Contributions of Productivity and Relative Price Changes to Wheat Profitability Changes in China

International Journal of Engineering and Management Research e-ISSN: 2250-0758 | p-ISSN: 2394-6962
Volume-11, Issue-4 (August 2021)
www.ijemr.net https://doi.org/10.31033/ijemr.11.4.34
263 This Work is under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Contributions of Productivity and Relative Price Changes to Wheat
Profitability Changes in China
Li Xiu-Shuang1
and Yu Kang2
1
College of Economics and Management, Zhejiang A & F University, Hangzhou 31130, CHINA
2
Research Academy for Rural Revitalization of Zhejiang Province, Zhejiang A & F University, Hangzhou 31130, CHINA
1
Corresponding Author: haileydeng@163.com
ABSTRACT
Using the provincial panel data of wheat from 1998
to 2018, based on the total price and quantity framework
proposed by O'Donnell(2010), and using Färe-Primont
productivity index, this artical decomposes the change of
wheat profitability into the change of input-output relative
price (TT) and the change of total factor productivity (TFP),
and further decomposes the change of TFP into technological
progress and efficiency change based on input orientation.
The results showed that the overall profitability of wheat
decreased by 24.9% compared with 1998, which was
attributed to the decrease of TT by 32.6% and the increase of
TFP by 11.4%. Results indicate that profitability change is
mainly driven by TT change, and the impact of TT change on
wheat profitability was alleviated by the compensatory
change of TFP. The main driving factor of wheat TFP growth
is technical progress, Compared with the growth of technical
progress, the technical efficiency grows slowly.
Keywords-- Profitability, Total Factor Productivity,
Relative Price Change, Färe-Primont Index, Data
Envelopment Analysis
I. INTRODUCTION
Since the reform and opening, China has rolled out a
series of major policies and measures to support
agriculture, so as to promote the sustained and stable
development of agriculture, ensure food production and
increase farmers' income. In 2020, the No.1 Document of
the Central Committee clearly pointed out that it is
necessary to further ensure the effective supply of grain
and promote the continuous increase of farmers' income.
Nowadays, although the output value of agricultural
production is increasing year by year, the cost of
agricultural production is also rising. Under the double
pressure of the limited price of agricultural products and
the increasing cost of agricultural production, farmers'
income is lower or even loss, farmers' income and
production enthusiasm cannot be effectively improved
(Wu Fangwei and Kang Jiaojiao, 2020). In addition, the
environmental and natural resource constraints of
agricultural development are becoming increasingly
severe, and the possibility for improving production by
increasing input factors is getting smaller and smaller
(Chen Xiwen, 2013), the future growth of agricultural
output depends fundamentally on the growth of TFP (Chen
Xiwen, 2012). In addition, the situation is complicated and
severe, the market fluctuates frequently, and the risks and
uncertainties increase. These factors make it increasingly
difficult to stabilize grain production and improve farmers'
income. When input prices, output prices and policies
change, farmers should adjust their production decisions,
and the output mix, input mix, production scale or
production technology will change, and these adjustments
will eventually affect farmers' production efficiency, cost,
income and profitability (Li Wenfu et al., 2015). Therefore,
under the new situation, it is of special practical
significance to research the changes of TFP, input-output
relative price and profitability of China's agriculture, and
to explore the main driving factors of profitability changes,
so as to stabilize food supply, improve farmers' production
enthusiasm and increase farmers' income.
Previous empirical studies have focused on study
the dynamics of productivity growth in the agricultural
sector, but few studies have investigated the relationship
between the input-output relative price, the change of
productivity and profitability of agricultura (Yeager and
Langemeier 2011, Mugera and Langemeier 2011). There
are two main types of measurement methods used in the
research of total factor productivity, the first is the
stochastic frontier method based on parameters (Wang Li
and Han Yali, 2016), the second is the nonparametric
method, and the nonparametric method mainly has two
forms, one is Malmquist index method based on data
envelopment analysis (Yu Hailong and Li Binglong, 2012;
Zhou Zhizhuan, 2014), and the other is the total quantity
analysis framework (Zhang Haixia, Han Peijun, 2018;
Zhao Liang, Yu Kang, 2019). Most of the above studies
only focuses on productivity and efficiency, which are
based on production technology and show how input is
transformed into output, but do not involve the profitability
closely related to the feasibility of farmers' management.
This is the problem that farmers are most concerned about.
Their goal may be to maximize profits or improve
profitability, but high profits are not always related to high

productivity, and vice versa.
In view of this, this paper intends to select wheat
as the research object, and use the framework first
proposed by O'Donnell(2010) to decompose the
profitability index of wheat production. The decomposition
process is divided into two stages. In the first stage, this
paper decompose the profitability index into two
multiplication parts: the relative price index which
measures the relative price changes of output and input and
the total factor productivity index. In the second stage, the
TFP index is decomposed into four multiplicative
efficiency measures: technological progress, technological
efficiency index, scale efficiency index and mixed
efficiency index. The framework clearly connects the three
variables of relative price, total factor productivity and
profitability, taking into account the production technology
and price, and provides a novel analysis framework for
measuring the main driving force of the change in wheat
profitability.
II. CONCEPTUAL FRAMEWORK
(1) Profitability Index Decomposition
By definition, TFP is the ratio of total output to
total input, input-output relative price is the ratio of total
output price to total input price, and profitability is the
ratio of total revenue to total cost. ( )
1 , ,
it it Mit
x x 
= 
x
， ( )
1 , ,
it it Nit
q q 
= 
q denote the input and output
quantity vectors for farm i in period t，
， denote the
input and output price vectors for farm i in period t.
( )
it it
X X
 x and ( )
it it
Q Q q
 denote the total intput and
total output, ( )
.
Q and ( )
.
X is a non-decreasing linear
homogeneous aggregate function, Total factor productivity
of i province in t year is defined as:：
it it it
TFP Q X
= （1）
Then the TFP index of i province in t year and h province in s year is defined as：
, , ,
it hs it hs it hs it hs
TFPI TFPI TFPI QI XI
= = （2）
( ) ( )
,
it hs i hs
t
Q Q Q
 q q is the output quantity
index, ( ) ( )
,
it hs i hs
t
X X X
 q q is the input quantity index,
O’Donnell（2012）called the TFP index defined by this
as the multiplicatively-complete TFP index.
The total input prices index and total output
prices index is defined as: it it it it
W X

= w x and
t
it
it it i
P Q

= p q , Therefore, the profitability of i
province in t year is defined as：
it it it
it
it it it it
it
p q P Q
PROF
w x W X
=

=

（3）
The profitability indexes of i province in t year and h province in s year are as follows：
,
it it it hs hs
it hs
hs hs
h it
s it
PROF P Q P Q
PROFI
W X W X
PROF
= = （4）
, ,
it hs it hs
W
P is the ratio of the total output price
index to the total input price index, That is, relative price
index, which measures the increase of output price relative
to input price. Obviously, the index to measure the change
of profitability can be decomposed into the total factor
productivity index and relative price index. The former
component captures the difference in profitability between
the two province that is due purely to differences in output
and input prices, while the latter captures the difference in
profitability between the two provience that is due purely
to differences in output and input quantities. In other
words, the former component is a pure price effect, while
the latter is a pure quantity effect.
(2) Multiplicatively-Complete TFP Index Decomposition
The production efficiency is defined by the ratio
of practical TFP to the maximum feasible TFP under the
current technology, so the efficiency of i province in t year
is defined as:
( )
1 , ,
t t Mt
w w 
= 
w ( )
1 , ,
t t Mt
p p 
= 
p

* * *
it
it
i
it
i
it
t it
t X
TFP
T
F
Q X
Q
FPE
T P
=
= （5）
it
TFP
is the largest TFP that can be realized by
using the technology of t period. We choose the efficiency
index based on input orientation, which is defined as
follows：
input-oriented technical efficiency index (ITE): ITE t
t
t
X
X
=
input-oriented scale efficiency index（ISE）：
° °
ISE t
t
t
t
t
Q X
Q X
=
input-oriented mix efficiency index（IME）：
µ
IME t
t
t
X
X
=
input-oriented residual scale efficiency（RISE）:
µ
IME t
t
t
X
X
=
input-oriented residual mix efficiency（RME）:
µ
RISE t t
t
t
t
Q X
Q X
 
=
°
t
Q and °t
X are the total output and total input
when holding the output and input mixes fixed and TFP
reaches the maximum. t
X is the minimum total input when
holding the output and input mixes fixed, µ
t
X is the
minimum total input when the input combination is
variable.
Combined with formula (5), the decomposition of
TFP index is obtained:
( )
, , , , ,
hs it hs it hs it hs it
hs it TFP ITE
T ISE RM
FP E

=    （6）
(10) The first item on the right side of the
equation is the technological progress index, the first item
in brackets is the technological efficiency index, the
second item is the scale efficiency index, and the third item
is the residual mixed efficiency index.
There is another decomposition path of TFP
index, which is decomposed into technical progress index,
technical efficiency index, mixed efficiency index and
residual scale efficiency index, namely.
( )
, , , , ,
hs it hs it hs it hs it
hs it TFP ITE
T IME RIS
FP E

=    （7）
Considering that multiplicatively-complete TFP
index has two decomposition paths, if TFP index is
measured and decomposed according to only one
decomposition path, deviation may occur. Therefore, we
geometrically average the two decompositions paths.
, , , , , ,,
hs it hs it hs it hs it hs it hs it
st TFP ITE ISE
T RISE IME E
FP RM

 
=     
  （8）
(3) Estimate Method In this paper, TFP is estimated by Färe-Primont
TFP index, which is constructed as follows：

（9）
0
q and are nonzero vector,
0 0
( ) ( , , )
t t
Q q D x q t
= ， 0 0
( ) ( , , )
t t
X x D x q t
= are distance
functions with constant input or output vectors in t period.
III. DATA DESCRIPTION
According to the output and input characteristics
of wheat production in china, One outputs, six inputs, and
their respective price indices are used in the computation
of profitability and productivity measures (see Table 1).
Among them, the sown area index was obtained from《
China Rural Statistical Yearbook》, and the other indexes
was obtained from《Compilation of cost-benefit data of
national agricultural products》. The《Compilation of
cost-benefit data of national agricultural products》
provides the average input per mu, so the total input was
obtained by multiplying the average input per mu by the
total sown area of corn. Since there is no specific data on
the quantity and price of machinery input in the《
Compilation of cost-benefit data of national agricultural
products》, we referring to Chen Shuzhang's(2003)
practice, the machinery input is measured as the
mechanical operation cost, the machinery price is
measured as price index of agricultural machinery. The
input of other factors is measured as the cost of other
factors, the price of other factors is measured as
agricultural production price index.
Tab1e 3.1: Index of variables
Quantity variables Price variables
Output
variables
Wheat production (ton) Wheat price (yuan/ton)
Input variables
Seed input (ton) Average price of seed(yuan/ton)
Chemical fertilizer input (ton) Average price of chemical fertilizer (yuan/ton)
Sown area (thousand hectares) Land rent (yuan/thousand hectares)
Employment days Labor price (yuan/day)
Mechanical operation cost(yuan) Price index of agriculturel machinery
Other factor inputs(yuan) Price index of Agricultural Means of Production
Considering the completeness and availability of
data, we selected the data of 15 provinces in China from
1998 to 2018, including Hebei, Shanxi, Inner Mongolia,
Heilongjiang, Jiangsu, Anhui, Shandong, Henan, Hubei,
Sichuan, Yunnan, Shaanxi, Gansu, Ningxia and Xinjiang.
IV. EMPIRICAL RESULTS
This section reports the estimated changes of
wheat profitability and its components, and all the
estimates were obtained using the professional version of
DPIN 3.0 software.
1. Changes of wheat profitability and its
components.
2. Dynamic analysis of national level
1. Changes of Wheat Profitability and its Components
Table 4.1 and Figure 1 provide the indexes of
wheat profitability, relative price and total factor
productivity compared with the base year in 1998. When
the index is greater than 1, it indicates that the index has
increased compared with 1998; otherwise, it indicates that
the index has decreased compared with 1998.
( )
( )
( )
( )
0 1
0 0 0 0
0 0 0 1 0 0
, , , ,
, , , ,
t
s t
s s
t
st
st
q t
Q
TF
q t
x x
D D
D x q t x t
X D q
P = =
0
x

Table 4.1: Index of wheat profitability and its components
Year Profitability Relative price Output price Input price TFP
1998 1.000 1.000 1.000 1.000 1.000
1999 0.981 0.919 1.049 1.142 1.068
2000 0.932 0.958 0.970 1.013 0.973
2001 1.000 0.987 0.963 0.975 1.013
2002 1.065 1.007 1.079 1.071 1.057
2003 1.096 1.080 1.140 1.056 1.015
2004 1.304 1.101 1.294 1.175 1.184
2005 1.061 0.954 1.187 1.245 1.113
2006 1.001 0.845 1.150 1.362 1.186
2007 0.843 0.724 1.101 1.519 1.163
2008 0.841 0.697 1.156 1.660 1.207
2009 0.778 0.658 1.198 1.820 1.181
2010 0.610 0.561 1.099 1.960 1.088
2011 0.592 0.505 1.104 2.185 1.172
2012 0.546 0.472 1.088 2.307 1.157
2013 0.502 0.463 1.131 2.442 1.085
2014 0.539 0.453 1.145 2.527 1.189
2015 0.507 0.433 1.101 2.545 1.172
2016 0.489 0.422 1.088 2.575 1.157
2017 0.511 0.434 1.137 2.622 1.177
2018 0.441 0.407 1.064 2.617 1.085
Average 0.751 0.674 1.104 1.639 1.114
Figure 1: Index of wheat profitability and its components
In the study period, the profitability of wheat was
lower than that in 1998 except in 2001-2006, and the
profitability index fluctuated between 0.441 and 1.304,
which was 24.9% lower than that in 1998 , Average annual
decline of 1.1%(dlnprof = ln (1.249/2018-2018)). This
indicates that the sharp decline of TT is the main driving
0.3
0.5
0.7
0.9
1.1
1.3
PROFI TTI TFPI

force for the decline of wheat profitability, and the impact
of TT change on wheat profitability is alleviated by the
compensatory change of TFP. In terms of stages, the
change of wheat profitability can be divided into two
stages: in the first stage (1998-2004), wheat profitability
showed an increasing trend, with an average increase of
4.8% compared with 1998, TT increased by 0.57%, and
TFP increased by 4.3%; In the second stage (2004-2018),
the profitability of wheat showed a downward trend, with
an average decrease of 51.3% compared with that in 2004,
which was attributed to the decrease of TT by 50% and
TFP by 2.8%.
From Table 4.1, it can be found that except for
2002, 2003 and 2004, TT is still significantly lower than
that in 1998. In order to explore the main reasons for the
significant decline in TT, we further decomposes TT
changes into total output price changes and total input
price changes. It was found that the total output price
increased by an average of 10.4% (0.49% per year)
compared with 1998, while the total input price increased
by an average of 63.9% (2.5% per year) compared with
1998, indicating that the slow growth of total output price
and the substantial increase of total input price were the
main reasons for the sharp decline of wheat TT. The
significant increase in the price of total inputs was mainly
due to the significant increase in the wages of employees
and land rentals, which increased by 144.5% and 117.8%
respectively compared with 1998. Compared with 1998,
the prices of seeds and other inputs increased by 14.2%
and 29.6% respectively, while the prices of chemical
fertilizers and machinery decreased.
During the study period, TFP of wheat showed a
fluctuating growth overall. Except in 2000, the TFP of
wheat increased compared with 1998, and the TFP index
fluctuated between 0.973 and 1.207, with an average
increase of 11.4% (average annual increase of 0.54%)
compared with 1998. Its growth is characterized by stages:
in the first stage (1998-2008), wheat TFP showed a sharp
growth trend, with an average annual growth rate of
1.71%; In the second stage (2009-2018), wheat TFP
decreased slightly in a fluctuating manner, with an average
annual decline rate of about 2.1%. Generally speaking,
China's wheat industry is facing the dilemma of relative
price deterioration, Although farmers strive to improve
production efficiency, their profitability is still declining.
2. Dynamic Analysis at Provincial Level
Table 4.2 provides the estimated value of the
profitability and its components of each province. Since
the Färe-Primont productivity index satisfy the transitivity
test, therefore, it can be used for cross-time and cross-
space comparison, not only for comparison within
provinces, but also for comparison among provinces. The
value marked with "a" in the table indicates the highest
among 15 provinces, and the value marked with "b"
indicates the lowest among 15 provinces.
Table 4.2: Index of profitability and its components of each province
Province
Profitability Relative price TFP
1998 2018 ∆ 1998 2018 ∆ 1998 2018 ∆
Hebei 2.256 1.358 0.602 3.324b
2.096 0.631a
0.679 0.648 0.954
Shanxi 1.815b
1.149 0.633 3.606 2.109 0.585 0.503 0.545 1.082
Inner Mongolia 3.329 1.909a
0.573 6.589 2.953 0.448 0.505 0.647 1.280
Heilongjiang 2.664 1.637 0.614 5.671 2.587 0.456 0.470 0.633 1.347
Jiangsu 1.979 1.397 0.706a
4.761 2.126 0.447 0.416b
0.657 1.581a
Anhui 2.913 1.400 0.481 6.567 2.518 0.383 0.444 0.556 1.254
Shandong 2.515 1.229 0.489 3.616 1.713 0.474 0.696a
0.717a
1.031
Henan 2.561 1.418 0.554 4.641 2.335 0.503 0.552 0.607 1.100
Hubei 3.504 0.836 0.239b
7.568 1.619b
0.214b
0.463 0.516 1.115
Sichuan 5.795 1.433 0.247 12.032a
2.790 0.232 0.482 0.514 1.067
Yunnan 5.884a
1.728 0.294 11.870 3.621a
0.305 0.496 0.477 0.962
Shaanxi 2.973 1.140 0.383 6.208 1.803 0.290 0.479 0.632 1.320
Gansu 2.450 0.808b
0.330 3.768 1.798 0.477 0.650 0.450b
0.692b
Ningxia 3.435 1.216 0.354 4.948 2.121 0.429 0.694 0.573 0.826
Xinjiang 2.478 1.211 0.489 3.622 1.803 0.498 0.684 0.672 0.982
Note: a indicates the highest value among 15 provinces, and b indicates the lowest value among 15 provinces.

It can be seen from Table 2 that the profitability
of 15 provinces decreased compared with that of 1998,
among which the profitability of Jiangsu Province
decreased the least (reduced by 29.4%), mainly due to its
higher TFP growth (incresed by 58.1%), and its TFP
growth rate was the highest among 15 provinces. Hubei
Province has the highest decline in profitability (reduced
by 76.1%), which is attributed to the sharp decline in TT
(reduced by 78.6%), and the decline in TT is the highest
among 15 provinces. Profitability varies greatly among
provinces, with Inner Mongolia having the highest
profitability (PROF=1.909) and Gansu having the lowest
profitability (PROF=0.808) in 2018. The relative price
levels of different provinces also show great differences. In
1998, the TT level in Sichuan Province was as high as
12.03, while that in Hebei Province was only 3.32.
Compared with 1998, TT in all provinces decreased in
2018, among which Hubei Province showed the largest
decrease, which was 78.6% lower than that in 1998. In
addition, the inter-provincial difference of TT in 2018 is
smaller than that in 1998, with Yunnan province having
the highest TT (TT=3.621) and Hubei province having the
lowest TT (TT=1.619). Compared with the decrease of
profitability and relative price level, TFP in most provinces
showed positive growth, among which Jiangsu Province
had the highest growth rate, which increased by 58.1%
compared with 1998, while TFP in some provinces showed
negative growth, such as Gansu (decreased by 30.8%) and
Ningxia (decreased by 17.4%).
1. TFP and its component changes.
2. Dynamic analysis at provincial level.
1. TFP and its Component Changes
Table 4.3: Changes of TFP and its components
Year TFP technical progress technical efficiency Scale efficiency Mixing efficiency
1998 1.000 1.000 1.000 1.000 1.000
1999 1.068 1.111 1.002 0.975 0.983
2000 0.973 0.976 1.009 0.974 1.015
2001 1.013 1.019 1.034 0.948 1.013
2002 1.057 1.052 1.035 0.940 1.034
2003 1.015 1.022 1.036 0.947 1.012
2004 1.184 1.183 1.038 0.924 1.044
2005 1.113 1.072 1.020 0.945 1.077
2006 1.186 1.094 1.021 0.960 1.106
2007 1.163 1.114 1.010 0.947 1.093
2008 1.207 1.178 1.015 0.939 1.074
2009 1.181 1.132 1.015 0.944 1.088
2010 1.088 1.058 1.010 0.933 1.091
2011 1.172 1.138 1.011 0.940 1.084
2012 1.157 1.069 1.025 0.965 1.095
2013 1.085 1.048 0.999 0.953 1.087
2014 1.189 1.184 0.990 0.954 1.063
2015 1.172 1.163 0.996 0.969 1.044
2016 1.157 1.128 1.009 0.979 1.038
2017 1.177 1.155 0.993 0.966 1.062
2018 1.085 1.031 1.010 0.979 1.065
Average 1.114 1.090 1.013 0.956 1.055

Figure 2: TFP and its component changes
Table 4.3 and Figure 2 provide the index of TFP
and its components compared with 1998. It can be seen
from Figure 2 that the main driving factor of wheat TFP
growth is technical progress, which is 9% higher than that
in 1998 (with an average annual growth of 3.2%). This is
also directly manifested in that the changing trends of the
two are consistent, and the direction of the wave, the
location of the wave trough, and the wave crest are highly
consistent. This may be due to the planned introduction of
international advanced agricultural science and technology
projects from abroad in 1994, including the introduction of
new varieties and technologies (Zhu Mande and Huang
Qingqing, 2020), which significantly promoted
technological progress. Compared with the substantial
increase of technological progress, the change of
technological efficiency is relatively gentle, with an
increase of only 1.3% (average annual increase of 0.61%)
compared with that in 1998, indicating that the best use of
existing technologies has not contributed much to the
growth of TFP, which may be due to the fact that farmers
are mainly engaged in small-scale decentralized
operations. Even if the government has been promoting
agricultural technology for a long time, the farmers are
limited by their knowledge level and resource
endowments, and have low acceptance of new
technologies and insufficient adoption motivation, which
results in poor use of new technologies.
The change of wheat mixing efficiency showed
the characteristics of stages: in the first stage (1998-2006),
the wheat mixing efficiency showed a large increase with
an average annual growth rate of 1.3%; In the second stage
(2007-2018), the mixing efficiency of wheat showed a
downward trend, with an average annual decline of 1.3%.
This means that the change of wheat factor input structure
promotes the growth of wheat TFP in the first stage, but
hinders the growth of wheat TFP in the second stage. This
may be due to the fact that China began to implement the
policy of minimum purchase price of wheat in Jiangsu,
Shandong, Hebei, Anhui, Henan and Hubei provinces in
2006, which prompted a large number of cultivated land
and other factors in these areas to flood into wheat
production. At the same time, farmers expected the future
increase of wheat planting income, and tended to increase
fertilizer input, which was not conducive to the
optimization of factor input structure and hindered the
growth of mixing efficiency, thus inhibiting the growth of
wheat TFP to a certain extent.
Scale efficiency showed a downward trend from
1998 to 2004 and slowly increased from 2004 to 2018, but
on the whole, scale efficiency still decreased by an average
of 4.4% as compared with 1998 (with an average annual
decrease of 0.23%), indicating that the change in wheat
scale not only did not promote the growth of wheat TFP,
but even hindered its growth to a certain extent. In recent
years, the government has encouraged moderate scale
operation of agriculture through various forms, such as
land transfer and subsidies from scale operators, which has
promoted the scale operation of wheat. The scale
efficiency of wheat has also shown an increasing trend in
recent years, but the scale efficiency is still low. It is still
an important task to improve wheat TFP in the future to
promote moderate scale operation of wheat and to promote
the improvement of scale efficiency.
2. Dynamic Analysis at Provincial Level
0.90
0.95
1.00
1.05
1.10
1.15
1.20
TFP technical progress technical efficiency
Scale efficiency Mixing efficiency

Table 4.5: Changes in TFP and its Components by Province
Provience
TFP Technical efficiency Scale efficiency Mix efficiency
1998 2018 ∆ 1998 2018 ∆ 1998 2018 ∆ 1998 2018 ∆
Hebei 0.679 0.648 0.954 1.000a
0.917 0.917 1.000a 0.997 0.997 0.976 0.988 1.012
Shanxi 0.503 0.545 1.082 0.859 0.823b
0.959 0.954 0.976 1.024a
0.884 0.944 1.069
Inner Mongolia 0.505 0.647 1.280 1.000a
0.985 0.985 1.000a
0.978 0.979 0.727 0.936 1.288
Heilongjiang 0.470 0.633 1.347 1.000a
1.000a
1.000 1.000a
0.939 0.940 0.676b
0.939 1.390a
Jiangsu 0.416b
0.657 1.581a
0.744b
1.000a
1.344a
0.991 0.999 1.008 0.811 0.917 1.131
Anhui 0.444 0.556 1.254 0.918 1.000a
1.089 0.980 0.987 1.007 0.709 0.786 1.109
Shandong 0.696a
0.717a
1.031 1.000a
1.000a
1.000 1.000a
1.000a
1.000 1.000a
1.000a
1.000
Henan 0.552 0.607 1.100 1.000a
1.000a
1.000 1.000a
0.920 0.920 0.793 0.920 1.160
Hubei 0.463 0.516 1.115 0.953 0.944 0.991 0.986 0.947 0.960 0.708 0.805 1.136
Sichuan 0.482 0.514 1.067 1.000a
1.000a
1.000 1.000a
0.988 0.988 0.693 0.725 1.047
Yunnan 0.496 0.477 0.962 1.000a
1.000a
1.000 0.999 0.972 0.972 0.713 0.684b
0.960
Shaanxi 0.479 0.632 1.320 0.902 1.000a
1.109 0.995 0.983 0.988 0.768 0.896 1.168
Gansu 0.650 0.450b
0.692b
1.000a
0.828 0.829b
0.975b
0.984 1.009 0.959 0.769 0.802b
Ningxia 0.694 0.573 0.826 1.000a
1.000a
1.000 0.999 0.902b
0.903b
0.999 0.886 0.887
Xinjiang 0.684 0.672 0.982 1.000a
1.000a
1.000 1.000a
0.996 0.996 0.984 0.940 0.956
Note: a represents the highest of the 15 provinces; b represents the lowest of the 15 provinces
The input-oriented efficiency estimates reported
in Table 4.5 show that, throughout the sample period, most
provinces have relatively high technical efficiency
(exceptions include Jiangsu and Shanxi provinces in 1998,
Shanxi and Gansu provinces in 2018) and relatively high
scale efficiency, but overall mixing efficiency is relatively
low, especially Heilongjiang and Sichuan provinces in
1998 and Yunnan and Gansu provinces in 2018. From the
growth rate of TFP, except for Hebei, Yunnan, Gansu,
Ningxia and Xinjiang, the TFP value of other provinces
increased positively compared with that of 1998, with
Jiangsu province having the highest growth rate. From the
perspective of input-oriented efficiency growth rate,
Jiangsu Province's technological efficiency grew from the
lowest in 1998 to the highest in 2018, becoming the
province with the highest growth rate of technological
efficiency, up 34.3% as compared with 1998, while Gansu
Province's technological efficiency showed a downward
trend, with the highest decline rate among 15 provinces,
down 17.1% as compared with 1998. Except for a few
provinces, the scale efficiency of most provinces decreased
compared with that of 1998, while the mixed efficiency as
a whole showed positive growth (except for Gansu,
Ningxia and Xinjiang).
V. CONCLUSIONS
This paper uses the provincial panel data from
1998 to 2018 to study the profitability and TFP changes of
wheat in China. By decomposing the changes of wheat
profitability into the changes of input-output relative price
and TFP, and further decomposing the changes of TFP into
the changes of technical progress and input-oriented
efficiency, the main driving forces of wheat profitability
and TFP changes were explored. The main conclusions are
as follows:
(1) During the research period, the national wheat
profitability showed a downward trend, with an average
decrease of 24.9% as compared with 1998, due to the
decrease of TT by 32.6% and the increase of TFP by
11.4%. The significant decrease in TT is the main driving
force for the decrease in wheat profitability. The impact of
TT change on wheat profitability is mitigated by the
compensatory change in total factor productivity. By
further decomposing the change of TT, it is found that the
slow growth of total output price and the sharp increase of
total input price are the main reasons for the sharp
decrease of TT in wheat. The significant increase in total
input price was due to the significant increase in the wages
of employees and land rentals.
(2) The growth of wheat TFP presents the
characteristics of stages: in the first stage (1998-2008),
wheat TFP shows a significant growth trend with an
average annual growth rate of 1.71%; In the second stage
(2009-2018), the wheat TFP showed a fluctuating and
small decrease trend, with an average annual decrease rate
of 2.1%. The main driver of wheat TFP growth was
technological progress, which increased by 9% on average
(3.2% on average) as compared with 1998. Compared with
the significant increase in technological progress, the

change in technological efficiency is relatively gentle, with
an increase of only 1.3% as compared with 1998 (with an
average annual growth rate of 0.61%), indicating that the
best use of existing technologies has little contribution to
the growth of TFP. The change of wheat mixing efficiency
also presents the characteristics of stages: in the first stage
(1998-2006), the wheat mixing efficiency is in positive
growth with an average annual growth rate of 1.3%; In the
second stage (2007-2018), the wheat mixing efficiency
showed a downward trend, with an average annual
decrease of 1.3%. This means that the change of mixing
efficiency gradually changes from promoting the growth of
wheat TFP to hindering the growth of wheat TFP. On the
whole, the scale efficiency is still lower than that of 1998,
with an average decrease of 4.4% (with an average annual
decrease of 0.23%).
The policy implications of this research
conclusion are as follows:
First, the slow increase in production efficiency and the
sharp drop in the relative prices are the two major factors
that restrict the improvement of wheat profitability. To
improve the profitability of wheat cultivation, the
government needs to shift its perspective from a single
demand-side price incentive to a multi-directional supply-
side support, such as controlling the growth of input prices,
enhancing the level of intensification, and providing more
and better public services. In addition, we should further
promote the function of agricultural cooperatives and
strengthen the standard of farmers' organization. Farmers
are at a disadvantage in the market and only passively
accept the market price. If the standard of farmers'
organization is improved, farmers can improve their
bargaining power in all aspects of production and sales by
virtue of their scale advantages.
Secondly, TFP of wheat grows slowly, which has a limited
effect on profitability. In the future, it is still necessary to
promote the improvement of agricultural TFP . In view of
the fact that technological progress and the growth of
mixing efficiency are the two main driving forces for the
improvement of wheat TFP, the improvement of wheat
TFP should focus on technological progress and the
optimization of factor input structure. On the one hand, we
should strengthen the research and development of new
technologies; on the other hand, we should attracting high-
quality talents into the agricultural field through the
preferential subsidy policy for agricultural innovation, and
improve the overall quality of labor in the agricultural
field. At the same time, we should popularize the training
of agricultural production technologies in rural areas, and
vigorously promote new crop varieties and emerging
technologies. In addition, we should vigorously promote
mechanized operation, integrate land resources.
Meanwhile, we should increase investment in research and
development of agricultural machinery manufacturing
technology to effectively solve the problems in the process
of mechanical operation, which can not only replace labor
input, but also effectively reduce material input, promote
the optimization of factor input structure and improve
production efficiency.
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