A spatiotemporal analysis of comparative advantage in tea production in China

https://doi.org/10.17221/85/2020-AGRICECONCitation:

Chen Y., Li M., Abu Hatab A. (2020): A spatiotemporal analysis of comparative advantage in tea production in China. Agric. Econ. – Czech, 66: 550–561.

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Tea is one of the most important cash crops and widely consumed beverages worldwide and plays a significant role in rural development, poverty reduction, and food security in many developing countries. Nevertheless, very few empirical studies have analysed the comparative advantage of the tea industry in developing countries. Taking Fujian Province, China, as the object of a case study, we carried out a spatiotemporal analysis of the determinants of the tea industry's revealed comparative advantage (RCA) during the period 2010–2018. The empirical analysis relied on a calculation of RCA and an estimation of a geographically and temporally weighted regression (GTWR) using data from 67 counties in Fujian. The results confirmed that the effect and significance of RCA determinants vary considerably across different spatial areas and over time. With the exception of ‘disposable income', all other determinants had a positive and statistically significant effect on a region's RCA in the tea industry. Specifically, the results indicated that regional specialisation had the strongest positive effect on tea competitiveness. Local governments' sectoral strategies and institutional policies were essential elements in building and maintaining regional tea competitiveness. Infrastructure development, which traditionally went hand-in-hand with urbanisation processes, had a significant impact on tea competitiveness. These findings imply that competitiveness of the tea sector can be improved by adopting local polices that support producers and processors through fiscal investment, technology provision, and capacity building as well as measures to improve rural road infrastructure and link small farmers to other actors along tea supply chains.

References:
Abu Hatab A., Romstad E. (2014): Competitiveness analysis of Egyptian cotton exports with special focus on the Chinese market. China Agricultural Economic Review,  https://doi.org/10.1108/CAER-02-2013-0026
 
6: 248–263.
 
Agricultural Development Plan of Fujian (2018): Construction Plan for the advantageous area of special agricultural products in Fujian Province (2018–2020). Fujian Provincial Development and Reform Commission. Available at http://fgw.fujian.gov.cn/zfxxgkzl/zfxxgkml/ghjh/201807/t20180706_3407010.htm (accessed Nov 1, 2020).
 
Ahmed S., Griffin T., Cash S.B., Han W.Y., Matyas C., Long C., Orians C.M., Stepp J.R., Robbat A., Xue D. (2018): Global climate change, ecological stress, and tea production. In: Han W.Y., Li X., Ahammed G.J. (eds): Stress Physiology of Tea in the Face of Climate Change. Singapore, Springer: 1–23.
 
Balassa B. (1965): Trade liberalisation and 'revealed' comparative advantage. The Manchester School, 33: 99–123. https://doi.org/10.1111/j.1467-9957.1965.tb00050.x
 
Boehm R., Cash S.B., Anderson B.T., Ahmed S., Griffin T.S., Robbat A., Stepp J.R., Han W., Hazel M., Orians C.M. (2016): Association between empirically estimated monsoon dynamics and other weather factors and historical tea yields in China: Results from a yield response model. Climate, 4: 20. https://doi.org/10.3390/cli4020020
 
Brunsdon C., Fotheringham A.S., Charlton M. (1999): Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science, 39: 497–524. https://doi.org/10.1111/0022-4146.00146
 
Chen Y., Li M. (2019): Evaluation of influencing factors on tea production based on random forest regression and mean impact value. Agricultural Economics – Czech, 65: 340–347. https://doi.org/10.17221/399/2018-AGRICECON
 
Commendatore P., Kubin I. (2016): Source versus residence: A comparison from a new economic geography perspective. Papers in Regional Science, 95: 201–222. https://doi.org/10.1111/pirs.12134
 
FAO (2018): Current market situation and medium-term outlook. In: 23rd Session of the Intergovernmental Group on Tea. Hangzhou, May 17–20, 2018: 1–16.
 
FAOSTAT (2020). Statistical Databases. [Dataset]. The Food and Agriculture Organization of the United Nations. Available at http://www.fao.org/faostat/en/#home (accessed March 7, 2020).
 
Fujian Statistical Yearbook (2019): Fujian Statistical Yearbook 2011–2019. Fujian Provincial Statistics Bureau. Available at http://tjj.fujian.gov.cn/xxgk/ndsj/ (accessed Nov 1, 2020).
 
Gunathilaka R.P.D., Smart J.C., Fleming C.M. (2018): Adaptation to climate change in perennial cropping systems: Options, barriers and policy implications. Environmental Science & Policy, 82: 108–116.
 
Han W., Zhang Y., Cai J., Ma E. (2019): Does urban industrial agglomeration lead to the improvement of land use efficiency in China? An empirical study from a spatial perspective. Sustainability, 11: 986. https://doi.org/10.3390/su11040986
 
He Q., Huang B. (2018): Satellite-based high-resolution PM2.5 estimation over the Beijing-Tianjin-Hebei region of China using an improved geographically and temporally weighted regression model. Environmental Pollution, 236: 1027–1037.
 
Henderson J.V. (2010): Cities and development. Journal of Regional Science, 50: 515–540. https://doi.org/10.1111/j.1467-9787.2009.00636.x
 
Hong L., Song W. (2015): Trade competitiveness of tea from Fujian, China: Analysis based on Porter Masonry Model. In: 2015 International Conference on Engineering Management, Engineering Education and Information Technology (EMEEIT 2015). Guangzhou, Oct 24–25, 2015: 5–9.
 
Huang J.K., Yang G.L. (2017): Understanding recent challenges and new food policy in China. Global Food Security, 12: 119–126. https://doi.org/10.1016/j.gfs.2016.10.002
 
Jiang Y.H., Shen J.F. (2010): Measuring the urban competitiveness of Chinese cities in 2000. Cities, 27: 307–314. https://doi.org/10.1016/j.cities.2010.02.004
 
Li E., Coates K., Li X., Ye X., Leipnik M. (2017): Analyzing agricultural agglomeration in China. Sustainability, 9: 313. https://doi.org/10.3390/su9020313
 
Li Y., Ma C. (2015): Circular economy of a papermaking park in China: A case study. Journal of Cleaner Production, 92: 65–74. https://doi.org/10.1016/j.jclepro.2014.12.098
 
Liu H., Fan J., Zhou K. (2018): An empirical study on spatial-temporal dynamics and influencing factors of tea production in China. Sustainability, 10: 3037. https://doi.org/10.3390/su10093037
 
Liu Z., Li Q., Lan J., Abu Hatab A. (2020): Does participation in the sloping land conversion program reduce the sensitivity of Chinese farmers to climate change? Land Use Policy, 99: 105021. https://doi.org/10.1016/j.landusepol.2020.105021
 
OECD, CDRF (2010): Trends in Urbanisation and Urban Policies in OECD Countries. What Lessons for China. Paris, OECD Publishing.
 
Xiao Z., Huang X., Zang Z., Yang H. (2018): Spatio-temporal variation and the driving forces of tea production in China over the last 30 years. Journal of Geographical Sciences, 28: 275–290. https://doi.org/10.1007/s11442-018-1472-2
 
Yu R., Cai J., Leung P.S. (2009): The normalized revealed comparative advantage index. Annals of Regional Science, 43: 267–282. https://doi.org/10.1007/s00168-008-0213-3
 
Zheng R., Zhan J., Liu L., Ma Y., Wang Z., Xie L., He D. (2019): Factors and minimal subsidy associated with tea farmers' willingness to adopt ecological pest management. Sustainability, 11: 6190. https://doi.org/10.3390/su11226190
 
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