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A Comparison from Matching Surveys in Africa and China: Plan in China

A Comparison from Matching Surveys in Africa and China: Plan in China. Jinxia Wang Center for Chinese Agricultural Policy (CCAP) Chinese Academy of Sciences Collaborating with: Robert Mendelsohn and S. Niggol Seo . Low Agri. Productivity in Africa. The growth of agri. Productivity Africa:

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A Comparison from Matching Surveys in Africa and China: Plan in China

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  1. A Comparison from Matching Surveys in Africa and China:Plan in China Jinxia Wang Center for Chinese Agricultural Policy (CCAP) Chinese Academy of Sciences Collaborating with: Robert Mendelsohn and S. Niggol Seo

  2. Low Agri. Productivity in Africa • The growth of agri. Productivity Africa: --- stalled or declined --- value added per agricultural worker now averages around 12% below 1980 levels in SubSaharan Africa Other countries (such as in Asia): --- growth quickly --- benefited from capital investments and the Green Revolution over the past decades

  3. Why Low Productivity in Africa? • Lack the education and knowledge to be aware of modern technology; • Lack access to capital; • Not having the secure property rights or government policies that provide sufficient incentives for long term investment; • Farmland below average climate and soil condition that constrain the investment opportunity (marginal investment opportunity)

  4. Growth of Grain Outputs in China Unit: million tons

  5. Annual Increase Rate of Outputs, Areas and Yield in China Unit: %

  6. Reasons for the Growth of Agricultural Productivity in China • The institutional reform and the implementation of household production responsibility (Lardy, 1983;Sicular, 1988 and 1991; McMillan et al.,1989 and Lin, 1992) ; • Improvement of technologies (Huang and Rosegrant.1993; Huang and Rozelle, 1995; Huang and Rozelle, 1996); • Recent research found the significant relationship between the change of climate and net crop revenue (Wang, et al., 2008)

  7. Purpose of this Research • Try to distinguish the effects of immutable agro-climate factors from the conditioning effects of markets and household characteristics on technology choice

  8. Theoretical Hypothesis • African farms have poor natural endowments (climate and soils) which reduce productivity and make investments into inputs unattractive; • African farmers do not have access to capital, labor, or technology; • African farmers lack education, experience, and access to extension • African governments have policies which discourage investment including poor property rights, restricted trade, and crop price controls. To test which hypothesis is more important, and compared with China

  9. Empirical Approach:Two sets of models • First, estimate Ricardian functions to determine the net productivity of land, regress crop net revenue per hectare on a set of exogenous variables that reflect each of the competing hypotheses . • Second, estimate input demand functions for capital, irrigation, modern crop varieties, hired labor, and household labor. • Adopt the similar approach in Africa and China, and using the similar household data

  10. Model Specified in China:First Mode (Ricardian Approach) • V: Net crop revenue per hectare • N: Natural endowments of the household, village and county • I: Availability of inputs and technology • K: Knowledge • P: Policies Test: which one is more important and their contributions

  11. V: Net Crop Revenue • Gross crop revenue (or total sales for each crop) less all expenditures for production (seed, fertilizer, irrigation, pesticide, machinery, plastic sheeting, hired labor and custom services; • All of the output that was consumed by each household was given a value based on a price of the output as if it was sold on the market; • Neither family labor nor a household’s rent for contracted land is counted as a cost; • Therefore, net revenue is a measure of returns to land and family labor; • Based on the total cultivated land area of each household (measured in hectares), we can calculate net crop revenue per hectare.

  12. N: Natural endowments of the household, village and county • Climate: linear and nonlinear (quadratic form) variables of temperature and precipitation in four seasons; • Soil type at the county level: -- clay, sand and loam soils -- share of cultivated area with each type of soil • Topographical environment: -- Elevation of the county -- Terrain of the village (1 if the village is located on a plain and 0 if the village is in a mountain).

  13. I: Availability of Inputs and Technology • Input price: the labor wage rates and fertilizer price; • Irrigation availability: share of irrigated areas • Access to capita: -- the distance between the village and township government; -- value of agricultural fix asset per ha -- share of sown areas serviced by machine (such as tillage, seeding and harvesting • Seed variety: share of sown areas of wheat, maize and rice that were planted by some high quality seeds; • Land endowment: land areas for each household; • Access to markets: if there is a road that connects the village to the outside world

  14. K: Knowledge • Education: average education level of each member of the household that is in the labor force • Production cooperative: if participate or not

  15. P: Policies • It is pity that we have no good policy variables, and have to use the provincial dummy variables to represent the policy influence. • Of course, this will capture all wide differences by province, not just policy distinctions.

  16. Model Specified in China:Second Mode (Input Demand) • F: input of agricultural production Capital, irrigation, modern crop variety and agricultural labor • N: Natural endowments of the household, village and county • I: Availability of inputs and technology • K: Knowledge • P: Policies

  17. Data… • Climate data: - Source: National Meteorological Information Center - Monthly temperature and precipitation from meteorological 733 stations - 1951~2001 - Divide into four seasons: Spring: 3~5; Summer: 6~8 Fall: 9~11; Winter: 12~2 - Calculate the average annual temp. and prec. for each season using data from 1951~2001

  18. Data… • Socio-economic Data -- Source: China’s National Bureau of Statistics Nation-wide Household Income and Expenditure Survey -- Sample: Counties having both meteorological stations and HH 8405 HH in 915 villages, 124 counties and 28 provinces

  19. Data • Soil Data -- FAO -- Clay, sand and loam soils -- Share of cultivated areas with each type of soil at county level

  20. Welcome comments and suggestions

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