IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v48y2014i2p1045-1073.html
   My bibliography  Save this article

What are the main explanations of occupational diseases and accidents at work in the agricultural sector? A panel analysis for Italian regional data

Author

Abstract

The aim of this paper is to investigate the causes of occupational diseases and accidents at work (ODA) in the Italian agricultural sector. To this end, we proceed with a two-stage analysis of Italian regional data for the period 1976–2004. The first phase of the analysis shows that in the Italian agricultural sector productivity Granger-causes ODA, and not vice versa. The results of the econometric estimates in the second stage of the analysis show that an increase in “productivity pressure” (associated with an increase in production rhythms) will produce, in the long run, an increase in accidents on less serious ODA (or temporary ODA); the same effect will not be observed on the more serious ODA (or permanent ODA). We verify how pro work legislation reduces ODA and how this effect is strengthened in the long-run. In addition, we observe that in the long-run the increase in the “pressure on workers” (associated with a high unemployment rate and a high probability of being fired) is the main cause of the increase in less and more serious ODA. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • M. Agovino, 2014. "What are the main explanations of occupational diseases and accidents at work in the agricultural sector? A panel analysis for Italian regional data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(2), pages 1045-1073, March.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:2:p:1045-1073
    DOI: 10.1007/s11135-012-9824-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11135-012-9824-y
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11135-012-9824-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Schultz, T. Paul, 2010. "Population and Health Policies," Handbook of Development Economics, in: Dani Rodrik & Mark Rosenzweig (ed.), Handbook of Development Economics, edition 1, volume 5, chapter 0, pages 4785-4881, Elsevier.
    2. Boone, Jan & van Ours, Jan C., 2006. "Are recessions good for workplace safety?," Journal of Health Economics, Elsevier, vol. 25(6), pages 1069-1093, November.
    3. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    4. Joakim Westerlund, 2007. "Testing for Error Correction in Panel Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(6), pages 709-748, December.
    5. Strauss, John, 1986. "Does Better Nutrition Raise Farm Productivity?," Journal of Political Economy, University of Chicago Press, vol. 94(2), pages 297-320, April.
    6. Holtz-Eakin, Douglas & Newey, Whitney & Rosen, Harvey S, 1988. "Estimating Vector Autoregressions with Panel Data," Econometrica, Econometric Society, vol. 56(6), pages 1371-1395, November.
    7. Dani Rodrik & Mark Rosenzweig (ed.), 2010. "Handbook of Development Economics," Handbook of Development Economics, Elsevier, edition 1, volume 5, number 6.
    8. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    9. Robinson, J.C., 1988. "The rising long-term trend in occupational injury rates," American Journal of Public Health, American Public Health Association, vol. 78(3), pages 276-281.
    10. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    11. Boone, Jan & van Ours, Jan C. & Wuellrich, Jean-Philippe & Zweimüller, Josef, 2011. "Recessions are bad for workplace safety," Journal of Health Economics, Elsevier, vol. 30(4), pages 764-773, July.
    12. Peter Pedroni, 2000. "Fully Modified OLS for Heterogeneous Cointegrated Panels," Department of Economics Working Papers 2000-03, Department of Economics, Williams College.
    13. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    14. Schultz, T. Paul & Tansel, Aysit, 1997. "Wage and labor supply effects of illness in Cote d'Ivoire and Ghana: instrumental variable estimates for days disabled," Journal of Development Economics, Elsevier, vol. 53(2), pages 251-286, August.
    15. Peter Pedroni, 1999. "Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 653-670, November.
    16. Maddala, G S & Wu, Shaowen, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 631-652, Special I.
    17. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2005. "Testing for PPP: Should we use panel methods?," Empirical Economics, Springer, vol. 30(1), pages 77-91, January.
    18. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    19. Choi, In, 2001. "Unit root tests for panel data," Journal of International Money and Finance, Elsevier, vol. 20(2), pages 249-272, April.
    20. Strauss, John & Thomas, Duncan, 1996. "Measurement and Mismeasurement of Social Indicators," American Economic Review, American Economic Association, vol. 86(2), pages 30-34, May.
    21. Clemente, Jesus & Montanes, Antonio & Reyes, Marcelo, 1998. "Testing for a unit root in variables with a double change in the mean," Economics Letters, Elsevier, vol. 59(2), pages 175-182, May.
    22. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    23. G. S. Maddala & Shaowen Wu, 1999. "A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(S1), pages 631-652, November.
    24. Pedroni, Peter, 1999. "Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 61(0), pages 653-670, Special I.
    25. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karikallio, Hanna, 2015. "Cross-commodity Price Transmission and Integration of the EU Livestock Market of Pork and Beef: Panel Time-series Approach," 2015 Conference, August 9-14, 2015, Milan, Italy 211832, International Association of Agricultural Economists.
    2. Pedro M. G. Martins, 2010. "Aid Absorption and Spending in Africa: A Panel Cointegration Approach," Working Paper Series 1010, Department of Economics, University of Sussex Business School.
    3. Acikgoz, Senay & Ben Ali, Mohamed Sami, 2019. "Where does economic growth in the Middle Eastern and North African countries come from?," The Quarterly Review of Economics and Finance, Elsevier, vol. 73(C), pages 172-183.
    4. Costantini, Valeria & Martini, Chiara, 2010. "The causality between energy consumption and economic growth: A multi-sectoral analysis using non-stationary cointegrated panel data," Energy Economics, Elsevier, vol. 32(3), pages 591-603, May.
    5. Hasanov, Fakhri J. & Liddle, Brantley & Mikayilov, Jeyhun I., 2018. "The impact of international trade on CO2 emissions in oil exporting countries: Territory vs consumption emissions accounting," Energy Economics, Elsevier, vol. 74(C), pages 343-350.
    6. Fang, Zheng & Chang, Youngho, 2016. "Energy, human capital and economic growth in Asia Pacific countries — Evidence from a panel cointegration and causality analysis," Energy Economics, Elsevier, vol. 56(C), pages 177-184.
    7. Sung, Bongsuk & Song, Woo-Yong, 2014. "How government policies affect the export dynamics of renewable energy technologies: A subsectoral analysis," Energy, Elsevier, vol. 69(C), pages 843-859.
    8. Hamit-Haggar, Mahamat, 2012. "Greenhouse gas emissions, energy consumption and economic growth: A panel cointegration analysis from Canadian industrial sector perspective," Energy Economics, Elsevier, vol. 34(1), pages 358-364.
    9. Edmore Mahembe & Nicholas Mbaya Odhiambo, 2019. "Foreign aid, poverty and economic growth in developing countries: A dynamic panel data causality analysis," Cogent Economics & Finance, Taylor & Francis Journals, vol. 7(1), pages 1626321-162, January.
    10. Juan Carlos Aquino & N. R. Ramírez-Rondán, 2020. "Estimating factor shares from nonstationary panel data," Empirical Economics, Springer, vol. 58(5), pages 2353-2380, May.
    11. In Choi, 2013. "Panel Cointegration," Working Papers 1208, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    12. Kahia, Montassar & Ben Aissa, Mohamed Safouane, 2014. "Renewable and non-renewable energy consumption and economic growth: Evidence from MENA Net Oil Exporting Countries," MPRA Paper 80776, University Library of Munich, Germany.
    13. Sadorsky, Perry, 2012. "Energy consumption, output and trade in South America," Energy Economics, Elsevier, vol. 34(2), pages 476-488.
    14. Trofimov, Ivan D., 2022. "Determinants of the profit rates in the OECD economies: A panel data analysis of the Kalecki's profit equation," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 380-397.
    15. Dedeoğlu, Dinçer & Kaya, Hüseyin, 2013. "Energy use, exports, imports and GDP: New evidence from the OECD countries," Energy Policy, Elsevier, vol. 57(C), pages 469-476.
    16. Sung, Bongsuk, 2015. "Public policy supports and export performance of bioenergy technologies: A dynamic panel approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 477-495.
    17. Yi Hu & Dongmei Guo & Mingxi Wang & Xi Zhang & Shouyang Wang, 2015. "The Relationship between Energy Consumption and Economic Growth: Evidence from China’s Industrial Sectors," Energies, MDPI, vol. 8(9), pages 1-15, August.
    18. Kahia, Montassar & Ben Aïssa, Mohamed Safouane & Charfeddine, Lanouar, 2016. "Impact of renewable and non-renewable energy consumption on economic growth: New evidence from the MENA Net Oil Exporting Countries (NOECs)," Energy, Elsevier, vol. 116(P1), pages 102-115.
    19. Rudra P. Pradhan & Mak B. Arvin & Sara E. Bennett & Mahendhiran Nair & John H. Hall, 2016. "Bond Market Development, Economic Growth and Other Macroeconomic Determinants: Panel VAR Evidence," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 23(2), pages 175-201, June.
    20. Dąbrowski, Marek A. & Papież, Monika & Śmiech, Sławomir, 2014. "Exchange rates and monetary fundamentals in CEE countries: Evidence from a panel approach," Journal of Macroeconomics, Elsevier, vol. 41(C), pages 148-159.

    More about this item

    Keywords

    Labour accidents; Severe and fatality injuries; Sick leave; Agricultural sector; Panel data; J28; J81; Q10; C23;
    All these keywords.

    JEL classification:

    • J28 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Safety; Job Satisfaction; Related Public Policy
    • J81 - Labor and Demographic Economics - - Labor Standards - - - Working Conditions
    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:qualqt:v:48:y:2014:i:2:p:1045-1073. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.