中国の地域間分業と地域の「位置」

1

【論文】

Environmental Efficiency and Employment
in China’s Soybean Farming Sector
Shota MORIWAKI［キーワード］environmental efficiency, Egaitsu-Shigeno type production function, soybeans,biochemical technology, mechanical technology［JEL 分類番号］O1, Q1, Q5

1 .Introduction
The purpose of this study is to concurrently

technology (an indicator of environmental
efficiency and waste price) and mechanical(M)

technology(over-employment).

Wemeasure environmental efficiency and over-establish that there is a relationship between

employment in China’s soybean farming sector

estimated environmental efficiency and theusing regional panel data from 2000 to 2012.

two independent variables, rural per-capita

We accomplish this by conducting a stochastic

income and waste price, which indicates if thefrontier analysis (SFA) on the Egaitsu–Shigeno

effect of rural development and waste levels on(E-S) type production function, which allows

environmental efficiency may be observed at a

us to distinguish between biochemical (BC)provincial level in this sector.

Figure 1

Nutrition Balance

Source: CNFPCBD───────────* This work was supported by JSPS KAKENHI ［grant number 24530323］

2

中国経済研究第13巻第 ２ 号

Table 1 The Ranking of The Harvested Area in China

(ha)

2000

2006

2012

1

Rice, paddy

29961890 Rice, paddy

29294650

Maize

34949000

2

Wheat26653290 Maize

26970880

Rice, paddy

30297000

3

Maize

23056270 Wheat

22961450

Wheat

24139000

4

Soybeans

9306760

Soybeans

9304400

Vegetables,
freshnes

9650000

5

Rapeseed

7494360

Vegetables,
freshnes

8300000

Rapeseed

7300000

6

Vegetables,
freshnes

6660000

Rapeseed5984000

Soybeans

6750000

7

Sweet potatoes

5815080

Seed cotton

5815700

Potatoes

5429000

8

Groundnuts, withshell

4855310

Potatoes

4214520

Groundnuts, with
4700000
shell

9

Potatoes

4723430Groundnuts, withshell

3955800

Seed cotton

4700000

10

Seed cotton

4041000

Sweet potatoes

3663000

Sweet potatoes

3472600

Source: FAOSTATWe focus on soybeans for three reasons. First,

effect on China’s rural economy. Althoughconsidering the process of biological fixation

the cultivated land area has decreased sincein addition to the use of chemical fertilizers,

the late 2000s, the output to land ratio hassoybean farming is a sector that could have aincreased (Figure 4). Focusing on the soybean

positive nutrition balance1.

farming sector enables us to establish how

Second, soybean farming could use nutrientsthe change to the “land-saving method” hasin intermediate goods (chemical fertilizer) andaffected environmental efficiency and resulted

arable land (biological fixation) in BC process

in over-employment.

and so, like applying cost minimization problem,

There are two approaches to measuring

we could resolve nutrient minimization

environmental efficiency that employ data

problem to estimate environmental efficiency

envelopment analysis (DEA); they produce

in our empirical methodology. Third, being one

input–output models that include bothof China’s top ten crops by cultivated land area

undesirable and desirable goods as variables.

indicates that soybean farming has a significant

The key difference lies in whether weak───────────
１ Nutrition balance is measured by subtracting
nutrient outputs from nutrient inputs in the
agricultural production process. We consider
a positive nutrition balance to be a primaryfactor of environmental waste in the agricultural
sector. For the method of estimation of nutrition
balance, see Hoang and Alauddin (2009).

disposability is imposed on the undesirable
goods.
Färe et al. (1989), Chung et al. (1997), andFäre et al. (2004) assume that the undesirable
outputs are weakly disposable. However,
Coelli et al. (2007) and Hoang and Coelli (2011)
measure total factor productivity (TFP) by

Environmental Efficiency and Employment in China’s Soybean Farming Sector

3

assuming that polluting agricultural materials

agriculture, and as such, we seek to fill this gap

are strongly disposable; additionally, theyin the literature by using the NBM to measureemploy the nutrient balance method (NBM),

environmental efficiency in China’s agriculturalwhich measures net agricultural pollutionsectors.

by subtracting nutrient outputs from nutrient
inputs.

It should be noted that waste’s shadow priceindex is a clear indicator of the cost of waste

While the aforementioned studies measure

reduction and is thus useful in the development

environmental efficiency through DEA andof tax and subsidy policies. Coelli et al. (2007)

linear programming, it is also possible tomeasure the waste price with optimal cost and

use SFA (Van Meensel et al., 2010; Hoang

nutrition values obtained from DEA, a method

and Nguyen, 2013). SFA affords two majorthat our study employs as well.

advantages; it does not require balanced panelInexamining

factorsthat

explaindata (which is useful for our own study as

environmental efficiency, we gained useful

China’s regional data is rarely balanced) andinsight from some studies that estimate an

it allows one to analyze the error term in theenvironmental Kuznets curve (EKC) whenestimation of the production frontier. Vanconsidering environmental efficiency measures.

Meensel et al. (2010) compare the environmental

Taskin and Zaim (2001) test whether the EKC

efficiency estimations from DEA and SFA

hypothesis holds by examining the relationshipusing data on pig furnishing in Flanders; they

between GDP per-capita and environmental

define feed use and the number of rotations

efficiency estimates in 49 countries. Färe andas inputs, indicating that their production

Grosskopf (2003) confirm the EKC hypothesis

function accounts for only BC technology. Vanusing data from the OECD countries from 1971

Meensel et al. (2010) also utilize the NBM in

to 1990. Managi and Jena (2008) employ Indian

their examination of the BC process. For ourindustrial panel data to estimate the EKC.

own study, we are able to reflect the entire

GDP or GDI per capita is used as explanatory

BC process in our environmental efficiency

variable to calculate macro environmental

estimation by taking into account nutrients

efficiency in several studies. However, ourproduced by bacterial nitrogen fixation rather

estimated environmental efficiency is limited

than just seeds and fertilization.2

to the soybean sector. Therefore, our study

Studiesthatexamineenvironmental

examines the relationship between estimatedefficiency in China include Kaneko and Managi

environmental efficiency and rural personal(2004), Managi and Kaneko (2006), Managi

income, which is a more limited concept thanand Kaneko (2009), and Fujii et al. (2010);

national income per-capita, and focuses on thethey do so by using DEA and focus on therelationship between increase in rural income

secondary industry rather than the primary.

and environmental efficiency. Additionally,

However, there are few studies that focus on

the effect of shadow waste price of soybean───────────
2 According to Shindo et al. (2003), rice,soybeans, and pulses grow through biologicalfixation. However, according to our estimation,
the biological fixation for rice farming per arable
land is significantly lower than that for soybeans.

farming on environmental efficiency is alsoestablished. The relationship between wastesocial cost and environmental efficiency has
not been examined and we could gain some
empirical suggestions for environmental policy.

4

中国経済研究第13巻第 ２ 号

It should be noted that the E-S productionof multicollinearity. This, in turn, causes theirfunction that we employ is also capable ofestimated results to be partially insignificant.

measuring over-employment through the

Our E-S production function requires fewer

M process. Ohkawa (1960) states that if one

dependent variables to separate BC technology

industry’s marginal productivity of labor (MPL)

from M technology and thus yields moreis lower than others industries’, then that

accurate results.

industry will exhibit over-employment. ThisThe rest of this study is organized as follows:

over-employment indicates an inefficient use

Our empirical model is presented in the Sectionof labor input in the M process and is one of

2. Section 3 describes the data. Sections 4

the main factors of rural poverty.

and 5 report the results of our statistical and

Additionally, when a developing economy

empirical analyses, respectively. In Section 6,

surpasses Lewis’ turning point, there will cease

we draw our conclusions and provide policy

to be over-employment and the real wage

implications.

will increase. According to Minami (1973), the2 .Theoretical background andmost effective means of determining whether

methodologies

this point has been reached is to estimate theproduction function and compare the real wage
rate with the estimated marginal productivity

2.1

E-S production function and nutrient
minimization problemof labor; if the real wage rate is greater, thenUnder the assumption that product andthe economy has yet to clear the turning point.

production factor markets are perfectly

Finding the Lewis turning point is not thecompetitive, the conventional agriculturalobjective of our study; however, prior studies,

production function may be written as follows

which estimate the production function, andestablish the Lewis turning point, could give us(1)

useful empirical suggestions.
Studies that estimate production functions
often evaluate the entire agricultural sector

where Q, K, L, M, and S represent output,
capital stock, labor power, intermediate goods,(Hondai and Ra, 1999; Islam and Yokota,

and arable land. Additionally, other production

2008; Minami and Ma, 2010); however, it may

factors can be substituted or supplemented

be beneficial to focus on a single crop as one

into this function. Egaitsu and Shigeno (1983)

cannot account for the disparities in farming

assume that there are a set of intermediatetechnologies when examining such a wide

goods and arable land and a set of labor power

variety of crops. For instance, Inada andand capital stock in Equation (1); the former

Yamamoto (2010) estimate the C-D production

set represents BC technology and the latterfunction for China’s japonica rice farming

M technology. We assume that the production

sector using regional panel data (as we do infactors in the set are substituted, while outputs

our study). In contrast to previous studies,

produced by the set are fully supplemented.

they do not use the value-added approach

Consequently, the E-S production function may

but rather the production approach. As such,

be written as

they are able to add intermediate goods as
dependent variables, which increase the risk

(2)

Environmental Efficiency and Employment in China’s Soybean Farming Sector

If the E-S production function is a C-D type,
we are able to write

5

where FM, FS, GL, and GK are each production
factors’ marginal productivity. Furthermore,
Equations (11) and (12) yield the equilibrium(3)

condition for the BC and M technologies.

(4)

F=G

(15)

A farmer’s cost minimization problem

Based on Equations (13) and (14), we confirm(subject to (3) and (4)) can be written as follows

total cost is minimized when both BC and M
technologies are at their optimal points, thatsuch thatMin
andis, when their production quantities are equal.
(5)

Environmental

efficiency

is

found

by

applying the cost minimization problem tothe BC technology process. We assume that

where TC indicates total cost and m, s, w, and renvironmental performance is determined

represent every production factor’s price. We

by the nutrient minimization process. If TN

obtain the optimum set of production factors

represents total nutrients and mn and sn are

by solving the following Lagrangian

the nutrients per production factor, we may
rewrite the cost minimization problem as
a nutrient minimization problem, which an(6)environmentally efficient soybean farm would
solve (we assume that nutrients are only in(7)arable land and intermediate goods).

(8)

min
and

such that(16)(9)
The equilibrium condition for nutrients is(10)

expressed as

(11)

(17)

(12)

We employ Farrell’s (1957) methodology inthe actual estimation. In Figure 2, CC´ is the

From the conditions in Equations (7)–(10), we

unit isoquant curve and GG´ is the isocost line. If

get equilibrium conditions for the production

a soybean farm produces at point A, they could

factor market:

proportionally reduce all of their production
factors and produce more efficiently in terms(13)of both BC and M technologies by producing
at point D instead. Technical efficiency (TE) is(14)

expressed as the ratio between DO and AO.

6

Figure 2

中国経済研究第13巻第 ２ 号

Technical Efficiency, Environmental Efficiency, and Cost Efficiency

Source: Farrell’s (1957) Diagram 1

Cost is minimized in the BC and the M(19)processes at point H, though not in terms ofTE at point D. Point I is equally as expensive as
point H and features a proportional reductionin production factors as compared to point A.
The allocative efficiency (AE) (TE evaluated by
the production cost) of point D is given by theratio of IO to DO in the BC or the M processes.
Cost efficiency (CE) is the ratio of IO to AO. The
ratios are related as follows:

Using Equation (19) in conjunction with
SFA, we are able to measure the production
efficiency while taking into account theenvironmental factor.
In estimating these efficiencies, we may
obtain the optimal sets of production factors,
which, according to Coelli et al. (2007), may
then be used to find the shadow price of waste.

(18)

Consequently, Figure 2 indicates the following(20)

We can measure these efficiencies in regard
to either the BC or the M processes.
If x1 and x2 in Figure 2 represent the arable
land and the intermediate goods in the BC
process, and BB´ represents the iso-nutrientline, then TE, environmental allocative
efficiency (EAE), and environmental efficiency(EE) are the ratios of DO to AO, FO to DO, andFO to AO, respectively. Furthermore,

whererepresents the shadow price of waste,

which is the monetary cost (factoring in the
reduction in environmental optimal points) of
reducing nutrients. By measuring the price ofpollutants, we may better understand the costof environmental conservation, which would, inturn, be useful in the development of regional
agricultural policies.

Environmental Efficiency and Employment in China’s Soybean Farming Sector

7

From the estimation of the M process’(25)

efficiency, we may determine optimal setsof capital stock and labor. We define overemployment as the difference between reallabor power and optimal labor power at the cost
minimizing point. If, in Figure 2, the production
function reflects M technology and x1 and x2
represent capital stock and labor power, therate of over-employment (ROE) iswhere

is a random, region-specific effect.

Again, the error term, v, and inefficiency, u,
are assumed to be normally and exponentiallydistributed, respectively.
Wemeasure

TE

and

EE

with

the

aforementioned models. For example, if
and
(21)

the

actualratio

of intermediate goods to arable land is, then TE BC (the efficiency of BC

2.2

SFA

technology) is the ratio of a set of Mte and Ste

In order to estimate the production function,and thereby the environmental efficiency and(that simultaneously realizeof observed

and

and

) to a set.

other indexes, we utilize SFA as it can be(26)

applied to unbalanced panel data and allows
us to include an error term. By transforming
Equations (3) and (4) into logarithms, we are

Similarly, TEM isable to obtain our SFA model for the production(27)

frontier:(22)

To measure EE, we solve the nutrient
minimization problem (Equation (16)) to obtain(23)

the nutrient function,

where i, t, v, and u represent the region, time,(28)

error term, and inefficiency, respectively. We
use Greene’s (2005) true fixed effect (TFE)
model and true random effect model (TRE) forwhere

the estimation. lnA in Equation (22) is given by
the TFE model(24)
where ai is a time invariant effect on the iregion. In the TFE model, the error term, v, is

By using Equation (28), we can find thenutrient minimizing set.

assumed to be normally distributed, while theinefficiency, u, is exponentially distributed.

(29)

In Equation (25), lnA is given by the TRE
model

(30)

8

中国経済研究第13巻第 ２ 号

the effect of the regional pollution level on(31)
We may also derive cost functions that canbe minimized with respect to either the BC or
the M process and thereby obtain the CE.

3 .Data
The Compilation of National Farm ProductCost-Benefit Data (CNFPCBD) is a national
sample survey that details the production

(32)
(33)
Kopp (1981), Bravo-Ureta and Rieger (1991),
Sharma et al. (1999), and Singh et al. (2001)
decompose cost efficiency as well, though theyignore time invariant fixed effects and random
effects (which Greene’s (2005) panel modelallows us to account for).
2.3

efficiencies.

costs of several agricultural products; we relyon it for regional soybean farming data in ourestimation of the production function.
As the CNFPCBD records data in units per
mu, we must modify the values for our study.
Furthermore, we must estimate arable landdata for all periods except 2000–2003.
3.1

Production function estimation through
SFA
Output: annual soybean production

The examination of explanatory variables
on environmental efficiency

Based on equation (19), we estimate equations
34–36 as follows:

Intermediate goods: total expenditure on
chemical fertilizer, seeds, pesticides, andother goods
These values are expressed in constant2007 prices calculated with the China Rural
Statistical Yearbook’s (CRSY) price index.

(34)

Capital input: capital service
Capital service is the total capital cost of(35)

machinery, draft animals, and depreciation
expressed in constant 2007 prices (CRSY).

(36)

Labor input: days of labor.
One day of labor is assumed to be eight

where y indicates the per-capita income in
the rural areas of each province. Considering

hours.
Arable land:

the nonlinearity of the relationship between

For the 2000–2003 period, we are able to use

several efficiencies and rural per-capita income,planted area values from the CNFPCBD. After

we include y2 in the estimated equations. We

2004, we estimate the values with the following

include the shadow price of waste,, to test

equation.

Environmental Efficiency and Employment in China’s Soybean Farming Sector

Figure 3

Real Fertilizer Expenditure to Land Ratio

9(Constant International Dollar/Ha)

Sources: CNFPCBD and the Ministry of Agriculture, Forestry, and Fisheries (MAFF)

The average between 2002 and 2003 isused. The data on the total area of soybean

capita income in rural areas deflated by the
consumer price index (CSY)

cultivation is obtained from the CRSY.
3.2

Measurement

of

EE

and

4 .Statistical Analysis
over-

employment

As Japan has transitioned from a developing
to a developed economy (surpassing Lewis’

Nutrients in the intermediate goods: total

turning point (Minami, 1973) and finishing

nutrients (i.e., nitrogen, phosphoric acid, andthe second export substitution (Ohkawa andpotassium) included in chemical fertilizers

Kohama, 1989)), we may compare its data with

(CNFPCBD), seeds (China Food Composition

that of China in order to determine China’s(CFC)), and arable land (estimated using

current stage of development and thereby

Shindo’s (2012) method)

supplement our empirical estimation.

Price of intermediate goods: implicit
deflator of intermediate goods
Price of capital input: implicit deflator of
capital services

Figure 3 shows real expenditures on chemicalfertilizer per Ha of arable land in Japan’s andChina’s soybean farming sectors from 2000 to
20113.

Real Wage: wages of hired-laborers in theThough Japan’s fertilizer expenditure to

regional soybean farming sector deflated byland ratio has trended downward, China’s

the consumer price index (China Statistical
Yearbook (CSY))
3.3

Estimation of factors explaining several
efficiencies
Real per-capita income in rural areas: per-───────────
3 It is difficult to obtain comparable data on the
nutrient content of Japan’s and China’s soybean
fertilizers, so we instead compare the monetary
value. The local currencies are converted using
the World Bank’s purchasing power parity
estimations.

10

Figure 4

中国経済研究

Soybean Output to Land Ratio

第13巻第 ２ 号

(Hg/Ha)Source: FAOSTAT

has trended upward to where the two nearly

for agricultural development. Unfortunately,

converged in the late 2000s. Reflecting thisfertilizer usage tends to pollute arable land andtrend, China’s output to land ratio has comeground water. Furthermore, on soybean farms,

to exceed Japan’s (Figure 4). As arable land

increases in the output to land ratio raise theis relatively scarce in East Asian countries,

nutrient to land ratio by means of biologicalincreasing the output to land ratio is integral

fixation, thus exacerbating the pollution

Figure 5

Nutrient Balance in the Soybean Farming Sector (2000-2012)

Sources: CNFPCBDEnvironmental Efficiency and Employment in China’s Soybean Farming Sector

Figure 6

Real Wage Rate of Hired -Labor in China’s Farming Sector

11(Yuan/day)

Sources: CNFPCBD and CSY

problem. As a result, the estimated average

sectors. We use the real wage rate of hired

nutrition surplus per mu during 2000–2012 inlabor as the proxy for the real wage rate. As

the soybean farming sector has increased from

Minami (1973) suggests, the real wage rate of

11 kg to 16 kg (Figure 5).

long-term contract labor would be a preferable

We believe that when the economy passes

proxy, but the CNFPCBD combines short-andthe turning point, it has a significant impact

long-term laborers in its hired-labor data. The

on over-employment in the soybean farming

resulting wage rate is thus slightly higher

sector. Minami (1973) states that, in order to

as it reflects the seasonal rise in short-term

determine whether a country has surpassedlaborers’ wages during the harvest. Taking thisLewis’ turning point, one must examine (i) wages

limitation into account, we use data on laborand the marginal productivity of labor (MPL)

power per hour.

in the subsistence sector, (ii) the correlation

In the early 2000s, the real wage rate in

between the two, (iii) the movements in the

China’s farming sector was constant, thoughsubsistence sector’s real wages, (iv) changes

later in the decade it rose rapidly.

in the wage differentials, and (v) the elasticity

The average growth rate over this periodof labor supply between the subsistence andwas 14.4 percent, which is roughly double

capitalist sectors. We use China’s and Japan’sJapan’s growth rate from 1961 to 1969 (i.e., thedata to examine (iii) and (iv).4

period during which it reached its turning point

Figure 6 shows the real wage rates in China’s
soybean, japonica rice, wheat, and corn farming───────────
4Minami (1973) states that (i) is the mostrigorous test for finding the turning point, but it
requires us to estimate the production function,
which we do not do until the next section.

and over-employment in agriculture became
nonexistent).5───────────
5

According to Minami (1973), the growth rateof annual contractors’ and daily workers’ real
wages was 7.15% and 7.27% from 1961 to 1969,
respectively.

12

Figure 7

中国経済研究第13巻第 ２ 号

Wage Differentials between the Crop Farming and Urban Sectors

Sources: CNFPCBD and CSY

Accordingto

Minami(1973),

wage

differentials between unskilled labor and

Therefore, to confirm over-employment, wemust estimate the E-S production function.

skilled labor may be used to determine the5.

turning point. We define unskilled laborers ascrop farmers (the data for whom is recorded
as daily pay) and skilled laborers as urban

5.1

Estimation results

Production function

Before estimating the production function,

workers (annual salaries), and present the

we must confirm whether our panel data isresulting wage differentials in Figure 7.

stationary (non-stationary data may yield

During the early 2000s, the wage in the

spurious correlations). Consequently, wesoybean and other crop farming sector

employ the Fisher-type panel unit-root test as

decreased relative to that in the urban sector,

it is suitable for our unbalanced panel dataset.

whereas it increased later in the decade;Table2 shows that the null (all panels containthese trends in the real wage rate and wage

unit roots) is rejected in both the Augmenteddifferentials would suggest that China surpassed

Dickey–Fuller (ADF) test and the Phillips andLewis’ turning point and over-employment in

Perron (PP) test, which include a time trend

these sectors became nonexistent. However,and subtract cross-sectional means from thethe rapid increase in the farming sector’s real

series. Thus, we conclude that our dataset iswage often reflects not only increased MPL

stationary.

but also improvements in the subsistence

We employ Belotti et al.’s (2012) methodology

level. Additionally, the wage differential is also

and sfpanel for STATA in our estimation ofaffected by the relative demand and supply

the production function. Table 3 shows theof unskilled and skilled labor. Furthermore,estimation results of the stochastic frontier

the exist of the over-employment in farming

production function. In both the BC and M

sector affects relative supply but not demand.

production functions (i) all of the coefficients’Environmental Efficiency and Employment in China’s Soybean Farming Sector

Table 2

13

Results of Panel Unit Root Test (H0: All panels contain unit roots)
ADF test

Inverse chisquared

Inverse normal

Inverse logit t

Modified inv. chisquared

statistics

p-values

statistics

p-values

statistics

p-values

statistics

p-values

49.67250.01-2.0278

0.02-2.44850.01

2.8961

0.00

48.12110.01-2.2151

0.01-2.57380.01

2.68880.00

47.5753

0.01-1.4189

0.08-1.86160.03

2.61590.00

49.83300.01-2.65140.00-3.14200.00

2.91760.00

56.7160

0.00-2.28900.01-2.6255

0.01

3.83730.00

PP testInverse chisquared

Inverse normal

Inverse logit t

Modified inv. chisquared

statistics

p-values

statistics

p-values

statistics

p-values

statistics

p-values

88.0217

0.00-4.94360.00-5.8507

0.00

8.02070.00

121.2031

0.00-6.0551

0.00-8.3556

0.00

12.45480.00

201.9714   0.00

-7.9505

0.00-14.3440

0.00

23.24790.00

142.7486

0.00-5.5836

0.00-9.5460

0.00

15.33390.00

113.52140.00-3.9955

0.00-7.0905

0.00

11.4283

0.00(a) We use one lag in each test.

Table 3

Regression Estimates (2000-2012, n=159)
BC Technology

Fixed Effects
Estimates

Standard
Error

0.2262***0.04840.7150***0.0433

M Technology

Random Effects
Estimates

Standard
Error

5.5567***0.31020.3090***0.0526

0.6810***0.0492

Fixed Effects
Estimates

Standard
Error

Random Effects
Estimates

Standard
Error

2.4565***0.07270.4361***0.0294

0.4934***0.03440.4345***0.04130.4396***

0.02690.1544***0.02020.1486***0.01820.1513***0.02570.1322***0.02320.0410***0.01480.0579***0.01180.0907***0.01850.1173***0.01553.7633***0.03242.5654***0.0264

1.6677***0.0413

1.1271***0.0349Statistics

p-value

7.16

0.01

Statistics

p-value

Statistics

p-value

Hausman
test

12.52

0.00

3.33

0.19

CRS test

4.08

0.04

(a) *** Statistically significant at the 1% confidence level; ** Statistically significant at the 5% confidence
level; * Statistically significant at the 10% confidence level

14

中国経済研究signs are as expected, (ii) all of the parameters

第13巻第 ２ 号

5.2

Estimation results for TE, EAE, EE, AE,
and CEare statistically significant, and (iii) are below
one, indicating that there are diminishing

The regional arithmetic means of TE, EAE,

returns to scale (DRS). Based on the Hausman

EE, AE, and CE are shown in Table 5. The

test, the estimation results from the TFE

average EE value across all of the provinces ismodel of the BC function and those from the

0.8269, indicating that China’s soybean farming

TRE model of the M function are selected to

sector has been environmentally inefficientmeasure EE and over-employment.

since 2000. Inner Mongolia, for example, could

In order to confirm the validity of (iii), we

reduce the quantity of nutrients by about 33%

use the Wald test to see whether there arewhile maintaining the same level of production,

constant returns to scale (CRS) (see Table 3).

while Shandong could reduce its nutrient level

The CRS hypothesis is rejected for both theby 11%. This, in turn, indicates a regional

BC and M functions, which actually seem todisparity in EE. We estimate each province’s

exhibit DRS.

potential for nutrient reduction by subtracting
are similar

the optimal number of nutrients from the real

to those of previous studies; that is, Japan andnumber of nutrients. The province with the

China enjoy CRS (or DRS that are near to

greatest potential for reduction is Heilongjiang

CRS). However, the estimation results forwith 32 kg, while the other provinces vary

indicate that Japan enjoys increasing returns

between 0.09 kg and 2.6 kg. In every province,to scale (IRS) to a greater degree than China.

there is greater potential for reduction throughIn contrast to previous studies, our results

TE (accounting for 67 percent of the reduction

suggest that China’s soybean farming sector

for EE) than there is through EAE.

Our estimation results forhas not exploited the IRS in the M process.

According to the CE in the BC process,China has also been inefficient in terms of cost
minimization since 2000 with a potential to

Table 4Previous Studies’ Results

ArticlesCountry

Data

Period andsector

Egaitsu andShigeno (1983)

Japan

Cross
section1957−1979
Japonica
rice0.14−0.26

Egaitsu (1985)

Japan

Cross
section1957−1980
Dairy
farming0.60−1.05

Shirasago(1986)

China

Cross
section1980
Agriculture

Shirasago(1991)

China

Cross
sectionKomagata(1998)

China

Panel

CRS

0.48−1.32−0.13−0.88

1.17−1.36

0.06−0.41

0.98−1.11

0.44−1.26

0.30−0.78

1.13−1.61

0.35

0.58

0.93

0.55

0.53

1.09

1981−1987
Agriculture

0.33−0.58

0.40−0.63

0.95−1.04

0.43−0.53

0.46−0.57

0.98−1.01

1991−96
Agriculture

0.67

CRS

0.36

0.61

0.97

(a) CRS indicates the assumption that.
(b) Egaitsu’s (1985) is the output elasticity of feed, whileis the output elasticity of dairy cow stock.

Environmental Efficiency and Employment in China’s Soybean Farming Sector

Table 515

Arithmetic Means of TE, EAE, EE, AE, and CE by Region and Time-series Variations(2000−2012)
TE

EAE

EE

AE

CE

TE

AE

CE

BC

BC

BC

BC

BC

M

M

M

Anhui

0.8721  0.9944

0.8671  0.8667

0.75790.87670.65950.5787

Yunnan0.85030.95290.8121  0.7464

0.63130.88230.67650.5964Henan

0.8676

0.98180.85170.92540.80080.82750.70740.5783Hebei

0.90670.9702  0.8792  0.9036

0.81860.88090.76480.6727

Jilin

0.89670.95270.8551  0.9239

0.82890.88290.81950.7238Hubei

0.79070.95150.75400.8882  0.7010

0.82630.7402  0.6172

Jiangsu

0.75100.9412  0.7182  0.8813

0.65590.82850.56470.4644

Shanxi

0.85330.92140.78940.81870.69680.87630.72950.6391

Shandong

0.90190.98650.88990.8756

0.78850.89340.60250.5372Chongqing

0.8881  0.9854

0.87540.78960.70830.87240.2501  0.2133

Inner Mongolia

0.70650.6672  0.8761  0.6240

Heilongjiang

0.8821  0.9371  0.8274

0.9137

Liaoning

0.89700.9646

0.9481  0.8497

Shaanxi

0.8697

0.9421  0.8212  0.8913

0.7767

Total

0.8596

0.95950.82690.8799

TE

EAE

EE

AE

CE

BC

BC

BC

BC

0.93780.86580.8471  0.8023

0.67920.8081  0.8911  0.9146

0.81470.8932  0.8440

0.75310.88290.74230.65260.7572  0.8720

0.71200.6200

TE

AE

CE

BC

M

M

M

0.72492000

0.81880.9681  0.7959

0.88500.84880.73780.62512001

0.80140.96430.9041  0.7231  0.8234

0.75180.6184

2002

0.90270.9712  0.8774

0.8792  0.7954

0.86900.72400.6331

2003

0.78490.95440.7511  0.8602  0.6730

0.81280.73070.5997

2004

0.86850.9521  0.8296

0.76090.66290.87640.71270.6213

2005

0.84400.95930.81280.82780.69890.8902  0.7072  0.6314

2006

0.85200.95530.81750.81840.69340.8497

2007

0.77850.94140.73550.85580.66380.8702  0.6871  0.5915

2008

0.88940.97030.8631  0.8584

0.76530.92370.6753

2009

0.85830.95180.81940.92030.78790.89090.6872  0.6101

2010

0.93680.96250.90200.94650.8871  0.9040

0.69600.63142011

0.9342  0.9622  0.8991  0.9558

0.89300.91350.68380.62502012

0.94590.96190.9096

0.95470.9026

0.89350.71790.6428Total

0.8596

0.95950.82690.87990.75720.87200.71200.62000.77540.72540.61090.6234

16

中国経済研究第13巻第 ２ 号

reduce total costs by 24 percent. In contrast to

EE, AE, and CE are shown in Table 5. In theprevious studies, we find that the CE in the BC

BC process, TE increased from 0.82 to 0.95

process is higher than the EE (Van Meensel etduring the study period, while EAE stayedal., 2010; Hoang and Nguyen, 2013). This would

between 0.94–0.97. Consequently, TE has had

seem to indicate that the soybeans farming

a greater impact on the improvements in EE.

sector is affected by a smaller amount ofFurthermore, while the CE decreased in thefertilizer than the other crops. The arithmetic

early 2000s, it eventually rose later in themean of TE in terms of the M process isdecade. TE did not improve in the M process

0.87 and the index is similar to that of theas it did in the BC process. Additionally, in

BC; however, there is less regional variation

the M process, AE declined during the study

in the former (coefficient of variation=0.099)

period, which caused CE to remain stagnant.

than there is in the latter (coefficient ofTable 6 reports the price of waste. In general,variation=0.14). Furthermore, a one-way

the cost of environmental improvement inanalysis of the variance rejects the hypothesis

China’s soybean farming sector has increased.

that the TEs in both processes are identical at

The cost of reducing nutrients is approximately

the 0.6 percent level. The M process’ AE and1.9 times higher in Henan (the province with

CE are significantly lower than the BC process’,

the greatest waste price) than it is in Shanxi

which suggests that M technology production(the province with the lowest price), indicating

factors are over-used to a greater extent thanthat there are wide regional variations in

BC technology factors.

reduction costs.

The time-series variations in TE, EAE,
Table 6

Arithmetic Means of Pollutant Prices and the ROE by Province and Year

Province

Pw

ROE

Year

Pw

ROE

Anhui

9.27020.64042000

6.93430.5419Yunnan6.25040.64502001

6.38730.5538Henan

10.3471 0.61492002

5.4502 0.5381

Hebei

7.0251 0.47182003

6.3086

0.5638Jilin

7.91070.51742004

6.53690.5870Hubei

7.1241 0.5682 2005

7.03070.5657

Jiangsu

7.98830.69792006

6.67050.5983

Shanxi5.45730.5923

2007

7.43170.6220Shandong

7.21460.65742008

8.7752 0.6001Chongqing

8.01860.86982009

9.06450.6124

Inner Mongolia

8.35760.5053

2010

9.43990.5972

Heilongjiang

7.68790.38842011

10.2286

0.6052

Liaoning

8.26780.49682012

11.3234

0.5682

Shaanxi

5.86700.5817Total

7.68410.57977.6841

Environmental Efficiency and Employment in China’s Soybean Farming Sector

5.3

17

Over-employment estimation results

to confirm that the series are stationary.

The ROE estimation results are presented

Table 7 and 8 show the results of Fisher’sin Table 6. The arithmetic mean of the ROE

panel unit- root test. From the ADF test in

is 58 percent. There is a 48 percentage pointlevels, we see that we cannot reject the nulldifference between the province with the lowest

hypothesis that all panels contain unit roots.

ROE (Heilongjiang at 39 percent) and that with

Meanwhile, the ADF test in first differences

the highest (Chongqing at 87 percent). Even

indicates that we can reject the null hypothesisthough over-employment began to decreasein the majority of cases. The PP test in levels

in 2007, it has consistently remained above 54shows that the null can be rejected for TE,

percent, thus indicating that the labor input in

EAE, and EE and that the other variables are

Chinese soybean farming is inefficient.

in most cases non-stationary. The PP tests
in first differences show thay the variables

5.4

Examination

of

the

impactonof interests are all stationary. The ADF test

environmental efficiency

indicates that our data for the estimation may

Before estimating equations 34–36, webe I(1). To confirm the long-term relationshipconduct Fisher’s panel unit-root test in order

between I(1) variables, we run Pedroni’s (2004)

Table 7

Results of the Panel Unit Root Test (H0: All panels contain unit roots)
ADF test

Inverse chisquared

Inverse normal

Inverse logit t

Modified inv. chisquared

Statistics

p-values

Statistics

p-values

Statistics

p-values

Statistics

p-values

TE

19.8291

0.87-0.17910.43-0.18390.43-1.0919

0.86

EAE

22.1900

0.77-0.2799

0.39-0.2668

0.40-0.77640.78

EE

18.86310.90-0.16500.44-0.14810.44-1.22100.89

lny

19.19510.89

0.06740.53

0.05210.52-1.1766

0.88(lny)221.0137

0.82-0.11820.45-0.1509

0.44-0.93360.82

pw

8.67101.00

3.14701.00

3.24751.00-2.5829

1.00

PP testInverse chisquared

Inverse normal

Inverse logit t

Modified inv. chisquared

Statistics

p-values

Statistics

p-values

Statistics

p-values

Statistics

p-values

TE

106.8710

0.00-6.7509

0.00-7.6695

0.00

10.53960.00

EAE

103.8412

0.00-5.96810.00-7.0793

0.00

10.13470.00

EE

102.9530

0.00-6.3961

0.00-7.28240.00

10.0160

0.00

35.82540.15

0.64820.26

0.53010.30

1.04570.15

35.6169

0.15-0.64160.26-0.52200.30

1.01790.15

42.43220.04

0.64590.74-0.1495

0.44

1.9286

0.03

lny
(lny)
pw

2

(a) We use one lag in each test.

18

中国経済研究Table 8

第13巻第 ２ 号

Results of the Panel Unit Root Test (H0: All panels contain unit roots)
ADF test

Inverse chi-squared

Inverse normal

Inverse logit t

Modified inv. chisquared

Statistics

p-values

Statistics

p-values

Statistics

p-values

Statistics

p-values

ΔTE

55.3800

0.00-3.49620.00-4.01810.00

4.07430.00

ΔEAE

51.9160

0.00-3.36290.00-3.63640.00

3.59390.00

ΔEE

47.2187

0.01-2.8299

0.00-3.15400.00

2.94250.00

35.2680

0.11-1.7287

0.04-1.74970.04

1.28500.10

33.9822

0.14

-1.63

0.05

-1.6204

0.06

1.10690.13

64.2338

0.00-3.2098

0.00-4.3373

0.00

5.30210.00

Δlny
Δ(lny)2

Δpw

PP testInverse chi-squared

Inverse normal

Inverse logit t

Modified inv. chisquared

Statistics

p-values

Statistics

p-values

Statistics

p-values

Statistics

p-values

ΔTE

305.7051

0.00-14.2337

0.00-25.5393

0.00

37.1099

0.00

ΔEAE

187.4767

0.00-10.6584

0.00-15.6652

0.00

21.31100.00

ΔEE

273.0350

0.00-13.1602

0.00-22.81640.00

32.7442

0.00

Δlny

108.1409

0.00-7.55420.00-8.9273

0.00

10.70930.00

Δ(lny)2

102.3158

0.00-7.2136

0.00-8.42350.00

9.93090.00

Δpw

129.7979

0.00-7.9002

0.00-10.7326

0.00

13.6033

0.00(a) We use one lag in each test.

and Kao’s (1999) panel co-integration tests,

parameters of lny are significantly positive andwhich show that the null may be rejected for(iii) those of (lny)2 are significantly negative. The

7 of the 12 statistics (Table 9 and 10). Thissigns of TE’s and EE’s estimated parameterssuggests that the panel variables in equations

are identical. Consequently, as increases in

34–36 are cointegrated.

per-capita income in China’s rural areas raise

Table 11 shows the estimation results

TE and EE will rise as well. However, after

for equations 34–36. The restriction over-per-capita income peaks, TE and EE will beginidentifying test (used instead of the conventional

to fall as income growth continues. In addition,

Hausman test) indicates that (i) the fixed effectthere is a significantly negative correlation

model is preferable.6 The TE and EE of the

between TE and pw; thus, we conclude thatsystem GMM reveal that (ii) the estimatedlowering the cost of agricultural waste would───────────
6 In our estimated results, the standard error
is clustered. Consequently, we use Arellano’s(1993) and Woodridge’s (2002) artificial regression
approach to select the fixed or random effectmodel.

improve TE and thereby have a positive
impact on EE.

Environmental Efficiency and Employment in China’s Soybean Farming Sector

Table 919

Results of Pedroni’s (2004) Cointegration Test (H0: No cointegration)
TE
Statistics

p-values

Weighted
statistics

p-values

Statistics

p-values

Panel v-Statistic-1.3547

0.9123-3.22790.9994

Group rho-Statistic

3.28830.9995

Panel rho-Statistic

2.20000.98612.53430.9944

Group PP-Statistic-16.5240

0.0000Panel PP-Statistic-9.8833

0.0000-8.85300.0000Group ADF-Statistic-5.6208

0.0000Panel ADF-Statistic-5.68350.0000-4.80380.0000Statistics

p-values

EAE
Statistics

p-values

Weighted
statistics

p-values

Panel v-Statistic-1.8301

0.9664-3.4205

0.9997

Group rho-Statistic

3.17260.9992

Panel rho-Statistic

1.79880.96402.63400.9958Group PP-Statistic-13.2374

0.0000Panel PP-Statistic-7.84560.0000-5.98240.0000Group ADF-Statistic-7.1491

0.0000Panel ADF-Statistic-5.53760.0000-6.27120.0000Statistics

p-values

Weighted
statistics

p-values

Statistics

p-values

Panel v-Statistic-1.8294

0.9663-3.0383

0.9988Group rho-Statistic

3.39820.9997

Panel rho-Statistic

2.4542

0.99292.55590.9947

Group PP-Statistic-15.2790

0.0000Group ADF-Statistic-5.24530.0000EE

Panel PP-Statistic-8.17120.0000-8.87300.0000Panel ADF-Statistic-4.7001

0.0000-4.9709

0.0000

Table 10

Results of Kao’s (1999) Cointegration Test (H0: No cointegration)
statistics

probability

TE

ADF-Statistic-7.30700.0000EAE

ADF-Statistic-4.29670.0000EE

ADF-Statistic-6.69370.00000.0074-0.0255***

TE-2.9970

0.0078-0.0248***-3.0400.00000.0101(a) The standard errors in fixed and random effect model are clustered on provinces.
(b) The system GMM standard errors are robust.
(c) We include a time dummy variable in each equation.

Test for restriction
over-identification
1011.700

2.33160.14130.3271-0.0258**-0.2684*4.6431**

9.5652Standard
Error

Test for 2nd orderserial correlation

0.00790.0965-18.9135**

Estimates

System GMM

0.0402-0.0214***-0.0485

1.5573

6.2991

Estimates
0.8895

Standard
Error

Random Effects

EE

0.0000

Test for 1st orderserial correlationpw

2.27560.1354

1.7913-0.0637(lny)2

Intercept

lny

Estimates

Standard
Error

808.805-0.0012-0.10440.3375

Estimates

0.00140.02880.4618

Standard
Error

Fixed Effects

EAE

1634.413

Statistics-0.0002-0.00930.17860.1470Estimates

0.0000p-values

0.00140.0226

0.35151.3799Standard
Error

Random Effects-0.0002-0.05451.0244-4.0676*

Estimates

0.13990.0074p-values

0.00110.02900.50552.2245

Standard
Error

System GMM

中国経済研究Dependent Variable

Test for restriction
over-identification

0.3309p-values

0.00930.14402.3822

9.8319Test for 2nd orderserial correlation-0.0264***-0.2589*

4.4055*-17.4524*

Estimates

Standard
Error

System GMM

0.04430.0074p-values

Statistics

0.0872-0.0229***-0.05091.41585.7524

Estimates
0.9074Standard
Error

Random Effects

Test for 1st orderserial correlation

Fixed Effects

0.1220-0.0660(lny)2pw

2.07201.7076

Intercept

lny

Estimates

Standard
Error

Fixed Effects

Regression Estimates (2000−2012, n=159)

Dependent Variable

Table 1120
第13巻第 ２ 号

Environmental Efficiency and Employment in China’s Soybean Farming Sector

6 .Conclusions and policy implications
EE

improved(thoughwithregional

21

M process, policies that promote the transferof labor and increase capital intensity in thefarming sector should be enacted.

disparities) within China’s soybean farming

According to our empirical results, both

sector throughout the study period; this is

BC and M production factors have been usedlargely due to increases in TE. Meanwhile,inefficiently in China’s soybean farming sector.

the waste price has increased since the late

The pollution and low income in rural areas is a

2000s. In spite of the improvement in EE, thedirect result of the excessive use of production

pollution from the BC process has increased.

factors. Clearly, institutions and policies that

Recently, the government has increased its

distort the production factor market are

direct subsidies to farmers as well thoseneeded to remedy this situation.

for purchasing agricultural materials. The

It should be noted that our research

Chinese government should take regional

methodology could also be applied to the rice

variations into account in their development offarming sector, where the rice fields are rich

subsidy policies, which should focus more on

in nitrogen. In fact, this line of inquiry could

conserving agricultural materials. Additionally,

provide useful insights into the green revolution

efforts should be made to educate farmers on

in China’s development process as the rice

the most efficient way to manage materials.

farming sector has seen a rapid increase in

For instance, multiple cropping, whereby one

output due to the extensive use of fertilizer.

crop is allowed to consume the nutrients from

Additionally, it may be beneficial for future

another crop (e.g., soybeans), appears to be an

studies to explore how the use of EE in total

effective means of reducing farm-level waste;agriculture is more suitable for the estimation

this method may be particularly beneficial inof the equations 34–36.

regions where the cost of waste reduction ishigh. The estimation results for the equations

References

34–36 suggest that a policy that focuses on

Arellano, M. (1993), “On the testing ofcorrelated effects with panel data”, Journalof Econometrics, 59, 1–2, 87–97.
Belotti, F.S., Daidone, G.I., and Atella V. (2012),“Stochastic frontier analysis using Stata”,
Stata Journal, 13, 4, 719–758.
Bravo-Ureta, B.E. and Rieger L. (1991), “Dairy
farm efficiency measurement using
stochastic frontiers and neoclassicalduality”, American Journal of Agricultural
Economics, 73, 2, 421–428.
Chung Y.H., Färe, R., and Grosskopf, S. (1997),“Productivity and undesirable outputs: A
directional distance function approach”,
Journal of Environmental Management,
51, 3, 229–240.
Coelli, T. Lauwers, L., and Van Huylenbroeck,
G. (2007), “Environmental efficiency
measurement and the materials balancehigher rural income could temporarily raise EE,
though it would eventually decrease again. In
order to improve EE and reduce waste, policies
that focus on the efficient usage of agricultural
materials must be implemented.
It should also be noted that the ROE in thesoybean farming sector exceeds 50% in mostprovinces. Recently, China’s government hasreformed the family register (hukou), which
disrupts transfers between rural and urban
areas. Further reforms may yet reduce overemployment to a greater extent. Moreover,TE and CE have not grown as rapidly in the
M process as they have in the BC process. In
order to improve these efficiencies within the

22

中国経済研究condition”,

Journalof

Productivity

Analysis, 28, 1–2, 3–12.
Egaitsu, F. (1985), “Rakunō No Seisan Kansū[Production function of dairy farming]”,[In Japanese] in Nihon Nōgyō No Keizai
Bunseki [Economic Analysis of Japan’s
Agriculture], Taimeidō, Tokyo, 120–133.
Egaitsu, F. and Shigeno, R. (1983), “Inasaku
Seisan Kansu No Keisoku to Kinkō Yōso
Kakaku”, [In Japanese] Nōgyō Keizai
Kenkyū [Journal of Rural Economics], 54,
4, 167–174.
Färe, R. and Grosskopf S. (2003), New Directions:
Efficiency and Productivity, Springer
Science+Business Media, Inc., Boston, MA.
Färe, R., Grosskopf, S., Lovell, C.A.K.,
and Pasurka, C. (1989), “Multilateral
productivity comparisons when some
outputs are undesirable: A nonparametricapproach”, Review of Economics andStatistics, 71, 1, 90–98.
Färe, R., Grosskopf, S., and Hernandez-Sancho,
F. (2004), “Environmental performance: An
index number approach”, Resource andEnergy Economics 26, 4, 343–352.
Farrell, M.J. (1957), “The measurement of
productive efﬁciency”, Journal of the Royal
Statistical Society, 120, 3, 253–290.
Fujii, H., Kaneko S., and Managi, S. (2010),“Changes in environmentally sensitive
productivity and technological modernization in China’s iron and steel Industry
in the 1990s”, Environmental and Development Economics, 15, 4, 485–504.
Greene, W. (2005) “Fixed and random effects
in stochastic frontier models”, Journal of
Productivity Analysis, 23, 1, 7–32.
Hoang, V.N. and Allaudin, M. (2009), “A new
framework of measuring national
nutrients balance for international andglobal comparison”, Discussion Paper,
School of Economics, The University of
Queensland,389 .
Hoang, V.N. and Coelli, T. (2011), “Measurementof agricultural total factor productivity
growth incorporating environmental
factors: A nutrients balance approach”,
Journal of Environmental Economics andManagement, 62, 3, 462–474.

第13巻第 ２ 号

Hoang, V.N. and Nguyen, T.T. (2013), “Analysisof environmental efficiency variations: A
nutrient balance approach”, Ecological
Economics, 86, 37–46.
Hondai, S. and Ra, K. (1999), “Nōson Keizai
No Henbō to Rōdō Shijō [The change ofthe rural economy and labor market]”,[In Japanese] in Minami, R. and Makino,
F. (Eds.), Nagareyuku Taiga: Chūgoku Nō
Rōdō No Idō [The Flowing Great River:
The Labor Transfer in Rural China],
Nihonhyōronsha, Japan, 57–79.
Inada, M. and Yamamoto, H. (2010), “Analysis of
migration decisions of Chinese japonica rice
farmers: Estimation of internal wage on
output supply using agricultural household
model”, No.145, Discussion Paper Series
from Institute of Economic Research, Chuo
University, Tokyo, Japan, July 2010.
Islam, N. and Yokota, K. (2008), “Lewis growthmodel and China’s industrialization”, Asian
Economic Journal, 22, 4, 359–396.
Kaneko, S. and Managi, S. (2004), “Environmental
productivity in China”, Economics Bulletin,
17, 2, 1−10.
Kao, C. (1999), “Spurious regression andresidual-based tests for cointegration inpanel data”, Journal of Econometrics, 90,
1, 1–44.
Komagata, T. (1998), “Shokuryō Seisan [Thefood production]”, [In Japanese] in by
Kuribayashi, S. and Takahashi, H. (Eds.),
Chūgoku Ni Okeru Jizokuteki Seichō No
Kanōsei [The Frontier for Sustainable
Growth in China], Hitotobunkasha, Tokyo,
Japan, 31–54.
Kopp, R.J. (1981), “Productive efficiency: Areconsideration”, Quarterly Journal of
Economics, 96, 3, 477–503.
Managi, S. and Kaneko, S. (2006) “Productivityof market and environmental abatementin China” Environmental Economics andPolicy Studies, 7, 4, 459–470.
Managi, S. and Jena, P.R. (2008), “Environmental
productivity and Kuznets curve in India”,
Ecological Economics, 65, 2, 432–440.
Managi, S. and Kaneko, S. (2009),“Environmental performance and returns
to pollution abatement in China”, EcologicalEnvironmental Efficiency and Employment in China’s Soybean Farming Sector

Economics, 68, 6, 1643–1651.
Minami, R. (1973), The Turning Point
in Economic Development: Japan’s
Experience, Kinokuniya Bookstore, Japan.
Minami, R. and Ma, X. (2010), “The Lewis
turning point of Chinese economy:
Comparison with Japanese experience”,
China Economic Journal, 3, 2, 163–179.
Ohkawa, K. (1960), “Kajō Shgūyō No Riron[Theory of over-employment]”, [InJapanese] in Ohkawa, K. (Ed.), Kajō Shūgyō
to Nihon Nōgyō [Over-Employment andJapanese Agriculture], Shunjūsha, Japan,
15–29.
Ohkawa, K. and Kohama, H. (1989) Lectures on
Developing Economies: Japan’s Experienceand its Relevance, University of Tokyo
Press, Tokyo, Japan.
Pedroni, P. (2004), “Panel cointegration:
Asymptotic and finite sample properties ofpooled time series tests with an application
to the PPP hypothesis”, Econometric
Theory, 20, 3, 597–625.
Sharma, K.R., Leung, P., and Zaleski, H.M.
(1999) “Technical, allocative and economic
efficiencies in swine production in
Hawaii: A comparison of parametric andnonparametric approaches”, Agricultural
Economics, 20, 1, 23–35.
Shindo, J. (2012), “Changes in the nitrogenbalance in agricultural land in Japan and12 other Asian Countries based on anitrogen-flow model”, Nutrient Cycling inAgroecosystems, 94, 1, 47–61.
Shindo, J., Okamoto, K., and Kawashima, H.
(2003), “A model-based estimation of
nitrogen flow in the food production–
supply system and its environmental

23

effects in East Asia”, Ecological Modelling,
169, 1, 197–212.
Shirasago, T. (1986), Chūgoku Nōgyō No Keiryō
Keizai Bunseki [Econometric Analysis
of Chinese Agriculture], [In Japanese]
Taimeidō, Tokyo, Japan.
Shirasago, T. (1991), “Chūgoku Nōgyō Wa
Docomade Kitanoka [Econometric analysis
of Chinese agriculture and township-village
enterprises]”, [In Japanese] in Watanabe,
T. (Ed.), Chūgoku No Keizai Kaikaku
To Shin Hatten Mekanizumu [Chinese
Economic Reform and New Development
Mechanism], Tōyōkeizaishinpōsha, Tokyo,
Japan, 149–174.
Singh, S., Coelli, T., and Fleming, E. (2001),“Performance of dairy plants in thecooperative and private sectors inIndia”, Annals of Public and Cooperative
Economics, 72, 4, 453–479.
Taskin, F. and Zaim, O. (2001), “The role ofinternational trade on environmental
efficiency: A DEA approach”, Economic
Modelling, 18, 1, 1–17.
Van Meensel, J., Lauwers, L., Van Huylenbroeck,G., and Steven, V.P. (2010), “Comparing
frontier
methods
foreconomic–
analysis”,
environmental
trade-offEuropean Journal of Operational Research,
207, 2, 1027–1040.
Wooldridge, J.M. (2002), Econometric Analysis
of Cross Section and Panel Data, MIT
Press, Cambridge, MA.
Shota Moriwaki (Graduate School of
Economics, Osaka City University)

24

中国経済研究第13巻第 ２ 号

Environmental Efficiency and Employment
in China’s Soybean Farming Sector
Shota MORIWAKI (Graduate School of Economics,Osaka City University)
Keywords: environmental efficiency, Egaitsu-Shigeno type production function, soybeans,biochemical technology, mechanical technology
JEL Classification Numbers: O1, Q1, Q5
The purpose of this study is to measure the environmental efficiency, waste price, andover-employment in China’s soybean farming sector. We establish that there is a relationshipbetween estimated environmental efficiency and the two independent variables, rural per-capita
income and waste price, which indicates if the effect of rural development and waste levels on
environmental efficiency may be observed at a provincial level in this sector.
This study relies heavily on the Compilation of National Farm Product Cost-Benefit Data as
well as the Cob-Douglas production function developed by Egaitsu and Shigeno (E-S), whichallows us to distinguish between biochemical (BC) technology and mechanical (M) technology. For
the estimation, we apply the stochastic frontier model to Greene’s (2005) panel data.
Based on our environmental efficiency estimation, we conclude that the industry has beeninefficient since 2000; however, it has since generally improved (with regional variations) due
largely to technological advances. Additionally, we find that the waste price has increased
since the late 2000s and that the rate of over-employment exceeded 50% in the majority of theprovinces. Technical efficiency and cost efficiency have not grown as rapidly in the M process as
they have in the BC process. In order to improve these efficiencies within the M process, policies
that promote the transfer of labor and increase capital intensity in the farming sector should beenacted.
Furthermore, Both the technical efficiency and environmental efficiency of the system GMM
reveal that the estimated parameters of log( rural per-capita) are significantly positive and thoseof log (rural per-capita) squared and waste price are significantly negative.
According to our empirical results, both BC and M production factors have been used
inefficiently in China’s soybean farming sector. The pollution and low income in rural areas isa direct result of the excessive use of production factors. Clearly, institutions and policies that
distort the production factor market are needed to remedy this situation.