This paper estimated the Liquor and Beverage Consumption based on a Chinese survey data. The results showed that beer consumption has been relatively stable during the past 10 years. However, there exists large potential wine market in china.Food Consumption/Nutrition/Food Safety,

Fang, Cheng

Pan, Suwen

English

Research Papers in Economics

Liquor and Beverage Consumption in China: 
A Censored Demand System Approach 
 
 
 
Cheng Fang 
Asia Officer/Economist at Economic and Social Development, Food and 
Agriculture Organization of the United Nations 
 
 
Suwen Pan 
Research Assistant professor at department of Agricultural and Applied 
Economics, Texas Tech University, 79409 
 
 
 
 
 
Paper prepared for presentation at the American Agricultural Economics Association 
Annual Meeting, Montreal, Canada, July 27-30, 2003 
 
 
 
 
 
 
 
 
 
 
 
 
 
Copyright 2003 by C. Fang and S. Pan.  All rights reserved.  Readers may make verbatim 
copies of this document for non-commercial purposes by any means, provided that this 
copyright notice appears on all such copies. Introduction  
       Over the last two decades, Chinese consumers’ preference for liquor and beverage 
consumption has changed significantly as per capita income has increased. Traditionally, 
Chinese people used to drink spirits and wine liquors. However, beer consumption 
increased rapidly in the 1980s and continued to grow in 1990s. Per capita beer 
consumption increased by 21 percent from 1990 to 1999, while per capita liquor 
consumption declined by 15 percent. Although the market for wine consumption is its 
initial shape, more and more people in the emerging middle and upper class drink more 
table wine. Per capita wine consumption increased by 89 percent from 1995 to 1999. The 
market potential for wine is huge considering the current low wine consumption at about 
0.3 liter per year. In comparison, the world per capita average per year is 7 liters; in 
Japan, 4 liters; in the United States 7.4 liters, and in France , more than 59 liters.  
          After 15 years of negotiations, China finally joined the World Trade Organization 
(WTO) in December 2001. China’s inclusion in the WTO has been a significant trade 
issue for the United States and the other large trading countries. China promised to 
eliminate tariffs on beer by 2005 from the current 70 percent, to cut tariffs on wine from 
current 65 percent to 14 percent by 2004, and to reduce tariff on barley from the current 
16 percent to 9 percent by 2004.  
In order to analyze China market potential for beer, wine, liquors and derived demand for 
barley and sorghum, the demand for these liquor and beverage need to be estimated. This 
study uses recent urban household-level survey data to estimate the demand for liquor 
and beverages by using a censored demand system approach.  
 Data and Estimation Procedures  
The data 
      The data used are urban household surveys conducted by the National Statistical 
Bureau (NSB) of China in 1993 and 1998. The household survey was carried out by local 
agencies. The families selected in the surveys were drawn from a very large population 
frame in different years, based on proportionate stratification. This was a random sample. 
We used about 2298 household observations from Beijing, Taijin, Shanghai, and Jiangsu.  
      In the 2298 households, 96 percent at least bought liquor and beverage related 
products sometime in the survey periods. 51 percent drank wine, 43 percent drank beer, 
14 percent drank juice, and 53 percent drank other related products. Detailed definitions 
and sample statistics for all variables are presented in Table 1.  
  
Estimation Procedures 
 The households who consume the liquor and beverage provide best picture in our 
analysis purpose. However, the sample selection bias may occur when we discard the 
sample without liquor and beverage consumption. To solve the problem, an inverse-mills 
ratio is calculated based on whether the households consume or not, and the variable is 
used in the demand analysis in the earlier work. However, we did not find any of these 
variables is statistically significant in the equations. Therefore, it is dropped from the 
analysis.  
          The method used in the paper follows Shonkwiler and Yen (SY 1999), Su and Yen 
(UY 1999)’s consistent two-step estimation procedure for a system of censored equations. Following their approach, we model whether a household consumes wine, 
juice wine, beer, and others using latent variables with selection mechanism 
otherwise
it i it Z it d if it i it X it y
0
0 ' '
=
> + = + = ν α ε β
 
(i=1,2,…,n; t=1,2,..,T)                                      (1) 
where , for the ith equation and tth observation,  yit is the dependent variable, Xit and Zit 
are vectors of exogenous variables determining level and participation, respectively, αi 
and βi  are comfortable parameter vectors, and εi and νi are error terms.  
The first step is a general probit mechanism, which includes four equations, whether 
consuming wine, juice wine, beer, and others in our analysis. Based on the step, a set of 
CDF ) ˆ ( i it Z α φ ′  and PDF  ) ˆ ( i it Z α ′ Φ  are calculated and used in the second step to estimate 
the different wine consumption.  
          For the second step, a standard Almost Ideal Demand Systems (LA/AIDS) is used 
in the estimation. The advantages of using LA/AIDS include: flexible functional form, 
satisfying exact aggregation across consumers, non-linear Engel curves, and a suitable 
linear approximation (Fabiosa and Jensen 2002). The form used in the second estimation 
is  
it i it i it i
t
i
n
j
jt ij i i it it Z D
P
Y
p Z W ξ α φ δ γ β γ β α + ′ + + 





+ + ′ Φ = ∑
=
) ˆ ( } ln ln ){ ˆ (
*
1
0                   (2) 
where W is the budget share of the different wine consumption in the total wine 
expenditure; Y is the predicted total wine expenditure; p is the nominal price of different 
wines; lnP
* is the stone index defined as   ∑ =
k k k p W P ln ln
* ; Dit  is a vector of 
demographic variables; ξ  is stochastic error term distributed as iid (0,Ω); and (α,β,γ) 
is a vector of parameters. 
        Based on SY and UY, the standard error calculated by SUR or MLE based on the 
equation (2) are biased due to the heteroscedastic problem. To correct the bias,  let log-
likelihoods of the probit model be L11(α1), L12(α2), …, L1n(αn), the second-step model as 
L2( β α α α , ˆ ,..., 2 ˆ , 1 ˆ n ), the covariance matrix of β ˆ is 1
2 ] 4
1
1
/
3 3
1
1
/
4 3
1
1
/
3 [ 1
2
1
2
− − − − − − − + − = ∑ R R R R R R R R R R R R                           (3) 
Where  
)] ( .,..., ),....,... ( [ ) ( 1 1 11 1 n n R R diag R α α α =                                                                           (4) 
)] ( .,..., ),....,... ( [ ) ( 2 1 21 2 n n R R diag R β β α =                                                                         (5) 
)] , ( | ... | ) , ( [ ) , (
/
3 1
/
31 3 β α β α β α n n R R R =                                                                            (6) 
)] , ( | ... | ) , ( [ ) , (
/
4 1
/
41 4 β α β α β α n n R R R =                                                                            (7) 
and  
) ( ) , ( , ) , (
, ) ( , ) (
2 1
4
2
2
3
/
1
2
2 /
1
2
1
′
∂
∂
∂
∂
− =
′ ∂ ∂
∂
− =
∂ ∂
∂
− =
∂ ∂
∂
− =
β α
β α
β α
β α
β β
β
α α
α
L L
E R
L
E R
L
E R
L
E R
i
i
i i
i
i i
i
i i
i
i i
                                                   (8) 
        As usual, we impose the theoretical restrictions of adding-up, homogeneity, and 
symmetry on the parameters as requested by LA/AIDS (Deaton and Muelbauer 1980). 
The expenditure and price elasticities are estimated using the following formula based on 
the model structure and the standard LA/AIDS formula given by Green and Alston (1990, 
1991). 
        Because we only include expenditure in the consumption equation, the expenditure 
elasticity can be calculated as 
()
i
i i it
i W
Z β α
ε
′ Φ
+ =1                                                                                                        (9) 
 The price elasticities are a little complicated because the effects of prices comes from 
both the participation and consumption equation. Let θij be the Kronecker delta (θij=1 for 
i=j; θij =0 for i≠0), uncompensated own-price and cross-price elasticities can be derived 
as follows:  () ij it it
it i
t
i
n
j
jt ij i ij ij i it i it i
K
kj kj k K
i
i
i
j i
i
ij
i
j
i
ij
j
i
ij
Z
D
P
Y
p Z Z
p W
W W
W
W
Z
p d
W d
p d
q d
α α φ
γ β γ β θ α α φ α δ
θ ε
β β γ
α θ ε
′
+ 





+ + + − ′ ′ −
 


 


+ − − ′ Φ = + − = =
∑
∑
=
*
} ln ln { ) ( ) (
) ( ln ) (
ln
ln
ln
ln
*
1
0 (10) 
 To simplify the elasticity calculation, based on suggestion from Green and Alston 
(1990), the error from using the following formula is small: 
() ij it it it i
t
i
n
j
jt ij i ij
ij i it i it i
i
j i
i
ij
i
j
i
ij
j
i
ij
Z D
P
Y
p
Z Z
W
W
W
Z
p d
W d
p d
q d
α α φ γ β γ β θ
α α φ α δ
β γ
α θ ε
′ + 





+ + + −
′ ′ −  


 


− ′ Φ = + − = =
∑
=
* } ln ln {
) ( ) ( ) (
ln
ln
ln
ln
*
1
0 (11) 
Because total liquor and beverage consumption expenditure and share of different wines 
are highly correlated, it is possible that the covariance of independent variable and the 
residuals of the demand equation is non-zero, a condition that would bias parameter 
estimates of the impact of wine expenditures on the different types of wine demand. In 
other words, wine expenditure maybe endogenous to wine demand equation system. To 
avoid this possible econometric problem, an Instrumental Variable (IV) approach is 
adopted and the predicted wine expenditures based on whether a household consumes 
wine or not is used in equation   (2).   
 
 
Results 
ML probit estimates for wine, juice, beer and others are presented in Table 2. At the 10 
percent level of significance, our results suggest that consumption of beer, wine, juices 
and other beverages in China is responsive to income and price changes. As income 
increase, the probabilities of wine, juice, beer and other related products consumption increase.  The probabilities of four types of consumption decrease as their own price 
increase. One interesting results here is that the probability of juice consumption increase 
as the price of beer increase, the probability of beer increase as wine price increase, 
which imply there is substitution between beer and juice, as well as between wine and 
beer. 
        In addition to responses to changes in prices and income, demographic variables 
also affect the probabilities of different wine type consumption. A household head who is 
male is more likely to drink wine and beer than those with female household head; those 
with higher education are more likely to drink wine, beer but less juice than those with 
less education, partly because they have a more secure job and more social life than those 
with less education.  The positive effects of employment also show that those with a full 
time or part time job are more likely to consume beer than those not. Those residing in 
north are more likely to drink wine, and beer than those in south. But the southern people 
are more interested in juice consumption.  
           Table 3 presents ML results for the system of equations, including parameter 
estimates, corrected standard errors. The explanation of the coefficient is complicated due 
to the model structure. The problem can be solved by calculating the elasticities. Table 4 
provides the income and price elasticities calculated based on equation (9) and (11).    
Juice and beer demand had total expenditure elasticities less than one (necessities). The 
expenditure elasticity for wine is larger than one (luxury). The price elasticities for wine 
and juice are larger than that for beer. Both wine and juice are elastic in marshallian 
elasticity. However, only juice is elastic in Hicksian elasticity. 
              The results show that beer has been the most popular alcoholic related drink in 
China. Beer and juice are the normal goods in the country but wine is still a luxury good. 
The important implication is that the demand for wheat will increase slowly due to the 
stable consumption of beer in China. However, wine consumption will increase sharply 
as per capita income increases.  
 
Concluding Remarks 
         This paper estimated the Liquor and Beverage Consumption based on a Chinese 
survey data. The results showed that beer consumption has been relatively stable during 
the past 10 years.  However, there exists large potential wine market in china. The results 
have significantly trade implications as China’s income growth, urbanization, and tariff 
reduction committed in the WTO agreement.   
       
 
Reference 
  
Fabiosa, Jacinto and Jensen, H. Helen. “Microeconomic Adjustments of Households to 
Macroeconomic Shocks: Household Level Welfare Impacts of the Indonesian Economic 
Crisis.” Paper presented in American Agricultural Economics Annual Meeting in Long 
Beach, California, July 28-31, 2002. 
 
Green, Richard and Alston, Julian M.  “Elasticities in AIDS Models.” Amer. J. Agr. 
Econ. 72(Nov., 1990): 442-445. 
 
Su, Shew-jiuan B. and Yen, Steven T. “ A Censored System of Cigarette and Alcohol  
Consumption,” Applied Economics 332 (2000): 729-737.  
 
Shonkwiler J. Scott and Yen, Steven T. “ Two-Step Estimation of A Censored System of 
Equations,” Amer. J. Agr. Econ. 81(Nov., 1999): 972-982. 
 Table 1 Definition and Sample Statistics (n=2298) 
Variables   Definiftion  Mean  Std Dev 
WEXP  Liquor and Beverage Expenditure per capita  111.21 117.32 
WSHARE  Wine share in the total expenditure  0.31  0.33 
JSHARE  Juice share in the total expenditure  0.06  0.16 
BSHARE  Beer share in the total expenditure  0.39  0.31 
OSHARE  Other wine share in the total expenditure  0.20  0.29 
WPRICE Wine  price  12.93  12.52 
JPRICE Juice  price  10.18  4.63 
BPRICE Beer  price  2.55  1.05 
OPRICE Other  related  price  4.87  4.24 
INCOME Income  per  capita(yuan)  5585.26  3838.93 
EDUCATION Years of formal education for the household head  9.81  2.65 
AGE   Age in years for the household head  50.17  12.46 
     
Dummy variables (yes=1, no=0)     
NORTH   Resides in North  0.48  
CITY  Resides in Beijing, Tianjin, Shanghai  0.91  
YDUM  Year in 1998  0.50  
MALE  Male household head  0.59  
EMPLOY  Household head is employed half or  full time  0.75   
Source: Calculated by Authors.      
 Table 2 Univariate Probit Regression Explaining Consumption Porbabilities for 
wine, Juice, Beer and others  
                         
 Wine   Juice   Beer   Others  
    Coef  Std  error Estimate Error  Estimate Error  Estimate Error 
Intercept -1.0849*  0.2501 2.3284* 0.3183 0.419 0.3083 -0.0486 0.2908
wprice -0.0076*  0.003 0.0004 0.003 0.0057* 0.0033  0.006* 0.0029
jprice -0.0046  0.0063 -0.0688* 0.0078 0.0121* 0.0073  -0.0007 0.0072
bprice 0.0448  0.034 0.0735* 0.0372 -0.1161* 0.0422  -0.0687* 0.0407
oprice -0.0113*  0.0062 -0.0085 0.0073 -0.034* 0.0107  -0.0265* 0.0115
Income 0.0203*  0.0112 0.0378* 0.0137 0.0711* 0.0134 0.0755* 0.013
male 0.2932*  0.0589 0.0087 0.077 0.2123* 0.0726  0.2737* 0.0683
north 0.6518*  0.059 -0.1554* 0.0774 2.1648* 0.0756  1.8038* 0.0676
edu  0.0645* 0.0111 -0.0211* 0.0103 0.0253* 0.0136 0.0125 0.0128
age  0.0155**  0.0031 -0.0029 0.0039 -0.0173*      0.0038  -0.0151* 0.0036
employ 0.0933  0.0868 -0.0241 0.1125 0.1723* 0.1058 0.1898* 0.1024
ydum -0.0713  0.0905 0.1816 0.1198 0.7028* 0.1124 0.5518* 0.1054
Log-likelihood -1379.64  -731.61 -842.85   -979.76
*Indicate significant at 10% level. Table 3 ML Estimation of System of Equations for Wine, Juice, Beer 
(Dependent variables: Wshare, Jshare, Bshare)  
                   
 WINE   JUICE   BEER    
   Coeff  Std Error  Coeff  Std Error  Coeff  Std Error 
Φ(Z,α)  -4.976*** 0.464 0.156*** 0.068 0.256*** 0.191 
North*Φ(Z,α)  -1.023*** 0.102 -0.036*** 0.009 0.665 0.115 
YDUM*Φ(Z,α)  -0.036 0.031 0.011 0.011 -0.029 0.032 
Age*Φ(Z,α) 0.023***  0.002 -0.0004 0.00046 -0.002  0.0013 
EDU*Φ(Z,α)  0.108*** 0.010 -0.003 0.0016 -0.002 0.004 
EMPLOY*Φ(Z,α)  0.162*** 0.034 -0.009 0.012 -0.051 0.035 
MALE*Φ(Z,α)  -0.452*** 0.045 -0.007 0.0084 0.057*** 0.023 
        
WPRICE*Φ(Z,α)  -0.144*** 0.024 -0.003 0.0099 0.0082 0.022 
JPRICE*Φ(Z,α)  -0.155*** 0.032 -0.054*** 0.012 -0.048 0.030 
BPRICE*Φ(Z,α)  0.324*** 0.038 0.086*** 0.014 -0.030 0.027 
log(WEXP/SPI)* Φ(Z,α)  0.267*** 0.049 -0.036*** 0.014 -0.112*** 0.049 
φ(Zα)  4.130*** 0.238 1.134 0.044 1.312*** 0.105 
Log-likelihood     702.499        
 Table 3 Expenditure elasticity 
   Elasticity  Average Share 
Wine 1.394  0.31 
Juice 0.51  0.06 
Beer 0.85  0.39 
Others 0.74  0.20 
 
Table 4 Marshallian elasticity 
      wine  juice   beer  others 
wine   -1.362  -0.371 0.490 -0.414
juice   -1.410  -1.891 1.651 1.084
beer   0.776  0.178 -0.901 -0.831
others     -0.178  0.508 -1.452 0.579
 
 
Table 5 Hicksian elasticity of the different prices 
      wine  juice   beer  Others 
wine   -0.829  -0.261 1.158 -0.067
juice   -1.228  -1.853 1.879 1.203
beer   1.028  0.229 -0.590 -0.667
others     -0.003  0.544 -1.233 0.692
 
 

Microeconomic Adjustments of Households to Macroeconomic Shocks: Household Level Welfare Impacts of the Indonesian Economic Crisis.” Paper presented in American Agricultural Economics Annual Meeting in

LIQUOR AND BEVERAGE CONSUMPTION IN CHINA:A CENSORED DEMAND SYSTEM APPROACH

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