of Munich, Germany.
  13. Johannes van Biesebroeck, 2002. "The Effect of Technology Choice on Automobile Assembly Plant Productivity," The Economic and Social Review, Economic and Social Studies, vol. 33(1), pages 65-73.

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