International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014)
The Cause Analysis of China Agricultural Information Efficiency in Different Provinces —Based on the comparison of the 31 provinces and autonomous regions
Wang huipo* Zhang lipeng Department of Economics and Management Department of Economics and Management Dalian University Dalian University Dalian , China Dalian , China [email protected] [email protected]
Abstract—The government wants to achieve the goal put information efficiency helps to make full use of forward in the eighteenth Congress of the Chinese agricultural information resources and raise the level of the Communist Party. It needs to improve efficiency. Therefore, development of agricultural information at a low cost. the efficiency of agricultural information is particularly Therefore, the paper has evaluated the agricultural important for a large agricultural country like China in the information efficiency by constructing evaluation index information age. Through constructing agricultural input and using the statistic data of 31 provinces and and output efficiency evaluation index system and using autonomous regions through our country. And this paper Data Envelopment Analysis, the article analyzed the analyzes the motivation of agricultural information Agricultural Information Efficiency in China’s 31 provinces efficiency difference forms in various provinces and and autonomous regions. As the result, we found that the autonomous regions and sensitivity of each agent. agricultural information efficiency of 11 provinces and autonomous regions does not reach optimal efficiency. So the In this paper, the structure arrangement is as follows: paper explored the sensitive of various input and output Section 2 is literature review; Section 3 has given the indicators in these cities. Finally, this paper gives some research methods and the corresponding index system; policy advice from a objective perspective to improve the Section 4 is using DEA method to deal with data and agricultural information efficiency. explaining and analyzing the statistical results; the last is coming to the conclusion and putting forward the outlook. Keywords- Agricultural Information Efficiency; DEA; Indicator System; Efficiency sensitivity II. LITERATURE REVIEW Agricultural information is the important component of I. INTRODUCTION national economic information and is of great significance Report of the eighteenth Congress of the Chinese to promote China’s agricultural information and realize Communist Party proposed that our country will build a agricultural modernization and the comprehensive moderately prosperous society and realize GDP and urban construction well-off society. [5]Agricultural information and rural incomes will be more double than 2010 until the efficiency decides the ability to use factors of agricultural year 2020.In order to achieve the target, the productivity is information and the effect of it. While the research that critical, that is to say, the problem of productivity most domestic scholars have done in recent years mainly efficiency is the core issue of economic and social focus on the current development of agricultural development of our country in the next decade or even two information, the existing problems, the improving decades. Similarly, the problem of efficiency is also the measures and the effect of agricultural information and so core issue of agricultural development of our country. on. Such as Cao Junjie (2007) analyzed 4 prominent Many scholars’ study shows that information has a pivotal problems in the agricultural information of our country and significance in promoting the development of rural areas put forward 5 practical agricultural information and agriculture. Li Youzhu and others have done some construction measures; [6]Liu Jinai (2009) point out three corresponding studies on agricultural Information main status, five problems and six countermeasures; technology investment and the contribution rate of [7]Long Bing, Du Tongqing (2012) analyzed the role of agricultural output in 2012; [1]Yu Shumin has studied the agricultural information in our country’s modernization impact of agricultural information on agricultural TFP in and point out that agricultural information is the important 2011. [2]Guo Qingran has studied the strategy that content and inevitable choice of agricultural modernization. agricultural information promotes agricultural [8]However, recent research on agricultural information in industrialization in 2008[3] and also Zhang Hong has overseas refers only to its system, approaches, degree and studied the influence agricultural information has on circumstance and so on. Nuray Kizilaslan (2006) agricultural economic growth [4] and so on. mentioned the benefit agricultural information has on From the above papers, we can learn that agricultural farmers in his paper by studying the agricultural Information plays an important role in improving the information system in Turkey. [9]Adebambo Adewale efficiency of agricultural production, but there is a lack of Oduwole , Chichi Nancy Okorie (2010) analyzed farmers research about it. The improvement of the agricultural in Abeokuta, Ogun State developed agricultural
© 2014. The authors - Published by Atlantis Press 792 information through electronic, print media, the village square commence, religion and the market.[10]William T max j yuh 0 Mokotjo,Trywell Kalusopa(2010) obtained the degree of 0 agricultural information service through investigating 300 T T .. j j ,,2,1,0 njyuxwts farmers in Lesotho.[11] L.O. Aina（2012）indicated that P)( T the information environment related to agricultural xw 0 1 stakeholders in Botswana. [12]It is rare to see the research on agricultural information efficiency. In this paper, it uw 0,0 regard the provincial differences of agricultural Us information as the starting point, using the method of DEA ing dual planning theory and further introduction of slack (Data Envelopment Analysis), combining the presented and surplus variables above fractional programming can be agricultural information input and output index, parsing transformed into a linear programming problem, as the data of 31 provinces, cities and autonomous regions, formula (2) shown: and conclude that all provinces and autonomous regions in min China agricultural information efficiency ranking and n analyze the causes of the differences, for low efficiency .. xsxts area and optimization of agricultural information inputs jj 0 j1 (2) used to provide the direction of improvement. n jj ysy 0 III. METHODOLOGY AND INDICATOR SYSTEM j1 ,,2,1,0 nj A. Methodology j ss 0,0 DEA (Data Envelopment Analysis) method is an effective method for multi-input and multi-output system ** * which make the relative efficiency evaluation. It not only If ss 0,0,1 , the decision-making unit calculates the efficiency of the production units and can j is DEA valid. The economic activities are both the best analyze the reasons for the different efficiency. Regional 0 agricultural information efficiency evaluation is a multi- of technology effectively and scale efficiently; If * 1 input and multi-output the same type of system evaluation. and at least one of the input or output is greater than 0, the Therefore this paper adopted the method to evaluate * unit j is weak DEA valid. If 1, the unit j is not agricultural information efficiency of 31 provinces and 0 0 autonomous regions with DEA and analyze DEA valid. The economic activities are neither the best of theMaintaining the Integrity of the Specifications technology effectively nor scale efficiently. difference motivation. B. Indicator system This paper adopts CCR model to study efficiency of agricultural information in 31 provinces and autonomous Agricultural information with quantify measure first regions studied. The CCR model is given as follow (1): began in Machlup (Macluph) and Borat (Porat), while China's agricultural information construction began in the s yu early 1990s. It is clear measure of agricultural information r rj0 max h r1 for research lagged behind and because China's agriculture j0 m xv has a high dispersion, variety, small-scale, family-run and i ij0 non-standard features. As a result, there are some i1 s difficulties in selecting indicators, and data availability is yu r rj also limited. So far, the presence of agriculture information r1 (1) ts j ,2,1,1.. ,n measurement indicators has big different with different i xv ij scholars, as shown in Table 1. ,0 ovu TABLE1 SCHOLARS HAVE BUILT EVALUATION INDEX SYSTEM OF AGRICULTURAL INFORMATION No Author Document Indicators The above plan is a fractional programming model, Classification using Charnes-Cooper transformation: 1 Zhou Hong Research on Three levels and （2001） agricultural four categories information 1 questions[14] t t w tv,, u tu 2 Xu Aiping.et Research on 8 level xv 0 （2004） agricultural indicators and a 1 information series of ， t measurement secondary t t xw 0 1 xv 0 index system indicators [15] It can be turned into the following linear programming 3 Wang Zhengyu The 20 indicators in model P: （2005） development of six categories Chinese agricultural
793 information and TABLE2 THE INPUT INDICATORS OF AGRICULTURAL INFORMATION empirical EFFICIENCY EVALUATION research [16] Level Secondary Source Indicat 4 Sheng Qifeng Agricultural 6 level Indicators Indicators ors （2005） information indicators and construction and 18 secondary Code evaluation indicators Number of TV every Yearbook of
research [17] Agricultural hundred rural Rural x1 5 Lu Anxinag, etc Research on the 20 indicators in Information Household （2006） development of six aspects Infrastructur Survey rural information e Number of computer Yearbook of
index system every hundred rural Rural x2 [18] Household 6 Chen Zhen, Cao Agricultural 4 level Survey Dianli information indicators and Number of Yearbook of
（2007） evaluation 18 secondary telephone every Rural x3 research based indicators hundred rural Household on principal Survey component The proportion of China Statistical x analysis [19] Agricultural primary industry in Yearbook、 4 7 Lu Lina The structure 21 indicators in information personnel China （2007） building of six categories professionals employment Agricultural agricultural Development information Report measurement Number of every Statistical
index system hundred high school Yearbook of the x5 [20] education or more provinces about 8 Liu Shihong.etc China’s rural 6 level the rural （2008） information indicators and economy、 evaluation 25 secondary China method research indicators Agricultural [21] Development 9 Xin Liyuan.etc Shallow of 5 level Report Tianjin indicators, 20 （2008） Long-distance fiber China Statistical agricultural secondary optic coverage ratio Yearbook x6 information indicators Information construction of Agricultural Agricultural Yearbook of evaluation index Information investment Rural x7 system [22] Resources accounted for the Household 10 Yang Cheng, The rural Five elements proportion of Survey、China Jiang Zhihua information indicators and investment areas Statistical （2009） evaluation index 28 constitute Yearbook system indicators construction in Technology our country [23] Amount of CNNIC x8 11 Deng Peijun, Agricultural Four areas, 13 agriculture-related Statistics Chen Yizhi information and specific websites in Millions of people （2010） the rural indicators economy growth Rural per capita China Yearbook x correlation reserves of published 9 studies [24] agricultural books 12 Lu Lina, Yu Agricultural 6 large elements Fengcheng.etc information and 20 TABLE3 THE OUTPUT INDICATORS OF AGRICULTURAL INFORMATION （2010） level measure indicators EFFICIENCY EVALUATION theory and Level Secondary Source Indicator application Indicator Indicators Code research of our s country [25] Farmer Per capita net China
Reference to the above scholars constructed the index Output income of rural Agricultural y1 system of agricultural information, taking into account the residents Statistics degree of difficulty index data obtained. This article Yearbook Farmers per capita China selected input indicators from the infrastructure, talent and y resources and output indicators from farmers and social food production Agricultural 2 perspectives. Finally, we selected nine input indicators and Statistics Yearbook four output indicators totally. Social Per capita amount China
output of Posts and Statistical y3 Telecommunication Yearbook s Information The proportion of China agricultural added Agricultural y4 value in total output Statistics value Yearbook
794
1.05 IV. DATA ANALYSIS 1 0.95 A. The initial data analysis 0.9 It is concluded in this paper that the optimal point 0.85 envelope of the output/input ratio to determine the efficient 0.8 frontier by using the CCR-O model of DEA and choosing 0.75 Social Output 9 input indicators and 4 output indicators. Obviously, the 0.7 decision unit deviating farther from the efficient frontier is 0.65 lower relatively, while those closer is higher. 0.6 Through DEA software processing of input and output 0.55 data of 31 provinces, cities and autonomous regions in our 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 country, we calculate their efficiency and the result is Farmer Output shown in Fig.1. The efficiency of decision making for 1 unit constitutes the efficient frontier. The Fig.1 shows Figure 2. Interval efficiency cities in the efficient frontier are Beijing, Shanghai, Tianjin, Neimenggu, Jilin, Heilongjiang, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Guangxi, Guangdong, Hainan, C. Sensitivity Analysis Chongqing, Sichuan, Guizhou, Xizang, Xinjiang and other 1 20 provinces and cities autonomous region; in the second 0.95 Initial Efficiency level is Shandong, Liaoning, Hebei, Henan, Fujian, etc. 0.9 0.85 Efficiency of Shanxi’s agricultural information is the x1x2x3 0.8 lowest, the value is 0.6612. 0.75
indicators(B) 0.7 x4x5 Initial Efficiency 0.65 The efficiency of input 0.6 x6x7x8x9 BJ TJ HB SX LN JL SH JS ZJ AH FJ JX SD HN HB HN GD GX HN CQ SC GZ YN XZ SX GS QH NX XJ 1 NMG HLJ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 0.95 The unit 0.9 0.85 Initial Figure 3. The efficiency of input indicators (B) 0.8 Efficiency 0.75 1 0.7 0.95 Initial 0.65 0.9 Efficiency 0.6 0.85 BJ TJ HB SX LN JL SH JS ZJ AH FJ JX SD HN HB HN GD GX HN CQ SC GZ YN XZ SX GS QH NX XJ
NMG HLJ 0.8 1 2 3 4 5 6 7 8 9 10 11 12 1314 15 16 17 1819 20 21 22 2324 25 26 27 2829 30 31 0.75 y1y2 indicators(B) 0.7 0.65 The efficiency of output Figure 1. Initial Efficiency 0.6 y3y4 BJ TJ HB SX LN JL SH JS ZJ AH FJ JX SD HN HB HN GD GX HN CQ SC GZ YN XZ SX GS QH NX XJ NMG HLJ From Fig.1, we can see that agricultural information 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 efficiency in 11 provinces, cities, and autonomous regions The unit is non DEA effective. Figure 4. The efficiency of output indicators (B) B. Fractal dimension efficiency analysis
1 Initial The efficiency of various provinces, cities and Efficiency autonomous regions on the two kinds of output indicators 0.95 x1 0.9 x2 are shown in Fig.2.In Fig.2, the efficiency of farmers’ 0.85 x3 output and social output in China’s 31 provinces, cities and 0.8 x4 0.75 autonomous regions is divided into 4 parts by using two x5 0.7
parallel and perpendicular lines. indicators(S) x6 0.65 As is shown in Fig.2, both farmer output and social 0.6 x7 The efficiency of input BJ TJ HB SX LN JL SH JS ZJ AH FJ JX SD HN HB HN GD GX HN CQ SC GZ YN XZ SX GS QH NX XJ output efficiency in Beijing, Shanghai, Tianjin and Jiangsu NMG HLJ x8 1 2 3 4 5 6 7 8 9 1011 121314 151617 181920 212223 2425 262728 293031 x9 are higher, greater than 0.85; Hebei, Liaoning, Henan, The unit Qinghai and other provinces have a lower farmers’ produce efficiency but higher social output efficiency; Figure 5. The efficiency of input indicators (S) Gansu, Guangxi and other provinces have a lower social output efficiency but higher farmers’ output; Shanxi province in the two aspects of output efficiency is very low.
795 sensitivity of agricultural information efficiency to the 1 Initial 0.95 Efficiency total value added of Shanxi province is the largest. 0.9 Through sensitive analysis, we can know clearly the y1 0.85 most effective improvement direction of the provinces, 0.8 cities and autonomous regions, which benefits for the 0.75 y2 optimal allocation of the elements of the agricultural indicators(S) 0.7 information and reduces resource waste. 0.65
The efficiency of output y3 0.6 V. CONCLUSION AND OUTLOOK BJ TJ HB SX LN JL SH JS ZJ AH FJ JX SD HN HB HN GD GX HN CQ SC GZ YN XZ SX GS QH NX XJ NMG HLJ y4 1 2 3 4 5 6 7 8 9 10 11 1213 1415 16 1718 1920 21 2223 24 2526 2728 29 3031 The unit A. Conclusion The paper constructs the agricultural information Figure 6. The efficiency of output indicators (S) evaluation efficiency index system and uses the method of DEA to analyze agricultural information efficiency in 31 In this paper, the sensitivity analysis is divided into provinces and autonomous regions in our country, then input index sensitivity and output index sensitivity analysis comes to the conclusion: of two aspects, both of which are parsed according to both a) The efficiency of Beijing, Shanghai, Tianjin, primary and secondary indicators. Jiangsu, Zhejiang and other 20 provinces and First of all, analyze the sensitivity according to the municipalities autonomous regions is 1, which is in the primary index sensitivity and output index sensitivity, namely investigating the fluctuation of efficiency of efficient frontier. But efficiency of the rest 11 like various provinces, cities and autonomous regions from the Shandong, Liaoning, Henan, Hebei, Fujian is less than 1, investment of agricultural information structure, talents among which Shanxi province is lowest. But there are and resources and farmers’ output benefits and social small overall differences in our country’s agricultural output benefits. If the difference between the efficiency of information efficiency. decision-making unit is the largest when other indicators b) The provinces and autonomous regions who is not remain the same after removing some indicators, this in efficient frontier and has efficiency less than 1 indicate means that the index for decision-making unit is the most there exists improvement and room for promotion. First, sensitive factors. we can use some reference set and weight compared with According to Fig.3, efficiency in Beijing, Jilin, effective decision making unites to improve the Heilongjiang, Jiangsu, Zhejiang, Xizang and Xinjiang information efficiency. Second, the efficiency will be remains the same after removing any one input indicator, divided into “four quadrants” according to the social while in Fig.4, that the efficiency value has not changed is output indicators and farm output. Hebei, iaoning and Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Hainan, other provinces in the second quadrant which have high Chongqing, Sichuan, Guizhou, Tibet and Xinjiang after social output and lower farm output. Therefore, they removing any one primary output indicator. Fig.3 tells us should focus on strengthening elements in peasant that the efficiency of Guizhou changes from 1 to 0.7835, making a difference of 0.2165 after removing the influence household production in agricultural information. The factor of agricultural information resources. That indicates decision unite in the forth quadrant has higher farm output that the agricultural information resources of Guizhou have and lower social output. So they need to increase the the greatest effect on the agricultural information agricultural information production elements into social efficiency. Therefore, if you want to improve the output. And both farmers output and social output in the agricultural information efficiency in Guizhou in terms of third quadrant of Shanxi province are at a lower level, the index, you can achieve good results. which shows that space has great progress in agricultural From Fig.4, we can see efficiency in Guangdong is the information in Shanxi province and it requires planning largest after removing farmers’ output benefit index, being from the two aspects of farmers and social or conversion 0.3807, which indicates that Guangdong province must for elements in agricultural information; last, give specific strengthen the farmers’ output benefit if he wants to guidance in the direction of agricultural information improve the agricultural information efficiency. factors of production. For instance, the most sensitive In addition, we can analysis the influence the 9 second factors that influence agricultural information efficiency input index and 4 second output index have on agricultural of Guizhou is agricultural information resources, information efficiency. From Fig.5, we can that the indicating that if Guizhou wants to improve efficiency, television, computer and telephone have small effect on efforts in this aspect can make obvious progress mostly. agricultural information efficiency, and the efficiency of the majority of decision-making units changes little or B. Outlook even remains unchanged. The efficiency difference of Though it is not hard to find there are small differences Shanxi province is the largest, being 0.1790 after removing overall in our country’s agricultural information efficiency, per capita agricultural books inventory, suggesting that for there are limitations in choosing index of measuring input Shanxi province, the per capita agricultural book capacity and output indicators of agricultural information efficiency. had the largest influence on its efficiency. So errors may be obtained through the data. Hence in the After removing the proportion of the added value of future research we need to continually improve the agriculture according to Fig.6, the largest difference of measure indicators of the agricultural information efficiency value in Gansu province is 0.2559, namely the efficiency in order to probe into the factors of agricultural
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