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Spatial variation of output-input elasticities: Evidence from Chinese county-level agricultural production data

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  • Seong-Hoon Cho
  • Zhuo Chen
  • Steven T. Yen
  • Burton C. English

Abstract

An agricultural production function is estimated using a Chinese county-level dataset along with associated geographic information. County-specific output-input elasticities are computed using the geographically weighted regression (GWR) and mapped with the geographical information system (GIS). A comparison of the ordinary least squares and GWR estimates confirms that allowing spatial variation in the parameters improves model fit of the agricultural production function, and provides valuable insights into the relative importance of inputs in different regions. Moran's indices reveal the spatial dependence of output per unit of land, and four inputs across regions. Mappings of GWR estimates help to detect a few clusters of high output-input elasticities: for labour in the Northeast, North, Southwest and Northwest China, for land in the Central and Southwest, for mechanical power in the North, Northwest and coastal area of the East and South and for fertiliser in the East. The county-specific scale elasticities suggest constant return to scale is likely to hold in the Central, East, Southwest and South. Copyright (c) 2007 the author(s). Journal compilation (c) 2007 RSAI.

Suggested Citation

  • Seong-Hoon Cho & Zhuo Chen & Steven T. Yen & Burton C. English, 2007. "Spatial variation of output-input elasticities: Evidence from Chinese county-level agricultural production data," Papers in Regional Science, Wiley Blackwell, vol. 86(1), pages 139-157, March.
  • Handle: RePEc:bla:presci:v:86:y:2007:i:1:p:139-157
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    References listed on IDEAS

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    1. Miranowski, John & Monchuk, Daniel C., 2004. "Spatial Labor Markets and Technology Spillovers - Analysis from the US Midwest," Staff General Research Papers Archive 12196, Iowa State University, Department of Economics.
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    Cited by:

    1. Lahr, Michael L., 2008. "Introduction to The Review of Regional Studies' First Special Issue on Regional Development in China," The Review of Regional Studies, Southern Regional Science Association, vol. 38(3), pages 283-287.
    2. Murakami, Tomoaki & Nakajima, Shinsaku & Takahashi, Taro & Nishihara, Yukinaga & Imai, Asako & Kikushima, Ryousuke & Sato, Takeshi, 2014. "Spatially Varying Impacts of Farmers Markets on Agricultural Land Use," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170668, Agricultural and Applied Economics Association.
    3. Tun-Hsiang “Edward” Yu & Seong-Hoon Cho & Ahmet Ali Koç & Gülden Bölük & Seung Gyu Kim & Dayton M. Lambert, 2014. "Evaluating spatial and temporal variation in agricultural output elasticities in Turkey," Agricultural Economics, International Association of Agricultural Economists, vol. 45(3), pages 279-290, May.
    4. Chloé Duvivier, 2013. "Does Urban Proximity Enhance Technical Efficiency? Evidence From Chinese Agriculture," Journal of Regional Science, Wiley Blackwell, vol. 53(5), pages 923-943, December.
    5. Lambert, Dayton M. & Cho, Seong-Hoon, 2008. "Forecasting Input Demand Shocks on China's Gross Value of Agricultural Output," The Review of Regional Studies, Southern Regional Science Association, vol. 38(3), pages 343-359.
    6. Ito, Junichi, 2010. "Inter-regional difference of agricultural productivity in China: Distinction between biochemical and machinery technology," China Economic Review, Elsevier, vol. 21(3), pages 394-410, September.
    7. Носов В.В. & Цыпин А.П., 2015. "Эконометрическое Моделирование Цены Однокомнатной Квартиры Методом Географически Взвешенной Регрессии," Izvestiya of Saratov University. New Series. Series: Economics. Management. Law Известия Саратовского университета. Новая серия. Серия Экономика. Управление. Право, CyberLeninka;Федеральное государственное бюджетное образовательное учреждение высшего образования «Саратовский национальный исследовательский государственный университет имени Н. Г. Чернышевского», vol. 15(4), pages 381-387.

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