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Research on the impact of insurance policies on the welfare effects of pork producers and consumers: Evidence from China

  • Jing Zhao , Xiaoyang Li EMAIL logo and Chengxu Zhou
Published/Copyright: November 3, 2025
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Open Agriculture
From the journal Open Agriculture Volume 10 Issue 1

Abstract

This study aims to provide a theoretical basis and empirical support for optimizing hog insurance policies, thereby enhancing the welfare levels of both producers and consumers. Based on the concept of compensatory variables, this article develops a model to measure the welfare effects of the hog industry. It analyzes the impact of insurance policies on producer welfare and consumer welfare effects in hog production and marketing balance zones based on data from China’s Production and Sales Balance Area from 2010 to 2022. It is found that the net return of hog production in most provinces in the balance of production and marketing area is positive, and the total welfare effect is significantly improved; the implementation of hog insurance policy promotes the producer welfare effect and the total welfare effect, and there is an inconsistency in the trend of changes in hog insurance and consumer welfare effects; the pig insurance policy in provinces with large pig production and consumption has a relatively weak impact on improving production welfare benefits. Based on the findings, several practical policy recommendations are proposed, including differentiated insurance subsidy adjustments by province, dynamic optimization of welfare enhancement strategies, innovation in pork consumption-oriented insurance products, enhanced data collection, and policy monitoring.

Keywords: pork prices; hog production prices; hog insurance policies; welfare effects

1 Introduction

The construction of a diversified food supply system is a powerful guarantee for the welfare of agricultural and livestock producers and consumers. It requires the government to expand the effective supply, not only to safeguard the interests of the main body of food supply by optimizing the structure of food production and the sustainable supply of food and other connotations, but also to meet the growing consumer demand of the population at the same time [1]. This suggests that focusing on improving producer welfare and consumer welfare in the process of diversifying and sustaining food consumption markets can contribute to the overall level of economic welfare in the food sector. Globally, livestock production faces systemic challenges such as epidemics, price volatility, and market risks [2], which not only threaten the stability of producers’ incomes, but also affect consumer welfare through price transmission [3]. Agricultural insurance, as an internationally recognized core risk management tool [4], plays a crucial role in livestock policy [5], which not only affects the productivity, product quality, and market stability of the livestock sector, but also has far-reaching impacts on the welfare of producers and consumers. The welfare effects of agricultural and livestock insurance have been widely explored in the international academic community [6,7], but empirical studies on pig insurance are still lacking. Academics have not systematically analyzed the producer–consumer dual welfare effects of pig insurance and have paid insufficient attention to the regional heterogeneity of the policy’s effects, especially the lack of research on the typical market structure of the “production and marketing equilibrium zone.”

The stable supply of pork consumer goods has an important impact on social consumer welfare and producer welfare. However, the “pig cycle” phenomenon in pig production and pork supply is evident, and the ability to maintain a stable supply of pig production capacity needs to be continuously improved [8]. The fluctuation of pork prices is still significant, which causes dual damage to consumer welfare and producer welfare. In order to stabilize the production of pigs and the supply of pork products, China launched the pig insurance business in 2007 and promoted the work mechanism of coordinated promotion of pig insurance and epidemic prevention in 2009. In 2013, Beijing, China, launched the country’s first pig price index insurance, and subsequently, pig price insurance was piloted in multiple provinces across the country. In recent years, various local governments in China have explored the financial tool of “insurance + futures” for pigs. This insurance model fully utilizes the dual functions of providing “price insurance” for breeding entities and “reinsurance” for insurance [9], further reducing the risks of live pig production.

So, has the implementation of the pig insurance policy promoted the welfare effect of pig production? What impact has it had on consumer welfare effects? Are there differences in the welfare effects of pigs among different regions? This article aims to fill the above research gaps by utilizing inter-provincial panel data from China’s production and marketing balance areas to empirically test the non-linear impacts of hog insurance policies on producer welfare, consumer welfare, and total welfare and to reveal their regional heterogeneity characteristics under the framework of welfare economics. Since pork price is an important monitoring object of the government and serves as the main basis for adjusting pig production capacity and pork storage strategies, it plays an important role in the process of changes in consumer welfare effects and production welfare effects. Therefore, it is of great theoretical and practical significance to explore the issues of consumption welfare effects and production welfare effects based on pork price changes and to study the role of hog price insurance policy in the process. The research outcomes are expected to provide actionable policy insights for improving the design of hog insurance schemes, ultimately contributing to stabilized pork supply, enhanced market efficiency, and improved welfare for both producers and consumers.

2 Literature review

It has been shown that hog farming scale structure and market distribution mode are factors that affect the welfare effects of producers and consumers [10,11]. Scholars have focused on the study of welfare effects based on pork price fluctuations, calculated the consumer welfare loss caused by rising pork prices [12], and believed the increase in pork prices led to a decline in consumer welfare levels through the income effect [13,14]. However, Liu et al. [15] argued that cyclical price fluctuations are more capable of enhancing consumer welfare than stabilizing prices. Some scholars have also analyzed the impact of hog production prices on the short-term and long-term welfare effects on farmers, pointing out that hog production price fluctuations positively affect farmers’ production welfare [16]. Other scholars have further studied the transmission mechanism that leads to changes in the welfare effects of the pig industry. Li et al. [17] used an economic surplus model under open conditions to calculate and analyze the impact of manure treatment on social and economic welfare. The results showed that manure treatment caused an increase in pork prices, a decrease in production, and a significant reduction in surplus for pig farming entities and consumers.

There are more current studies on the welfare effects of food policies on farm households [18,19,20,21], while there are fewer studies on welfare effects in the field of animal husbandry. Scholars have begun to pay attention to the impact of policy factors on the welfare effects of the hog industry and have introduced policy factors into the study of welfare effects [22,23]. Ho et al. [24] concluded that credit policies are conducive to promoting hog production and can significantly improve the production welfare of extremely poor farming subjects. Some studies have also researched the effects of quarantine policy, monetary easing policy, and subsidy policy on farmers’ welfare [25,26,27]. To mitigate diseases, market fluctuations, and rising feed costs [28,29], pig insurance policies have emerged. Pig insurance aims to provide risk protection for farmers. By implementing a risk sharing mechanism to safeguard the interests of farmers, pig insurance policies can reduce losses caused by risks and promote the healthy development of the pig industry [30,31]. However, some studies have found that although pig insurance has improved the welfare benefits of farmers to some extent, direct government subsidies may be more effective [32]. Some scholars have studied the impact of specific types of insurance on the welfare of the pig industry [33]. For example, Nie and Duan [34] found that pig disease insurance stabilizes the income capacity of farmers by increasing investment in breeding funds and labor. In addition, researchers also discussed the impact of specific insurance policies on the stability of pig production, including breeding sow insurance [35], pig gross margin insurance [36], pig target price insurance [37,38], and pig price index insurance [39,40].

The analysis of the welfare effects of agricultural insurance is rooted in risk-sharing theory and the welfare economics framework [41,42]. International studies have shown that agricultural insurance, by reducing production risk premiums and marginal costs, can enhance producers’ expected utility and expand the scale of production [43], which in turn affects consumer surplus through market equilibrium [44]. In the field of livestock, Roll [45] finds that livestock insurance has an enhancing effect on production and efficiency and changes the input utilization mix, but its welfare distributional effects are not discussed in depth. However, some studies have argued that the welfare effects of livestock insurance are ambiguous and uncertain [6]. In recent years, studies have begun to focus on the effects of new insurance tools. Park et al. [46] proposed a blockchain-based pig insurance product and verified that this insurance product can maximize the benefits of both policyholders and insurance companies, but still lacks a quantitative analysis of consumer welfare.

In summary, while there are more studies on the welfare effects of pork price volatility, there are fewer studies that consider policy factors. Even the studies that consider policy factors only resort to theoretical explanations and simulation methods. Existing studies mainly measure the welfare effect based on pork price fluctuations through the compensatory change method, but they do not incorporate policy factors into the supply and demand functions and lack in-depth analysis of policy influences. In addition, there is also a relative lack of existing literature that examines the differences in welfare benefits across production regions. Therefore, the potential innovations of this article include the inclusion of hog insurance as a key variable in the supply and demand function, the use of inter-provincial panel data to quantify the changes in producer and consumer welfare at the same time, to reveal the effectiveness of insurance in influencing welfare effects, and to provide empirical evidence for risk management in the global livestock sector from China’s balance-of-production and marketing zones.

3 Theoretical analysis

3.1 Pig insurance policy and welfare effects

The concept of “welfare” discussed herein is rooted in neoclassical welfare economics, which aggregates and allocates total social welfare through producer surplus and consumer surplus [47]. This theoretical framework can be employed to conduct quantitative analyses of how policy impacts alter the welfare of different groups within the pork market.

In the swine farming industry, producer welfare refers to the economic well-being of pig farming entities (e.g., individual farmers, specialized cooperatives, and large-scale farms). It is measured by producer surplus, defined as the difference between the market selling price of pork and the marginal cost of production. An increase in producer surplus signifies enhanced producer welfare. Similarly, consumer welfare denotes the economic well-being of pork consumers. It is measured by consumer surplus, defined as the difference between the maximum price consumers are willing to pay and the actual price paid. Holding other factors constant, a decrease in the pork market price increases consumer surplus, thereby improving consumer welfare. Total welfare is the sum of producer surplus and consumer surplus. If a policy or event increases total welfare, it can be considered a Pareto improvement even if the distribution of benefits between producers and consumers changes.

The current insurance policies for hog production are of various types, including insurance for breeding pigs, breeding sows, piglets, fattening pigs, and other types of hogs as the subject, as well as insurance for the end of the industrial chain, such as hog feed cost insurance, price index insurance, and hog revenue insurance. Property insurance companies sign insurance agreements with hog farms (households) and pay out in the event of peril according to the policy agreement. The implementation of the hog insurance policy significantly influences the production decisions of farming entities through the mechanism of risk-taking theory. Specifically, the insurance policy reduces the production risk faced by the farming subjects, enabling them to be partially or fully compensated when facing potential losses. This risk protection enhances the production willingness of the farmers, making them more willing to expand the production scale and invest more production materials. Ultimately, this expansion of production scale increases the scale of hog farming and pork production, providing more pork supply to the market.

In order to deeply analyze the impact of insurance policy on pig production and welfare, this article constructs a formal optimization model under uncertainty based on expected utility maximization as a framework to explore the relationship among insurance, production, and welfare. Assuming that the main body of hog farming faces stochastic production risk, its benefit function can be expressed as follows:

(1) U = E [ π ( Q , θ ) ] ,

where U is the expected utility of the farmer, π ( Q , θ ) is the actual revenue under the yield Q and the stochastic production state θ , and C ( Q ) is the deterministic production cost, including but not limited to the cost of feed, labor, and insurance. The farming entity maximizes its expected utility by choosing the optimal yield Q *

(2) Q * = arg max Q E [ π ( Q , θ ) ] .

With the introduction of hog insurance, the farming entities can pay a premium M to purchase insurance to reduce the production risk. When the production risk occurs, the insurance company will pay the compensation S according to the contract, thus directly reducing the actual loss of the farming subject. At this time, the expected return function of the farming main body is adjusted as follows:

(3) U ′ = E [ min ( π ( Q , θ ) , π ( Q , θ ) + S − M ) ] − C ( Q ) − M .

The min ⁡ ( · ) function reflects the compensatory effect of insurance claims on actual losses.

Assuming that the marginal cost of the farming body before the implementation of the hog insurance policy is MC 0 . The optimal decision condition for maximizing economic welfare is that the marginal cost is equal to the marginal benefit, i.e., MC 0 = P 0 , and the corresponding optimal supply of pork is Q 0 . At this time, the maximized economic welfare (the sum of the consumer surplus and the producer surplus) is △ A N 0 D in Figure 1.

Figure 1 Changes in economic welfare before and after the implementation of the hog insurance policy.
Figure 1

Changes in economic welfare before and after the implementation of the hog insurance policy.

This change is due to the compensation of potential losses by insurance payments, which allows farming entities to invest more actively in the means of production in the face of uncertainty.

According to the principle of expected utility maximization, the farming entity will choose to increase the production to Q 1 to maximize its expected utility after the marginal cost is reduced. At this time, due to the reduction of marginal cost, the farming entity can obtain higher profit at the same production or obtain the same profit as before at a higher production. As a result, the level of production welfare is enhanced. After the farmer enrolls in the insurance policy, the marginal cost is reduced to Q 1 due to the expected effect of insurance compensation

(4) MC 1 = ∂ [ C ( Q ) + M − E [ S · I ] ] ∂ Q .

where I is an indicator function that is 1 when the risk occurs and 0 otherwise.

The premium increases operating costs, assuming that the increment of this cost is C 0 . However, when a hog production risk occurs, the insurance company is contractually obligated to dispose of the risk and pay compensation, which directly reduces operating costs, assuming that this cost reduction is C 1 . Due to the compensation being significantly greater than the premium, under normal circumstances, C 1 > C 0 . Therefore, when the insurance company conducts a loss assessment for pig production and pays compensation, the production cost of pig farming entities will significantly decrease, with a decrease of C 1 − C 0 , and their marginal cost will decrease to M C 1 . At this time, the pork production becomes Q 1 , the production welfare level is △ P 1 N 1 E , and the total welfare level becomes △ A N 1 E . Relative to before the implementation of the insurance policy, the level of production welfare increases △ P 1 N 1 E − △ P 0 N 0 D , and the total welfare increases ◇ DN 0 N 1 E . Therefore, when the insurance policy is implemented, the production welfare and total welfare effects of the hog industry are positive.

Combined with the theory of elasticity of supply and demand, the price elasticity of demand for pork is relatively low, implying that consumers are insensitive to changes in the price of pork. In contrast, supply price elasticity may change due to factors such as insurance policies. In this study, the implementation of the insurance policy increased the supply of pork by reducing the risk of the farming entities, thus changing the supply–demand balance in the market. Due to the low price elasticity of demand, the increase in supply exerted significant downward pressure on market prices. This price decline allowed consumers to purchase pork at lower prices, which in turn enhanced consumer welfare. At this time, the level of consumer welfare increases by △ P 1 N 1 A − △ P 0 N 0 A .

However, in special cases, such as when the cost of enrollment is too high and is not effectively converted into insurance proceeds, it may lead to higher product prices, which in turn affects consumer welfare. Since the cost of hog production is usually passed on to the consumer through the price of the product, the cost of insuring hog farmers as a cost of production will generally drive up the price of pork by translating it into a portion of the price of the product. Specifically, if the insurance premium is too high and not converted into insurance benefits, it will lead to a high increase in product prices, which will significantly affect the market consumption demand for pork products that are sensitive to demand elasticity. Moreover, pig farming in the balanced production and sales area is mainly aimed at meeting the needs of local consumers and ensuring basic self-sufficiency in pork production. The transmission space of production costs to product prices is small, and the transmission speed is fast. Therefore, assuming that in this scenario, the production cost of live pigs increases, the marginal cost increases to MC 2 , and the increase in pork prices leads to the demand curve D 0 becoming D 1 . At this point, consumer welfare will decrease from △ P 0 N 0 A to △ P 2 N 2 B , and the consumer welfare effect will be negative.

3.2 Pork prices and welfare effects

The compensation variable method is a technique frequently used to measure the impact of price fluctuations on changes in welfare, which refers to the calculation of the amount of money that residents need to pay to maintain the level of utility in the base period after a change in price to measure the change in consumer welfare due to the price change [48]. This article adopts the above methodology and draws on the idea of compensating variables proposed by Minot and Goletti [49] to carry out the research. This article studies the industry welfare effect of pork price volatility in China, and it is more appropriate to adopt the idea of compensating variables to measure and decompose the welfare effect of the hog industry. This article will focus on the short-term welfare effects and long-term welfare effects of pork price volatility in China.

3.2.1 Consumer welfare effects

Changes in consumer welfare can be measured by the compensating variable method, which calculates the amount of money that consumers would have to pay to maintain the base period utility level after a price change. Let equation (5) be as follows:

(5) CV = e ( p 1 , u 0 ) − e ( p 0 − u 0 ) ,

where CV is the compensation variable, e ( p 1 , u 0 ) denotes the expenditure equation when the pork price is p 1 and the utility level is u 0 ; e ( p 0 , u 0 ) denotes the expenditure equation when the pork price is p 0 and the utility level is u 0 .

Assume that the utility level u 0 remains constant during price changes. A second-order Taylor series expansion of the expenditure function e ( p 0 , u 0 ) at p 0 , ignoring higher-order infinitesimal items, and incorporating Shephard’s principle, i.e.,

(6) ∂ e ( p , u ) ∂ p i = h i ( p , u ) ,

where h i is the Hicks demand, which is collapsed to give equation (7):

(7) CV ≌ ∑ i = 1 n h i ( p 0 c , u 0 ) ∆ P i c + 1 2 ∑ i = 1 n ∑ j = 1 n ∂ h i ( p 0 c , u 0 ) ∂ P i c ∆ P i c ∆ P j c ,

where ∆ p i and ∆ p j replace ( p 1 i c , p 0 i c ) and ( p 1 j c , p 0 j c ) in the second-order Taylor series expansion, and h i ( p 0 c , u 0 ) denotes the Hicks demand conditional on an initial price of p 0 c and a utility level of u 0 .

Assuming that the demand function is continuous and less variable with respect to price, a Marshallian approximation of the demand function can be used in place of the Hicks demand function. It is assumed that the self-price effect of pork demand is dominant. For computational convenience, the Hicks self-price elasticity ε H and the Marshallian demand q i ( p 0 c , x 0 ) are used in place of the Hicks demand, collapsing to equation (8):

(8) CV ≌ q ( p 0 c , x 0 ) ∆ p c + 1 2 ε H q ( p 0 c , x 0 ) p 0 c ∆ p c ∆ p c ,

where q ( · ) denotes the quantity demanded of pork, p c denotes the price of pork, ε H denotes the Hicks self-price elasticity of pork, reflecting the change in demand excluding the income effect, ∆ p c denotes the amount of change in the price of pork, x 0 is the level of income in the base period, q ( p 0 c , x 0 ) is the base-period Marshallian demand, and approximate substitution for Hicks’ demand presupposes a small price elasticity. Divide each side of equation (8) by x 0 , and multiply the numerator and denominator on the right side of the equation by p 0 c :

(9) CV x 0 ≌ p 0 c q ( p 0 c , x 0 ) ∆ p c x 0 p 0 c + 1 2 ε H p 0 c q ( p 0 c , x 0 ) x 0 ∆ p c p 0 c 2 .

Define CR as the ratio between the consumption expenditure on pork and the total consumption expenditure of the population, which is expressed by the formula:

(10) CR = p c q ( p c , x ) x .

Substituting equation (10) into equation (9), it can be deduced:

(11) CV x 0 = CR ∆ p c p 0 c + 1 2 ε H CR ∆ p c p 0 c 2 .

3.2.2 Production welfare effects

Changes in production welfare can be represented by changes in income, i.e., by calculating the change in the producer’s income as a result of a price change. In this article, it is assumed that the producer operates in a perfectly competitive market and that the profit function π ( p s , w , z ) is determined by the output price p s , the input price w , and the fixed factor of production z . In the short run, w and z are held constant, and the effect of price changes on production welfare is expressed as a profit differential ∆ x

(12) ∆ x = π ( p 1 s , w 0 , z 0 ) − π ( p 0 s , w 0 , z 0 ) ,

where ∆ x denotes the profit margin and ( p 0 s , w 0 , z 0 ) denotes the profit equation for a change in the production price in the base period to p 1 , leaving the input price w and the fixed factor z constant. After a similar derivation and organization as for the change in consumer welfare, equation (13) is obtained:

(13) ∆ x x 0 ≌ PR ∆ p s p 0 s + 1 2 ε s PR ∆ p s p 0 s 2 ,

where ∆ p s denotes the change in the price of production, i.e., p 1 s − p 0 s , PR denotes the ratio between the value of pork production and total revenue, and ε s denotes the self-price elasticity of pork supply.

Meanwhile, based on the values of PR and CR , it can be derived that the net benefit rate (NBR) is the ratio of the net sale value of pork to the income, which is the difference between the ratio of the value of pork production to the income and the ratio of the value of pork consumption to the expenditure, i.e., NBR = PR − CR ; when NBR > 0 , the producer is in the position of a net seller in the process of pork production and consumption; when NBR < 0 , the producer is in the position of a net buyer in the process of pork production and consumption process is in the position of a net buyer.

3.2.3 Total welfare effects

Assuming that the consumption welfare effect and the production welfare effect can be added linearly, the long-run total welfare effects of pork price changes are modeled as follows:

(14) ∆ w 2 x 0 ≌ PR ∆ p s p 0 s + 1 2 ε s PR ∆ p s p 0 s 2 − CR ∆ p c p 0 c − 1 2 ε H CR ∆ p c p 0 c 2 ,

where ∆ w 2 is a second-order approximation of the net welfare effect due to price changes taken as the long-run effect. The Hicks elasticity of demand formula is given below:

(15) ε H = E + CR · η ,

where E represents the price elasticity of demand for pork, η represents the income elasticity of demand for pork, and CR is the ratio between consumer spending on pork and income.

4 Empirical analysis

According to the “14th Five-Year Plan” for the Development of the National Animal Husbandry and Veterinary Industry, this article divides the pig breeding industry into regions and selects the panel data of 13 provinces (excluding Xizang) in the pig production and marketing balance area from 2010 to 2022 for empirical analysis. Compared with the transfer out area and the main sales area, the production and sales balance area covers the largest number of provinces. In addition, some provinces in the production and sales balance area are traditional main pig production areas with a certain level of scale operation, focusing on promoting characteristic pig breeding. Overall, the implementation of pig insurance policies in the pig production and sales balance zone is relatively extensive and has typical and representative research significance.

The data of the study mainly come from national statistical yearbooks such as China Statistical Yearbook, China Animal Husbandry and Veterinary Medicine Yearbook, China Rural Statistical Yearbook, China Household Statistical Yearbook, etc., as well as local statistical yearbooks and statistical bulletins, etc., from 2011 to 2023, and the individual missing values are replaced by the method of mean substitution. The above data are used to measure the consumption welfare effect, production welfare effect, and total welfare effect. To eliminate the effect of inflation, all price data have been deflated by the CPI index (with 2010 as the base period).

In order to avoid pseudo-regression and ensure the validity of the empirical results, the panel series smoothness test is first carried out by using the panel unit root test methods such as Fisher and Hadri LM. The test results show that all the variables in the supply function and demand function have passed the test, and the p-values of the corresponding statistics are all less than 0.01, which significantly rejects the original hypothesis at the 1% level, i.e., the original series of each variable does not contain a unit root and is smooth.

4.1 Estimation of supply function and demand function

4.1.1 Estimation of price elasticity of pork supply

This article uses the classic economic model “Cobb Douglas (C-D) production function model” to estimate the supply elasticity of pork prices. The logarithmic form of the C-D function can not only achieve dimensionless variables and reduce heteroscedasticity, but also yield coefficients that precisely represent the price elasticity of supply. The supply function model is set as follows:

(16) LNQ it = α 0 + α 1 LNPIG it + α 2 LNRIS it + α 3 LNPRO it α 4 LNBEEF it + α 5 LNINS it + u it ,

where i represents each province, t represents the year, and Q it represents the pork production; PIG it represents the year-end pig output; RIS it represents the market risk, with the pig-to-grain ratio (the ratio of pig prices to corn prices) as a proxy variable; PRO it represents the production price of live pigs; BEEF it represents the beef production. INS it represents the payout intensity of pig insurance, which serves as a proxy variable for pig insurance policy. The calculation method of hog insurance usually involves several factors, including the breeds of hogs, the scale of farming, market prices, and so on. However, these data belong to the internal confidential data of insurance companies and are not currently available in the public or other channels. Therefore, this article adopts an approximate estimation to find the amount of hog insurance and its intensity in each province. Although this approach cannot completely and without error find the amount of hog insurance, it also reflects the intensity of hog insurance to a large extent. The specific calculation formula is as follows:

(17) INS it = POM it × AMO it ARM it × STO it ,

where POM it represents the pig breeding output value of province i in year t , ARM it represents the total agricultural output value of province i in year t , AMO it represents the agricultural insurance compensation amount of all insurance companies in province i in year t , and STO it represents the pig inventory of province i in year t .

In this article, the Hausman test for the pork supply function was first conducted, and the P-value of the Hausman test under the clustering standard error was less than 0.001, which significantly rejected the use of random effects at the 1% level. The above results indicate that the data in this article are suitable for a fixed effects model rather than a random effects estimation model. Considering the complex intersection of supply and demand between pork supply in each province, as well as the large differences in environmental carrying capacity, resource endowment, consumption preference, and other factors in each place, to avoid the estimation bias caused by the separate estimation of each province, this study takes all the provinces of the balance-of-production and marketing area as a whole for the estimation of pork supply elasticity [20]. It applies its estimation results to the subsequent calculation of the welfare effect. After estimation, the following pork supply function equation is obtained:

(18) LNQ it = − 1.8587 + 0.9096 LNPIG it − 0.0083 LNRIS it − 0.0357 LNPRO it + 0.0386 LNBEEF + 0.0089 LNINS it .

The estimation results show that the estimated coefficients of production price and insurance intensity passed the 1% significance level test. The 95% confidence interval of hog production price is [0.8481, 0.9711], and the confidence interval does not include 0. The production price of hogs is negatively correlated with pork production, which may be because the increase in production costs (such as the increase in feed prices, the increase in the cost of epidemic prevention and control, etc.) pushes up the production price. The farmers, in order to maintain their profitability, will reduce the scale of farming or increase the price of hogs, leading to a decrease in the supply of pork in the market. The 95% confidence interval for hog insurance intensity is [0.0023, 0.0154], and the confidence interval does not include 0. Hog insurance intensity is positively correlated with pork production, indicating that the hog insurance policy improves the profitability of hog farming entities so that they can invest more in hog production. Therefore, the implementation of the insurance policy has an obvious promotional effect on pork production.

Although panel fixed effects models control for individual heterogeneity that does not vary over time, insurance intensity may still be affected by unobserved factors. For this reason, this article uses lagged one-period insurance intensity as an explanatory variable to mitigate endogeneity due to reverse causation. The results show that hog insurance intensity remains significantly and positively associated with pork production at the 5% level, with an impact coefficient of 0.0082. To further test the robustness of the findings, this article changes the sample range. After excluding data from the earliest year, the regression results show that the impact coefficient of hog insurance intensity is 0.0084 at the 5% level of significance. The above results imply that the results of the benchmark regression are robust.

4.1.2 Estimation of demand elasticity and income elasticity

According to the basic definition of the demand function, this article constructs the pork demand function model as follows:

(19) LND it = β 0 + β 1 LNINC it + β 2 LNRET it + β 3 LNCOS it + β 4 LNBEPR it + β 5 LNINS it + u it ,

where D it represents the per capita pork consumption of all households in each province, INC represents the per capita net income of all residents, RET represents the price of pork, COS represents the cost price of pork per unit of production, BEPR it means beef price, and INS it represents the intensity of hog insurance payouts.

Before carrying out the estimation, it is necessary to carry out the Hausman test for the pork demand function, and the results of the Hausman test under the clustered standard error show that the statistical P-value is less than 0.001, which significantly rejects the use of random effects at the 1% level. The above results suggest that the pork demand function should also be estimated using a fixed effects model rather than a random effects estimation model. Again, taking into account the complex differences in supply and demand and conditions between provinces, the elasticity of demand for pork and income elasticity were estimated for all provinces in the production and marketing balance area as a whole, and their estimates were applied to the subsequent calculation of welfare effects. As a result of the estimation, the following pork demand function equation is obtained:

(20) LND it = − 0.7903 + 0.3589 LNINC it − 0.3198 LNRET it + 0 . 1250 LNCOS it + 0.1022 LNBEPR − 0.0630 LNINS it .

The estimation results show that the 95% confidence interval of per capita net income of all residents is [0.2202, 0.4975], and the 95% confidence interval of pork price is [−0.4615, −0.1780], and both passed the 1% significance level test. The income elasticity is 0.3589, that is, a 10% rise in per capita disposable income of residents will increase the consumption of pork by 3.589%; the price elasticity of demand is −0.3198, that is, a 10% rise in the price of pork will decrease the demand for pork by 3.198%. The intensity of hog insurance claims is negatively associated with pork consumption at the 1% significance level, with a 95% confidence interval of [−0.1062, −0.0197], implying that the implementation of the hog insurance policy reduces residents’ demand for pork consumption, which is consistent with the results of the theoretical analysis. The reason may be the expenditure of hog premiums in the balance of production and marketing areas, as well as the raising of production costs by the farming entities without obtaining insurance proceeds, which leads to an increase in the price of pork and suppresses the consumption of residents.

The same consideration is given to the fact that the impact of insurance intensity on residential pork consumption may be influenced by unobserved factors, thus generating endogeneity. For this reason, this article still uses lagged one-period insurance intensity as an explanatory variable to mitigate endogeneity due to reverse causality. The results show that hog insurance intensity remains negatively associated with pork production. To further test the robustness of the findings, this article changes the sample range. After excluding the data of the earliest year, the regression results show that the impact coefficient of hog insurance intensity is −0.0670 with a significance level of 1%. The above test results imply that the baseline regression results between the explanatory and interpreted variables are robust.

4.2 Analysis of welfare effects in the hog industry

4.2.1 Analysis of net yield of hog production

Based on the data of per capita pork consumption, pork price, per capita consumption expenditure of residents, etc., the CR value is calculated. The PR value is calculated by utilizing the data of pig rearing output value, per capita disposable income of residents, total population at the end of the year, and so on. The CR value and PR value were used to find the net return value of hog farming NBR . According to the calculation results, the trend of NBR value of each province in the production and marketing balance area shows consistency from 2010 to 2022, i.e., although slightly fluctuating, it gradually decreases to between 0.02 and −0.02. Some provinces have differences, the NBR value of Yunnan is always higher than 0.02, which is the province with the highest net return in the production and marketing balance area; in addition, the NBR value of Shaanxi and Xinjiang provinces is always positive, but the value is not high, which is the case of positive net return of pig production. The above results show that most of the provinces in the production and marketing balance area are basically in a situation of balance between production value and consumption value, and a small number of provinces are in a situation of positive net return of hog production.

As shown in Table 1, Fujian, Inner Mongolia, Gansu, Qinghai, and Chongqing provinces have an average NBR of less than 0 and a negative net return on pig production from 2010 to 2022, which means that the share of pork consumption in total consumption expenditure in these provinces exceeds the share of pork production value in total income. Among the 13 provinces, Inner Mongolia, Gansu, and Qinghai rank low in pork production and per capita pork consumption; while Fujian and Chongqing have higher pork production but also higher per capita pork consumption, leading to their negative NBR means. Although Xinjiang, Ningxia, and other provinces have lower pork production, the folklore and dietary culture of these provinces determines that the per capita pork consumption is extremely low, and the per capita pork consumption is also very low, which, on the contrary, leads to their NBR means being ranked in the top.

Table 1

Ranking of production and consumption averages by province in the production and marketing balance area, 2010–2022

Province CR (%) PR (%) NBR (%) NBR sort Pork production (10,000 tons) Pork production sort Pork consumption (kg/person) Pork consumption sort
Yunnan 6.3228 9.8133 3.4904 1 297.53 2 26.72 3
Sichuan 6.3199 8.8921 2.5722 2 473.72 1 32.77 2
Shaanxi 2.1748 4.4650 2.2902 3 86.66 6 11.13 10
Hainan 5.3223 6.7795 1.4572 4 40.32 10 24.20 6
Xinjiang 0.8608 2.0410 1.1802 5 35.68 11 4.04 13
Shanxi 2.1133 2.2877 0.1743 6 63.88 8 10.03 11
Ningxia 1.3860 1.4466 0.0606 7 8.04 13 7.154 12
Guizhou 7.1185 7.1334 0.0149 8 158.77 3 26.94 4
Fujian 3.2457 3.2207 −0.0251 9 132.99 5 24.49 5
Inner Mongolia 2.8826 2.8364 −0.0461 10 70.55 7 18.66 7
Gansu 3.2158 2.8121 −0.4038 11 51.82 9 13.11 8
Qinghai 2.5809 2.1680 −0.4129 11 8.50 12 11.91 9
Chongqing 5.5394 4.6231 −0.9164 13 141.76 4 34.25 1

4.2.2 Welfare effect analysis of the hog industry

Based on the CR and PR values as well as supply elasticity, price elasticity, and income elasticity calculated in the previous section, the consumption welfare effect and production welfare effect from 2011 to 2022 are calculated using 2010 as the base year. Although the size of the welfare effect values does not reflect the absolute amount of producer and consumer welfare, according to the principle of the compensating variable method, it is possible to analyze both the time trend of the welfare level of individual provinces vertically and the differences in the welfare level of each province horizontally.

Overall, the provinces in the production and marketing balance area have a predominantly positive total welfare effect. Table 2 shows the total welfare effect of each province in the production and marketing balance area in 2011–2022. It can be seen that, relative to 2010, the hog industry in each province in 2011–2022 was dominated by the state of welfare enhancement, and the total welfare effect enhancement in 13 provinces amounted to 108.8748%. Individual provinces and individual years showed heterogeneity in the total welfare effect. First, Hainan’s total welfare effect is negative in most years, with a cumulative total welfare effect of −8.3666%. This may be because Hainan belongs to the geographical location of the island, and the feed supply and logistics costs of hog farming are higher than those of the mainland provinces, which makes the cost of hog production high, and at the same time, due to the greater demand for its pork consumption, which results in higher pork prices, and ultimately leads to the consistently low welfare effect of its hog economy. Second, Sichuan has the largest cumulative value of total welfare effect, reaching 35.7252%, and Yunnan, Shaanxi, Guizhou, and Chongqing are ranked second to fifth. These provinces have high pork production, relatively stable pork prices, and the enhancement of the total welfare effect of the hog industry is obvious. Third, the total welfare effect is negative in most provinces in 2020–2022. Due to the deterioration of the environment in the hog farming and consumption markets caused by public health events, hog feed and products are poorly circulated, the operating costs of farming enterprises rise, and the price of pork soars. At the same time, outdoor consumption of pork decreased sharply and total consumption fell significantly, leading to a negative total welfare effect in most provinces.

Table 2

Total welfare effects by province in the production and marketing balance area, 2011–2022 (%)

Province 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Cumulative
Shanxi 0.0338 0.1059 0.0808 0.0700 0.0697 0.1208 0.0972 0.0710 0.4021 −0.1688 −0.6090 −0.5553 −0.2817
Inner Mongolia −0.1363 0.4566 0.4279 0.4460 0.3966 0.6827 0.4677 0.1515 −0.2580 −2.6228 −0.7698 −0.3946 −1.1524
Fujian 0.4868 0.2955 0.2682 0.1664 0.1494 0.2635 0.2614 0.0812 0.1945 −0.3176 −0.6192 −0.3673 0.8629
Hainan −0.3835 −0.7625 −1.0158 −0.6042 −0.5541 −0.1679 −0.9831 −0.6924 0.8387 1.2394 −3.2169 −2.0642 −8.3666
Chongqing 0.2195 1.4512 1.2788 1.1485 1.0537 1.7243 1.0479 0.8542 1.6495 1.6126 0.9285 0.4471 13.4158
Sichuan 2.7277 2.8823 2.6054 2.1450 2.6577 3.3988 2.1126 1.7949 2.3729 7.1947 3.3862 2.4469 35.7252
Guizhou −0.1287 1.1894 1.4102 1.5715 1.6876 2.3573 2.5762 1.0791 0.8421 −0.2561 0.6696 0.3480 13.3464
Yunnan 1.9536 3.2643 1.8954 1.2889 1.2487 1.2386 1.1337 0.3606 2.1369 7.6389 1.9561 0.4732 24.5890
Shaanxi 2.1201 2.3106 1.9088 1.2959 1.4462 1.8373 1.2193 0.7827 0.9078 0.2645 0.0840 −0.1265 14.0507
Gansu −0.2471 0.4534 0.7116 0.6520 0.5514 0.4209 0.1786 0.1045 0.0098 −1.8104 −0.6335 −0.3447 0.0467
Qinghai 0.7927 1.3764 1.3298 1.1321 0.9294 1.0118 1.1223 0.8445 0.8187 −1.2075 0.3834 −0.0016 8.5320
Ningxia 0.3479 0.2414 0.0712 0.0282 0.0795 0.3329 0.1707 0.1296 0.4123 0.2230 −0.1657 −0.1373 1.7338
Xinjiang 0.2155 0.8005 0.8119 0.4202 0.4670 0.8275 0.3816 0.2198 1.0667 1.3516 −0.0293 −0.1597 6.3731
Total 8.0021 14.0650 11.7841 9.7606 10.1827 14.0487 9.7863 5.7811 11.3941 13.1416 1.3645 −0.4360 108.8748

In order to analyze the relationship between the variables of insurance policy, pork price, and hog production price and the welfare changes in the hog industry and to further compare the differences among the provinces in the production and marketing balance area, it is necessary to examine the relationship between the trends of the variables. Overall, pork price changes in the opposite direction to the consumption welfare effect, and hog production price changes in the same direction as the production welfare effect and the total welfare effect; the intensity of hog insurance policy changes in the same direction as the production welfare effect and in the opposite direction to the consumption welfare effect. However, by observing the trend changes in each province, there are some differences among provinces.

Since the insurance policy is an important variable analyzed in this article, the 13 provinces can be divided into two categories according to the trend of hog insurance intensity. First, the intensity of hog insurance in the provinces of Xinjiang, Shanxi, Qinghai, Inner Mongolia, Shaanxi, Ningxia, Gansu, and Hainan first rises and then falls. Taking Xinjiang and Shanxi as an example (Figures 2 and 3), their pig insurance intensity rises slightly between 2011 and 2018, rises sharply in 2019 and 2020, and declines significantly in 2021 and 2022. This trend is almost the same as the movement of its production welfare effect, indicating that the implementation of the hog insurance policy has obviously promoted the enhancement of production welfare. Second, the level of hog insurance intensity in Sichuan, Chongqing, Guizhou, Yunnan, Fujian, and other provinces is not high, showing a slow rise and remaining relatively stable in the later years. Taking Sichuan and Fujian as an example (Figures 4 and 5), their pig insurance intensity was relatively stable and at a low level before 2018 and did not increase significantly until 2019 and remained stable in the following years. Although this trend of change is not very consistent with that of the production welfare effect, it still shows a high law of same-direction change.

Figure 2 Trend of welfare effect changes in the Xinjiang pig industry. Note: For the convenience of observing the trend of changes in the strength of pig insurance, the values are enlarged by ten times in the figure. Welfare effects are on the left axis and strength of pig insurance are on the right axis. The same applies below.
Figure 2

Trend of welfare effect changes in the Xinjiang pig industry. Note: For the convenience of observing the trend of changes in the strength of pig insurance, the values are enlarged by ten times in the figure. Welfare effects are on the left axis and strength of pig insurance are on the right axis. The same applies below.

Figure 3 Trend of welfare effect changes in the Shanxi pig industry.
Figure 3

Trend of welfare effect changes in the Shanxi pig industry.

Figure 4 Trend of welfare effect changes in the Sichuan pig industry.
Figure 4

Trend of welfare effect changes in the Sichuan pig industry.

Figure 5 Trend of welfare effect changes in the Fujian pig industry.
Figure 5

Trend of welfare effect changes in the Fujian pig industry.

By summarizing the change pattern of variables in each province, it is found that the change direction of hog insurance intensity and the consumption welfare effect is inconsistent. In 2018 and the previous years, hog insurance intensity and consumption welfare effect moved in the same direction. And since 2019, although the insurance intensity of each province increased significantly, the consumption welfare effect decreased significantly, and the two showed obvious changes in the opposite direction. This may be due to the drastic changes in the external environment in these years, which led to severely high pork prices and a sharp decline in the consumption welfare effect, offsetting the welfare level-enhancing effect of the hog insurance policy.

5 Conclusion and recommendation

This article constructed a model for measuring the consumption welfare effect and production welfare effect in the hog industry. Based on the panel data of 13 provinces in China’s production and marketing equilibrium zone from 2010 to 2022, the implementation effect of the hog insurance policy is analyzed by measuring the CR value, PR value, consumption welfare effect, and production welfare effect of each province. The research conclusions of this article are as follows: the CR value fluctuates frequently but remains stable overall in all provinces, the PR value and NBR have a clear downward trend, but the NBR value is mostly positive; the total welfare effect of the hog industry is positive in most provinces, and the trend of welfare level enhancement during the 12 years is obvious. The implementation of the hog insurance policy promoted the hog production welfare effect and the total welfare effect as a whole; the intensity of the insurance policy showed a significant positive relationship with the consumption welfare effect in 2018 and before, while a negative relationship was observed after 2019.

The contribution of this study to welfare economics is mainly in three aspects. First, the mechanism of the dual welfare effects of livestock insurance is verified. It confirms that hog insurance significantly enhances supply and production welfare by lowering producers’ marginal costs, which is consistent with the expectation of international agricultural insurance theory [50]; and it also reveals that in markets with low price elasticity of demand, increased supply can indirectly enhance consumer welfare through downward price movement (pre-2018 stage). Second, the characterization of changes in insurance effects is discovered. The finding that premiums may offset consumer welfare gains through a price pass-through mechanism when external shocks (e.g., African Swine Fever in 2019, COVID-19 in 2020) lead to a sudden increase in the cost of production enriches the theoretical boundaries of the distribution of agricultural insurance benefits. Third, the policy implications of regional heterogeneity are revealed. In provinces with high production–consumption duals, such as Sichuan, the promotion effect of insurance on production welfare is weak, suggesting that insurance design needs to take into account regional market structure characteristics, which is informative for the development of differentiated livestock risk management programs.

Based on the above conclusions, this article puts forward the following policy recommendations. First, differentiated insurance subsidy adjustments by province. Empirical evidence shows that the premium cost pass-through after 2019 leads to a decline in consumption welfare, and the insurance effect is weaker in provinces with double-high production-consumption (e.g., Sichuan and Chongqing). Policymakers should adjust premium rates dynamically based on the pig–grain ratio. When the ratio exceeds the breakeven point, premium rates should be lowered to avoid cost pass-through to consumers; in net-producing provinces with NBR > 0, such as Yunnan and Shanxi, the premium subsidy should be maintained to stabilize production incentives and enhance producer welfare; and in net-consuming provinces with NBR < 0, such as Qinghai and Chongqing, the payout cap should be set to prevent excessive premium cost transfer to consumers. Second, dynamic optimization of welfare enhancement strategies. The policy optimization target is set based on the total welfare enhancement of 108.8748% in the balance area, future policies should set incremental welfare enhancement targets at the provincial level. For inefficient provinces such as Hainan (total welfare −8.3666%) and Inner Mongolia (total welfare −1.1524%), reallocate insurance subsidies toward technological upgrades, breeding improvement, and production training rather than blanket insurance coverage. Third, innovation in pork consumption-oriented insurance products. Empirical results show that the consumption welfare effect is negative due to price increases. Insurance companies should be encouraged to innovate pork consumption product insurance, such as introducing pork product quality guarantee insurance and price cap insurance payout triggers. Government should explore public–private partnerships to pilot consumption voucher programs linked to insurance payouts during high-price periods to maintain consumer welfare. Fourth, enhanced data collection and policy monitoring. The government needs to establish a centralized data platform for hog insurance to improve the accuracy of insurance intensity calculations and enable real-time policy adjustments. Concurrently, it should regularly evaluate the effectiveness of insurance policies on the livestock industry and adjust parameters such as premium rates, coverage scope, and subsidy levels accordingly.

This study also has some limitations. First, in conducting a study on the intensity of hog insurance affecting pork supply and demand, the instrumental variables approach was not used in this article due to data limitations. Future research can further strengthen the causal identification by excluding endogeneity through the instrumental variables method. Second, due to the lack of statistical data, this article has not been able to formally obtain accurate data on the amount of hog insurance from official sources and has adopted the calculation method related to agricultural insurance to approximate the estimation of hog insurance. In the future, the results of the study will be more accurate if the statistical data can be obtained.

tel: +86-13883897986

  1. Funding information: This research was funded by the National Livestock Technology Innovation Center Pioneer Science and Technology Project (No. NCTIP-XD/C18) and Chongqing Social Science Planning Key Project (No. 2021NDZD08).

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal. JZ: data curation, formal analysis, methodology, writing–original draft, writing–review and editing. XL: conceptualization, funding acquisition, project administration, supervision. CZ: investigation, resources, software.

  3. Conflict of interest: Authors state no conflict of interest.

  4. Data availability statement: All data generated or analyzed during this study are included in this published article.

References

[1] Hyochung K, Meera K. Concerns about hazardous elements on foods and recognition of the roles of government, food producers, and consumers in securing food safety. East Asian Soc Diet Life. 2011;21(3):401–17.Search in Google Scholar

[2] McKendree MGS, Tonsor GT, Schulz LL. Management of multiple sources of risk in livestock production. J Agric Appl Econ. 2021;53(1):75–93. 10.1017/aae.2020.31.Search in Google Scholar

[3] Hashmiu I, Adams F, Etuah S, Quaye J. Food-cash crop diversification and farm household welfare in the Forest-Savannah Transition Zone of Ghana. Food Secur. 2024;16(2):487–509. 10.1007/s12571-024-01434-3.Search in Google Scholar

[4] Frascarelli A, Del Sarto S, Mastandrea G. A new tool for covering risk in agriculture: The revenue Insurance policy. Risk. 2021;9(7):131. 10.3390/risks9070131.Search in Google Scholar

[5] Williams R. Looking to livestock: Gauging the evolution of the EU’s Agri-climate law and policy. Transnatl Environ Law. 2025;14(1):69–93. 10.1017/S2047102524000256.Search in Google Scholar

[6] Bulte E, Haagsma R. The welfare effects of index-based livestock insurance: Livestock herding on communal lands. Environ Resour Econ. 2021;78(4):587–613. 10.1007/s10640-021-00545-1.Search in Google Scholar

[7] Boyer CN, Griffith AP. Livestock risk protection subsidies changes on producer premiums. Agric Financ Rev. 2023;83(2):201–10. 10.1108/AFR-05-2022-0066.Search in Google Scholar

[8] Rembisz W, Zawadzka D. The issue of stability of revenue in pig production in Poland. Agric Econ-Zemedelska Ekonomika. 2021;367(2):84–100. 10.30858/zer/134769.Search in Google Scholar

[9] Xu YY, Zhang S, Cui XN, Qi HT. Pricing mechanism and effect analysis of “insurance + futures” service for risk management in the pig breeding market. Agric Econ Issues. 2024;8:112–26.Search in Google Scholar

[10] Wang XD, Zhu ZY, Zhao AP, Jin SY. Research on the changes and impact of pork market circulation mode under the background of African swine fever: taking the “production area slaughtering + pork transportation + designated operation” model in Beijing as an example. Price Theory Pract. 2021;4:16–9. 10.19851/j.cnki.CN11-1010/F.2021.04.173.Search in Google Scholar

[11] Wang ML, Li PC, Ma XP. The impact of scale selection on the high-quality development of animal husbandry and its path optimization: based on the perspective of pig farming scale. China Rural Econ. 2022;3:12–35.Search in Google Scholar

[12] Huang H, Yan HM. Empirical study on the rise of pork prices and consumer welfare Losses: a case study of low-income individuals in Ruichang City, Jiangxi Province. Stat Inf Forum. 2008;10:52–5.Search in Google Scholar

[13] Ye F. Research on the fluctuation of pork prices in China and its impact on the welfare of urban residents. Jingzhou, China: Changjiang University; 2019.Search in Google Scholar

[14] Kashyap P, Suter JF, McKee SC. Measuring changes in pork demand, welfare effects, and the role of information sources in the event of an African swine fever outbreak in the United States. Food Policy. 2024;126:102672. 10.1016/j.foodpol.2024.102672.Search in Google Scholar

[15] Liu SX, Liu XO, Zhang LX. The causes of retail price fluctuations and their impact on consumer welfare: a case study of pork. Agric Technol Econ. 2016;12:13–22. 10.13246/j.cnki.jae.2016.12.002.Search in Google Scholar

[16] Fu LL, Tong XY. The heterogeneous impact of fluctuations in pig prices on the welfare effects of farmers. Stat Decis Mak. 2021;37(16):90–4. 10.13546/j.cnki.tjyjc.2021.16.019.Search in Google Scholar

[17] Li PC, Wang ML, Wang SB. Analysis of the current situation and economic welfare effects of pig farming manure treatment in China. Agric Econ Manag. 2020;5:90–102.Search in Google Scholar

[18] Li BX, Wang YP. Research on the impact of wheat minimum purchase price policy on farmers’ welfare effect. J Huazhong Agric Univ. 2016;4:47–52. 10.13300/j.cnki.hnwkxb.2016.04.007.Search in Google Scholar

[19] Wang WX, Qi CJ. Festival research on the producer welfare effect of fruit price fluctuations. Price Monthly. 2016;4:25–30. 10.14076/j.issn.1006-2025.2016.04.06.Search in Google Scholar

[20] Luo CP, Niu K, Dan B. Grain prices, agricultural subsidies, and welfare effects of farmers in major production areas. Macroecon Res. 2017;4:122–35. 10.16304/j.cnki.11-3952/f.2017.04.013.Search in Google Scholar

[21] Wang G, Shi Q, Qian L. Can the minimum purchase and price subsidy policies enhance the welfare effect of farmers: calculation based on panel data from 5 major wheat producing provinces from 1996 to 2016. Agric Econ Issues. 2019;(10):63–73. 10.13246/j.cnki.iae.2019.10.006.Search in Google Scholar

[22] Yu Y, Zhang H. The impact of pork reserve policy on prices and welfare analysis. J Nanjing Agric Univ. 2017;17(6):101–9.Search in Google Scholar

[23] Chen YQ, Yu XH. Does the centralized slaughtering policy create market power for Pork industry in China? China Econ Rev. 2018;50:59–71. 10.1016/j.chieco.2018.03.005 Search in Google Scholar

[24] Ho NN, Do TL, Tran DT, Nguyen TT. Indigenous pig production and welfare of ultra-poor ethnic minority households in the northern mountains of Vietnam. Environ Dev Sustainability. 2022;24(1):156–79. 10.1007/s10668-021-01348-6.Search in Google Scholar

[25] Beghin J, Melatos M. The trade and welfare impact of Australian quarantine policies: the case of pigmeat. World Econ. 2012;35(8):1006–21. 10.1111/j.1467-9701.2012.01459.x.Search in Google Scholar

[26] Yu XH. Monetary easing policy and long-run food prices: evidence from China. Econ Model. 2014;40:175–83. 10.1016/j.econmod.2014.03.029 Search in Google Scholar

[27] Liu HY. Finding the way out to African swine fever: analysis of Chinese government’s subsidy programs to farms and consumers. Comput Ind Eng. 2021;160:107543. 10.1016/j.cie.2021.107543.Search in Google Scholar

[28] Correia-Gomes C, Mendonca D, Vieira-Pinto M, Niza-Ribeiro J. Risk factors for Salmonella spp in Portuguese breeding pigs using a multilevel analysis. Prev Vet Med. 2013;108(2–3):159–66. 10.1016/j.prevetmed.2012.07.013.Search in Google Scholar PubMed

[29] Tani S, Pineiro C, Koketsu Y. Characteristics and risk factors for severe repeat-breeder female pigs and their lifetime performance in commercial breeding herds. Porcine Health Manag. 2017;3(12):1–8. 10.1186/s40813-017-0059-0.Search in Google Scholar PubMed PubMed Central

[30] Ju GW. Research on the micro effects and policy optimization of China’s Livestock Insurance. Beijing, China: Chinese Academy of Agricultural Sciences; 2016Search in Google Scholar

[31] Singbo A, Larue B, Tamini LD. Total factor productivity change in hog production and Quebec’s revenue insurance program. Can J Agric Econ-Revue Can D Agroecon. 2020;68(1):21–46. 10.1111/cjag.12220.Search in Google Scholar

[32] Zhang YY. Research on demand preference and effect evaluation of policy insurance for pig farmers. Nanjing, China: Nanjing Agricultural University; 2017.Search in Google Scholar

[33] Li YR, Zhang HL. Research on the role of pig profit insurance in pig market regulation: a case study of sichuan province. Insur Res. 2022;12:74–89. 10.13497/j.cnki.is.2022.12.005.Search in Google Scholar

[34] Nie YB, Duan YY. Can pig disease insurance stabilize the income of farmers: Based on survey data of pig farming households in 6 provinces across the country. Insur Res. 2024;12:13–22. 10.13497/j.cnki.is.2024.12.002.Search in Google Scholar

[35] Tan Y, Zhang W, Qiu JJ. Analysis of farmers’ willingness to pay for insurance policies for breeding sows. Insur Res. 2014;6:11–7. 10.13497/j.cnki.is.2014.06.007.Search in Google Scholar

[36] Xia YG, Huang L, Fu J. The operating mechanism and inspiration of US pig gross profit insurance. Price Theory Pract. 2015;7:43–5. 10.19851/j.cnki.cn11-1010/f.2015.07.014.Search in Google Scholar

[37] Machinski PA, Faria MC, Moreira VR, Ferraresi AA. Agricultural insurance mechanisms through mutualism: the case of an agricultural cooperative. Rev de Adm. 2016;51(3):266–75. 10.1016/j.rausp.2016.06.004.Search in Google Scholar

[38] Zhang L, Wen T. Experience, problems and countermeasures of target price insurance pilot for agricultural products - based on a survey of three pilot areas. Econ J. 2019;7:74–82. 10.16528/j.cnki.22-1054/f.201907074.Search in Google Scholar

[39] Liao P, He YP, He SY, Liu XY. Can pig price index insurance suppress the “pig cycle”? Manag Rev. 2022;34(3):31–40. 10.14120/j.cnki.cn11-5057/f.2022.03.006.Search in Google Scholar

[40] Wu JQ, Ren CY. Market prices, government subsidies, and demand for pig price index insurance: Evidence from large sample policy data from insurance companies. Financ Sci. 2020;11:54–64.Search in Google Scholar

[41] Hockett R. Just insurance through global macro-hedging: Information, distributive equity, efficiency, and new markets for systemic-income-risk-pricing and systemic-income-risk-trading in a “new economy”. Univ Pa J Int Econ. 2004;25(1):107–257.Search in Google Scholar

[42] Nshakira-Rukundo E, Kamau JW, Baumüller H. Determinants of uptake and strategies to improve agricultural insurance in Africa: a review. Environ Dev Econ. 2021;26(5–6):605–31. 10.1017/S1355770X21000085.Search in Google Scholar

[43] Semenov NN, Averin AY. Assessment of the effectiveness of state support for insurance in agriculture in Russia. Regionol Russ J Reg Stud. 2022;30(2):299–323. 10.15507/2413-1407.119.030.202202.299-323.Search in Google Scholar

[44] Lusk JL. Distributional effects of crop insurance subsidies. Appl Econ Perspect Policy. 2017;39(1):1–15.10.1093/aepp/ppw002Search in Google Scholar

[45] Roll KH. Moral hazard: the effect of insurance on risk and efficiency. Agric Econ. 2019;50(3):367–75. 10.1111/agec.12490.Search in Google Scholar

[46] Park BK, Yeo SH, Lee S, Yang H. A Case study on the application of blockchain technology for the InsurTech: Development of livestock insurance product. J Inf Technol Serv. 2020;19(4):125–38. 10.9716/KITS.2020.19.4.125.Search in Google Scholar

[47] Just R, Heuth DL, Schmitz A. The welfare economics of public policy: a practical approach to project and policy evaluation. London, United Kingdom: Edward Elgar Publishing; 2005. p. 72–85.Search in Google Scholar

[48] Luo CP, Niu K, Zhang ZY, Dan B. Fluctuations in grain prices and the welfare effects of farmers in major production areas: an analysis based on inter provincial panel data in major production areas. Chin Soft Sci. 2017;2:37–53.Search in Google Scholar

[49] Minot N, Goletti F. Rice market liberalization and poverty in Vietnam. Washington, D.C.: International Food Policy Research Institute; 2000. p. 42–89.Search in Google Scholar

[50] Hueth DL, Furtan WH Economics of agricultural crop insurance: Theory and evidence. Kluwer Academic, Boston. 1994.10.1007/978-94-011-1386-1Search in Google Scholar
Received: 2025-03-24
Revised: 2025-08-31
Accepted: 2025-09-10
Published Online: 2025-11-03

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/opag-2025-0469
Keywords for this article
pork prices; hog production prices; hog insurance policies; welfare effects
Creative Commons
BY 4.0

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