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Stochastic Technical Change and Procyclical TFP The Case of Italian Agriculture

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  • Roberto Esposti

Abstract

The paper questionsthe use of Total Factor Productivity (TFP) measures as a proxyfor the technological level in Italian agriculture. TFP growthreveals a cyclical behaviour and a short-run component due toshocks on both the demand and supply sides. In order to identifyreal long-run technological change, a Kalman filter procedureis applied to a stochastic process depicting the generation oftechnical change induced by R&D and Extension expenditure.The empirical evidence reveals that short-term shocks greatlyaffect the traditional measure of productivity. A better indicatorof technological progress can be obtained by estimating the long-runcomponent of productivity, which seems to be significantly inducedby R&D-Extension public expenditure. Copyright Kluwer Academic Publishers 2000

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  • Roberto Esposti, 2000. "Stochastic Technical Change and Procyclical TFP The Case of Italian Agriculture," Journal of Productivity Analysis, Springer, vol. 14(2), pages 119-141, September.
    • Handle: RePEc:kap:jproda:v:14:y:2000:i:2:p:119-141
    DOI: 10.1023/A:1007846907896
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    Cited by:

    1. Ludwik Wicki & Hanna Dudek, 2019. "Factors influencing cereals yield in Polish agriculture," Economia agro-alimentare, FrancoAngeli Editore, vol. 21(3), pages 793-806.
    2. Edoardo Baldoni & Roberto Esposti, 2018. "Agricultural Productivity in Space - An econometric assessment on Italian farm-level data," Working Papers 428, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    3. Silvia Coderoni & Roberto Esposti, 2014. "Is There a Long-Term Relationship Between Agricultural GHG Emissions and Productivity Growth? A Dynamic Panel Data Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 58(2), pages 273-302, June.
    4. Coderoni, Silvia & Pagliacci, Francesco, 2023. "The impact of climate change on land productivity. A micro-level assessment for Italian farms," Agricultural Systems, Elsevier, vol. 205(C).
    5. Edoardo Baldoni & Roberto Esposti, 2021. "Agricultural Productivity in Space: an Econometric Assessment Based on Farm‐Level Data," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(4), pages 1525-1544, August.
    6. Matthew Andersen & Julian Alston & Philip Pardey, 2012. "Capital use intensity and productivity biases," Journal of Productivity Analysis, Springer, vol. 37(1), pages 59-71, February.
    7. Baldoni, Edoardo & Coderoni, Silvia & Esposti, Roberto, 2017. "The Productivity-environment Nexus At The Farm Level. The Case Of Carbon Footprint Of Lombardy FADN Farms," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 260895, European Association of Agricultural Economists.
    8. Esposti, Roberto, 2002. "Public agricultural R&D design and technological spill-ins: A dynamic model," Research Policy, Elsevier, vol. 31(5), pages 693-717, July.
    9. Sturla Furunes Kvamsdal, 2016. "Technical Change as a Stochastic Trend in a Fisheries Model," Marine Resource Economics, University of Chicago Press, vol. 31(4), pages 403-419.

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