数据

China Wheat Producer Survey
International Food Policy Research Institute (IFPRI). Washington, DC 2019
China wheat wholesaler survey is the study of the wholesalers in the wheat value chain in the province of Henan. These data allow quantifying the extent of food loss in the wholesaler level using consistent approaches that are comparable across commodities and regions. They also enable characterizing the nature of food loss, specifically the production stages and the particular processes at which loss is incurred.

China Wheat Middlemen Survey
International Food Policy Research Institute (IFPRI). Washington, DC 2019
China wheat middlemen survey is the study of Chinese middlemen in the wheat value chain in the province of Hena. These data allow us to quantify the extent of food loss in the middlemen level using consistent approaches that are comparable across commodities and regions. They also enable us to characterize the nature of food loss, specifically the production stages and the particular processes at which loss is incurred.

China Wheat Wholesaler Survey
International Food Policy Research Institute (IFPRI). Washington, DC 2019
China Wheat Producer Survey is the study of the producers in the wheat value chain in the province of Henan. These data allow quantifying the extent of food loss in the producer level using consistent approaches that are comparable across commodities and regions. They also enable characterizing the nature of food loss, specifically the production stages and the particular processes at which loss is incurred.

The Pest Severity, Insecticide Application, and Land Use Data for 51 Counties in China, 1991-2015
International Food Policy Research Institute (IFPRI); Chinese Academy of Agricultural Sciences (CAAS); Ministry of Agriculture and Rural Affairs;Chinese Academy of Sciences (CAS);. Washington, DC 2018
This is a compiled unique long-term panel dataset that consists of data on (i) pest severity and insecticide applications per annum per county by pest species, and (ii) land cover/use data. The counties in the database are the 51 most important cotton-growing counties, by production, in the Yangtze River valley and Yellow River valley cotton production regions, while the data covered the years 1991–2015, with complete coverage of counties in all years when cotton was cultivated. Between 2011 and 2013, eight counties in our sample stopped cultivating cotton. The number increased to 11 and 12 counties in 2014 and 2015, respectively, resulting in 47 missing records.

The national cotton pests monitoring network, maintained by the Ministry of Agriculture mandates the main cotton-producing counties to collect yearly data on pest infestation levels and insecticide applications for key cotton pests following national standardized monitoring and categorization methods. Tailored scouting methods were used for different pests. In each county, 10–20 fields were selected for pest monitoring in each year. Insect populations were recorded every 3–10 days from early June to late August each year, and the seasonal average abundance across the surveyed fields were used for scoring using a five-point scale of levels I–V. Data on the number of insecticide applications targeted at specific pests were collected by interviewing farmers at each scouting to estimate yearly pest-specific total number of sprays for each county. While the detailed data collection methods and protocols should inspire confidence in the data, the reliability of the pest level data depends on the accuracy, knowledge, and honesty of the respondents, as is the case with any non–first-hand data.

County-level land use data were drawn from a national land cover/use database developed by the Chinese Academy of Sciences, using satellite remote-sensing data from the Landsat Thematic Mapper/Enhanced Thematic Mapper images. The database offers the most comprehensive coverage of China’s land use/cover and has been used in a number of published studies. The land use data for 6 years (1990-2015) at 5 years interval were extracted. The proportional area for each six main land use classes as well as the Shannon index for land use diversity for each county for six years was computed. Land use proportions in the intermediate years (e.g., 1991, 1992, 1993, 1994, 1996, etc.) were calculated by linear interpolation between the data.

A 2007 Social Accounting Matrix for China
Zhang, Yumei; Diao, Xinshen. Washington, D.C. 2013
This paper documents a 2007 Social Accounting Matrix (SAM) for China. This SAM was constructed for the China CGE model to assess the impact of the 2008-09 global recession shocks and the Chinese government's stimulus policy on China's economic growth. The SAM is constructed using data from various sources including an existing input-output table, national accounts, government budgets, balance of payments, commodity exports and imports, labor employment and wage statistics, household expenditure surveys and agricultural production statistics. Cross-entropy estimation techniques are used to balance the SAM. This SAM is a detailed representation of China’s economy in 2007. It covers 61 production activities and commodity sectors, 4 types of factors (low skilled labor, skilled labor, capital, and land), and 2 representative household (rural and urban) groups. The structural characteristics of China’s economy presented in the SAM would be helpful to better understand the economic linkages. And the SAM also provides an ideal tool for economy-wide impact assessments, such as a SAM-based multiplier analysis and computable general equilibrium (CGE) modeling.

A 2007 Social Accounting Matrix for China
Zhang, Yumei; Diao, Xinshen. Washington, D.C. 2013
This paper documents a 2007 Social Accounting Matrix (SAM) for China. This SAM was constructed for the China CGE model to assess the impact of the 2008-09 global recession shocks and the Chinese government's stimulus policy on China's economic growth. The SAM is constructed using data from various sources including an existing input-output table, national accounts, government budgets, balance of payments, commodity exports and imports, labor employment and wage statistics, household expenditure surveys and agricultural production statistics. Cross-entropy estimation techniques are used to balance the SAM. This SAM is a detailed representation of China’s economy in 2007. It covers 61 production activities and commodity sectors, 4 types of factors (low skilled labor, skilled labor, capital, and land), and 2 representative household (rural and urban) groups. The structural characteristics of China’s economy presented in the SAM would be helpful to better understand the economic linkages. And the SAM also provides an ideal tool for economy-wide impact assessments, such as a SAM-based multiplier analysis and computable general equilibrium (CGE) modeling. (2013-04-17)

ASTI China database
Agricultural Science and Technology Indicators (ASTI). Washington, DC 2012
This dataset includes national-level time series data on researcher capacity by qualification level, age bracket, discipline mix, and commodity, as well as a detailed breakdown of agricultural research investment across government, higher education, nonprofit, and (where possible) private for-profit agricultural research agencies. These data were derived through a series of primary survey rounds conducted by IFPRI’s Agricultural Science and Technology Indicators (ASTI) and in close collaboration with a large network of national collaborators. The ASTI data constitute a powerful resource for national and regional research managers, policymakers, donor organizations, and other stakeholders. ASTI’s key indicators provide both a diagnostic tool for assessing the allocation and use of existing resources and an advocacy tool for increasing resources and improving the efficiency and effectiveness of resource use. The ASTI website offers interactive pages that allow users to access country-level time series data, make cross-country comparisons, create graphs, and download country datasets and publications as well as detailed institutional information on agencies involved in agricultural research. The interactive benchmarking tool on the ASTI website is a convenient map-based instrument allowing users to make cross-country comparisons and rankings based on a wide set of financial and human resource indicators. The detailed ASTI datasets are available in an easy-to-use data download tool. Detailed information on definitions, methodology, and calculation procedures are available at www.asti.cgiar.org

China, Government Expenditure, Growth, Poverty, and Infrastructure, 1952-2001
Washington, D.C. 2004
This dataset provides information on key economic indicators, agricultural output and inputs, public investments, poverty, and various social indicators in China. Cross-section (29 provinces) and time-series (50 years from 1952 to 2001) data are included in this dataset. The dataset consists of 50 variables altogether, including agricultural and nonagricultural GDP, agricultural labor, agricultural output, agricultural population, arable land, share of rural population with college education, total telecommunication expenditures (rural and urban), draft animals, education expenditures, rural electricity consumption, total expenditures in electricity construction, fertilizer use in pure nutrients, rural illiteracy rate, machinery use, official rural poverty rates, rural education expenditures, agricultural research expenditures, road construction expenditures, rural telephones, etc.