An Empirical Examination of Aging’s Ramifications on Large-scale Agriculture: China’s Perspective

1 Introduction

Aging is one of the most important social trends of the 21st century, affecting several areas of economy and society. According to the United Nations World Population Prospects (2019 Revision), one in six people worldwide will be over 65 years old (16%) by 2050, whereas this number will be only 11 (9%) by 2019. As one of the most populous countries worldwide, the age structure of China’s population has changed dramatically over the past decade. According to the World Health Organization classification criteria, China’s elderly population will exceed 400 million in 2037, and China will gradually become an older society (Fang et al., 2015; Peng & Hu, 2011; Wang & Ai, 2015). In the last two decades, China’s urbanization has accelerated, and the trend of a large number of agricultural labourers moving to cities in search of non-farm jobs has accelerated significantly (Cai & Wang, 2007; Cai, 2018), leading to more rapid aging in rural areas of China than in cities. The average age of the population engaged in agricultural labour is also significantly higher than in other sectors (Chen, 2013; Hu & Zhong, 2012; Yang et al., 2016).

The impact of the rapid aging of the agricultural population on agricultural production has become an area of increasing academic interest, with attention focused on the effects of aging on production factor inputs (Guo et al., 2015; Qin et al., 2011) and agricultural output (Li & Li, 2009; Li & Sicular, 2013). Some literature argues that there is an imbalance in the supply of effective agricultural labour due to the gradual decline in the physical functioning of the rural agricultural workforce and the decreasing number of effective labourers for agricultural production inputs (Wang et al., 2024; Yi et al., 2023). In addition, studying the effects of aging on land use efficiency (Lin & Deng, 2012; Sui et al., 2022) and the impact of aging on agricultural production behaviour decisions (Hu & Zhong, 2012; Li & Zhao, 2009; Zhong & Hu, 2008) has also been the focus of research. Through the study, it was found that population aging has profoundly affected the land transfer behaviour of the elderly left behind in rural areas (He & Yi, 2024; Wang & Liu, 2023). However, few studies have examined the effects of aging on large-scale agriculture. As an important initiative to expand agricultural investment and increase land capital accumulation, the Chinese government has been committed to promoting the development of large-scale agriculture and has introduced various supportive policies to encourage farmers to join cooperatives and professional associations or build platforms to facilitate cooperation between rural households, companies, and land trusts (Clegg, 2006). The development of new agricultural enterprises has been affected by the outflow of labour (Deng et al., 2024). It can be argued that large-scale agriculture is extremely important for enhancing agricultural productivity and modernizing agricultural development (Teng et al., 2021). There is no doubt that aging will also have far-reaching effects on large-scale agriculture, and the conclusions drawn from existing studies are divergent. Some scholars believe that aging can positively affect large-scale agriculture by enabling farming households to rent out their land that is difficult to operate (Liu et al., 2021). However, some argue that labour outflows can significantly increase the labour costs of large-scale agriculture (Lu et al., 2017). Other scholars have questioned the negative effects of aging, arguing that the existing agricultural mechanization can fully compensate for the impact of aging on large-scale agriculture (Lu et al., 2022; Yang et al., 2016).

Previous studies provide the necessary reference for us to address the impact of aging on large-scale agriculture, but they have room for further refinement. First, previous studies have a single statistical calibre of aging, failing to break down to different age groups or analyse different dimensions such as households and cities. Aging is a long-term process in which quantitative changes cause qualitative changes. It is also a process in which microscopic households accumulate effects until they change the age structure of the macroscopic county city population. Using only one type of variable for analysis will inaccurately describe the impact, which is one of the reasons for the divergent conclusions of previous studies. Second, research on large-scale agriculture at the rural household level is limited. Unlike large-scale operations conducted by agricultural capital, large-scale agriculture involving local rural households reflects the true level of rural economic development. Previous studies have failed to use census data effectively. Although the research subjects remain in the category of large-scale agriculture, the conclusions drawn come from efficient agricultural capital and ignore the outflow of agricultural production returns. This would overestimate the actual returns of rural households and disguise the negative effects of aging in rural areas. Finally, there have been recent changes in the rural economy, such as the revalidation of land ownership and use rights and the upgrading of rural infrastructure. These changes, along with aging, have affected large-scale agriculture. Therefore, further research on per capita arable land area and farm machinery is necessary. Therefore, this study uses multiple dimensions to describe aging, and regressions using a logistic model to verify the effects of aging on large-scale agriculture and further analyse other related effects. First, from the farm household perspective, aging leads to an increase in the age of the rural household labour force and a decrease in the probability of farm household participation in large-scale agriculture. Second, in the county-city dimension, aging leads to a decrease in the proportion of the participating population in large-scale agriculture, which is not conducive to the development of large-scale agriculture. Third, increasing arable land per capita does not weaken the negative effects of aging on large-scale agriculture. In some cases, the negative effects of aging on large-scale agriculture are reinforced in areas with high levels of agricultural mechanization.

Our study contributes in three ways. First, it uses data from the Third National Agricultural Census 2016. Census data are the most intuitive and comprehensive, reflecting the changing trends in China’s rural large-scale agriculture in the face of aging. Second, we measure aging in two dimensions, rural households and county cities, and divide them into different age groups. Third, we analysed the moderating effects of per capita acreage and mechanization level on the impact of aging on large-scale agriculture, providing a reference for formulating targeted policies.

The remainder of the paper is structured as follows: Section 2 presents the theoretical analysis and research assumptions. Section 3 presents the data and estimation strategy used in this study. Section 4 presents the empirical results. Section 5 provides further analyses and discusses the findings and shortcomings of the study. Finally, Section 6 presents the conclusions of this study.

2 Theoretical Analysis and Research Hypothesis

Aging has different transmission paths based on the dimensions of farmers and cities, and the impact on large-scale agriculture has different transmission paths that eventually converge into a complete impact. As rural households age, the health and strength of their labour force gradually decline, and they are no longer able to perform overly strenuous physical labour (Wang et al., 2021). Farmers are more inclined to rent out their land than to operate their own land alone and are more reluctant to participate in various types of cooperative farming (Gao, 2022). In addition, participation in large-scale agriculture requires not only physical strength but also the necessary learning ability and ability to apply agricultural technology. For individual farmers, although increasing age increases their farming experience to a certain extent, their experience forms a fixed mindset that is not conducive to the adoption of new technologies, which creates further barriers for older farmers to engage in cooperative farming (Gao, 2022; Hu & Zhong, 2012). Therefore, we propose hypothesis 1:

In the county-city dimension, we further subdivided the rural population into youth, middle-aged, and elderly groups according to age, and the degree of aging was intuitively represented by fluctuations in the proportion of the population in different age groups. County cities with a lower degree of aging have higher proportions of middle-aged and younger populations. As local aging increases, the share of the local middle-aged and young population continues to decrease, whereas the share of the older population increases (Chang et al., 2021; Čiutienė & Railaitė, 2015). Attitudes toward large-scale agriculture differ significantly among people of different age groups; the main participants in large-scale agriculture are middle-aged rural populations. They generally have some urban work experience. As they enter middle age, the benefits of remaining in urban, high-intensity non-farm employment may already be lower than the benefits they would receive if they returned to rural areas to participate in large-scale agriculture (Nie & Li, 2015; Xiao & Luo, 2019). In addition, the relatively stable earnings and low cost of living from agricultural production are among the reasons why the middle-aged cohort chose to return to the countryside (Zhang et al., 2023). They tend to have experience in technology applications and management, which motivates them to expand their business areas and participate in large-scale agriculture (Xu & Zhao, 2022). Young people can also participate in large-scale agriculture. However, compared with the middle-aged population, their physical condition is more suitable for high-intensity urban non-farm industry positions. The rural youth cohort is constrained by a weaker level of education and is therefore employed in urban areas, mainly in labour-intensive industries. These industries have lower skill thresholds, and income returns are directly proportional to the labour input (Liu & Lei, 2020). Currently, the rising cost of human resources in China has led to rural youth earning higher wages for farming in cities than in rural areas. In the context of the scissor difference between urban and rural wage returns, they are more inclined to work (Song & Gao, 2022; Wang & Luo, 2022). As their income is focused on non-agricultural jobs in urban areas, they pay less attention to rural land, and more than half of rural youth have indifferent attitudes toward land (Yang & Zuo, 2015). However, the elderly population is constrained by their health status and learning ability to meet the general requirements for participation in large-scale agriculture (Ma et al., 2023). Therefore, an increase in aging in the county-city dimension will lead to a decrease in the share of middle-aged and young people as the main participants in large-scale agriculture, which, in turn, will have a negative impact on the level of large-scale agriculture in their cities. Therefore, we propose hypothesis 2:

3 Data, Variables, and Models

4 Empirical Results

Table 2 presents the regression results with stepwise inclusion of key explanatory and control variables. Column (5) shows the results of adding all explanatory and control variables, indicating that as the age of the rural household head increases, the probability of the household’s participation in cooperative farming decreases significantly. This indicates that in terms of the household dimension, the probability of rural households participating in large-scale agriculture decreases with increasing age, verifying Hypothesis 1. In terms of the population share indicator, which measures the degree of aging in county cities, when the remaining control variables are not included, the probability of participating in large-scale agriculture increases as the proportion of rural youths and middle-aged people aged 20–60 years increases. When the remaining control variables were included, the positive effect of an increase in the proportion of rural youth population on the probability of participation in large-scale agriculture was not significant, while the positive effect of rural middle-aged people between the ages of 40 and 60 remained highly significant. Both youth and middle-aged people from rural areas are the main participants in cooperative farming, but youth tend to work more in urban areas, while middle-aged people have a higher tendency to return to their hometowns to expand their production scales compared to youth. In terms of another measure of the impact of aging, the proportion of migrant workers in different age groups and the probability of participating in large-scale agriculture decrease as the proportion of rural people aged 30–60 years who go out increases, which is consistent with the previous analysis. This is because young and middle-aged populations are the main groups involved in large-scale agriculture. The higher the proportion of migrant workers among the 60–70 year old population, the higher the probability of participating in large-scale agriculture. An increase in the proportion of the 20–30-year-old population going out of the country also increases the probability of participating in large-scale agriculture. The youngest population lacks experience in agricultural production and their choice of urban employment can facilitate the rental of unused land for large-scale agriculture. Thus, deepening aging (decreasing the share of the young and middle-aged population and increasing the share of the older population) decreases the probability of participation in large-scale agriculture by affecting the proportion of the participating subject population, as proved by Hypothesis 2.

In addition, other control variables have different effects on large-scale agriculture: Education has a positive effect on the probability of participation in large-scale agriculture. As the level of education increased, farmers became more likely to participate in cooperative farming. Additionally, large-scale agriculture benefits harmonious and intact families, whereas family situations such as divorce and widowhood reduce the probability of rural households participating in large-scale agriculture. Most of China’s agricultural population has experience working outside the home in non-farm employment, and those with employer work experience have an increased probability of participating in large-scale agriculture. More household assets will also increase the probability of participation in large-scale agriculture, and their investment and management strategies will be more active.

5 Discussion

Based on the findings of the baseline regression, we discuss whether arable land area and agricultural mechanization produce moderating effects, summarise the strengths and limitations of this study, and propose targeted policy recommendations based on these findings.

6 Conclusion

In this study, based on microdata from the Third National Agricultural Census of 2016, a logistic model was used for regression to verify the impact of aging on large-scale agriculture. It was found that, first, from the microscopic rural household dimension, aging led to an increase in the age of the rural household labour force and a significant decrease in the probability of farmers’ participation in large-scale agriculture. Second, in the county-city dimension, aging at the macro level reduces the share of young and middle-aged populations, which are the main group of rural participants in large-scale agriculture, and the probability of participating in large-scale agriculture decreases as a result. Third, the use of agricultural machinery can exacerbate the negative effects of aging on large-scale agriculture. Meanwhile, owing to the limitations of cross-sectional data, we could not further analyse the impact of aging dynamics on large-scale agriculture, which provides a direction for our future research. Based on the above conclusions of the study, we put forward the following policy recommendations. First, we should pay attention to the important role played by young and middle-aged people between 30 and 40 years in the field of large-scale rural agriculture and give them more precise policy support. They are the most active participants in land transfer and have certain business and management knowledge, which is the most likely fresh blood to inject vitality into the rural economy; secondly, we should improve the publicity of land transfer-related policies among the youth. Secondly, we should improve the publicity of land transfer policies among young people. They pay little attention to land transfer and do not realise the importance of revitalizing their own property. In order to solve the problem of land abandonment and improve agricultural production capacity, the publicity of land transfer among young people should be strengthened and the convenience of participating in land transfer should be improved; third, the regulation of the land transfer market should be emphasised. Aging will lead to an oversupply in the land transfer market, which will jeopardise the interests of farmers who rent out their land. The government should actively cultivate large-scale agricultural entities and give them tax incentives to increase the demand for land transfer; fourth, the government should improve the comprehensive quality of the aging rural population. Through analysis, we find that for the same elderly population, the more educated they are, the more inclined they are to participate in large-scale agriculture. Therefore, re-education and adult training for the rural population can offset some of the negative impacts of aging; Fifth, continue to provide subsidies for farmers to purchase agricultural machinery. Investments in agricultural machinery can increase the level of large-scale rural operations and mitigate the impact of ageing on agricultural production.