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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 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: --- 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
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)
Growth of Grain Outputs in China Unit: million tons
Annual Increase Rate of Outputs, Areas and Yield in China Unit: %
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)
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
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
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
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
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.
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).
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
K: Knowledge • Education: average education level of each member of the household that is in the labor force • Production cooperative: if participate or not
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.
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
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
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
Data • Soil Data -- FAO -- Clay, sand and loam soils -- Share of cultivated areas with each type of soil at county level