A Spatial Econometrics Analysis On Regional Disparities Of Agricultural Mechanization In China

Min Min College of Land Management

Huazhong Agricultural University Wuhan, China

Chen Jiaying College of Resources and Environment

Huazhong Agricultural University Wuhan, Chin

Abstract—The aim of the paper is to discover the spatial distribution of agricultural mechanization and investigate the possible influential factors of spatial disparities of agricultural mechanization in China. The statistic data at provincial-level in the year of 2009 were collected to achieve the objective. Method of exploratory spatial data analysis (ESDA) is applied to examine the spatial structure and spatial distribution of agricultural mechanization. With global and local spatial autocorrelation analysis, it can be drawn that there is evidence of spillover in the agriculture mechanization among different provinces. Further, we use spatial regression models to analyze the influential factors. The result shows that rural per capita net income, educated population, and per capita cultivated land have significant influence on present spatial disparities of agricultural mechanization in China.

Keywords- spatial econometric analysis; regional disparities; agricultural mechanization; spatial autocorrelation; Moran's I

I. INTRODUCTION The growth and development of the agricultural sector is

important for all the countries in the world. Since 20th century, the effect that mechanization has had on farm productivity and on society itself is so profound that agricultural mechanization was named among top 20 engineering achievements by the National Academy of Engineering[1]. However, the number of machines used in agriculture is vastly different among the various countries and regions of the world. Most of developed countries have high levels of mechanization in the 20th century. Agricultural mechanization has been taken as a symbol of agricultural modernization. Nowadays, agricultural mechanization has become indispensable in agricultural growth and development for developing country because merely expanding the area under cultivation to meet the increasing food needs of growing populations is no long sufficient.

China is a traditional agricultural country and has large population and inadequate land, the improvement of agricultural mechanization is urgent to raise productivity and speed up urbanization progress. The agricultural mechanization in China has still been very uneven. The issue has been brought into public focus. Previous empirical studies on agricultural mechanization have often been descriptive and have focused primarily on the agricultural mechanization development environment, agricultural mechanization demand, the contributions of mechanization, and so on [2, 3]. In contrast,

limited empirical research has addressed the spatial disparities of agricultural mechanization. Smit et al. use the method of Exploratory Spatial Data Analysis (ESDA) to reveal that indicators related to agriculture show relatively strong spatial clustering effect in Europe[4].

The aim of the paper is to discover the spatial distribution of agricultural mechanization and investigate the determinant influential factors of spatial disparities. We use Geoda, an exploratory spatial data analysis tool, to visualize the spatial distribution and identify global and local spatial autocorrelation and thus characterize the spatial patterns of agricultural mechanization. Further, we employ spatial regression models to investigate the influential factors accounting for spatial dependencies. The article is arranged as follows. In section I, background is introduced. In section II, the exploratory spatial data analysis methods and spatial econometrics models is defined briefly. Section III describes the data and study design.. Section IV presents the empirical findings and explanation. Finally at the last section conclusions are made and suggestions are given.

II. METHODS

A. Exploratory Spatial Data Analysis Exploratory Spatial Data Analysis is considered as an

extension of exploratory data analysis (EDA) to detect spatial properties of data. It includes a collection of techniques to describe and visualize spatial distributions, identify atypical locations or spatial outliers, discover patterns of spatial association (spatial clustering), and suggest spatial regimes or other forms of spatial heterogeneity[5]. The essential of ESDA is spatial auto-correlation and spatial heterogeneity.

Spatial autocorrelation represents the relationship between nearby spatial units and can be simply defined as the coincidence of value similarity with location similarity[6]. Traditionally, spatial autocorrelation measures and tests can be separated into ‘global’ and ‘local’ categories by analysis scale. Global implies all associations of spatial units one with another are included in any calculation of spatial autocorrelation. Moran’s I is a classic index of spatial autocorrelation and it ranges from -1 to +1, indicating respectively negative and positive spatial autocorrelation. Compared with the global measures and tests, local measures usually access the spatial autocorrelation associated with one particular spatial unit,

The research behind this paper was partially supported by National Natural Science Foundation of China (71103072, 41101192), China Postdoctoral Science Foundation (20100471004) and the Fundamental Research Funds for the Central Universities (2011RW006)

which can detect the presence of ‘hot spots’. Anselin’s LISA (local indicator spatial autocorrelation) is widely used to individuate local spatial clusters[7]. The LISA significance map can be generated with significant LISA values and influential observations (hot spots) can thus identified on the map.

B. Spatial Regression models With the considerable development of spatial econometrics

from mid 1990s, spatial regressions have been increasingly adopted in various research works, such as the real estate literature[8], the environmental economics literature[9], crime research literature[10], et al. .Unlike Classical Linear Regression Model (CLRM) which assumes explanatory variable and dependent variable is independent from each other, spatial regression models assume that positively spatially dependent exits in the sample data, which means that observations from one location tend to exhibit values similar to those from nearby locations. Since the spatial dependence occurs in two different forms[6],spatial regression model includes Spatial Lag Model (SLM) and Spatial Error Model (SEM), which usually requires full maximum likelihood estimation (ML) instead of Ordinary Least Squares (OLS) of CLRM.

SLM usually includes a spatially lagged dependent variable and is represented by the following equation[11]:

y Wy Xρ β ε= + + (1)

Where y is an 1N × vector of the dependent variable, W is an N N× spatial weight matrix, ρ is the spatial autoregressive coefficient, X is a N K× matrix of

independent variables, β is the 1K × vector of corresponding coefficients, ε is the 1N × vector of the error term.

SEM includes a spatial autoregressive error term and is given by,

y X Wβ λ ξ ε= + + � (2)

Where λ is the spatial error coefficient, ξ is the 1N × linear model disturbance term, and ε is the 1N × vector of the uncorrected disturbance term and the other notation is as (1).

In order to select the alternative specification, Lagrange Multiplier test statistics are suggested to be applied[12,13]. LM-Lag (LMLAG) and Robust LM-Lag (R-LMLAG) refer to the spatial lag model while LM-Error (LMERR) and Robust LM-Error (R-LMERR) refer to the spatial error model as the alternative. Firstly, the standard LM-Lag and LM-Error test statistics is considered, if LM-Error rejects the null, but LM-Lag does not, estimate a spatial error model, and vice versa. If both standard version (LM-Lag and LM-Error) are significant, only Robust versions of LM statistics will be considered.

Moran’s I statistic is used to detect spatial autocorrelation and misspecifications in the model. Besides OLS, there are other classic specification tests, such as Likelihood Ration (LR), Log-Likelihood, AIC (Akaike information criterion) and SC (Schwarz criterion). The increase of Log-Likelihood and the decrease of AIC and SC suggest an improvement of fit for the specification.

III. DATA AND VARIABLE DEFINITION The study area covers 31 administrative regions at the

provincial level in China (except Taiwan, Meco and Hong Kong).

Data analyzed in this study were collected and derived from China agricultural mechanization statistics 2009 annual year report, China Year book of Rural Household Survey (2010), China Rural Statistical Yearbook (2010), china agricultural machinery industry yearbook (2010). Geographic boundary data were collected from National Geomatics Center of China. The variable values were added to the attribute table as the attribute fields in Geoda.

Literatures about agricultural mechanization show that the level of agricultural mechanization is influenced by the input of land, labor, capital and knowledge[14]. In this paper, we use rate of agricultural mechanization (RAM) as the dependent variable. The explanatory variables include rural per capita net income (RPFI), government financial investment (FI), educated population in agricultural machinery technology (EP), Per Capita Cultivated Land (PCCL), rate of agricultural labor transfer (RLT). Among these explanatory variables, RPFI and FI are used to reflect the capital input, PCCL is used to reflect the land fragment, RLT is used to reflect the labor input, and EP can reflect the level of education in agricultural machinery knowledge.

IV. RESULTS

A. ESDA results ESDA is a descriptive step before suggesting dynamic

factors to explain the spatial patterns under study and before estimating and testing more sophisticated regression models[15]. Firstly, we map the phenomena and perform an exploratory spatial data analysis (ESDA) to explore patterns of spatial autocorrelation. Here we present the box map (1.5 hinge) of the distribution of agricultural mechanization in Chinese provinces. The map can show the position of each province in the entire distribution of agricultural mechanization, and allow us to detect the presence of outliers.

Figure 1 shows that the spatial distribution of agricultural mechanization in Chinese provinces is significantly uneven. Agricultural mechanization is much higher in the northern provinces than in the southern provinces of China. Since the box map show that spatial autocorrelation seems to affect agricultural mechanization. Then we use Moran’s I and LISA to detect the presence of ‘global’ and ‘local’ spatial autocorrelation.

Based on the weight contiguity matrix created using to the rook procedure, Moran’s I value is 0.5099 and shows that

positive spatial autocorrelation exists for the agricultural mechanization. In order to gain more insight into the way provinces with high or low agricultural mechanization are located in China Then, LISA cluster map is used to individuate local clusters.

Figure 1. Box Map of Agricultural Mechnization

Figure 2 shows LISA cluster maps for agricultural mechanization. It shows a clear distinction between two positive local spatial clusters: a northern cluster (high-high) involving Jilin, Shandong and Hebei, which therefore perform well in terms of agricultural mechanization compared to the province average, and a southern spatial cluster (low-low) involving Jiangxi, Yunnan, Guangxi, Guizhou, Chongqing, Guangdong, Sichuan, Hunan. The map also detects atypical regions characterized by negative local spatial autocorrelation, for examples, Anhui and Hainan province perform well compared to their low-low cluster neighbors since they are significantly low-high quadrant.

Figure 2. Cluster Map of Agricultural Mechnization

All the results relative to ESDA in this section reveal that agricultural mechanization in Chinese provinces is affected by spatial autocorrelation and detect the intra-regional disparities. Therefore, the feature of spatial autocorrelation should be take into account in the next econometric estimations.

B. Spatial regressions results The analysis above can verify the existence of spatial

autocorrelation of agricultural mechanization, but failed to detect the influential factors of the spatial clustering and explain the spatial disparity. In this section we propose a cross-sectional analysis of provincial agricultural mechanization in China in 1999. A double log model was established in which the dependent variable and exploratory variables are defined in section III.

The estimable statistical model used for the estimation is thus the following:

0 1 2

3 4

5

log( ) * log( ) *log( )*log( ) *log( )* log( )

AM RPFI FIRLT PCCLEP

β β ββ ββ ε

= + ++ ++ +

(3)

Where β is the regression parameters, ε is the error term.

Firstly, we use OLS estimation, Moran’s I test, and Lagrange Multiplier test statistics to suggest the alternative spatial regression models, the result is in Table 1.

TABLE I. OLS AND ML FOR AGRICULTURAL MECHNIZATION

Variable Coefficient Std. Error t-Statistic Probability

Constant -1.5028 2.5829 -0.5818 0.5659

RPFI 0.5707*** 0.3135 1.8208 0.0806

FI 0.0513 0.1221 0.4206 0.6777

RLT -0.2010 0.2094 -1.0030 0.3255

PCCL -0.1725** 0.2517 -2.1607 0.0405

EP 0.5440 0.1014 -0.2931 0.7718

R2 0.4128

Adjusted R2 0.2954

F 3.5150**

LogL -15.5902

AIC 43.1804

SC 51.7843

DIAGNOSTICS FOR SPATIAL DEPENDENCE

Test MI/DF Value Prob

Moran’s I 0.2928 3.8183* 0.0001

LMLAG 1 0.7064 0.4006

R-LMLAG 1 0.0206 0.8858

LMERR 1 5.5384** 0.0186

R-LMERR 1 4.8526** 0.0276

Lagrange Multiplier (SARMA)

2 5.5590** 0.0621

Notes: *,**,*** indicate significance at the 1, 5 and 10 percent levels, respectively

The estimation result in table I shows the regression yield significant and negative coefficient for PCCL (p<0.05), confirming earlier findings of Hou[16]. The RPFI index is only marginally significant (at p<0.10). And FI, RLT and EP failed to pass the significant test. Meanwhile, the adjusted R2 is only 0.2954. The estimation result shows the explanatory variables have not significant effect to explain the spatial disparity of agricultural mechanization. There are two possible reasons: one is that vital variables are missed, the other relates to the selection of the spatial econometric model.

In fact, significant spatial autocorrelation has been detected in part A of section IV, so we need to select an alternative specification instead of adopt the classic linear regression model. Diagnostics for spatial dependence in table I shows that Moran’s I (0.2928) demonstrates the significant spatial dependence (spatial autocorrelation), and LMERR (Z=5.5385, P=0.0186) and R-LMERR (Z=4.8526, P=0.0276) statistics were both significant while LMLAG (Z=0.7064, P=0.4006) and R-LMLAG (Z=0.0206, P=0.8858) were clearly not. SEM was finally chosen to interpret spatial disparity of agricultural mechanization according to spatial regression decision process.

TABLE II. ML FOR AGRICULTURAL MECHANIZATION

Variable Coefficient Std. Error t-Statistic Probability

Constant -4.8706*** 2.6373 -1.8468 0.0648

RPFI 0.9889* 0.3056 3.2363 0.0012

FI -0.1681 0.1072 -1.5673 0.1170

RLT -0.4206*** 0.2476 -1.6976 0.0896

PCCL -0.6118** 0.2419 -2.5287 0.0114

EP 0.2142** 0.0977 2.1935 0.0282

λ -2.1040* 0.0388 -54.2776 0.0000

Test Value Prob

LogL 0.0000

LR 31.1804 0.0000

AIC 12.0000

SC 20.6039

Notes: *,**,*** indicate significance at the 1, 5 and 10 percent levels, respectively

Table II shows the estimation result of SEM with full maximum likelihood estimation. Compared values of LogL, LR, AIC and SC in table I and table II, LogL (0.0000) obtained by SEM was higher than that (-15.5902) by CLRM, AIC (12.0000 ) and SC (20.6039) obtained by SEM was lower than that (AIC=43.1804, SC=51.7843) by CLRM, which proved the better fitness of SEM.

The significant value of λ in SEM implies that agricultural mechanization in province i depends directly on the agricultural mechanization in other neighboring provinces; in. other words, provinces with high, or low, percentage of agricultural mechanization are clustered and interacted.

Moreover, the spatial differences of agricultural mechanization are explained by RPFI, EP and PCCL, while FI

and RLT are not significant at P=0.05 in SEM. The positive sign of the coefficient of RPFI implies that the percentage of agricultural mechanization depends strongly on farmer’s per capita income. i.e. an increase of farmer’s per capita income gives a more than proportional increase of agricultural mechanization. With respect to the other variables, EP variable shows a lower strength to explain the provincial agricultural mechanization difference. Even if the coefficient is weakly significant its positive sign indicates that a marginal increasing of education population in agricultural mechanization knowledge gives a less proportional increase of agricultural mechanization. The negative relation between agricultural mechanization and per capita cultivated land seems to conflict with the general hypothesis that land fragmentation will impede the pace of mechanization. But it is of great significance in the present development status of agriculture and usage of agricultural mechanic in China. Since the family-based contracted responsibility was established in late 1970 in China, small-scale operations based on households is the main feature of the agricultural production. Agricultural households investing in agricultural machinery can be divided into two kinds. One is the agricultural skilled-worker who buys the agricultural machines to provide service for other farmers to increase income. The other buys mini-sized agricultural machines, e.g. small four-wheel tractor, farming three-wheel truck, cultivator, and half human and animal power and half mechanization equipment, for their own farming use, which lead to the machinery is left unused most of the time. Moreover, it is universal phenomenon that mini-sized tractor and farming three-wheel truck are used as vehicle in farmers’ daily life in Chinese rural area. This implies the low-level, redundant investment in present agricultural mechanization..

V. CONCLUSIONS The result above suggested that it may lead to model

misspecification and biased estimation of coefficients in the form of ignoring spatial dependence and spatial heterogeneity to apply the primary statistical method of OLS regression in regional development research. Agricultural mechanization roots in the regional sio-econometric environment which is not spatially homogeneous. Considering spatial effects, spatial regression models (SLM and SEM) will improve the analysis of determinants of agricultural mechanization distribution. Farmer’s per capita income has a significant positive effect on the spatial disparity of agricultural mechanization while government financial invest shows no significance, and the education in knowledge of agricultural machinery by government shows a lower effect, which implies that it is farmer household econometric behavior instead of government behavior influence the level of agricultural mechanization. Moreover, the land fragmentation influencing the agricultural mechanization in a lower and redundant way implies the family-run system of small-scale farmer’s land needs to be improved and innovated.

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