







































 
 
 

Agriculture and Food Sciences Research 
Vol. 4, No. 1, 37-44, 2017 

ISSN(E) 2411-6653 / ISSN(P) 2518-0193 
DOI: 10.20448/journal.512.2017.42.37.44 

 
 
 
 
 
 

 

37 
 

The Role of Institutional Policies in Promoting Agribusiness Development in Rural 
China 

 
Owusu Samuel Mensah1

    

Zhuang Jincai2    

Asare Bediako Isaac3    

Acheampong Patrick4    

 

 
( Corresponding Author) 

 
1,2,3,4School of Management, Jiangsu University, Jiangsu, P.R. China 

 

 
Abstract 

Sustainable agricultural growth has become an area of interest for many researchers in the quest to 
increase food production in the midst of an escalating population. However, the evidence remains 
largely scanty, isolated and devoid of an in-depth analysis of how some economic policies promote 
agribusiness development in mainland China.  Using time series data from 1990 to 2013, this paper 
adopts semi-parametric quantile regression to study the complex relationship between institutional 
policies in rural areas and agribusiness development. The study revealed the role of family 
household in promoting agribusiness development at the higher quantiles of the distribution. 
Moreover, government investment in rural health and education contributed significantly across the 
quantile distribution. The impact of research and development on agribusiness development is 
positively significant across the quantile points of the distribution within the study period. The 
result from the quantile graph clearly shows the disparities between OLS and quantile regression 
coefficients. 

 
Keywords: Agribusiness development, Institutional policies, Quantile regression, Rural China. 

 
Citation | Owusu Samuel Mensah; Zhuang Jincai; Asare Bediako 
Isaac; Acheampong Patrick (2017). The Role of Institutional Policies 
in Promoting Agribusiness Development in Rural China. 
Agriculture and Food Sciences Research, 4(1): 37-44. 
History:  
Received: 29 September 2017 
Revised: 7 November 2017 
Accepted: 13 November 2017 
Published: 16 November 2017 
Licensed: This work is licensed under a Creative Commons 

Attribution 3.0 License  
Publisher: Asian Online Journal Publishing Group 
 

Contribution/Acknowledgement: For comments, suggestions and data 
collection, the authors would like to thank Dr. Emmanuel Opoku Marfo, Li 
Na, Chen Cong, Li Junqiang and Liu Jiayi. 
Funding: National Natural Science Foundation of China (NSFC) supported 
this research work, with grant number 7147310. 
Competing Interests: The authors declare that they have no conflict of 
interests. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study was reported; that no vital 
features of the study have been omitted; and that any discrepancies from the 
study as planned have been explained. 
Ethical: This study follows all ethical practices during writing.   

 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 38 
2. Related Literature............................................................................................................................................................................ 38 
3. Data Source and Model Specification .......................................................................................................................................... 39 
4. Conclusion ......................................................................................................................................................................................... 42 
References .............................................................................................................................................................................................. 43 
 

 

 

 

 

 

 

http://crossmark.crossref.org/dialog/?doi=10.20448/journal.512.2017.42.37.44&domain=pdf&date_stamp=2017-01-14
http://creativecommons.org/licenses/by/3.0/
http://creativecommons.org/licenses/by/3.0/
https://orcid.org/orcid-search/quick-search?searchQuery=Owusu%20Samuel%20Mensah
https://orcid.org/orcid-search/quick-search?searchQuery=Zhuang%20Jincai
https://orcid.org/orcid-search/quick-search?searchQuery=Asare%20Bediako%20Isaac
https://orcid.org/orcid-search/quick-search?searchQuery=Acheampong%20Patrick


Agriculture and Food Sciences Research, 2017, 4(1): 37-44 

38 
 

 

1. Introduction 
The contribution of agriculture to the growth of the world’s economy has been a key policy issue in both Less 

Economically Developed Countries (LEDC) and More Economically Developed Countries (MEDC). Currently, 
agriculture, which was previously perceived as a low-tech industry designed for small families and rural dwellers has 
changed dramatically due to liberalization of the economy and availability of agricultural markets [1, 2]. The 
contribution of agriculture sector to economic growth and development is manifested through the creation of 
agricultural and non-agricultural job opportunities and   provision of food for both human and animal consumption 
[3-6]. Currently, China’s agricultural sector is facing many challenges due to increasing urbanization and 
industrialization, which has decreased the productivity growth of agriculture. However, the large growth of human 
population and the need to increase food production has compelled the Chinese government to introduce  agricultural 
reform policies to remedy the situation [3, 7]. 

Literature has revealed that the rural reforms instituted by China’s government have been the backbone of 
agricultural productivity growth [8-13]. According to Pufahl and Weiss [14] the impact of the central government 
programs on agricultural production and farm structure is a major issue in the international trade negotiation on 
agriculture. Moreover, the introduction of the household responsibility system, adjustments of prices of major farm 
products, tax exemptions given to smallholder farmers, and public investments have contributed enormously to the 
growth  of agricultural sector [12, 15-17]. For instance, with less than 8-10% of the world’s arable, China feeds 
about 20% of the world population. In 2008, the total grain production for the world- wide by China amounted to 
18%, it then produced 27% of its meat and 43% of its poultry products. 
 

 
Figure-1. Agribusiness growth showing at Different Quantiles [18]. 

Source: Authors’ Construct 

 
In addition, the domestic consumption of edible beans in China increased to 4.4 million tons in 2011, which was 

equivalent to 3.27kgper capital as compared to 9.9kgper capital in Japan in 2011 [19]. The agricultural sector in 
mainland China, aside its continual food supply to the large populace, gives more than eight hundred million jobs to 
farmers dwelling in both rural  and urban areas [20]. China’s agricultural sector has experienced rapid growth in 
other sectors such us livestock, fishery and forestry aside the crop-first agriculture.  After 1978 reforms, the output 
growth of crop reduced from 82% in 1970 to 55% in 2010, whilst the fishery production also moved from 2% in 1979 
to 10% in 2010 and forestry sector recorded 4% in 2010 [8].  For the past decade, there has been a rapid growth in 
the output of livestock and fishery production as compared to that of crop production due to dietary changes.  

The effect of the various institutional reforms in promoting China’s agriculture sector has been subjected to 
rigorous research [8, 12, 21-29]. In theory, the growth of agriculture sector is attributed to many factors, such as 
fertilizer application, the use of agricultural machinery, increase in agricultural and non-agricultural labor, and more 
importantly the introduction of household responsibility system, which was formally called production teams [30, 
31].  

The aim of this paper is to adopt flexible statistical techniques to investigate the effect of the various reforms on 
the growth of agribusiness in Mainland China. Surprisingly, few studies on this topic has employed semi-parametric 
regression model to study the complex relationship between rural policies and growth in agribusiness sector. 
However, we adopt quantile regression, which is the appropriate quantitative technique to measure the effect 
predictor variables on agricultural growth in rural China.  

The rest of the study is organized as follows. Section 2 reviews the related literature. Section 3 presents the 
source of data. Section 4 describes methodology and model specification. Section 5 summarizes empirical results and 
discussion of findings. Section 6 provides concluding remarks. 
 

2. Related Literature 
After successful rural reforms in China, the change from the commune system to the household responsibility 

system (HRS) in the late 1970s and 1980 has been a major contributor to agribusiness sector and agricultural 
productivity growth [17, 20, 25, 29]. According to Huang, et al. [32] the implementation of  household 
responsibility system increased agricultural production by 8.2% as compared to 2.7% before the reform period. 



Agriculture and Food Sciences Research, 2017, 4(1): 37-44 

39 
 

 

Moreover, the study conducted by Fan and Pardey [28] also revealed that between the period of 1965 to 1993, the 
institutional reform policy contributed to the growth of agriculture by 17.6 percent. 
 

 
Figure-2.  The trend of family household and public investments in health, education and agricultural research  from  1990-2013 [18]. 

Source: Authors’ Construct 

 
Moreover, government investment in agricultural research and development  has resulted in  the introduction of 

new varieties of crops, which has increased food production in rural China [33]. In addition, the massive economic 
reforms and rapid economic growth in China has increased government investment in the health sector, which has 
resulted in the provision of quality health services in rural China [34, 35].  

According to  literature, the monetary investments in education, which consist of in-school and out-of-school 
expenditure, which serve as a major aspect of human capital investment has  improved the standard of education 
among farmers resulting into an increase in agricultural production [36]. Most studies conducted in China’s 
agricultural sector have ascribed the rapid growth of agriculture to several factors.  For example, McMillan, et al. 
[37] investigated into the impact of China’s economic reforms and agricultural productivity growth. They 
established that price adjustment policies have increased the productivity growth of agriculture. Wu, et al. [38] use a 
non-parametric Malmquist procedure to investigate the temporal and spatial nature of agricultural productivity 
growth over the study period (1988-1995). The authors concluded that agricultural productivity growth in mainland 
China has increased through continuing innovation, the use of modern inputs and competitive market. In this line of 
research Huang and Ma [15] analyzed the determinants of public and farm household capital stock formation and 
their effects on the productivity growth of agriculture. By using Cobb-Douglas production function, the study reveals 
that capital investment in agriculture, private capital formation and technological changes have increased 
agricultural growth while fertilization application has had mild  agricultural growth. [12] using province-level panel 
data investigated the effect of rural reforms on agricultural growth in rural China. By employing Cobb-Douglas 
production function, the author indicated that decollectivation and adjustment in state procurement prices have 
augmented the growth of agricultural production. Lezin and Long-Bao [16] explored the impact of production 
variables used on agricultural productivity growth. However, using 1989-2002 data for Zhejiang Province and 
employing Cobb-Douglas production function, the study indicates that there is a strong correlation between the 
production variables and agricultural productivity growth. Xu [17] used of Cobb-Douglas production function to 
synthesis the empirical literature on the subject Chinese agricultural growth in post-reform Era.  The study reveals 
that there is a relationship between household responsibility system and agricultural productivity growth. Li [39] 
adopted Stochastic Frontier Analysis and employed 1995-2009 village-level data to investigate the effect of China’s 
Agricultural reforms on productivity changes.  The study disclosed that for the past fifteen years, Chinese 
Agricultural productivity has increased due to the institution of agricultural policies (such as tax exemptions, 
insurance for farmers), and technological changes. Based on the related literature, an important aspect of measuring 
the productivity growth of agriculture, which is necessary but have been over-looked, is the estimation of the 
conditional median or the other quartiles of agricultural growth and not only the mere conditional mean. 

 

3. Data Source and Model Specification 
3.1. Data Source  

The data set consist of a country-data for 23 years observation over the period 1990-2013, which is obtained 
from China Statistical Yearbook ,various issues, 2015. The data on agribusiness development consists of a country-



Agriculture and Food Sciences Research, 2017, 4(1): 37-44 

40 
 

 

data from the period of 1990 to 2013 obtained from China Statistical Yearbook, various issues, 2015. Agribusiness 
performance (AGB) is measured by total profit of agro-industries in rural China within the study period. Family 
household refers to the total number of farmers in the various households from 1990 to 2013 in China. Expenditure 
in education includes government appropriations for education, which consists of rural expenditure on education, 
taxes, and fees, collected for education by the government, which is measured in 10 000 Yuan. Agricultural Research 
& Development expenditure capture the public total expenditure on research and development in agricultural sector 
that is measured in 10 000 Yuan. Government expenditure in health is the previous and current expenditure by the 
government in the health of rural people measured in 100 million Yuan. All the variables were converted into log 
form to avoid heteroskedasticitity, which will help us to estimate elasticity. 
 

Table-1. Variables Definition 

Variable Definitions Unit Source 

AGB Agribusiness Growth 100 million Yuan China Statistical Yearbook                                                
HSD The total number of  farmers in the various family  

households 
Household  China Statistical Yearbook  

EDU Government investment in rural education 10 000 Yuan China Statistical Yearbook 
HLT Government expenditure in rural health 100 million Yuan China Statistical Yearbook 
R&D Government expenditure in agricultural research and 

development 
10 000 Yuan  China Statistical Yearbook 

    Note: All the variables are  annual data from 1990-2013. 
    Data source: National Bureau of Statistics of China China Statistical Yearbook [18] 

 

3.2. Model Estimation 
The quantile regression method proposed by Koenker and Bassett [40] performs regression analysis of sample 

data at different quantile points. Thus conditional quantile estimates are performed by quantile regression with each 
nth quantile showing different behavior in the conditional distribution [41]. The quantile regression generalized 
median regression estimate to other quantiles. However, the use of traditional regression (OLS) only pays attention 
to the mean effects, which may result in either over estimation or under estimation of coefficients and even fail to 
identify the actual relationships that exist between variables [42]. 

To investigate the role of institutional policies in promoting agribusiness development, the study adopts quantile 
regression (QR) over the ordinary least square model (OLS) for the estimation. The Least squares regression only 
measures how the changes in the vectors of the covariates   affects the conditional mean function of  . Furthermore, 
OLS regression is very sensitive to outliers, which may lead to inaccurate predictions due to the presence of 
multicollinearity, abnormal distribution and the independent nature of the residuals [43]. Moreover, QR is invariant 
to monotonic transformation which is different from OLS [40, 43]. In addition, QR is more robust to the non-normal 
error term and outliers in the model and takes into consideration the major effects of the covariates on the 
distribution of the dependent variable holistically and not only the conditional mean. However, this study employs 
the specifications of the previous literature on quantile regression. 

As stated by Chen [44] the quantile regression generalizes the concept of a univariate quantile to a conditional 
given qauntile or many covariates and is modeled as follows; 
For a random variable Y with probability distribution function 

( ) ( )PrF y ob Y y=  ,                                                                                                                    (1) 

 The th  quartile of  Y*  is defined as the inverse function  

( ) ( ) inf :Q y F y =  ,                                                                                                             (2) 

Where 0 1  . In particular, the median is ( )1/ 2Q . According to Chen [44] for random sample  1,..., ny y of Y, 

it is known that the  sample median minimizes  the sum of absolute deviations 
1

min
d

i
R

i

y





−

− . 

As compare to the general th sample quantile ( )  , which is analogue of  ( )Q  is donated by 

( )
1

min
d

i
R

i

y


 


−

− ,  where ( ) ( )( )0 ,0 1z z I z  = −     

   

Thus, ( ).I is the indicator function. Chen [44] further argues that likewise the sample mean, which tries to 

minimize the sum of the squared residuals  

( )
2

1

1

ˆ arg min
d

i

R y 

−

= −                                                                                                         (3) 

is extended to linear conditional mean function as ( )E Y X x x = = by solving  

( )
2

1

ˆ arg min
d

H

i i

i

R y x  

−

= −                                                                                           (4) 

the linear conditional quantile function modeled as ( ) ( ) ,Q X x x  = = is also estimated by solving  

( ) ( )
1

ˆ arg min
d

H

i i

i

R y x   

−

= −                                                                                       (5)                                           

for any quantiles ( )0,1  .The, quantity ( )̂  represents the th  regression quantiles.  



Agriculture and Food Sciences Research, 2017, 4(1): 37-44 

41 
 

 

The 0.5 = , denotes the median regression called the 1L regression, following Chen [44]. 

   

3.3. Discussion of Results   
The study, however, measured the effect of the dependent variables on the response variable by adopting quantile 

regression model. For effective comparison, we present the OLS estimate, which serves as the baseline of mean effect 
and compare to the results of the selected quantiles in the conditional distribution of output produced in table 5. The 
estimation of quantile regression is reported in table 5 at 15th, 25th, 35th, 45th, 55th, 65th, 75th, 85th, and 95th 
quantiles. 

Table 5 presents the estimation of a quantile regression model whereby family household and government 
investments in rural health, rural education and agricultural research and development are used to measure the agro-
industry performance in China. From the results, the values of the parameters used for the estimation vary across 
OLS and the selected quantiles both in magnitude and direction. For example, Table 2 shows that the coefficient of 
family household in the 90th quantile is 24 times more than the OLS estimate results. 

According to the coefficients of family household in Table 2, the contribution of farmers in each household to the 
growth of agribusiness sector in the lower quantiles is negative. However, the coefficients of OLS estimate to the 
lowest quantile (15th, 25th, 35th, 45th, and 55th quantile), are significant at the 10 % level. Moreover, the influence 
of individual-based farming system in the various households on agro-industry growth increases at the higher 
quantile and significant at the lower quantile (Fig. 3). This implies that an increase in farmers in the family household 
corresponds with the agribusiness development in the higher quantile of the distribution. In our opinion, this due to 
the technological changes in the agricultural sector, whereby farmers use simple farm tools to boost food production. 
In addition, land fragmentations, which is a major problem facing smallholder farmer, and increase in off-farm 
employment among the youth and the able-bodied people in the rural areas are contributing factors contributing to 
the poor performance of family household to the agricultural growth in the lower quantiles and OLS estimate results. 
From Table 2, the OLS results indicate that a unit increase in family household reduces agricultural productivity 
growth by 2.62 percent.  

Furthermore, the results of the study indicate some variations across selected percentiles in the conditional 
distribution of agribusiness growth. This shows the heterogeneity in the effect of rural health investments in 
agribusiness performance. The coefficient is significant and has a positive impact across the selected quantiles and 
OLS estimates at 1% and 10% levels, whereby, the magnitude increases and then decreases toward the higher 
quantile (Fig. 3). The results imply that, all things being equal, improve health status among rural farmers in China 
contributes greatly to the development in the agribusiness sector at both the OLS estimate and the selected quantiles. 
This is due to increasing government spending in rural health, as a means of developing the human capital in the 
rural areas to increase food production. According to Sckokai and Moro [45] effective government policies promote 
agribusiness sector and agricultural growth and more importantly, create  agricultural and non-agricultural jobs that 
increase farmers’ income level. Report from Chappell and LaValle [3] indicates that life expectancy increased from 
67.77% in 1981 to 74.83% in 2010 and, there was a dropped in Infant Mortality Rate (IMR) from 32.89% in 1990 to 
13.93% in 2012. 
 

Table-2. Institutional policies and agribusiness development: OLS versus quantile regressions. (Dependent variable is total profit 
of agro-industries from 1990 to 2013) 

Coefficients          Selected Quantiles      

 

       
OLS 

  Q        
(.15) 

   Q 
(.25) 

   Q     
(.35) 

   Q 
(.45) 

   Q 
(.55) 

   Q 
(.65) 

   Q 
(.75) 

  Q 
(.85) 

   Q 
(.95) 

constant 29.29* 22.93 26.35* 36.42* 45.28 48.78 5.65 13.22 7.93 5.65 

 (1.48) (0.82) (0.87) (1.09) (1.38) (1.71) (0.11) (0.34) (0.17) (0.11) 

HSD -2.62 -1.92* -2.31* -3.35* -4.28* 4.63* 0.11 0.93 0.35 0.11 

 (1.29) (0.67) (0.67) (0.97) (1.26) (1.55) (0.02) (0.23) (0.07) (0.02) 

HLT 0.89*** 0.59*** 0.71*** 0.76** 0.88** 0.83** 0.87* 0.65* 0.75* 0.88* 

 (4.32) (1.92) (1.96) (2.03) (2.01) 1.72 (1.12) (1.16) (1.01) (1.12) 

EDU -0.07 0.17 0.11 0.1 0.42 0.94 0.24 0.36 0.08**   0.24** 

 (0.33) (0.58) (0.33) (0.310 (0.12) (0.25) (0.43) (0.08) (0.16) (0.43) 

R&D 0.03* 0.01* 0.03 0.02** 0.26 0.02 0.02* 0.04** 0.02* 0.02* 

 (1.48) (1.15) (2.56) (1.94) (2.43) (1.99) (0.68) (1.79) (0.88) (0.68) 
Observation         23         23         23                          23          23       23     23       23    23        23 
R -square 0.9843          
Pseudo  0.9295 0.9157 0.9055 0.899 0.8943 0.889 0.8823 0.8789 0.8903 

           Note: t statistics are in parentheses. 
         *P< 0.10, **P< 0.05, ***P< 0.01 

 
The per-capita health spending of urban area as compared to rural health investment was less than 2% in early 

1990s, but increased to 2.67% in 2010. The 2.67% increased in health expenditure recorded in 2010 is attributed to 
the implementation of the new Rural Cooperate Medical Scheme (NRCMS) and the current reforms [46-48]. This 
led to  rapid increase in agro-industry performance in the agribusiness distribution. 

 In addition to these, the contribution of rural investment in education to agro-industry performance is 
statistically insignificant across the selected quantiles but recorded positively at the OLS estimate. Table 2 clearly 
shows that the coefficients of the number of variables used differ across OLS and the quantile levels. As demonstrated 
in Figure 3, the impact of investment in rural education in agribusiness development across different quantiles is 
positive and its influence under 55th quantile is higher than the estimates above 90th quantile. The results of the 
OLS estimate suggest that a percentage increase in rural investment in education decreases agribusiness performance 
by 0.07 percent. This supports why the study adopted quantile regression model to measure the effect at the various 
quintile points other than using only OLS model. However, the contribution of public investment in education to the 
agro-industries performance across the selected quantiles recorded positively. For instance, from Table 2 the output 



Agriculture and Food Sciences Research, 2017, 4(1): 37-44 

42 
 

 

growth at 55th quantile is greater than those in15th, 25th, 35th, 45th, 65th, 75th, 8th, and 95th quantiles. From 
Figure 3, results for both family household and rural education give clear indications of the changes in the 
coefficients throughout the distribution of agribusiness development. The quantile regression shows significantly 
negative effect of household at 15th to 45th quantiles, however, the impact is positive at the upper quantiles of the 
conditional distribution. Although, the results of family household contradicts the results of various studies, we will 
not make any policy conclusion from the results, but has to be thorough investigated. 
 

 
Figure-3. The effects of family household (hsd), rural investment in education (edu), health (hlt) and agricultural R&D (rd) agribusiness 
development (ABD): OLS versus quantile regression. 
Notes: The dashed lines represent OLS parameter estimate, and the dark shaded areas are confidence intervals for the estimation of quantile regression 
parameters.  
Data source: National Bureau of Statistics of China China Statistical Yearbook [18] 

 
In our view, the growth in the various quantiles from Table 2 is due to improvement in the educational system in 

rural areas, which has gone a long way to improve the skills of agricultural entrepreneures in rural China.  
Finally, for agricultural R&D, the results indicate that the impact of research and development on agribusiness 

performance is positively significant across the quantiles at 1%, 5% and 10% level. However, the effect on 
agribusiness development differs slightly, despite its positive effect on at all selected quantiles. For instance, the 
coefficient of R&D at 25th quantile is the same as compare to the coefficient in OLS regression. According to OLS 
results, an increase in entrepreneurial knowledge increases agribusiness growth by 0.03 % within the study period. 
Moreover, the parameter estimate of the OLS regression is quite lower than the estimate results in the 45th quantile 
but higher than the values for 15th, 35th, 55th, 65th, 85th, and 95th quantiles. This confirms study conducted by 
Audretsch, et al. [49] which maintained  that investment in new knowledge promotes productivity growth. The 
contribution of R&D to agribusiness development may attribute to high investment in research and development. 
According to Liu, et al. [50]  patent applications and grants in the agricultural sector has increased  over the past 
decades, which has promoted agricultural production. 
 

4. Conclusion 
Using time series data in PR China from 1990-2013, this paper measures the linkage between institutional 

policies and agribusiness development in China. That is the impact of government investment in education, health 
research and development and the introduction family households on agribusiness sector. The study however, 
adopted OLS mean regression and compared with quantile regression because OLS regression model only expresses 
the conditional mean in a linear form. The results indicate that the impact of rural investments on agribusiness 
development in China is quite heterogeneous. For instance, effects family household on growth of agribusiness sector 
across the selected quantiles are negative but significant at 15th, 25th, 35th, 45th, and 55th quantiles. However, 
agribusiness development continuously increases at 85th and 95th quantiles. The influence of rural investment in 
health on the agribusiness sector increases monotonically across the quantile distribution. For instance, the output 
growth in 15th and 75th quantiles are lower than 25th, 35th, and 85th quantiles but at the 45th, 55th, and 95th 
quantiles, health influence on agro-industry performance is stronger. This is due to the desire of the government to 
ensure the well-being of the people and promote human capital development through provision of effective health 
care system.  Moreover, the contribution of educational investment to agribusiness development at 55th quantile of 
the distribution is greater than the effect at the higher quantiles of the distribution.  The difference in the output 
growth at different quantiles may be attributed to the inability of smallholder farmers to identify the various 
opportunities in the farming business and have access to the technological market.   The reason is that most farmers 
in the rural areas still use traditional farm tools for farming activities and engage in      subsistence farming. Based on 



Agriculture and Food Sciences Research, 2017, 4(1): 37-44 

43 
 

 

the results from the quantile regression, we suggest that policy makers could be misled by using simple results 
achieved by OLS regression to determine the effect of institutional policies on agribusiness development. This is 
because simple aggregations achieved by simple regression fail to indicate the existence of predictability in the 
exogenous variables. 
 

References  
[1] J. Hansen, F. Tuan, and A. Somwaru, "Do China's agricultural policies matter for world commodity markets?," China Agricultural 

Economic Review, vol. 3, pp. 6-25, 2011. View at Google Scholar | View at Publisher 
[2] G. McElwee, "Developing entrepreneurial skills of farmers. A Literature Review of Entrepreneurship in Agriculture." Retrieved from 

http://www.esofarmers.org/documents/ESoFliteraturereview_000.pdf, 2005. 
[3] M. J. Chappell and L. A. LaValle, "Food security and biodiversity: Can we have both? An agroecological analysis," Agriculture and 

Human Values, vol. 28, pp. 3-26, 2011. View at Google Scholar | View at Publisher 
[4] X. Diao, P. Hazell, and J. Thurlow, "The role of agriculture in African development," World Development, vol. 38, pp. 1375-1383, 2010. 

View at Google Scholar | View at Publisher 
[5] J. M. Dieterle, "Just food: Philosophy," Justice and Food, 2015. 
[6] X. Zhu, "Understanding China's growth: Past, present, and future," Journal of Economic Perspectives, vol. 26, pp. 103-124, 2012. View at 

Google Scholar | View at Publisher 
[7] J. C. Aker, "Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in 

developing countries," Agricultural Economics, vol. 42, pp. 631-647, 2011. View at Google Scholar | View at Publisher 
[8] J. Huang and S. Rozelle, The role of agriculture in China’s development: Performance, determinants of successes and future challenges emerging 

economies: Springer, 2015. 
[9] B. F. Johnston, "Agriculture and structural transformation in developing countries: A survey of research," Journal of Economic 

Literature, vol. 8, pp. 369-404, 1970. View at Google Scholar   
[10] D. Kleine, "Who will feed China?," Journal of Soil and Water Conservation, vol. 52, pp. 398-399, 1997. View at Google Scholar   
[11] W. M. Liefert and P. C. Westcott, "Modifying agricultural export taxes to make them less market-distorting," Food Policy, vol. 62, 

pp. 65-77, 2016. View at Google Scholar | View at Publisher 
[12] J. Y. Lin, "Rural reforms and agricultural growth in China," American Economic Review, pp. 34-51, 1992. View at Google Scholar   
[13] X. Xin and F. Qin, "Decomposition of agricultural labor productivity growth and its regional disparity in China," China Agricultural 

Economic Review, vol. 3, pp. 92-100, 2011. View at Google Scholar | View at Publisher 
[14] A. Pufahl and C. R. Weiss, "Evaluating the effects of farm programmes: Results from propensity score matching," European Review of 

Agricultural Economics, vol. 36, pp. 79-101, 2009. View at Google Scholar | View at Publisher 
[15] J. Huang and H. Ma, Capital formation and agriculture development in China: Rome: FAO, 2010. 
[16] W. A. B. H. Lezin and W. Long-Bao, "Agricultural productivity growth and technology progress in developing country agriculture: 

Case study in China," Journal of Zhejiang University-Science A, vol. 6, pp. 172-176, 2005. View at Publisher 
[17] M. Xu, "Chinese agricultural growth in post-reform Era," Advances in Computer Science and Engineering, pp. 711-715, 2012. View at Google 

Scholar | View at Publisher 
[18] National Bureau of Statistics of China China Statistical Yearbook, Retrieved from 

http://www.stats.gov.cn/enGliSH/Statisticaldata/AnnualData/. [Accessed 24 January 2015], 1985-2014. 
[19] Y. F. Chen, Z. G. Wu, T. H. Zhu, Y. Lei, and H. P. Chien, "Agricultural policy, climate factors and grain output: Evidence from 

household survey data in rural China," Journal of Integrative Agriculture, vol. 12, pp. 169-183, 2013. View at Google Scholar | View at Publisher 
[20] L. Yunhua and W. Xiaobing, "Technological progress and Chinese agricultural growth in the 1990s," China Economic Review, vol. 16, 

pp. 419-440, 2005. View at Google Scholar | View at Publisher 
[21] D. G. Johnson, "China's rural and agricultural reforms in perspective: Publisher not identified," 1998. 
[22] Z. Ling, "Rural reform and peasant income in China," Impact of China's Post Mao Rural, 1991. 
[23] A. Nin-Pratt, B. Yu, and S. Fan, "Comparisons of agricultural productivity growth in China and India," Journal of Productivity 

Analysis, vol. 33, pp. 209-223, 2010. View at Google Scholar | View at Publisher 
[24] C. Po-Chi, Y. Ming-Miin, C. C. Chang, and H. Shih-Hsun, "Total factor productivity growth in China's agricultural sector," China 

Economic Review, vol. 19, pp. 580-593, 2008. View at Google Scholar | View at Publisher 
[25] L. J. Zhang, Y. Tang, and B. H. Liu, "Changes in agricultural system as farmers adapt to economic-social and climatic changes in the 

min upriver rural areas in Western Sichuan, South Western China," Journal of Mountain Science, vol. 12, pp. 747-758, 2015. View at 

Google Scholar | View at Publisher 
[26] L. Zhou and H. P. Zhang, "Productivity growth in China's agriculture during 1985–2010," Journal of Integrative Agriculture, vol. 12, 

pp. 1896-1904, 2013. View at Google Scholar | View at Publisher 
[27] L. Zhu, Rural reform and peasant income in China. London: Blacnillan, 1991. 
[28] S. Fan and P. G. Pardey, "Research, productivity, and output growth in Chinese agriculture," Journal of Development Economics, vol. 

53, pp. 115-137, 1997. View at Google Scholar | View at Publisher 
[29] S. Fan, L. Zhang, and X. Zhang, "Reforms, investment, and poverty in rural China," Economic Development and Cultural Change, vol. 

52, pp. 395-421, 2004. View at Google Scholar | View at Publisher 
[30] C. Ding, "Policy and praxis of land acquisition in China," Land Use Policy, vol. 24, pp. 1-13, 2007. View at Google Scholar | View at Publisher 
[31] D. Goodman, "Organic and conventional agriculture: Materializing discourse and agro-ecological managerialism," Agriculture and 

Human Values, vol. 17, pp. 215-219, 2000. View at Google Scholar   
[32] J. Huang, K. Otsuka, and S. Rozelle, "Agriculture in China’s development: Past disappointments, recent successes, and future 

challenges," China’s Great Economic Transformation, pp. 467-505, 2008. View at Google Scholar | View at Publisher 
[33] S. Fan, C. Fang, and X. Zhang, "Agricultural research and urban poverty: The case of China," World Development, vol. 31, pp. 733-741, 

2003. View at Google Scholar | View at Publisher 
[34] K. Fuglie and D. Schimmelpfennig, "Introduction to the special issue on agricultural productivity growth: A closer look at large, 

developing countries," Journal of Productivity Analysis, vol. 33, pp. 169-172, 2010. View at Google Scholar | View at Publisher 
[35] S. Tang, Q. Meng, L. Chen, H. Bekedam, T. Evans, and M. Whitehead, "Tackling the challenges to health equity in China," Lancet, 

vol. 372, pp. 1493-1501, 2008. View at Google Scholar | View at Publisher 
[36] W. Chi and X. Qian, "Human capital investment in children: An empirical study of household child education expenditure in China, 

2007 and 2011," China Economic Review, vol. 37, pp. 52-65, 2016. View at Google Scholar | View at Publisher 
[37] J. McMillan, J. Whalley, and L. Zhu, "The impact of China's economic reforms on agricultural productivity growth," Journal of 

Political Economy, vol. 97, pp. 781-807, 1989. View at Google Scholar | View at Publisher 
[38] S. Wu, D. Walker, S. Devadoss, and Y. C. Lu, "Productivity growth and its components in Chinese agriculture after reforms," Review 

of Development Economics, vol. 5, pp. 375-391, 2001. View at Google Scholar | View at Publisher 
[39] Z. Li. The change of Chinese agricultural productivity technology for education and learning. Springer. pp: 43-50, 2012. 
[40] R. Koenker and J. G. Bassett, "Regression quantiles," Econometrica: Journal of the Econometric Society, pp. 33-50, 1978. View at Google 

Scholar   
[41] B. Xu and B. Lin, "A quantile regression analysis of China's provincial CO 2 emissions: Where does the difference lie?," Energy Policy, 

vol. 98, pp. 328-342, 2016. View at Google Scholar | View at Publisher 
[42] H. Zhu, L. Duan, Y. Guo, and K. Yu, "The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-

5: Evidence from panel quantile regression," Economic Modelling, vol. 58, pp. 237-248, 2016. View at Google Scholar | View at Publisher 
[43] H. Uematsu, A. R. Khanal, and A. K. Mishra, "The impact of natural amenity on farmland values: A quantile regression approach," 

Land Use Policy, vol. 33, pp. 151-160, 2013. View at Google Scholar | View at Publisher 
[44] C. Chen, "An introduction to quantile regression and the QUANTREG procedure," presented at the Proceedings of the Thirtieth 

Annual SAS Users Group International Conference, 2005. 

https://scholar.google.com/scholar?hl=en&q=Do%20China's%20agricultural%20policies%20matter%20for%20world%20commodity%20markets?
http://dx.doi.org/10.1108/17561371111103516
http://www.esofarmers.org/documents/ESoFliteraturereview_000.pdf,
https://scholar.google.com/scholar?hl=en&q=Food%20security%20and%20biodiversity:%20Can%20we%20have%20both?%20An%20agroecological%20analysis
http://dx.doi.org/10.1007/s10460-009-9251-4
https://scholar.google.com/scholar?hl=en&q=The%20role%20of%20agriculture%20in%20African%20development
http://dx.doi.org/10.1016/j.worlddev.2009.06.011
https://scholar.google.com/scholar?hl=en&q=Understanding%20China's%20growth:%20Past,%20present,%20and%20future
https://scholar.google.com/scholar?hl=en&q=Understanding%20China's%20growth:%20Past,%20present,%20and%20future
http://dx.doi.org/10.1257/jep.26.4.103
https://scholar.google.com/scholar?hl=en&q=%22Dial%20
http://dx.doi.org/10.1111/j.1574-0862.2011.00545.x
https://scholar.google.com/scholar?hl=en&q=Agriculture%20and%20structural%20transformation%20in%20developing%20countries:%20A%20survey%20of%20research
https://scholar.google.com/scholar?hl=en&q=Who%20will%20feed%20China?
https://scholar.google.com/scholar?hl=en&q=Modifying%20agricultural%20export%20taxes%20to%20make%20them%20less%20market-distorting
http://dx.doi.org/10.1016/j.foodpol.2016.04.001
https://scholar.google.com/scholar?hl=en&q=Rural%20reforms%20and%20agricultural%20growth%20in%20China
https://scholar.google.com/scholar?hl=en&q=Decomposition%20of%20agricultural%20labor%20productivity%20growth%20and%20its%20regional%20disparity%20in%20China
http://dx.doi.org/10.1108/17561371111103570
https://scholar.google.com/scholar?hl=en&q=Evaluating%20the%20effects%20of%20farm%20programmes:%20Results%20from%20propensity%20score%20matching
http://dx.doi.org/10.1093/erae/jbp001
http://dx.doi.org/10.1631/jzus.2005.as0172
https://scholar.google.com/scholar?hl=en&q=Chinese%20agricultural%20growth%20in%20post-reform%20Era
https://scholar.google.com/scholar?hl=en&q=Chinese%20agricultural%20growth%20in%20post-reform%20Era
http://dx.doi.org/10.1007/978-3-642-27948-5_94
http://www.stats.gov.cn/enGliSH/Statisticaldata/AnnualData/
https://scholar.google.com/scholar?hl=en&q=Agricultural%20policy,%20climate%20factors%20and%20grain%20output:%20Evidence%20from%20household%20survey%20data%20in%20rural%20China
http://dx.doi.org/10.1016/s2095-3119(13)60217-8
https://scholar.google.com/scholar?hl=en&q=Technological%20progress%20and%20Chinese%20agricultural%20growth%20in%20the%201990s
http://dx.doi.org/10.1016/j.chieco.2005.03.006
https://scholar.google.com/scholar?hl=en&q=Comparisons%20of%20agricultural%20productivity%20growth%20in%20China%20and%20India
http://dx.doi.org/10.1007/s11123-009-0156-4
https://scholar.google.com/scholar?hl=en&q=Total%20factor%20productivity%20growth%20in%20China's%20agricultural%20sector
http://dx.doi.org/10.1016/j.chieco.2008.07.001
https://scholar.google.com/scholar?hl=en&q=Changes%20in%20agricultural%20system%20as%20farmers%20adapt%20to%20economic-social%20and%20climatic%20changes%20in%20the%20min%20upriver%20rural%20areas%20in%20Western%20Sichuan,%20South%20Western%20China
https://scholar.google.com/scholar?hl=en&q=Changes%20in%20agricultural%20system%20as%20farmers%20adapt%20to%20economic-social%20and%20climatic%20changes%20in%20the%20min%20upriver%20rural%20areas%20in%20Western%20Sichuan,%20South%20Western%20China
http://dx.doi.org/10.1007/s11629-014-3386-5
https://scholar.google.com/scholar?hl=en&q=Productivity%20growth%20in%20China's%20agriculture%20during%201985–2010
http://dx.doi.org/10.1016/s2095-3119(13)60598-5
https://scholar.google.com/scholar?hl=en&q=Research,%20productivity,%20and%20output%20growth%20in%20Chinese%20agriculture
http://dx.doi.org/10.1016/s0304-3878(97)00005-9
https://scholar.google.com/scholar?hl=en&q=Reforms,%20investment,%20and%20poverty%20in%20rural%20China
http://dx.doi.org/10.1086/380593
https://scholar.google.com/scholar?hl=en&q=Policy%20and%20praxis%20of%20land%20acquisition%20in%20China
http://dx.doi.org/10.1016/j.landusepol.2005.09.002
https://scholar.google.com/scholar?hl=en&q=Organic%20and%20conventional%20agriculture:%20Materializing%20discourse%20and%20agro-ecological%20managerialism
https://scholar.google.com/scholar?hl=en&q=Agriculture%20in%20China’s%20development:%20Past%20disappointments,%20recent%20successes,%20and%20future%20challenges
http://dx.doi.org/10.1017/cbo9780511754234.014
https://scholar.google.com/scholar?hl=en&q=Agricultural%20research%20and%20urban%20poverty:%20The%20case%20of%20China
http://dx.doi.org/10.1016/s0305-750x(03)00008-1
https://scholar.google.com/scholar?hl=en&q=Introduction%20to%20the%20special%20issue%20on%20agricultural%20productivity%20growth:%20A%20closer%20look%20at%20large,%20developing%20countries
http://dx.doi.org/10.1007/s11123-010-0168-0
https://scholar.google.com/scholar?hl=en&q=Tackling%20the%20challenges%20to%20health%20equity%20in%20China
http://dx.doi.org/10.1016/s0140-6736(08)61364-1
https://scholar.google.com/scholar?hl=en&q=Human%20capital%20investment%20in%20children:%20An%20empirical%20study%20of%20household%20child%20education%20expenditure%20in%20China,%202007%20and%202011
http://dx.doi.org/10.1016/j.chieco.2015.11.008
https://scholar.google.com/scholar?hl=en&q=The%20impact%20of%20China's%20economic%20reforms%20on%20agricultural%20productivity%20growth
http://dx.doi.org/10.1086/261628
https://scholar.google.com/scholar?hl=en&q=Productivity%20growth%20and%20its%20components%20in%20Chinese%20agriculture%20after%20reforms
http://dx.doi.org/10.1111/1467-9361.00130
https://scholar.google.com/scholar?hl=en&q=Regression%20quantiles
https://scholar.google.com/scholar?hl=en&q=Regression%20quantiles
https://scholar.google.com/scholar?hl=en&q=A%20quantile%20regression%20analysis%20of%20China's%20provincial%20CO%202%20emissions:%20Where%20does%20the%20difference%20lie?
http://dx.doi.org/10.1016/j.enpol.2016.09.003
https://scholar.google.com/scholar?hl=en&q=The%20effects%20of%20FDI,%20economic%20growth%20and%20energy%20consumption%20on%20carbon%20emissions%20in%20ASEAN-5:%20Evidence%20from%20panel%20quantile%20regression
http://dx.doi.org/10.1016/j.econmod.2016.05.003
https://scholar.google.com/scholar?hl=en&q=The%20impact%20of%20natural%20amenity%20on%20farmland%20values:%20A%20quantile%20regression%20approach
http://dx.doi.org/10.1016/j.landusepol.2012.12.007


Agriculture and Food Sciences Research, 2017, 4(1): 37-44 

44 
 

 

[45] P. Sckokai and D. Moro, "Modelling the impact of the CAP single farm payment on farm investment and output," European Review of 
Agricultural Economics, vol. 36, pp. 395-423, 2009. View at Google Scholar | View at Publisher 

[46] S. Hu, S. Tang, Y. Liu, Y. Zhao, M. L. Escobar, and D. De Ferranti, "Reform of how health care is paid for in China: Challenges and 
opportunities," Lancet, vol. 372, pp. 1846-1853, 2008. View at Google Scholar | View at Publisher 

[47] W. Yip and W. Hsiao, "China's health care reform: A tentative assessment," China Economic Review, vol. 20, pp. 613-619, 2009. View at 

Google Scholar | View at Publisher 
[48] L. Zhu, "A review of rural healthcare system weaknesses in China: Moving toward universal coverage by applying the benchmarks of 

fairness," 2013. 
[49] D. B. Audretsch, A. Coad, and A. Segarra, "Firm growth and innovation," Small Business Economics, vol. 43, pp. 743-749, 2014. View at 

Google Scholar   
[50] L. J. Liu, C. Cao, and M. Song, "China's agricultural patents: How has their value changed amid recent patent boom?," Technological 

Forecasting and Social Change, vol. 88, pp. 106-121, 2014. View at Google Scholar | View at Publisher 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

  

Asian Online Journal Publishing Group is not responsible or answerable for any loss, damage or liability, etc. caused in relation to/arising out of the use of the content. 
Any queries should be directed to the corresponding author of the article. 
 

https://scholar.google.com/scholar?hl=en&q=Modelling%20the%20impact%20of%20the%20CAP%20single%20farm%20payment%20on%20farm%20investment%20and%20output
http://dx.doi.org/10.1093/erae/jbp026
https://scholar.google.com/scholar?hl=en&q=Reform%20of%20how%20health%20care%20is%20paid%20for%20in%20China:%20Challenges%20and%20opportunities
http://dx.doi.org/10.1016/s0140-6736(08)61368-9
https://scholar.google.com/scholar?hl=en&q=China's%20health%20care%20reform:%20A%20tentative%20assessment
https://scholar.google.com/scholar?hl=en&q=China's%20health%20care%20reform:%20A%20tentative%20assessment
http://dx.doi.org/10.1016/j.chieco.2009.08.003
https://scholar.google.com/scholar?hl=en&q=Firm%20growth%20and%20innovation
https://scholar.google.com/scholar?hl=en&q=Firm%20growth%20and%20innovation
https://scholar.google.com/scholar?hl=en&q=China's%20agricultural%20patents:%20How%20has%20their%20value%20changed%20amid%20recent%20patent%20boom?
http://dx.doi.org/10.1016/j.techfore.2014.06.018

