1. Introduction
The inferiority of agriculture has always been a restriction for the development of Chinese agriculture for a long time. In the karst regions, due to the widespread mountains and hills, the rugged terrain, and depleted soils, it is hard to develop agricultural production [
1]. In this case, the foundation of agricultural production in the karst regions is relatively weak. This results in the problems of agricultural frangibility and inferiority being more serious. Agricultural insurance, as an important tool to spread the risks of agricultural production, plays an ever-increasingly important role in promoting farm modernization and sustainability, promoting rural industry revitalization, and guaranteeing farmers’ basic income. It is also an essential tool to safeguard agricultural production in the karst regions. Therefore, it is of great necessity to study the development of agricultural insurance in the rural karst regions, to help promote the participation rate of agricultural insurance in rural China.
The Chinese government has always attached great importance to the development of agricultural insurance: In 2022, Central Document No. 1 listed “strengthening financial services for rural revitalization” as an important directive and stressed the importance of “actively developing agricultural insurance and reinsurance”. With the tireless efforts of the government and departments, China’s agricultural insurance has made great progress: According to the data provided by the China Banking and Insurance Regulatory Commission (CBIRC) and China’s Agricultural Insurance Protection Research Report (2021), China’s agricultural insurance premium income in 2020 totaled CNY 81.49 billion, and the premium scale has surpassed the United States. China has become the largest agricultural insurance market worldwide. At the same time, China’s agricultural insurance depth reached 1.05%, and the density reached CNY 460 per person, achieving a year-on-year growth rate of 27.75%. Although the insurance density and insurance depth have achieved rapid growth, the overall level is still far below the average level of the overall insurance industry. The relatively low insurance density and insurance depth also reflect that the number of farmers participating in insurance is insufficient, and their willingness to participate in insurance is still not high. To better solve this problem, the farmers’ willingness to purchase agricultural insurance needs to be further studied. Comprehensively, we should systematically clarify the farmers’ decision-making in purchasing agricultural insurance and its internal influencing mechanism.
Farmers’ decisions are easily influenced by the behaviors of their peers. Especially in the case of inadequate information, farmers’ decisions are more likely to be influenced by the group members. In other words, farmers’ decisions will be affected by the peer effect. As a typical “acquaintance society” in rural China, farmer’s decision-making behavior is more likely to be influenced by each other. Furthermore, some studies have proved that the peer effect can significantly promote individuals to participate in voluntary social programs, such as China’s New Rural Pension Scheme [
2], pension plans, social medical insurance, etc. [
3]. Meanwhile, the peer effect also plays an important role in many individual decision-making processes, such as risk-taking, fertility issues, financial investment issues, etc. [
4,
5,
6]. However, in the field of studying farmers’ behavior to participate in agricultural insurance, the peer effect still lacks sufficient attention.
In previous studies, many scholars conducted their studies from an objective aspect: Some scholars believed that the main reasons for the agricultural insurance market failures are information asymmetry and high systemic risk [
7,
8,
9]. In that case, the expected utility theory is used to explain the insufficient demand for agricultural insurance, including that the initial wealth accumulation will reduce farmers’ risk aversion consciousness [
10]; the larger or more efficient the farm is, the more likely the farmer is to participate in agricultural insurance [
11]. However, Chinese scholars attributed the agricultural insurance market failures to the nature of agricultural insurance as a quasi-public good. They believed that the main reason for the insufficient supply of agricultural insurance was inadequate government subsidies, which ultimately resulted in the “double freezes in supply and demand” in the agricultural insurance market [
12]. In 2004, China officially established the policy-based agricultural insurance system and achieved nationwide coverage in 2012 [
13]. China’s agricultural insurance has made great progress since then, but the overall situation is still not ideal. Since then, many studies have been published on the demand-side research of the agricultural insurance market. Zhang et al. [
14] proposed that policy-based agricultural insurance essentially belonged to an agricultural support policy, whose importance lies not in the characteristic itself, but the features compared with other agricultural support tools. It is the insufficient demand that results in market failures. Wu et al. [
15] pointed out that one of the keys to the success of agricultural insurance is the demanding analysis. Overall, the studies carried out by Chinese scholars on the problem of insufficient demand can be summarized as follows: (1) From a macro perspective, the suggestions put forward for the reform of the agricultural insurance system consist of the rural revitalization strategy. These suggestions include improving relevant laws and regulations, perfecting management systems, clarifying policy objectives, etc. [
16], or refer to the policy frames of agricultural insurance development in other developed countries, such as developing and utilizing the income support function of agricultural insurance [
17], gradually improving the guaranteed level of agricultural insurance, implementing differentiated regression proportional subsidy [
18], etc. (2) From the micro perspective, studies focused on the influencing factors of farmers’ purchase of agricultural insurance, such as gender, education level, health conditions, and other individual characteristics; meanwhile, household factors such as cultivated land areas and household total income will also significantly affect farmers’ insurance participation decisions [
19,
20,
21]. With the deepening study on farmers’ behavior to participate in agricultural insurance, a growing body of literature has noticed that the previous studies were mostly from the traditional expected utility theory, which did not explain the real insurance participation behavior, and made it hard for the conclusions to be applied to practice. Hence, from the perspective of behavioral economics, some scholars began to distinguish the difference between the farmers’ willingness for insurance participation and their participation behavior. They tried to explore the real law of farmers’ insurance participation behavior [
22]. Some scholars also began to study the influence of social capital on farmers’ agricultural insurance purchasing decisions [
23], or to explore the dynamic equilibrium of the agricultural insurance market by the evolutionary equilibrium principle [
24].
As mentioned above, seldom has empirical literature tested the peer effect on farmers’ decisions to participate in agricultural insurance. Therefore, the main objective of this paper is to quantify the importance of peer effect in household agricultural insurance participation and further explore its internal mechanism. In this paper, we adopt an IVprobit model to identify the peer effect on the farmers’ participation in agricultural insurance using the 2020 China Karst Area Rural Economic Survey (CKRS) data. The CKRS data include Hubei, Guangdong, Guangxi, Sichuan, Guizhou, Hunan, Chongqing, and Yunnan Provinces in China, with 9452 farmers in total. We introduce two mediators of risk perception and insurance cognition, to further explore the main functionary channels of the farmers’ decision-making to buy agricultural insurance. Moreover, we find the peer effect is asymmetrical by dividing the whole sample into different groups.
Compared with the existing literature, our study contributes to the literature by showing that the peer effect can be applied to the study of agricultural insurance decision-making through empirical analysis. Our study finds that the probability of individual farmers participating in agricultural insurance will increase by 3.25% when the farmers’ neighbors’ participation rate increases by 10%. Specifically, we conducted the survey in the karst regions to help increase the agricultural insurance participation rate in the areas with weak agricultural production, and to provide some references for the future development of agricultural insurance in rural China. Meanwhile, the differential distribution of farmers’ participation rate may be caused by the particularity of geographical conditions, which may provide a new probable perspective for regional research on agricultural insurance in the future. The remainder of the paper is organized as follows.
Section 2 briefly describes the theoretical framework of this study.
Section 3 outlines our materials and the estimation strategy.
Section 4 presents our empirical findings, and
Section 5 presents further discussions about the peer effect.
Section 6 concludes and discusses the policy implications of our findings.
4. Results
We use Stata 16.0 for statistical analysis and
Table 2 presents our baseline results using the probit model. The LR chi2(19) of the model is 1487.91, and the pseudo R-squared is 0.2908, which shows good goodness of fit for further study. Column (1) shows the variables’ coefficients of regression, and column (2) shows the marginal effects. The result shows that the peer effect has a significant influence on farmers’ participation in agricultural insurance: A 10% increase in farmers’ neighbors’ participation in agricultural insurance increases the likelihood of farmers’ participation by 3.25%. Hypothesis H1 is verified.
Moreover, from the perspective of individual characteristics. The coefficient of farmers’ age is negative and statistically significant at 1%. With a farmer’s age increasing by 1 year, the probability of participating in agricultural insurance will decrease by 0.1%. As farmers get older, they are more inclined to use traditional risk avoidance tools, and their understanding of policies could be weaker, so their willingness to buy insurance is also lower. Meanwhile, the coefficient of the farmers’ gender variable is positive and statistically significant at 10%, which means that male farmers are more likely to buy agricultural insurance. Compared with female farmers, the probability of male farmers participating in agricultural insurance will increase by 0.9%. Male farmers usually play the role of breadwinners and decision-makers in the household, so they are exposed to more new information about agricultural production. Therefore, male farmers are more likely to buy agricultural insurance to spread the agricultural production risks. The coefficients for the farmers’ political status are also statistically significant and positive, and the probability of party members participating in agricultural insurance will increase by 1.6% compared to non-party members. The most likely explanation is that party members are more likely to learn the relevant policies for the promotion and publicity of agricultural insurance, so they will have a deeper cognition and understanding of policies and insurance clauses. So, they are more likely to participate in agricultural insurance. The same argument can be used for the result of farmers’ subsidy policies cognition, which indicates that if a farmer’s insurance cognition increases by one point, the probability of buying agricultural insurance will increase by 9.1%.
Additionally, for farmers’ households, the coefficient of farmers’ household types is positive and statistically significant at 1%. The probability of pure farmers participating in agricultural insurance will increase by 2% compared with the part-time farmers. Pure farmers spend more time on agricultural production and may have a deeper understanding of relevant information, that is, they are more inclined to participate in agricultural insurance. At the statistical level of 1%, household cultivated land area significantly promotes farmers’ probability of buying agricultural insurance. With the expansion of farmers’ planting scale, natural risks will have a greater and greater impact on agricultural production. To spread risks and reduce probable losses, farmers with a larger planting scale could be more willing to purchase agricultural insurance at that time. In the meantime, as another indicator related to production risk, household monthly average expenditure significantly promotes farmers’ probability of buying agricultural insurance at the statistical level of 5%, which can reflect the cash flow of farmers’ households in production and living to some extent. With the household monthly average expenditure increasing by 1%, the probability of a farmer participating in agricultural insurance will increase by 0.6%. It may indicate that a farmer household with larger cash flows may face more uncertain risks in production, thus, they are more likely to spread the uncertain risks with agricultural insurance.
5. Discussion
Table 3 reports the IVprobit estimation results when the farmers’ neighbors’ average political status is used as the instrument variable. As shown in
Table 2, the farmers’ neighbors’ political status significantly promotes their probability of buying agricultural insurance. Meanwhile, the neighbors’ political status, as a personal characteristic, will not directly influence the agricultural insurance buying decision of the respondent farmer.
The first-stage IVprobit result is reported in column (1), the farmers’ neighbors’ political status has positive and statistically significant coefficients; the first-stage F-statistic is above 10, which indicates that the instrument is statistically strong [
37]. The second-stage IVprobit result is presented in column (2), the coefficient of the peer effect is also positive and statistically significant. Hence, from this table, we can conclude that the peer effect increases the likelihood that a farmer participates in agricultural insurance. Furthermore, the coefficient of the peer effect using IVprobit is less than the coefficient estimated in
Table 1, which indicates that the probit model will overestimate the positive impact of the peer effect, and the estimations of the IVprobit model are closer to the real situation. In addition, in the case of this model, when the number of endogenous explanatory variables is equal to the number of instrumental variables, the overidentified test is not necessary according to the existing study [
38].
Table 4 reports the robustness test results of the IVprobit model by replacing dependent variables and sample groups. Firstly, we use the logarithm of the payment for agricultural insurance as the dependent variable to repeat the above estimation. Column (2) shows the results that are consistent with the baseline estimation: The peer effect still has a significant influence on the farmers’ agricultural insurance buying decision. In addition, there are probable differences between local governments’ invisible implications, and some specific villages may be affected by promotions and publicity, which results in a higher overall participation rate to interfere with the test of the peer effect. Therefore, our study excludes the village with an insurance participation rate that is higher than the average level of the whole sample. The results are shown in column (3): The marginal effect of the peer effect is slightly reduced, but the peer effect still significantly promotes farmers’ agricultural insurance decisions, which also indicates that the empirical model in this paper is robust.
As the empirical analysis shows, the peer effect has a significant positive impact on farmers’ purchase of agricultural insurance. We introduce the proxy variables for risk perception and insurance cognition to further explore the mechanism. According to the above inference in this paper, if the peer effect promotes the farmers’ insurance decisions by influencing their risk perception and insurance cognition, the influence of peer effect in the groups with higher risk perception or insurance cognition should be greater. Hence, we divide the whole sample into two groups by the threshold of the average level of risk perception and insurance cognition, respectively, to test the peer effect in each group. The results are shown in
Table 5. The farmers with higher risk perception and insurance cognition are significantly affected by the peer effect, suggesting that risk perception and insurance cognition may be the channels of peer effect in farmers’ agricultural insurance decision-making. Moreover, the KHB method is used to test the mechanism and further quantify the indirect effect.
Table 6 shows the results of the KHB method. The total effect of the peer effect is 0.470 and the direct effect is 0.299, both of which are statistically significant at 1%. The indirect effect is 0.171, composed of risk perception and insurance cognition, which is statistically significant at 10% and accounts for 36.38% of the total effect. Hence, the group regression and KHB decompositions indicate that the peer effect could promote farmers’ purchase of agricultural insurance directly and indirectly through promoting risk perception and insurance cognition. Therefore, there are no grounds for rejecting Hypothesis H2.
Studies have shown that when individuals are making decisions under the influence of their peers, there may be “leaders” in the group that influence the decision of the whole group [
39]. It is of great significance for us to clarify the possible leaders in the purchase decision of agricultural insurance. By mobilizing the participation enthusiasm of leaders and giving full play to the significant demonstration effect of leaders, can we better carry out policy publicity and promotions of agricultural insurance in the future.
It has been proved that male farmers are more likely to buy agricultural insurance in
Table 1. Therefore, our study further divides the whole sample into a male group and female group to test the influence of the full-sample peer effect, and we also test the influence of the peer effect within the groups on their insurance participation, respectively. The results are shown in
Table 7: Both the peer effects in the whole sample and the subsample have a greater influence on the male sample’s insurance buying decision. In other words, the male farmers participating in agricultural insurance may have a demonstration effect. Similarly, the larger the areas of cultivated land are, the greater the risk that farmers may have to bear which results in the increasing demand for agricultural insurance. Therefore, the whole sample is divided into larger-scale and smaller-scale farmers with an average cultivated land area of 3.49 acres as the boundary. The results are the same as the groups divided by gender, as shown in
Table 7. The peer effect in the whole sample and subsample plays a more important role among the larger-scale farmers. In summary, male farmers and larger-scale farmers have more significant effects on their peers and probably lead the participation in agricultural insurance.
6. Conclusions
In this paper, we examine the peer effect on farmers’ agricultural insurance decisions, based on the survey data of 9452 farmers in the karst regions of Hubei, Guangdong, Guangxi, Sichuan, Guizhou, Hunan, Chongqing, and Yunnan Provinces. We find that the peer effect has a significant positive influence on farmers’ participation in agricultural insurance within a village, and a 10% increase in farmers’ neighbors’ participation in agricultural insurance increases the likelihood of farmers’ own participation by 3.25%. We also find that the peer effect promotes farmers’ agricultural insurance participation by enhancing farmers’ risk perception and insurance cognition. Meanwhile, we find the heterogeneous effects that exist in the peer effect: Male farmers and larger-scale farmers have larger effects on their peers and probably lead the farmers’ participation in agricultural insurance.
As a related finding to the existence of opinion leaders, our evidence suggests: (1) Increasing policy publicity and enforcing policy advocacy, which would magnify the positive impact of the peer effect, such as building a digital information-sharing platform so that the farmers can access more comprehensive technical and policy information. (2) Increasing the participation rate of specific groups by policy interventions, as male and larger-scale farmers have larger effects on their peers and probably lead the agricultural insurance decisions, we could perfect the subsidy policy to encourage moderate scale management of agriculture to give full play to the positive demonstration effect of specific groups. (3) Innovating insurance publicity methods to enhance farmers’ risk awareness in agriculture production and improving farmers’ insurance cognition. On the one hand, we could conduct more relative educational lectures. On the other hand, it would be more efficient if we simplify the publicity materials, avoiding just a single jumble of insurance product details, as we can add more simple cases and process diagrams to explain the operation process of agricultural insurance. (4) Focusing more on primary-level governance and construction in rural China. As the basic administrative unit in China, village communities occupy most of the production and living activities in time and space for residents. We should make full use of village resources, strengthen elementary education, and organize more group activities to advance the stock of rural social capital on all fronts, increasing the sense of identity among farmers and the sense of belonging, to leverage the role of the peer effect within rural areas.