１【論文】

Effects of Household Heads Wage Work on Farm
Technology Level and Technical Efficiency in China＊
Bohan YUE
Keywords: Wage Work, Technology Level, Technical Efficiency, Chinese Farmers
JEL Classification: Q16, Q18

purchase of inputs. Participation in wage work

1. Introduction

can also increase information about farm
farm

inputs and technology through better access tohouseholds have more opportunities in theurban areas (Herdt and Mandac, 1981).

wage employment sector. More farmers will

However, on the other hand, farm productivity

choose to reallocate their time from the

could decline if households increase wage

agricultural sector to the non‑agriculturalwork. According to Goodwin and Mishra

sector if the latter offers higher wages than(2004), wage work participation decreases farm

the reward from farming. For example, thehouseholds attention to the optimal use ofChinese Household Income Project survey ofvariable inputs and farm technology. It also

2002 (hereinafter CHIP2002; Li, 2002) verifies

changes the quality of inputs. More specifically,that wage work has recently gained economic

Mu and van de Walle (2011) and Chang, Dong,

importance in rural China: nearly 60% of

and Macphail (2011) explain that the migration

household heads participate in wage work andof males increases farm work hours of females

the share of wage income in total income

and the elderly left behind in rural areas. If

exceeds 30%.

increased participation of rural households inAs

China seconomydevelops,

rural

wage work deteriorates farm productivity,

households in wage work has been shown tofurther economic development will threaten

have important effects on farm productivity. A

stable domestic supply of farm commodities infarm household s participation in wage work

China.

Such

increased

participationof

could raise its farm productivity for several

Nonetheless, only a few studies havereasons. Wage income can be used to facilitate

investigated the effects of more wage work onfarm management through a more flexible

farm productivity and especially on technicalefficiency. Mochebelele and Winter‑Nelson───────────＊ The author thanks Yuko Arayama, Tomoko
Kinugasa, Tadashi Sonoda, Jinjun Xue and two
anonymous referees for their valuablecomments and suggestions.

(2000)

estimate

Cobb‑Douglasstochastic

production frontiers (SPF) separately forhouseholds with and without migrants inLesotho. They find that the technical efficiency

２

中国経済研究第１１巻第１号

of households with migrants is 12% higher.

estimates

Cobb‑Douglas

SPF

for

farm

Chang and Wen (2011) estimate a similar SPF

households with and without a wage worker

for rice farmers in Taiwan. They find that the

separately to compare the technology leveltechnical efficiency of households without off‑

and technical efficiency of the two groups offarm workers is slightly higher than that ofhouseholds.１ After estimating the two frontiers,

households with off‑farm workers. Pfeiffer,

we follow the method of Kumbharkar, Tsionas,

Lopez‑Feldman, and Taylor (2009) specify¨
and Sipilainen(2009) to compare them. We

technical inefficiency as a function of off‑farm

focus on wage work participation of householdincomes. They find for Mexican households

heads because they are most likely tothat more off‑farm incomes have a positive

participate in wage work, as we will see in theeffect on farm technical efficiency. For Chinese

next section, and because their decisions onhouseholds, many studies, including Chen,

wage work are likely to have the greatest

Huffman, and Rozelle (2009), have estimatedeffect on farm technology and technicalfarm technical efficiency, but no studies haveefficiency.２ We adopt a similar approach toestimated the effects of more wage work onthat of Mochebelele and Winter‑Nelson (2000),

technical efficiency.

rather than following Pfeiffer, Lopez‑Feldman,

Furthermore, it is important to decompose

and Taylor (2009), because there are no

productivity into technology level and technicalplausible ways to split the sample if we use

efficiency when estimating the effects of more

wage incomes or hours to explain technicalwage work on farm productivity. Technology

efficiency.３

level represents the best production technology

affecting technical efficiency as most otherthat is potentially available for all households.

studies estimating technical efficiency do.

On

the

other

hand,

technical

efficiency

represents how efficiently each household
actually produces its outputs in comparison
with those that could be produced using thebest technology. The distinction between
technology level and technical efficiency isimportant because some policies might havedifferent implications. For example, improved
education of farmers will raise the technology
level through their use of new inputs and cropvarieties. However, it may not always raisetechnical efficiency. While education improves
farmers managerial ability, it also inducesthem to participate in off‑farm activities,
thereby reducing their attention toward farm
production. Few studies have estimated theeffects of more wage work on technology level
and technical efficiency separately.
Using data from CHIP2002, this study

Finally,

weexaminefactors

The second section describes wage work───────────
１ This study prefers SPF to DEA method
partly because the stochastic disturbance plays
an important role in agricultural production due
to weather shocks and partly because
heteroskedasticity of technical inefficiency term
is assumed to test its homoskedasticity.
２ Some studies are interested in the effect of
migration (mostly by young family members
rather than the household head) on farm
outputs for Chinese farm households (e.g., Yang,
2003). However, their results show that thiseffect is not significant.
３ This method might suffer from sample
selection bias. However, a standard way tocorrect this bias (i.e., adding the inverse Mills
ratio as a regressor) does not solve the problem,¨
as Kumbharkar, Tsionas, and Sipilainen(2009)
explain. For this reason, we just follow
Mochebelele and Winter-Nelson (2000) andChang and Wen (2011), although we need tocarefully interpret our empirical results.

Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

administered３

participation of Chinese rural households anddirectlymunicipalities

makes some inferences on the effect of thiseastern, central, and western regions.５

intoparticipation on farm productivity. The third
section introduces SPF with a heteroskedastic
error term and its estimation method. It also
briefly2．
1 Wage work participation of rural households
We first examine wage work participation ofcompute

4,391 rural households in CHIP2002. Table 1

technical efficiency and production frontiers.

presents the participation rate of wage work

The

explains

procedures

tosectionexamines

estimatedby the household head, his wife, other adultoftechnical

efficiency,

males (excluding the head) and other adultproduction frontiers, and factors to determine

females (excluding the head s wife). Wage

technical

sectionwork includes migration (working out of his or

summarizes the analysis and concludes theher home county at least 90 days in the survey

paper.

year),

fourthparameters

SPF,

efficiency.

The

final

2. Wage Work Participation and Farm
Production in Rural China

the

rate

of

which

is

shown

in

parentheses. Over 50% of the household heads
participate in wage work in all regions, but
only 6−8% of them migrate. The participation

This empirical analysis uses data of 9,200

rate of the wife is the lowest, and it is only 17%

rural households in CHIP2002. Gustafsson, Li,in the most developed region. Other adultand Sicular (2008) and Knight, Deng, and Li

males (most of them are the household head s(2011) provide a detailed description of thesons) work for wages at a relatively high rate

survey.

4,391

in all regions, and their migration rate (14−households for which data on relevant variables25%) is much higher than the household head.

are not missing and the following conditions

The participation rate of other adult females

are satisfied: 1) total production value from(most of them are the household head s

grains,livestockdaughters) is lower than other adult males, but

production is positive, 2) both crop production

it is much higher than the head s wife. These

costs and cultivated land areas are positive, 3)

results are consistent with those found by Mu

the household head is a married male and he,and van de Walle (2011) who use the China

as well as his wife, works on his own farm, andHealth and Nutrition Survey over the period4) each village includes at least two sample

1997−2006.

We

specificallyeconomiccrops,

examineandhouseholds.４ The empirical analysis also uses

In summary, migrants are mainly composed

data on village‑level variables from theof young adults, while most of the household

Administrative Village Questionnaire annexedheads and wives do not migrate but engage into CHIP2002. To allow for regional differences

farm work and/or local wage work −in farm production and labor markets, weclassify 22 provinces, autonomous regions, and───────────
４ We need at least two households in eachvillage to estimate the coefficient of village
dummy variables (or to control for village fixed
effects) in estimating SPF.

───────────
５ The eastern region includes Beijing, Hebei,
Liaoning, Jiangsu, Zhejiang, Shandong, andGuangdong. The central region includes Shanxi,
Jilin, Anhui, Jiangxi, Henan, Hubei, and Hunan.
The
western
regionincludesGuangxi,
Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi,
Gansu, and Xinjiang.

４

中国経済研究第１１巻第１号

Table 1. Rate of Wage Work Participation by Rural Households (%)
Head

Wife

Other males

Other females

East

65.1 (6.2)

17.4 (0.7)

50.5 (13.5)

31.3 (10.2)

Center

62.9 (7.0)

10.1 (1.1)

44.2 (24.8)

26.7 (17.8)

West

55.4 (8.0)

11.0 (0.9)

36.3 (19.2)

20.1 (11.7)

Note: Rate of migration is shown in parentheses.
CHIP2002 shows that the average farm work

work hours of the family and hired workers inhours are 1,021, 1,140, 655, and 636 for the head,

the production of grains, economic crops, andhis wife, other adult males, and other adult

livestock products.８ Variable input VC

females, respectively. Also, the household headcrop production is the sum of production costs

is most likely to participate in local wage work,of grains and economic crops (excluding costs

while his wife is least likely to do so.

of hired labor) and the value of grains used forConsequently, it seems plausible to focus on

seeds and seedlings. Variable input VL for

the household head when we examine thelivestock production is the sum of costs inrelationship between farm production and thelivestock production (excluding costs of hired

wage work participation of rural households.

labor) and the value of grains used for feed.

For this reason, the subsequent analysis

Farm capital K is the sum of the value offorseparates households with and without a wage

large‑ and medium‑sized tools, machinery andworker depending on wage work participationequipment for farming, and livestock used forlabor and food. Land T is the sum of cultivatedof the head.

own and rented land.９ The share irr̲share of2．
2 Comparisonofrelevant

variablesforhouseholds with and without a wage
workerirrigated land areas is used to control for thequality of land.
Table 2 presents means and standard

We next compare farm inputs and outputs ofdeviations of these variables. In all regions,households with and without a wage worker.

households without a wage worker produce a

Output Y is the sum of gross revenues fromhigher amount of output than those with agrains, economic crops, and livestock products

wage worker: the former produce 92%, 41%,

and 10% of the livestock value.６ When theand 47% more output in the eastern, central,gross revenues are not available, values

and western regions, respectively. The muchconsumed of those commodities produced by
the household are used.７ Labor L is the sum of───────────
６ Jacoby (1993) adds 20% of the livestock value
in computing the value of outputs, althoughmany other studies do not. The present study
uses the intermediate value.
７ For households which miss the data on grossrevenues, they are assumed to consume exactly
what they produce on their farm. Of the 9,200
households originally included in the survey, the───────────shares of households that miss the data forgross revenues from grains, economic crops,
and livestock production are 6%, 8%, and 15%,
respectively.
８ We focus on total work hours because hours
of hired workers occupy only about 1% in the
total hours of family and hired workers on
average.
９ Most households in our sample do not rent
land from other households. The average shareof rented land in total land T is about 8%.

Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

５

Table 2. Means and Standard Deviations of Variables for Households with and without a Wage
WorkerRegionEastCenterWest

With or withouta wage workerWithworkerWithout
workerWith
workerWithout
workerWith
workerWithout
workerSample size

852

456

1062

627

772

622

X [yuan]

6434(7727)

12332(22766)6526(5121)

9209(7622)

6426(5108)

9424(8747)

L [hours]

2002(1538)

3225(2297)

2061(1358)

2925(1716)

2933(1719)

3810(2206)VC [yuan]

1248(1235)

2098(3021)

1142(1123)

1634(1482)

1056(1435)

1811(2394)VL [yuan]

1485(5518)

3962(19940)1164(3091)

1480(4285)

2012(3409)

2084(5200)

K [yuan]1970(4444)

3317(4943)

2068(4190)

3212(4923)

1601(2584)

3126(5467)

T [mu]

5.477(5.000)7.962(9.531)

7.968(8.005)10.297(9.639)5.808(4.841)

9.850(10.10)irr̲share

0.679(0.391)0.609(0.411)

0.573(0.421)

0.452(0.424)0.497(0.358)0.511(0.397)large̲scale

0.266(0.442)0.439(0.497)0.282(0.450)0.394(0.489)0.206(0.405)0.407(0.492)

educ6

0.388
(0.488)0.452(0.498)0.503(0.500)0.445(0.497)0.418(0.494)0.354(0.479)

educ9

0.383
(0.486)0.296(0.457)0.261(0.439)0.255(0.436)0.281(0.450)0.159(0.366)

educ120.095(0.293)0.042(0.200)0.043(0.204)0.018(0.131)

0.043(0.202)

0.032(0.177)age

45.97(9.452)49.33(9.646)41.72(8.756)

46.55(9.911)

42.62(9.287)

46.61(10.90)nonlabor̲inc[yuan/1000]

0.510(1.646)0.493(1.807)0.437(1.710)0.494(1.720)0.283(1.052)0.303(1.544)num̲hh3.609(1.050)3.950(1.195)3.920(0.994)3.997(1.194)4.187(1.260)4.682(1.531)num̲childlt6

0.092(0.297)0.138(0.364)0.179(0.405)0.169(0.419)0.210(0.491)0.259(0.494)collective̲pest

0.174(0.379)0.149(0.357)0.116(0.320)0.150(0.357)0.108(0.310)0.272(0.445)collective̲purchase

0.059(0.235)0.039(0.195)0.048(0.214)0.041(0.200)0.039(0.193)0.114(0.318)

Note: Standard deviations are shown in parentheses and units are shown in brackets. One mu isapproximately equal to 0.067 ha.

６中国経済研究第１１巻第１号

higher output of households without a wage

for livestock production. A similar inference

worker is quite natural because their farmfor the central region is not so clear at thiswork hours should be much longer. In fact,

point. We estimate SPF to check these

their farm work hours are 61%, 42%, and 30%

inferences and decompose the differences inlonger in the eastern, central, and western

productivity into differences in technology

regions, respectively.

level and technical efficiency.

They also use much larger amounts of otherFor the subsequent analysis, we introduce

inputs. In the eastern region, they use 45−68%

10 variables to explain technical efficiency TE

more inputs (excluding VL ) to produce 92%

based on other studies (e.g., Chen, Huffman,

more output. They use 167% more of VL

and Rozelle, 2009; Sherlund, Barrett, andpartly because they tend to produce more

Adesina, 2002), which are shown in Table 3.

livestock products when the household head

The last column shows the sign of the effect ofdoes not participate in wage work: the revenue

each variable on TE, which is explained in theshare of livestock products is 22% and 25% forfollowing way. A household with larger farmhouseholds with and without a wage worker inland (large̲scale = 1) tends to have higher TE

this region, respectively. In the western region,

because it can introduce more effective

households without a wage worker use 70−

machinery or inputs in farm production. An

95% more inputs (excluding VL ) to produce

increase in the number of household members

47% more output. They use only 4% more of(num̲hh ) improves TE partly because larger

VL, unlike those households in the easternhouseholds can more easily mobilize labor toregion, partly because they tend to produce

meet peak demands at the time of planting andmore crops when the household head does notharvesting. An increase in the number ofparticipate in wage work: the revenue share ofyoung children (num̲childlt6 ) tends to reduce

livestock products is 41% and 34% for

TE because family members have to devote

households with and without a wage worker inmore time and attention to rearing young

this region, respectively. Compared to thechildren. Furthermore, collective pest control

eastern and western regions, the input‑output(collective̲pest = 1) and collective purchase of

ratio seems similar between households with

inputs (collective̲purchase = 1) are likely to

and without a wage worker in the central

facilitate farm management to raise TE.

region: households without a wage worker use

Other variables are expected to have

27−55% more inputs to produce 41% morepositive or negative effects on technicaloutput.

efficiency. More education (educ6 = 1 or educ9

These results allow us to make an inference= 1 or educ12 = 1)１０ basically tends to raise

about the productivity of households with and

TE because they increase the head s ability inwithout a wage worker. The productivity offarm management. On the other hand, higher

households without a wage worker is inferrededucation of the household head can cause

to be higher in the eastern region, although

lower TE

they use much more amounts of inputs forlivestock production. On the other hand, it isinferred to be lower in the western region,
although they use much less amounts of inputs

partly because it raises the───────────
１０ For households with educ6 = educ9 = educ12= 0, years of education of the household head
are shorter than six.

Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

７

Table 3. Variables to Explain Technical Efficiency (TE) and Their Expected Effects on TE
Variables

Description of the variables

Effecton TElarge̲scale

Dummy variable taking value 1 if the household has larger land
than the regional average.

+

educ6

Dummy variable taking value 1 if schooling years of thehousehold head are between 6 and 8.

?

educ9

Dummy variable taking value 1 if schooling years of thehousehold head are between 9 and 11.

?

educ12

Dummy variable taking value 1 if schooling years of thehousehold head are more than 12.

?

Age of the household head?

Household non-labor incomes [yuan]?

Number of household members+

num̲childlt6

Number of children younger than six years old−collective̲pest

Dummy variable taking value 1 if the village collectively
prevents and cures plant diseases and insect pests+

collective̲purchase

Dummy variable taking value 1 if the village provides theservice of purchasing farm inputs unified+

age
nonlabor̲incnum̲hhNote: In the column Effect on TE , the signs show expected effects of the corresponding
variables on technical efficiency.
possibility for him to participate in wage work

Table 2 shows that the proportion ofor work longer for wages, which reduces his

households whose head has more than 12

attention to farm management. Higher age ofeducation years is 56%, 58%, and 26% smaller

the household head (age ) may raise TE due tofor households without a wage worker in the

his longer farm experience, while it may lower

eastern,central,andwestern

regions,

TE due to his weaker physical strength. An

respectively. It also shows that the proportion

increase in non‑labor incomes (nonlabor̲inc )１１of households whose head has 9−11 years of

can raise TE through more flexible purchaseeducation is 23%, 2%, and 43% smaller forof production inputs, while it might reduce

households without a wage worker in the same

householdregions. This result is consistent with themembers

attention

to

farm

production because those incomes in this study

resultmight include incomes from non‑agriculturalmembers with higher education tend to engage

family operation.

in───────────
１
１ nonlabor̲inc is the sum of non‑wage incomes,
subsidies received by joining the survey, nettransfers received from the village and town
(excluding taxes paid for production activities
and wage work), and other incomes. Thisvariable is deflated by the provincial price index
estimated by Brandt and Holtz (2006).

ofJolliffe

non‑agricultural(2004):

farm

activities.

householdHouseholds

without a wage worker also tend to have morechildren younger than six years old in theeastern and western regions. Finally, thosehouseholds tend to receive a much larger
amount of collective services of pest control
and input purchases in the western region,
showing that households can receive those

８

中国経済研究第１１巻第１号

services if the head mainly engages in farmvariables in equation (1) are omitted fromproduction.

conditional variables for simplicity. If
increase in

3. Empirical Method, anraises the variance, whichmeans that non‑negative inefficiency u can

A farm household uses labor L, input VC

appear more rightward (non‑positive efficiency

for crop production, input VL for livestock

can appear more leftward), which in turnproduction, farm capital K, and land T to
We specify its SPF as １２

produce output Y.

means

decreases. Hence,variablewith positive coefficientisinterpreted as a negative factor of TE .
Conversely, if(1), an increase in

the variance

where irr̲share denotes the share of irrigatedu (and efficiency

land areas. We assume that v is a normal

near 0, which means

random variable with mean 0 and constantincreases. Therefore, variablevariance

negative coefficientandfollows a half normal

distribution with variancereduces, which means that inefficiency) is more likely to appear
with theis interpreted as apositive factor of TE.

.

Caudill, Ford, and Gropper (1995) emphasizeWe

estimate

the

SPF

(1)

and

the

that the heteroskedasticity of inefficiency

heteroskedasticity function (2) jointly using thevariable u can have a serious effect on the

maximum likelihood method for each group of

estimated technical efficiency index. We apply

households. Specifically, the density function ofthis reason to specify the varianceas １３

the error term(2)

For variable, we use the 10 variables

(3)
where

and

respectively

introduced in the second section.
Equation (2) not only gives a specification ofunder the present

assumptions is written as

cumulative

denotedistribution

the

.

and

densityandfunctionsoftheheteroskedasticity of u but also represents thestandard normal variable. After substituting

effects of variable

equation (2) into

on an index of technicalefficiency,
includes all variablesof equation, where W(3), this density function is used to construct, and explanatory

the likelihood function and to estimate───────────
１２ Livestock inputs VL and farm capital K takethe value of 0 for some households. In this case,
we follow Sherlund, Barrett, and Adesina (2002)
to replace the values of these variables with/10 ( : the smallest positive value of therelevant variable in the sample).
１
３ A more popular way might be to assume atruncated normal distribution of technicalinefficiency u and specify its mean as a functionof. We did adopt this method but could not
obtain convergence in the parameters of SPF
for our data.

parametersinequations

(1)

and

(2)

simultaneously. After this estimation, we
follow Battese and Coelli (1988) to computetechnical efficiency TE as

(4)
where

and
.

To compare the (deterministic) production
frontiers for households with and without aEffects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

９

wage worker, we use two approaches. The

results. Production elasticities of L, VC, VL, K,

first is to compare the two sets of coefficientsand T are estimated to be significantly positive, in equation (1) using the Wald test.

at the 5% level for all groups of households.

The second is to compute an index of predicted

Although the coefficient of irr̲share is not

output in a manner similar to that ofstatistically significant for most cases, we keep¨
Kumbhakar, Tsionas, and Sipilainen(2009). Let

this variable to achieve convergence in the

,…,

and

denote outputs that can be predictedusing deterministic frontiers (the right handparameter estimation and obtain plausible
results for technical efficiency. １６side of equation (1) excluding the terms u andOur production elasticities for the easternv ) of households with and without a wage

and western regions can be compared withworker. We compute both these predicted

those for the eastern and southwestern

outputs for each household by substituting theregions estimated by Chen, Huffman, andactual amount of the five inputs and the actual

Rozelle (2009) (hereinafter CHR) and those for

share

Jiangsuofirrigated

land

into

the

two

To explain the second method in more detail,we simply express the production frontier ofhouseholds with a wage worker as
and that of households without a wage worker, wheredenotes vector of

inputs. Suppose household has a wage worker
and uses the amountof inputs. Then, we

can compute two predicted outputs
andWhereasfor household.

represents the largest amount of

output when this household uses its own
technology,

represents thelargest amount of output if it could use theother technology
and. Comparison of

for the same household allows us tocontrol the amount of inputs and therefore tocompare the distributions of

and.

4. Empirical Results
The SPF specified by equations (1) and (2) isestimated for each region and each type ofhousehold

after

and

Sichuan(western)

provinces estimated by Liu and Zhuang (2000)

deterministic frontiers.

as(eastern)

addingvillagedummyvariables. １４１５ Table 4 presents the estimation───────────
１
４ The numbers of villages in our sample are204, 242, and 189 for households with a wage

(hereinafter LZ). Although our production───────────
worker in the eastern, central, and westernregions,
respectively.
The
corresponding
numbers for households without a wage worker
are 133, 171, and 175.
１５ Output Y includes both crops and livestock
products in our empirical study. If we estimate
equations (1) and (2) using this output, thedifference in production frontiers or technicalefficiency between different groups can arise
from the difference in the revenue share of
livestock products between them. To examine
this possibility, equations (1) and (2) werereestimated by adding the revenue share of
livestock products as a regressor in the two
equations. The result showed that our essential
conclusion was not affected by this consideration.
In addition, when this share was computed forhouseholds with and without a wage worker, it
was 22% and 25% for the eastern region, 24%
and 23% for the central region, and 41% and
34% for the western region. The result showsthat the difference in composition of farm
products is negligible between the two groups
for the eastern and central regions, although it
might not for the western region.
１
６ When irr̲share is excluded, convergence inparameter estimation is not achieved for one
group of households and estimated technicalefficiency TE extremely concentrates near 1
for two groups of households.

１０

中国経済研究第１１巻第１号

Table 4. Estimated Parameters of Stochastic Production Frontiers and Variance Functions forHouseholds with and without a Wage Worker
RegionWith or withouta wage worker

East
With
workerCenterWithout
workerWith
workerWest

Without
workerWith
workerWithout
workerSample size

852

456

1062

627

772

622

lnL

0.2857(9.15)

0.2268(4.66)

0.1619(6.28)

0.1199(3.00)

0.0732(2.65)

0.1306(3.44)

lnVC

0.2486(10.45)0.2438(6.23)

0.2149(10.97)0.2578(8.09)

0.2612(11.81)0.1542(6.33)

lnVL

0.0723(11.44)0.0768(8.32)

0.0831(15.23)0.0851(10.27)

0.0943(11.63)0.0903(9.72)

lnK

0.0306(3.51)

0.0268(2.04)

0.0274(4.72)

0.0185(2.15)

0.0536(5.91)

0.0181(2.44)

lnT

0.2368(5.60)

0.2523(4.36)

0.3202(10.20)0.2998(6.62)

0.2283(7.12)

0.2572(6.14)

irr̲share

0.1286(1.24)

0.2041(1.47)

0.0767(0.77)‑0.0655(0.52)

0.0024(0.03)

0.5213(4.54)

Variables inlarge̲scale‑2.6660(1.56)

1.2399(1.90)‑0.5028(0.83)-0.0532(0.07)

0.2481(0.55)‑0.9241(2.57)

educ6

0.0589(0.12)‑1.2710(0.84)‑1.1264(2.77)‑1.1913(1.13)

0.5948(1.32)‑0.3349(1.30)

educ9‑0.2438(0.46)

1.8546(1.84)‑1.1666(2.74)‑1.6632(1.18)

0.3884(0.79)

0.3105(1.00)

educ12‑1.7068(1.46)

2.5133(2.01)‑2.3071(1.58)

0.2469(0.18)‑0.7545(1.01)‑1.7696(1.74)

age‑0.0054(0.30)

0.0010(0.02)‑0.0164(0.77)‑0.0145(0.34)‑0.0639(2.70)

0.0092(0.83)

nonlabor̲inc‑0.0896(0.57)

0.4776(2.43)‑0.7884(1.60)

0.2446(1.87)

0.2017(2.07)

0.2859(3.28)

num̲hh

‑0.5781(1.96)‑0.3531(0.95)‑0.1058(0.60)‑0.2104(0.53)‑0.8946(3.44)‑0.0202(0.25)

num̲childlt6

1.1339(2.07)‑4.2839(1.28)

0.5063(1.53)

0.6688(1.21)

0.4211(1.39)

0.1387(0.62)

collective̲pest‑1.7716(0.78)

5.3993(3.78)

1.4708(2.12)

2.5197(1.45)

3.0937(5.05)‑0.6131(1.24)

collective̲purchase1.0109(1.05)

1.8216(0.65)‑0.1508(0.16)‑5.1524(1.14)‑0.5240(0.79)‑27.6097(0.02)

constant‑0.2193(0.15)‑6.2973(2.11)‑1.5029(1.40)‑2.7877(1.11)

1.5701(1.12)‑2.1557(2.99)

0.3633(24.25)0.3858(27.64)0.2779(22.85)0.2793(19.96)

0.2426(25.31)0.2157(14.94)log-likelihood‑405.34‑241.67‑227.68‑114.00‑89.30‑104.35

LR test ofhomoskedasticity

26.08[0.00]

36.44[0.00]

26.90[0.00]

16.71[0.08]

Not
available

44.22[0.00]

Note: Absolute values of the t statistics are shown in parentheses and the upper tail area forχ２(10) is in brackets. To save space, the estimated coefficients of the village dummy
variables and the constant term of SPF are not shown.

Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

that

can

be

１
１

elasticity of labor (0.29 or 0.23 for east and 0.07

outputs

or 0.13 for west) tends to be higher than that oftechnologies of households with and without a

CHR and LZ, it is similar to CHR (0.12 for east

wage worker) are 7,792 (6,557) and 9,560

and 0.07 for southwest) because the elasticity(18,347) for the eastern region, 7,942 (4,795) andis higher in the eastern region. On the other8,493 (8,409) for the central region, and 8,116

hand, our production elasticity of land (0.24 or(5632) and 9,641 (6,801) for the western region,

0.25 for east and 0.23 or 0.26 for west) is muchwhere standard deviations are shown inlower than that of CHR and LZ, which isparentheses.

higher than 0.40. Our production elasticity ofdeterministic frontier is higher for households

farm capital (0.03 for east and 0.05 or 0.02 forwithout a wage worker in all regions afterwest) also tends to be lower than that of CHR

controlling for input amounts.

and LZ, which is between 0.01 and 0.20. The

Because the average of

Therefore,

produced

on

using

average,andthecan bedifferences in these results arise probably

sensitive if their distributions are skewed, we

because outputs and inputs in this study covercompare their kernel density (a smootherlivestock production, whereas those in CHR

version of the histogram) for each region inand LZ do not.

Figure 1 (a)−(c). In these figures, the horizontal
axis measures the amount of predicted output

4．
1 Comparison of production frontiersFirst, we briefly compare the production
elasticities between households with and(or) and the vertical axis measures its

density, with the dotted and solid lines
respectively drawn forand. For thewithout a wage worker. Although production

eastern and central regions,elasticities of VL and T are similar between

have a similar distribution, although

the two groups for any region, those of L, VC,

little higher density at smaller output andandseem tohas aand K differ for most cases. We use the Waldtends to have a little higher density at larger

test to check equivalence across the five

output. For the western region, the distribution

production elasticities between the two groups.

ofis located more rightward for any output

The test statistics are computed as 1.29 [0.94],
2.53 [0.77], and 24.89 [0.00] for the eastern,
central, and western regions, respectively,
which can be compared with critical values ofχ２ distribution with five degrees of freedom (p‑values are shown in brackets). The resultshows that equivalence of the deterministic
production frontiers of the two groups is not
rejected for the eastern and central regions,whereas it is rejected for the western region.
Next, we compare the deterministic frontiers
between the two groups of households byfollowing the method of Kumbharkar, Tsionas,¨
and Sipilainen(2009). １７ The averages of thepredicted outputs

and(maximum───────────
１７ Because villages in the sample differ for thetwo groups of households, we evaluate theintercept of the SPF in the following way. For avillage that includes both groups of households,
we evaluate its intercept using the constantterm and the coefficient of the corresponding
village dummy. For a village that includes only
households with a wage worker in the sample,
we evaluate its intercept to computein thesame way as above. For this village, however,
we evaluate its intercept to computeusing
the constant term and the average of available
coefficients of village dummies. A similar
procedure is applied to the evaluation of theintercept for a village that includes only
households without a wage worker in thesample.

１２

中国経済研究第１１巻第１号

Figure 1 (a). Distributions of Predicted Outputs under Production Technologies of Households withand without a Wage Worker (Eastern Region)(Output produced using technologyof households with a wage worker)(Output produced using technologyof households without a wage worker)

Figure 1 (b). Distributions of Predicted Outputs under Production Technologies of Households withand without a Wage Worker (Central Region)(Output produced using technologyof households with a wage worker)(Output produced using technologyof households without a wage worker)

Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

１３

Figure 1 (c). Distributions of Predicted Outputs under Production Technologies of Households withand without a Wage Worker (Western Region)(Output produced using technologyof households with a wage worker)(Output produced using technologyof households without a wage worker)

level. Consequently, the deterministic frontier

frontier is inferred to come from their higherof households without a wage worker is higher

intercept or their higher managerial ability

only for households with relatively large

and land quality which cannot be observed byoutput in the eastern and central regions,researchers. A similar explanation seems towhereas it is always higher in the western

apply to the result for the central region,

region. This finding is consistent with thealthough households without a wage worker

result of the Wald test above.

have a slightly higher production elasticity of

The higher production frontier of households

crop inputs.

without a wage worker can be attributed to

For the western region, households without

their higher production elasticities, theira wage worker have a higher production

higher coefficient on irrigation share, or theelasticity of labor and a higher coefficient ofhigher intercept of their production frontier.

irrigation share. Their higher coefficient ofFor

production

irrigation share suggests that it is difficult toelasticities are similar between the two types

effectively manage water use without theof households and the production elasticity of───────────eastern region suggests the increasing numberof farm households which are managed by theelderly. In fact, Table 2 shows that the averageage of the household head is the highest forhouseholds without a wage worker in thisregion.

theeastern

region,thelabor is lower for households without a wage
worker. １８ Therefore, their higher production───────────
１
８ The lower production elasticity of labor forhouseholds without a wage worker in the

１
４

中国経済研究household head working on farm actively

第１１巻第１号

Beforeinterpretingthesignificant

because the western region is more likely tocoefficients in Table 4, we compare technicalsuffer from natural disasters. １９ Regarding theefficiency (TE ) between households with andhigher production elasticity of labor, thewithout a wage worker, which is estimatedhousehold head tends to have lower farmusing equation (4). The average TE

productivity due to lower quality (or effort) of

households with and without a wage worker

his labor when he engages in full‑time wage

are 0.86 (0.09) and 0.92 (0.13) for the eastern

employment and works on farm only at peakregion, 0.88 (0.07) and 0.92 (0.07) for the central

seasons.

region, and 0.88 (0.10) and 0.81 (0.13) for theforwestern region, where standard deviations are
4．
2 Comparison of technical efficiency and its
determinants
Table

shown in parentheses. The average TE ishigher for households without a wage worker

estimatedin the eastern and central regions, while it iscoefficients of the heteroskedasticity function

higher for those with a wage worker in the(2). Many of the individual coefficients arewestern region. The two studies CHR and LZ

4

also

presents

thestatistically insignificant at the 5% level,cited above estimate TE at 0.73−0.77 for thesuggesting some difficulty in estimating effects

eastern region and 0.55−0.69 for the (south)of those factors on technical inefficiency using

western regions using the data on crop

the variance. Nonetheless, the likelihood

production in China. Our estimates are higher

ratio test for homoskedasticity of technicalthan theirs but are similar in that the averageinefficiency ureveals joint significance of

TE is lower in the western region. Our higher

coefficients( ＝1,…,10) in equation (2), as

estimates seem plausible partly because ourshown at the bottom of Table 4. ２０ For this

inputs and outputs cover livestock production,

reason,under

for which Latruffe et al. (2004) find a 15%

subsequent

higher TE than for crop production in Poland.

we

use

heteroskedasticity

the
for

resultsthediscussion.２１

Figure 2 (a)−(c) depict the kernel density of

TE for the two groups of households in each───────────
１
９ The Administrative Village Questionnaireannexed to CHIP2002 shows that 45%, 49%, and
66% of villages in the eastern, central, andwestern regions had natural disasters in 2002.
２
０ For households with a wage worker in thewestern region, we cannot compute the test
statistic because parameter convergence is notachieved under homoskedasticity of u .
However, the individual coefficients of age,nonlabor̲inc, num̲hh, and collective̲pest are
significant,suggesting
rejectionofhomoskedasticity for this case.
２
１ Another reason to use all those factors is toachieve parameter convergence and obtainplausible results for technical efficiency. When
we assume homoskedasticity of u, parameter
convergence is not achieved for one case andregion. The horizontal axis measures TE andthe vertical axis measures its density, with thesolid and dotted lines respectively drawn forhouseholds with and without a wage worker.
Unlike Figure 1, Figure 2 exhibits distinct
distributions for the two groups in all regions.
For the eastern and central regions, TE forhouseholds

withoutawage

workerconcentrates around 0.95−0.99, whereas TE
for households with a wage worker has awider distribution with its mode locating───────────
technical efficiency extremely
near 1 for four cases.

concentrates

Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

１５

Figure 2 (a). Distributions of Technical Efficiency Index for Households with and without a Wage
Worker (Eastern Region)

Households with a wage worker

Households without a wage worker

Figure 2 (b). Distributions of Technical Efficiency Index for Households with and without a Wage
Worker (Central Region)

Households with a wage worker

Households without a wage worker

１６

中国経済研究第１１巻第１号

Figure 2 (c). Distributions of Technical Efficiency Index for Households with and without a Wage
Worker (Western Region)

Households with a wage worker

Households without a wage worker

around 0.90−0.94. For the western region, on

Table 4. Therefore, we use the result in Tablethe

5 to examine statistically significant effects ofcontrary,

theformer

has

a

wider

distribution with its mode locating around 0.85,

on TE.

whereas the latter concentrates around 0.92.

Variables large̲scale, num̲hh, num̲childlt6,

In summary, households without a wage

collective̲pest, and collective̲purchase haveworker are more technically efficient in the

expected effects on TE in most cases. As weeastern and central regions, whereas thoseexamined in Table 3, larger cultivated land,with a wage worker are more technicallymore household members, fewer children, andefficient in the western region. To investigate

more collective pest control and input purchasereasons for this result, we first examine effects

raise technical efficiency. The three educationof the variables

in equation (2) on technicaldummies have positive coefficients for mostefficiency TE. Table 5 presents the result ofcases, implying that households with higher

regression of estimated TE on these variables.

education (than those with schooling years

Although this method might cause biased

shorter than six years) have higher TE. The

estimates of the parameters, as pointed out by

negative coefficient for households without aWang and Schmidt (2002), we find the result inwage worker in the eastern region suggests

Table 5 to be plausible because we obtain athat the household head tries harder to searchsimilar result by applying the interpretation of

for wage work if he has relatively highthe coefficient

education(variablewith> 0 has anegative effect on TE ) to their estimates in(educ9 = 1)

but

he

does

not

participate in the labor market, which can

Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

１７

Table 5. Results of Ordinary Least Squares Regression of Technical Efficiency
RegionWith or withouta wage worker

East
With
workerCenterWithout
workerWith
workerWest

Without
workerWith
workerWithout
workerSample size

852

456

1062

627

772

622

large̲scale

0.1101(14.07)‑0.0351(1.80)

0.0248(4.16)‑0.0068(0.93)‑0.0170(1.62)

0.0713(3.69)

educ6‑0.0081(1.08)

0.0149(1.11)

0.0653(10.32)0.0494(8.45)‑0.0265(3.34)

0.0266(2.38)

educ9

0.0119(1.49)‑0.0556(3.82)

0.0671(10.55)0.0621(11.67)‑0.0212(2.35)

0.0097(0.53)

educ120.0777(8.50)‑0.0471(0.90)

0.1039(11.32)‑0.0167(1.11)

0.0382(1.94)

0.1035(2.75)

age

0.0001(0.32)

0.0004(1.01)

0.0008(4.13)

0.0004(2.15)

0.0024(7.81)‑0.0013(2.17)

nonlabor̲inc

0.0039(3.15)‑0.0204(6.42)

0.0106(5.08)‑0.0122(7.37)‑0.0112(3.06)‑0.0065(0.57)

num̲hh

0.0306(10.99)0.0068(1.83)

0.0046(2.63)

0.0078(4.67)

0.0339(13.12)0.0094(2.21)

num̲childlt6‑0.0593(7.15)

0.0372(3.04)‑0.0295(5.20)‑0.0276(3.59)‑0.0286(4.51)‑0.0292(2.79)

collective̲pest

0.1451(2.34)‑0.0698(1.23)‑0.0367(1.59)

0.0466(1.51)‑0.1912(3.69)

0.1967(0.79)

collective̲purchase‑0.1087(1.68)

0.0162(0.30)‑0.0061(0.33)

0.0482(4.29)

0.0727(1.38)

0.1244(3.73)

0.59950.7071

0.48920.70720.59200.2964

Note: Absolute values of the t statistics (based on robust standard errors) are shown inparentheses. To save space, the estimated coefficients of the village dummy variables andthe constant term are not shown.
denotes the adjusted coefficient of determination.
lower TE.

The negative coefficient forand they might lose their attention to farmhouseholds with a wage worker in the western

production. On the other hand, non‑laborregion suggests that the household head tends

incomes tend to have a positive effect on TE

to have a relatively stable job (rather than afor those with a wage worker partly becauseseasonal or temporary job) as wage work,

non‑labor incomes in addition to wage incomes

which can reduce attention to farm production

further

to lower TE.

production inputs.

facilitate

flexiblepurchaseofFurthermore, age of the household head

Finally, we find some reasons for the(age ) has a positive effect on TE for mostdifferent TE between households with andcases, implying that longer farm experience

without a wage worker by comparing thetends to raise technical efficiency. Non‑labor

regression coefficients in Table 5 and theincomes (nonlabor̲inc ) have a negative effectsample means ofon TE for households without a wage worker
partly because their family members might
engage in non‑agricultural family operationin Table 2. ２２ If we focus───────────
２２ The idea is similar to the Oaxaca‑Blinder
decomposition.

１８

中国経済研究第１１巻第１号

only on statistically significant coefficients andand without a wage worker to determine

marked difference in sample means of, weeffects of the head s participation in wage

find the following factors important to explain

work on farm productivity in China. Using thehigher TE for the relevant households. For

estimated frontiers, it compares deterministic

the eastern region, households without a wage

production frontiers (technology levels) andworker are more technically efficient because

technical efficiency of the two groups ofthe higher coefficient of num̲childlt6 and thehouseholds and examines determinants ofhigher mean of large̲scale contribute to thetechnical efficiency.

higher TE for these households. ２３ For the

In the eastern and central regions, typicalcentral region, households without a wage

households with a wage worker tend to haveworker are more technically efficient because

lower farm productivity because of their lower

the higher coefficient of collective̲purchasetechnical efficiency. We can attribute thisand the higher mean of large̲scale contributelower technical efficiency to their smaller farmto the higher TE

for these households.

land. One implication of this result is that

Therefore, households without a wage worker

domestic food supply in these regions willin both the eastern and central regions arestagnate or deteriorate as rural household

more technically efficient partly because ofheads participate more in wage work as

their larger farm land. For the western region,

economic

households with a wage worker are more

opportunities to increase nonfarm income. To

technicallyhigherimprove the lower technical efficiency in thesecoefficient of age and num̲hh and the higher

regions, an important policy is to make themean of educ12 contribute to the higher TE

land market more active and allow farmfor these

efficientbecausethehouseholds. ２４ Therefore,

they havedevelopment

providesmorehouseholds to expand their cultivated land.

higher TE because they have more members

In the western region, typical householdswith higher education and because they arewith a wage worker may have higher farm

better at raising technical efficiency fromproductivity because of their higher technicallonger experience on farm and more family

efficiency.

members.

technical efficiency to better education of the

5. Concluding Remarks

We

can

attribute

thishigherhousehold head and better utilization offarming knowledge and family members. One

This study uses a Chinese household income

implication of this result is an increase insurvey from 2002 to estimate stochastic

domestic food supply in this region along withproduction frontiers for farm households withfurther

economicdevelopment.

This

improvement, however, might be limited───────────
２
３ Note that num̲childlt6 has a positive effecton TE for these households.
２
４ Note that age has a negative effect on TE
for households without a wage worker probably
because weaker physical strength of older
members can decrease TE in the westernregion where the value share of livestock
production is much higher.

because it depends on more farm work by
women and the elderly. To make the domestic
food supply sustainable, it is important toencourage farmers in this region to use more
farm machinery and a newer variety of crops,
which not only helps women and the elderly
improve farm production but also boosts theEffects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

production frontier to increase the domestic
food supply in this region.
References
Battese, G. E. and T. J. Coelli (1988). Predictionof Firm‑Level Technical Efficiencies witha
Generalised
Frontier
Production
Function and Panel Data. Journal of
Econometrics 38, pp. 387−399.
Brandt, L. and C. Holtz (2006). Spatial Price
Differences in China: Estimates and
Implications. Economic Development andCultural Change 55, pp. 43−86.
Caudill, S. B., J. M. Ford and D. M. Gropper(1995). Frontier Estimation and Firm‑
Specific Inefficiency Measures in the
Presence of Heteroscedasticity. Journalof Business & Economic Statistics 13, pp.
105−111.
Chang, H., X. Dong, and F. Macphail (2011).
Labor Migration and Time Use Patternsof the Left‑behind Children and Elderly in
Rural China. World Development 39, pp.
2199−2210.
Chang, H. and F. Wen (2011). Off‑Farm Work,
Technical Efficiency, and Rice Production
Risk in Taiwan. Agricultural Economics
42, pp. 269−278.
Chen, Z., W. E. Huffman, and S. Rozelle (2009).
Farm
Technology
andTechnical
Efficiency: Evidence from Four Regions in
China. China Economic Review 20, pp.
153−161.
Goodwin, B. K. and A. K. Mishra (2004).
Farming Efficiency and the Determinantsof Multiple Job Holding by Farm
AmericanJournalofOperators.
Agricultural Economics 86, pp. 722−729.
Gustafsson, B. A., S. Li, and T. Sicular (2008).
Inequality and Public Policy in China.
New York: Cambridge University Press.
Herdt, R. W. and A. M. Mandac (1981).
Modern Technology and Economic
Efficiency of Philippine Rice Farmers.
Economic Development and Cultural
Change 29, pp. 375−399.
Jacoby, H. G. (1993). Shadow Wages andPeasant Family Labour Supply: An

１
９Econometric Application to the Peruvian
Sierra. Review of Economic Studies 60,
pp. 903−921.
Jolliffe, D. (2004). The Impact of Education in
Rural Ghana: Examining Household Labor
Allocation and Returns on and off the
JournalofDevelopment
Farm.
Economics 73, pp. 287−314.
Knight, J., Q. Deng, and S. Li (2011). The
Puzzle of Migrant Labour Shortage andRural Labour Surplus in China. China
Economic Review 22, pp. 585−600.
Kumbharkar, S. C., E. G. Tsionas, and T.
¨
Sipilainen(2009). Joint Estimation ofTechnology
ChoiceandTechnical
Efficiency: An Application to Organic andConventional Dairy Farming. Journal of
Productivity Analysis 31, pp. 151−161.
Latruffe, L., K. Balcombe, S. Davidova, and K.
Zawalinska (2004).
Determinants ofTechnical Efficiency of Crop and Livestock
Farms in Poland. Applied Economics 36,
pp. 1255−1263.
Li, S. (2002). Chinese Household Income Project,
2002 [Computer file]. ICPSR21741‑v1.
Ann
Arbor,MI:Inter‑university
Consortium for Political and Social
Research [distributor], 2009‑08‑14. doi:
10.3886/ICPSR21741.
Liu, Z. and J. Zhuang (2000). Determinants ofTechnical Efficiency in Post‑Collective
Chinese Agriculture: Evidence from
Journalof
Farm‑Level
Data.
Comparative Economics 28, pp. 545−564.
Mochebelele, M. T. and A. Winter‑Nelson
Migrant Labor and Farm
(2000).
Technical Efficiency in Lesotho. World
Development 28, pp. 143−153.
Mu, R. and D. van de Walle (2011). Left behind
to Farm? Women s Labor Re‑Allocationin Rural China. Labour Economics 18, pp.
S83−S97.
Pfeiffer, L., A. Lopez‑Feldman, and J. E. Taylor(2009). Is Off‑Farm Income Reforming
the Farm? Evidence from Mexico.
Agricultural Economics 40, pp. 125−138.
Sherlund, S. M., C. B. Barrett and A. A. Adesina(2002). Smallholder Technical Efficiency
Controlling for Environmental Production

２０

中国経済研究Conditions. Journal of Development
Economics 69, pp. 85−101.
Wang, H. and P. Schmidt (2002). One‑Step and
Two‑Step Estimation of the Effects of
Exogenous
VariablesonTechnical
Efficiency Levels. Journal of Productivity
Analysis 18, pp. 129−144.

第１１巻第１号

Yang, L. (2003). Rural Labor Migration Choice
in China and Its Impacts on Rural
Households. Chicago: Department of
Economics, University of Chicago.
(Graduate School of Economics,
Nagoya University)Effects of Household Heads Wage Work on Farm Technology Level andTechnical Efficiency in China

２
１

Effects of Household Heads Wage Work on Farm Technology
Level and Technical Efficiency in China
Bohan YUE (Graduate School of Economics, Nagoya University)
Keywords: Wage Work, Technology Level, Technical Efficiency, Chinese Farmers
JEL Classification: Q16, Q18
This study investigates the effects of a household head s participation in wage work on farm
productivity in China. To this end, we use a Chinese household income survey from 2002, which
includes 4,391 households in 22 provinces, autonomous regions, and directly administeredmunicipalities. For this data set, we estimate stochastic production frontiers (SPF) separately forfarm households whose head is a wage worker (households with a wage worker) and those whose
head is not a wage worker (households without a wage worker). After estimating SPF for eachgroup of households, we compare the deterministic production frontiers and technical efficiency.
The empirical results for the eastern and central regions show that typical households with awage worker tend to have lower farm productivity because of their lower technical efficiency. This
lower technical efficiency is due to their smaller farm land. To improve technical efficiency in theseregions, the government should try to make the land market more active and allow farmhouseholds to expand their land.
The empirical results for the western region show that typical households with a wage worker
may have higher farm productivity because of their higher technical efficiency. This higher
technical efficiency is due to better education of the household head and better utilization offarming knowledge and family members. However, the higher technical efficiency depends on more
farm work being carried out by women and the elderly, which might not be sustainable in the
future. To address this concern, the government should encourage farmers to use more farmmachinery and newer varieties of crops so that their production frontier can shift upward.