Analysis of Agriculture Insurance Technical Efficiency Based on COLS Approach for Rice

Farmers in Liaoning province

Yang Yu Department of finance, Liaoning University of International

Business and Economics, Dalian, China, 116052

Yuyang770727@163.com

Erda Wang Faculty of Management and Economics,

Dalian University of Technology, Dalian, China, 116024 edwang@dlut.edu.cn

Abstract—The purpose of this study was to describe the effect of agriculture insurance on crop production among rice farmers in Liaoning province. The population used in this study consisted of rice farmers. We used stratified multi-stage cluster sampling method to collect data of this study and face to face interviewed with 206 farmers in three different climate regions: cold, temperate and tropical during 2010-2011 crop years. Corrected Ordinary Least Square (COLS) was used to determine farmers' technical efficiency. The study adds to findings that agriculture insurance has positive effect on temperate and tropical regions. However, there was non-significant production difference between insured and uninsured farmers in cold region. Therefore, the conclusion was that technical efficiency of agricultural production is a function of agriculture insurance as well as other variables such as production risk management practices, personal characteristics and fair distribution of crop inputs.

Keywords-agriculture insurance; efficiency analysis; production risk; rice farming

I. INTRODUCTION Liaoning province is situated in the south of Northeast

China. It is China's northeast economy area and is located between 38 and 43° in the North, 118 and 125° in the East. According to the risk assessment of company Maple-croft announced the “natural disaster risk index" ranking, that are based on a 1980 to 2010 Global occurs in a variety of natural disaster. China is the twelfth ranked " extreme risk level” in the world[1].

Among wide range of natural disasters in china, drought has been a recurring phenomenon in Liaoning Province and farmers have to cope with the high rainfall variability continually. The arid climatic condition implies that if no optimized use of water resources, it must cause widespread damage. Liaoning province is not only the national characteristics of the commodity grain production base, but also the important high quality rice production base. Due to the low temperature, the meteorological factor and the other environmental conditions, Liaoning grain crop yield is annual

fluctuation, measured by the coefficient of variation ( CV, coefficient of variability ) is 0.12 ~ 0.29 [2], describing the area of agricultural production risk and the area farmer 's resilience is poor. Agricultural production risk management is therefore an important business due to a variety of weather, disease, pest and essential input cost related risks that Chinese farmers face.

II. REVIEW OF THE LITERATURE The role of agriculture in economic development has been

recognized for years. However, in recent years, very little attention has been given to technical efficiency on agriculture insurance across diverse climatic regions, while a great deal of attention to crop yield insurance and revenue insurance in domestic research perspectives. Due to uncertain natural growth process of crops, the production risk comes from the weather, disease, pests and other factors. Crop production risk management program is call for crop insurance. Technical efficiency was first reported by J Guillaume et al. (2011) that farmers living in cold regions were no inclined to buy insurance coverage for their crops, but the farmers living in tropical and temperate regions were opposite [3]. Moreover, in a recent study by G. Y. Jiao et al. (2010) revealed that profitability was the biggest motivating factor in using crop insurance coverage in Liaoning province of China[4]. Furthermore, B. K. Goodwin (2008) indicated that agriculture insurance has a great influence on the intention to utilize indigenous approach to farming practices [5]. Agriculture insurance is one tool that can assist Liaoning crop producers in managing the risk of yield loss related with disasters.

Unfortunately, farmer is often reluctant to buy insurance coverage for his crop considering the arid climate and being one of the disaster prone regions in China. An agriculture production productivity unit, defined as the ratio of its output to its input, different from production technology and in the efficiency of the production process. Although insurance agents encourage dry farmers to buy insurance coverage for their crops, only a few farmers have come to believe that crop insurance is important if they are to make at their maximum efficiency.

The purpose of this study was to analyze the technical efficiency of agriculture insurance and to determine the effect

This research was financially supported by the Youth Foundation of Social Science and Humanity, China Ministry of Education (#11YJC630267) and Scientific Research General Project of Department of Education in Liaoning Province (#W2011151)

978-1-4577-2025-3/12/$26.00 ©2012 IEEE

of crop insurance among insured and uninsured rice farmers in Liaoning province.

III. METHODS AND MATERIALS The population in this study is formed from insured and

uninsured rice farmers of Liaoning province. Using stratified multi-stage cluster sampling method 206 farmers were interviewed among three different regions: Tropical, temperate and cold during 2010-2011 crop years. In specifying the model in this paper, it is assumed that rice farming use seven output criteria to produce output (Y): land (X1), pesticide (X2), phosphate fertilizer (X3), nitrogen fertilizer (X4), seed (X5), labor (X6) and machinery (X7).

Cobb-Douglas Production Function as a statistical deterministic production function was used to represent the production technology of Liaoning rice farmers. Cobb-Douglas Production was selected as the result of popularity in specification and empirical estimation frontiers. However, It is because logarithmic nature, simple use is to make econometric parameters estimation of production function. As F. Aitsahlia (2011) points out, that function may be criticized for its restrictive assumptions such as constant returns to scale and unitary elasticity of substitution, but alternatives such as trans-log production function also has problems such as being susceptible to degrees of freedom and multi- linearity and other limitation [6]. The Cobb-Douglas statistical deterministic production technology characterized by variable return to scale is specified as:

∑

=

−−+=7

10 ,2,1

miminmi niXLLnY εδδ

(1)

In Equation1, the“ ith ”rice farmer output is represented by the amount of the “mth ”farm input. Constant returns to scale in production is imposed on the parameters by the following restriction:

∑=

=7

1

1m

mδ

(2)

The production frontier is deterministic due to a one-side non-negative error term included, which has a non-negative mean and constant variance and is assumed to be identically and independently distributed. To estimate this production frontier, using Ordinary Least Squares (OLS) will come out problems. As Greene (1980) presented, while OLS provides best linear unbiased estimates for the slope parameters and computed standard errors appropriately, but not an unbiased estimate for the intercept parameter. There further has no significant in the OLS residuals of the model. Because the dependence of technical efficiency on these residuals being non-negative, a correction for the biased by shifting, upward via the largest positive OLS residual. As the Corrected Ordinary Least Squares (COLS) method is a two-step procedure, the unbiased estimator for the intercept parameter is presented by:

*ˆˆ0

*0 εδδ += (3)

The estimates are non-negative in terms of the corrected OLS residuals are all non-positive, which implying none of the

forms is more than 100 percent efficient. The calculation of Technical Efficiency (TE) for the ith farm is used by the following equation:

*)exp()exp( εε −=−= iii EET

(4)

Where, “ iε ”=OLS residual for the ith farm; and “ *ε ”= defined “ iε ”.

The production of rice farming inputs are included by the following: land, pesticide, phosphate fertilizer, nitrogen fertilizer, seed, labor and taken machinery. Land is all of the fragments for rice farming per hectare during a crop year. Pesticide is represented by the cost in liter per hectare. Phosphate and Nitrogen fertilizer is represented by the amount of consumption in kilogram per hectare. Seed is measured by the amount of consumption in kilogram per hectare. Labor means total hours that rice farmer worked per hectare. Machinery is represented by the total operated hours per hectare. Statistics collected is shown in Table 1.

TABLE I. DESCRIPTIVE STATISTICS FOR THE SAMPLE OF 251 RICE FARMERS IN LIAONING PROVINCE

Variables (unit) Max. Min. Mean SD. Output (ton/ha) 5 0.2 1.39 0.88 Land (ha) 10 0.2 1.77 3.85 Pesticide (L/ha) 5 0.5 1.68 0.70 Phosphate (k/ha) 2000 3 109.42 117.75 Nitrogen (k/ha) 500 3 114.59 63.27 Seed (k/ha) 300 120 158.87 29.21 Labor (hour/ha) 30 5 13.25 5.36 Machinery(h/ha) 13 2 5.64 2.03

IV. STUDY RESULTS To obtain estimates of parameters for the statistical

deterministic production models in each climate region separately, The Corrected Ordinary Least Squares (COLS) method was conducted. The parameters intercept, pesticide, nitrogen fertilizer and seed are statistically significant at 1 % level of significance in cold region as shown in Table 2.

TABLE II. TABLE 2. ESTIMATED COLS FOR RICE FARMERS IN COLD REGION.

Variables Coe. T-stat Prob. R-squ. Intercept -1.9923** -2.8563 0.00 0.8722

Adj. R 0.8034

N=52

X1 0.2037 -0.7515 0.51 X2 0.3012** 2.9724 0.00 X3 -0.0166 -0.1745 0.78 X4 0.2250* 2.0157 0.03 X5 0.7237** 2.7570 0.00 X6 -0.1816 -1.1373 0.30 X7 0.1694 1.3743 0.22

** Significant at 5% and *Significant at 10% All parameters in temperate region as shown in Table 3

have meaningful signs except land, phosphate fertilizer and labor that were not significant statistically.

TABLE III. TABLE 3. ESTIMATED COLS FOR RICE FARMERS IN TEMPERATE REGION.

Variables Coe. T-stat Prob. R-squ. Intercept -1.6023** -2.3160 0.01 0.6944

X1 0.1937 -0.4965 0.59 Adj. R 0.6537

N=89

X2 0.2734* 1.6572 0.08 X3 0.0131 0.0742 0.95 X4 0.2693* 1.8943 0.07 X5 0.5929* 1.9803 0.06 X6 -0.2890 -1.3473 0.17 X7 0.2656* 1.8749 0.05

** Significant at 5% and *Significant at 10% Only land, phosphate and nitrogen fertilizers were

statistically significant in tropical region as shown in Table 4.

TABLE IV. ESTIMATED COLS FOR RICE FARMERS IN TROPICAL REGION.

Variables Coe. T-stat Prob. R-squ. Intercept 0.5834 0.6033 0.59 0.7753

Adj. R 0.7390

N=65

X1 1.4990** 2.6812 0.01 X2 -0.2013 -0.3865 0.70 X3 -0.3537* -1.6242 0.06 X4 0.4630* 1.8547 0.07 X5 -2.2892 -0.4890 0.72 X6 -0.2054 -0.8634 0.45 X7 0.0795 -0.3082 0.73

** Significant at 5% and *Significant at 10% Technical efficiency estimate distributions for each climate

regions are shown in Table 5.

TABLE V. FREQUENCY OF DISTRIBUTIONS OF TECHNICAL EFFICIENCY ESTIMATE FROM COLS IN INDIVIDUAL REGION

TE Cold Temperate Tropical(0,0.2] 1 8 5

(0.2,0.4] 3 20 23(0.4,0.6] 24 54 36(0.6,0.8] 22 7 1(0.8,1.0] 3 0 0

Total 52 89 65Mean 62.1 39.9 38

To determine the effect of agriculture insurance across individual insured or uninsured rice farmers in three climate regions in Liaoning province, T-test for independent sample was used. As a result, there were no significant differences in cold regions. There is a significant difference among insured and uninsured rice farmers in temperate and tropical regions that shown in Table 6.

TABLE VI. HYPOTHESIS TEST REGARDING THE MEAN TECHNICAL EFFICIENCY IN INDIVIDUAL CLIMATE REGIONS

Region T-value Decision

Cold 0.803 Do not reject H0 at the 5%level of significance

Temperate 4.519 Reject H0 at the 1%level of significance Tropical 2.508 Reject H0 at the 1%level of significance H0: Technical efficiency for insured rice farmer is equal to that of uninsured rice farmer

V. DISCUSSION Based on COLS approach, both insured and uninsured rice

farmers' technical efficiencies were estimated in this study. The results revealed a positive effect of crop insurance in temperate

and tropical regions. However, in cold regions farmer’s crop insurance coverage did not affect due to higher rainfall in Liaoning province with cold climate. The average annual precipitation in Liaoning province is 250.5 mm and most of this precipitation occurs in the cold region since 2009 to 2011. Thus farmers that living in these regions may not be willing to buy insurance policy for their crops. Implications from this study come out the result for agricultural extension agents as well as agricultural policy-makers in China. First, county related insurance agents should conduct educational programs for farmers in cold, tropical and temperate regions in order to emphasize the importance of policy-oriented agriculture insurance in farming practices. One of the effective ways to do this would be to invite insured farmers to extension classes as guest speakers to share their benefit and experience with uninsured farmers. Second, insurance agents should target farmers in cold regions about the benefits of agriculture insurance. The other effective method would be to distribute insurances extension showing drought patterns that have shown to occur every two to three years in China. Farmers would be alert in these awareness programs about a possible drought season that in turn would encourage those farmers uninsured to consider agriculture insurance. Third, agricultural policy-makers could provide incentives for those farmers who buy agriculture insurance. These incentives could be in the form of free fertilizer or free soil sample analysis to those who need insurance coverage. It may take a while before agriculture insurance policies are institutionalized in the country but a pre-requisite to that would be to policy-oriented crop insurance across the country. Fourth, agricultural policy-makers in China should use mass media as a means to diffuse crop insurance benefits and recognize those who adopt agriculture insurance across the country. Finally, government should provide enough incentives (free soil tax, free fertilizer etc.) in drought years, in order to encourage farmers to cover the risk of dry season through policy-oriented agriculture insurance.

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