Fiscal Policy Dilemma in Resolving Agricultural Risks: Evidence from China’s Agricultural Insurance Subsidy Pilot

Abstract

The agricultural insurance subsidy policy (AISP) encourages farmers to expand production scale by mitigating production risks. Under the high-input production patterns of traditional agriculture, the implementation of AISP is conducive to increase farmers’ income, but it also leads to the destruction of the agricultural environment. Achieving agricultural green development (AGD) has been hindered in China. In this context, this paper attempts to analyze the impact of AISP on farmers’ income and the agricultural environment. Based on the panel data of 316 prefecture-level cities from 2003 to 2012 in China, this paper empirically tests the effects of AISP by employing methods such as time-varying difference-in-difference (DID). The results show that AISP has significantly promoted the growth of farmers’ incomes but has negatively impacted the agricultural environment. Furthermore, the mechanism analysis shows that the policy effects are realized by affecting the quantity of main productive fixed assets (Mpfa) and grain sown area per capita (Gsa). In addition, the policy effect is heterogeneous in different regions. Therefore, the government should appropriately raise the subsidy standard for farmers who adopt environmental-friendly production patterns. At the same time, the government should give more subsidies to the large grain-producing areas.

Whether the Agricultural Insurance Policy Achieves Green Income Growth—Evidence from the Implementation of China’s Total Cost Insurance Pilot Program

“…The second method is the placebo test. In the third method, the explained variables in Equation (1) are replaced by net operating income of farmers’ households, the number of environmental incidents, and the death rate, respectively [ 1 , 38 ].…”

“…In order to test the triple-effect of TCI on farmers’ income, environment, and public health by using DID model, this paper selects the per capita disposable income of rural residents (Income), air pollutant emissions (Pollution) and the incidence of major diseases (Disease) as the explained variables. According to the empirical analysis in the existing literature, farmers’ income is measured by the per capita disposable income of farmers (Income) [ 1 ], the emission of air pollutants (Pollution) represents environmental pollution [ 25 ], and the health of residents is measured by the incidence of major diseases (Disease) [ 32 ]. In this paper, control variables include industrial structure (Str), per capita sown area (Sown), the degree of damage (Disaster), agricultural modernization level (Modern), urbanization level (Urban), regional average education level (Edu), economic development (Eco) and inflation rate (Inf) [ 14 , 15 ].…”

“…In order to analyze the triple-effect of TCI and test whether TCI can achieve green income growth, this paper uses the DID model for empirical analysis, referring to the methods of Beck et al [ 30 ], Li et al [ 31 ] and Gao et al [ 1 ]. The specific difference-in-difference model is as follows: where is the explained variable, including Income, Pollution or Disease.…”

“…With the rise and promotion of concepts related to green sustainable development, the issue of green income growth in agricultural insurance policies has attracted wide attention. Whether green income growth can be achieved has become an important criterion for measuring an agricultural insurance policy [ 1 ].…”

How Air Quality Affect Health Industry Stock Returns: New Evidence From the Quantile-on-Quantile Regression

“…There are multiple explanations for the relationship between AQ and SR in China's health care industry. First, China is a rapidly developing country and suffers serious environmental problems ( 20 – 23 ). In 2020, China emitted 9.89 billion tons of carbon dioxide (CO 2 ), 3.7 million tons of sulfur dioxide (SO 2 ), and 11.57 million tons of nitrogen oxide (NOx).…”