An Analysis of Food Demand in China: A Case Study of Urban ...agecon.okstate.edu/faculty/publications/3709.pdf · their per capita at-home consumption of foods of animal origin (meats,

Review of Agricultural Economics—Volume 31, Number 4—Pages 873–893

An Analysis of Food Demandin China: A Case Study of UrbanHouseholds in Jiangsu Province

Zhihao Zheng and Shida Rastegari Henneberry

China has had one of the world’s most rapidly developing economies for atleast the past two decades. Population growth, accompanied by recent eco-

nomic growth and rapid urbanization, has led to an increase in food demandand a considerable change in the composition of foods consumed in China. Ru-ral households (roughly 60% of China’s consumers) decreased their per capitaat-home consumption of food grains from 262 kg per person in 1990 to 219kg per person in 2004, a decrease of over 16%. At the same time, they raisedtheir per capita at-home consumption of foods of animal origin (meats, poultry,eggs, aquatic products, and dairy products), from 28 kg per person in 1990 to42 kg per person in 2004, an increase of 50%. Urban at-home consumption offoods has changed even more drastically. The per capita consumption of foodgrains declined by 40%, from 131 kg in 1990 to 78 kg in 2004; whereas per capitaconsumption of foods of animal origin increased by 78%, from 41 kg in 1990 to73 kg in 2004 [China’s National Bureau of Statistics (NBS), 1991–2005]. Consid-ering that China has over one-fifth of the world’s consumers and an economythat has grown at an average rate of 9–10% annually since 1978, this country’schanging food consumption patterns have the potential to significantly impactthe global magnitude and pattern of food demand. Research is needed to providea better understanding of China’s food buyer preferences and the potential formarketing food in China.

Several studies have been conducted on China’s household demand for food.However, these studies have not taken into account the more recent changes ineconomic structure in China, including the rapidly rising incomes during thepast decade. These past studies have used a variety of data, including aggregatetime-series data (Lewis and Andrews), aggregate city-level cross-sectional data(Wu, Li, and Samuel), pooled time-series and cross-sectional data at the provincial

� Zhihao Zheng is an associate professor in the College of Economics and Managementat China Agricultural University and a former postdoctoral research associate in theDepartment of Agricultural Economics at Oklahoma State University.� Shida R. Henneberry is a professor in the Department of Agricultural Economics,Oklahoma State University.

DOI: 10.1111/j.1467-9353.2009.01471.x

874 Review of Agricultural Economics

level (Fan, Wailes, and Cramer), and pooled time-series and cross-sectional dataat the county level (Zhang, Mount, and Boisvert). Some of the more recent studieshave used household survey data collected by NBS. The NBS household surveydata are considered superior compared to the available time-series data for re-search because they include detailed demographic characteristics that allow forthe assumption of heterogeneity in preferences across households. Additionally,the large sample size included in the NBS household survey data allows esti-mating a relatively large demand system. Among the published studies based onthe household survey data, Halbrendt et al. and Gao, Wailes, and Cramer focuson rural households in Guangdong and Jiangsu provinces, respectively. Zhangand Wang; and Yen, Fang, and Su concentrate on urban households in China in1998 and 2000, respectively. Liu and Chern; and Gould and Villarreal analyze thefood demand of urban families using the household survey data for Shandong,Jiangsu, Heilongjiang, Henan, and Guangdong provinces in 1997 and 2001, re-spectively. Some studies have taken advantage of the availability of householdsurvey data over the years and have analyzed food demand using the avail-able pooled time-series and cross-sectional data at the household level. Goulduses three consecutive years of NBS urban household survey data (1995–97) forJiangsu, Shandong, and Guangdong provinces to estimate a system of demandsfor food commodities; and Guo et al. use data for 1989, 1991, and 1993 from theChina Health and Nutrition Survey to examine food consumption behaviors ofboth urban and rural households across income levels.

This study goes beyond the previous studies in data use by utilizing a morerecent data set—the 2004 NBS urban household survey data. Furthermore, thisstudy uses a generalized almost ideal demand system (GAIDS) that “allows thedemand shifters to be included in a fashion that is flexible, parsimonious, andmaintains the model’s invariance to changes in units of measurement” (Alston,Chalfant, and Piggott, p. 77). The objective of this study is to estimate the impactsof economic factors (prices and expenditures) and noneconomic factors (demo-graphic variables) on urban household demand for ten broad food categories inJiangsu province. Jiangsu is one of China’s major provinces with its gross do-mestic product (GDP) accounting for more than 9% of China’s national GDP.Jiangsu’s urban per capita disposable income was ranked seventh among thirty-one provinces in the nation in 2004 (NBS 2005). Thus, a study of urban householdfood consumption patterns in this province may help in understanding China’snational demand and the factors that affect it.

China has undergone a massive urbanization during the twenty-first century,which has had a dramatic effect on its food demand (Hsu, Chern, and Gale).According to China’s official statistics, only 42% of China’s population lived incities and towns in 2004 (NBS 2005). This urban population share is expectedto grow to 50% by 2020 (Hsu, Chern, and Gale). Given that urban residents inChina have much higher per capita incomes compared to rural residents, urbanhouseholds have been the driving force behind the growth in food demand and theemerging demand for better quality foods in China.1 This changing food demandhas led to a significant increase in the number of supermarkets, conveniencestores, and food-away-from-home (FAFH) outlets that offer greater convenience,variety, and quality to consumers (Gale and Huang). By shedding light on China’scontemporary consumer preferences, the results of this study are expected to

An Analysis of Food Demand in China 875

be useful to policy makers and exporters in the United States and other foodexporting countries in developing effective trade policies and marketing strategiesfor trade with China. More specifically, the food demand elasticities provided bythis study may be used to analyze the impacts of trade policies on China’s economyand the world food markets.

The remainder of this study is organized as follows. A model of urban house-hold food demand in China is presented in the following section. The data andestimation procedures are then described. Results are presented next, followedby the summary and conclusions.

Model SpecificationThe almost ideal demand system (AIDS), developed by Deaton and Muell-

bauer, has been widely used in empirical demand studies due to its theoreticaladvantages (Henneberry, Piewthongngam, and Qiang). The AIDS specification,however, does not maintain the desired property of estimated economic effects(elasticities) being invariant to disproportionate changes in units of measure-ment when incorporating demand shifters in a traditional way (Alston, Chal-fant, and Piggott). One solution suggested by Alston, Chalfant, and Piggott isto adopt a generalized version of the AIDS, the GAIDS, first derived by Bollino.The GAIDS model includes precommitted quantities as parameters. These pre-committed quantities are independent of prices and expenditure. By augmentingthese precommitted quantities to be a function of demand shifters, the lack ofinvariance problem that may be present in the AIDS specification can be avoidedin the GAIDS model. Following Bollino, as well as Piggott and Marsh, the GAIDSmodel can be expressed in share form as

wi = pi ci

M+ M∗

M

[�i +

N∑j=1

�i j ln(p j ) + �i ln(

M∗

P

)], i, j = 1, . . . , N,(1)

where wi represents the budget share associated with the ith good; pi is the priceof the ith good; ci denotes the precommitted quantity of good i; M is the total ex-penditure exhausted in the system; M∗ = M − ∑N

i=1 pi ci denotes supernumeraryexpenditure, where

∑Ni=1 pi ci is the mathematical representation of the total pre-

committed expenditure; ln(P) = �0 + ∑Ni=1 �i + 0.5

∑Ni=1

∑Nj=1 �i j ln(pi ) ln(p j ) is

a nonlinear price index; and �i , �i j , and �i are parameters to be estimated.Demographic variables that affect consumption behavior are incorporated into

the model by allowing the precommitted quantities in equation (1), the c ′i s, to be

a function of these variables. That is

ci = ci0 +K∑

k=1

cikdk ,(2)

where k = 1, . . . , K is used to identify the demographic variables considered in thestudy, ci0 and c ′

iks are parameters to be estimated, and dk is the kth demographic


variable. Thus, the resulting share equations become

wi = pi

M

(ci0 +

K∑k=1

cikdk

)+ M∗

M

[�i +

N∑j=1

�i j ln(p j ) + �i ln(

M∗

P

)](3)

where

M∗ =[

M −N∑

i=1

(ci0 +

K∑k=1

cikdk

)pi

].

The properties from neoclassical demand theory can be imposed on modelequation (3) by restricting its parameters.

The adding-up restriction (∑

wi = 1) is given by

N∑i=1

�i = 1,N∑

i=1

�i j = 0, andN∑

i=1

�i = 0.(4a)

Homogeneity (wi unchanged by a proportional change of all prices and income)is imposed as

N∑j=1

�i j = 0 for any j.(4b)

Slutsky symmetry is given by

�i j = � j i for any i and j.(4c)

A major problem when estimating a complete demand system is the endo-geneity of expenditure term in the system (LaFrance). To control for the expen-diture endogeneity in the GAIDS model, a nonlinear full information maximumlikelihood (FIML) estimation procedure is used in this study. As pointed out byGreene and Dhar, Chavas, and Gould, this procedure can generate consistent andasymptotically efficient estimates under the assumption that the error terms arenormally distributed. Similar to Blundell, Pashardes, and Weber, this study spec-ifies a reduced form expenditure equation where household food expenditure isa function of disposable household income, prices of the studied goods, and de-mographic variables that are the same as those used in equation (2). The reducedform expenditure equation is specified as

M = a0 +K∑

l=1

aldl +N∑

r=1

br pr + cy ln(y)(5)

where dl is the lth demographic variable defined above; pr is the price of the rthgood; y denotes disposable household income, which is used as an identifyinginstrument; and a0, a ′

l s, b ′r s, and cy are parameters to be estimated.


The uncompensated (Marshallian) price elasticities of demand are calculatedas

eui j = �i j −

(1 − ci

qi

)×

( p j c j

M∗)

+[

�i j − �i

(� j +

N∑k=1

�k j ln(pk) + p j c j

M∗

)]×

(M∗

pi qi

)(6)

where qi denotes the quantity of good i, and �i j = −1 + ci/qi if i = j and zerootherwise.

The expenditure elasticity of demand is

ei = (1 − ci/qi ) × (M

/M∗) + �i/wi .(7)

The compensated (Hicksian) price elasticities of demand are

eci j = eu

i j + w j ei .(8)

The demographic elasticity of demand is

edik =

[1 − �i −

N∑j=1

�i j ln(p j ) − �i ln(

M∗

P

)− �i

]×

(cikdk

qi

),(9)

where dk = 1 if dk is a binary (0/1) variable and the mean of the variable otherwise.

Data and Estimation ProceduresThe data set used for this study was collected by NBS for Jiangsu province in

2004. The NBS conducts a nationwide urban household survey annually. As anofficial statistical activity, the urban household survey collects extensive socioeco-nomic information on income, consumption, employment, housing, demograph-ics, education, and asset ownership. Unlike most income and expenditure surveysthat cover only a short period, the urban household survey in China captures ex-penditures and consumptions through a diary kept by the chosen householdsover the course of an entire year. These households are selected by NBS and rep-resent the households belonging to various income classes in urban Jiangsu. Thus,the data set used for this study reflects actual consumption patterns of a set ofhouseholds during an entire year (Gale and Huang).

The sample of households selected for the survey in Jiangsu province is chosenfrom twenty-eight cities and towns and has 4,600 households, which accountsfor about 0.05% of total urban households in the province in 2004. However,the data set available for this study has only 922 households, which are drawnsystematically from the 4,600 sample households. After deleting the householdsthat contain missing observations for two or more food categories, only usabledata for 902 households remain that are used for this study.2 The food productsthat are analyzed in this study consist of ten broad food categories: grains, oilsand fats, meats (encompassing pork, beef, and mutton), poultry, eggs, aquaticproducts, dairy products, vegetables, fruits, and other foods (including starches


and tubers, alcoholic beverages, beverages, and cakes). On a per capita basis, theaverage total expenditure on the studied food products accounts for 82.6% of totalexpenditures for food consumed at home and 69.4% of total food expenditures inthe data set for the 902 surveyed households.

Households report their food expenditures and the physical quantities that per-tain to their food consumption in the survey diary. The prices paid by the house-holds are calculated by dividing the consumer expenditures on a food productby its corresponding quantity. Hence, the price calculated in this manner (unitvalue) is household specific, representing household purchase decisions. In mostinstances, consumers choose both the quantity and the quality of consumptionsimultaneously. Therefore, the calculated price should be adjusted for quality dif-ferences among households before it can be used to estimate commodity demandfunctions from cross-sectional data. The quality and price adjustments follow theprocedure proposed by Cox and Wohlgnant. Not all households purchased allfood products during the survey period. If no expenditure or quantity occurs, thequality-adjusted price is equal to the regional (city or town) average price for theconsuming households in that region.3

In this study, the food products examined are treated to be weakly separablefrom other food and nonfood items in the consumer’s budget. Consequently, thedemand for an individual food product depends only on the expenditure on thestudied foods, the prices of the foods within the system, and the included demandshifters representing demographic changes. Demographic variables used in thisstudy include region (south vs. north), city size (towns vs. cities), household size,ratio of the number of seniors (aged sixty-one and above) to total householdmembers, ratio of the number of children (aged seventeen and below) to totalhousehold members, educational levels of household heads (college and abovelevel vs. others), and ratio of expenditures for FAFH to total food expenditures.

Table 1 contains summary statistics and a description of the variables used inthe estimation. The average household size consists of three persons. The averageper capita disposable income is 10,551 Yuan (equivalently US$1,286) per year. Theaverage household in urban Jiangsu allocates 12% of its expenditures on the stud-ied foods to grains, with 22%, 12%, and 13% allocated to meats, aquatic products,and vegetables, respectively. Thus, grains, meats, aquatic products, and vegeta-bles are the main components in a consumer’s diet in urban Jiangsu. Additionally,as shown in the table, the low percentage of the number of children and the highpercentage of the number of adults aged sixty-one and above indicate that popu-lation in urban Jiangsu has been aging, a result of government policy advocatinglater marriages, fewer births, and one birth per couple in urban areas. This chang-ing age structure of the population is expected to affect the composition and thequantity of the food products consumed.

Data are not available from NBS household survey data for oils and fats anddairy products for 5% and 11% of households, respectively. Thus, the consistenttwo-step (CTS) estimation procedure for a system of equations with limited vari-ables, proposed by Shonkwiler and Yen, is used to account for zero expenditureshares resulting from missing values for these two dependent variables. In thefirst step of CTS, a probit model is estimated separately for oils and fats and dairyproducts, using the maximum likelihood estimation method to obtain the univari-ate standard normal probability density functions (pdf) and the standard normal


Table 1. Summary statistics, urban Jiangsu province, China, 2004a

Variables Mean Standard Deviation

Budget ShareGrains 0.124 0.067Oils and fats 0.047 0.029Meats 0.219 0.068Poultry 0.081 0.046Eggs 0.041 0.023Aquatic products 0.120 0.063Dairy products 0.060 0.056Vegetables 0.130 0.046Fruits 0.087 0.053Other foods 0.091 0.059

Quality-Adjusted Price (Yuan/Kg)Grains 3.036 0.831Oils and fats 8.718 2.833Meats 16.206 2.086Poultry 13.849 2.716Eggs 6.126 0.951Aquatic products 12.528 4.908Dairy products 6.131 4.957Vegetables 2.159 0.638Fruits 2.981 1.237Other foods 6.380 4.786

Demographic VariablesPer capita income 10551 8221Household size 3.004 0.989Ratio of seniorsb 0.206 0.352Ratio of childrenb 0.143 0.162Ratio of FAFH spendingb 0.135 0.134South (yes = 1; 0 otherwise) 0.459 0.499Town (yes = 1; 0 otherwise) 0.282 0.450College (yes = 1; 0 otherwise) 0.074 0.262

Source: Calculated based on the National Bureau of Statistics (NBS) data regarding 902 households inurban Jiangsu, China, 2004.aStatistics refer to food-consumed-at-home data.bRatio of seniors, ratio of children, and ratio of food-away-from-home (FAFH) spending refer to theratio of seniors (aged sixty-one and above) to total household members, the ratio of children (agedseventeen and below) to total household members, and the ratio of FAFH spending to total foodexpenditures, respectively.

cumulative distribution functions (cdf). The explanatory variables included in theestimation are logarithm of household disposable income, logarithms of prices ofthe ten studied food products, and the demographic variables that are the sameas those used in equation (2). In the second step of CTS, the ten-good GAIDSsystem for nine equations of food categories encompassing grains, oils and fats,meats, poultry, eggs, aquatic products, dairy products, vegetables, and fruits arethen estimated simultaneously with the reduced form expenditure equation (5)


using the FIML estimation method, with homogeneity and symmetry imposed.For equations for oils and fats and dairy products, the estimated cdf and pdf fromthe first-step probit estimations are applied (see Yen, Kan, and Su for a detailedexplanation). The “other foods” category here is treated as a residual categorywith no specific demand of its own. Consequently the price and expenditure elas-ticities for the “other foods” category are derived using the adding-up restrictionsspecified as

∑Ni=1 wi ei = 1,

∑Ni=1 wi eu

i j = −w j , and∑N

j=1 eui j + ei = 0 (Yen, Lin, and

Smallwood);4 while the demographic elasticities for “other foods” category arecalculated directly using the parameters of the adding-up restrictions.5

ResultsThe estimated parameters and the adjusted R2s of the GAIDS model are pre-

sented in table 2. Of the 190 parameters estimated in the system, more than one-third (88) are significantly different from zero at the 10% level. Moreover, mostof the constant components (ci0) of the estimated precommitted quantities arenegative, which is in contradiction with the results reported by Park et al. andPiggott and Marsh. However, regularity conditions do not require the c ′

i0s to benonnegative and, consequently the signs of c ′

i0s should be regarded as empiri-cal questions (Pollak and Wales). Additionally, parameter estimates for �i (i.e.,�i ) are statistically significant in the oils and fats and dairy products equations,providing evidence that it is important to account for zero observations in thesegoods.6

Price and Expenditure ElasticitiesAll price and expenditure elasticities are evaluated based on parameter es-

timates and sample means of explanatory variables. Standard errors of theseelasticities are approximated using the delta method. The full matrix of the un-compensated (Marshallian) price elasticities for the ten studied food products isreported in table 3. Consistent with economic theory, all own-price elasticities arenegative. With the exception of own-price elasticity for aquatic products, which isnot statistically significant, own-price elasticities for the other studied food prod-ucts are significant at the 10% level. Own-price elasticities for grains, oils and fats,dairy products, and other foods (including starch and tubers, alcohol beverage,beverages, and cakes) are greater than unity in absolute terms. The elasticities forthe other studied food products (including meats, poultry, eggs, aquatic products,vegetables, and fruits) are less than unity in absolute values. The other foods cat-egory has the highest own-price elasticity in absolute value (−1.61), whereas theaquatic products category has the lowest own-price elasticity in absolute value(−0.10) among all the food products considered.

Table 3 also presents the expenditure elasticities, which are all positive and sig-nificantly different from zero at the 5% level. Yet, only some of these elasticities(including grains, oils and fats, eggs, and vegetables) are found to be signifi-cantly different from 1.0 at the 10% level.7 Because the studied food products aretreated as being weakly separable from other food and nonfood items in the con-sumer’s budget, the expenditure elasticities are conditional. The unconditionalexpenditure elasticity for any of the studied food categories may be calculated asthe product of the expenditure elasticity for that food category and the elasticity


Tab

le2.

Est

imat

edG

AID

Sp

aram

eter

s,u

rban

Jia

ngs

up

rovi

nce

,Ch

ina,

2004

a

Coe

ffici

ents

of

Oil

san

dA

qu

atic

Dai

ryO

ther

Exp

lan

ator

yV

aria

ble

sG

rain

sFa

tsM

eats

Pou

ltry

Egg

sP

rod

uct

sP

rod

uct

sV

eget

able

sFr

uit

sFo

ods

Con

stan

t(c 0

)−4

0.65

6−6

.915

−28.

969∗∗

−21.

443∗∗

−9.5

94−1

7.20

2∗−6

.118

−92.

215∗∗

6.04

3−5

.364

Sout

h25

.831

∗∗2.

733∗

13.3

50∗∗

15.4

45∗∗

4.73

0∗∗12

.774

∗∗7.

932∗∗

77.6

19∗∗

43.0

77∗∗

7.14

3∗∗To

wn

−16.

421∗

−3.3

74∗∗

3.46

82.

377

2.07

47.

567∗∗

1.76

1−1

6.92

7−2

.037

3.00

7H

ouse

hold

size

30.9

30∗∗

3.04

7∗∗6.

271∗∗

2.23

1∗3.

960∗∗

2.27

7−3

.426

27.4

48∗∗

−4.4

127.

114∗∗

Rat

ioof

seni

ors

36.7

46∗∗

0.84

15.

081

1.48

94.

622∗

5.00

91.

124

48.3

25∗∗

−8.9

906.

594

Rat

ioof

child

ren

−154

.873

∗∗−4

.921

−35.

259∗∗

−12.

592∗

−20.

451∗∗

−34.

624∗∗

39.5

27∗∗

−202

.464

∗∗−5

.384

−21.

620∗∗

Col

lege

−28.

523

1.41

70.

695

0.65

73.

212

−1.9

46−2

.479

4.61

2−3

.464

7.18

8R

atio

ofFA

FHsp

end

ing

−170

.159

∗∗−1

8.08

0∗∗−3

8.77

4∗∗−2

1.73

3∗∗−2

4.02

7∗∗−1

9.57

7∗∗22

.370

∗−2

63.5

22∗∗

−3.2

13−2

5.62

7∗∗

Con

stan

t(�

)0.

255∗∗

0.18

4∗∗0.

287∗

0.18

4∗0.

077

−0.0

80−0

.209

∗∗0.

456∗∗

−0.1

34–

ln(p

-gra

ins)

−0.0

08ln

(p-o

ilsan

dfa

ts)

−0.0

02−0

.005

ln(p

-mea

ts)

0.01

30.

004

0.06

0∗∗ln

(p-p

oult

ry)

−0.0

06−0

.003

−0.0

21∗

0.05

9∗∗ln

(p-e

ggs)

0.00

00.

002

0.01

3∗∗−0

.003

0.01

0ln

(p-a

quat

icpr

oduc

ts)

−0.0

08−0

.006

−0.0

41∗∗

−0.0

23∗∗

−0.0

050.

093∗∗

ln(p

-dai

rypr

oduc

ts)

0.02

1∗∗0.

007∗∗

−0.0

060.

001

0.00

2−0

.002

−0.0

24∗∗

ln(p

-veg

etab

les)

−0.0

12−0

.004

−0.0

19∗

0.00

1−0

.015

∗∗−0

.020

∗∗0.

012∗∗

0.05

7∗∗ln

(p-f

ruit

s)0.

004

0.00

7∗∗0.

000

−0.0

07−0

.003

0.01

6∗∗−0

.012

∗∗−0

.001

−0.0

06ln

(p-o

ther

food

s)−0

.001

0.00

1−0

.004

0.00

2−0

.001

−0.0

06∗∗

0.00

10.

000

0.00

2–

ln(m

∗ /P)

−0.0

24−0

.019

∗∗−0

.011

−0.0

14−0

.009

0.02

8∗∗0.

042∗∗

−0.0

34∗∗

0.02

9∗∗–

�b

−0.0

41∗∗

0.04

1∗∗A

dju

sted

R2

0.17

00.

119

0.04

20.

096

0.19

40.

244

0.13

60.

197

0.14

9–

a Est

imat

edus

ing

2004

Chi

na’s

NB

Sho

useh

old

surv

eyd

ata.

b�

ind

icat

esth

eun

ivar

iate

stan

dar

dno

rmal

pdfe

stim

ated

inth

efir

st-s

tep

prob

itre

gres

sion

s.∗ ,

∗∗Si

gnifi

cant

atth

e10

%an

d5%

leve

ls,r

espe

ctiv

ely.


Tab

le3.

Un

com

pen

sate

d(M

arsh

alli

an)

pri

cean

dex

pen

dit

ure

elas

tici

ties

,urb

anJ

ian

gsu

pro

vin

ce,C

hin

a,20

04a

Pri

ceof

Oil

sA

qu

atic

Dai

ryO

ther

Com

mod

ity

Gra

ins

and

Fats

Mea

tsP

oult

ryE

ggs

Pro

du

cts

Pro

du

cts

Veg

etab

les

Fru

its

Food

sE

xpen

dit

ure

Gra

ins

−1.2

21∗∗

0.03

20.

285∗

∗0.

007

0.03

2−0

.091

0.18

6∗∗

−0.0

120.

011

−0.0

250.

795∗

∗(0

.171

)(0

.057

)(0

.099

)(0

.083

)(0

.043

)(0

.076

)(0

.048

)(0

.068

)(0

.059

)(0

.050

)(0

.092

)O

ilsan

dfa

ts−0

.417

∗∗−1

.312

∗∗0.

400∗

∗0.

341∗

∗−0

.218

∗∗−0

.169

∗∗0.

200∗

∗0.

182∗

∗−0

.153

∗∗0.

429∗

∗0.

717∗

∗(0

.109

)(0

.116

)(0

.135

)(0

.096

)(0

.069

)(0

.082

)(0

.044

)(0

.082

)(0

.071

)(0

.079

)(0

.085

)M

eats

0.12

1∗∗

0.05

3−0

.853

∗∗−0

.090

0.10

1∗∗

−0.2

50∗∗

−0.0

64∗∗

−0.0

61−0

.023

0.02

41.

040∗

∗(0

.052

)(0

.040

)(0

.122

)(0

.058

)(0

.034

)(0

.049

)(0

.030

)(0

.049

)(0

.041

)(0

.034

)(0

.062

)Po

ultr

y−0

.022

0.00

4−0

.225

−0.3

47∗

−0.0

24−0

.378

∗∗−0

.049

0.13

7−0

.155

∗0.

058

1.00

1∗∗

(0.1

14)

(0.0

73)

(0.1

55)

(0.1

93)

(0.0

69)

(0.0

98)

(0.0

46)

(0.0

98)

(0.0

83)

(0.0

51)

(0.1

09)

Egg

s0.

100

0.14

30.

648∗

∗−0

.029

−0.8

49∗∗

−0.2

02∗

−0.0

14−0

.409

∗∗−0

.151

∗−0

.060

0.82

4∗∗

(0.1

33)

(0.0

99)

(0.1

97)

(0.1

51)

(0.2

60)

(0.1

17)

(0.0

54)

(0.1

30)

(0.0

87)

(0.0

69)

(0.0

97)

Aqu

atic

pro.

−0.1

44∗∗

−0.1

03∗∗

−0.4

73∗∗

−0.2

79∗∗

−0.0

79∗∗

−0.1

010.

038

−0.2

94∗∗

0.19

50.

041∗

∗1.

198∗

∗(0

.068

)(0

.041

)(0

.084

)(0

.064

)(0

.034

)(0

.107

)(0

.036

)(0

.058

)(0

.053

)(0

.038

)(0

.085

)D

airy

pro.

0.27

6∗∗

0.00

6−0

.316

∗∗−0

.133

∗−0

.035

0.01

2−1

.209

∗∗−0

.024

−0.1

90∗∗

0.24

0∗∗

1.37

2∗∗

(0.0

98)

(0.0

48)

(0.1

14)

(0.0

72)

(0.0

37)

(0.0

89)

(0.1

63)

(0.0

71)

(0.0

74)

(0.0

68)

(0.1

47)

Veg

etab

les

−0.0

220.

027

−0.0

490.

109∗

−0.1

25∗∗

−0.2

16∗∗

0.04

8∗−0

.500

∗∗−0

.067

−0.0

190.

814∗

∗(0

.062

)(0

.040

)(0

.089

)(0

.065

)(0

.039

)(0

.058

)(0

.029

)(0

.100

)(0

.045

)(0

.036

)(0

.065

)Fr

uits

−0.0

240.

042

−0.0

78−0

.175

∗∗−0

.079

∗∗0.

299∗

∗−0

.105

∗∗−0

.162

∗∗−0

.865

∗∗0.

170∗

∗0.

978∗

∗(0

.078

)(0

.049

)(0

.105

)(0

.082

)(0

.039

)(0

.082

)(0

.051

)(0

.068

)(0

.141

)(0

.057

)(0

.094

)O

ther

food

s0.

249∗

∗−0

.033

−0.0

52−0

.063

0.15

4∗∗

0.06

8−0

.028

−0.0

240.

158∗

∗−1

.609

∗∗1.

180∗

∗(0

.071

)(0

.035

)(0

.078

)(0

.053

)(0

.034

)(0

.054

)(0

.039

)(0

.045

)(0

.049

)(0

.224

)(0

.112

)

a Est

imat

edus

ing

2004

Chi

na’s

NB

Sho

useh

old

surv

eyd

ata.

bSt

and

ard

erro

rsar

egi

ven

inth

epa

rent

hese

s.∗ ,

∗∗Si

gnifi

cant

atth

e10

%an

d5%

leve

ls,r

espe

ctiv

ely.


of total expenditure on the studied food categories with respect to income. Con-sidering that income elasticity of food expenditure on the studied food productsis positive, the signs of the resulting income elasticities indicate that all the stud-ied food products are normal goods. Expenditure elasticities for meats, poultry,aquatic products, dairy products, and other foods are greater than unity, withdairy products elasticity having the largest value at 1.37. Expenditure elasticitiesfor grains, oils and fats, eggs, vegetables, and fruits are less than one, with theoils and fats elasticity having the smallest value at 0.72. These findings suggestthat as consumers’ food expenditures increase, consumers in urban Jiangsu spendproportionately more on dairy products, aquatic products, other foods, poultry,and meats; and less on fruits, eggs, vegetables, grains, and oils and fats followingin order.

The meat category (including pork, beef, and mutton) has a relatively lowerexpenditure elasticity compared to aquatic products, dairy products, and otherfoods (including starch and tubers, alcohol beverage, beverages, and cakes). Sincepork accounts for more than 70% of the studied total meat expenditures in thisstudy, the relatively low expenditure elasticity for meats is consistent with the factthat pork is the most widely consumed and affordable meat in China. Accordingto the findings of this study, a consumer’s expenditure for the meat category inurban Jiangsu is expected to increase by a larger amount than that of poultry,fruits, eggs, vegetables, grains, and oils and fats as household incomes rise.

The aquatic food category in this study has a relatively higher expenditureelasticity and lower own-price elasticity in absolute term compared to the otherfood categories examined in this study, which may suggest that aquatic foodconsumption is driven more by a change in expenditures than in price. Thus, thedemand for aquatic products is expected to increase as the result of the rising percapita incomes. Moreover, these results for urban Jiangsu support the findings ofShono, Suzuki, and Kaiser, and Yen, Fang, and Su that indicate China’s dietarypattern is moving toward the diets of consumers in Japan, South Korea, Taiwan,and Hong Kong. These developed Asian countries and regions depend more onseafood as their source of protein than the western countries.

Similar to the aquatic product category, poultry meat has a relatively largerexpenditure elasticity and lower own-price elasticity in absolute value comparedto other studied food products. Per capita at-home consumption of poultry meatin urban China almost tripled between 1990 and 2003, before leveling off duringrecent years (2004–6). The slow growth in poultry meat consumption in urbanChina since 2004 has been mainly due to frequent outbreaks of diseases in poultryproduction, which has caused consumers to switch to other meat products such aspork, beef, and mutton (personal communication with an NBS official). Accordingto the estimated expenditure elasticity, poultry meat consumption in urban Chinais expected to substantially increase as household incomes continue to grow inthe future and if poultry meat regains the reputation of being a safe product.

The dairy category is more responsive to changes in own price and expenditurethan most food products examined in this study. The high expenditure and own-price elasticities (in absolute value) for dairy products illustrate that both incomeand own price play important roles in dairy food consumption. If the currentprice structure remains unchanged, the demand for dairy products is expectedto increase as household incomes and food expenditures rise. Additionally, the


per capita consumption of dairy products in 2004 in urban Jiangsu is reported as22 kg. The figure is much lower than those reported by Yen, Fang, and Su forJapan (65.8 kg), South Korea (28.6 kg), Taiwan (43.0 kg) and the United States(256.6 kg) in 2002. According to China’s official statistics, dairy product consump-tion has significantly increased in both urban Jiangsu and urban China over thepast decade. The per capita consumption of fresh milk and yogurt in urban Jiangsuhas increased from 4 kg in 1995 to 12 kg in 2000 and to 22 kg in 2004, an increase ofmore than 400% over the past ten years (NBS 1996–2005). Dairy demand in Chinais anticipated to increase in the future with growing urbanization and westerniza-tion associated with the growth in per capita incomes (Fuller, Beghin, and Rozelle;Yen, Fang, and Su).

The nondiagonal elements in table 3 are the estimated uncompensated cross-price elasticities. These cross-price elasticities indicate a mixture of gross comple-ments and substitutes. About half of the cross-price elasticities are significantlydifferent from zero at the 10% or lower levels. Results indicate that the meats cat-egory is a gross substitute for grains and eggs; but a gross complement to aquaticproducts and dairy products. Moreover, the vegetables category is a gross sub-stitute for poultry and dairy products; while it is a gross complement to eggsand aquatic products. Similar patterns also exist for other food products. Relativeto own-price and expenditure elasticities, the cross-price effects are smaller inmagnitude, which may indicate that China’s consumers are more responsive tochanges in products’ own prices and food expenditures.

Compensated (Hicksian) price elasticities are reported in table 4. Similar tothe uncompensated elasticities, all compensated own-price elasticities are signifi-cant and negative, except for the poultry category and aquatic products category.The former is negative but statistically insignificant, while the latter is positivealthough very small in magnitude and statistically nonsignificant. Unlike theiruncompensated counterparts that indicate a mix of gross substitutes and com-plements, the compensated cross-price elasticities suggest that net substitutionis a dominant pattern. More specifically, among the ninety compensated cross-price elasticities, more than one-third (thirty-four) are positive and significantindicating net substitution; while about 10% (ten) are negative and significantindicating net complementarity. The findings are consistent with those reportedby Yen, Fang, and Su for urban China.

Effects of Demographic Variables on Household Food DemandThe effects of demographic variables on household food demand are measured

by the estimated demographic elasticities in table 5. According to the results,households to the south of the Yangtze River (south, table 5) spend more of theirannual income on nine of the ten studied food products (except for oils and fatscategory) compared to those living in the north of Yangtze River. Geographically,Jiangsu province consists of two parts, one to the north of the Yangtze River—aregion that is less economically developed relative to the south, and another tothe south of the Yangtze River—a region that is more economically developed.Thus, the estimated regional demographic elasticities in this study may mirrorthe difference in economic development levels between the south and the northregions.


Tab

le4.

Com

pen

sate

d(H

ick

sian

)p

rice

elas

tici

ties

,urb

anJ

ian

gsu

pro

vin

ce,C

hin

a,20

04a

Pri

ceof

Oil

san

dA

qu

atic

Dai

ryO

ther

Com

mod

ity

Gra

ins

Fats

Mea

tsP

oult

ryE

ggs

Pro

du

cts

Pro

du

cts

Veg

etab

les

Fru

its

Food

s

Gra

ins

−1.1

22∗∗

0.06

80.

460∗∗

0.07

20.

065

0.00

40.

234∗∗

0.09

20.

081

0.04

7(0

.169

)(0

.057

)(0

.096

)(0

.082

)(0

.043

)(0

.075

)(0

.048

)(0

.068

)(0

.057

)(0

.054

)O

ilsan

dfa

ts−0

.328

∗∗−1

.280

∗∗0.

557∗∗

0.39

9∗∗−0

.188

∗∗−0

.083

0.24

3∗∗0.

276∗∗

−0.0

910.

494∗∗

(0.1

09)

(0.1

15)

(0.1

33)

(0.0

96)

(0.0

69)

(0.0

82)

(0.0

44)

(0.0

82)

(0.0

70)

(0.0

82)

Mea

ts0.

250∗∗

0.10

0∗∗−0

.624

∗∗−0

.006

0.14

4∗∗−0

.125

∗∗−0

.001

0.07

50.

068∗

0.11

9∗∗

(0.0

52)

(0.0

40)

(0.1

17)

(0.0

59)

(0.0

34)

(0.0

48)

(0.0

30)

(0.0

50)

(0.0

40)

(0.0

36)

Poul

try

0.10

20.

050

−0.0

05−0

.266

0.01

7−0

.258

∗∗0.

011

0.26

8∗∗−0

.068

0.15

0∗∗

(0.1

14)

(0.0

73)

(0.1

55)

(0.1

90)

(0.0

68)

(0.0

97)

(0.0

46)

(0.0

98)

(0.0

82)

(0.0

56)

Egg

s0.

201

0.18

0∗0.

829∗∗

0.03

8−0

.816

∗∗−0

.103

0.03

5−0

.301

∗∗−0

.079

0.01

5(0

.133

)(0

.098

)(0

.195

)(0

.151

)(0

.258

)(0

.117

)(0

.054

)(0

.131

)(0

.088

)(0

.074

)A

quat

icpr

o.0.

005

−0.0

49−0

.209

∗∗−0

.181

∗∗−0

.030

0.04

30.

110∗∗

−0.1

38∗∗

0.30

0∗∗0.

151∗∗

(0.0

68)

(0.0

41)

(0.0

82)

(0.0

63)

(0.0

34)

(0.1

02)

(0.0

37)

(0.0

59)

(0.0

53)

(0.0

41)

Dai

rypr

o.0.

446∗∗

0.06

8−0

.015

−0.0

210.

021

0.17

6∗∗−1

.126

∗∗0.

155∗∗

−0.0

700.

365∗∗

(0.0

98)

(0.0

47)

(0.1

14)

(0.0

70)

(0.0

36)

(0.0

88)

(0.1

60)

(0.0

68)

(0.0

72)

(0.0

74)

Veg

etab

les

0.07

90.

063

0.13

00.

175∗∗

−0.0

91∗∗

−0.1

18∗∗

0.09

7∗∗−0

.394

∗∗0.

004

0.05

5(0

.063

)(0

.041

)(0

.085

)(0

.064

)(0

.039

)(0

.058

)(0

.029

)(0

.097

)(0

.045

)(0

.039

)Fr

uits

0.09

70.

086∗

0.13

6−0

.095

−0.0

400.

416∗∗

−0.0

46−0

.034

−0.7

80∗∗

0.25

9∗∗

(0.0

80)

(0.0

49)

(0.1

04)

(0.0

80)

(0.0

39)

(0.0

82)

(0.0

50)

(0.0

68)

(0.1

37)

(0.0

62)

Oth

erfo

ods

0.39

5∗∗0.

020

0.20

7∗∗0.

033

0.20

2∗∗0.

210∗∗

0.04

30.

130∗∗

0.26

1∗∗−1

.501

∗∗

(0.0

72)

(0.0

35)

(0.0

83)

(0.0

56)

(0.0

34)

(0.0

58)

(0.0

38)

(0.0

48)

(0.0

49)

(0.2

19)

a Est

imat

edus

ing

2004

Chi

na’s

NB

Sho

useh

old

surv

eyd

ata.

bSt

and

ard

erro

rsar

egi

ven

inth

epa

rent

hese

s.∗ ,

∗∗Si

gnifi

cant

atth

e10

%an

d5%

leve

ls,r

espe

ctiv

ely.


Tab

le5.

Dem

ogra

ph

icel

asti

citi

es,u

rban

Jia

ngs

up

rovi

nce

,Ch

ina,

2004

a

Oil

san

dA

qu

atic

Dai

ryO

ther

Var

iab

leG

rain

sFa

tsM

eats

Pou

ltry

Egg

sP

rod

uct

sP

rod

uct

sV

eget

able

sFr

uit

sFo

ods

Sout

h0.

107∗∗

0.05

20.

134∗∗

0.40

7∗∗0.

128∗∗

0.17

4∗∗0.

113∗∗

0.20

3∗∗0.

231∗∗

0.06

7∗∗

(0.0

43)

(0.0

35)

(0.0

41)

(0.0

55)

(0.0

52)

(0.0

51)

(0.0

58)

(0.0

40)

(0.0

52)

(0.0

32)

Tow

n−0

.068

∗−0

.362

∗∗0.

035

0.06

30.

056

0.10

3∗∗0.

025

−0.0

44−0

.011

0.02

8(0

.039

)(0

.049

)(0

.040

)(0

.059

)(0

.048

)(0

.047

)(0

.058

)(0

.038

)(0

.049

)(0

.028

)H

ouse

hold

size

0.38

3∗∗0.

932∗∗

0.18

9∗∗0.

177∗

0.32

2∗∗0.

093

−0.1

470.

216∗∗

−0.0

710.

201∗∗

(0.0

82)

(0.1

06)

(0.0

76)

(0.1

04)

(0.0

84)

(0.0

94)

(0.1

11)

(0.0

72)

(0.0

95)

(0.0

66)

Rat

ioof

seni

ors

0.03

1∗∗0.

058∗∗

0.01

10.

008

0.02

6∗0.

014

0.00

30.

026∗∗

−0.0

100.

013

(0.0

12)

(0.0

12)

(0.0

13)

(0.0

18)

(0.0

15)

(0.0

16)

(0.0

21)

(0.0

13)

(0.0

15)

(0.0

10)

Rat

ioof

child

ren

−0.0

91∗∗

−0.1

93∗∗

−0.0

51∗∗

−0.0

47∗

−0.0

79∗∗

−0.0

67∗∗

0.08

1∗∗−0

.076

∗∗−0

.004

−0.0

29∗∗

(0.0

17)

(0.0

25)

(0.0

17)

(0.0

25)

(0.0

21)

(0.0

21)

(0.0

26)

(0.0

17)

(0.0

23)

(0.0

13)

Col

lege

−0.1

18−0

.335

∗∗0.

007

0.01

70.

087

−0.0

26−0

.035

0.01

2−0

.019

0.06

8(0

.074

)(0

.075

)(0

.076

)(0

.106

)(0

.089

)(0

.088

)(0

.156

)(0

.073

)(0

.083

)(0

.058

)R

atio

ofFA

FH−0

.095

∗∗−0

.208

∗∗−0

.053

∗∗−0

.077

∗∗−0

.088

∗∗−0

.036

∗∗0.

043∗

−0.0

93∗∗

−0.0

02−0

.033

∗∗

spen

din

g(0

.017

)(0

.025

)(0

.014

)(0

.023

)(0

.017

)(0

.017

)(0

.023

)(0

.013

)(0

.018

)(0

.008

)

a Est

imat

edus

ing

2004

Chi

na’s

NB

Sho

useh

old

surv

eyd

ata.

bSt

and

ard

erro

rsar

egi

ven

inth

epa

rent

hese

s.∗ ,

∗∗Si

gnifi

cant

atth

e10

%an

d5%

leve

ls,r

espe

ctiv

ely.


City size exhibits a significant effect on the demand for several food productsconsidered. Relative to households located in cities, households located in towns(town, table 5) tend to consume more aquatic products at home; but less grainsand oils and fats. This finding is reflective of the fact that consumers in townshave a more limited access to the varieties and qualities of foods that are offeredto consumers in cities through FAFH channels. Given their small living quarters,it is very common for urban Chinese households to eat some of their main mealsaway from home in eating establishments. Therefore, the results of this studysupport the urban food culture in China that consumers in bigger cities tend toconsume less meats and aquatic products at home than those in smaller towns,with all else being the same.

Household size has a positive relationship on the demand for grains, oils andfats, meats, poultry, eggs, vegetables, and other foods. Because of the govern-ment’s policy related to the one child per couple rule in urban China, householdsize in urban China has decreased significantly over the past two decades, from3.50 persons per household in 1990 to 2.98 persons per family in 2004. As house-hold size continues to decrease, the consumption of grains, oils and fats, meats,poultry, eggs, vegetables, and other foods is expected to decrease, with all elsebeing equal.

As expected, households with more seniors (aged sixty-one and above, ratio ofseniors in table 5) tend to consume more grains, oils and fats, eggs, and vegetables.Households with more children (aged seventeen and below, ratio of children intable 5) tend to consume more dairy products; but less grains, oils and fats, meats,poultry, eggs, aquatic products, vegetables, and other foods. Hence, the oldergeneration has generally different dietary habits compared to younger people.This finding is also consistent with the findings related to Japanese seniors in astudy by Hsu, Chern, and Gale.

Variables associated with educational levels of household heads have a statisti-cally significant effect only on the consumption of grains (at the 11% significancelevel) and oils and fats. Results show that households headed by a better educatedperson (college and above, college in table 5) tend to consume less grains and oilsand fats. This result might be because higher educated households usually havehigher living standards than other families in contemporary China. Consequently,those higher income households can afford to have more higher valued productsin their diets.

The ratio of expenditures for FAFH to total food expenditures (ratio of FAFHspending, table 5) is one of the variables that significantly influence demand formost of the food products considered. This variable has a statistically significantpositive effect on the at-home consumption of dairy products; but a negative effecton the at-home consumption of grains, oils and fats, meats, poultry, eggs, aquaticproducts, vegetables, and other foods. According to a survey conducted by theChinese Academy of Agricultural Sciences in 1999, the urban Chinese consumerconsumes about 27% of pork, 37% of beef and mutton, 51% of poultry, 13% ofeggs, 43% of aquatic products, and 4% of dairy products outside his/her homeduring the course of a year (Wang and Zhou, pp. 96–97).8 Given that consumersspend a higher proportion of their FAFH expenditures on foods of animal origin;as the proportion of expenditures on FAFH increases, the at-home consumption ofgrains, oils and fats, meats, poultry, eggs, aquatic products, vegetables, and other


foods is expected to decrease; while the at-home consumption of dairy productsis expected to increase, with all else being equal.

Elasticity Comparisons with Other StudiesTable 6 presents a comparison of own-price and food-expenditure elasticities

from this study with those that have used the NBS urban household survey dataafter 1995. The studies that have used post-1995 data are more relevant to thisstudy, compared to those that have utilized data prior to 1995. While expenditureelasticities for oils and fats and poultry from this study, particularly oils and fats,differ from those reported by past studies (Gould and Villarreal; Liu and Chern;Yen, Fang, and Su; Zhang and Wang); expenditure elasticities for the remainingstudied food categories from this study fall within the range of estimates as re-ported in other studies (listed above). The differences between the magnitudeof elasticities reported in this study and those given by others might be mainlyattributable to the differences in model specification, make-up of food categoriesin the demand system, and data used. In particular, this study employs the non-linear generalized version of the AIDS model and corrects for endogeneity of theexpenditure term. This study is therefore different from most past studies thathave used the linear or nonlinear versions of the AIDS model, but have not takeninto consideration the possibility of the existence of expenditure endogeneity.

Own-price elasticities for meats, eggs, dairy products, vegetables, and fruitsfrom this study are very similar to the estimates reported by the past studies(listed above). However, own-price elasticities for grains and oils and fats fromthis study are higher (in absolute terms) while own-price elasticities for poultryand aquatic products from this study is smaller in absolute value, compared tothose reported by these past studies. While own-price elasticity for aggregate oilsand fats from this study is higher (in absolute terms) than those reported by Gouldand Villarreal; Liu and Chern; Yen, Fang, and Su; and Zhang and Wang, it lieswithin the range of estimates reported by Fang and Beghin for disaggregate oilsand fats (−0.22 to −1.32). The relatively high own-price elasticity for grains inabsolute value might be attributed to the unexpected high grain prices in 2004.According to China’s official statistics, triggered by a sharp rise in rice prices inthe late 2003, the grain price index in 2004 was 26.4% higher than those in 2003;while the consumer price index for food in 2004 was only 9% higher than theprevious year (NBS 2005). Therefore, the relatively high own-price elasticity (inabsolute value) for grains may be a reflection of household reaction to the unusualgrain market situation in urban Jiangsu in that particular year (2004).

The consumption of poultry meats accounts for a notable percentage of house-hold meat consumption in urban Jiangsu. About 35% of at-home meat consump-tion in urban Jiangsu during the period 2002–2004 is poultry meats, considerablyhigher than those for the national average (28%). Moreover, the three-year aver-age (2002–2004) per capita at-home consumption of poultry meat is 12.5 kg perperson in urban Jiangsu, while it is 8.9 kg per person for the national average.Thus, the low own-price elasticity for poultry category may be a reflection of thefood preferences of consumers in Jiangsu. Similar situation has occurred in theconsumption of aquatic products in urban Jiangsu. Given that the consumers inJiangsu already allocate a higher percentage of their food expenditures to poul-try and aquatic products compared to the national average (NBS 1991–2005), the


Tab

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Ela

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ity

com

par

ison

sb

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this

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oth

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end

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last

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ins

0.82

1.18

0.79

−0.9

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Mea

ts1.

04−0

.85

Pork

1.17

1.09

0.94

0.97

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181.

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0.79

0.92

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prices of poultry and aquatic products might not play such an important role inconsumer purchasing decisions.

Summary and ConclusionsThis study examines the impacts of economic factors (price and expenditures)

and noneconomic factors (demographic characteristics) on food consumption pat-terns in China using the 2004 NBS urban household survey data for Jiangsuprovince. A complete demand system of households is estimated for ten ma-jor food products (grains, oils and fats, meats, poultry, eggs, aquatic products,dairy products, vegetables, fruits, and other foods) using a nonlinear generalizedversion of the AIDS model. Moreover, the endogeneity of expenditure term inthe GAIDS model is corrected using the full information maximum estimationprocedure. Finally, the CTS estimation procedure proposed by Shonkwiler andYen is used to account for zero budget shares resulting from missing values. Twomajor findings of this study are summarized as follows.

First, the results of this study clearly indicate that the changing demographicprofile of urban consumers in Jiangsu has had a significant impact on food de-mand. The most significant demographic effects come from region (south vs.north), city size (towns vs cities), household size, the ratio of seniors to householdsize, the ratio of children to total household members, and the ratio of expendi-tures for FAFH to total food expenditures. Variables related to the educationallevels of household heads have a significant impact only on the demand for foodgrains and oils and fats.

Second, the relatively large positive and statistically significant expenditureelasticities for the ten food categories analyzed in this study imply that incomehas been a notable driving force behind the changing food consumption patternsin the urban Jiangsu province of China. If the current price structure remainsconstant, expenditures on each of the studied food products are expected to growas household incomes rise. However, the amount of the increase in demand inresponse to an increase in food expenditures varies across products. The demandfor foods of animal origin (such as meats, poultry, aquatic products, and dairyproducts) is expected to grow by a larger magnitude than the other food categoriesincluded in this study.

The findings of this study have important implications for both China and U.S.agriculture. This study indicates that with increases in per capita food expendi-tures, the demand for foods of animal origin is expected to increase by a largermagnitude than the demand for food grains in urban China. As a result, as incomesrise, the demand for feed grains to be used in livestock production is expected toincrease by a larger percentage than the demand for food grains. China’s croppingpatterns have changed considerably in the past decade in response to the growthin demand for foods of animal origin and the consequent increase in demand forfeed grains. Between 1996 and 2006, planted hectares of corn and soybeans haveincreased by 16.2% and 25.9%, respectively; while planted hectares of rice andwheat have decreased by 6.6% and 8.5% correspondingly (NBS 2008). However,China’s ability to increase production of feed grains, particularly corn, might behindered by its limited land and water resources. Given that China’s economy isexpected to continue to grow in the future, China will be expected to continue todemand more foods of animal origin and, consequently, China will likely have to


resort to imported feed grains in order to increase its livestock inventory in thefuture (Crook and Colby; Fan, Wailes, and Cramer; Hayes). As a major feed grainexporting country and a prominent trading partner, the United States is expectedto play an important role in feed grain markets in China.

AcknowledgmentsThis study was partially funded by the Hatch Project No. 02702 of the Oklahoma State University

Agricultural Experiment Station and the Cooperative Agreement No. 58-8000-8-0086 from the ERS,USDA on Study of China Livestock Industry Structure. This research benefited from the constructiveinput of Fred Gale, senior economist with China team at ERS, USDA regarding food consumptiontrends in China. The opinions expressed in this article are those of the authors and not necessarilythose of the U.S. Department of Agriculture.

Endnotes1Average per capita income for urban Jiangsu province in 2004 was 10,482 Yuan, compared to 4,754

Yuan for rural Jiangsu (NBS 2005).2In order to examine how comparable the expenditures of the selected households (902) are with

the expenditures of the NBS sample households for Jiangsu (4,600) published in China’s statisticalyearbook, we compared the means of expenditures on each of the ten studied food products calcu-lated from the two data sets. The results show that per capita expenditures on each of the studied foodproducts based on the 902 households are consistent with the 2004 NBS sample household expendi-tures for Jiangsu. Moreover, only food consumed at home is considered in this study. Food away fromhome (FAFH) is not included because of the unavailability of data. China’s NBS only publishes dataon total FAFH expenditures.

3The approach developed by Cox and Wohlgnant may lead to bias because of sample selectivityand simultaneity problems. However, for a ten-good demand system using a data set with 902 ob-servations, it would be computationally difficult to perform quality adjustment simultaneously withthe estimation of the underlying model parameters. Consequently, this study employs the traditionalprocedure to generate quality-adjusted prices.

4The elasticity estimates are not invariant to the residual good selected when using this approachto accommodate the adding-up restrictions (Dong, Gould, and Kaiser). However, “if there is a naturalchoice for this residual good, invariance is not of primary interest.” (Yen, Lin, and Smallwood, p. 460).For this study, the last food category (other foods) fits into the residual good category.

5The unconditional mean of dependent variables in the second step of CTS procedure is specifiedas E(si ) = �(z′

i �i )w(x′i i ) + �i �(z′

i �i ), where si represents the observed budget share, wi denotes thedeterministic budget share, zi and xi are vectors of exogenous variables used in the first-step probitand the second-step share estimations, and i represents parameters in the second-step estimation.Note that the common variables (i.e., price and demographic variables) that are used in both the probitand share equations (first- and second-step estimations), are expected to affect the dependent variablesthrough x′

i i , standard normal probability �i (z′i �i ), and density �i (z′

i �i ). Therefore, the formulae forprice and demographic elasticities for oils and fats and dairy products should measure the full effectsof the changes in the common variables. Based on Su and Yen (p. 733), the marginal effect of a commonvariable zi in xi and zi is specified as:

∂ E(si )∂zi

= �(z′i �i ) × ∂w(x′

i i )∂zi

+ w(x′i i )�(z′

i �i )�i − �i �(z′i �i )�i .(10)

The price and demographic elasticities for oils and fats and dairy products (equations 11 and 13) aresubsequently revised as the following:

eui j = �i j −

(1 − ci

qi�̂i

)×

(p j c j

M∗)

+[�i j − �i

(� j +

N∑k=1

�k j ln(pk ) + p j c j

M∗

)]×

(M∗

pi qi

)�̂i + �̂i �i j

(1 − �i

wi

),

(11)

ei = ({1 − ci �̂i /qi }) × ({M/M∗}) + �i �̂i /wi ,(12)


and

edik =

[1 − �i −

N∑j=1

�i j ln(p j ) − �i ln(

M∗

P

)− �i

]×

(cikdk

qi

)�̂i + �̂i �ikdk

(1 − �i

wi

),(13)

where �i j = −1 + ci �̂i /qi if i = j and 0 otherwise; �̂i and �̂i denote univariate standard normal cdfand pdf; �i j s and �ik s are the parameters of logarithm of prices and demographic variables on fooddemand estimated in the first-step probit functions, respectively; and �i is the parameter of �̂i in thesecond-step estimation, which also represents the error covariance between error terms of the censoredsystem of equations (the second-step estimation) and error terms in a binary indicator function (thefirst-step estimation) (for a more detailed discussion, see Shonkwiler and Yen).

6To save space, results for the reduced-form regression and the probit regressions are not reportedhere; however, they are available upon request.

7The hypothesis test on expenditure elasticities being different from 1.0 was conducted using aKrinsky-Robb evaluation method. Specifically, a distribution of 1,000 values of each elasticity estimatewas generated using a bootstrapping procedure proposed by Krinsky and Robb. The proportion ofobservations in this distribution with values greater than 1.0 is the p-value associated with the one-sided hypothesis test that each elasticity estimate is greater than 1.0 (see Tonsor and Marsh for a moredetailed explanation).

8The Chinese Academy of Agricultural Sciences survey was conducted in six provinces and in-cludes 359 urban households. Although the survey may have suffered from typical problems relatedto surveys conducted in China (not necessarily being a solid representation of the entire urban pop-ulation, and being unofficial and ad hoc) it did support the fact that FAFH spending is an importantcomponent in urban Chinese consumer’s budget.

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An Analysis of Food Demand in China: A Case Study of Urban ...agecon.okstate.edu/faculty/publications/3709.pdf · their per capita at-home consumption of foods of animal origin (meats,