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Xu P.,California State University, Fresno | Zeng Y.,Renmin University of China | Fong Q.,University of Alaska Fairbanks | Lone T.,California State University, Fresno | Liu Y.,Research Center for Rural economics
Food Control | Year: 2012

As the world's largest seafood consumer and exporter, China is challenged by frequent seafood contamination incidents. To restore consumer confidence in seafood safety, China's Ministry of Agriculture (MOA) mandated a nation wide quality standard that awards a green label to qualified safer seafood. MOA is also planning for an environmental friendly label to address consumers' concerns about wild sea species sustainability. This study developed a three-stage purchase framework model and applied a multivariate Probit regression to analyze questionnaire information collected from 14 supermarkets in Beijing, China. The results show that Chinese consumers consider the seafood label a more important information source than previous consumption experience. They are willing to pay more for green-labeled seafood for the protection of individual benefits. Moreover, consumers are willing to pay more for the eco-labeled seafood for the protection of societal benefits. Gender, shopping venues, education, seafood expenditure and knowledge of the labeled products affected purchase intention and willingness to pay. Price was not a statistically significant factor affecting purchase decisions. © 2012 Elsevier Ltd. Source

Qin L.,Anhui University of Finance and Economics | Pan S.,Texas Tech University | Wang C.,Texas Tech University | Jiang Z.,Research Center for Rural economics
China Agricultural Economic Review | Year: 2012

Purpose - The purpose of this paper is to examine the adverse selection in participation in the New Rural Cooperative Medical Scheme (NRCMS), as well as in outpatient and inpatient service utilization, in Chaoyang, Beijing, China. Design/methodology/approach - Probit model is established to test whether the rural Hukou family member in Combined Household (CH) is statistically different from the Pure Rural Household (PRH) in enrollment in NRCMS. Seemingly Unrelated Regression (SUR) model is adopted to examine the difference in the utilization of outpatient and inpatient between the rural Hukou family members in the two kinds of households. Findings - This paper finds that the rural Hukou family member in CH has more probability to enroll in NRCMS than the counterpart in PRH. In the period of six months, the rural Hukou family member in CH exceeds PRH by 0.73 times in outpatient visit number per capita. The former average spends yuan 157 more in outpatient service and is reimbursed yuan 53 more from NRCMS than the latter. Moreover, on average, rural Hukou family member has no difference in the inpatient service utilization between the two kinds of households in the period of 12 months. Originality/value - This is the first study to empirically test the adverse selection in China's medical insurance market from the perspective of two different types of households, which are CH and PRH. © Emerald Group Publishing Limited. Source

Zhan J.-T.,Nanjing Agricultural University | Wu Y.-R.,University of Western Australia | Zhang X.-H.,Research Center for Rural economics | Zhou Z.-Y.,James Cook University
China Agricultural Economic Review | Year: 2012

Purpose - The number of farms engaged in grain production in China has been declining in recent years. Limited efforts have been devoted to examine why producers quit from grain production and how such exits affect China's grain output. Such information, however, is invaluable in understanding whether the exit from grain production should be encouraged and if so, how. The purpose of this paper is to identify the factors that influence farmers' decision to quit from grain production, with a view to drawing implications for devising policies to deal with such exits. Design/methodology/approach - Both descriptive statistics and econometric techniques are used to analyse a set of unique and comprehensive farm-level survey data to identify key factors that affect farmers' decision to quit from grain production. Findings - Key factors that influence a farm to quit from, or stay in, grain production include: family size, the share of farming labour out of total family labour, per capita arable land, the proportion of land used for grain production, the share of family income from grains. It was also found that the level of grain prices and the sunk cost in farming, chiefly in grain production, also affect the likelihood that a household will stay or exit from grain production. Further, farmers in more economically developed regions are more likely to quit from grain production. Originality/value - The paper's findings clearly indicate that farms with a larger scale of grain production and earning higher income from grain are the major contributors to China's grain production. Potential exists for China to raise its total grain output if the land from those exiting farmers is readily made available to larger producers, enabling them to further benefit from the economies of scale. © Emerald Group Publishing Limited. Source

He A.-H.,Research Center for Rural economics | Liu T.-S.,Renmin University of China | Kong X.-Z.,Renmin University of China
Zhongguo Renkou Ziyuan Yu Huan Jing/ China Population Resources and Environment | Year: 2014

In recent years, the heterogeneity of Chinese farmer increasingly strengthens with the acceleration of land circulation and the emergence of farmer professional cooperatives. This paper takes the behavior of farmers in renting land or joining cooperative as the main manifestation of their heterogeneity, and investigates their influence on farmers'participation in agricultural technology training. This paper selects Zero-Inflated Negative Binomial( ZINB) and tests the model's robustness, according to the nature of the survey data from 1 039 peasant households in Shandong, Shanxi and Ningxia provinces. The result indicates that the land tenancy behavior of farmers doesn't effect their participation behavior of agricultural technology training, but the possibility of participation agricultural will dramatically increase if a household joins in a farmers professional cooperative or only engages in agriculture management. In practical terms, the expected frequency of the farmers who rent land to participate in agricultural technology training is 1. 044 0 times of the others, while the farmers who join cooperative is 2. 112 9 times of the others. The current too run-of-mill agricultural technology training is an important reason of limiting farmers' demand for it. So the government should take the heterogeneity of farmers into account, organize agricultural technology training in differentiated positioning and give more subsidies to the cooperatives which carry out the agricultural technical training.. Source

Chen Y.-F.,China Agricultural University | Wu Z.-G.,Research Center for Rural economics | Zhu T.-H.,Tianjin University | Yang L.,China Agricultural University | And 2 more authors.
Journal of Integrative Agriculture | Year: 2013

This paper estimates a stochastic frontier function using a panel data set that includes 4 961 farmer households for the period of 2005-2009 to decompose the growth of grain production and the total factor productivity (TFP) growth at the farmer level. The empirical results show that the major contributor to the grain output growth for farmers is input growth and that its average contribution accounts for 60.92% of farmer's grain production growth in the period of 2006-2009, whereas the average contributions sourced from TFP growth and residuals are only 17.30 and 21.78%, respectively. The growth of intermediate inputs is a top contributor with an average contribution of 44.46%, followed by the planted area (18.16%), investment in fixed assets (1.05%), and labor input (-l2.75%), indicating that the contribution from the farmer's input growth is mainly due to the growth of intermediate inputs and that the decline in labor inputs has become an obstacle for farmers in seeking grain output growth. Among the elements consisting of TFP growth, the contribution of technical progress is the largest (32.04%), followed by grain subsidies (8.55%), the average monthly temperature (4.26%), the average monthly precipitation (-0.88%), the adjusted scale effect (-5.66%), and growth in technical efficiency (-21.01%). In general, the contribution of climate factors and agricultural policy factor are positive and significant. © 2013 Chinese Academy of Agricultural Sciences. Source

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