Cross-Commodity Analysis of China’s Grain Sector: Sources of Growth and

Supply Response. By Hunter Colby, Xinshen Diao, and Agapi Somwaru, U.S.

Department of Agriculture, Economic Research Service, Market and Trade Economics

Division. Technical Bulletin No. 1884.

Abstract

We investigate sources of output growth and supply response in rice, wheat, corn, and

soybeans, the four most important crops in China’s grain sector, during 1978-97. Using

a growth accounting methodology, we found large total factor productivity (TFP) contributions to growth in grain production immediately following China’s rural economic

reform (1978-85). In 1995-97, the TFP contribution dropped to only 16 percent of

growth in grain production, as greater use of inputs increasingly drove growth. In the

supply response analysis, the results of the econometrically estimated restricted profit

function confirm a joint and nonseparable multiproduct technology for China’s grain

sector. Complementarity prevails in the grain sector among different outputs and inputs,

meaning that an increase in the price of intermediate inputs/capital or wages would

result not only in an absolute reduction in all outputs but also in a change in the composition of these outputs. The expansion (or scale) effects subsided during 1986-97,

implying a relatively slow outward shift of the production frontier during this period. If

the current government policy environment remains unchanged, China’s grain production will become more costly, constraining its future growth and competitiveness in

world markets. 

Keywords: China agriculture, growth accounting, total factor productivity (TFP), multiproduct supply response.

Acknowledgments

The authors have benefited from discussions with Terry Roe and Eldon Ball. The

authors are also grateful to M. Gopinath, Shenggen Fan, Terry Roe, Praveen Dixit, Joy

Harwood, John Dyck, Steven Zahniser, Bill Kost, Scott Rozelle, and Francis Tuan for

their critical comments and suggestions. An early version of the paper was presented at

the International Agricultural Trade Research Consortium, "China's Agricultural Trade

and Policy," San Francisco, CA, June 26, 1999. The authors are solely responsible for

any remaining errors.

Washington, DC 20005-4788 May 2000
ii • China’s Grain Sector Economic Research Service/USDA • TB-1884 

Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .iii

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Growth Accounting Analysis—Sources of Output Growth . . . . . . . . . . . . . . . . . . . . .1

Previous Studies and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

Growth Accounting Analysis—Contribution of TFP . . . . . . . . . . . . . . . . . . . . . . .4

Growth Accounting Analysis—Contribution of 

Intermediate Inputs and Capital . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7

Structural Change in Input Use—Rise in Capital/Land Ratio 

and Fall in Labor/Land Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8

Analyzing Supply Response in the Grain Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Product Supply and Input Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

Estimates of Supply and Demand Elasticities . . . . . . . . . . . . . . . . . . . . . . . . . . . .12

Technological Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16
TB-1884 • Economic Research Service/USDA China’s Grain Sector • iii

Executive Summary

This report analyzes the sources of output growth and supply response in rice, wheat,

corn, and soybeans, the four most important crops in China’s grain sector, during 1978-

97. The study, using a growth accounting methodology, finds large total factor productivity contributions to growth in grain production immediately following China’s rural

economic reform (1978-85). These findings are reinforced by additional analysis using

a multiproduct supply response approach. 

Most of the growth in total factor productivity during 1978-85 was from efficiency

gains due to institutional reforms. After 1985, when the efficiency gains had diminished, the growth rate of productivity fell sharply and contributed less than 20 percent of

growth in grain production. In recent years (1995-97), increased use of inputs, especially intermediates/capital, became major sources of growth in China’s grain sector.

Historically, there has been a decline in the ratio of labor relative to land used in China’s

grain production, though in recent years this decline has slowed significantly. The current land tenure system, which blocks scale economies by preventing land transfers, is a

major factor constraining the development and expansion of labor-saving technology in

China’s grain sector. Given that increased input use, including labor, drove growth in

China’s grain sector in recent years, grain production will become more costly as wages

and prices for intermediate/capital inputs rise. This could constrain growth in China’s

grain production and weaken its competitiveness in world grain markets. However, the

gap between productivity growth in China’s grain sector and overall productivity growth

in developed countries such as the United States suggests that China can improve its

grain production technology if the economic and policy environment encourages investment in agricultural research and development, water control systems, and land infrastructure.

This report also examines output and input adjustments in China’s grain sector due to

changes in prices following adoption of the Household Responsibility System and

expansion of the system of rural free markets. The econometric analysis captures the

relationships among outputs supplied and inputs demanded in a non-separable and joint

production system characteristic of China’s multiproduct grain farms. Multiple production activities are the dominant structure of technology in China. Grain outputs are

jointly produced and inputs are jointly employed, an important factor underlying this

study’s finding of a strong complementarity among outputs and among inputs. Study

results also find that grain output is highly responsive to changes in input prices. An

increase in the price for intermediate inputs/capital or wages would result in absolute

reductions in all grain production as well as changes in the composition of grain production.

Each grain’s output is highly responsive to changes in that particular grain’s price, but

there is also a strong complementary supply/price relationship with other grains. That

is, an increase in the output price for a particular grain would result in increased production of all grains. This result indirectly supports the finding from the growth accounting

analysis that the current heavy dependence on intermediate input use for production

growth could constrain growth in China’s grain production should wages or the prices of

inputs rise.
iv • China’s Grain Sector Economic Research Service/USDA • TB-1884

Future work in identifying and measuring the sources of growth in China’s agriculture

should focus on disaggregating to regions (or even provinces). National analysis may

obscure some important regional differences in productivity because of differences in

economic development and agricultural practices across regions.
Introduction

China’s agricultural output grew at an astonishing rate

over the last 20 years following the introduction of

economic reforms in 1978. Grain output more than

doubled, rising to 490 million tons in 1998. During

that same period, China’s agricultural trade also grew

very rapidly (especially in the 1990’s), but more erratically than agricultural output. Only in the 1990’s did

both output and trade move upward. China’s agricultural imports rose from US $5.5 billion in 1990 to $10

billion in 1996. Exports also rose substantially, but

mostly in the early 1990’s as government policies discouraged grain exports after the mid-1990’s.

China’s changing role in world trade, both as an

importer and exporter, has heightened the need to

understand its production potential. Anticipating

China’s production growth and adoption of production

technologies calls for an understanding of China’s past

production growth and output and input utilization

choices. Especially important, for a whole host of

political, social, and economic reasons, is to identify

the sources of growth in China’s grain sector and

examine the structure of its grain production.

Moreover, in an era of stronger market orientation, it

is also important to understand the role of output and

factor prices in generating supply or production

response. 

Growth Accounting Analysis—

Sources of Output Growth

Previous Studies and Background

The existing literature on China’s agricultural growth

is insufficient to predict its potential. Most of these

studies were conducted in the late 1980’s and early

1990’s, focusing on the period immediately following

the introduction of rural reform. Moreover, with only

a handful of very recent exceptions, these studies were

based on the gross value of agricultural output

(GVAO), with little information on joint production by

China’s household farms.

Most studies examining the early reform period (1978-

85) are strongly influenced by the unusually high

growth rate of China’s agricultural production in that

period. Even though many researchers recognized the

importance of institutional changes in the early growth

of China’s agricultural production (see McMillan et

al., 1989; Fan, 1991; Wen, 1993), they were unable to

pinpoint contributions of these changes versus technological changes.

Lin (1992) attempted to identify the sources of total

factor productivity (TFP) growth. TFP captures the

growth in outputs not accounted for by the growth in

production inputs (e.g., labor, capital, and fertilizer).

In Lin’s study, TFP was analyzed by breaking out the

individual contributions of the introduction of the

household responsibility system (HRS) during 1978-

84, changes in government purchase prices, and the

expansion in the system of rural free markets.

However, because the study used aggregated agricultural data and covered only a few post-HRS years, it is

difficult to accurately gauge the potential of China’s

future growth based on Lin’s analysis.

Huang and Rozelle (1996) successfully separated the

contribution of technological change in the growth of

production from other factors (including institutional

change). However, the study covered only rice production.

In this study, we apply a growth accounting method to

disaggregated national data for rice, wheat, corn, and

TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 1

Cross-Commodity Analysis 

of China’s Grain Sector

Sources of Growth and Supply Response
soybeans from 1978 to 1997, measuring the different

sources of growth for these four grains. Data were

compiled from provincial data and aggregated to a

national level to indicate China’s potential growth in

grain production. Carter, Chen, and Chu (1999) provide a similarly disaggregated analysis of six crops

and two livestock products, but for only one province

(Jiangsu). Agricultural production structure and productivity growth vary greatly across regions (Fan,

1991), so it is hard to extrapolate national growth

potential from their study. 

Our growth accounting analysis is divided into three

periods that correspond to major shifts in China’s agricultural policy. The first period (1978-85) covers the

transformation of the old commune system to the 

family-based HRS. In the second period (1986-94),

the reforms shift from the rural to the urban economy.

The provincial governor’s grain responsibility system,

which promotes grain production and self-sufficiency,

marks the third period (1995-97). The growth rates of

the four grains are observed to be quite different in

these three periods, implying strong institutional and

policy impacts.

Over the last two decades, China’s grain production

increased by nearly 200 million tons, led by rice,

wheat, corn, and soybeans. These four grains accounted, on average, for 96 percent of the growth in total

grain production. The share of these four grains’ contribution to total grain output rose from 80 percent in

the late 1970’s to 86-87 percent in the late 1990’s.

Among these four grains, soybeans and wheat grew

most rapidly, while wheat and rice had the largest contribution share (table 1). 

The contribution share of each grain is defined as the

ratio of the output growth rate for each grain over the

growth rate for the four grains in total, weighted by

the share of each crop in the value of the four grains’

output. Between 1978 and 1997, wheat accounted for

27 percent of the value of the four-grain total, and contributed the most (38 percent) to total grain growth,

while rice accounted for 46 percent of the value of the

four-grain total and contributed less (30 percent) to

total grain growth than wheat. The rapid growth in

wheat output, 4.5 percent annually, made it the largest

contributor to growth in grain output.

The growth rate of soybean output was 4.9 percent per

year. Due to this rapid growth rate, soybeans contributed 12.5 percent of growth in total grains, though

they accounted for only 8 percent of grain output in

value terms. Corn accounted for 19.4 percent of total

grain value and contributed a similar share (19.5 percent) to total growth, implying that corn’s growth rate

is similar to the growth for total grains.

We now turn to the growth accounting analysis to

understand how and why the rates differed across time

and among crops.

Method

The purpose of growth accounting analysis is to determine the sources of growth in output. However, the

choice of output and input indices can strongly influence the accounting results. The traditional output

index is usually measured in terms of constant output

prices, while the input index is calculated using a fixed

share to weight individual inputs. Consequently, the

aggregated output and input indices, and hence the

2 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

Table 1—Growth rate and contribution of each crop

1978-85 1986-95 1995-97 1978-97 

Growth Growth Growth Growth 

rate Contribution rate Contribution rate Contribution rate Contribution

Annual average percent

Four-crop total 4.0 100.0 2.0 100.0 4.8 100.0 3.2 100.0

Rice 3.0 36.9 0.5 11.5 4.5 38.9 2.0 30.3

Wheat 6.9 46.9 1.6 21.9 7.5 40.2 4.5 37.7

Corn 1.9 8.3 5.0 46.7 1.7 7.8 3.3 19.5

Soybeans 4.9 8.0 4.8 19.9 4.5 13.1 4.9 12.5

Source: Calculated by ERS from China Statistical Yearbook (1978-98) data.
estimated TFP, are likely to be biased if such traditional measures are used (Fan, 1997). This method would

be particularly problematic for the period we study

because of China’s institutional reforms and subsequent changes in production and input use structures.

To capture the effects of change in production or input

combinations, we applied an index number procedure.

Specifically, we computed a Tornqvist input index for

each crop and a Tornqvist output index for aggregate

grains. The Tornqvist index is an approximation of the

Divisia index and can be derived from a flexible multiproduct structure of production constrained to constant

returns to scale. This index measure accounts for

changes in the share of an individual input in total production costs, and the share of an individual crop’s

output in total grain output, over time. These structural changes are often outcomes of technological innovations or institutional changes (see Diewert, 1976, and

Caves et al., 1982, for additional explanations of the

Divisia index approach). The input and output indices

are defined as follows:

where Sf,j,t = (pf,tXf,j,t)/(pj,tYj,t) is the share of the cost

of input Xf(f are inputs, e.g., labor or land) in the revenue of crop Yj

(j are outputs) at time t. Sf,t =

(pf,tXf,t)/Sj(pj,tYj,t) is the share of the cost of input Xfin

the revenue of total grains (pfand pjare prices for

inputs and outputs, respectively). Sj,t = (pj,tYj,t)/

Si(pi,tYi,t) is the share of each crop in the revenue of

total grains. Vj,t and Vtare input indices for crop Yj

and aggregate grains, respectively. Qtis an aggregate

output index. Furthermore, the levels of output and

inputs are normalized to 1 in a specific year and then

accumulated over time. The TFP indices in logarithmic form can be expressed as:

for crop j, and

for the whole grain sector.

The growth rate of output and the contributions of

inputs and TFP to the growth in each crop and aggregate grains can be calculated from equations 1-5. For

the whole grain sector, the output growth rate at time t

is 100 •(Qt- 1), the growth rate of TFP is 100 •

[EXP(Gt) - 1], and the contribution of TFP to output

growth at time t roughly equals 100 •[EXP(Gt)/Qt]. 

In growth accounting analysis, TFP is obtained by subtracting an input index, which captures labor or other

physical inputs, from an output index (for example,

total grain output). As a residual term, TFP captures

all nonphysical input factors that affect output growth

over time. Technological change, weather, policy

change, institutional change, and other external shocks

can all affect production efficiency (e.g., change in the

output level given input levels).

Technological change is usually a sustainable source

of TFP growth (i.e., it is a long-term effect). Similarly,

changes in institutions or policy can provide a longterm impact. But most other sources of TFP growth—

good weather, for example—provide only short-term

boosts to productivity. The growth accounting method

by itself cannot identify short- versus long-term

sources of TFP growth. The influence of short-term

effects on TFP growth can be moderately reduced by

studying a longer period of time.

Data

Data for each crop’s output and sown area, and price

indices for the outputs and inputs were obtained from

the China Statistical Yearbook, published by China’s

National Bureau of Statistics (NBS). The cost data,

including person-day time of labor use, wages, and

intermediate input costs by crop, were drawn from the

annual household survey, “National Crop Production

Cost and Labor Productivity Survey,” published in the

TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 3

G

TFP

TFP

Y

Y

j t V

j t

j t

j t

j t

, j t

,

,

,

,

, ≡ ln ln ln

















=

















−

−1 −1

ln , , ,ln , ,

, ,

V (S )

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X

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f j t

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2 1 Σ

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ln ( , ,)ln ,

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X

t

f

f t f t

f t

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= +

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TFP

TFP t Q V

t

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≡ t t

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1

(1)

(3)

(2)

(5)

(4)
China Rural Statistical Yearbook. The survey covers

the costs of intermediate inputs (fertilizers, pesticides,

seeds, plastic sheeting, irrigation, energy, draft animals, and capital depreciation, including small farming

tools, agricultural machinery, and other capital). The

intermediate input price index was obtained from the

China Statistical Yearbook and the China Commerce

Yearbook.

We aggregated inputs into three categories: land, labor,

and intermediates/capital. Because only aggregate

data for variable expenses (costs of intermediate

inputs) and fixed expenses (capital depreciation) were

published, we defined intermediate and capital inputs

as a single input. The implicit quantity associated

with this input category is calculated by dividing its

expenditure by the price index.

For land, we used area sown to each grain instead of

cultivated area because of the extensive multiple-cropping of grain in China. There were no data for land

prices or returns to land. Thus, we assumed that net

revenue from each grain’s production—that is, gross

revenue minus the cost of labor and intermediates/capital as well as tax payments—was the return to land.

China’s official statistics under-report actual cultivated

area (Crook, 1993). This problem may spill over to

sown area statistics, since sown area is the number of

times a piece of land is planted multiplied by the

amount of cultivated area. The majority of the reporting error is believed to have occurred during the

1960’s and early 1970’s as a result of collectivization,

the formation of communes, and the Cultural

Revolution. Even though the under-reporting of land

artificially inflates the level of yields, the yield growth

rates analyzed in this study are largely unaffected

because they reflect a more recent time period.

We measure labor as time spent (person-days) on each

crop rather than the number of laborers in agriculture

for two reasons. First, most households raise many

different crops. Second, farmers generally spend only

part of their time in agriculture because the small scale

of household plots provides for opportunities in nonfarming sectors. The wage data from the survey were

too low to accurately reflect the opportunity costs of

rural nonfarm labor. Since land is farmed mainly by

individual households rather than hired or nonfamily

labor, the underestimated returns to labor would be

captured in the returns to land. The indices for all

these data are reported in tables A1-A2 (see appendix).

Growth Accounting Analysis—

Contribution of TFP

On average, growth in TFP contributed more than 70

percent of the increase in total output of China’s four

major grains over the last two decades (table 2). The

TFP contribution is defined as the ratio of the TFP

growth rate over the growth rate of grain output.

Growth in rice production can be explained by TFP

growth alone, while TFP contributed 48-65 percent of

output growth for soybeans, corn, and wheat. 

Our study covers more of the post-reform contributions of TFP to the growth of China’s grain sector than

most. One recent exception, Carter et al. (1999), covers a similar time period (1978-96) and compares agricultural productivity growth in China at national and

provincial (Jiangsu) levels. The study calculated TFP

for six crops in Jiangsu province, while the national

analysis is based on gross value of agricultural output.

When the inputs were weighted, the TFP growth rates

calculated for the four grains in Jiangsu (1.9, 2.6, 3.3,

and 2.7 percent for rice, wheat, corn, and soybeans)

are comparable with our rates except for corn, which

is rarely produced in south China.

Many studies examine China’s agricultural productivity during the 1980’s. For example, using a parametric

approach, Lin (1992) found that growth in TFP contributed about 50 percent of growth in total grain output in the periods 1978-84 and 1985-87. Based on

Fan’s (1997) growth accounting analysis, which also

used Divisia input and output indices, TFP growth was

found to contribute 77 and 70 percent of the growth in

aggregate agricultural output (including livestock and

other commodities) over the periods 1979-84 and

1985-95, respectively. These results are comparable

with our 1978-97 estimates (table 2). However, our

results show a much larger TFP contribution in the

early period and a much smaller TFP contribution in

more recent periods.

We also compared our results with studies of other

countries’ agricultural TFP. For example, using a similar index number method, Evenson et al. (1999) found

that TFP growth contributed 55 percent of the growth

in India’s total crop production during 1956-87. In a

4 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884
study of the sources of sectoral growth in U.S. agriculture and using a similar index number method,

Gopinath and Roe (1997) found that TFP explains all

of the growth in U.S. agriculture over 1960-90, as

input effects are negative. 

For the aggregate grain category, the contribution of

TFP to the growth in output fell over time with each

subperiod (table 2). In 1978-85, the growth rate of

TFP exceeded the growth rate of output for aggregate

grain, rice, and corn. When output grows more rapidly than the use of production inputs, TFP contributes

significantly to the growth in output. If use of production inputs falls while output increases, TFP’s contribution to the growth in output exceeds 100 percent.

On the other hand, if more inputs are used while output falls, TFP’s contribution to the growth of output

becomes negative.

TFP growth in the early period (1978-85) captures the

efficiency gains from institutional changes. Before

1978, China’s agricultural production was centrally

planned and quite inefficient. In other words, China’s

production was well within its “production possibility

frontier”—the set of efficient input combinations chosen by producers on the basis of profit maximization.

This period (1978-85) saw a shift from the collective

production system to the household responsibility system (HRS); less administrative intervention in agricultural production and reduced mandatory quotas for

grain purchased by the government; increases in government procurement prices; and the blossoming of

free-market activities.

Lin (1992) found that the introduction of the HRS contributed more than 90 percent to growth in agricultural

productivity during the early period. In contrast, based

on a parametric approach, Huang and Rozelle (1996)

found that technological change was relatively more

important. Using rice as an example, they found that

nearly 40 percent of growth in rice output during

1978-84 was due to the rise in yields resulting from

the adoption of new varieties. This finding, like our

result, indicates the importance of TFP in the early

period. In rice, we found that TFP accounted for 173

percent of the growth in rice production over 1978-85. 

The efficiency gains from institutional reforms can

occur at any given level of technology. Therefore, the

impact on growth lasts only for a limited time. A slowdown in TFP growth after the first period (1978-85)

indicates that when China’s grain production moved to

its production possibility frontier at the given level of

technology, efficiency gains from further reform

became smaller. Hence, additional growth in TFP

would have to come from technological change.

TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 5

Table 2—Contribution of TFP to grain production growth

Years Total 4 Rice Wheat Corn Soybean

Annual average percent

Output growth rate:

1978-97 3.2 2.0 4.5 3.3 4.9

1978-85 4.0 3.0 6.9 1.9 4.8

1986-94 2.1 0.5 1.6 5.0 4.5

1995-97 4.8 4.5 7.5 1.7 6.3

TFP growth rate:

1978-97 2.3 2.3 2.9 1.7 2.3

1978-85 5.4 5.3 6.6 4.7 3.8

1986-94 0.4 -0.3 0.3 1.5 2.8

1995-97 0.8 3.0 2.6 -4.1 -2.3

TFP contribution:

1978-97 73 112 65 52 48

1978-85 136 173 96 241 79

1986-94 19 -58 18 30 63

1995-97 16 67 36 -256 -39

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
The growth rate of aggregate output fell to 2.1 percent

in the second period (1985-94), and the growth rate of

TFP fell to 0.4 percent (table 2). Thus, the contribution of TFP to output growth fell to 19 percent for all

grains. And when rapid growth in rice production

came to a sudden halt during the second period, the

TFP growth rate for rice turned negative (table 2).

The slowdown in grain output growth may have been

due to changes in relative agricultural prices. Markets

for vegetables, fruits, and fishery products were further

liberalized after 1985, and prices for these commodities rose relative to grains. The slowdown in TFP

growth may be related to the reduced public investment in agricultural research and development and in

infrastructure (irrigation, flood control, etc.) after the

early reform period (Huang and Rozelle, 1996; Fan

and Pardey, 1997).

The average growth rate of grain production rebounded dramatically during the third period (1995-97),

reaching 4.8 percent (higher even than during the first

period). Soybean and wheat growth was especially

rapid, while the growth rate for corn was just 1.7 percent (table 2). TFP growth was only 0.8 percent per

year in the third period, so the contribution of TFP to

grain growth fell to 16 percent from 19 percent in the

previous period.

We hypothesize that the rise in grain prices relative to

other agricultural products (except for rice) boosted

production growth in the third period (table 3). This is

confirmed by the econometric analysis of supply

response discussed in the next section. Higher grain

prices were due not only to changes in market prices,

but also to increases in government procurement

prices. In addition, when the “governor’s grain bag”

policy was introduced in late 1994, provincial governments were required by the central government to

ensure sufficient grain production. The provincial

governments subsequently introduced various production subsidies, especially subsidies on agricultural

inputs such as pesticides and fertilizer. In some

provinces, governments reinstated administrative

measures to stabilize grain prices and maintain the

area sown to grain crops (Fang and Beghin, 1999). As

the policy environment shifted toward supporting

grain production, inputs into the grain sector grew

while the contribution of TFP to growth actually fell.

In summary, the high TFP growth rate and its large

contribution to grain production growth in the period

immediately following China’s rural economic reforms

was largely due to efficiency gains arising from institutional change. After 1985, the annual growth rate of

TFP fell sharply. Increased grain output in recent

years was due more to rising grain prices than to

improvements in production technology. A fall in the

contribution of TFP implies, on the one hand, that the

recent growth in the grain sector will be shortlived

since it is largely due to increased input use. On the

other hand, lower TFP growth also implies that the gap

between TFP growth in China’s grain sector and other

countries’ agricultural sectors (Evenson et al., 1999),

especially developed countries (Gopinath and Roe,

1997), is quite large. If China’s economic environment and government policy can encourage more

investment in agricultural research and development,

water control systems, and land infrastructure, China

can further increase productivity in its grain sector.

6 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

Table 3—Grain prices normalized by an index for total agricultural products

Years Rice Wheat Corn Soybean Total grains

Price index for same period's total agricultural products is 100

1978-97 107.5 98.4 106.3 157.5 120.0

1978-85 106.3 108.7 107.9 143.2 115.6

1986-94 105.0 87.2 97.5 106.5 98.5

1995-97 98.2 112.0 110.4 110.7 106.7

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
Growth Accounting Analysis—

Contribution of Intermediate 

Inputs and Capital

In this section, we analyze the contributions of labor

and intermediate inputs/capital to grain production

growth. The increase in production factors contributed

27 percent of growth in China’s grain output during

1978-97. This contribution was primarily due to the

increased use of intermediate inputs and capital. Land

use was nearly constant and the use of labor fell by

more than 3 percent annually (table 4). The use of

intermediate inputs and capital in grain production

increased in each sub-period over the last two decades.

Moreover, the growth rate of intermediates/capital use

rose over time, from 4.0 percent in 1978-85 to 5.1 percent in 1995-97. This indicates that grain production

is becoming more intermediate/capital-intensive, as

does the decreased use of labor and relatively stable

use of land over the 1978-97 period. Sown area (land)

and labor days (time spent working) used in production of the four grains fell by 0.6 and 9 percent in the

first period (1978-85). In this period, rice and corn

acreage fell 1.0 and 1.7 percent, while wheat and soybean acreage rose slightly (table 5). National sown

area statistics indicate that the reductions in land use

were due to changes in cropping intensity, as farmers

moved from triple- and double-cropping to double- or

even single-cropping (Weins, 1982). During 1978-85,

total sown area for all crops fell by 0.63 percent,

implying that grain area was not simply shifting to

nongrain production (table 6).

Although labor-day statistics in the first period (1978-

85) may be suspect, labor efficiency certainly rose due

to the reforms. Before 1978, individual peasant

income in China was calculated according to time

spent in the collective field without reference to production outcome. This system strongly encouraged

peasants to participate in collective work assignments

but to put little effort into their actual work (or chu

gong bu chu li in Chinese). The HRS encourages

peasants to efficiently use labor time as incomes are

determined solely by production output.

Labor’s contribution to production growth differed significantly depending on whether it was based on laborday (or time spent) of peasants or the number of persons engaged in grain production. For example, in

Fan and Pardey (1997), with increasing numbers of

laborers, the contribution of labor to production

growth was 5.6 percent in 1979-84. However, when

the labor contribution to growth is based on the labor

days of peasants, as in our study, time spent on grain

production declined 9 percent annually and the contribution of labor to growth was negative. Given that

most farmers in China allocate their time among many

different crops and livestock, as well as to nonagricultural work, we believe that time spent on grain production is the most accurate measure of labor’s contribution to growth in grain production.

In the second period (1986-94), area sown to grains

was quite stable (with a 0.36-percent annual increase)

and nearly identical to the change in total sown area

(table 6). Land sown to rice and wheat fell slightly

(table 5), while land sown to corn and soybeans both

rose by 2 percent. Labor used in production of the

four grains fell 0.83 percent per year over this period

(though rising slightly in production of corn and soybeans). This is not consistent with national statistics

that show the number of agricultural laborers rising by

TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 7

Table 4—Input contribution to grain production growth

Years Total input contribution Growth rate of

Land use Labor use Intermediates/

capital use

Percent ------------------------- Annual average percent -----------------------

1978-97 27 0.34 -3.58 4.35

1978-85 -36 -0.64 -9.08 4.03

1986-94 81 0.36 -1.44 4.35

1995-97 84 2.62 3.58 5.14

Note: A negative share implies input use fell and the growth rate of TFP was greater than the growth rate of output.

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
0.8 percent during 1986-94. However, in the national

statistics, agricultural laborers were classified by their

main production activities. That is, those engaged primarily (more than 50 percent of labor time) in agriculture were counted as agricultural laborers. Our conclusion, based on household survey data, is that peasants working primarily in agriculture spent less time

on grain production than before.

In the final period (1995-97), land and labor returned

to grain production, rising by 2.6 and 3.6 percent

annually (table 6). Compared with the increase of 1.3

percent in total area sown, grains successfully competed for additional land at the expense of other crops.

Moreover, 1995-97 was the only period in which

changes in land and labor use move in the same direction (rising) across the four grain crops. These observations all suggest that the market and policy environment during this period favored grain production.

While increased input use contributed more than 80

percent of the growth in grain output in the second and

third periods (table 4), different inputs played different

roles in that growth. In 1986-94, increased intermediate input/capital use was nearly the sole source of

growth, while in 1995-97, increased labor and land use

together contributed more than intermediate

inputs/capital to grain output growth.

Structural Change in Input Use—

Rise in Capital/Land Ratio and 

Fall in Labor/Land Ratio

With rapid economic development, China’s technological change should move toward less use of labor (as

wages increase) and more use of intermediate inputs

and capital. We calculated the ratios of labor and land

as well as intermediates/capital and land in order to

examine whether or not technological change affected

the structure of input use. The ratio of intermediates/

capital over land rose by 110 percent for total grain

production over the last two decades. That is to say, if

the ratio in 1978 is normalized to be 100, then by 1997

the ratio is more than 200, with an annual increase of

4 percent (table 7). On the other hand, the ratio of

labor over land for total grain production fell by more

than 50 percent, with an annual decline of 4 percent.

The results suggest both a substitution effect and a

bias in the direction of technological change, which is

labor-saving and capital/intermediate input-using. The

8 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

Table 5—Growth in output and input use in grain

production

Crop/ Output Land Labor Intermediate/

year use use capital use

Annual average percent

Rice:

1978-97 2.03 -0.42 -4.04 2.72

1978-85 3.01 -1.01 -8.56 1.66

1986-94 0.48 -0.68 -2.46 4.27

1995-97 4.49 1.73 2.26 0.60

Wheat:

1978-97 4.46 0.16 -4.29 5.76

1978-85 6.89 0.02 -9.25 7.22

1986-94 1.64 -0.09 -2.20 3.91

1995-97 7.48 1.22 1.58 8.01

Corn:

1978-97 3.33 0.92 -2.58 5.26

1978-85 1.90 -1.71 -10.37 3.38

1986-94 5.03 2.00 0.81 6.33

1995-97 1.66 3.97 6.81 6.47

Soybeans:

1978-97 4.89 2.38 -1.24 6.41

1978-85 4.79 1.11 -7.82 9.35

1986-94 4.50 2.00 1.39 0.96

1995-97 6.33 6.58 7.19 16.90

Source: Calculated by ERS from China Statistical Yearbook (1978-

98), China Rural Statistical Yearbook (1985-98), and China

Commerce Yearbook (1987-97) data.

Table 6—Growth in agricultural labor use and sown area

Years Total sown acreage Land in 4 grains Agricultural labor Labor in 4 grains

Annual average percent

1978-97 0.14 0.34 0.87 -3.57

1978-85 -0.63 -0.64 1.43 -9.08

1986-94 0.35 0.36 0.83 -1.44

1995-97 1.27 2.62 -0.26 3.58

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
substitution effect reflects the change in the opportunity cost of labor relative to that of capital and intermediate inputs. Whether technological bias is increasing

the marginal productivity of capital/intermediate inputs

more than that of labor is less certain. The rise in the

opportunity cost of labor and the tendency to pull

labor from the grains sector reflects the growing efficiency in the economy outside of primary agriculture. 

In the subperiods 1978-85 and 1986-97, the

capital/land ratio rose by 38 and 53 percent, with

annual increases of 4.7 and 3.6 percent, respectively.

The labor/land ratio fell by 46 percent in 1978-85 and

13 percent in 1986-97, or 8.5 percent and 1.1 percent

per year (table 7). We performed statistical tests to

formally identify structural change in input use during

the 1978-97 period. The Van der Waerden (VW), the

Kolmogorov-Smirnov (KS), and the Wilcoxon score

tests are applied to both the labor/land and capital/land

data series. The results indicate that the location

parameters of labor and capital use per unit of land are

statistically significant across the two time periods,

1978-85 and 1986-97. The calculated chi-square values for the VW, KS, and Wilcoxon tests are 12.62,

2.19, and 13.71. All three results suggest that there is

structural change between these two subperiods. 

We argue that the current land tenure system is a

major constraint to additional declines in labor use per

unit of land. In fact, the growth rate of labor use in

grain production actually rose almost 3.6 percent in

the 1995-97 period. Under the current system, agricultural land cannot be sold, and in many areas cannot

even be rented legally. Incomplete property rights

mean farmers are reluctant to invest in land improvements (Perkins, 1994, pp. 28-29). Moreover, this system constrains the ability of farms to increase in size

and limits the adoption of labor-saving technology.

A more extensive study is needed to explain why the

decline in the labor/land ratio in grain production

slowed during the last decade. However, if the land

tenure system is unchanged, it will likely continue to

constrain the development and expansion of labor-saving technology in China’s grain sector. With increases

in wages and the opportunity costs of grain production, China’s competitiveness in world grain markets

may well weaken.

In summary, increased input use has driven growth in

China’s grain sector during the last decade. Increased

use of intermediate inputs and capital was the largest

contributor to growth in the grain sector. The recent

policy bias toward grain production has stimulated

input use rather than productivity growth in the grain

sector. With a slowdown in the decline of labor use

per unit of land, the cost of grain production will continue to rise, which will either curb China’s competitiveness in world grain markets or else restrict further

growth in grain production.

Analyzing Supply Response in the

Grain Sector

In the previous section, we applied a growth accounting analysis to capture the sources of growth due to

technological change, differences in production efficiency, or, in general, growth in outputs not accounted

for by growth in inputs. In this section, we attempt to

capture the product supply and input demand responsiveness of grain producers that result from changes in

prices induced primarily by shifts in government policies. This econometric approach endeavors to estimate the sources of input and output growth associated

with producers’ response to changes in price regimes.

TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 9

Table 7—Change in ratio of labor use and intermediate/capital use per unit of land

Labor/land Intermediates and capital/land

Years Total change Annual change Total change Annual change

Percent

1978-97 -53.1 -4.0 110.6 4.0

1978-851 -46.3 -8.5 37.9 4.7

1986-972 -12.6 -1.1 52.7 3.6

1 The ratio in 1977 was set at 100. 2 The ratio in 1985 was set at 100.

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
In the growth accounting analysis, we did not need to

address functional representation of the underlying

technology or structural assumptions—such as separability in output prices and nonjointness in inputs—that

are associated with multiproduct technology in agricultural supply response studies. In this section, we

capture all information relevant to China’s grain production structure using a restricted profit function, and

empirically estimate grain production technology. We

also measure the substitution and expansion (scale)

effects for both inputs and outputs and conduct tests to

determine whether the data are consistent with separable and nonjoint technology.

The parametrical approach attempts to capture the

adjustments of output and input utilization choices in

China’s grain sector due to changes in prices mainly

induced by adoption of the HRS and the expansion of

the rural free market system. We estimate a coherent

set of interrelated supply functions and input demand

functions for the four grains, approximate the technology within a multiproduct framework by a restricted

profit function, and test various production decisions.

The input indexes constructed for each grain subsector

and for the aggregate sector are consistent with the

economic principles linking aggregate and individual

sector accounts without compromising the economic

integrity of the accounts. The data used for the parametric analysis are the same as used in the previous

section (see appendix tables 1-2). The supply

response analysis helps to identify the economic

behavior of farmers and their responsiveness within

the properties of the multiproduct production specification. Together, the growth accounting and supply

response approaches provide complementary perspectives on China’s agricultural growth potential.

Separability of technology assumes that prices within

a group satisfy the requirements for consistent aggregation, in our case, into a total grain sector (Berndt

and Wood, 1975; Lau, 1978). That is, with separability, the prices of each individual grain subsector do not

depend on prices of other production activities outside

the grain sector as a whole. The grain subsectors can

be combined into an aggregate grain sector, the price

of which is then a function of output and input production activities (outside the grain sector) that always

move in fixed proportions. On the other hand, the

rejection of separability in output prices implies that

the individual grain subsectors cannot consistently be

aggregated across the system into one aggregate grain

sector and the production of grains in this case can be

characterized by a nonseparable technology.1

Nonjointness in inputs means that when a commodity

is produced by a nonjoint technology, decisions about

its production are independent of decisions about the

other products in the group (Lau, 1978). Nonjointness

in inputs implies that the supply of a grain subsector

can be examined without regard to other product

prices and that the level of each output is independent

of the prices of competing outputs. This is a necessary

implicit assumption made for single-commodity production studies. A single-output approach does not

allow measurement of the interdependencies among

outputs and the differential effects of various outputs

on factor demand. The rejection of nonjointness in

inputs of the grain subsectors, which is explored later

in the study, implies that important interactions are

present in the production of all grains in China.

Because of the potential interactions among grains

induced by China’s rural economic reforms, and the

potential for nonjointness in particular, we estimated

output supply and input demand elasticities by a system of equations derived from a restricted profit-maximization specification. In China, a season of winter

wheat is often followed by a season of late rice in the

south, while winter wheat is followed by a season of

summer corn in the north. Soybeans are often intercropped with other crops in both the south and the

north. These multiproduct farming systems within

households strongly suggest that the production supply

response of each grain cannot be estimated independently and, thus, that the system is likely to be characterized by joint production. This means that the production level of each grain can be affected by the

prices of other competing grains. 

The grain sector’s technology is assumed to relate two

variable inputs (labor and intermediates/ capital), a

fixed input (land), and four outputs (the four grains).

Let Y = (Y1,...,Y6) be the vector of output and variable inputs. When Yi > 0, i=1,...,4, it represents an

output, when Yi < 0, i=5,6, it represents a variable

input, and X is a fixed input (land). In addition, tech10 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

1 In the growth accounting analysis, we constructed individual

indexes for total outputs and inputs and for each grain subsector.

The estimates of productivity growth do not require that outputs

and inputs be separable (see Ball, 1985).
nology is assumed to exhibit constant returns to scale.

Let P = (P1,...,P6) denote a vector of prices for outputs and inputs. Then the restricted profit, p(P;X), is

approximated by the translog function with arguments,

P and t, where t indexes the time:

with the following restrictions:

Using Hotelling’s lemma,

on equation 6 yields the share equations (Si) that are

linear in normalized prices:

Equation 7, representing the maintained or unconstrained model, is used to estimate product supply and

input demand relations and to test the hypotheses of

nonjoint and separable technology. The input data

used to estimate equation 7 can be found in appendix

table A3. In estimating equation 7, we used a maximum-likelihood approach, taking into account convexity restrictions, meaning that the Hessian matrix of the

restricted-profit function is positive semidefinite (Lau,

1978; Ball, 1988). The imposition of the constraints

does not affect the Cramer-Rao lower bound for the

variance of the estimator (Rothenberg, 1974). The

system is estimated using the General Algebraic

Modeling System. 

Product Supply and Input Demand

Estimates of the parameters of the six supply-demand

equations are reported in appendix table A4. The estimated parameters provide new evidence about the

structure of production on China’s grain farms during

1978-97. All own-price coefficients have the expected

sign, which is consistent with expectations, and all are

significantly different from zero at the 5-percent level.

In other words, the output supply and factor demand

equations possess properties that are consistent with

underlying profit maximation theory. The parameter

estimates satisfy the homogeneity and symmetry conditions and the estimated model was constrained to

give a positive semidefinite Hessian matrix in prices.

This implies that the output-supply equations have the

expected own-price elasticity signs. 

Consistent with the disaggregated estimates, rice and

wheat are product substitutes with each other, and with

corn and soybeans, since the estimated parameters are

negative (see appendix table A4). On the other hand,

the estimated positive parameters for corn and soybeans indicate a complementary or joint production

technology. 

After we incorporated into the maintained model the

appropriate restrictions that are associated with weak

separability in output prices, we estimated this restricted model. Then, we performed a maximum likelihood

test for the restricted and the maintained model based

on the hypothesis that the production technology

exhibits weak separability in output prices. Our test

rejected the null hypothesis at the 1-percent level of

statistical significance (see appendix table A5), meaning that the system is nonseparable. This implies that

we cannot aggregate the grain subsectors into one grain

sector. Also, a third model was specified and estimated

to account for nonjointness in inputs by incorporating

the appropriate restrictions into the maintained model.

When we performed the maximum-likelihood test comparing the nonjointed technology model with the maintained model, we rejected the hypothesis that the technology of grains exhibits nonjointness in inputs at the

1-percent level of statistical significance (see appendix

table A5). Thus, we conclude that China’s grain system follows a joint technology. 

The results of these tests on the structure of grain technology are consistent with the observation that multiple production activities are the dominant cropping

style in China, where the production or input requirements relate multiple outputs and multiple inputs.

This also implies that the supply response of each

grain should not be estimated independently as the

production of grains is interrelated and depends upon

the decisions regarding other grains. In short, aggreTB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 11

ln ln ln ln

ln ln ln

π α α β

ρ γ θ

= + + ⋅

+ + ⋅+ +

= = =

= =

0

1

6

1

2

1

6

1

6

1

6

1

6

1

2

2

t

i i

i j

ij i j

i

i i

i

i i

P P P

P X P t t t

Σ ΣΣ

Σ Σ Θ

β β α β ρ γ ij ji

i

i

i

ij

i

i

i

i

= = = = =

= = = =

; ;

1

6

1

6

1

6

1

6

Σ 1 Σ Σ Σ 0

∂ π

∂ π

ln

lnP

PY

S

i

i i

i

= =

S P X t i

i i

j

ij j i i

= + + + =

=

α β ρ γ

1

6

Σ ln ln , 1,...,6

(6)

(7)
gation across grain subsectors is not consistent with

the structure of grain technology in China.

Estimates of Supply 

and Demand Elasticities

The elasticities of output supply and input demand

from the maintained model are presented in table 8.

These elasticities are derived from the estimated equations that satisfy the homogeneity, symmetry, and convexity restrictions. In most studies, the calculated

elasticities are obtained by evaluating the parameter

estimates either at the sample mean values of the variable involved or at the point of approximation of the

functional form. In this study, we computed the elasticities using the average factor shares for two periods,

1978-85 and 1986-97.

An important advantage in estimating a multiproduct

production system is that no endogenous variables

need to be used as explanatory variables in estimating

it. That is, the profit function approach (in contrast

with a cost or revenue function approach) avoids

inconsistencies in the econometric estimates due to

simultaneous equation problems in the variables

involved. However, the profit function provides the

gross elasticities (Marshallian elasticities) but not the

structural input substitution (along an isoquant) and

output expansion trade-offs (along a production possibility frontier). In this study, we separated substitution

and expansion effects (see Sakai, 1974; and Nagatani,

1973, for discussions of substitution and expansion

effects in production theory), and followed Lopez

(1984) to obtain the same information from the profit

function as from direct approaches like the cost function or revenue function specifications. A change in

an output price causes the relative prices of the four

grains to change. This induces technical substitution

among the grains produced along the existing isoquant

frontier (the substitution effect). Furthermore, relative

price changes induce changes in input demand. With

increases in the use of all variable inputs, output will

change along the new expansion path by shifting the

production possibility frontier outward (the expansion

or scale effect). The substitution effect usually causes

output of other grains to decline when the price for a

specific grain rises and hence output of this grain

increases. The expansion effect, on the other hand,

can result in increases in all grains’ output.

The estimated results confirm the hypotheses that

grain outputs are jointly produced and that inputs are

jointly employed (table 8; columns 1-4 and rows 1-4,

the supply elasticities with respect to the prices of outputs; columns 5-6 and rows 1-4, the supply elasticities

with respect to the prices of inputs; columns 1-4 and

12 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

Table 8—Output supply and input demand elasticities

Price of

Commodity Rice (1) Wheat (2) Corn (3) Soybean (4) Labor (5) Intermediates

and capital (6)

Part 1, 1978-85:

(1) Rice 1.48 0.96 0.46 0.04 -1.51 -1.43

(2) Wheat 1.75 0.96 0.40 0.02 -1.69 -1.45

(3) Corn 1.29 0.61 1.03 0.46 -0.84 -2.55

(4) Soybean 0.32 0.10 1.29 3.72 -0.79 -4.65

(5) Labor 2.30 1.41 0.46 0.15 -2.53 -1.79

(6) Intermediates and capital 1.61 0.89 1.01 0.65 -1.32 -2.85

Part 2, 1986-97:

(1) Rice 1.01 0.58 0.20 -0.03 -1.09 -0.67

(2) Wheat 0.98 0.96 0.15 -0.03 -1.32 -0.74

(3) Corn 0.47 0.21 1.17 0.53 -0.25 -2.12

(4) Soybean -0.14 -0.09 1.11 3.25 -0.37 -3.77

(5) Labor 1.76 1.25 0.18 0.12 -2.13 -1.18

(6) Intermediates and capital 0.74 0.48 1.01 0.86 -0.81 -2.29

Source: Estimated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
rows 5-6, the demand elasticities with respect to the

prices of outputs; and columns 5-6 and rows 5-6, the

demand elasticities with respect to the prices of

inputs). Input usage varies across grain subsectors.

The labor and capital elasticities (table 8, columns 1-4

and rows 5-6) capture the effects of output prices on

the marginal cost of each grain subsector’s production.

As the elasticities for rice and wheat are very elastic,

any increase in labor or capital cost would affect the

production of these crops much more than the production of the other grains. The supply elasticity of each

grain’s production to its own price is generally greater

than unity and more elastic than to prices for the other

grains, which implies the dominance of own effects

over cross effects (Sakai, 1974). Own-price elasticities range from 0.96 for wheat to 3.72 for soybeans in

the period 1978-85 (table 8, part 1, columns 1-4 and

rows 1-4). Since the own-price elasticities of soybeans, corn, and rice are greater than unity, while that

of wheat is less than unity, the supply response and

consequently planting options for soybeans, corn, and

rice producers are greater than for wheat producers.

The cross-elasticities are positive, suggesting that an

increase in the output price for a particular grain

would result in increased production of all grains’ outputs (the scale effect). However, the cross-elasticities

for all grains are not price elastic. 

When the 1986-97 period is compared with 1978-85,

the own-price elasticity of rice and soybeans decreased

by 32 and 13 percent respectively, the own price elasticity of corn increased by 3 percent, and the own

price elasticity for wheat remained the same. Also

during the latter period, a competitive relationship is

observed for soybeans and rice as well as soybeans

and wheat (the substitution effect). In general, the

magnitude of all elasticities, own and cross, decreased

in this period, implying both smaller substitution and

scale effects. This might have been the consequence

of changes in relative agricultural prices as prices for

non-grain crops started to increase relative to that of

grains. This finding complements the conclusions of

the growth accounting analysis, which indicated that

growth rate of aggregate output slowed significantly

during 1986-97, especially for rice and wheat.

The large magnitude of the labor and intermediates/

capital elasticities in the 1978-85 period indicates that

the increase in input usage for rice and wheat production might have resulted in the shift of the product

transformation frontier outwards (the expansion effect,

see table 8, part 1, columns 1-4 and rows 5-6). This

shift consequently allowed increased production in all

outputs (gross complementarity of outputs) in this

period. During the 1986-97 period, however, the labor

and intermediates/capital elasticities declined compared with that of the early period (table 8, part 2,

columns 1-4 and rows 5-6). The results in table 8

indicate that the rice and wheat subsectors are more

sensitive to increases in labor costs than the corn and

soybean subsectors, since their responses to change in

wages are very elastic (1.76 and 1.25 respectively).

This also implies that as the opportunities for farm

labor employment in the non-agricultural sector

increase, due to the overall economic development of

China, the cost of rice and wheat production might

also increase considerably. On the other hand, the

elasticity of capital in soybean production increased

compared with that in the early period by 32 percent,

implying that the soybean subsector became more sensitive to the cost of intermediate inputs and capital.

The supply response elasticities to factor prices are

negative and elastic during the first period (table 8,

columns 5-6 and rows 1-4), as are the own-price elasticities of the input demand functions (table 8,

columns 5-6 and rows 5-6). This is consistent with

economic theory, which indicates that the marginal

revenue of an input increases when quantities of other

inputs increase or when output prices increase (Sakai,

1974). During the 1986-97 period, however, the own

elasticities of labor and intermediate inputs slightly

declined. At the same time, labor demand became

more inelastic to changes in corn and soybean prices

while it remained elastic to changes in rice and wheat

prices. Regarding intermediate inputs/capital, the

changes are more drastic as the demand became

inelastic with respect to rice and wheat production

while remaining elastic to corn and soybean prices.

This result might shed some light regarding the structural changes in input usage and input intensity, as

indicated by the growth accounting method. However,

further indepth analysis is needed on this issue in order

to make concrete inferences.

The estimates of the factor demand elasticities suggest

that returns to labor and intermediates/capital may

decrease quite drastically as a consequence of increasing wages or intermediate/capital costs. Moreover, an

increase in the price of intermediates/capital or wages

TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 13
would not only constrain the expansion of outputs, it

would also change the composition of outputs because

the elasticities are different across commodities. For

example, an increase in wages would affect rice and

wheat production more than corn and soybeans. On

the other hand, changes in intermediate/capital costs

would affect mostly corn and soybean production.

This reinforces the findings of the role of intermediate

inputs and capital in the growth accounting analysis—

that increased input use has been the driving force

behind China’s growth in grain production. 

In sum, complementarity prevails among the inputs

used and the outputs produced and an increase in the

price of intermediates/capital or wages would result in

absolute reductions in all outputs as well as changes in

the composition of outputs as the elasticities are different across commodities. In other words, higher labor

costs would affect rice and wheat costs of production

significantly, with a smaller effect on corn and soybean production. On the other hand, higher intermediate inputs/capital costs would affect corn and soybean

production significantly, with a smaller effect on rice

and wheat production. This indicates the dependency

of grain production on labor and intermediate

inputs/capital costs.

Technological Change

The estimated coefficient of time trend, t, captures the

systematic bias in technological change (also called

the constant rate of bias) for the estimation period

(appendix table A4). The negative t for rice, corn, and

soybeans implies that the growth rate of technological

change for these three grains is below the average

growth rate in the grain sector, while the opposite is

true for wheat with a positive t. This is consistent

with the finding from the growth accounting analysis

that wheat, on average, had a higher annual TFP

growth rate than the other grains (table 2).

The sign of t for inputs (labor and capital) is positive,

and the value for capital is greater than that for labor

(table A4). This implies the presence of a positive

technological change and that such change is more

capital biased. This result supports the finding that

technological change resulted in higher

intermediates/capital use per unit of land.

Summary and Conclusions

We used a growth accounting method to analyze the

sources of output growth in rice, wheat, corn and soybeans, the four most important crops in China’s grain

sector, during 1978-97. We found TFP contributed

greatly to growth in grain production in the period

immediately after China’s rural economic reform

(1978-85). Most of this growth was from efficiency

gains due to institutional reforms. After 1985, when

efficiency gains had diminished, the growth rate of

TFP fell sharply and contributed less than 20 percent

to growth in grain production. In recent years (1995-

97), increased use of inputs, especially intermediates/

capital, became major sources of growth in China’s

grain sector. These results imply that recent growth in

grain output may fall as wages and prices for intermediate inputs increase. The gap between TFP growth in

China’s grain sector and TFP growth in developed

countries suggests that China can improve its grain

production technology if the economic and policy

environment encourages investment in agricultural

research and development, water control systems, and

land infrastructure.

The changes in the labor-land ratio and the intermediates/capital-land ratio over the last two decades in

China are consistent with economic theory. That is,

with economic growth, technological change induces

less use of labor and more use of capital. However,

the decline in China’s labor-land ratio slowed significantly in recent years. The current land tenure system

is a major factor constraining the development and

expansion of labor-saving technology in China’s grain

sector. Given that increased input use, including labor,

drove growth in China’s grain sector in recent years,

grain production will become more and more costly as

wages and opportunity costs rise. This will likely constrain growth in China’s grain production and weaken

its competitiveness in world grain markets.

We also estimated the supply response for the four

grains using a multiproduct framework. The parametric approach confirms the hypothesis of a nonseparable

and joint production system in China’s grain sector. 

We separated substitution and expansion effects to

obtain the same information from the restricted profit

function as from direct approaches. We accounted for

both the substitution and the expansion effects. The

14 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884
expansion effects subsided in the more recent period,

implying a relatively slow outward shift in the production frontier. This result indirectly supports our finding from the growth accounting analysis that TFP grew

much more slowly in recent years than immediately

following rural economic reform. 

Own-price elasticities of soybeans, corn, and rice are

greater than unity while wheat’s elasticity is less than

unity. Hence, the supply response and consequently

planting options for soybean, corn, and rice producers

are greater than for wheat producers. Since the input

use elasticities for rice and wheat are very elastic, any

increase in labor or capital costs will affect the supply

of these crops much more than the production of other

grains. 

The own- and cross-supply elasticities decreased in the

1986-97 period. This finding supports the conclusions

of the growth accounting analysis, which indicates that

growth in TFP slowed significantly during 1986-97,

especially for rice and wheat. As labor and intermediates/capital elasticities declined in this period compared with the early period, changes in input use

would not affect the composition of grain production

as much as in the first period. Rice and wheat production would be affected more by increases in wages

than corn and soybean production, while soybean production is overall most sensitive to the costs of intermediate inputs and capital. In general, complementarity prevails among the inputs used and the outputs produced. In addition, an increase in the price of intermediates/capital or wages would result in absolute reductions in all outputs as well as changes in the composition of outputs. 

The input demand functions are price elastic, which

has important implications for grain production. The

estimates suggest that returns to labor and intermediates/capital may decrease quite drastically as a consequence of increasing wages or intermediate/capital

costs. Changes in intermediate/capital costs would

affect mostly corn and soybean production while

changes in wages would affect mostly rice and wheat

production. This reinforces the findings of the role of

intermediate inputs and capital in the growth accounting analysis and implies that the cost of intermediate

inputs and capital as well as wages might become very

important in determining the grain sector’s production

dynamics. 

Future work in identifying and measuring the sources

of growth in China’s agriculture should focus on disaggregating to regions (or even provinces). National

analysis may obscure some important regional differences in productivity because of differences in economic development and agricultural practices across

regions. In addition, future work should quantify the

sources of TFP growth in China’s grain sector.

Sustainable TFP growth is a key factor in maintaining

growth in China’s agriculture. Although we point out

that TFP growth slowed after 1985 due to the diminishing effect of institutional changes, a parametric

approach is needed to quantitatively analyze the different sources of TFP growth.

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Appendix

Formulas used to calculate supply and input demand

elasticities:

Elasticity with respect to own price:

Elasticity with respect to prices of other commodities/

factors of production:

Values of bij are displayed in appendix table 1, while

Siis the dependent variable in equation 7. The average shares of Si

for the two periods 1978-85 and 1986-

97 are used in computing the elasticities.

16 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

η

β

ii

ii

i

i

S

= + S −1

η

β

ij

ij

i

j

S

= + S
TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 17

Appendix table 1—Quantity indices of grain outputs and inputs

Wheat Rice 

----------------Input------------------ -----------------Input-----------------

Output Land Labor Capital1 Output Land Labor Capital1

1978 0.795 1.067 1.883 0.845 0.598 0.904 1.202 0.454

1979 0.835 1.050 1.572 0.906 0.697 0.910 1.125 0.649

1980 0.812 1.050 1.541 0.911 0.613 0.894 1.066 0.657

1981 0.836 1.032 1.320 0.894 0.662 0.877 0.904 0.663

1982 0.938 1.025 1.184 0.886 0.760 0.866 0.756 0.673

1983 0.981 0.900 1.009 0.848 0.904 0.900 0.676 0.733

1984 1.035 0.917 0.962 0.891 0.975 0.917 0.670 0.738

1985 0.979 0.994 1.007 0.948 0.953 0.906 0.609 0.739

1986 1.000 1.000 1.000 1.000 1.000 0.918 0.583 0.792

1987 1.012 0.998 0.971 1.062 0.954 0.893 0.558 0.796

1988 0.982 0.991 0.970 1.181 0.949 0.892 0.556 0.794

1989 1.046 1.013 0.984 1.286 1.009 0.925 0.580 0.793

1990 1.099 1.025 0.979 1.386 1.091 0.953 0.618 0.873

1991 1.067 1.010 0.938 1.360 1.066 0.959 0.575 0.988

1992 1.081 0.995 0.888 1.212 1.128 0.945 0.533 1.033

1993 1.031 0.941 0.836 1.119 1.182 0.937 0.564 1.003

1994 1.022 0.935 0.805 1.381 1.103 0.898 0.499 1.043

1995 1.076 0.953 0.839 1.357 1.135 0.894 0.525 0.991

1996 1.133 0.973 0.856 1.376 1.228 0.918 0.527 1.154

1997 1.166 0.984 0.861 1.406 1.369 0.932 0.523 1.314

Corn Soybean

------------------Input------------------- -----------------Input------------------

Output Land Labor Capital Output Land Labor Capital

1978 0.790 0.619 0.891 0.302 0.652 0.221 0.228 0.076

1979 0.847 0.625 0.735 0.398 0.642 0.225 0.198 0.079

1980 0.883 0.631 0.727 0.405 0.684 0.223 0.196 0.084

1981 0.836 0.602 0.605 0.386 0.803 0.249 0.184 0.102

1982 0.855 0.575 0.503 0.381 0.778 0.261 0.165 0.113

1983 0.963 0.583 0.495 0.404 0.840 0.235 0.140 0.110

1984 1.036 0.575 0.448 0.411 0.835 0.226 0.126 0.101

1985 0.901 0.548 0.414 0.381 0.904 0.239 0.129 0.142

1986 1.000 0.593 0.447 0.438 1.000 0.257 0.137 0.118

1987 1.118 0.626 0.472 0.506 1.049 0.262 0.141 0.121

1988 1.092 0.610 0.467 0.499 0.999 0.252 0.135 0.125

1989 1.114 0.631 0.491 0.547 0.881 0.249 0.131 0.124

1990 1.366 0.663 0.501 0.614 0.947 0.234 0.131 0.118

1991 1.394 0.669 0.452 0.609 0.836 0.218 0.105 0.105

1992 1.346 0.652 0.493 0.589 0.887 0.224 0.111 0.113

1993 1.449 0.641 0.453 0.545 1.317 0.293 0.152 0.162

1994 1.401 0.656 0.445 0.662 1.343 0.286 0.146 0.155

1995 1.580 0.706 0.525 0.749 1.162 0.252 0.124 0.139

1996 1.799 0.759 0.562 0.814 1.542 0.327 0.174 0.208

1997 1.472 0.737 0.542 0.799 1.615 0.346 0.179 0.247

1 Intermediates/capital and the same for the following tables.

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
18 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

Appendix table 2—Price indices of grain outputs and inputs

Output Input 

Wheat Rice Corn Soybean Land Labor Capital

1978 0.562 0.560 0.504 0.327 0.074 0.323 0.804

1979 0.732 0.734 0.655 0.405 0.194 0.533 0.808

1980 0.790 0.791 0.706 0.448 0.249 0.533 0.812

1981 0.831 0.833 0.743 0.707 0.371 0.667 0.826

1982 0.837 0.864 0.771 0.735 0.663 0.667 0.841

1983 0.922 0.952 0.849 0.809 1.121 0.667 0.867

1984 0.922 0.958 0.849 0.809 1.000 1.000 0.944

1985 0.941 0.959 0.865 0.832 0.801 1.000 0.989

1986 1.000 1.000 1.000 1.000 1.000 1.000 1.000

1987 1.132 1.034 1.041 1.034 0.820 1.333 1.070

1988 1.357 1.191 1.090 1.128 0.799 1.467 1.243

1989 1.773 1.452 1.436 1.385 1.103 1.733 1.478

1990 1.642 1.336 1.402 1.363 0.870 1.833 1.560

1991 1.574 1.259 1.236 1.361 0.456 1.933 1.605

1992 1.533 1.386 1.338 1.626 0.741 2.267 1.664

1993 1.911 1.460 1.595 1.995 0.952 2.733 1.899

1994 2.942 2.222 2.413 2.286 1.510 3.967 2.309

1995 3.554 2.958 3.400 2.585 2.609 4.860 2.942

1996 3.703 3.230 3.244 3.314 2.358 6.467 3.189

1997 3.266 2.875 3.056 3.318 1.263 6.467 3.173

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
TB-1884 • Economic Research Service/USDA Cross-Commodity Analysis of China’s Grain Sector • 19

Appendix table 3—Share of value of output value of each grain and input value 

in total grain net revenue (SjSj = 1)

Year Rice (S1) Wheat(S2) Corn(S3) Soybean(S4) Labor(S5) Capital(S6)

1978 4.856 2.419 1.700 0.499 -3.470 -5.005

1979 2.914 1.622 1.041 0.268 -2.169 -2.676

1980 2.451 1.233 0.937 0.252 -1.691 -2.183

1981 1.837 0.971 0.646 0.324 -1.249 -1.528

1982 1.297 0.722 0.428 0.204 -0.675 -0.976

1983 0.941 0.596 0.335 0.153 -0.378 -0.646

1984 1.112 0.724 0.403 0.170 -0.604 -0.805

1985 1.239 0.818 0.413 0.218 -0.683 -1.006

1986 1.039 0.691 0.409 0.224 -0.529 -0.834

1987 1.254 0.719 0.502 0.256 -0.735 -0.996

1988 1.393 0.787 0.490 0.254 -0.768 -1.156

1989 1.287 0.676 0.437 0.183 -0.618 -0.965

1990 1.442 0.775 0.602 0.223 -0.768 -1.274

1991 1.921 1.021 0.776 0.281 -1.076 -1.922

1992 1.724 1.082 0.737 0.324 -1.123 -1.745

1993 1.341 0.782 0.620 0.386 -0.878 -1.252

1994 1.305 0.708 0.578 0.288 -0.767 -1.111

1995 1.171 0.685 0.648 0.199 -0.705 -0.998

1996 1.337 0.841 0.732 0.351 -1.027 -1.235

1997 1.844 1.268 0.857 0.560 -1.550 -1.979

Source: Calculated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.
20 • Cross-Commodity Analysis of China’s Grain Sector Economic Research Service/USDA • TB-1884

Appendix table 4—Parameter estimates for the translog restricted profit function

Parameter Estimated Parameter Estimated Parameter Estimated

value value value 

(Standard error) (Standard error) (Standard error)

α1 1.491 β23 -0.388 β66 -0.001

(0.161) (0.792) (0.436)

α2 1.118 β24 -0.271 τ1 -0.039

(0.083) (0.228) (0.295)

α3 0.840 β25 -0.367 τ2 0.006

(0.062) (0.382) (0.234)

α4 0.948 β26 0.458 τ3 -0.047

(0.421) (0.281) (0.026)

α5 -1.079 β33 0.956 τ4 -0.056

(0.161) (0.598) (0.019)

α6 -2.318 β34 0.145 τ5 0.028

(0.539) (0.172) (0.032)

β11 0.836 β35 0.384 τ6 0.108

(1.128) (0.289) (0.031)

β12 -0.371 β36 -0.511

(0.577) (0.211)

β13 -0.586 β44 1.164

(0.436) (0.116)

β14 -0.459 β45 0.150

(0.446) (0.194)

β15 -0.295 β46 -0.729

(0.747) (0.142)

β16 0.875 β55 0.220

(0.548) (0.741)

β22 0.939 β56 -0.092

(0.839) (0.543)

Note: 1 is rice, 2 wheat, 3 corn, 4 soybean, 5 labor, and 6 intermediates/capital.

Source: Estimated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.

Appendix table 5—Chi-square statistics for hypothesis tests

Hypothesis Calculated value Degree of freedom Critical value

0.05 0.01

Output separability 36.59 12 21.03 26.22

Input nonjointness 41.67 6 12.59 16.81

Source: Estimated by ERS from China Statistical Yearbook (1978-98), China Rural Statistical Yearbook (1985-98), and China Commerce

Yearbook (1987-97) data.