Study of the Impact of Agricultural Insurance on the Livelihood Resilience of Farmers: A Case Study of Comprehensive Natural Rubber Insurance

Abstract

Against the backdrop of increasingly frequent extreme weather events and heightened market price volatility, investigating the relationship between agricultural insurance and farmers’ livelihood resilience is crucial for ensuring rural socioeconomic stability. This study utilizes field survey data from 1196 households across twelve county-level divisions (three cities and nine counties) from China’s Hainan and Yunnan provinces, specifically in natural rubber-producing regions. Using propensity score matching (PSM), we empirically examine agricultural insurance’s impact on household livelihood resilience. The results demonstrate that agricultural insurance increased the effect on farmers’ livelihood resilience by 1%. This effect is particularly pronounced among recently poverty-alleviated households and large-scale farming operations. Furthermore, the analysis highlights the mediating roles of credit availability, adoption of agricultural production technologies, and production initiative in strengthening insurance’s positive impact. Therefore, policies should be refined and expanded, combining agricultural insurance with credit support and agricultural technology extension to leverage their value and ensure the sustainable development of farm households.

1. Introduction

As global climate change intensifies, triggering frequent extreme weather events and market price fluctuations, agricultural production faces substantially heightened uncertainty. According to the United Nations Office for Disaster Risk Reduction (UNDRR), the proportion of agricultural losses from climate-related disasters relative to GDP increased by 23% between 2000 and 2021, with smallholder farmers accounting for 76% of these losses [1]. China, as a climate-sensitive agricultural powerhouse, experienced damage to 120 million mu of crops from extreme weather in 2022. Concurrently, price fluctuations led to the “bumper crop but no harvest” phenomenon, exposing 15.7% of farmers to the risk of falling back into poverty [2]. Consequently, establishing a long-term poverty prevention mechanism is imperative to effectively mitigate large-scale relative poverty and prevent marginalized populations from re-entering poverty. Simultaneously, China’s rural revitalization strategy explicitly calls for “improving the agricultural support and protection system”.

Based on this context, agricultural insurance plays a critical role as a market-based risk dispersion mechanism. Its significance lies not only in serving as a key instrument for addressing developmental challenges but also in generating spillover effects across industries. From the perspectives of agriculture itself and farmers’ livelihoods, agricultural insurance effectively prevents vulnerable farmers from falling into poverty or returning to poverty because of disasters by compensating for losses caused by natural hazards or market fluctuations, thereby reducing the poverty rate among smallholder farmers [3] and stabilizing their livelihoods. Simultaneously, insurance coverage enhances farmers’ confidence and capacity to adopt new technologies and expand production [4] facilitating agricultural modernization [5]. The stabilizing effect of agricultural insurance also benefits related industries. Furthermore, the substantial agricultural insurance market itself directly drives the development of the insurance and reinsurance sectors.

Therefore, agricultural insurance has been entrusted with a critical mission within China’s Rural Revitalization Strategy. However, China’s current agricultural insurance system suffers from the issue of “high coverage but low protection.” The “low protection” is primarily manifested in low coverage levels, narrow scopes of liability, and stringent claim standards, making it difficult to meet the differentiated needs of farmers. This “quantity-over-quality” status quo may result in insufficient disaster compensation payouts, causing farmers to experience sudden income drops, depletion of savings, or even forced asset sales, thereby undermining their risk resistance and post-disaster recovery capacities. When uncovered risks materialize, insurance fails to provide an effective buffer, leaving farmers’ livelihoods still exposed to significant shocks. Consequently, beneath the facade of “high coverage,” the reality of “low protection” may substantially diminish the effectiveness of agricultural insurance in enhancing farmers’ livelihood resilience, falling far short of theoretical expectations. Whether it can fully translate into a substantive improvement in farmers’ risk resistance capabilities and strengthen their livelihood resilience and sustainable development in the face of compound risk remains a core question demanding urgent empirical validation.

Natural rubber, as a national strategic material, has production stability directly linked to industrial chain security and national livelihood. Furthermore, China’s primary natural rubber production regions are concentrated in ecologically sensitive areas such as Hainan and Yunnan. Therefore, safeguarding the livelihood stability of rubber farmers holds significance not only for individual household welfare but also for maintaining regional economic development, stability, and ecological barrier functions.

Considering this, this study takes natural rubber insurance as the entry point and employs the propensity score matching (PSM) method to explore the impact of agricultural insurance on farmers’ livelihood resilience and its underlying mechanisms at the micro level. The marginal contributions of this paper are primarily manifested in the following aspects: First, whereas most existing studies use a single income factor to measure the policy effectiveness of agricultural insurance, this research considers the multidimensionality and complexity of policy effects. It constructs a multidimensional indicator system from the perspective of farmers’ livelihood resilience to evaluate the policy impact of agricultural insurance. Second, it empirically analyzes the transmission mechanisms through which agricultural insurance affects farmers’ livelihood resilience—specifically, credit access, agricultural production technology adoption, and production initiative. Third, based on heterogeneity analysis categorizing farmers by whether they are formerly impoverished households and by planting scale, it clarifies the differential effects of agricultural insurance on livelihood resilience across distinct groups. This study not only helps address gaps in the existing literature and provides empirical evidence for constructing indicator systems and conducting empirical analyses related to livelihood resilience but also offers insights into further refining and advancing agricultural insurance policies, thereby informing decision-making to promote rural development.

2. Literature Review

Agricultural insurance may raise transaction and administrative costs because of moral hazard and adverse selection caused by information asymmetry [6]. Systemic agricultural risks prevent effective spatial risk dispersion, prompting private insurers to impose coverage restrictions under solvency constraints [7], resulting in market failure. Government intervention thus becomes essential to resolve this dilemma [8]. Fiscal subsidies reduce operational costs for insurers, ensuring the effective operation of agricultural insurance mechanisms to some extent.

The existing literature on agricultural insurance primarily covers three areas. First, studies of its impact on farmers’ production behavior, such as [9], who examined how federal crop revenue insurance influences planting systems in the US Corn Belt, finding it affects crop selection and rotation decisions. Further research reveals agricultural insurance shapes farmer behavior through altered risk attitudes [10], resource allocation adjustments [11], and production financing [12,13,14,15]. Second, research on income effects, such as that by [16], analyzed rainfall insurance’s impact on smallholder incomes in rural India, demonstrating significant household income increases. Subsequent studies indicate that agricultural insurance boosts farm income by enhancing production efficiency via indemnity payments [17] and credit support [18,19,20]. Third, environmental effect studies highlight its role in promoting green production [21], reducing air pollution [22], and improving agricultural carbon productivity [23]. Collectively, most research focuses on short-term food crops at the farm level using singular behavioral or income metrics, leaving the livelihood effects of insurance for long-cycle, high-volatility cash crops insufficiently explored.

The existing literature on agricultural insurance primarily examines three domains. First, investigations into its impact on farmers’ productive behavior. Studies demonstrate that agricultural insurance shapes production factor inputs by modifying income structures [24], risk attitudes [10], factor allocation [11], and production financing [12]. This encompasses technology adoption [12,25], fertilizer and pesticide application [13,26], and adjustments to operational scale [27], among others. Second, research assessing agricultural insurance’s influence on farmer income. Evidence indicates it elevates income levels via economic compensation [17], credit support [18], and enhanced production factors to boost agricultural efficiency [19]. Third, exploration of its spillover effects. Research on environmental impacts includes promoting green production [21], reducing air pollution [22], and enhancing agricultural carbon productivity [23]. Research on food security impacts reveals significant positive effects on grain planting areas [14], crop structure [15], and yield [20]. However, existing policy evaluations, predominantly household-focused, tend to analyze singular behavioral or income dimensions, lacking a comprehensive framework to fully capture impacts on household well-being. Consequently, research on agricultural insurance’s effect on livelihood resilience is critically significant for promoting farmers’ endogenous development momentum, consolidating production and livelihood foundations, and ensuring a smooth transition from poverty alleviation to rural revitalization strategies.

In summary, current research exhibits two unresolved issues. First, the existing literature predominantly focuses on farmers’ economic recovery and stability post-risk events—emphasizing ex-post management. Few studies adopt a livelihood perspective that simultaneously addresses economic concerns and farmers’ capacity to maintain and adjust livelihood strategies, spanning ex-post compensation to ex-ante prevention and long-term development. Second, the mechanisms underlying the livelihood resilience generation remain inadequately examined, while pathways and measures for enhancing farmers’ livelihood resilience require further refinement.

3. Policy Background, Conceptual Explanation, Theoretical Analysis

3.1. Background of the Natural Rubber Insurance Policy

China has developed a comprehensive policy-based agricultural insurance system through an extensive exploration of institutional frameworks, structural optimization, and implementation pathways since the resumption of agricultural insurance operations in 1982. Nationally implemented natural rubber insurance currently comprises two primary types: disaster insurance and price insurance, designed to mitigate losses from risk exposure during coverage periods. Analysis of government policy documents on natural rubber insurance since 2007 (

Table 1. Summary of key natural rubber insurance policies.

Policy Phase	Key Documents
Policy exploration phase (2007–2017)	2007: The Ministry of Finance issued Interim Measures for Central Government Subsidies on Agricultural Insurance Premiums. Hainan Province launched a pilot rubber tree wind disaster insurance program with provincial subsidies.
	2008: Natural Rubber Advantageous Region Layout Plan (2008–2015) first proposed establishing policy-based agricultural insurance and disaster compensation systems.
	2010: Rubber tree wind disaster insurance was included in nationally subsidized categories.
Policy structuring phase (2018–2022)	2018: Hainan Provincial Government implemented the 2018 Hainan Agricultural Insurance Implementation Plan, integrating natural rubber income insurance into policy programs to achieve full price insurance coverage.
Policy refinement phase (2023–present)	2023: Joint notice by the Ministry of Finance, Ministry of Agriculture, and National Financial Regulatory Administration: Implementation Guidelines for Comprehensive Natural Rubber Insurance Policy specified operational rules (insured entities, coverage types, protection levels, and subsidy ratios) for Hainan and Yunnan, covering all growers and enterprises.

) reveals continuous institutional refinements aimed at strengthening natural rubber farmers’ resilience to market fluctuations and natural disasters, ensuring stable and sustainable rubber production.

3.2. Conceptual Explanation

Livelihood resilience refers to the ability of livelihood groups to maintain individual balance or enhance it through intrinsic attributes under external pressures and shocks [28]. It not only emphasizes passive adaptive capacity but also active resistance capacity [29], serving as an important dimension and effective tool for analyzing how individuals cope with uncertainties and sustainable livelihood characteristics. The term ‘resilience’ traces its origins to the Latin word “resilio.” It was first introduced into academic research by ecologist Holling ([30]) to describe the ability of ecosystems to maintain core functions and achieve self-repair after experiencing disturbances. As the scope of research expanded, development economists Chambers et al. (1994 [31]) first proposed the concept of “livelihood resilience,” defining it as the foundational survival capacity demonstrated by residents when facing environmental pressures, representing the most basic form of productivity. This study draws on Speranza et al. ([32]) to select relevant indicators for measuring farmers’ livelihood resilience. Among the three dimensions of livelihood resilience, buffer capacity refers to the ability of farmers to utilize existing resources to mitigate risks and maintain livelihood stability when facing external shocks; self-organization capacity refers to the ability of farmers to spontaneously organize themselves when responding to risk shocks, leveraging social networks and institutional advantages to collectively mitigate risks [33]; learning capacity refers to farmers’ ability to acquire knowledge and skills, as well as their ability to convert knowledge into livelihood activities [32].

3.3. Theoretical Analysis

3.3.1. Analysis of the Impact of Agricultural Insurance on Farmers’ Livelihood Resilience

As a key risk management instrument, agricultural insurance enhances farmers’ livelihood resilience across multiple dimensions. According to risk-sharing theory (Arrow, 1963) [34], it transfers individual risks via the law of large numbers, mitigating natural and market risks [35], thereby establishing foundations for enhanced resilience. First, at the risk response level, insurance compensation and credit support rapidly replenish production funds during shocks [36,37], preventing asset liquidation-induced poverty cycles and stabilizing household economies [38]. Agricultural insurance further bolsters financial capital through direct and indirect income effects [24]. Per Prospect Theory [39], it reduces risk aversion by stabilizing expected income, not only promoting agricultural labor allocation [40] but also improving mental health [41], thus optimizing human capital. Additionally, farmers can release precautionary savings for productive investment [42], enhancing efficiency and material capital accumulation. Second, at the social connectivity level, agricultural insurance mechanisms partially substitute informal risk-sharing channels such as kinship borrowing, alleviating social network burdens and preventing social capital depletion [43]. Concurrently, policyholder experience-sharing fosters learning networks [44], broadening social connections while strengthening internal bonds. Third, at the cognitive and development level, stable expected income significantly boosts educational investment [45]. Insurance promotion generates knowledge spillovers: participants access weather alerts, market intelligence [46], risk management training, and informational support [47], heightening risk awareness and management capabilities [48]. Insurer-provided technical training further improves comprehension and adaptability [49]. Based on this analysis, we propose the following research hypotheses.

H1: 

Agricultural insurance has a significant positive impact on farmers’ livelihood resilience.

3.3.2. Mechanism Analysis of Agricultural Insurance on Farmers’ Livelihood Resilience

Credit functions as a vital funding source for farmers’ development, providing essential financial support for increased production inputs or cash flow relief [50]. Nonetheless, farmers face difficulties obtaining loans from financial institutions because of low solvency [51]. Agricultural insurance enhances credit access through innovative bank–insurance linkages that serve as collateral, reducing traditional collateral requirements and alleviating rural credit rationing [52]. Furthermore, insured farmers demonstrate stronger risk resilience, deemed more risk-aware and responsible, enhancing their creditworthiness [53]. Insurance coverage may also prompt lenders to lower interest rates. Such credit access enables farmers to borrow against income shocks, maintaining basic needs and production recovery [54], thereby reducing livelihood disruption risks and strengthening buffer capacity. Credit access further supports production investments and technical training [55,56,57], enhancing learning capabilities. Based on this analysis, we propose the following research hypothesis.

H2: 

Enhancing loan accessibility is the primary mechanism through which agricultural insurance influences farmers’ livelihood resilience.

In traditional agriculture, farmers frequently hesitate to adopt new agricultural technologies because of uncertainties, capital requirements, and limited risk management capacity. However, when agricultural insurance provides risk protection, farmers become more willing to adopt new, potentially higher yielding yet riskier agricultural technologies as production risks are partially covered [58]. Furthermore, insurance facilitates credit access for policyholders, alleviating financial constraints on technology adoption and enhancing implementation capacity [52]. Additionally, the risk protection mechanism improves farmers’ income expectations, incentivizing them to invest in new technologies for greater long-term returns and strengthening adoption motivation [4]. Advanced agricultural technologies typically boost production efficiency and reduce costs, thereby increasing farm profits [59] and enhancing livelihood stability. The adoption process necessitates enhanced knowledge and skills through learning, strengthening future risk-coping abilities, and providing crucial safeguards for livelihood development. Based on this analysis, we propose the following research hypothesis.

H3: 

Promoting the adoption of agricultural production technologies is the primary pathway through which agricultural insurance influences farmers’ livelihood resilience.

Agricultural insurance’s risk mitigation function reduces farmers’ risk apprehension, thereby stabilizing income volatility [60]. This enhances agricultural engagement security, a crucial psychological foundation for heightened production initiative. Insurance coverage reduces precautionary savings through risk protection [61], releasing disposable capital for productive investment and strengthening production incentives [42]. When farmers exhibit high production enthusiasm, they optimize existing resources to improve utilization efficiency and productive investments, thereby increasing income [62] and enhancing buffer capacity. Concurrently, motivated farmers invest in human capital through agricultural training and skill acquisition [63], enhancing household labor capabilities. Heightened production initiatives not only generate short-term income gains but, more significantly, achieve livelihood improvements in efficiency gains, enhanced risk-coping capacity, and long-term development [64], thereby bolstering livelihood resilience. Based on this analysis, we propose the following research hypothesis.

H4: 

Enhancing production initiative is a key pathway through which agricultural insurance impacts farmers’ livelihood resilience.

Based on H1–H4, this study develops a conceptual framework illustrating agricultural insurance’s impact mechanisms on farmers’ livelihood resilience (see Figure 1).

4. Materials and Methods

4.1. Data Sources

Data presented in this paper were collected through field surveys conducted by the research team between December 2024 and February 2025, targeting natural rubber farmers in Yunnan and Hainan provinces, China. The surveys were administered through one-on-one interviews. Hainan and Yunnan are major natural rubber-producing regions in China, with their rubber planting areas and yields occupying an important position nationwide. Conducting research in these regions ensures the representativeness and typicality of the research samples. Additionally, natural rubber insurance in the surveyed areas has been implemented for a long time with a high coverage rate. To ensure scientific and representative survey data, a stratified random sampling method was adopted for sample selection. The selection of survey samples was as follows (see

Table 2. Sample distribution.

Province	City (County)	Number of Villages	Number of Questionnaires	Sample Proportion	Province	City (County)	Number of Villages	Number of Questionnaires	Sample Proportion
Hainan	Chengmai	7	110	9.20%	Yunnan	Mengla	11	253	21.15%
	Danzhou	8	141	11.79%		Jinghong	13	212	17.73%
	Baisha	7	120	10.03%		Gengma	2	57	4.77%
	Qionghai	2	60	5.02%		Mojiang	1	31	2.59%
	Qiongzhong	7	120	10.03%		Jinping	1	31	2.59%
	Ledong	1	30	2.51%		Jiangcheng	1	31	2.59%

): first, 6 cities (counties) were selected from the natural rubber production areas in Hainan and Yunnan Provinces, respectively, namely Chengmai County, Danzhou City, Baisha County, Qionghai City, Qiongzhong County, Ledong County in Hainan, Mengla County, Jinghong City, Gengma County, Mojiang County, Jinping County, and Jiangcheng County in Yunnan. The research team randomly selected 1 to 3 sample towns in each city (county), totaling 33 towns. Second, 1 to 4 natural villages were randomly selected from each sample town. Finally, the research team randomly interviewed 25 to 45 rubber farmers in each sample village, and team members filled out the questionnaires based on the interview content. The interviewees were family members aged 18–78 who were familiar with their family’s basic situation and could communicate effectively. The survey content mainly included the characteristics of the interviewees’ family members, such as gender, age, health status, and education level; the family’s buffer capacity, self-organization capacity, learning capacity, as well as the policy implementation, infrastructure, and natural conditions of the village where they lived. A total of 1241 questionnaires were distributed. After excluding invalid questionnaires with outliers and missing values, 1196 valid questionnaires were finally obtained (96.4%). This study has a limitation in the form of cross-sectional data. To mitigate the endogeneity problem caused by omitted variables, this study will use the PSM model and instrumental variable method to identify causal relationships more robustly.

Among surveyed households, the average household members were 4.23 persons; mean household head age was 52.96 years; 82.27% of household heads were male versus 17.73% female; only 13.54% attained high school education or above, while 86.46% possessed junior high school education or lower; 45.74% had purchased agricultural insurance compared with 54.26% non-purchasers. Comparisons with statistical yearbook data, including household size, head age, and education level, revealed minor deviations, indicating strong representativeness of the survey sample.

4.2. Construction of a Household Livelihood Resilience Indicator System

The measurement of household livelihood resilience is modeled after the indicator system proposed by [32] and incorporates insights from Quandt ([65]), who emphasizes that constructing household-level resilience metrics should integrate both subjective and objective indicators. This study operationalizes livelihood resilience through a multi-indicator framework spanning three dimensions—buffer capacity, self-organization capacity, and learning capacity—thereby establishing a comprehensive evaluation system. The dimensional definitions are as follows:

4.2.1. Buffer Capacity

Based on the buffer capacity’s conceptual definition, this study scientifically validates using livelihood capital indicators as measurement standards, where greater capital possession corresponds to stronger buffer capacity. Livelihood capital encompasses human, natural, physical, financial, and social capital. Labor capacity—human capital’s core element—directly influences livelihood status through quantitative proportion and health status, leading to “labor force proportion” and “per capita health level” as the measurement standard. For natural capital, land being foundational for survival and development necessitates “per capita arable land area” representation. Financial capital assessment combines “total household income” (foundational livelihood support) with “household debt amount” (financial health indicator), reflecting resource status. Material capital—the infrastructure for production and daily life—is quantified via “per capita residential area” and “number of production/transportation tools.” Given the conceptual overlap between social capital and self-organization capacity regarding social networks, this paper employs social capital indicators for self-organization capacity measurement without further elaboration.

4.2.2. Self-Organization Capacity

Based on self-organization capacity’s conceptual definition, this study selects social participation, social trust, social networks, and social connectivity as specific indicators: farmers’ social networks are measured through “the total number of village officials, government officials, and public institution staff among relatives/friends” and “mobile phone contacts”; social participation is quantified by “frequency of participation in village activities”; social trust is represented by “trust in village officials” and “trust in neighbors”; while social connectivity reflects villagers’ social norms and reciprocity through “level of village cohesion” and “frequency of mutual assistance”.

4.2.3. Learning Capacity

Based on learning capacity’s conceptual definition, this study employs “average education level per capita” to assess farmers’ information processing capabilities and knowledge reserves; “number of times receiving rubber technology extension services” evaluates technical learning and application capacity; “internet learning time” measures self-directed learning and information acquisition abilities; “whether exchanging production techniques with village officials” and “whether exchanging production techniques with other farmers” gauge access to authoritative information and social interaction capabilities; while “channels for obtaining information” assess information integration capabilities and learning flexibility.

Specific indicators are shown in

Table A1. Construction of a resilience index system for farmers’ livelihoods.

Variable	Description	Measurement	Attribute	Weighting
Buffer capacity	Per capita health level	Disability = 0; Unhealthy = 1; Generally healthy = 2; Completely healthy = 3	+	0.002
	Labor force share	Labor force size/household population	+	0.006
	Per capita arable land area	Arable land area/household population (mu)	+	0.047
	Per capita residential area	Residential area/household population (square meters)	+	0.026
	Number of production and transportation tools	Number of production and transportation tools in normal use (vehicles or units), such as cars, tractors, etc.	+	0.018
	Annual total household income	2024 household total income (ten thousand yuan)	+	0.051
	Household debt amount	2024 household debt amount (ten thousand yuan)	−	0.001
Self-organization capacity	Number of relatives and friends working in village committees, government departments, and public institutions	Number of relatives and friends working in village committees, government departments, and public institutions	+	0.155
	Mobile phone contacts	Mobile phone contacts (number)	+	0.084
	Frequency of participation in collective activities	Rated from low to high on a scale of 1 to 5: Very low = 1; Low = 2; Average = 3; High = 4; Very high = 5	+	0.015
	Trust in village officials	Rated from low to high on a scale of 1 to 5: Very low = 1; Low = 2; Average = 3; High = 4; Very high = 5	+	0.005
	Trust in neighbors	Rated from low to high on a scale of 1 to 5: Very low = 1; Low = 2; Average = 3; High = 4; Very high = 5	+	0.003
	Frequency of mutual assistance	Rated from low to high on a scale of 1 to 5: Very low = 1; Low = 2; Average = 3; High = 4; Very high = 5	+	0.014
	Village unity	Rated from low to high on a scale of 1 to 5: Very low = 1; Low = 2; Average = 3; High = 4; Very high = 5	+	0.004
Learning capacity	Average education level	Total years of education per household/household population	+	0.007
	Internet learning time	Daily internet learning time (hours)	+	0.222
	Information acquisition channels	Total number of information acquisition channels	+	0.113
	Natural rubber technology promotion sessions	Number of natural rubber technology promotion sessions attended	+	0.117
	Whether exchanging production techniques with other farmers	Yes = 1; No = 0	+	0.056
	Whether exchanging production techniques with village officials	Yes = 1; No = 0	+	0.051

.

4.3. Variable Selection and Descriptive Statistics

4.3.1. Dependent Variable: Household Livelihood Resilience

Based on the established indicator system, this study measures farmers’ livelihood resilience using the entropy weight method—an objective weighting approach that determines indicator weights based on the information quantity provided by each indicator’s entropy value. This method minimizes human bias in results; thus, we apply it to calculate weighted evaluation indices and derive a composite livelihood resilience score.

4.3.2. Explanatory Variable: Natural Rubber Insurance

This study operationalizes the variable through the survey question “Has the household purchased natural rubber insurance?”. Current comprehensive coverage includes rubber tree materialized cost insurance, full cost insurance, and natural rubber income insurance. Farmers purchasing any type are coded as 1; otherwise, 0.

4.3.3. Covariates

Drawing on established research regarding agricultural insurance and livelihood resilience determinants, this study selects household characteristics (total household population, average household age, and presence of Communist Party membership) and location characteristics (distance to village committee, distance to logistics point, and provincial location in Yunnan) as covariates for propensity score matching.

4.3.4. Mediating Variables: Credit Availability, Adoption of Agricultural Production Technologies, and Production Initiative

Credit availability is coded as 1 if farmers obtain credit, 0 otherwise; agricultural technology adoption is coded as 1 for adopters, 0 for non-adopters; production initiative is measured through rubber-tapping frequency categorized into five ordinal levels: 1 (abnormal tapping: rarely taps), 2 (extremely low-frequency: ≤0.25 taps/day), 3 (low-frequency: 0.25–0.5 taps/day), 4 (regular: 0.5–0.7 taps/day), and 5 (high-frequency: >0.7 taps/day).

4.3.5. Instrumental Variable

The instrumental variable indicates whether agricultural insurance was promoted by the government or village cadres, coded as 1 if promoted and 0 otherwise.

The descriptive statistics of the variables are shown in

Table A2. Descriptive statistics.

Variable	Variable Name	Variable Definition	Min	Max	Mean	Standard Deviation
Dependent variable	Farmers’ livelihood resilience	Sum of buffer capacity, self-organization capacity, and learning capacity	0.024	0.399	0.114	0.046
	Buffer capacity	Comprehensive value calculated using the entropy weight method	0.007	0.023	0.023	0.009
	Self-organization capacity	Comprehensive value calculated using the entropy weight method	0.005	0.184	0.040	0.016
	Learning capacity	Comprehensive value calculated using the entropy weight method	0	0.266	0.052	0.038
Explanatory variable	Natural rubber insurance	Whether natural rubber insurance was purchased: Yes = 1; No = 0	0	1	0.457	0.498
Mediating variable	Credit availability	Whether borrowing occurred in 2024: Yes = 1; No = 0	0	1	0.171	0.376
	Adoption of agricultural production technology	Whether agricultural production technology will be applied in 2024: 1 = Yes; 0 = No	0	1	0.188	0.399
	Production initiative	Assigned values from low to high 1–5: Abnormal tapping = 1; Extremely low-frequency tapping = 2; Low-frequency tapping = 3; Regular tapping = 4; High-frequency tapping = 5	1	5	3.751	0.721
Instrumental variable	Promoted agricultural insurance	Whether agricultural insurance was promoted by government/village cadres: Yes = 1; No = 0	0	1	0.508	05
Covariate	Household population	Total number of household members	1	8	4.401	1.463
	Average household age	Total age of household members/Number of household members (years)	17	76	38.084	10.64
	Whether there are Party members in the household	Whether there are Party members in the household: Yes = 1; No = 0	0	1	0.222	0.416
	Distance to the village committee	Distance from home to the village committee (km)	0.01	30	3.708	5.689
	Distance to the logistics station	Distance from home to logistics station (km)	0.01	40	5.091	6.951
	Regional dummy variable	Hainan Province = 1; Yunnan Province = 0	0	1	0.514	0.500

.

Comparative results between natural rubber insurance purchasers and non-purchasers regarding livelihood resilience, credit availability, agricultural technology adoption, and production initiative (

Table 3. Purchasers vs. non-purchasers of natural rubber insurance.

Variable	Mean		Standard Deviation		Minimum		Maximum
	Participated	Non-Participated	Participated	Non-Participated	Participated	Non-Participated	Participated	Non-Participated
Farmers’ livelihood resilience	0.115	0.114	0.048	0.045	0.023	0.026	0.399	0.321
Credit availability	0.161	0.179	0.368	0.383	0	0	1	1
Adoption of agricultural production technology	0.194	0.183	0.409	0.391	0	0	1	1
Production initiative	3.872	3.650	0.610	0.789	1	1	5	5

) demonstrate purchasers’ slightly higher average livelihood resilience (0.115 vs. 0.114), alongside significantly greater proportions in credit access, technology adoption, and production initiative, indicating insurance’s positive effect on resilience enhancement and synergistic relationships with these activities.

4.4. Model Construction

4.4.1. Propensity Score Matching Method

To ascertain whether livelihood resilience differences between the treatment group (natural rubber insurance purchasers) and the control group (non-purchasers) stem from insurance intervention, this study employs propensity score matching (PSM). PSM constructs a counterfactual framework by identifying comparable control units, thus approximating randomized conditions in observational data and minimizing sample bias. When multiple observed characteristics exist, direct matching proves challenging. PSM resolves this by condensing multidimensional variables into a scalar propensity score. Let ${\mathrm{Y}}_1$ denote the treatment group’s livelihood resilience, ${\mathrm{Y}}_0$ be the control group’s resilience, and Treat be the intervention variable. The average treatment effect on the treated (ATT) of natural rubber insurance is then defined as:

$$\mathrm{A}\mathrm{T}\mathrm{T}={\mathrm{E}}_{\left(\mathrm{P}\right(\mathrm{X}\left)\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\right)}\left\{\mathrm{E}\right[{\mathrm{Y}}_1|\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}=1,\mathrm{P}(\mathrm{X}\left)\right]-\mathrm{E}\left[{\mathrm{Y}}_0\right|\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}=0,\mathrm{P}\left(\mathrm{X}\right)\left]\right\}$$

(1)

The PSM method typically involves selecting covariates, estimating propensity scores, choosing matching algorithms, assessing matching quality, quantifying treatment effects, and performing sensitivity analyses. Propensity scores are commonly estimated via Logit or Probit models. In this study, we utilize the Logit model to derive linearized propensity scores. The formula is expressed as follows:

$$\mathrm{P}\left(\mathrm{z}\right)=\mathrm{P}\mathrm{r}[\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{r}=1|\mathrm{X}]=\mathrm{E}[\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{r}\left|\mathrm{X}\right]$$

(2)

In Equation (2), P denotes the fitted conditional probability of farmers purchasing natural rubber insurance, while X signifies the covariates.

Three matching methods were employed: caliper-based k-nearest neighbor matching (k = 6, caliper = 0.01), radius matching, and kernel matching. These approaches prioritize matching quality versus quantity differently, with no consensus on superiority. Their distinctions mainly manifest in estimation consistency.

4.4.2. Mediator Analysis Method

Mediation analysis examines causal pathways between variables, advancing beyond conventional analyses that focus exclusively on direct independent–dependent variable relationships. This approach generates deeper insights into the underlying mechanisms. To clarify how natural rubber insurance affects farmers’ livelihood resilience, we adopt Baron et al.’s (1986) [66] methodology to develop mediation regression equations modeling key variable relationships.

$$\mathrm{Y}={\mathrm{a}}_0+{\mathrm{a}}_1\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}+\sum {\mathrm{a}}_2\mathrm{X}+\epsilon$$

(3)

$$\mathrm{M}={\beta }_0+{\beta }_1\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}+\sum {\beta }_2\mathrm{X}+\epsilon$$

(4)

$$\mathrm{Y}={\gamma }_0+{\gamma }_1\mathrm{T}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}+{\gamma }_2\mathrm{M}+\sum {\gamma }_3\mathrm{M}+\epsilon$$

(5)

In these equations, Y denotes household livelihood resilience, M denotes the mediating variable, and ε denotes the random disturbance term. Equation (3) captures the total effect of natural rubber insurance on livelihood resilience. Equation (4) estimates its effect on the mediator, while the coefficient in Equation (5) measures the mediator’s direct effect on resilience. Substituting (4) into (5) yields the indirect effect (${\gamma }_2{\beta }_1$)—natural rubber insurance’s impact on resilience mediated through M.

5. Empirical Results

5.1. Common Support Domain and PSM Matching Results Analysis

Using regression Equation (1), we compute agricultural insurance purchase propensity scores for each farmer as the matching foundation. To validate PSM estimates, both common support and conditional independence assumptions require testing. Given space limitations, only kernel matching results are presented in an exemplary manner (Figure 2). Substantial overlap exists between treatment and control group propensity scores, with most observations within the common support range—confirming high matching quality and fulfillment of the common support assumption.

As shown in Figure 3, standardized covariate deviations between treatment and control groups are below 10% after matching, indicating effective elimination of pre-matching differences. Moreover, kernel matching retained 1176 matched samples with only 20 exclusions (

Table 4. PSM matching results.

	Unmatched Samples	Matched Samples	Total
Control group	17	632	649
Treatment group	3	544	547
Total	20	1176	1196

), confirming robust matching quality.

5.2. Balance Test

To ensure matching reliability, covariate balance was verified—meaning no significant systematic differences existed between control and treatment groups post-matching, except in livelihood resilience. Balance test results (

Table 5. Results of explanatory variable balance tests before and after propensity score matching.

Matching Method	Pseudo R2	LR Chi-Square	Standardized Bias (%)
Before matching	0.194	320.25	115.7
Kernel matching	0.002	2.36	9.3
Radius matching	0.003	4.75	13.3
Caliper-based k-NN matching (k = 6, caliper = 0.01)	0.004	5.92	14.9

) indicate that after sample matching, the standardized bias of explanatory variables decreased from 115.7% to 9.3–14.9%, with total bias reduced significantly to below the 20% threshold. Pseudo R² declined from 0.194 pre-matching to 0.002–0.004 post-matching, while the LR statistics dropped from 320.48 to 2.36–5.92. These results confirm that PSM effectively reduced explanatory variable distribution differences and eliminated self-selection bias.

5.3. Impact Effect Calculation

We calculate the average treatment effect (ATT) of natural rubber insurance on farmers’ livelihood resilience, buffer capacity, self-organization capacity, and learning capacity. The results (

Table 6. Average treatment effect (ATT) of propensity score matching.

	Farmers’ Livelihood Resilience	Buffer Capacity	Self-Organization Capacity	Learning Capacity
Kernel matching	0.011 *** (0.003)	0.001 ** (0.001)	0.003 ** (0.001)	0.006 ** (0.003)
Radius matching	0.010 *** (0.004)	0.001 ** (0.001)	0.003 ** (0.001)	0.006 ** (0.003)
Caliper-based k-NN matching (k = 6 caliper = 0.01)	0.009 *** (0.004)	0.001 ** (0.000)	0.003 ** (0.001)	0.005 ** (0.003)
Average value	0.010	0.001	0.003	0.006

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively. The numbers in parentheses are standard errors.

) demonstrate consistent estimates across three matching methods, confirming data robustness. For empirical analysis, the arithmetic mean is selected to represent the impact effect.

Following propensity score matching counterfactual estimation, we obtained the average treatment effect (ATT) for the treatment group. Table 6 results demonstrate that, after controlling for household and location characteristics, natural rubber insurance purchase exerts a significantly positive impact (1% level) on livelihood resilience, with a net effect of 0.010. This indicates that after addressing selection bias, insured farmers show 0.01 units higher livelihood resilience on average than non-insured farmers, confirming H1. Two factors may explain this observation. First, the livelihood resilience scores calculated via the entropy weight method are relative values with limited inter-household variation, leading to an attenuated linear relationship. Second, as a multidimensional, complex, and relatively stable composite indicator, livelihood resilience inherently resists substantial short-term fluctuations. Although the absolute coefficient value is small, according to Hou et al. ([67])’s economic interpretation measured by livelihood resilience standard deviation, the treatment effect is 0.217 standard deviations. This indicates agricultural insurance improves farmers’ livelihood resilience by approximately 21.7% in standard deviation change, demonstrating it as an identifiable factor enhancing resilience. This represents a practically meaningful small-to-medium effect size ([68]). Specifically, insured farmers climb 21.7 percentile points within the overall farmer population’s livelihood resilience ranking, demonstrating agricultural insurance’s demonstrable policy value in bolstering risk resilience and guarding against poverty. Additionally, insurance significantly enhances buffer capacity (ATT = 0.001), self-organization capacity (ATT = 0.003), and learning capacity (ATT = 0.006)—translating to 0.1%, 0.3%, and 0.6% improvements, respectively. Consistent results across matching methods reinforce robustness.

5.4. Robustness Tests

5.4.1. Alternative Models

Robustness was tested by estimating ordinary least squares (OLS) regression for natural rubber insurance’s impact on livelihood resilience. The results in

Table 7. Robustness tests using alternative models and measurement methods.

Variable	Farmers’ Livelihood Resilience
	Alternative Model (1)	Alternative Measurement (2)
Agricultural insurance	0.011 *** (0.003)	0.011 ** (0.004)
	1196
R2/Pseudo R2	0.180	0.188

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively. The numbers in parentheses are standard errors.

(1) show largely consistent significance and direction with our core findings, confirming robustness. However, when compared with radius matching results, the OLS regression may introduce bias in assessing livelihood resilience and learning capacity improvements because of unaddressed sample selection issues.

5.4.2. Alternative Measurement Methods

For livelihood resilience measurement, this study adopted the entropy weight method to calculate composite scores. To avoid methodological bias, we re-measured resilience using equal-weight assignment. The results in Table 7(2) remain largely consistent with matching estimates, confirming that agricultural insurance enhances farmers’ livelihood resilience.

5.4.3. Instrumental Variable Approach

All sampled farmers reside in insurance-available areas, potentially weakening the counterfactual inference basis of PSM. To address endogeneity from unobservable factors, this study selects whether agricultural insurance was promoted by the government or village cadres as the instrumental variable. This choice satisfies two conditions: promotion enhances farmers’ insurance awareness and trust, thereby increasing participation; yet promotion itself has no direct link with livelihood resilience, meeting exogeneity requirements. The model results in

Table 8. Robustness tests using the instrumental variable approach.

Variable	First Stage	Second Stage
	Agricultural Insurance	Livelihood Resilience
Whether agricultural insurance was promoted by government/village cadres	0.251 *** (0.026)	0.011 ** (0.004)
Agricultural insurance	0.180	0.188
Constant term	0.321 ***	0.078 ***
Covariates	Controlled	Controlled
Kleibergen–Paap rk LM	87.24 ***
Cragg–Donald Wald F	103.37
Sample size	1196
R2/Pseudo R2	0.185	0.113

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively. The numbers in parentheses are standard errors.

show a Cragg–Donald Wald F-statistic of 103.37, exceeding the 16.38 threshold and rejecting weak instrument concerns. The significant Kleibergen–Paap rk LM statistic (1% level) confirms instrument validity. The first stage regression indicates a significant correlation between the instrument and explanatory variable, while the second stage shows a 0.388 coefficient for the core variable (significant at 1%), maintaining consistent direction and significance after controlling for selection bias.

5.5. Mechanism Exploration

While prior analysis established whether natural rubber insurance improves farmers’ livelihood resilience and its impact magnitude, how it achieves this improvement requires examination. This section performs regression analysis on Equations (3)–(5), employing Sobel and Bootstrap tests. Columns (1)–(2) use credit availability as the mediating variable; Columns (3)–(4) agricultural production technology adoption; Columns (5)–(6) production initiative. The results are presented in

Table 9. Test results of the mechanism.

	(1)	(2)	(3)	(4)	(5)	(6)
Variable	Credit availability	Farmers’ livelihood resilience	Adoption of Agricultural Production Technologies	Farmers’ livelihood resilience	Production initiative	Farmers’ livelihood resilience
Natural rubber insurance	0.053 ** (0.024)	0.010 *** (0.003)	0.07 *** (0.003)	0.010 *** (0.003)	0.174 *** (0.05)	0.011 *** (0.003)
Credit availability		0.016 *** (0.003)
Adoption of agricultural production technologies				0.009 *** (0.003)
Production initiative						0.004 ** (0.002)
Covariates	Controlled
Sobel test z-statistic		1.980 **		1.928 *		2.011 **
95% Confidence interval (CI)		(0.000, 0.002)		(0.000, 0.001)		(0.000, 0.001
Matched observations (N)	1196

Note: *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. The numbers in parentheses are standard errors.

.

First, Columns (1), (3), and (5) of Table 9 demonstrate significantly positive coefficients for natural rubber insurance (5% and 1% levels), confirming its effectiveness in promoting farmers’ credit accessibility, adoption of agricultural technologies, and production initiative. Second, loan accessibility, technology adoption, and production initiatives significantly enhance livelihood resilience through mediating pathways, as manifested by significantly positive coefficients of the natural rubber insurance variable in Columns (2), (4), and (6) of Table 9. These results validate hypotheses H2, H3, and H4.

Building on these regression results, we performed Sobel and Bootstrap tests to examine the mediating effects of credit accessibility, agricultural technology adoption, and production initiative. Sobel tests yielded significant z-statistics at the 5% level, while Bootstrap tests showed confidence intervals for livelihood resilience excluding zero—confirming significant mediation effects.

5.6. Heterogeneity Analysis

The existing literature confirms that natural rubber insurance enhances farmers’ livelihood resilience. To investigate nuanced dimensions of this impact, we analyze differential effects across two dimensions: poverty-stricken household status and natural rubber cultivation scale. The detailed results appear in

Table 10. Heterogeneity results of the impact of natural rubber insurance on farmers’ livelihood resilience.

Variable	Farmers’ Livelihood Resilience
	Coefficient (1)	Standard Error	Coefficient (2)	Standard Error
Natural rubber insurance	0.009 ***	0.003	0.006 **	0.003
Natural rubber insurance × whether a household has escaped poverty	0.009 **	0.004
Natural rubber insurance × natural rubber planting scale			0.135 ***	0.004
Control variables	Controlled		Controlled
Sample size	1196		1196
R2	0.183		0.187

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively.

.

5.6.1. Whether They Are Poverty-Stricken Households

Poverty-stricken households exhibit weaker economic foundations, resource acquisition capacity, and risk resistance compared with ordinary households. Natural rubber insurance may affect them differently because of greater reliance on agricultural income, potentially amplifying insurance’s buffer effect on their livelihood resilience. However, premium affordability constraints may lower enrollment rates among poverty-alleviated households, potentially diminishing actual policy impacts. Interaction term analysis reveals (Table 10(1)) a significantly positive coefficient (5% level) for the poverty alleviation × insurance interaction, indicating greater marginal utility of insurance on livelihood resilience in poverty-alleviated households.

5.6.2. Natural Rubber Planting Scale

Households of different scales face distinct risks and coping capacities. Large-scale operations possess greater resources (e.g., capital and technology), enhancing disaster resilience where insurance serves supplementary purposes. Conversely, small-scale households rely more critically on insurance, as single risk events may cause irreparable livelihood damage. Differential impacts of price fluctuations and disasters also yield varied insurance roles. Following Dennis et al. ([69]), we classify households above the mean cultivation area (≥35 acres) as large-scale and those below (<35 acres) as small-scale. Table 10(2) shows a significantly positive interaction term (1% level) between cultivation scale × insurance, indicating greater marginal utility of insurance on livelihood resilience among large-scale households.

6. Discussion

This study analyzes agricultural insurance’s impact on farmers’ livelihood resilience. The results show a statistically significant positive effect (0.01, p < 0.01), consistent with prior qualitative findings by Pratiwi et al. (2018 [70]). Notably, while Pratiwi et al. documented resilience improvements through income-based qualitative assessment, they did not quantify the effect size. Our multidimensional resilience framework empirically establishes this 0.01-unit insurance effect at 1% significance. This stems from insurance enhancing buffer capacity through indemnities and credit access, strengthening self-organization via cooperation and information sharing, and boosting learning capacity by reducing risk aversion and facilitating knowledge transfer—collectively improving resilience. Therefore, optimizing agricultural insurance policies is essential to maximize their protective function.

Furthermore, agricultural insurance enhances livelihood resilience primarily through credit accessibility, agricultural technology adoption, and production incentives. Credit accessibility functions as a mechanism by improving farmers’ credit ratings and enabling loan collateralization via bank–insurance partnerships, thus mitigating credit constraints [52]. Reduced credit constraints bolster risk-coping capacity for livelihood maintenance and production recovery [57]. Agricultural technology adoption operates through insurance’s risk reduction and financing facilitation, lowering barriers to new technologies and increasing adoption willingness [58]. Technological application enhances productivity and income [59], strengthening resilience. Production initiatives arise as insurance stabilizes income expectations, reduces risk aversion, and facilitates productive investments and technology adoption [42]. Enhanced initiatives improve resource utilization efficiency, drive human capital investment, and foster livelihood improvements [64]. Therefore, governments should strengthen the integration of agricultural insurance with credit policies and technology extension to maximize synergies.

Finally, agricultural insurance demonstrates greater marginal utility on livelihood resilience for poverty-alleviated households and large-scale farmers. For poverty-alleviated households, high-subsidy policies lower participation barriers, enabling low-cost risk protection [71]. Greater reliance on agricultural income also allows insurance to buffer risk shocks more effectively, reducing income volatility and enhancing resilience. For large-scale farmers, heightened marginal utility presumably originates a lot from higher input costs, extended initial investment cycles, and elevated risk exposure. Insurance covers larger losses while their stronger risk management initiative—including better utilization of insurers’ risk information and prompt implementation of scientific risk measures—reduces moral hazard [72]. Consequently, governments should develop differentiated insurance policies.

This study has limitations: (1) Findings based on natural rubber growers in Hainan and Yunnan may have limited generalizability to other crops or regions with distinct socioeconomic conditions. Future research should expand to encompass diverse crops and geographic areas to verify robustness. (2) Constrained by cross-sectional data, we cannot capture long-term dynamics of insurance impacts on livelihood resilience. Future work should employ panel tracking data to examine temporal policy effects. (3) The limited value range of livelihood resilience indices constructed via the entropy weight method may yield low absolute linear correlation coefficients with other variables, despite high statistical significance. Future research could develop comprehensive indicators capturing all dimensions or explore more complex functional forms to further validate this relationship.

7. Conclusions and Recommendations

This study focuses on natural rubber insurance, utilizing field survey data from 1196 rubber-growing households across twelve county-level divisions (three cities and nine counties) of China’s Hainan and Yunnan provinces. Through propensity score matching, we assess agricultural insurance’s impact on farmers’ livelihood resilience and further examine its mechanisms and heterogeneity. The key findings indicate: (1) agricultural insurance significantly enhances livelihood resilience, buffer capacity, self-organization capacity, and learning capacity; (2) credit accessibility, agricultural technology adoption, and production incentives serve as effective mechanisms for resilience improvement; (3) heterogeneity analysis reveals greater marginal effects among poverty-alleviated households and large-scale farmers, who derive more substantial benefits from insurance dividends compared with general and small-scale households.

Based on these findings, this study proposes the following recommendations: First, optimize and expand agricultural insurance policies. Policymakers should develop multi-tiered, diversified, region-specific, and differentiated insurance products. Streamline claim settlement mechanisms to reduce processing time and improve efficiency. Maintain and refine premium subsidies with higher rates for lifted-out-of-poverty and small-scale farmers to lower participation costs. Concurrently, enhance farmers’ awareness and engagement through social media, training programs, and village assemblies. Second, strengthen policy synergies. Establish an insurance-anchored synergy system integrating credit support and agricultural technology extension to amplify incentives for production initiative and learning capacity. Third, implement robust monitoring mechanisms. Governments must track real-time metrics—participation rates, claim settlement duration, and farmers’ satisfaction—while providing regular feedback to ensure effective risk protection. Crucially, to mitigate moral hazard and subsidy over-reliance, establish risk management standards clarifying farmers’ post-enrollment responsibilities, ensuring policy sustainability and effectiveness.