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Productivity growth patterns in US dairy products manufacturing plants

Author

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  • Pinar Celikkol Geylani
  • Spiro Stefanou
Abstract
We analyse the productivity growth patterns in the US dairy products industry using the Census Bureau's plant-level data set. We decompose Total Factor Productivity (TFP) growth into the scale and technical change components and analyse variability of plants' productivity by constructing transition matrices. We observe a cross-sectional dispersion in plant-level productivity growth in the industry. Even though the industry aggregate shows a small TFP growth rate -0.3%, quartile rank analysis shows that while the lowest productivity quartile plants average 1.9% loss in productivity, the highest productivity quartile plants average 1.1% growth annually. Our results show considerable movements of plants in their productivity rank categories overall and across age groups, and we find that the scale effect contribution to TFP growth accounts for about 90% of TFP growth on average in the industry. These plants extract scale efficiencies over technological progress to fuel TFP growth. The youngest plants start with the lowest productivity growth at the initial time period, but they catch up older plants productivity, which present the highest average growth rate through years. This may indicate a 'learning-by-doing' process for the industry.

Suggested Citation

  • Pinar Celikkol Geylani & Spiro Stefanou, 2011. "Productivity growth patterns in US dairy products manufacturing plants," Applied Economics, Taylor & Francis Journals, vol. 43(24), pages 3415-3432.
  • Handle: RePEc:taf:applec:v:43:y:2011:i:24:p:3415-3432
    DOI: 10.1080/00036841003636326
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    References listed on IDEAS

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    1. Phoebus J Dhrymes, 1991. "The Structure Of Production Technology Productivity And Aggregation Effects," Working Papers 91-5, Center for Economic Studies, U.S. Census Bureau.
    2. Pakes, Ariel & Ericson, Richard, 1998. "Empirical Implications of Alternative Models of Firm Dynamics," Journal of Economic Theory, Elsevier, vol. 79(1), pages 1-45, March.
    3. Eric J. Bartelsman & Wayne Gray, 1996. "The NBER Manufacturing Productivity Database," NBER Technical Working Papers 0205, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Magdalena Kapelko, 2019. "Measuring productivity change accounting for adjustment costs: evidence from the food industry in the European Union," Annals of Operations Research, Springer, vol. 278(1), pages 215-234, July.
    2. Magdalena Kapelko & Alfons Oude Lansink & Spiro E. Stefanou, 2017. "Input-Specific Dynamic Productivity Change: Measurement and Application to European Dairy Manufacturing Firms," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 579-599, June.
    3. Pinar Geylani & Spiro Stefanou, 2013. "Linking investment spikes and productivity growth," Empirical Economics, Springer, vol. 45(1), pages 157-178, August.
    4. Wei Zhang & Long Gao & Mohammad Zolghadr & Dawei Jian & Mohsen ElHafsi, 2023. "Dynamic incentives for sustainable contract farming," Production and Operations Management, Production and Operations Management Society, vol. 32(7), pages 2049-2067, July.
    5. Chieh-Wen Chang & Kun-Shan Wu & Bao-Guang Chang & Kuo-Ren Lou, 2019. "Measuring Technical Efficiency and Returns to Scale in Taiwan’s Baking Industry―A Case Study of the 85 °C Company," Sustainability, MDPI, vol. 11(5), pages 1-14, February.
    6. Xinyue Lin & Lingli Qi & Haoran Pan & Basil Sharp, 2022. "COVID-19 Pandemic, Technological Progress and Food Security Based on a Dynamic CGE Model," Sustainability, MDPI, vol. 14(3), pages 1-18, February.
    7. Sansi Yang & C. Richard Shumway, 2020. "Knowledge accumulation in US agriculture: research and learning by doing," Journal of Productivity Analysis, Springer, vol. 54(2), pages 87-105, December.

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