The evaluation of Chinese Crop Insurance in farmer’s perspective
1
Ke Wang, 2Qiao Zhang, 3Shingo Kimura and 4Suraya Akter
1 Ke Wang is a Ph.D. Student of Graduate School of CAAS, assistant professor at Agricultural Information Institute of CAAS
and Key Laboratory of Digital Agricultural Early-warning Technology of Ministry of Agriculture of China. Address: No.12 South
Zhongguancun St. Beijing 100081 China. Tel: 86-10-5949 2969; Fax: 86-10-8210 6261. E-Mail: wangke01@caas.cn
wangkeable@gmail.com
2 Qiao Zhang is a professor at Agricultural Information Institute of Chinese Academy of Agricultural Science (CAAS) and Key
Laboratory of Digital Agricultural Early-warning Technology of Ministry of Agriculture of China. Address: No.12 South
Zhongguancun St. Beijing 100081 China.Tel: 86-10-8210 9883; Fax: 86-10-8210 6261. E-Mail: zhangqiao@caas.cn
3 Shingo Kimura is a senior researcher at Trade and Agricultural Directorate of Organization for Economic Cooperation and
Development (OECD). Address: 2 rue André-Pascal; 75775 Paris Cedex 16. Tel: +33 (0) 1 45 24 95 35; Fax: +33 (0) 1 44 30 61
01; E-mail: shingo.kimura@oecd.org
3 Suraya Akter is a Ph.D. Student of Graduate School of CAAS, Address: No.12 South Zhongguancun St. Beijing 100081 China.
E-Mail: surayabd78@gmail.com
Abstract
Purpose - This paper aims to evaluate the effectiveness of the Chinese crop insurance program in
terms of farmer utility and welfare.
Design/Methodology/Approach – A simulation model based on the power utility function was first
developed to evaluate the effectiveness of crop insurance. Then, the Monte Carlo approach was used
to generate the datasets of area, price, yield, cost, and income based on the characteristics of
representative farmers, which were clustered and calibrated using the farm-level data of 574
individual farmers from five Chinese provinces. Finally, the effectiveness of Chinese crop insurance
was evaluated by comparing the certainty equivalence (CE) of farmer’s utility/welfare under
alternative crop insurance scenarios.
Findings - Government subsidy is a necessary premise for implementing the crop insurance program.
The government should subsidize more than 50% of the crop insurance premium to motivate more
farmers to participate in the program. The findings also show that the current crop insurance program
in China has increased the welfare of farmers but still need to be improved to achieve the Pareto
improvement and to make full use of the financial fund of the government.
Originality/Value- The current study is the first to quantitatively evaluate the effectiveness of Chinese
crop insurance according to the welfare of individual farmers. This paper is believed to not only extend
academic research but also contribute to the better design of the Chinese crop insurance program.
Keywords:
Paper type:
Crop insurance, effectiveness evaluation, expected utility model, China
Research Paper
Introduction
Agriculture is an important industry for almost all countries, especially for developing countries with
high population density such as China. However, agriculture is also considered a high-risk sector
because it continually faces risks in production due to adverse weather conditions that farmers cannot
control. Droughts, floods, and other natural disasters may result in serious consequences, such as crop
failure, poverty, and food insecurity. To minimize the effect of adverse weather on the income of
farmers, more than 100 countries have conducted crop insurance program (Mahul & Stutley, 2010).
The Chinese government has also paid more attention to developing a crop insurance program. The
Chinese agricultural insurance program has been very successful since its establishment in 2007, when
the central government began to provide a premium subsidy. The premium of Chinese agricultural
insurance rose to CNY 24.6 billion (appropriately USD 4 billion) in 2012 from CNY 5.2 billion in 2007
and CNY 24.06 billion in 2012. Until now, China is the first and the second agricultural insurance
market in terms of premiums in Asia and in the world, respectively (CIRC*).
Along with rapid development, the Chinese government had allotted an increasing fund to subsidize
crop insurance program. Now, the Chinese government subsidizes more than 70% of crop insurance
premiums. In 2012, the central government of China, who can afford to pay 40%-50% of the premium
subsidy, had paid CNY 10 billion to the agricultural insurance program. At the same time, the low
insured value of the Chinese crop insurance program was criticized in China. Current Chinese crop
insurance only covered some of the physical costs during crop planting, which account for 25% to 40%
of crop returns. Thus, government officers, agricultural economists, and farmers argue that the insured
value of crop insurance is excessively low and may be of no use for farmers. Therefore, the serious
question of valuing Chinese crop insurance has been proposed, and the effective evaluation of the
Chinese crop insurance program in China was highlighted.
In the literature from the end of the 1990s, agricultural economists began to study the effect of crop
insurance, revenue insurance, hedging, loans, and other risk management tools. Certain scholars
investigated the effect of crop insurance on agricultural production (Coffey, Skees, Dillon, & Anderson,
2001; Hennessy, 1998; O'Donoghue, Key, & Roberts, 2005), and some explored the effect of crop
insurance on farmer welfare (Chen, Wang, & Makus, 2007; H. Holly Wang, Hanson, Myers, & Black,
1998). Other scholars studied the interaction of alternative risk management tools (Antón & Kimura,
2009; Keith H Coble & Heifner, 1998; K. H. Coble, Miller, Zuniga, & Heifner, 2004; H Holly Wang,
Makus, & Chen, 2004). Most of them had evaluated the effect of risk management tools under the
*
China Insurance Regulatory Commission: http://www.circ.gov.cn
expected utility framework and conducted empirical studies that adopted a robust stochastic simulation
approach. However, in China, the majority of studies focused on the theoretical discussion of the
necessary and potential effect of subsiding crop insurance (Tuo, 2003; Zhang et. al, 2005; Wu, 2005;
Shi, 2008; Hou et al, 2010), with the exceptions of Zhang & Shi (2007) and Sun & Zhong (2008).
Zhang and Shi (2007) investigate the crop insurance effect in theory by analyzing the issues of crop
insurance subsidy and social welfare, but he fails to conduct an empirical study. Sun and Zhong (2008)
estimated the net welfare of subsidized crop insurance by calculating the willingness of farmers to pay
(WTP) based on survey data. However, this approach, compared to the stochastic simulation approach,
lacks the feasibility to be used for hypothesis research.
Agriculture insurance is initially designed to stabilize the income of farmers by helping them fight
against yield loss due to adverse weather. Thus, evaluating the effect of crop insurance according to
farmers is valuable and meaningful. However, no studies evaluate the effectiveness of the Chinese crop
insurance program from the perspective of farmers and no studies have adopted the stochastic
simulation approach, which has the advantage of analyzing the interactions among different policies,
allowing the analysis of the possible consequences of modifying crucial points in each policy (Kimura
& Thi, 2011). This study is the first to conduct a quantitative assessment of Chinese crop insurance
effectiveness based on the welfare of individual farmers. In this paper, using farm-level data of 574
individual farmers from the provinces of ShanDong (SD), HeNan (HN), JiangSu (JS), SiChuan (SC),
and Shan’Xi (SX), the effectiveness of Chinese crop insurance is evaluated by comparing the certainty
equivalence (CE) of the utility/welfare of representative farmers under alternative crop insurance
policy scenarios. This paper is believed to not only extend academic research but will also contribute to
the better design of the Chinese crop insurance program.
The rest of this paper is organized as follows: the second section describes the methodology. The
sample data and empirical results are shown in the third section. The fourth section presents the
discussion based on empirical results, and the conclusion and policy implications are presented in the
final section.
Method
The basic idea to evaluate the effect of Chinese crop insurance in this paper is to compare farmer
welfare in alternative scenarios. Four scenarios, including no crop insurance (NOCI), Chinese current
crop insurance (CCI), modified crop insurance (MCI) and directly subsiding farmers (DSF) are
hypothesized in this paper. The process to achieve this goal can be divided into four steps.
Step 1: Estimating farmer welfare
In economic theory, using expected utility is the most general approach for comparing risky choices
and studying risk behaviors under uncertainty. Thus, a simulation model based on expected utility
function was developed in this paper to estimate farmer welfare. Similar to previous studies (K. H.
Coble, Heifner, & Zuniga, 2000; Lin, 2001; Turvey, 1992; H. Holly Wang et al., 1998), this paper also
adopted the power utility function (Eq. 1) to compute for farmer utility.
U(w0 + w) =
1
1−𝜃
(𝑤 + 𝑤0 )(1−𝜃)
(1)
Where θ depicts the constant relative risk aversion (CRRA) and was set to 2, 4, and 6, respectively,
to test the robustness of the analysis. Moreover, w0 depicts farmer’s initial wealth, and w is farmer’s net
income, which can be expressed as Equation (2)
w = 𝑤0 + ∑ 𝑃𝑖 ∗ 𝑌𝑖 ∗ 𝐴𝑖 + 𝑓𝑎𝑟𝑚𝑖𝑛𝑐𝑜𝑡ℎ𝑒𝑟 + 𝑜𝑓𝑓 + 𝑇𝑟𝑖𝑛𝑐𝑜𝑚𝑒 + 𝑍𝑐𝑖𝑛𝑐𝑜𝑚𝑒 − 𝑐𝑜𝑠𝑡 + 𝐼𝑛𝑑 − 𝑃𝑟𝑒 + 𝑠𝑢𝑏
(2)
Where Pi is the output price of crop i, Yi is the output yield of crop i, Ai is the area of land
cultivated for crop i, and farminc_other is the farm income from minority crops, livestock, and other
agricultural production activities. Off is the income obtained from working in a city and other non-farm
activities, cost is the crop planting costs, including physical cost and labor cost, Ind and Pre is the
indemnity and premium* of crop insurance, respectively, and sub is the Gov. Premium subsidy for crop
insurance. The indemnity and premium of crop insurance can be calculated as follows:
Ind = A ∗ Ivalue ∗ max(
(1−𝑎)∗𝑦𝑡−𝑦
𝑦𝑡
, 0)
(3)
Where A is the insured acreage, Ivalue is the insured value per unit (which equals price times yield
per unit times coverage level), a is the deductible level, yt is the regular yield in a normal year, and y is
the actual yield.
Given that the CE is the guaranteed amount that has the same utility as farmer’s excepted utility in risk
prospect but has the advantage of being easily compared, the certainty equivalent of farm income is
used to compute for farmer’s welfare within a given level of risk aversion.
1
𝐶𝐸 = {(1 − 𝜃)𝐸[𝑈(𝑤0 + 𝑤)]}(1−𝜃) − 𝑊0
(4)
Step 2: Defining representative farmers
The contribution of crop insurance to farmer welfare may be different to different farmers, depending
on the individual characteristics and risk exposures of farmers. Thus, the problem of which welfare
should be chosen as the standard for evaluating crop insurance first needs to be addressed. The most
standard method is defining the representative farm. This approach is widely used in previous studies
and has the advantage of producing results that are easily interpreted and flexible structures for
carrying out a large number of scenarios (Kimura & Thi, 2011). When defining the representative farm,
ensuring that the representative farm fully reflects the risk exposure of farmers is important. In this
paper, the cluster analysis approach is adopted to group the sample farms into several clusters that are
homogenous in terms of risk characteristics. Specifically, hierarchical clustering analysis is applied to
group farmers in a province into several homogenous clusters according to the variance of agri. income
and the proportion of agri. income to household income.
Step 3: Calibrating risk characteristics of individual farmers
Given that, in a region, the good yield of one farmer may be offset by another farmer with a serious
yield loss, assessing farmer production risk using aggregated data can be misleading (Ketih H Coble,
Dismukes, & Thomas, 2007). Micro data from agricultural planting and agribusiness are used to
calculate and aggregate the characteristics of individual farmers to calibrate the characteristics of
representative farmer. To calibrate risk exposures, the crop yields of individual farmers are detrended at
the county-level† to eliminate the influence of technology improvement. In addition, all monetary
* Note, in theory, premium of crop insurance should be calculated based on actuarial principles. The current premium of crop
insurance, however, is set based on intuitional experiences in China. Thus, when we evaluate the effectiveness of current crop
insurance, the premium is provided based on current crop insurance policies in China instead of being calculated.
†Given that the panel data of agricultural production is short (only six or seven years), historical data collected for
variables such as price and income are deflated to avoid the effects of inflation. Rural CPIs are adopted
to deflate all monetary variables including price, agricultural income, off income, and covert nominate
price into real price at the base year. Log-linear trends are estimated to get the annual percentage
growth of yield at the region level.
Log(Yt ) = a + b ∗ t + ε
(5)
Where Yt is the yield level at the county level in year t, and b can be estimated by log-linear
regression to indicate the annual percentage growth of yield. Subsequently, b is subtracted from
individual observations to estimate the yield risk of individual farmers, as shown in Equation (6).
Dyit = yit + yi1 ∗ (1 − (1 + b)t−1 )
(6)
Where 𝐷𝑦𝑖𝑡 is the detrended yield for farmer i in year t, 𝑦𝑖𝑡 is the yield observation of farmer i
in year t, and yi1 is the yield observation of farmer i in the base year.
After the micro data of farmers are detrended or deflated, the statistic features (mean, standard
deviation) of yield, price, cost, and returns from crop. Other farming and off-farm data are calculated
by crops, by cluster, and by province. The correlation coefficient of yield and price by crops are also
calculated to reflect agricultural production diversity and the relationship of yield and price.
Step 4: Calculating representative farmer’s welfare using simulated approach
Although following the methods in Steps 1 to 3, representative farmer’s welfare could be calculated
using historical data. Thus, we use the stochastic simulation approach in this paper to estimate farmer’s
welfare because a) the time span of micro-level historical data is usually limited and unbalanced; b) the
simulation approach allows us to specify a joint price and yield generating process that reflects actual
conditions (H. Holly Wang et al., 1998); and c) the simulated approach has the advantage of analyzing
the interactions among different policies and allows us to analyze the possible consequences of
modifying crucial points in each policy (Kimura & Thi, 2011).
In spite of the arguments that normal distribution cannot appropriately capture crop yield and price
generation process (Harri, Erdem, Coble, & Knight, 2009), multivariable normal distribution is still
primarily favored when fitting the joint distribution of crop yield and price because it is relative simple
and the conclusion that normal distribution is inappropriate are doubted (Just & Weninger, 1999; H
Holly Wang et al., 2004). In this paper, we still assume that the joint distribution of crop yields and
prices can be captured through multivariable normal distribution, and the Monte Carlo approach was
adopted to generate 1,000 combinations of crop yield and price for each representative farmer.
Regarding other variables, including area, household income, other farm income, cost, and off-farm
income, we assume that they will be fixed and equal to the value of the previous year in the process of
stochastic simulation so that the effect of crop insurance can be highlighted.
Results
Data source
In this paper, 574 individual farmers from 12 counties in 5 provinces (JS, SD, HN, SX, and SC) were
each farmer may underestimate the yield risk when yield trend by farmer is removed. Therefore, the yield trend
should be removed at the regional level. Taking account the huge geographic area of each province in China, the
regional level is set to the county level in this paper.
selected as the sample for collecting farm-level data. Farm-level data were obtained from the China
Rural Fixed Observation Office (CRFOO), Ministry of Agriculture of China. The time span was from
2003 to 2009. Figure 1 shows the geographic location of sample farmers. Table 1 lists the description
of sample farmers.
[Figure 1 here]
[Table 1 here]
Table 1 shows that the crop planting characteristics are reasonable because most farmers in the five
sample provinces plant more than two crops. Moreover, the crop varieties planted by farmers in each
province are consistent with local cropping practices and weather conditions. Although the
observations and the number of farmers are unbalanced in Table 1, which may result in inconsistent
explanatory power, we believe that the sample data are acceptable because this paper is aim to provide
evidence about the effect of Chinese crop insurance on farms instead of infer the whole effect of the
Chinese agricultural insurance program.
Characteristics of representative farmers
As mentioned above, hierarchical clustering analysis was adopted to ensure the homogeneity of
representative farmers. Using hierarchical clustering analysis, the sample farmers in HN are classified
into four groups. Moreover, farmers in JS, SD, SX and SC are divided into two groups, as shown in
Table 2.
[Table 2 here]
After grouping the sample farmers, the characteristics of representative farmers in terms of crop
planting and income were summarized in Table 3. The following can be observed:
1) The crop sowing area in China is relatively small. Farmers in SC and JS have the lowest sowing area,
mostly less than 1.5 Mu (0.1 hectare) per crop. Wheat farmers in SD plant 6.9 Mu of wheat on average,
which is the biggest in the sample but still less than 0.5 hectare.
2) Wheat and corn are the main crops for farmers in SD, HN, and SX, whereas, in JS and SC, farmers
also plant rice and oilseed.
3) The yield risk of crop planting are high in China because the coefficient of variations (CVs) of crop
yield in the five provinces is above 20% on average, ranging from 10% to 40%.
4) The majority of Chinese farmers may face higher risk of yield variation than price fluctuations
because the CVs of yield are higher than the CVs of price for almost all sample farmers. A notable
exception is the oilseed farmers in JS who were more concerned about price than yield because they
have modest yield risk but may sell their oilseed with huge price fluctuations. A possible explanation
for this finding is that JS is one of the best oilseed producers in China.
5) Although part-time farming is increasing at a high pace in China, agriculture remains vital for
Chinese farmers because agricultural income contributes more than 50% of household income in all
sample provinces, which is consistent with the values published by the National Statistics Bureau of
China.
6) However, revenue from grain crops (wheat, rice, corn, and oilseed) only accounts for 3% to 8% of a
farmer’s total income. This fact highlights the necessity and importance of stabilizing the grain revenue
of farmers by crop insurance in China because insurance can help to increase the willingness of farmers
to be involved in grain production.
[Table 3 here]
In practice, diversity production is a common strategy for farmers for stabilizing their crop income. The
negative relationship between crop price and crop yield also contribute to the stability of the farmer’s
income. Table 4 shows the correlation characteristics between yield and price by crop and by province.
Notably, certain crops in Table 4 are not planted at the same time. For example, corn and wheat are
usually rotated every year in SD and HN. Calculating the correlation of crops not planted at the same
time is arguably problematic. However, although certain crops are planted at different times, they are
planted within the same year and the crops planted in the same year experience a similar climate
environment. Therefore, we believe that calculating the correlation matrix as in Table 4 is reasonable.
[Table 4 here]
Based on the characteristics shown in Tables 3 and 4, the Monte Carlo approach was used to generate
the simulated data for representative farmers following the assumption and approach shown in Step 4
in the methodology section.
Effectiveness of current crop insurance program
Although certain pilots of weather index insurance and crop price index insurance exist in one or two
regions in China, the dominant crop insurance program in China is yield insurance, which works
similar to the multi-peril crop insurance (MPCI) in the U.S. but with much lower insured value. At
present, the central, provincial, and local government in China subsidizes more than 70% of crop
insurance premiums. Table 5 lists the crop insurance policies in sample provinces.
[Table 5 here]
Using the simulation model mentioned in the methodology section, the effectiveness of the current
Chinese crop insurance program is evaluated by comparing the representative farmer’s welfare (CE)
under three alternative scenarios. The first scenario is NOCI, the second is with the CCI, and the last is
DSF or transferring government subsidy to farmers directly instead subsidizing crop insurance. Figure
2, which demonstrates the change in the welfare of representative farmers under the alternative
scenarios, shows the following:
1) The current crop insurance program has no doubt increased the welfare of Chinese farmers despite
complaints that CCI only providing low-risk guarantees.
2) Given that revenue from grain crops only contribute 3% to 8% to famers’ total income (as shown in
Table 3), limited welfare improvement due to CCI, which ranged from 0.1% to 0.6% in all sample
provinces except HN, is appropriate.
3) Crop insurance in China remains in need of improvement because the CE under CCI of farmers is
less than DSF in all provinces except HN.
4) The exception of HN farmers in choosing from CCI and DSF may imply that crop insurance is more
useful to HN farmers because HN is one of the main producers of grain in China.
5) The constant result in terms of the representative farmer’s choice between CCI and DSF for different
risk aversions implies that the above findings are robust.
6) The elasticity difference in farmer’s welfare change with the degree of risk aversion degree. This
finding demonstrates the importance of setting appropriate risk aversion coefficients for farmers with
different characteristics, which future studies must address. Future studies that find the underlying
reason for elasticity differences in terms of farmer welfare according to changes in risk aversion
coefficients would also be of research interest.
[Figure 2 here]
Simulated analysis on the modification of crop insurance
Figure 2 shows that farmers in four out of five provinces in China prefer DSF to WCI, which may be
linked to the complaint of insufficient insured value. However, is this complaint the reason why
farmers prefer DSF? To answer this question, this paper increases the insured value of crop insurance
(time insured value with 1.2, 1.5, and 2, respectively) with the premium, subsidy level, and other
factors fixed to test whether increasing insured value will affect the results. The simulation result of
increasing the insured value of crop insurance in JS, SD, SC, and SX are shown in Figure 3. Farmers in
all provinces would benefit more when the insured value of the crop insurance program is increased.
However, farmer’s welfare under the modified crop insurance scenario remains lower than farmers’
welfare under the DSF scenario even when the insured value of crop insurance is doubled. The finding
drawn from Figure 3 implies that only increasing the insured value of crop insurance would not make
farmers prefer crop insurance compared with DSF.
[Figure 3 here]
When the premium subsidy ratio is fixed, increasing insured value of crop insurance will definitely
increase the premium expenditures of the government. The government may wonder whether they can
lower the premium subsidy ratio while increasing insured value. If so, how much can they do so?
Therefore, we allow the government to change the subsidy ratio in the future to investigate whether the
subsidy ratio can be decreased while insured value increases. For simplicity, the simulation results with
the hypothesis that CRRA equals two are demonstrated in Figures 4 and 5. The following can be
observed:
1) The government subsidy is necessary for the implementation of Chinese crop insurance because
farmer’s welfare with NOCI is less than farmer’s welfare with crop insurance only when government
subsidy is higher than 60% in JS, 55% in SD, 20% in SC, and 45% in SX.
2) As expected, farmers’ welfare increased as the premium subsidy ratio increased, but increasing the
insured value of crop insurance decreased, rather than increased, farmer’s welfare when the subsidy
ratio are lower.
3) To maintain farmer’s welfare at par, the premium subsidy ratio can be reduced by increasing the
insured value of crop insurance. When increasing insured value by 50%, the government can reduce the
premium subsidy ratio by 5% in JS, 5%~10% in SD, 20% in SD, and 10%~15% in SX.
[Figure 4 here]
Conclusions
With the rapid development of agricultural insurance in China, the effectiveness of agricultural
insurance program had elicited an increasing amount of attention and has been heavily debated by
government officers, academic experts, and farmers. Unlike previous studies that evaluate the effect of
Chinese crop insurance, this paper adopted the stochastic simulation model (which was originally used
to compare the interaction effects of crop insurance, hedging, contract farming and other risk
management tools) to evaluate the effectiveness of Chinese crop insurance. Based on the simulated
results, three key conclusions can be made.
The first conclusion is that the Chinese crop insurance program has increased the welfare of farms.
However, the crop insurance program in China requires improvement to achieve Pareto improvement
because the majority of farmers prefer DSF, in which the government subsidizes the money of farmers
directly to the current crop insurance program, under the DSF scenarios.
The second meaningful conclusion is that, in most cases, the government should subsidize more
than 40% of premiums to make farmers participate in the crop insurance program. Although the
minimum subsidy ratios of crop insurance differ across provinces, the government should provide more
than 50% of crop insurance premiums to stimulate the willingness of farmers to participate in the
program in China. Such finding is consistent with that of Hazell, Pomareda, & Valdés (1986), who
found that the crop insurance for maize and beans would require a subsidy of two-thirds of the total
premium to be attractive to farmers in Mexico. The conclusion in this paper provides empirical
evidence to the theoretical inference that crop insurance will not work unless premium subsidy is
available.
Although many criticize the low insured value of the Chinese crop insurance program, Chinese
crop insurance will not be improved significantly by only increasing the coverage level or insured
value because these two factors would not make the majority of farmers choose crop insurance
compared to DSF. However, in case of increasing the insured value of crop insurance, it is possible to
reduce the premium subsidy ratio without lowering farmers’ welfare and decreasing the crop insurance
participating ratio. This final conclusion has obvious policy implications for policymakers to improve
Chinese crop insurance program.
Finally, this paper could be improved in several aspects. For example, this paper assumes that the
joint distribution of yield and price fits multi-normal distribution. However, alternative multivariable
distribution, such as joint kernel distribution, could be used to capture the underlying joint distribution
of yield and price more appropriately. We also assume that the degree of farmer’s risk aversion across
sample provinces is the same, which may not be consistent with practical conditions. The two
drawbacks will be improved in following works. Moreover, exploring the difference of the choice
between CCI and DSF in HN and other provinces is not explained in depth because it is beyond the
scope of this paper, but we believe that finding the underlying factor to explain the significant
discrepancy between HN and other provinces is meaningful.
Reference
Antón, J., & Kimura, S. (2009). Farm Level Analysis of Risk, and Risk Management Strategies and
Policies: Evidence from German Crop Farms. Paper presented at the International Association
of Agricultural Economists Conference, Beijing.
Chen, X., Wang, H. H., & Makus, L. D. (2007). Production risk and crop insurance effectiveness: organic
versus conventional apples. Paper presented at the Economics and Management Risk in
Agriculture and Natural Resources, Gulf Shores
Coble, K. H., Dismukes, R., & Thomas, S. (2007). Policy Implications of Crop YIeld and Revenue
Variability at differing levels of Disaggregation. Paper presented at the American Agricultural
Economics Association Annual Meeting 2007, Portland, Oregon.
Coble, K. H., & Heifner, R. (1998). The Effect of Crop or Revenue Insurance on Optimal Hedging. Paper
presented at the Proceedings of the NCR-134 Conference on Applied Commodity Price
Analysis, Forecasting, and Market Risk Management.
Coble, K. H., Heifner, R. G., & Zuniga, M. (2000). Implications of crop yield and revenue insurance for
producer hedging. Journal of Agricultural and Resource Economics, 25(2), 432-452.
Coble, K. H., Miller, J. C., Zuniga, M., & Heifner, R. (2004). The joint effect of government crop
insurance and loan programmes on the demand for futures hedging. European Review of
Agricultural Economics, 31(3), 309-330. doi: DOI 10.1093/erae/31.3.309
Coffey, B. K., Skees, J. R., Dillon, C. R., & Anderson, J. D. (2001). Potential Effects of Subsidized Livestock
Insurance on Livestock Production. Paper presented at the 2001 Annual meeting, August 5-8,
Chicago, IL.
Harri, A., Erdem, C., Coble, K. H., & Knight, T. O. (2009). Crop Yield Distributions: A Reconciliation of
Previous Research and Statistical Tests for Normality. Review of Agricultural Economics, 31(1),
163-182. doi: DOI 10.1111/j.1467-9353.2008.01431.x
Hazell, P. B., Pomareda, C., & Valdés, A. (1986). Crop insurance for agricultural development: issues
and experrience: IICA Biblioteca Venezuela.
Hennessy, D. A. (1998). The Production Effects of Agricultural Income Support Policies under
Uncertainty. American Journal of Agricultural Economics, 80(1), 46-57. doi: 10.2307/3180267
Hou, L.L., Y. Y., Mu and Y. Z. Zeng. 2010. Empirical Analysis on the Farmers Willing of Buying Insurance
Effects and Subsidies Policy ofAgricultural Insurance, Issues in Agricultural Economy, 4, 19-25.
(In Chinese)
Just, R. E., & Weninger, Q. (1999). Are Crop Yields Normally Distributed? . American Journal of
Agricultural Economics, 81, 287-304.
Kimura, S., & Thi, C. L. (2011). Farm Level Analysis of Risk and Risk Management Strategies and Policies:
TECHNICAL NOTE (Vol. OECD Food, Agriculture and Fisheries Working Paper). Paris: OECD
Publishing.
Lin, C. Y. (2001). An Economic Analysis of Alternative Rice Insurance Policies in Taiwan. Taiwanness
Agricultural Economic Review, 6(2), 235-253.
Mahul, O., & Stutley, C. J. (2010). Government Support to Agricultural Insurance: Challenges and
Options for Developing Countries. Washington D.C: The World Bank.
O'Donoghue, E. J., Key, N., & Roberts, M. J. (2005). Does risk matter for farm businesses? The effect of
crop insurance on production and diversification. Paper presented at the 2005 AAEA meeting,
Providence, TO.
Turvey, C. G. (1992). An Economic Analysis of Alternative Farm Revenue Insurance Policies. Canadian
Journal of Agricultural Economics, 40, 403-426.
Wang, H. H., Hanson, S. D., Myers, R. J., & Black, J. R. (1998). The Effects of Crop Yield Insurance
Designs on Farmer Participation and Welfare. American Journal of Agricultural Economics,
80(4), 806-820.
Wang, H. H., Makus, L. D., & Chen, X. (2004). The impact of US commodity programmes on hedging in
the presence of crop insurance. European Review of Agricultural Economics, 31, 331-352.
Wu, Y. 2005. Theoretical Basis of Agricultural Insurance and the Analysis of Its Effectiveness, Social
Sciences, 12, 20-25 (In Chinese)
Shi, H. 2008. Review on the fiscal subsidy system for Agricultural Insurance in the United States.
Insurance Studies, 4, 91-94. (In Chinese)
Sun, X. Y., & Zhong, F. N. (2008) The welfare economic anlysis of crop insurance subsidy. Issues in
Agricultural Economy, 2, 1-11 (In Chinese)
Tuo, G.Z. and G. J., Wang. 2003. Study on Chinese Agricultural Insurance and Rural Social Security,
Beijing: Capital University of Economics and Business Press (In Chinese)
Zhang, Y. H., & Shi, H. (2007). Subsideis, welfare and policy-oriented agricultural insurance: an
intensive study based on welfare economics. Journal of Zhejiang University (Humanities and
Social Sciences), 37(6), 138-146. (In Chinese)
Zhang, Y. H., Q. H., Shi and H. Y., Gu. 2005. A Theoretical and Positive Study on the Demand of Crop
Insurance —— Based on the questionnaires from 662 Farmers’ households of Henan
province, The Journal of Quantitative & Technical Economics, 4, 65-75(In Chinese)
Annex
Figure 1 Location of sample farmers
Table 1 Description of Sample data
Province
JiangSu
ShanDong
HeNan
SiChuan
Shan'Xi
Total
Farm Type
Crop Farm
Crop Farm
Crop Farm
Crop Farm
Crop Farm
Crop Farm
Major
Commodity
Num of
Sample
Length of
the data
Corn,Oilseed Corn,oilseed Corn,Oilseed Corn,Oilseed
,Rice, Wheat
, Wheat
,Wheat
,Rice,Wheat
Corn,Wheat
97
39
282
75
81
574
6 years
(2003-2008)
6 years
(2003-2008)
7 years
(2003-2009)
7 years
(2003-2009)
7 years
(2003-2009)
6 or 7
years
Table 2 Cluster analysis for sample farmers in five provinces
cluster
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Total
# of Individual Farmers
Risk of Agri. income (Normalized)
% of Agincome in Household income
# of Individual Farmers
Risk of Agri. income (Normalized)
% of Agincome in Household income
# of Individual Farmers
Risk of Agri. income (Normalized)
% of Agincome in Household income
# of Individual Farmers
Risk of Agri. income (Normalized)
% of Agincome in Household income
# of Individual Farmers
Risk of Agri. income (Normalized)
% of Agincome in Household income
JS
84
-0.389
52.90%
13
2.025
36.10%
SD
37
-0.512
63.80%
2
1.966
69.00%
97
-0.0657
50.6%
39
-0.385
64.1%
HN
235
-0.051
40.80%
37
-0.156
85.60%
9
3.304
47.50%
1
6.805
22.60%
282
0.0667
46.9%
SC
69
-0.129
48.00%
6
3.297
50.30%
SX
75
-0.351
51.40%
6
2.431
73.20%
Total
75
0.145
48.2%
81
-0.145
53.0%
574
-0.00593
49.7%
Table 3 Summary of representative farmers, by crops, by cluster, and by provinces
JS
Cluster 1
mean
Area
(Mu)
Price*
(CNY)
sd
mean
Cluster 1
sd
corn
1.2
0.6
oilseed
1.7
0.9
1.6
0.8
rice
1.5
0.6
1.4
0.4
wheat
Yield*
(kg/Mu)
SD
Cluster 2
mean
HN
Cluster 2
sd
mean
Cluster 1
sd
mean
Cluster 2
sd
mean
SC
Cluster 3
sd
mean
3.1
1.9
3.2
1.1
2.9
1.5
2.9
1.6
0.3
0.1
0.2
0.1
2.0
1.4
2.2
2.2
3.1
Cluster 4
sd
1.5
mean
0.8
Cluster 1
sd
mean
0.3
SX
Cluster 2
sd
mean
Cluster 1
sd
1.3
0.9
0.9
0.3
1.3
0.7
1.3
0.5
1.4
0.9
1.0
0.5
mean
4.7
Cluster 2
sd
2.0
mean
sd
5.6
2.2
1.7
1.1
6.9
2.7
6.1
3.2
4.3
2.6
4.2
3.2
3.3
1.9
0.8
0.3
1.2
0.7
0.9
0.5
6.7
3.2
5.9
1.8
corn
345.3
123.1
398.5
71.6
407.2
64.1
342.4
143.4
354.4
297.4
340.8
95.9
498.6
26.8
355.7
122.5
348.9
118.3
257.0
66.6
234.8
60.0
oilseed
181.1
29.4
172.7
8.1
168.1
68.3
186.6
59.2
56.5
43.4
62.1
33.1
163.8
58.0
139.9
38.0
rice
592.9
44.2
602.5
45.7
407.5
159.9
401.3
101.3
wheat
257.3
69.4
350.8
58.0
326.4
35.8
336.8
156.8
326.4
69.8
353.8
61.8
389.8
30.3
304.2
80.6
297.1
76.7
226.7
62.5
245.4
72.5
corn
1.4
0.3
1.1
0.1
1.1
0.1
1.0
0.1
1.0
0.1
1.0
0.1
1.0
0.1
1.3
0.2
1.3
0.2
1.0
0.1
1.0
0.1
oilseed
9.7
9.2
15.5
12.3
3.4
1.3
3.3
1.4
6.4
1.6
5.8
2.2
2.3
0.5
2.2
0.5
rice
1.5
0.1
1.5
0.1
2.4
1.3
2.3
1.2
1.1
0.1
1.3
0.1
1.3
0.1
1.2
0.1
1.2
0.1
1.2
0.1
1.2
0.1
1.3
0.1
1.3
0.1
1.3
0.1
1.3
0.1
Total income*
wheat
28353
24732
32228
11896
17265
7830
29426
34192
16806
13378
18158
14195
25727
23143
20988
18533
20862
10050
24004
17465
15888
23531
19978
20162
Agri. income*
15921
25587
12676
13372
10530
4237
22298
25562
6601
9489
15667
13580
15113
24147
10578
21606
9011
5167
13692
17022
9588
24166
16481
16812
G_Crops income*
1776
1632
1467
1509
1107
361
1183
661
735
303
706
306
774
186
907
154
1748
630
1681
608
441
195
419
203
Other Agri_income*
14145
25838
11209
13649
9423
4242
21115
25547
5866
9477
14961
13589
14339
24140
9670
21556
7263
5115
12011
17001
9147
24160
16062
16797
Cost_Farming
1184
677
1143
444
4097
1817
3936
1940
5204
88339
2032
1787
2238
1133
959
243
1996
1229
1643
1000
2756
1317
2570
1252
Non-Ag income*
1770
5682
2764
8424
1783
5174
1410
3445
3252
7776
496
2289
3147
8992
1873
4156
3820
8677
3404
7513
1371
3240
264
1195
NAInc_Local*
1234
4173
1717
5931
408.1
3937
0
0
207.5
1989
0
0
1088
7769
0
0
169.5
2107
35.56
346.6
120
1033
47.38
299.7
NAInc_Migrate*
680.4
4064
1047
5515
1375
3540
1410
3445
3045
7471
495.8
2289
2059
5002
1873
4156
3651
8481
3369
7454
1251
3094
216.7
948.2
428
1444
150
546
546
1545
3177
5904
500
1616
386
934
505
1206
27
60
890
1747
622
979
309
985
897
3038
10327
9599
16663
10143
4368
4694
2602
3989
6474
8147
1684
2874
6983
8666
8545
4881
7203
7742
6335
9158
4747
4521
2364
3129
Trincome*
A&R income*
Note：The unit of all monetary variables in this table is Chinese Yuan, and * indicates deflated or detrended. G_crops income=income from grain crops (wheat, rice, corn and oilseed); Other Agri_income= other agricultural income from
other crops, livestock and other farming activities; Cost_farming includes the cost of grain crops and other crops; NAinc_local and NAinc_migrate means the wage income from working in local s and outside city, respectively; Trincome
means the Transfer income; A&R income means the Assert and residual income
Table 4 Correlation matrix of yield and price by crops, by cluster, and by provinces
JS_Cluster 1
Yield
Corn Oilseed Rice
Corn
1.0
-1.0
-1.0
Oilseed
1.0
1.0
yield
Rice
1.0
Wheat
Corn
Oilseed
Price
Rice
Wheat
JS_Cluster 2
Price
Wheat Corn Oilseed Rice
-0.8
-0.6
-0.8
0.8
0.8
0.6
0.8
-0.8
0.8
0.6
0.8
-0.8
1.0
1.0
1.0
-0.3
1.0
1.0
0.0
1.0
-0.3
1.0
Yield
Wheat Corn Oilseed Rice
1.0
Na
Na
Na
-1.0
1.0
0.0
-0.9
1.0
-0.9
-0.8
-0.9
0.6
1.0
SD_Cluster 1
Yield
Corn Oilseed Rice
Corn
1.0
-0.2
Na
Oilseed
1.0
Na
yield
Rice
Na
Wheat
Corn
Oilseed
Price
Rice
Wheat
Yield
Wheat Corn Oilseed Rice
-0.4
1.0
-0.3
Na
0.0
1.0
Na
Na
Na
0.2
0.4
-0.3
Na
1.0
Yield
Wheat Corn Oilseed Rice
0.02 1.00 0.44
Na
-0.02
1.00
Na
Na
Na
0.36
0.27
-0.63
Na
1.00
Price
Wheat Corn Oilseed Rice
0.59 -0.04
Na
Na
Na
Na
Na
Na
Na
Na
Na
Na
1.00 0.08
Na
Na
1.00
Na
Na
Na
Na
Na
Yield
Wheat Corn Oilseed Rice
0.08 1.00
Na
Na
Na
Na
Na
Na
Na
0.00
0.63
Na
Na
1.00
HN_Cluster 3
Yield
Corn Oilseed Rice
Corn 1.00
Na
Na
Oilseed
Na
Na
yield
Rice
Na
Wheat
Corn
Oilseed
Price
Rice
Wheat
Price
Wheat Corn Oilseed Rice
0.3
-0.1
-0.2
Na
0.8
0.2
0.9
Na
Na
Na
Na
Na
1.0
-0.1
0.6
Na
1.0
0.7
Na
1.0
Na
Na
Wheat
0.0
-0.7
Na
-0.9
0.5
-0.3
Na
1.0
HN_Cluster 2
Price
Oilseed Rice
-0.09
Na
0.21
Na
Na
Na
-0.50
Na
-0.25
Na
1.00
Na
Na
Wheat Corn
0.15 -0.01
-0.09 0.23
Na
Na
1.00 -0.02
1.00
Wheat
Na
Na
Na
Na
Na
Na
Na
Na
SD_Cluster 2
Price
Wheat Corn Oilseed Rice
0.0
-0.3
0.0
Na
0.0
0.0
0.0
Na
Na
Na
Na
Na
1.0
0.1
0.0
Na
1.0
0.7
Na
1.0
Na
Na
HN_Cluster 1
Yield
Corn Oilseed Rice
Corn 1.00 -0.05
Na
Oilseed
1.00
Na
yield
Rice
Na
Wheat
Corn
Oilseed
Price
Rice
Wheat
Price
Wheat Corn Oilseed Rice
Na
Na
Na
Na
Na
Na
0.2
-1.0
Na
Na
-0.1
0.1
Na
Na
Na
Na
Na
Na
Na
1.0
-0.3
1.0
Price
Wheat Corn Oilseed Rice
0.67 0.25 -0.50
Na
0.29 -0.39 -0.52
Na
Na
Na
Na
Na
1.00 -0.15 -0.78
Na
1.00 0.27
Na
1.00
Na
Na
Wheat
0.58
0.54
Na
0.53
0.04
-0.71
Na
1.00
HN_Cluster 4
SC_Cluster 1
Yield
Price
Corn Oilseed Rice Wheat Corn Oilseed Rice Wheat
Corn 1.00 0.36 0.32 0.28 -0.03 0.25 -0.32 0.23
Oilseed
1.00 0.43 0.25 -0.09 0.15 -0.44 0.08
yield
Rice
1.00 0.47 0.05 0.16 -0.75 0.16
Wheat
1.00 0.02 0.13 -0.42 0.15
Corn
1.00 -0.47 -0.12 0.33
Oilseed
1.00 -0.15 0.05
Price
Rice
1.00 -0.05
Wheat
1.00
SX_Cluster 1
Yield
Price
Corn Oilseed Rice Wheat Corn Oilseed Rice Wheat
Corn 1.00
Na
Na
0.26 -0.21
Na
Na
-0.02
Oilseed
Na
Na
Na
Na
Na
Na
Na
yield
Rice
Na
Na
Na
Na
Na
Na
Wheat
1.00 -0.04
Na
Na
-0.02
Corn
1.00
Na
Na
0.52
Oilseed
Na
Na
Na
Price
Rice
Na
Na
Wheat
1.00
Price
Wheat Corn Oilseed Rice
0.86 -0.22
Na
Na
Na
Na
Na
Na
Na
Na
Na
Na
1.00 0.01
Na
Na
1.00
Na
Na
Na
Na
Na
Wheat
-0.42
Na
Na
-0.01
-0.17
Na
Na
1.00
SC_Cluster 2
Yield
Price
Corn Oilseed Rice Wheat Corn Oilseed Rice Wheat
1.00 -0.17 0.34 0.79 0.99 0.15 0.37 0.77
1.00 0.38 0.30 -0.23 0.94 -0.33 0.49
1.00 0.79 0.23 0.54 0.73 0.51
1.00 0.73 0.56 0.49 0.88
1.00 0.09 0.30 0.74
1.00 -0.14 0.73
1.00 0.07
1.00
SX_Cluster 2
Yield
Price
Corn Oilseed Rice Wheat Corn Oilseed Rice Wheat
1.00
Na
Na
0.05 0.10
Na
Na
0.09
Na
Na
Na
Na
Na
Na
Na
Na
Na
Na
Na
Na
Na
1.00 -0.53
Na
Na
0.13
1.00
Na
Na
0.57
Na
Na
Na
Na
Na
1.00
Table 5 Practical crop insurance policies in five Chinese provinces
Insured Value (CNY/Mu)
Gov. subsidy ratio*
Premium Ratio (%)
Province
Corn
Oilseed
Rice
Wheat
Corn
Oilseed
Rice
Wheat
JS
300
300
300
300
5
5
5
5
SD
300
300
320
3.3
5
HN
192
300
263
311
6
6
6
6
SC
300
280
300
300
7
5.5
7
7
SX
280
300
300
7
5
Crops
3.1
80%
5
CRRA=2
CRRA=4
5
4
3
2
1
0
JS
SD
HN
SC
SX
SC
SX
JS
SD
HN
SC
SX
CRRA=6
5
4
3
2
1
0
JS
SD
HN
CCI
DSF
Note: NOCI is the Baseline,=0
Figure 2 Welfare change of representative farmers under alternative scenarios
*
In practice, the crop insurance subsidy ratios range from 75% to 80% in China, depending on the region and
crop. For simplicity, the subsidy ratio in this paper is set to 80% for all crops in all provinces.
CRRA=2
CRRA=4
JS
JS
SD
SD
SC
SC
SX
SX
-.4
-.2
0
.2
.4
.6
CRRA=6
JS
SD
SC
SX
-.4
-.2
0
.2
.4
.6
Change of Farmer's CE, %
MCI120%
MCI150%
MCI200%
NOCI
DSF
Note: CCI is the Baseline equal to zero; Farmers in cluster 1 are selected;
MCI120% means increaing insured value of CCI to 120%
Figure 3 Simulation results of increasing insured value of crop insurance
MCI200%
MCI150%
MCI100%
CCI
DSF
JS
SD
SC
SX
NOCI
1.0050
1.0000
0.9950
0.9900
0.9850
1.0050
1.0000
0.9950
0.9900
0.9850
0
10
20
30
40
50
60
70
80
90
0
10
20
30
40
50
60
70
80
90
Premium subsidy ratio by government (%)
Note: CCI is the Baseline equal to one; Farmers in cluster 1 are selected;
MCI150% means increaing insured value of CCI to 150%
Figure 4 Farmer’s Welfare of alternative crop insurance program in SD, HN, SC, and SX