China Agricultural Economic Review

Feng, Fan; Jia, Ningyuan; Lin, Faqin

2023 China Agricultural Economic Review

doi: 10.1108/caer-07-2022-0156

Considering the importance of Russia and Ukraine in agriculture, the authors quantify the potential impact of the Russia–Ukraine conflict on food output, trade, prices and food security for the world.Design/methodology/approachThe authors mainly use the quantitative and structural multi-country and multi-sector general equilibrium trade model to analyze the potential impacts of the conflict on the global food trade pattern and security.FindingsFirst, the authors found that the conflict would lead to soaring agricultural prices, decreasing trade volume and severe food insecurity especially for countries that rely heavily on grain imports from Ukraine and Russia, such as Egypt and Turkey. Second, major production countries such as the United States and Canada may even benefit from the conflict. Third, restrictions on upstream energy and fertilizer will amplify the negative effects of food insecurity.Originality/valueThis study analyzed the effect of Russia–Ukraine conflict on global food security based on sector linkages and the quantitative general equilibrium trade framework. With a clearer demonstration of the influence about the inherent mechanism based on fewer parameters compared with traditional Global Trade Analysis Project (GTAP) models, the authors showed integrated impacts of the conflict on food output, trade, prices and welfare across sectors and countries.

Li, Wenjing; Zhang, Lu; Yue, Meng; Ruiz-Menjivar, Jorge; Zhang, Junbiao

2023 China Agricultural Economic Review

doi: 10.1108/caer-03-2021-0062

The purpose of this study was threefold: (1) to measure farmers' health poverty, (2) to examine the effect of health vulnerability on health poverty and (3) to identify countermeasures that may alleviate health poverty in rural China.Design/methodology/approachThis study built a health poverty measurement model based on the multi-dimensional poverty framework to evaluate farmers' health vulnerability. Further, this paper used an econometric model to assess the impact of health vulnerability on health poverty. The sample for this study comprised 1,115 rice farmers from Hubei province, China.FindingsThe medical affordability poverty ratio was 17.95%, where farmers in the low-income group faced severe medical affordability poverty (27.46%). Results from the multi-dimensional analysis showed that, the health poverty ratios were 17.95 and 30.50%, respectively. Our results indicated that climate change vulnerabilities, living habits, medical facilities and medical accessibility were positively related to health poverty, whereas the regular physical examinations reduced mental health poverty.Research limitations/implicationsBased on this study's findings, we proposed that: (1) to address illness-induced poverty among members of the agricultural community, national and provincial strategies and programs grounded on a multi-dimensional health poverty framework ought to be formulated and implemented, (2) mechanisms of health knowledge exchange may facilitate the improvement of farmers' health status, (3) robust and comprehensive metrics should be employed to understand and improve farmers' ability to absorb and mitigate the negative health impacts and (4) the improvement in both quality and quantity for medical facilities and medical affordability in the rural areas should be key priorities in governmental-wide initiatives.Originality/valueExisting studies for alleviating poverty caused by disease mainly focus on medical service support to those economic vulnerabilities after a disease happens. However, few studies have focused on the root causes of poverty caused by disease, particularly from the preventive perspective of health vulnerability. To fill this gap, this study, therefore, proposes the health poverty index and analyzes the impact of health vulnerability on health poverty.

Shen, Zheng; Brown, Derek S.; Yu, Kang

2023 China Agricultural Economic Review

doi: 10.1108/caer-03-2022-0042

Off-farm employment is an important factor associated with fertility transition in many developing countries. The purpose of this paper is to investigate the impact of female off-farm employment on their fertility desire in rural China.Design/methodology/approachBased on the data from the China Labor-force Dynamics Survey, the authors adopt an instrumental variable approach to address the endogeneity issue. Desired number of children and desire for a second child are used to measure fertility desire.FindingsThe results show that off-farm employment participation significantly reduces women's desired number of children and the likelihood of their desire for a second child. Moreover, off-farm employment reduces women's fertility desire mainly through pathways including the weakening of son preference and a decrease in job autonomy, rather than the changes in leisure hours. Further evidence suggests that social health insurance plays an important role in moderating the adverse relationship between off-farm employment and the desire for a second child. The fertility-reducing effects are more pronounced among younger women, among those participating in off-farm wage employment and among families with only wives' participation in the off-farm labor market.Originality/valueThis paper contributes to the existing research by investigating the causal impact of off-farm employment on fertility desire in a rural developing context and the possible underlying mechanisms responsible for this relationship. This study provides important insights on this topic in developing countries and may have important implications for theory and practice.

Zhu, Lin; Liao, Hua; Zhou, You

2023 China Agricultural Economic Review

doi: 10.1108/caer-06-2022-0109

Promoting clean heating in rural areas is crucial for achieving a low-carbon transition of energy consumption and China's dual-carbon target. The study aims to consider the energy stacking behavior in heating energy use, reveals the determinants that affect household cleaner heating choices under the winter clean heating plan (WCHP), and proposes policy recommendations for the sustainable promotion of clean heating.Design/methodology/approachWith unique rural household survey data covering the clean heating pilot regions in northern China in 2020, this study estimates the relationship between driving factors and heating energy choices through binary and multivariate probit models.FindingsThe regression estimates show that the main drivers of heating energy choices include household income per capita, education level of household head, knowledge of the WCHP, access to heating subsidies and perception of indoor air pollution. There is energy stacking behavior in rural household heating energy use. Household decisions to adopt electricity or clean coal heating are correlated with firewood or soft coal use.Originality/valueThis study is one of the few to investigate the heating energy use of rural households by allowing for the adoption of multiple energy types. Combined with a unique microsurvey dataset, it could provide rich information for formulating proper energy transition planning. The findings also shed light on the importance of heating subsidies, households' knowledge of WCHP and awareness of environmental health in choosing clean heating energy, which has not been fully valued in related research.

Zhang, Yating; Tsai, Chung-Han; Liu, Wei; Weng, Kun

2023 China Agricultural Economic Review

doi: 10.1108/caer-06-2022-0122

This research examines farmers’ cognitions to the policy and how such cognitions influence their intentions and behaviors of land transfer, with the implementation of the Three Rights Separation (TRS) policy.Design/methodology/approachUsing data collected from the Beijing area, this research tests the relationship between farmers’ policy cognition and their intention/behavior through the mediation of their psychological constructs. Both Causal step test and Bootstrap test are adopted.FindingsFarmers’ intention of land transfer is influenced by their cognition of the TRS policy. In this process, farmers’ psychological constructs play a mediating role between their policy cognition and their intentions of land transfer, thereby eventually influencing their behaviors. This research confirms that institutions are not exogenous and the policy is not wishful thinking from the government. Instead, any policies, even enacted by governmental authority, have to be internalized within target groups’ cognition to be implemented.Originality/valueLand transfer deserves close attention since it is the direct aim of the TRS reform. In this regard, this paper, based on an institutional perspective, aims to extend our understanding on the incentives of land transfer. This research proposes a revised model of planned behavior and argues that farmers’ intention of land transfer is influenced by their cognition of the TRS policy. On one hand, this study is the first to examine farmers’ cognition formed through the implementation of the TRS policy. On the other hand, it reveals the path of how policy can finally influence farmers’ intentions and behaviors through shaping their cognitions and changing subjective perceptions, which enriches our understanding of the mechanism of how policy has a concrete impact on society.

Zhang, Yan; Jin, Shaosheng; Lin, Wen

2023 China Agricultural Economic Review

doi: 10.1108/caer-05-2022-0104

The contradiction and conflict between grassland conservation and economic development are prominent in the Qinghai–Tibet Plateau (QTP) with its fragile environment and ecosystem. How to promote sustainable grazing in the plateau without hurting the economic welfare of local residents is a key challenge facing the Chinese government. This study explores the potential of market-based grassland conservation policies by evaluating consumer preferences and valuations for forage–livestock balance certification labeled yak products.Design/methodology/approachThis study adopts a choice experiment with four attributes of yak meat, including forage–livestock balance certification, feeding type, age at slaughter and price. A sample size of 2,999 respondents from Beijing, Shanghai, Wuhan, Guangzhou and Chengdu was collected by a professional online survey company.FindingsThe result reveals that urban Chinese consumers are willing to pay highest price premiums for forage–livestock balance certified yak meat, followed by grass-fed claim labeled meat. Consumers on average place negative valuations for grain-fed claims, meat from yak slaughtered above 2 and 4 years old. Heterogeneous analysis indicates that individuals who are female, younger, married, and better educated, and with above median income, Tibet travel or yak consumption experience, are more receptive to the forage–livestock balance certification.Originality/valueIt is the first study to explore demand-driven mechanisms for grassland conservation by focusing on consumer valuation for the forage–livestock balance certification.

Yu, Xiaohua; Maruejols, Lucie

2023 China Agricultural Economic Review

doi: 10.1108/caer-05-2023-307

Maruejols, Lucie; Wang, Hanjie; Zhao, Qiran; Bai, Yunli; Zhang, Linxiu

2023 China Agricultural Economic Review

doi: 10.1108/caer-03-2022-0051

Despite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require identifying who are the households that judge their income is insufficient to meet their basic needs, and what factors are associated with subjective poverty.Design/methodology/approachHouseholds report the income level they judge is sufficient to make ends meet. Then, they are classified as being subjectively poor if their own monetary income is inferior to the level they indicated. Second, the study compares the performance of three machine learning algorithms, the random forest, support vector machines and least absolute shrinkage and selection operator (LASSO) regression, applied to a set of socioeconomic variables to predict subjective poverty status.FindingsThe random forest generates 85.29% of correct predictions using a range of income and non-income predictors, closely followed by the other two techniques. For the middle-income group, the LASSO regression outperforms random forest. Subjective poverty is mostly associated with monetary income for low-income households. However, a combination of low income, low endowment (land, consumption assets) and unusual large expenditure (medical, gifts) constitutes the key predictors of feeling poor for the middle-income households.Practical implicationsTo reduce the feeling of poverty, policy intervention should continue to focus on increasing incomes. However, improvements in nonincome domains such as health expenditure, education and family demographics can also relieve the feeling of income inadequacy. Methodologically, better performance of either algorithm depends on the data at hand.Originality/valueFor the first time, the authors show that prediction techniques are reliable to identify subjective poverty prevalence, with example from rural China. The analysis offers specific attention to the modest-income households, who may feel poor but not be identified as such by objective poverty lines, and is relevant when policy-makers seek to address the “next step” after ending extreme poverty. Prediction performance and mechanisms for three machine learning algorithms are compared.

Zeng, Qiyan; Chen, Xiaofu

2023 China Agricultural Economic Review

doi: 10.1108/caer-03-2022-0045

Development of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.Design/methodology/approachThe study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.FindingsThe study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.Practical implicationsThe machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.Originality/valueThis is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.

Liu, Chang; Zhou, Lin; Höschle, Lisa; Yu, Xiaohua

2023 China Agricultural Economic Review

doi: 10.1108/caer-01-2022-0003

The study uses machine learning techniques to cluster regional retail egg prices after 2000 in China. Furthermore, it combines machine learning results with econometric models to study determinants of cluster affiliation. Eggs are an inexpensiv, nutritious and sustainable animal food. Contextually, China is the largest country in the world in terms of both egg production and consumption. Regional clustering can help governments to imporve the precision of price policies and help producers make better investment decisions. The results are purely driven by data.Design/methodology/approachThe study introduces dynamic time warping (DTW) algorithm which takes into account time series properties to analyze provincial egg prices in China. The results are compared with several other algorithms, such as TADPole. DTW is superior, though it is computationally expensive. After the clustering, a multinomial logit model is run to study the determinants of cluster affiliation.FindingsThe study identified three clusters. The first cluster including 12 provinces and the second cluster including 2 provinces are the main egg production provinces and their neighboring provinces in China. The third cluster is mainly egg importing regions. Clusters 1 and 2 have higher price volatility. The authors confirm that due to transaction costs, the importing areas may have less price volatility.Practical implicationsThe machine learning techniques could help governments make more precise policies and help producers make better investment decisions.Originality/valueThis is the first paper to use machine learning techniques to cluster food prices. It also combines machine learning and econometric models to better study price dynamics.