Pub Date : 2022-12-02DOI: 10.1108/caer-02-2022-0039
C. Kuhlgatz, Jiaqi Huang, Gerrit Antonides
PurposeThe purpose of this paper is to evaluate the effects of price and income changes on food and nutrient demand of rural households by including own-produced food and production-side effects in the demand estimation to correct potential measurement bias in the income and price elasticities for rural households in underdeveloped areas. Simulation results of income and grain price changes on food and nutrition security are provided for economic nutrition security policy applications.Design/methodology/approachThis study analyzes survey data of 1,555 households from underdeveloped rural areas of China to find out how price and income changes affect food and nutrition insecurity of rural households. The authors employ the quadratic almost ideal demand system (QUAIDS) in a two-stage budgeting framework, using quality adjusted prices that were retrieved with regressions of the difference between the unit value surveyed at household level and its village average on household characteristics. The bias correction is implemented by using an augmented IV (instrumental variable) method, in which each market price is instrumented with farm-specific variables. Important macro- and micronutrient elasticities are computed for (a) households with agriculture as main income and (b) other households (of which still many have agriculture as a side business). Finally, the authors use these elasticities to simulate how changes in income or grain prices affect the food and nutrition security in the studied areas.FindingsIn general, food income elasticities of agricultural households are at a higher level than those for other households, and so are the food price elasticities. Income changes also have a greater nutritional effect on agricultural households than on other households. Nutrient income elasticities ranged from 0.22 (energy) to 0.27 (Vitamin A) for agricultural households and from 0.19 (energy) to 0.23 (Vitamin A) for other households. Grain price increases have greater effect on nutritional status of non-agricultural households, while a grain price reduction is not clearly favoring the nutritional situation of a particular household group.Originality/valueThis demand study contributes to the literature by taking into account differences in consumption of own production between households and the potential endogeneity of prices resulting thereof. The authors also demonstrate that merely reporting nutrient elasticities might not be sufficient for policy recommendations, and simulations should be reported as a valuable addition.
{"title":"Food demand and the nutrient intake of households in underdeveloped rural regions of China: an instrumental variable approach","authors":"C. Kuhlgatz, Jiaqi Huang, Gerrit Antonides","doi":"10.1108/caer-02-2022-0039","DOIUrl":"https://doi.org/10.1108/caer-02-2022-0039","url":null,"abstract":"PurposeThe purpose of this paper is to evaluate the effects of price and income changes on food and nutrient demand of rural households by including own-produced food and production-side effects in the demand estimation to correct potential measurement bias in the income and price elasticities for rural households in underdeveloped areas. Simulation results of income and grain price changes on food and nutrition security are provided for economic nutrition security policy applications.Design/methodology/approachThis study analyzes survey data of 1,555 households from underdeveloped rural areas of China to find out how price and income changes affect food and nutrition insecurity of rural households. The authors employ the quadratic almost ideal demand system (QUAIDS) in a two-stage budgeting framework, using quality adjusted prices that were retrieved with regressions of the difference between the unit value surveyed at household level and its village average on household characteristics. The bias correction is implemented by using an augmented IV (instrumental variable) method, in which each market price is instrumented with farm-specific variables. Important macro- and micronutrient elasticities are computed for (a) households with agriculture as main income and (b) other households (of which still many have agriculture as a side business). Finally, the authors use these elasticities to simulate how changes in income or grain prices affect the food and nutrition security in the studied areas.FindingsIn general, food income elasticities of agricultural households are at a higher level than those for other households, and so are the food price elasticities. Income changes also have a greater nutritional effect on agricultural households than on other households. Nutrient income elasticities ranged from 0.22 (energy) to 0.27 (Vitamin A) for agricultural households and from 0.19 (energy) to 0.23 (Vitamin A) for other households. Grain price increases have greater effect on nutritional status of non-agricultural households, while a grain price reduction is not clearly favoring the nutritional situation of a particular household group.Originality/valueThis demand study contributes to the literature by taking into account differences in consumption of own production between households and the potential endogeneity of prices resulting thereof. The authors also demonstrate that merely reporting nutrient elasticities might not be sufficient for policy recommendations, and simulations should be reported as a valuable addition.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46839535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-24DOI: 10.1108/caer-01-2021-0020
Yali Han, K. Paudel, Junyi Wan, Qinying He
PurposeChina's economy has transformed from a high-speed growth phase to a high-quality development phase. The agriculture sector has grown substantially since the economic reform in 1978. Considering the five-year plan (FYP) as a collection of policies, this study explores the relationship between the FYP and agricultural total factor productivity (TFP).Design/methodology/approachThis study uses 31 provincial-level panel data of the five FYPs from 1996 to 2020. The data envelopment analysis (DEA) is used to compute Malmquist productivity indexes. The authors analyze the temporal and spatial changes and convergences of China's agricultural TFP, and investigate the impact of economic planning on China's agricultural TFP and its regional difference.FindingsThere is a slow but upward growth trend in China's agricultural TFP. The technical change has played a leading role in the growth of China's agricultural TFP. The agricultural TFP of all provinces has shown a “catch-up” effect and is developing toward their respective steady-state levels. The regional difference in productivity growth among the eastern, central and western regions exists. Test results show that the FYP has a positive effect on the agricultural TFP, and the effect has obvious regional heterogeneity. The FYP also plays a positive role in the gross value of agricultural output, and the impact effect is greater than that on the improvement of agricultural productivity.Originality/valueThere are many forms of industrial policy in China, among which the FYP is the guiding document of industrial policy, which makes a systematic plan for industrial development in the subsequent five years. The development objectives, guidelines and overall deployment for agriculture in the FYP not only describe the general context of China's agricultural development but also show the key ideas of agricultural development. Therefore, this study explores its impact on agricultural quality development from the perspective of FYP. The results provide evidence for examining the governance performance of the government and the objective evaluation and restraint of the FYP. As agriculture moves toward the stage of high-quality development, the Chinese government should strengthen the critical guiding role of the FYP and pay attention to quality indicators such as technical progress, efficiency improvement and regional coordination in the formulation of the FYP.
{"title":"Five-year plan and agricultural productivity in China","authors":"Yali Han, K. Paudel, Junyi Wan, Qinying He","doi":"10.1108/caer-01-2021-0020","DOIUrl":"https://doi.org/10.1108/caer-01-2021-0020","url":null,"abstract":"PurposeChina's economy has transformed from a high-speed growth phase to a high-quality development phase. The agriculture sector has grown substantially since the economic reform in 1978. Considering the five-year plan (FYP) as a collection of policies, this study explores the relationship between the FYP and agricultural total factor productivity (TFP).Design/methodology/approachThis study uses 31 provincial-level panel data of the five FYPs from 1996 to 2020. The data envelopment analysis (DEA) is used to compute Malmquist productivity indexes. The authors analyze the temporal and spatial changes and convergences of China's agricultural TFP, and investigate the impact of economic planning on China's agricultural TFP and its regional difference.FindingsThere is a slow but upward growth trend in China's agricultural TFP. The technical change has played a leading role in the growth of China's agricultural TFP. The agricultural TFP of all provinces has shown a “catch-up” effect and is developing toward their respective steady-state levels. The regional difference in productivity growth among the eastern, central and western regions exists. Test results show that the FYP has a positive effect on the agricultural TFP, and the effect has obvious regional heterogeneity. The FYP also plays a positive role in the gross value of agricultural output, and the impact effect is greater than that on the improvement of agricultural productivity.Originality/valueThere are many forms of industrial policy in China, among which the FYP is the guiding document of industrial policy, which makes a systematic plan for industrial development in the subsequent five years. The development objectives, guidelines and overall deployment for agriculture in the FYP not only describe the general context of China's agricultural development but also show the key ideas of agricultural development. Therefore, this study explores its impact on agricultural quality development from the perspective of FYP. The results provide evidence for examining the governance performance of the government and the objective evaluation and restraint of the FYP. As agriculture moves toward the stage of high-quality development, the Chinese government should strengthen the critical guiding role of the FYP and pay attention to quality indicators such as technical progress, efficiency improvement and regional coordination in the formulation of the FYP.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46647113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-24DOI: 10.1108/caer-07-2021-0141
Y. Ge, Yongbing Yang, F. Yi, Haojie Hu, XiaoBai Xiong
PurposeThe purpose of this study is to investigate the impacts of surface ozone pollution on rice profit, output and variable inputs in China.Design/methodology/approachThis study estimates the rice profit function using county-level rice production data and ozone monitoring data in 2014 and 2015 to capture the impact of ozone pollution on rice profit. Then, it uses dual approach to identify the impacts of ozone on the supply of rice and the demand for variable inputs. The ozone concentration data are obtained from 1,412 monitoring stations established by the National Environmental Monitoring Centre of China.FindingsThe results show that surface ozone would significantly reduce rice profits; a 1% increase in (the daily average ozone concentration from 9 am to 4 pm) leads to a 0.1% decrease in profits. In addition, ozone has a negative impact on the levels of inputs and the supply of rice, and the elasticities of rice output, fertilizer input and labour input with respect to are −0.87, −0.86 and −0.78%, respectively. These results suggest that ozone pollution affects rice production via two channels: the direct damage on rice growth and the indirect negative impact of reducing variable inputs.Originality/valueThis study estimates the impacts of surface ozone pollution on rice profit and output, and quantifies its influence on variable inputs in China, which provides a better understanding of farmers' adaptation behaviour.
{"title":"Measuring the impact of surface ozone on rice production in China: a normalized profit function approach","authors":"Y. Ge, Yongbing Yang, F. Yi, Haojie Hu, XiaoBai Xiong","doi":"10.1108/caer-07-2021-0141","DOIUrl":"https://doi.org/10.1108/caer-07-2021-0141","url":null,"abstract":"PurposeThe purpose of this study is to investigate the impacts of surface ozone pollution on rice profit, output and variable inputs in China.Design/methodology/approachThis study estimates the rice profit function using county-level rice production data and ozone monitoring data in 2014 and 2015 to capture the impact of ozone pollution on rice profit. Then, it uses dual approach to identify the impacts of ozone on the supply of rice and the demand for variable inputs. The ozone concentration data are obtained from 1,412 monitoring stations established by the National Environmental Monitoring Centre of China.FindingsThe results show that surface ozone would significantly reduce rice profits; a 1% increase in (the daily average ozone concentration from 9 am to 4 pm) leads to a 0.1% decrease in profits. In addition, ozone has a negative impact on the levels of inputs and the supply of rice, and the elasticities of rice output, fertilizer input and labour input with respect to are −0.87, −0.86 and −0.78%, respectively. These results suggest that ozone pollution affects rice production via two channels: the direct damage on rice growth and the indirect negative impact of reducing variable inputs.Originality/valueThis study estimates the impacts of surface ozone pollution on rice profit and output, and quantifies its influence on variable inputs in China, which provides a better understanding of farmers' adaptation behaviour.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41947595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-11DOI: 10.1108/caer-11-2022-306
Hua Liao, Z. Mi
{"title":"Guest editorial: Agricultural and rural development under the goal of carbon neutrality","authors":"Hua Liao, Z. Mi","doi":"10.1108/caer-11-2022-306","DOIUrl":"https://doi.org/10.1108/caer-11-2022-306","url":null,"abstract":"","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48042168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PurposeDespite 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.
{"title":"Comparison of machine learning predictions of subjective poverty in rural China","authors":"Lucie Maruejols, Hanjie Wang, Qiran Zhao, Yunli Bai, Linxiu Zhang","doi":"10.1108/caer-03-2022-0051","DOIUrl":"https://doi.org/10.1108/caer-03-2022-0051","url":null,"abstract":"PurposeDespite 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.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47936719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-08DOI: 10.1108/caer-01-2022-0003
Chang Liu, Lin Zhou, Lisa Höschle, Xiaohua Yu
PurposeThe 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.
{"title":"Food price dynamics and regional clusters: machine learning analysis of egg prices in China","authors":"Chang Liu, Lin Zhou, Lisa Höschle, Xiaohua Yu","doi":"10.1108/caer-01-2022-0003","DOIUrl":"https://doi.org/10.1108/caer-01-2022-0003","url":null,"abstract":"PurposeThe 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.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42230883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-08DOI: 10.1108/caer-03-2022-0045
Qiyan Zeng, Xiaofu Chen
PurposeDevelopment 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.
{"title":"Identification of urban-rural integration types in China – an unsupervised machine learning approach","authors":"Qiyan Zeng, Xiaofu Chen","doi":"10.1108/caer-03-2022-0045","DOIUrl":"https://doi.org/10.1108/caer-03-2022-0045","url":null,"abstract":"PurposeDevelopment 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.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44286518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-07DOI: 10.1108/caer-01-2022-0014
J. G. Ribeiro, S. M. Piedade
PurposeThe state of Mato Grosso represents the largest producer and exporter of soybeans in Brazil; given this importance, it was aimed to propose to use the univariate imputation tool for time series, through applications of splines interpolations, in 46 of its municipalities that had missing data in the variables soybean production in thousand tons, production value and soy derivatives in R$ thousand, and also to assess the differences between the observed series and those with imputed values, in each of these municipalities, in these variables.Design/methodology/approachThe proposed methodology was based on the use of the univariate imputation method through the application of cubic spline interpolation in each of the 46 municipalities, for each of the 3 variables. Then, for each municipality, the original series were compared with each observed series plus the values imputed in these variables by the Quenouille test of correlation of time series.FindingsIt was observed that, after imputation, all series were compared with those observed and are equal by the Queinouille test in the 46 municipalities analyzed, and the Wilcoxon test also showed equality for the accumulated total of the three variables involved with the production of soybeans. And there were increases of 5.92%, 3.58% and 2.84% for soy production, soy production value and soy derivatives value accumulated in the state after imputation in the 46 municipalities.Originality/valueThe present research and its results facilitate the process of estimates and monitoring the total soy production in the state of Mato Grosso and its municipalities from 1990 to 2018.
{"title":"Missing data estimates related to soybean production in the state of Mato Grosso, Brazil, from 1990 to 2018","authors":"J. G. Ribeiro, S. M. Piedade","doi":"10.1108/caer-01-2022-0014","DOIUrl":"https://doi.org/10.1108/caer-01-2022-0014","url":null,"abstract":"PurposeThe state of Mato Grosso represents the largest producer and exporter of soybeans in Brazil; given this importance, it was aimed to propose to use the univariate imputation tool for time series, through applications of splines interpolations, in 46 of its municipalities that had missing data in the variables soybean production in thousand tons, production value and soy derivatives in R$ thousand, and also to assess the differences between the observed series and those with imputed values, in each of these municipalities, in these variables.Design/methodology/approachThe proposed methodology was based on the use of the univariate imputation method through the application of cubic spline interpolation in each of the 46 municipalities, for each of the 3 variables. Then, for each municipality, the original series were compared with each observed series plus the values imputed in these variables by the Quenouille test of correlation of time series.FindingsIt was observed that, after imputation, all series were compared with those observed and are equal by the Queinouille test in the 46 municipalities analyzed, and the Wilcoxon test also showed equality for the accumulated total of the three variables involved with the production of soybeans. And there were increases of 5.92%, 3.58% and 2.84% for soy production, soy production value and soy derivatives value accumulated in the state after imputation in the 46 municipalities.Originality/valueThe present research and its results facilitate the process of estimates and monitoring the total soy production in the state of Mato Grosso and its municipalities from 1990 to 2018.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48042338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-05DOI: 10.1108/caer-10-2020-0237
Tingting Liu, S. Tang
PurposeImproving the agricultural products market integration is conducive to developing provincial comparative advantage, optimization of agricultural and industrial organization and enhanced competitiveness. The relationship between the emergencies and the agricultural products market integration in the production and consumption provinces is of great significance for stabilizing market prices and improving the efficiency of agricultural resource allocation.Design/methodology/approachThe authors reviewed the literature on the market integration of agricultural products. Then, they adopted a two-way fixed effect model to investigate the impact of emergencies on the poultry market integration in the production and consumption provinces in China.FindingsHighly pathogenic avian influenza (HPAI) caused abnormal fluctuations in the poultry market price and decreased the poultry market integration. The negative impact of HPAI on poultry market integration was strengthened in the main production provinces and weakened in the main consumption provinces.Originality/valueThis is the first study that applies empirical analysis to identify the emergencies’ impact on the poultry market integration considering production and consumption characteristics. The results indicate that the impact of avian influenza is more serious in production provinces than in consumption provinces. Due to the heterogeneity of production and consumption provinces, the government implements precise compensation policies to resume production quickly after the disaster. It can be conductive to market integration and promote the development of agricultural products market.
{"title":"Research on the impact of emergencies on the poultry market integration in China","authors":"Tingting Liu, S. Tang","doi":"10.1108/caer-10-2020-0237","DOIUrl":"https://doi.org/10.1108/caer-10-2020-0237","url":null,"abstract":"PurposeImproving the agricultural products market integration is conducive to developing provincial comparative advantage, optimization of agricultural and industrial organization and enhanced competitiveness. The relationship between the emergencies and the agricultural products market integration in the production and consumption provinces is of great significance for stabilizing market prices and improving the efficiency of agricultural resource allocation.Design/methodology/approachThe authors reviewed the literature on the market integration of agricultural products. Then, they adopted a two-way fixed effect model to investigate the impact of emergencies on the poultry market integration in the production and consumption provinces in China.FindingsHighly pathogenic avian influenza (HPAI) caused abnormal fluctuations in the poultry market price and decreased the poultry market integration. The negative impact of HPAI on poultry market integration was strengthened in the main production provinces and weakened in the main consumption provinces.Originality/valueThis is the first study that applies empirical analysis to identify the emergencies’ impact on the poultry market integration considering production and consumption characteristics. The results indicate that the impact of avian influenza is more serious in production provinces than in consumption provinces. Due to the heterogeneity of production and consumption provinces, the government implements precise compensation policies to resume production quickly after the disaster. It can be conductive to market integration and promote the development of agricultural products market.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46340426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-30DOI: 10.1108/caer-10-2021-0201
Yi Luo, Dong Huang, Yan Han, Laping Wu
PurposeThe purpose of this paper is to analyse the impacts of storage losses and market development on the maize-selling behaviours of rural households in China.Design/methodology/approachBased on the survey data of 543 households from nine major maize production provinces in China, the authors introduce storage losses to a household's maize-selling decision-making model and use fractional logit model and ordered probit model to empirically analyse the impact of maize storage losses and market development on household maize-selling decisions in China. To overcome potential endogeneity problems, the authors select the weather at drying (whether bad weather occurs during the drying process) and harvest loss as instrumental variables and re-estimate the model.FindingsThe results show that increased storage losses prompt farmers to increase the proportion of maize sold within three months after harvest and sell maize in advance. Meanwhile, the degree of market development has a significant impact on farmers' maize-selling decisions. Other factors, such as the maize output, non-agricultural employment and awareness of loss control, also affect farmers' maize-selling behaviours.Research limitations/implicationsThe government should promote advanced storage facilities, reduce household storage losses, decrease the phenomenon of centralised sales after harvest and help farmers freely choose the suitable time for sales. The government also needs to strengthen market information releases and publicity, reduce transaction costs and help farmers make reasonable sales decisions.Originality/valueThe authors introduce storage losses as a separate variable in a farmer's grain-selling decision model to empirically analyse the impact of storage losses on farmers' grain-selling behaviours. Moreover, the authors analyse the impact of market development on household grain-selling behaviours in China. These findings can help avoid oversupply in the market during the harvest season and alleviate the pressure on the market from the supply and demand imbalance. These results are also beneficial for farmers waiting for a higher price and increasing their income.
{"title":"Storage losses, market development and household maize-selling decisions in China","authors":"Yi Luo, Dong Huang, Yan Han, Laping Wu","doi":"10.1108/caer-10-2021-0201","DOIUrl":"https://doi.org/10.1108/caer-10-2021-0201","url":null,"abstract":"PurposeThe purpose of this paper is to analyse the impacts of storage losses and market development on the maize-selling behaviours of rural households in China.Design/methodology/approachBased on the survey data of 543 households from nine major maize production provinces in China, the authors introduce storage losses to a household's maize-selling decision-making model and use fractional logit model and ordered probit model to empirically analyse the impact of maize storage losses and market development on household maize-selling decisions in China. To overcome potential endogeneity problems, the authors select the weather at drying (whether bad weather occurs during the drying process) and harvest loss as instrumental variables and re-estimate the model.FindingsThe results show that increased storage losses prompt farmers to increase the proportion of maize sold within three months after harvest and sell maize in advance. Meanwhile, the degree of market development has a significant impact on farmers' maize-selling decisions. Other factors, such as the maize output, non-agricultural employment and awareness of loss control, also affect farmers' maize-selling behaviours.Research limitations/implicationsThe government should promote advanced storage facilities, reduce household storage losses, decrease the phenomenon of centralised sales after harvest and help farmers freely choose the suitable time for sales. The government also needs to strengthen market information releases and publicity, reduce transaction costs and help farmers make reasonable sales decisions.Originality/valueThe authors introduce storage losses as a separate variable in a farmer's grain-selling decision model to empirically analyse the impact of storage losses on farmers' grain-selling behaviours. Moreover, the authors analyse the impact of market development on household grain-selling behaviours in China. These findings can help avoid oversupply in the market during the harvest season and alleviate the pressure on the market from the supply and demand imbalance. These results are also beneficial for farmers waiting for a higher price and increasing their income.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43768835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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