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The purpose of this paper is to present a nonparametric comparative study on the HCMS consumption of urban and rural households in China.

This paper applies the panel data for China's thirty provinces in 1991‐2007 as a sample and presents a local linear estimation to the nonparametric Working‐Leser panel data models on the HCMS consumption of the rural and urban households in China. The nonparametric Hausman test is applied to test the random effects specification against the fixed effects.

Both the parametric and nonparametric estimation of the Working‐Leser panel data models give us a similar result: that the HCMS commodity is quite elastic in both the rural and urban China. Nonparametric estimate also shows that the urban‐rural difference of the HCMS expenditure share in the total expenditure is mainly due to the large urban‐rural difference of total expenditure. Under the same total expenditure, the HCMS is more elastic in the urban than in the rural regions.

To decrease the urban‐rural difference in the HCMS consumption, the government should enhance the income or total expenditure level of the rural households, especially in the impoverished and remote areas in China. Urbanization plays a critical role in access to health care and can help make substantial changes in rural health care in China.

Compared to the parametric estimation, the nonparametric estimation gives us the added information that the expenditure elasticity of the HCMS consumption in China gradually declines as one moves up the per capita total expenditure distribution. The paper could make a contribution to the relatively thin literature on the Chinese medical and health consumption market.

Urbanization is driving the growth of China’s carbon footprint. It’s important to investigate what factors, how and to what extent, affect carbon footprints embedded in various categories of rural and urban households’ consumption.

We employ an environmental extended input-output model to assess and compare the rural-urban household carbon footprints and perform a multivariant regression analysis to identify the varying relationships of the determinants on rural and urban household carbon footprints based on the panel data of Chinese households from 2012 to 2018.

The results show evidence of urbanity density effect on direct carbon footprints and countervailing effect on indirect carbon footprints. The old dependency ratio has no significant effect on rural family emissions but has a significantly negative effect on urban direct and indirect carbon footprints. A higher child dependency ratio is associated with less rural household carbon emissions while the opposite is true for urban households. Taking advantage of recycled fuel saves direct carbon emissions and this green lifestyle benefits urban households more by saving more carbon emissions. There is a positive relationship between consumption structure ratio and direct carbon footprints while a negative relationship with indirect carbon footprints and this impact is less significant for urban households. The higher the price level of water, electricity and fuel, the lower the rural household’s direct carbon footprints. Private car ownership consistently augments household carbon footprints across rural and urban areas.

This paper provides comprehensive findings to understand the relationships between an array of determinants and China’s rural-urban carbon emissions, empowering China’s contribution to the global effort on climate mitigation.

Health is an important issue in our life. A person with good health will have peace of mind and will be able to contribute to nation-building. We cannot expect performance from an ill person with a low morale. In the present paper, the authors tried to understand the ground realities of health-care facilities provided in India and more specifically in Odisha, India.

This empirical paper used a non-experimental design to test a proposed model based on a review of relevant literature. In this paper, an initial pilot study was conducted by taking 44 various variables; however, after the study and taking the expert opinion, the variables were restricted to only 30. For the purpose of study, only state-sponsored hospitals were considered on a random sampling method.

The analysis of data is conducted on a simple percentage method with closed-end options. It is found that even after 67 years of independence, people do not have access to basic medical care facilities in the rural areas and to some extent in semi-urban areas also. The major stumbling block is inadequate infrastructure in these hospitals.

While this study offers some insight into the status of healthcare infrastructure in rural India, the sample was limited to respondents in state-sponsored hospitals, which may not represent the views about private hospitals.

It seems that in some interior areas of Odisha, people rely more on their fate than then these health-care services.

Various governments claim that they are spending millions of rupees on health-care service, but the benefits are not being felt by the masses. We are sure that our attempt to highlight the scenario of health-care services in the state of Odisha will be an eye opener and will compel the various stake holders to introspect their involvement in the health-care services provided in these areas.

A considerable amount of research has been done evaluating the status of healthcare in India, but this is the first empirical research study to date based on respondents from the rural parts of the state of Odisha in India. Some of these areas are not reachable to researchers due to the poor infrastructure. This contribution is also of special importance amid the recent criticism of the healthcare infrastructure in India by prominent management scholars.

The digital economy is expected to revive the countryside and reduce the current level of urban–rural inequality. Nevertheless, whether rural e-commerce can narrow the urban–rural income gap still requires further analysis. The purpose of this paper is to clarify whether this goal is, in fact, being achieved.

Taobao villages have become the epitome of rural e-commerce development in China. Therefore, this paper matches the data of Taobao villages and the data of prefecture-level cities from 2014 to 2019, and employs a two-way fixed effect model, nonlinear model, instrumental variable model and interactive fixed effects model to explore the impact of rural e-commerce on the urban–rural income gap.

Firstly, the ability of urban residents to share rural e-commerce development is higher than that of rural residents, which actually widens the urban–rural income gap. Secondly, the migration to cities of rural families that have profited from e-commerce, and the return of working-class people to the countryside, are two factors that are contributing to the widening of the urban–rural income gap. Thirdly, the farther the distance from the urban area and the higher the spatial agglomeration of the rural e-commerce cluster is, the weaker the impact on widening the urban–rural income gap will be. Finally, while industrial-led rural e-commerce is responsible for widening the urban–rural income gap, agricultural-led rural e-commerce has no significant impact on the urban–rural income gap.

To the best of the authors' knowledge, this paper is the first to analyze the impact of rural e-commerce on the urban–rural income gap from the perspective of the coverage of Taobao villages. This empirical study will enrich existing theoretical perspectives on urban–rural integration under the backdrop of the digital economy.

Since 2017, China's digital economy has accounted for more than 30% of the country's GDP. The digital economy has become the main driving force of China's economic development. Moreover, the digital economy has also changed the traditional modes of production and distribution between urban and rural areas. This paper aims to explore the influential mechanism of digital economy infrastructure (DEI) on the urban-rural income gap (URIG).

By analyzing the theoretical model of the URIG, this paper constructs a theoretical analysis framework and clarifies the key roles of rural land circulation (RLC) and resident population urbanization (RPU) in the relationship between DEI and the URIG.

The DEI can effectively reduce the URIG; the regression coefficient (RC) was −0.109. The reduction effect is mainly reflected in: 1) the wage income gap between urban and rural residents (RC = −0.128) and 2) the net property income gap of urban and rural residents (RC = −0.321). Also, for the spatial spillover effect, the path effect of “DEI – RLC – URIG” is almost equal to the path effect of “DEI – RPU – URIG”; for the local effect, the path effect of the former is far smaller than the latter. Moreover, when the RPU reaches the threshold of 86.29%, the DEI will expand the URIG (RC = 0.201).

This paper proposes a theoretical framework for the impact of DEI on the URIG, explores the mechanism of RLC and RPU in the DEI and URIG and enriches the theory of traditional research on URIG.

This paper studies the political economy of the endogenous urban–rural divide in two dimensions: labor market and provision of public goods.

This paper gives a dual-sector model endogenously depending on the consumption of public goods (club goods), the number of rural–urban migrants and the tax rate (transfer payments).

According to the research findings in this paper, the constraints on the participation of rural residents portray the rural residents' bargaining power, and in the game between the urban elites and the rural residents, tax rates depend on the preferences of the urban elites and the constraints urban elites and the rural residents jointly face. Therefore, the urban elites have to set tax rates deviating from the most preferred ones. The model in this paper can explain a series of empirical findings and yield new theoretical findings for empirical testing.

Significantly, the paper finds that the increase in agricultural productivity will lead to industrialization, accompanied by the disintegration of the dual-sector model. However, though the increase in industrial productivity can accelerate industrialization, it will further expand the urban–rural divide.

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.

The 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.

The 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.

The 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.

This 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.

Challenges from the urban–rural disparity immensely burden the world's progress fulfilling Sustainable Development Goals and the goals' central promise, particularly for developing countries experiencing rapid structural change and urbanization. A knowledge gap lies between the epistemology of urban–rural disparity and the practice of integrated urban-rural development. This paper aims to provide a new approach to bridge the knowledge gap based on the recent Chinese experience.

This qualitative research reviewed major economic and multidisciplinary studies regarding urban-rural development and the growth-equality tradeoff. Chinese experience is employed to showcase concrete challenges from the urban–rural disparity and how the proposed approach works for urban-rural integration.

Theoretical and practical approaches with urban bias largely fail to counter the challenges. Building on China's recent practice probing beyond urban bias, IFPRI (2019) proposes the term rurbanomics with a highlight of equalized urban-rural economic partnership, whereafter Zhou and Chen (2021) enrich the term into a conceptual framework. This paper further improves rurbanomics as a new viable approach to integrated urban-rural development under the overarching goal of common prosperity. The approach prioritizes driving forces in the economic, demographic, eco-environmental, social institutional and technological aspects for the policy community to leverage. Long-term mechanisms are decerned to link urban-rural integration to common prosperity.

China has leveraged ingredients of the rurbanomics approach in the political deployment to integrated urban-rural development. However, the application of this approach is yet to be adapted with local heterogeneities and live up to application's potential. Long-term mechanisms recommended by the rurbanomics approach will need to be carried out. Future improvements will need substantial theoretical extension and micro-level empirical studies.

This paper streamlines the epistemological shreds regarding pursuits, challenges, global experiences and theoretical approaches of urban-rural development. The paper also develops rurbanomics to navigate urban-rural integration, Sustainable Development Goals and common prosperity. By decerning long-term mechanisms in the Chinese case accordingly, this paper provides clues for other economies to employ the new approach.

The determinants of income of rural and urban farm households, with emphasis on the role of off-farm employment by farm household members and of farm size, are examined using data from the 2016 Agricultural Resource Management Survey (ARMS) and quantile regression procedure. The implemented quantile regression technique is extended to allow for the decomposition of the income gap between the two groups of farm households. Findings indicate, regardless of the location of the farm, a positive and significant impact of a previous year's participation in off-farm work by household members on the distribution of current household income. Having operated a larger-sized farm in the previous year is shown with a similar effect in the upper range of the income distribution for urban households and with a comparable impact but across the whole income distribution for rural farm households.

Data from the 2016 ARMS are used in conjunction with quantile regression in order for decomposition of the income gap between the two groups of farm households.

Findings show that urban farm households who in a previous year have participated in off-farm work and operated larger-sized farms tend to earn higher incomes. Results further indicate higher rates of return to education for “urban” farm households in comparison to “rural” farm households, particularly for those with a college education and beyond who are at the lower portion of the income distribution.

To the extent that the ARMS is an annual cross-sectional data, the temporal impacts of factors that potentially may influence the incomes of farm households in urban and rural areas cannot be measured.

Findings from this research indirectly support previous published research where higher earnings by urban US population were documented in comparison to rural population and where earnings tend to rise as a result of participation in off-farm work and in expanding the size of the farming operation; this is in addition to the procurement of higher education.

The results of a higher rate of return to education for “urban” farm households in comparison to “rural” farm households have important policy implications for policymakers.

This is the first paper in the agricultural economic literature that implements a method of assessing the rural–urban divide across all of the quantiles of income distribution.

Since 1978, China has made tremendous economic achievements through industrial upgrading. However, these achievements are accompanied by an expanding income gap between rural and urban areas. The purpose of this paper is to examine the relationship between industrial structure and urban–rural income inequality in China.

Using the fixed-effects model and provincial data for the period 1985–2019, this paper estimates a linear relationship between industrial structure and urban–rural income inequality. By decomposing total income inequality into four components, the paper then analyzes how industrial structure affects each component.

The results show that industrial structure imbalance and industrial upgrading are positively associated with urban–rural income inequality. The positive effect of industrial imbalance mainly comes from widening the wage gap, while that of industrial upgrading mainly comes from aggravating business income inequality and property income inequality. Moreover, industrial balance and upgrading are conducive to increasing the share of wage income at the cost of property income.

By progressively examining the total inequality and the inequality of income components, this paper provides a better understanding of how industrial structure affects urban and rural income inequality. The findings of this study highlight the “inequality cost” associated with industrial structure adjustment, which provide policy-related insights on the balance development of urban and rural areas.